seventh framework programme research infrastructures

SEVENTH FRAMEWORK PROGRAMME

Capacities Specific Programme Research Infrastructures

Project No.: 227887

SERIES SEISMIC ENGINEERING RESEARCH INFRASTRUCTURES FOR

EUROPEAN SYNERGIES

Deliverable D13.1 covering Tasks JRA 2.1 and JRA 2.2

Work package [WP13/JRA2] Deliverable [D13.1] - [Report on advanced sensors, vision systems and control techniques for measuring structural/foundation response, improving test control and hybrid testing.

Dissemination of sensor and vision systems to partner infrastructures not directly involved in their development or application]

Deliverable/Editor: [CEA, UNITN] Reviewer: [UNITN]

Revision: Final

May, 2011

1

ABSTRACT

The main objective of this report, that covers the research activities both of Task JRA2.1

and of Task JRA2.2, respectively, is the presentation of the state of the art as well as of the

implementation and application of new types of sensors, time-integration and control techniques,

visualisation and device modelling tools capable of enhancing the measurement of the response

of test specimens and improving the quality of test control. In greater detail, firstly the following

topics and objectives are treated:

- modern monolithic and partitioned time integration schemes able to deal with pseudo-

dynamics and real-time tests; state-of-the-art controllers still based on linear systems

theory but capable to take into account both actuator dynamics and non-linear effects;

techniques for test error assessment.

- Procedures capable to identify transfer functions of servo valves and hydraulic actuators

that also operate shaking tables.

- Implementation and application of new types of sensors for improved sensing and control.

Specifically, new types of instruments like fibre optics, wireless sensors and 3D

visualization tools and techniques for measuring structural and foundation responses were

explored.

Lastly, experiments at different levels of complexity are presented. They were adopted to

calibrate/validate the proposed techniques and instrumentation.

Keywords: Integration methods. Internal model control. Servo valve model. Actuator model.

Error assessment. Fiber Optic Sensors. Wireless sensors. Sensor network. Vision systems.

Instrumented specimens. Shaking table.

3

ACKNOWLEDGMENTS

The research leading to these results has received funding from the European Community’s

Seventh Framework Programme [FP7/2007-2013] under grant agreement n° 227887.

This work has been developed by the partners of the WP13/JRA2 activities.

5

DELIVERABLE CONTRIBUTORS

AUTH K.D. Pitilakis CEA A. Le Maoult

L. Moutoussamy P. Mongabure

ITU A. Ilki JRC P. Capéran

KOERI E. Safak LCPC J-L Chazelas LNEC A.C. Costa NTUA I.N. Psycharis

H.P. Mouzakis S. Natsis

UCAM G. Madabhushi UNITN O. S. Bursi

M. S. Reza Z. Wang

UNIVBRIS C. Taylor P.D. Stoten I. Elorza

UOXF.DF M. Williams T. Blakeborough

UPAT S. Bousias

6

CONTENTS

List of Figures ................................................................................................................................11

List of Tables ..................................................................................................................................15

1 Study Overview .....................................................................................................................16

2 JRA 2.1 Advanced Sensing and Control Techniques for Improved Testing Control ...........18

2.1 Integration methods ....................................................................................................18

2.1.1 Introduction .....................................................................................................18

2.1.2 Monolithic Schemes ........................................................................................19

2.1.2.1 The LSRT2 Method ............................................................................. 19 2.1.2.2 The Chang method ............................................................................. 21 2.1.2.3 The CR Method ................................................................................... 22

2.1.3 Partitioned Schemes .......................................................................................23

2.1.3.1 The GC Method .................................................................................. 24 2.1.3.2 The PM Method .................................................................................. 25 2.1.3.3 The Partitioned Rosenbrock Method ................................................. 27

2.1.4 Conclusions ......................................................................................................29

2.2 Adaptive Control strategies for real-time substructuring tests ..................................31

2.2.1 Introduction .....................................................................................................31

2.2.2 Open loop indirect adaptive control for compensating transfer system

dynamics ..........................................................................................................34

2.2.3 Open loop direct adaptive control for compensating transfer system

dynamics ..........................................................................................................35

2.2.4 Indirectly adaptive modification of the feedback force..................................35

2.2.5 Adaptive control of the transfer system in parallel with the numerical

substructure .....................................................................................................36

2.2.6 Conclusions ......................................................................................................36

2.3 Internal model control..................................................................................................38

2.3.1 Introduction .....................................................................................................38

2.3.2 Internal model control .....................................................................................38

2.3.3 Basic Concepts of IMC .....................................................................................39

7

2.3.4 Some Extensions of IMC ..................................................................................40

2.3.5 IMC application in the TT1 test rig ..................................................................41

2.3.6 Conclusions ......................................................................................................42

2.4 Model predictive control ..............................................................................................43

2.4.1 Basic principle of MPC .....................................................................................43

2.4.2 Advantages and disadvantages of MPC ..........................................................44

2.4.3 MPC and hybrid simulation .............................................................................45

2.5 Combined Inverse-Dynamics and Adaptive Control for Instrumentation ..................47

2.5.1 Introduction .....................................................................................................47

2.5.2 Overview of Inverse-Dynamics (Inverse-Model) Control ...............................47

2.5.3 Overview of Adaptive Control .........................................................................48

2.5.4 Combined Inverse-Dynamics and Adaptive Control for Instrumentation .....49

2.5.5 Conclusions ......................................................................................................50

2.6 TT2 test: non linear hydraulic Actuator model ............................................................51

2.6.1 Actuator model ................................................................................................52

2.6.1.1 The Merritt servohydraulic model...................................................... 52 2.6.1.1.1 Fluids mechanics equations ........................................................ 52 2.6.1.1.2 Servohydraulic System ................................................................ 54 2.6.1.1.3 Final Merritt model equations ..................................................... 56

2.6.1.2 The modified Actuator Model ............................................................ 57 2.6.1.2.1 Flows decomposition for actuator modeling .............................. 58 2.6.1.2.2 Force equation on Piston ............................................................ 61 2.6.1.2.3 Servovalve equations .................................................................. 63 2.6.1.2.4 governing equations of the modified actuator model ............... 64

2.6.2 Experimental setup ..........................................................................................64

2.6.2.1 Identification tests .............................................................................. 68 2.6.2.1.1 Identification test n° 1: no velocity test ...................................... 68 2.6.2.1.2 Identification test n° 2: no load flow test .................................... 71 2.6.2.1.3 Identification test n° 3: Sine sweep test ..................................... 74

2.6.2.2 Results: model vs test ......................................................................... 76 2.6.2.2.1 Step test ....................................................................................... 77 2.6.2.2.2 Sine sweep test ............................................................................ 77 2.6.2.2.3 White noise test ........................................................................... 79 2.6.2.2.4 Conclusion ................................................................................... 79

2.7 Conclusions ..................................................................................................................80

2.8 Error assessment ..........................................................................................................81

8

2.8.1 Influences of errors ..........................................................................................81

2.8.2 Resources of errors ..........................................................................................81

2.8.3 Complication of error assessment...................................................................82

2.8.4 Approaches to assess errors ............................................................................82

2.8.5 Conclusions ......................................................................................................83

2.9 Sensors in presence of linear electro-magnetic actuators – initial analysis ...............83

2.9.1 Dynamic seating deck - outline of project ......................................................83

2.9.2 Description of seating deck .............................................................................84

2.9.3 Control system .................................................................................................85

2.9.4 Investigation into the transducer signals ........................................................87

2.9.4.1 Encoder ............................................................................................... 87 2.9.4.2 Load cells ............................................................................................ 88

3 JRA 2.2 Sensing and Verification Tests for Measuring Structural and Foundation

Performance ..........................................................................................................................93

3.1 Introduction ..................................................................................................................93

3.2 Fibre Optic Sensors ......................................................................................................93

3.3 Microelectromechanical systems ..............................................................................104

3.4 Wireless Sensors and Sensor Networks ....................................................................106

3.4.1 Introduction ...................................................................................................106

3.4.2 Hardware Design of Wireless Sensors ..........................................................107

3.4.3 State of the art of Academic Wireless Sensing Unit Prototype ...................109

3.4.4 Commercial Wireless Sensor Platforms ........................................................112

3.4.5 ZigBee and 802.15.4 Overview ......................................................................113

3.4.6 IEEE 802.15.4 Standard .................................................................................113

3.4.7 Field Deployment of Wireless Sensors in Civil Infrastructure Systems ........114

3.4.8 Reliability assessment of wireless sensors in the University of Trento ........117

3.4.8.1 Tests with wireless strain gauges ..................................................... 123 3.4.9 Concluding Remark .......................................................................................125

3.5 Sensors and techniques for vision systems ...............................................................126

3.5.1 Introduction ...................................................................................................126

3.5.2 Photogrammetric principles ..........................................................................127

9

3.5.3 Optical components, data collection and calibration ...................................129

3.5.3.1 Camera sensor .................................................................................. 129 3.5.3.2 Time of flight sensors ....................................................................... 138 3.5.3.3 Optical Calibration ............................................................................ 139

3.5.4 Tracking methods ..........................................................................................142

3.5.4.1 Targets networks and artificial texture on the bridge ..................... 143 3.5.4.2 Tracking method and image matching ............................................ 145

3.5.5 PsD methodology: an example of stereo-vision measurements on the Future

Bridge Project ................................................................................................148

3.5.5.1 Description of the experiment ......................................................... 148 3.5.5.2 Strong floor displacements .............................................................. 150 3.5.5.3 General drift of the beam ................................................................. 153 3.5.5.4 Opening and sliding between slab and sandwich ............................ 154 3.5.5.5 Shell buckling.................................................................................... 159

3.5.6 On some real time displacement measurements .........................................161

3.5.7 Shake table methodology: recent Research Efforts in using photogrammetry163

3.5.8 Commercial Integrated Systems ...................................................................164

3.5.9 Hardware Components for photogrammetry on shake table experiments 165

3.5.10 Photogrammetric System Configuration .....................................................169

3.5.11 Software development ..................................................................................171

3.5.11.1 Stereoscopic video capture .............................................................. 171 3.5.11.2 Stereoscopic video play-back .......................................................... 173 3.5.11.3 Camera calibration ........................................................................... 174 3.5.11.4 Target tracking and Triangulation ................................................... 177

3.5.12 Shake table methodology: an example of photogrammetry on the

CEA/AZALEE equipment ...............................................................................180

3.5.12.1 Presentation/Context ....................................................................... 180 3.5.12.2 Equipment ........................................................................................ 180 3.5.12.3 Stereovision system evaluation: test on a rocking and sliding block 183 3.5.12.4 Using the stereovision system during shaking table tests: drums stacked on AZALEE table ................................................................................... 189

3.5.13 Conclusion ......................................................................................................199

3.6 Stress and strain visualisation using thermal imaging ..............................................200

3.6.1 Calibration of temperature data ...................................................................201

3.6.2 Transformation of images to a fixed reference frame ..................................202

3.6.3 Conversion of temperatures to energy densities ..........................................203

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3.6.4 Conversion of energy density to stress and strain ........................................204

3.6.5 Conclusion ......................................................................................................205

4 Summary .............................................................................................................................206

References ...................................................................................................................................207

11

List of Figures

Fig. 2. 1 Schematic representation of a 2-DoF structure with substructuring: (a) emulated structure; (b) partitioned structure; (c) numerical substructure; and (d) physical substructure and transfer system. ............................................................................................. 19 Fig. 2. 2 Spectral radii ñ of real-time complatible integration methods vs non-dimensional frequency Ù.................................................................................................................................. 21 Fig. 2. 3 The GC method ............................................................................................................ 25 Fig. 2. 4 The interfield parallel solution procedure of the PM method.................................. 26 Fig. 2. 5 The multi-time-step partitioned algorithm with ss=2: (a) staggered procedure; (b) interfield parallel procedure. ............................................................................................... 27 Fig. 2. 6 The solution procedure of the improved interfield parallel algorithm. ................ 28 Fig. 2. 7 Comparison of test results between different partitioned methods. ........................ 29 Fig. 2. 8 Schematic for real-time substructuring tests ............................................................. 32 Fig. 2. 9 Block diagram for real-time substructuring tests ..................................................... 32 Fig. 2. 10 Substructuring test with a model of the physical specimen to improve the test characteristics (After Sivaselvan, 2006). ................................................................................... 34 Fig. 2. 11 Adaptive model of the physical specimen for palliating lack of knowledge (After Sivaselvan, 2006) ......................................................................................................................... 36 Fig. 2. 12 Block diagram of IMC ............................................................................................... 39 Fig. 2. 13 Two-degree of freedom IMC ..................................................................................... 40 Fig. 2. 14 Model reference adaptive inverse control system ................................................... 40 Fig. 2. 15 IMC applications in the actuators of the TT1 test rig ............................................ 41 Fig. 2. 16 Basic structure of MPC .............................................................................................. 43 Fig. 2. 17 MPC strategy .............................................................................................................. 44 Fig. 2. 18 Schematic of real-time test......................................................................................... 45 Fig. 2. 19 The scheme of open-loop inverse-dynamics control ................................................ 47 Fig. 2. 20 The scheme of parallel model-reference adaptive control ...................................... 49 Fig. 2. 21 The control block diagram for inverse dynamics + adaptive controllers ............. 50 Fig. 2. 22 Flows entering and leaving a control volume (Merrit, 1967) ................................. 53 Fig. 2. 23 Valve piston combination (Merrit 1967) .................................................................. 54 Fig. 2. 24 Flows in actuator ........................................................................................................ 58 Fig. 2. 25 Stiffness and pusation normalized variations .......................................................... 60 Fig. 2. 26 Forces acting on the piston ........................................................................................ 61 Fig. 2. 27 Stribeck model curve for friction forces variation depending on velocity (Jellali and Kroll, 2003) ........................................................................................................................... 62 Fig. 2. 28 Experimental setup..................................................................................................... 66 Fig. 2. 29 Drawing of the experimental servo-hydraulic setup ............................................... 66 Fig. 2. 30 Sensors of the actuator ............................................................................................... 67 Fig. 2. 31 Variations of displacement, drive and pressure depending on time are not significant ..................................................................................................................................... 68 Fig. 2. 32 Force (from load cell) depending on differential pressure ..................................... 69 Fig. 2. 33 No velocity flow depending on pressure ................................................................... 70 Fig. 2. 34 No velocity flow experimental and fitted curves...................................................... 70

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Fig. 2. 35 Charge loss coefficient 𝑲𝒄𝒆 ....................................................................................... 71 Fig. 2. 36 Flow depending onpiston velocity ............................................................................. 72 Fig. 2. 37 Friction force depending on velocity ........................................................................ 73 Fig. 2. 38 No load flow depending on drive............................................................................... 73 Fig. 2. 39 Non-linearities appearing on first servovalve (left) with an overlap and the second servovalve (right) with an underlap .......................................................................................... 74 Fig. 2. 40 Actuator with a rigid mass ........................................................................................ 75 Fig. 2. 41 Oil stiffness evaluation, sine sweep test .................................................................... 75 Fig. 2. 42 Drive signal of the reference test ............................................................................... 76 Fig. 2. 43 Model vs test velocity, reference test ........................................................................ 76 Fig. 2. 44 Model vs test velocity, step test.................................................................................. 77 Fig. 2. 45 Model vs test velocity, step test, zoom ...................................................................... 77 Fig. 2. 46 Model vs test velocity, 0.1 Hz and 10 Hz sinus test.................................................. 77 Fig. 2. 47 Model vs test velocity, 18 Hz and 40 Hz sinus test................................................... 78 Fig. 2. 48 Model vs test velocity, 60 Hz and 80 Hz sinus test................................................... 78 Fig. 2. 49 Model vs test velocity, 100 Hz and 120 Hz sinus test............................................... 78 Fig. 2. 50 Model vs test velocity, white noise test ..................................................................... 79 Fig. 2. 51 Model vs test acceleration, 15 Hz sinus test, zoom .................................................. 79 Fig. 2. 52 Schematic of seating deck frame with actuators and air springs........................... 84 Fig. 2. 53 Control loops for motion of seating deck ................................................................. 87 Fig. 2. 54 Encoder displacement of an actuator on the grandstand (upper), detail showing data points and glitch (lower) .................................................................................................... 88 Fig. 2. 55 Load cell output for actuator load cell – whole trace (upper), detail (lower) ....... 89 Fig. 2. 56 Power spectral density of load cell signal ................................................................. 90 Fig. 2. 57 Load cell signal from spectator cell – detail trace (upper) and psd (lower).......... 90 Fig. 3. 1 cyclic test N. 4: specimen cross-section (dimensions in mm) .................................... 96 Fig. 3. 2 Four load points scheme (dimensions in mm)............................................................ 96 Fig. 3. 3 Cyclic test N.4: top side internal vs external fiber data ............................................ 97 Fig. 3. 4 Cyclic test N.4: bottom side internal vs external fiber data ..................................... 97 Fig. 3. 5 Cyclic test N.4: moment-rotation curve ..................................................................... 98 Fig. 3. 6 Cyclic test N.4: Comparison between AEPs, strain gauges and fiber optic sensors........................................................................................................................................................ 98 Fig. 3. 7 Full scale test set-up of the tunnel ring (dimensions in cm)...................................... 99 Fig. 3. 8 Full scale test set-up of the tunnel ring (dimensions in cm).................................... 100 Fig. 3. 9 Comparison between actuator inner displacement and wire 2-6........................... 100 Fig. 3. 10 External unbounded AOS fiber data in Section 1 for the pre-straining phase. . 101 Fig. 3. 11 Inner bonded AOS fiber data in Section 2 during the ECCS phase. ................... 101 Fig. 3. 12 External unbounded AOS fiber data in Section 8 for the ECCS phase. ............. 102 Fig. 3. 13 Functional elements of a wireless sensor for structural monitoring applications..................................................................................................................................................... 109 Fig. 3. 14 Wireless network typologies for wireless sensor networks ................................... 110 Fig. 3. 15 A Base Station and a MOTE Unit ........................................................................... 118 Fig. 3. 16 Testing Scheme ......................................................................................................... 118 Fig. 3. 17 Laboratory Test Layout ........................................................................................... 119 Fig. 3.18 Sensors arrangements; (a) Tests on X axis, (b) Tests on Y axis, (c) Tests on Z axis..................................................................................................................................................... 120 Fig. 3.19 (a) Fitted time histories of the sample test using test’s parameters;..................... 121

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Fig. 3.20 Steel/aluminium frame placed on the shaking table instrumented with accelerometers ........................................................................................................................... 122 Fig. 3.21 Earthquake simulation fitted and synchronized time histories ............................ 123 Fig. 3.22 The wireless nodes to be tested (the nodes are packaged in plastic boxes of dimension 11x8x4cm, a 19cm high antenna; the weight of a sensor is 150g). ..................... 124 Fig. 3.23 Testing scheme with the wired and wireless strain gauges. ................................... 124 Fig. 3.24 (a) Strain gauges mounted in the bare bars; (b) strain gauges mounted in the bar in the concrete. .......................................................................................................................... 124 Fig. 3.25 Strain measured by wired and wireless strain gauges ........................................... 125 Fig. 3. 26 The Swissranger® SR4000 range camera .............................................................. 139 Fig. 3. 27 Calibration of the stereo rig .................................................................................... 141 Fig. 3. 28 Optical distortion of the right camera .................................................................... 142 Fig. 3. 29 a) close-up view of the random texture of the bridge, b) corresponding window on left camera c) corresponding window on right camera ......................................................... 143 Fig. 3. 30 Synopsis of the tracking method ............................................................................. 144 Fig. 3. 31 Illustration of the matching method ....................................................................... 145 Fig. 3. 32 Perspective view of the bridge ................................................................................. 148 Fig. 3. 33 Right view of the beam, with some measurement points and LVDT available for comparison................................................................................................................................. 151 Fig. 3. 34 Evidence of the floor displacement ......................................................................... 152 Fig. 3. 35 Evolution of the slope of the floor at points 13 and 17 .......................................... 153 Fig. 3. 36 Drifting of the bridge longitudinal to its axis (a) and perpendicular to it (b) for points 1, 41 9 and 11 .................................................................................................................. 154 Fig. 3. 37 right view of the concrete slab with targets indicated by red crosses. Cyan crosses correspond to sandwich and green ones to FRP .................................................................... 154 Fig. 3. 38 Left and right views of the LVDT 22. The profile of the lever is delineated on the left view ...................................................................................................................................... 155 Fig. 3. 39 Signal of the LVDT 22, compared to distance between targets 77 and 569, on its extremities. The green curves corresponds to sliding as measured from target 417 .......... 155 Fig. 3. 40 In a is exhibited the sliding profile of the concrete slab with respect to sandwich panel, at the successive loading maxima. In b is shown the corresponding opening .......... 157 Fig. 3. 41 Close up view of the green rectangle in Fig. 3. 9 (right view), for b) initial time, to be compared with a) and c). For c) the concrete slab has been registered to its initial state, so that relative displacement of targets on Sandwich panel and FRP are evidenced ......... 158 Fig. 3. 42 Perspective views of the surface of reference (black) and of its displacement at time step 2069 (red). A bulge and a declivity can be seen on the red surface, with respect to the reference one ....................................................................................................................... 160 Fig. 3. 43 The difference between out of plane displacement for time steps 2071 and 2069 reveals the shell buckling ......................................................................................................... 161 Fig. 3. 44 a) Experimental set-up, the actuator loading the damper is clearly visible on the right side of the photo. The camera on the left partially hide the damper in the back-ground, that is vertically loaded by a square plate and 4 Dividags. b) A detail of the piston on which the tracked target is stuck ........................................................................................ 162 Fig. 3. 45 a) comparison of optical results (green) with Heidenhain (red) and Temposonics (blue); b) difference between Heidenhain and optical methods ........................................... 162 Fig. 3. 46 a) longitudinal and lateral displacements; b) cycles.............................................. 163 Fig. 3. 47 Rolling Shutter and global shutter video capture ................................................. 167 Fig. 3. 48 Configuration of vision system developed at LEE/NTUA .................................... 171

14

Fig. 3. 49 Software for stereoscopic video capture developed at LEE/NTUA ..................... 173 Fig. 3. 50 Stereoscopic video play-back of the system developed at LEE/NTUA................ 173 Fig. 3. 51 Indicative camera positions for camera calibration .............................................. 174 Fig. 3. 52 Camera calibration software developed at LEE/NTUA ....................................... 175 Fig. 3. 53 Template and actual (captured) target ................................................................... 177 Fig. 3. 54 Targets on specimen at LEE/NTUA ....................................................................... 179 Fig. 3. 55 Trajectory along X axes (displacement in meters) for the experiment performed at LEE/NTUA ............................................................................................................................ 179 Fig. 3. 56 Carbon arm drawing................................................................................................ 181 Fig. 3. 57 Different pictures of the carbon arm ...................................................................... 181 Fig. 3. 58 VIDEOMETRIC target ........................................................................................... 182 Fig. 3. 59 Left and right images of stereovision system ......................................................... 183 Fig. 3. 60 Test rig for stereovision system evaluation ............................................................ 184 Fig. 3. 61 A theoretical Gaussian distribution with µ, mean value and σ, standard deviation ..................................................................................................................................... 185 Fig. 3. 62 Histogram “number of errors” versus “deviation from mean value” for 6 targets (error = deviation from mean value) ....................................................................................... 185 Fig. 3. 63 VIDEOMETRIC results for different check tests ................................................. 186 Fig. 3. 64 VIDEOMETRIC results for different tests............................................................ 187 Fig. 3. 65 VIDEOMETRIC results quality (measurement noise) ......................................... 188 Fig. 3. 66 Concrete floor with epoxy coating .......................................................................... 189 Fig. 3. 67 Drums stack on AZALEE table (top view) ............................................................ 189 Fig. 3. 68 Examples of accelerograms for drums stacks seismic tests .................................. 191 Fig. 3. 69 A typical 3 pallets and 3x4 drums on AZALEE .................................................... 192 Fig. 3. 70 Drums stacks testing instrumentation .................................................................... 193 Fig. 3. 71 Instrumentation implementation on drums stacks ............................................... 193 Fig. 3. 72 VIDEOMETRIC targets fixe on mock up ............................................................. 194 Fig. 3. 73 Comparisons of VIDEOMETRIC and LVDT sensors measurements for shaking table ............................................................................................................................................ 195 Fig. 3. 74 Comparisons of VIDEOMETRIC and LVDT sensors measurements for top drum ........................................................................................................................................... 196 Fig. 3. 75 VIDEOMETRIC measurements for pallets .......................................................... 197 Fig. 3. 76 VIDEOMETRIC measurements for top drums .................................................... 198 Fig. 3. 77 Thermal images from a fatigue test to failure on a yielding shear panel dissipative device .......................................................................................................................................... 200 Fig. 3. 78 Thermal images from tests on short beam sections............................................... 203 Fig. 3. 79 Plastic strain distributions deduced from thermal images for the beam pictured in Fig. 3. 60 ................................................................................................................................. 205

15

List of Tables

Table 3. 1 Maximum deformations for each instrumented section and comparison with εy of longitudinal reinforcing bars. ...................................................................................................... 102

16

1 Study Overview

The main objective of JRA2 is the implementation and application of new types of

sensors, control techniques and modelling tools capable of enhancing the measurement of the

response of test specimens and improving the quality of test control. The activity also aims at

developing numerical simulation tools, integrated with data processing, databases and

visualisation, for an improved design of test campaigns, including the equipment and for

enhanced interpretation of experimental results. In more detail, the following objective both in

Task JRA2.1 and Task JRA2.2 is pursued:

– implementation and application of new types of sensors for improved sensing and

control. Specifically, new types of instrumentation -wireless, fibre optics and 3D visualization

tools based on several individual sensor measurements or digital video-photogrammetry- and

techniques for measuring structural and foundation response -point and field, local and global

kinematic measurements, etc.- will be explored. Experiments at different levels of complexity

will be carried out to calibrate/validate the proposed instrumentation and techniques.

State-of-the-art report for JRA2

18

2 JRA 2.1 Advanced Sensing and Control Techniques for Improved Testing Control

2.1 INTEGRATION METHODS

2.1.1 Introduction

Hybrid Simulation (Saouma Sivaselvan Editors, 2008) or hererogenrous testing (Bursi O. S. and

Wagg D. Editors, 2008), i.e. a method capable to evaluate the dynamic response of substructured

systems, is under development. In the method, the structure is torn into at least two parts,

amongst which some parts called numerical subdomains are computationally simulated while

other parts called physical subdomains are simulated through actual tests in the laboratory.

Pseudo-dynamic testing, continuous pseudo-dynamic testing, fast hybrid testing, real-time

substructure testing and real-time dynamic substructure testing and so on are methodologies

developed within the hybrid simulation framework (Nakashima et al 1992; Darby et al 2001;

Yung & Shing 2006; Wagg & Stoten 2001). Equation Section 2

Integration schemes are one key element for these methods, and up to now a good number of

integrators has been developed and applied, such as the central difference method (Wu 2005), the

Newmark schemes (Bayer 2005), the α-method (Yung & Shing 2007), the Operator-Splitting

method (Wu et al 2006), the GC method (Gravouil and Combescure 2001), the PM method

(Pegon and Magonette 2002) and so on. All of these methods can be classified into two types:

monolithic methods and partitioned methods. More and more attentions are paid to partitioned

schemes because of their ability to evaluate responses of complicated structures.


19

In this brief summary, some new-developed schemes are introduced. They are two monolithic

schemes, namely, the LSRT2 method and the CR method, and several partitioned schemes,

namely, the GC method, the PM method and the Partitioned-Rosenbrock method.

2.1.2 Monolithic Schemes

2.1.2.1 The LSRT2 Method

Bursi et al (2008) proposed the use of Rosenbrock-based integrators for real-time hybrid

simulations in the linear regime. They have been recommended for their accuracy and easiness

of implementation. The LSRT2 scheme is in fact a variant of linearly semi-implicit Runge-Kutta

methods, commonly referred to as Rosenbrock methods (Rosenbrock 1963). Herein the LSRT2

scheme is introduced by taking a substructured 2-DoF system. As shown in Fig. 2.1, the state

equation of the system can be expressed as

Fig. 2. 1 Schematic representation of a 2-DoF structure with substructuring: (a) emulated structure; (b) partitioned structure; (c) numerical substructure; and (d) physical

substructure and transfer system.

2

2 1

( , ) 1 [ ]n ne sn

yf t

f f c y k ym

= =

+ − −

y y (0.1)

where 1 2T Tx x y y= =y defines the state vector; mⁿ, cⁿ and kⁿ denote the mass, damping

factor and stiffness of the numerical substructure, respectively; fe and fs are the external force on

the numerical substructure and the coupling force between the two substructure. The LSRT2

method reads


20

1 1 1 2 2k k b b+ = + +y y k k (0.2) with

[ ] 11 ( , )k kt t tγ −= − ∆ ∆k I J f y (0.3)

[ ] ( )( )21 2

12 21 1,k kt t tα αγ γ−

+ += − ∆ + ∆k I J f y J k (0.4)

where b₁ , b₂ , γ, γ₂₁and α₂ are scheme parameters, which are adjustable to obtain better

numerical properties; 21k α+y represents the estimate of the state vector at the time

2 2kt t tα α+ = + ∆

; I is the identity matrix 2×2; J is the Jacobian matrix, defined as

0 1= n n

n n

k cm m

∂ = ∂ − −

fJy

(0.5)

The parameters can be determined in such a way to achieve second-order accuracy and L-

stability. The following values are recommended:

212

γ = − , 2 21 1/ 2α α= = , 21γ γ= − , 1 0b = and 2 1b = (0.6)

The hybrid test is summarized in algorithmic form as follows:

(a) Compute the Jacobian matrix J from (2.5)

(b) Compute k₁ from (2.3) and evaluate 21k α+y .

(c) Impose 21k α+y to the PS, measure the coupling force

21,s kf α+ and evaluate k₂ and 1k +y

from (2.4) and (2.1)

(d) Impose 1k +y to the PS and measure the coupling force , 1s kf +

(e) Set k=k+1 and go to (b)

From the aforementioned description, the integrator doesn't require the knowledge of the state

(displacement and velocity) and of the coupling force ahead of the actual stage and/or of the end

of the time step Δt. This property is referred to as Real-time Compatibility. Furthermore, the

integrator is based on a Runge-Kutta scheme and it is explicit for displacements and velocities,

which is different from most schemes based on Newmark schemes. Because of the explicit

displacement and velocity, better control performance, such as rapid, accurate and stable

responses, should be easily obtained. Because the LSRT2 method is a linearly implicit method, it

is more suitable to real-time test than most monolithic integrators. Moreover, it is filtering

capabilities beyond the Nyquist frequency ΩN=π are favourable as shown in Fig. 2. 2. The

method works well also in the nonlinear regime (Bursi et al. 2010).


21

Fig. 2. 2 Spectral radii ñ of real-time complatible integration methods vs non-dimensional frequency Ù

2.1.2.2 The Chang method

The Chang scheme proposed by Chang (2002) provides explicit displacements and is spectrally

equivalent to the famous Newmark constant average acceleration scheme. The Chang scheme,

applied to real-time hybrid simulations, can be expressed as

1 1 1 , 1 , 1( , )k n k k e k s ku + + + + ++ = −M r u u f f (0.7)

1 1( )2k k k kt

+ +

∆= + +u u u u (0.8)

21 2k k k kt t+ = + ∆ + ∆u u uαu (0.9)

In order to obtain stability and better performance, the following parameters must be carefully selected

11 2 1

2 0 01 1 12 2 4

t t−

− − = + ∆ + ∆ βI M C M K (0.10)

11

1 2 0122

t−

− = ∆ ββM C (0.11)

Investigations (Chang 2002) show that its numerical properties are similar to those of the

constant average acceleration method. In this respect, see Fig. The scheme is said to be

unconditionally stable, to exhibit no numerical dissipation and to have no overshooting effect.

However, this is only demonstrated for a linear structure, where K0 represents the constant

-210 10

-1 010 10

1 210 10

0.2

0.6

0.8

1.0

0.4

0.

1.2

γ =

γ =

Ω

ρ

π


22

stiffness. Bonnet (2006) and Bonnet et al. (2007) investigated the accuracy of the scheme based

on experimental results that proved the scheme to yield satisfactory results.

2.1.2.3 The CR Method

The development and application to monolithic problems of the Chen & Ricles (CR) integration

scheme were first presented by Chen & Ricles (2008a, 2008b). The scheme is spectrally

equivalent to the Newmark constant average acceleration scheme, with γ=1/2, β=1/4 and is

therefore second order accurate, unconditionally stable, non-dissipative and shows minor period

distortion characteristics when applied to monolithic problems. See Fig. 2. 2 in this respect. The

CR scheme presents a major advantage for RTDS testing over the Chang-scheme (Chang 2002)

because it provides explicit displacements and explicit velocities. The CR scheme, applied to

RTDS tests is described in Equations (2.12), (2.13) and (2.14), as follows:

1 1 1 , 1 , 1( , )k n k k e k s ku + + + + ++ = −M r u u f f (0.12)

1 1k k kt+ = + ∆u uαu (0.13) 2

1 2k k k kt t+ = + ∆ + ∆u u uαu (0.14) The first step of the scheme involves calculating the updated displacements using the second

difference equation in Equation (2.14) and to apply them to the experimental substructure by the

adoption of the following α₁and α₂ matrices:

1 2 20 0

44 2 t t

= =+ ∆ + ∆

MααM C K

(0.15)

Before the start of the numerical integration process, the method requires an initial estimate of the stiffness and damping matrices:

0 ( )n e∂ ∂≈ +

∂ ∂r rKu u

0 ( )n e∂ ∂≈ +

∂ ∂r rCu u

(0.16)

where K₀ and C₀ are the initial estimation of the stiffness and damping matrix, corresponding to

the emulated structure. Because the properties of the numerical substructure are known at all

time, the numerical tangent stiffness and tangent damping matrices can be updated at each step,

if the computation time required is reasonably short.

Several RTDS tests were successfully conducted by Chen & Ricles (2009) and Chen et al.

(2009). It was experimentally demonstrated that the CR scheme is stable and accurate when


23

performing RTDS tests. In Chen & Ricles (2008b), the stability of the scheme was investigated

in both the linear and nonlinear regime and it was proven that the scheme is unconditionally

stable as long as the tangent stiffness of the system integrated is of softening type. In both the

aforementioned references, the effect of nonlinear damping in the experimental substructure

wasn't investigated and no conclusions were available on the stability of the scheme for that

particular case. Furthermore, the effect of erroneous estimates of K₀ and C₀ on the order of

accuracy of the scheme are yet unknown.

In fact, even through the velocity of the CR method is explicit, the velocity target is not used in

the test, and furthermore, the linear interpolation of displacement target will induce a velocity

response different from the target. Then the unconditionally stability property may be destroyed.

From this viewpoint, the OSM-RST developed by Wu (2006) might perform better.

2.1.3 Partitioned Schemes

In these methods, the emulated structure is torn into non-overlapping substructures, where an

incomplete solution of the primal field is evaluated using a direct solver, and intersubstructure

field continuity is enforced via Lagrange multipliers applied at substructure interfaces (Gravouil

and Combescure 2001). Given a structure split into two domains, A and B for instance, the

equations of equilibrium on subdomain A at time 1nt + and subdomain B at time

/ ( 1,..., )n j sst j ss+ = , can be written as

1 1 1 , 1 1( , )A A A A A A ATn n n ext n nM u R u u F L+ + + + ++ = + Λ (0.17)

/ / / , / /( , )B B B B B B BT

n j ss n j ss n j ss ext n j ss n j ssM u R u u F L+ + + + ++ = + Λ (0.18) where the state variables u(t) are nodal quantities arising from a spatial discretization and their

derivatives u and u with respect to time t are indicated with superposed dots; AL and BL are

the constraint matrices which express a linear relationship between the two connected

boundaries. In the case of an inelastic material, R depends also on internal variables that, in turns,

incrementally depends on the current kinematical state of the numerical structure. In particular,

for a linear elastic system with classical damping, it holds:

1 1 1 1( , )A A A A A A An n n nR u u K u C u+ + + += + (0.19)


24

and

/ / / /( , )B B B B B B Bn j ss n j ss n j ss n j ssR u u K u C u+ + + += + (0.20)

where AK and BK denote the stiffness matrices of the two subdomains, respectively, and the AC

and BC are the damping matrices of the two subdomains, respectively.

The kinematic interface constraints between the subdomains can be written as

/ / 0A A B Bn j ss n j ssL w L w+ ++ = (0.21)

where, in general, w can be a displacement (u), a velocity ( u ) or an acceleration (u ).

2.1.3.1 The GC Method

Gravouil and Combescure (2001) proposed a multi-time-step explicit-implicit coupling method,

labelled as the GC method, which is able to couple arbitrary Newmark schemes with different

time steps in different subdomains. Fig. 2. 3 shows the basic procedure of the GC method.

Gravouil and Combescure proved that the GC method is unconditionally stable as long as all

individual subdomains satisfy their own stability requirements. Moreover, they showed that for

multi-time-step cases the GC method entails energy dissipation at the interface, while for the

case of a single time step in all the subdomains the GC method is energy preserving. The GC

method is very appealing for Real-time testing and in particular for continuous PsD testing as

heterogeneous numerical and physical substructures can be solved with different implicit/explicit

Newmark schemes in different subdomains, according to their complexity and characteristics.

The possibility of performing a large amount of small time steps on a reduced number of DoFs at

the laboratory, at about 1kHz frequency, while computing a large time step on a large number of

DoFs on a remote computer, is mandatory for the proper implementation of the continuous PsD

technique with substructuring. In particular, it maintains the smoothness of the displacement

trajectory without using any extrapolation/interpolation assumption, preserving the optimum

signal/noise ratio of the continuous method.


25

Fig. 2. 3 The GC method

Unfortunately, the GC, as can be seen in Fig. 2. 3, is in essence a sequential staggered algorithm

where the tasks in different subdomains are not concurrent. Systematically, the process

performing the fine time steps has to stop in order to wait for the process involving the coarse

time step. This is a drawback for real-time test and continuous PsD applications. In order to solve

this problem Pegon and Magonette (Pegon and Magonette 2002) developed and implemented an

interfield parallel algorithm, the PM method, based on the GC method.

2.1.3.2 The PM Method

The PM method is an extention of the GC method to advance all the domain simutaneously and

continuously, as depicted in Fig. 2. 4. The method for advancing from 1nt − to 1nt + in subdomain

A and from nt to 1nt + in subdomain B can be summarized by the following pseudo-code.

1. Solve the free problem in subdomain A by using 2 At∆ , thus advancing from 1nt − to 1nt +

2. start the loop on ss substeps in subdomain B

3. solve the free problem in subdomain B by using Bt∆ , thus advancing from ( 1) /n j sst + − to

/n j sst + with j=1,…,ss

4. linearly interpolate the free velocity / ,n j ss fu + in subdomain A

5. compute the Lagrange multipliers /n j ss+Λ by solving the condensed global problem

6. solve the link problem in subdomain B at /n j sst +

7. compute kinematic quantities in subdomain B at /n j sst + by summing free and link

quantities


26

8. if j=ss, then end the loop in subdomain B

9. solve the link problem in subdomain A by using 2 At∆ , from 1nt − to 1nt +

10. compute kinematic quantities in subdomain A at 1nt + by summing free and link

quantities

Fig. 2. 4 The interfield parallel solution procedure of the PM method

With the PM method, one can divide the whole structure into a subdomain where an implicit

Newmark method can be used and a subdomain where an explicit Newmark method can be

adopted. Moreover, the time step in one subdomain can be ss times that of the other one. This

provides the possibility to synchronize the computations in the two subdomains according to

numerical or physical requirements. As a result, this method can be implemented for parallel

simulations of numerical systems but also for hardware-in-the-loop and continuous pseudo-

dynamic testing.

The method was shown to be conditionally stable as the stability of the explicit subdomain

determines the stability of the emulated problem. As soon as Bt∆ satisfies the stability condition

(Bonelli, 2008), a rising of ss does not have any impact on the stability. Regarding the accuracy,

the scheme is still second order accurate when ss is equal to one, but it becomes first order

accurate when ss is larger than one, typical of partitioned schemes. An explanation for that can

be found in Jia (2010). The numerical damping ratio which is determined by the energy

dissipated at the interface is rather limited and similar when the number of substep is different

except of being unity, which corresponds to a non-dissipative case. Compared with the GC

method, the PM method exhibits an accuracy which is related to 2 At∆ instead of At∆ and

numerical analysis shows that it results to be less dissipative than its progenitor GC method.


27

Bursi et al.(2010) extended the properties of the interfield parallel PM method by introducing the

Generalized-α method into it. In detail for this partitioned method the Generalized-α method was

developed while avoiding a balanced formulation of the equilibrium equations. It was shown that

the controllable numerical dissipation can be advantageous for solving coupled and/or

heterogeneous structural dynamic systems, where convergence and/or computational efficiency

can be adversely affected by spurious high-frequency components of the response, entailed by

spatial discretizations and/or kinematic constraints.

2.1.3.3 The Partitioned Rosenbrock Method

Bursi et al. (2009) developed the PM method based on Rosenbrock method, which is an linearly

implicit method. The partitioned problem can be expressed in compact form as

( ), Tt = +

=

Ay F y CΛCy 0

(0.22)

where the Lagrange multiplier vector can be obtained as

( )11 1 ,T t−− − = − ΛCA C CA F y (0.23)

Fig. 2. 5 shows the partitioned L-stable Rosenbrock method. Further information can be found in

Jia et al. (2011).

Fig. 2. 5 The multi-time-step partitioned algorithm with ss=2: (a) staggered procedure; (b) interfield parallel procedure.


28

The partitioned Rosenbrock methods are appealing for their accuracies and stabilities in some

cases. In detail, most partitioned schemes are first order accurate, while the partitioned

Rosenbrock methods are second accurate. Furthermore, they are L-stable and suitable to solve

stiff problem.

However, the partitioned method also exhibits disadvantages, such as displacement drifts in

distinct subdomains. To solve these problems, an improved version of the method was conceived

as shown in Fig. 2. 6. This algorithm reduces the drift by performing velocity projection,

improves and simplifies the computation procedure by using the Rosenbrock method with

different stage sizes. Simulations show that the projection also improves the robustness due to

the dissipation introduced. Unfortunately, the new method looses some accuracy owing to the

projection. Test result comparisons of a Two-DoF system made via the test rig TT1 are presented

in Fig. 2. 7, which confirm the drift reduction of the newly-developed method. For more

information, readers are referred to Bursi et al. (2011).

Fig. 2. 6 The solution procedure of the improved interfield parallel algorithm.


29

(a) Translational displacements provided by different algorithms

(b) Rotational displacements provided by different algorithms

Fig. 2. 7 Comparison of test results between different partitioned methods.

2.1.4 Conclusions

This brief summary introduced several integrators for hybrid simulations or heterogenrous

testing with substructuring. Amongst these schemes, LSRT2 and partitioned Rosenbrock

methods appear to be good choices owing to a series of advantages, such as real-time

compatibility, second order accuracy, L-stability and so on. More experimental verifications are

needed.

In addition, the implementations in this section are not dealing with the problem related to the

transfer system dynamics, even though delay compensation is another key problem to real-time

hybrid simulation. The control strategies presented in the following section, such as adaptive

control and internal model control, may reduce the effect of delay.


30


31

2.2 ADAPTIVE CONTROL STRATEGIES FOR REAL-TIME SUBSTRUCTURING TESTS

2.2.1 Introduction

In order to perform a substructured test we separate a structure into parts or substructures and we

enforce their coupling at the separation points or substructuring interfaces, with the intention to

reproduce the behaviour of the emulated structure. The quality of the coupling determines the

reliability of the test results, so that its enforcing and evaluation are capital.

The coupling may be implemented in a variety of ways, the most common of which is, perhaps,

tracking the displacement of the interface nodes of a numerical substructure with the

displacement of their counterparts in a physical one, which is enforced by a controlled transfer

system, typically linear actuators, such as hydraulic cylinders equipped with servo-valves, and

feeding back the measured forces into the numerical substructure as forcing terms.

A very simple, but nevertheless illustrative, example is what has been called the split mass

problem, a one-dimensional mass-spring-damper system which is separated into two subsystems

which we will indulge in calling substructures. The coupling of both is achieved in the way

described above.

Note that perfect coupling of this kind affords both perfect representation of the original system

and an impossible situation in which one of the substructures must behave as a non-causal

system, fact which is represented by its transfer function having more zeros than poles (Ogata,

1970). It is therefore in principle impossible to achieve perfect coupling and all depends on how

close we can get to it and how much the imperfections in the coupling affect the reliability of the

test results.


32

Fig. 2. 8 Schematic for real-time substructuring tests

Fig. 2. 9 Block diagram for real-time substructuring tests

The course of action goes therefore, very often, on the lines of improving the frequency response

of the controlled transfer system and reducing its effects on the test results. In pseudo-dynamic

tests, this is achieved by running experiments more slowly, thus virtually reducing the relative

response time of the transfer system. However, when that is not possible and real-time testing is

required, faster actuators are the only solution (Zhao et al., 2003).

At that point the engineering criteria compel some to seek some solution that needs not use

transfer systems with bandwidths several orders of magnitude higher than those of the

trajectories they will be used to follow.

A common strategy (Wallace et al., 2005a) involves characterizing the transfer system for typical

test frequencies, correcting the amplification by the use of a gain, considering the phase lag as a


33

time-delay, which can then be added to the other sources of delay, typically communications and

computation, and manipulating the reference signal sent to the transfer system in order to

anticipate that delay. The method of choice for the latter is, in many cases, the prediction of

future reference signal values through extrapolation of its past values. This type of technique has

given satisfactory results when the delay to be compensated was short in comparison to the

typical period of vibration of the relevant structure or, in other words, with relatively fast transfer

systems, communications and computing. The reason for the compensation to have been

considered necessary was that the effect of the delays in the feedback of interface forces was

magnified by the test dynamics (Wallace et al., 2005b). The test parameters have a great

influence in that. For example, it has been proven that, for the split mass problem described

above, any delay, no matter how small, will render a test unstable if the mass in the physical

substructure is greater than that in the numerical substructure (Bursi et al. 2008, Kyrychko et al

2006).

Traditionally more advocated by control engineers, another option to counteract the possible

pernicious effects of the transfer system not responding as fast as our confidence in test results

would require, is to characterize the transfer system as a (set of) differential equation(s) and,

provided that it is possible to do so, invert it and integrate it in the numerical model used as the

numerical substructure. The accuracy of the transfer system model is here very important,

especially when the structures involved have little damping.

The disadvantages presented by some test parameters can also be palliated by using the existent

knowledge of the structure, by integrating it into the numerical model representing the numerical

substructure. This has been analysed and compared to the Smith predictor control resource,

which was originally conceived to enable closed-loop control of processes with long dead

periods. The working principle is the reduction of the significance of the feedback forces in the

test, which results in the improvement of the stability of the substructuring test and therefore the

decay of errors caused by the transfer system. The extreme case, in which the feedback is

completely eliminated, would be a result of having an exact model of the structure and the

absurdity of the test.


34

It is then manifest that, unless the transfer system is fast and powerful enough to not be

significantly affected by the structure it is forcing or not to introduce significant dynamics into

the test, modelling the ongoing processes is capital for the test results to be reliable and that, the

better we are able to model them, the less necessary the test itself is (Gawthrop et al., 2007). To

cope with this contradiction, schemes have been designed in which uncertainties where

compensated by adaptation in the relevant algorithms.

Fig. 2. 10 Substructuring test with a model of the physical specimen to improve the test characteristics (After Sivaselvan, 2006).

2.2.2 Open loop indirect adaptive control for compensating transfer system dynamics

Following the line of modelling the transfer system dynamics and using the resulting information

to generate a demand signal that will ensure the motion of the physical substructure,

satisfactorily follows that calculated with the numerical model, different online identification

techniques were proposed. This open-loop control method is therefore indirectly adaptive, as it

relies on the identification of the transfer system for the redesign of the control algorithm

(Gawthrop et al., 2005).

Against this method, it may be argued that it is completely unable to reject disturbances – apart

form the disturbance rejection capabilities the transfer system itself may have -, due to its open-

loop control nature, quite independently of the identification and subsequent control design

methods we may choose. In addition, because the adaptive nature of the controller is indirect, we

entirely depend on the identification method to correct any deviations from the desired

trajectories. Identification methods are invariably based on the combined analysis of input and


35

output histories, during which the behaviour of the controlled system may or may not be

satisfactory.

2.2.3 Open loop direct adaptive control for compensating transfer system dynamics

A variation of the above method was proposed, e.g. by Lim et al. (2007), by which a plausible

model for the transfer system behaviour is designed, while it is controlled in a closed loop

capable of ensuring that behaviour. From measurement of the transfer system trajectories and

their comparison with those expected of the aforementioned model, the closed loop controller is

redesigned in every time-step. It is possible to implement this in a way that guarantees that both

trajectories will converge, with accuracy and speed of convergence depending on the noise signal

ratios and available computational ratio, as well as the relevance of unmodelled dynamics. The

model information is then used to generate the signal to be sent to the closed loop controller, in

an open loop control fashion.

This practice may improve on the previous one in that its implementation is normally easier and

that the behaviour of the transfer system is constantly checked upon, to drive it to the desired

one, but otherwise differs from it very little.

2.2.4 Indirectly adaptive modification of the feedback force

As mentioned above, it is possible to palliate disadvantages presented by unfavourable test

parameters by integrating a model of the physical substructure into the numerical substructure –

quite apart from the method chosen to palliate the effects of the transfer system dynamics -, to

then subtract an equivalent force from the feedback force (Sivaselvan 2006). However, our

ability to model the physical substructure is contrary to the pertinence of the test, so we may

choose to identify it during the test and subsequently modify both the numerical substructure and

the calculation of the quantities to be subtracted from the feedback force.

Against this method, it may be argued that modification of the numerical substructure is

laborious and that the computational overhead, besides the possible actuator dynamics

compensation scheme, is not easily justified.


36

Fig. 2. 11 Adaptive model of the physical specimen for palliating lack of knowledge (After Sivaselvan, 2006)

2.2.5 Adaptive control of the transfer system in parallel with the numerical substructure

A way of palliating both the effects of transfer system dynamics and unfavourable test

parameters at the same time is to design a closed loop controller (Stoten and Hyde 2006) around

the transfer system to ensure that it responds to the excitation signal in the same way as the

numerical substructure does to the excitation signal and the feedback force – which would not

be, strictly speaking, a feedback force any more -. The input to the transfer system is not

calculated from the output of the numerical substructure, but directly from the excitation signal,

so in that sense (although not strictly) the transfer system is in parallel, rather than in series, with

the numerical substructure.

The controller may be designed to ensure that the trajectories of the physical and numerical

substructures coincide, if the transfer system and the physical substructure are known. Again,

this is contrary to the pertinence of the test, so an adaptive controller may be chosen to

compensate for lack of knowledge.

2.2.6 Conclusions

Real-time substructuring test results are affected by the coupling established between the

numerical models and the physical specimens involved, in a case-specific way. To be able to


37

assess the reliability of the results of one particular test, it is not only necessary to monitor the

nature of the coupling, but also to establish the way in which it affects the quality of the test.

Once the relationship between the coupling in the numerical-physical interfaces and the test

reliability has been established, the latter may be improved by improving the former, through the

use of more suitable equipment and control techniques, and reducing its impact on the final

results through numerical manipulations. However, the degree of the success of both control

techniques and numerical manipulations depends on the knowledge of the test characteristics

available for their design, while the lack of that knowledge is the main reason for the test to be

performed.

Adaptive schemes of different kinds have been proposed by different authors to improve test

results by applying a limited knowledge of the test characteristics, which is subsequently

completed by observing its behaviour. The proposed algorithms are varied, but their immediate

objectives are similar. Here, we have given a possible generalist classification of the proposed

methods, quite independently of the particular algorithms used in each case.


38

2.3 INTERNAL MODEL CONTROL

2.3.1 Introduction

In the last chapter, several adaptive control strategies for hybrid simulation are introduced. Two

control strategies based on plant models, namely internal model control (IMC) and model

predictive control (MPC), are discussed in the following part of this chapter.

2.3.2 Internal model control

It is evident that both closed-loop control and open loop control exhibit advantages and

disadvantages. In the widely used closed-loop control, e.g. proportional-integral- derivative

(PID) control, the controller regulates the drive signal based on current and past errors and thus,

the controller cannot respond to the change or disturbance of the input before the error is

measured (Jung 2005). Conversely, open loop controllers directly regulate the drive signal based

on the reference input. However, open loop controllers can’t eliminate the errors between the

reference and the output, which is different from closed-loop control. Furthermore, open loop

controllers don’t reject the disturbance at all.

In hybrid simulation of heterogeneous testing, transfer systems are required to rapidly and

accurately respond to the reference input in order to enforce the coupling between the two

substructures. If the specimen is loaded with several transfer systems, disturbance rejection

performance of each control system should be considered to decline the coupling influence

amongst transfer systems. From these viewpoints, internal model control (IMC) may be a

preferable choice, which has the advantages of open-loop controls and closed-loop controls

(Morari and Zariou 1989).


39

2.3.3 Basic Concepts of IMC

Fig. 2. 12 Block diagram of IMC

Internal Model Control (Morari and Zariou 1989) is one control scheme with the advantages of

the open-loop control and closed-loop control. Fig. 2. 10 shows the block diagram of a single-

degree of freedom IMC. If the plant model can perfectly represent the plant, setpoint tracing

control is an open loop and the closed-loop can reject a disturbance. If the plant model is not

perfect, the closed loop can suppress the discrepancy between the plant output and the model

output. So, IMC can rapidly respond to the setpoints in that it’s an open loop. Secondly, IMC can

obtain accurate control performance in that it can reject the disturbance and correct model

mismatches through its closed loop. In fact, the closed loop can improve the system robustness.

Furthermore, IMC is similar to Smith’s Predictor control, which was conceived to control a

system with large delay.

To further explain the IMC properties we derive the transfer functions from the disturbance and

setpoint to the plant out

( ) ( ) ( )( )1 ( ( ) ( )) ( )

p s q s r sy sp s p s q s

=+ − (0.24)

(1 ( ) ( )) ( ) ( )( )1 ( ( ) ( )) ( )

dp s q s p s d sy sp s p s q s

−=

+ −

(0.25) In equation (2.24), if ( ) ( ) 1p s q s = and ( ) ( )p s p s= , we obtain ( ) 1y s = , which means perfect

setpoint tracing performance. In equation (2.25), we find ( ) 0y s = , which means perfect

disturbance rejection, if ( ) ( ) 1p s q s = , no matter whether ( ) ( )p s p s= .


40

As it is mentioned above, disturbance rejection performance is significant when the coupling

amongst actuators is strong. In several researches, the influence of the coupling are reported, see,

amongst others, Bonnet et al (2007). A 2-degree of freedom IMC, as depicted in Fig. 2. 11, may

solve the problem. In the figure, two controllers are designed: one for disturbance rejection and

the other for setpoint tracing. Then it is not needed to compromise between setpoint tracing and

disturbance rejection with one controller.

Fig. 2. 13 Two-degree of freedom IMC

Fig. 2. 14 Model reference adaptive inverse control system

2.3.4 Some Extensions of IMC

IMC is an open control scheme and many concepts, such as robustness, adaptiveness, can be

synchronized with it. For example let’s consider model reference adaptive control, which is

widely used as one type of adaptive control. Fig. 2. 12 shows the block diagram of Model

reference adaptive inverse control system (Widrow and Walach 2008), which is the connection


41

of two-degree of freedom IMC and MRAC. MRAIC inherits the advantages of both IMC and

MRAC.

2.3.5 IMC application in the TT1 test rig

The IMC controller is designed with a second-order actuator model for the TT1 test rig. Tests

without and with a specimen are conducted and compared with the corresponding tests by using

a PID tuned with the CHR scheme. Only the results with the specimen consisting of two springs,

one dampers and one mass, are presented here, as shown in Fig. 2. 13. In the figure, IMC-5 and

IMC-6 denote IMC with the filter time constants 0.005τ = and 0.006τ = , respectively. One can

see that the performance of the two types of control methods is very similar. Further simulations

were carried out and showed that the IMC controller was better in terms of setpoint tracking and

disturbance rejection when uncertainties were taken into account.

Fig. 2. 15 IMC applications in the actuators of the TT1 test rig

10-1

100

0

0.5

1

1.5

IMC-5IMC-6PID

10-1

100

-60

-40

-20

0

IMC-5IMC-6PID

10-1

100

-0.04

-0.03

-0.02

-0.01

0

IMC-5IMC-6PID


42

2.3.6 Conclusions

IMC is a method directly based on a plant model. Compared with other controls, the IMC

method applies to the plant model explicitly and directly. Then, it’s easier to tune IMC

parameters. Its robustness, ability of delay compensation and rapid response performance

indicate that it may be suitable for real-time tests with dynamic substructuring.


43

2.4 MODEL PREDICTIVE CONTROL

MPC designates an ample range of widely used control methods. Instead of difficult and tedious

finding the closed loop optimal feedback control, MPC is based on the repeated solution of open

loop optimal control using an updated state and knowledge of a plant predictive model.

2.4.1 Basic principle of MPC

The ideas in the predictive control family are basically:

o explicit use of a model to predict the plant output in the future horizon;

o calculation of a control sequence through minimizing an objective function;

o a receding strategy, so that at each instant the horizon is displaced toward the future,

which involves the application of the first control signal of the sequence calculated at

each step.

Fig. 2. 14 shows the basic structure of MPC. It mainly contains three parts: process, process

model and control computation part. From the figure, we can summarize the main steps of the

method as follows:

o Establish the plant model to predict the plant output in the future.

o Determine the cost function and then minimize it to obtain the control action in the

future.

o Send the right control action at the nearest instant.

o Go to the next step.

Fig. 2. 16 Basic structure of MPC


44

Fig. 2. 17 MPC strategy

Fig. 2. 15 shows the MPC procedure in a greater detail. At time k∆t the current plant state is

sampled and a cost minimizing control strategy is computed (via a numerical minimization

algorithm) for a relatively short time horizon in the future: [k∆t, (k+N)∆t]. Specifically, an

online calculation is used to explore state trajectories that emanate from the current state and find

a cost-minimizing control strategy until time (k+N)∆t. Only the first step of the control strategy

is implemented, then the plant state is sampled again and the calculations are repeated starting

from the now current state, yielding a new control and new predicted state path. Although this

approach is not optimal, in practice it has given very good results (Camacho E.F. and Bordons C.

2003).

2.4.2 Advantages and disadvantages of MPC

MPC has a series of special features, as shown in the following (Camacho E.F. and Bordons

C. 2003):


45

-It is particularly attractive to stuff with only limited knowledge of control because the

concepts are very intuitive and relatively easy.

-It can be used to control a great variety of processes, e.g. systems with long delay times and

non-minimum phase of unstable ones.

-The multivariable case can easily be dealt with.

-It introduces feed forward control in a natural way to compensate for measurable

disturbances.

-It is a totally open methodology based on certain basic principles which allows for future

extensions.

As it is logical, however, it also has its drawbacks. The greatest drawback is the need for an

appropriate model of the plant. In fact, it's not very easy to obtain an appropriate model. The

second drawback is that the derivation of MPC is more complex than that of the classical PID

controllers. Another drawback is that when constraints are considered, the amount of

computation is high .In spite of these drawbacks, MPC is widely used and has proved to be a

reasonable strategy.

2.4.3 MPC and hybrid simulation

Fig. 2. 18 Schematic of real-time test

One of the great features of MPC is that it predicts the plant output in the future based on the

plant model and the desired output or setpoints (Juang Jer-nan and Phan Minh Q.. 2001). For

generic control problem, the desired output can be preliminarily supplied. However, the desired


46

future plant output in real-time test can’t be obtained directly because they are the future

response of the numerical substructure, as shown in Fig. 2. 15. If we try to predict them, we have

to predict the response of the physical substructure firstly and the predictive error would be

accumulative. Because the future desired output can’t be obtained directly, which is different

from general control problem, MPC is difficult to use in hybrid simulation.


47

2.5 COMBINED INVERSE-DYNAMICS AND ADAPTIVE CONTROL FOR INSTRUMENTATION

2.5.1 Introduction

In the above three sections, several control strategies for real-time test (real-time hybrid

simulation) were introduced. The following part of this chapter will introduce another control

strategy, which combines the inverse-dynamics and the adaptive control. Even though something

is mentioned in the aforemented sections, the control strategy is presented in more detail.

2.5.2 Overview of Inverse-Dynamics (Inverse-Model) Control

Principally, inverse-dynamics controller (IDC) is implemented under the assumption of a good

level of parameter certainty. The effectiveness of IDC was proven in many implementation

works, eg. Nakanishi et al. (2007) and Zhou et al. (2006). Fig. 2. 16 shows the block diagram of

an open-loop, IDC-controlled system, whilst more complicated nonlinear, closed-loop IDC is

excluded in this context. In Fig. 2. 16, the plant, composed of sensor(s) or actuator(s), is

represented by Gp(s); the transfer function of the plant model is written as ( )PG s , and Gc(s)

denotes the IDC. The reference, output response and inverse dynamics control signals are

described by r, yp and uc, respectively.

Fig. 2. 19 The scheme of open-loop inverse-dynamics control

IDC is designed from a linear inverse model of the underlying plant dynamics, which is typically

parameterised via system identification. Therefore the IDC control law is written as:

( ) ( )1C PG s G s−= (0.26)

Ideally, when the parameters are known exactly, the plant output yp is given by:

ucr ( )PG s( ) ( )1C PG s G s−= yp


48

( ) ( ) ( ) ( ) ( )p P Cy s G s G s r s r s= = (0.27)

where the following assumptions are made:

( ) ( )P PG s G s≅ (0.28)

( ) ( ) 1P CG s G s = (0.29)

Nevertheless, as would be the normal case, the plant dynamics cannot be estimated

accurately:

( ) ( )P PG s G s= + ∆ (0.30)

where ∆ represents the unmodelled and uncertain dynamics. Therefore, the following issues

regarding inverse-model control need to be noted:

In many practical systems, GP(s) may contain nonlinear or non-proper transfer-

function dynamics, so that ( )PG s in (1.26) cannot be properly synthesised or

inverted.

The linearised inverse-dynamic equations can be coupled and complicated, and thus

solving the inverse-dynamics problems is time-consuming.

Parameter knowledge may be far from certain, which can not be known exactly via

system identification, thus GP(s)GC(s) ≠ 1 and yp(s) ≠ r(s).

The stability and control robustness cannot be guaranteed via a feedforward, open-

loop control policy, in the presence of significant parameter changes, uncertainties

and unknowns within the plant.

Therefore a feedback control policy is required to ensure closed-loop stability and robustness.

Nonlinear adaptive control method is introduced, to cater for nonlinear, uncertain parameter

control problems.

2.5.3 Overview of Adaptive Control

Different from linear, optimal and/or fixed-gain control strategies, an adaptive controller is

composed of nonlinear, time-varying control gains, that change with time to enable the controller

to accommodate varying or uncertain parameters within the plant. There are many methods to

formulate adaptive control algorithms. Model-reference adaptive control uses an ideal reference

model to form adaptive gains, eg. Stoten & Gómez (2001) and Wagg & Stoten (2001). The


49

control objective is to drive the nonlinear system to behave as the reference model, as presented

in Fig. 2. 17 and introduced in the next paragraph.

Fig. 2. 20 The scheme of parallel model-reference adaptive control

As shown in Fig. 2. 17 the reference model, adaptive controller and plant are written as GM, GC

and GP, respectively. The reference model output, plant output and adaptive control signals are

denoted by ym, yp and uc, respectively. The principle of model-reference adaptive control is to

design adaptive control algorithms, which enable yp to approach the trajectory of ym, thus

e = ym – yp ideally approaching zero. The beneficial properties of adaptive control are

summarised as follows:

Direct (black-box) adaptive control requires no a priori information on the plant

dynamic parameters, thus requiring no system identification. Adaptive gains are

directly synthesised from on-line signals to cater for time-varying, unknown or

nonlinear plant dynamics.

Adaptive algorithm is particularly suitable for control of nonlinear dynamic systems,

as in many cases the dynamics are not exactly known and the response

characteristics can therefore be unpredictable. Adaptive gains can compensate for

these problems in real-time, thus improving test accuracy.

2.5.4 Combined Inverse-Dynamics and Adaptive Control for Instrumentation

Combined inverse-dynamics and adaptive control method was considered and implemented

(Cheah et al. 2006; Stoten & Gómez 2001; Wang & Xie 2009). Instruments inevitably include

ymr

Plant(GP)

Reference model(GM)

Nonlinear system

Adaptive controller(GC)

ypuc


50

unwanted dynamics which can influence the measuring and testing accuracy. When an IDC and

a model-reference adaptive controller are combined for dynamic compensation of

instrumentation, the block diagram is then shown in Fig. 2. 18 and is addressed as follows:

the IDC gain can be envisaged as initial conditions for the adaptive algorithm, when

the plant dynamics are partially known, in order to pursue faster convergence and

even better performance than the ‘black-box’ approach to adaptive control.

Nonlinear, uncertain and changing parameters, which are not considered explicitly

by the IDC, can be compensated by an adaptive controller.

Fig. 2. 21 The control block diagram for inverse dynamics + adaptive controllers

The potential candidates for control of instrumentation/instruments are suggested: the inverse-

dynamics compensation via simulation (IDCS) method (Tagawa & Fukui 1994) and minimal

control synthesis algorithm (Stoten & Gómez 2001), developed by Professor Tagawa in Japan

and Professor David Stoten in Bristol, respectively. These two control algorithms were

implemented together on dynamic substructuring tests (Tu et al. 2009), and will be specifically

introduced in due course.

2.5.5 Conclusions

In this section, some basic ideas of inverse dynamics control and ccombined inverse-dynamics

and adaptive control method are introduced. From this brief overview, the advantages of the

combined method, such as easiness of implementations and better performances, are shown.

ymr

Plant

Referencemodel

Adaptive controllerypuc+

+

Inverse dynamicscontroller


51

2.6 TT2 TEST: NON LINEAR HYDRAULIC ACTUATOR MODEL

In earthquake engineering, Servohydraulic actuators are mostly used in dynamic shaking table

tests. For these tests, the control strategy is to measure the transfer function between the motion

and the electrical white noise sent to actuators. With the desired motion and the inverse transfer

function, the electrical drive can be calculated. This electrical drive is finally sent to actuators

which creates a motion quite similar to the desired motion. This strategy can not be used for real

time hybrid tests because:

• It does not take into account non-linearities of the mock up and the plant (the

transfer function does not change during the test).

• It does not take into account the modification of the mock up during the test (the

transfer function does not change during the test).

• The large rigid mass of the shaking table compensate non-linearities and mock up

modification.

• The desired motion has to be known before the test which is not the case for

hybrid tests.

• The CPU time necessary to calculate the electrical signal is too important for real

time.

For real time hybrid test, a real time compensation method should be used to compensate the

actuator dynamics. In the previous chapters, some real-time control strategies have been

introduced to compensate the transfer dynamics of the plant. A model of the plant should then be

used. For electro dynamic actuators (and for a suitable design of the plant), the transfer dynamics

can be evaluated by a delay. Nevertheless, hydraulic actuators have to be used for large force and

displacement, and their behavior is much more complex than a delay. In this chapter, we will

then focus on hydraulic actuator model. This model will first permit to better understand physical

phenomena in actuators. Second, it could permit to numerically test algorithms. Third, it could

give the oportunity to compensate the actuator dynamics. The first part will present and detail

equations of the Merritt physical model of an hydraulic actuator in time domain. This model has

been commonly used for actuator design during the last 40 years, but contrary to design studies,

a very high accuracy of the model is needed for control. We will then improve equations of the

Merritt model. To determine the parameters of these equations and to validate the complete


52

model, TT2 tests have been setup and performed at CEA (France). Experimental setup and

results will be presented. Finally, a comparison between the model and the test will be done.

2.6.1 Actuator model

This part will present and detail equations of the Merritt physical model of an hydraulic actuator

in time domain. In this part, we will also improve this model by introducing in equations:

• The servo valve dynamic.

• The variation of the oil stiffness along the stroke.

• The Stribeck friction force.

• The independency of the model with regard to the tested structure.

2.6.1.1 The Merritt servohydraulic model

This model is a reference model commonly used for analytical modelling of servovalve-actuator

systems (Alleyne and Liu, 2000, Conte and Trombetti ,Willimas and Williams and

Blakeborough, 2001, Kuehn and Epp and Patten 1999).

2.6.1.1.1 Fluids mechanics equations

Equation of state

In his model, Merritt assumes that temperature and pressure have small influence on fluid

density and uses a linear equation of state :

𝜌 = 𝜌0 + ∂𝜌∂𝑃𝑇

(𝑃 − 𝑃0) + ∂𝜌∂𝑇𝑃

(𝑇 − 𝑇0)

= 𝜌0 1 + 1𝛽𝑒

(𝑃 − 𝑃0) + 𝛼(𝑇 − 𝑇0) (1)

with:

ρ, mass density,

P, pressure,

T, temperature,

βe, effective isothermal bulk modulus,


53

α, volumetric thermal expansion coefficient,

P0, T0, ρ0, reference parameters.

Continuity equation

Fig. 2. 22 Flows entering and leaving a control volume (Merrit, 1967)

Following Fig. 2. 19:

∑ 𝑊𝑖𝑛 − ∑ 𝑊𝑜𝑢𝑡 = 𝑔 𝑑𝑚𝑑𝑡

= 𝑔 𝑑𝜌𝑉0𝑑𝑡

= 𝑔𝜌 𝑑𝑉0𝑑𝑡

+ 𝑔𝑉0𝑑𝜌𝑑𝑡

(2)

with:

Win/out, weight flow rates,

m, mass,

g, acceleration.

Merritt assumes that temperature is constant in the system. The equation of state (1) can now be

written:

𝜌 = 𝜌𝑖 + 𝜌𝑖𝛽𝑃 (3)

𝜌𝑖 and 𝛽: parameters at zero pressure.

Noting that 𝑊 = 𝑔𝜌𝑄, (2) becomes:

∑ 𝑄𝑖𝑛 − ∑ 𝑄𝑜𝑢𝑡 = 𝑑𝑉0𝑑𝑡

+ 𝑉0𝛽𝑑𝑃𝑑𝑡

(4)


54

because:

𝑑𝜌𝜌

= 𝜌𝑖𝑑𝑃𝜌𝑖+

𝜌𝑖𝛽𝑃

𝑛𝑒𝑔𝑙𝑒𝑐𝑡𝑒𝑑

= 𝑑𝑃 (5)

The neglected term in (5) can be considered as insignificant because we assumed that pressure

has small influence on 𝜌.

2.6.1.1.2 Servohydraulic System

The following figure presents the Valve-piston combination:

Fig. 2. 23 Valve piston combination (Merrit 1967)

On Fig. 2. 20:

− 𝑞1, 𝑞2, forward and return flows.

− 𝑝1,𝑝2, forward and return pressures.

− 𝐴𝑝, area of piston.

− 𝑥𝑝, displacement of piston.

− 𝑉01, initial volume of forward chamber.

− 𝑉02, initial volume of return chamber.


55

− Cip is the coefficient of leakage between chambers.

− Cep is the coefficient of each chamber end leakage.

Servo valve

Assuming the servovalve orifices are symmetrical, the equations of continuity for system are:

𝑞1 = 𝐾𝑞𝑥𝑣 − 2𝐾𝑐𝑝1 (6) 𝑞2 = 𝐾𝑞𝑥𝑣 + 2𝐾𝑐𝑝2 (7)

with:

Kq, valve displacement gain.

Kc, valve flow-pressure gain.

xv, valve displacement from neutral.

(6) and (7) give:

𝑞𝐿 = 𝐾𝑞𝑥𝑣 − 𝐾𝑐𝑝𝐿 (8) with:

qL = q1+q22

, average flow, pL = p1 − p2, load differential pressure.

Actuator

The equation of flow continuity for each chamber of the actuator are written:

𝑞1 − 𝐶𝑖𝑝(𝑝1 − 𝑝2) − 𝐶𝑒𝑝𝑝1 = 𝑑𝑉1𝑑𝑡

+ 𝑉1𝛽𝑒

𝑑𝑝1𝑑𝑡

(9)

𝐶𝑖𝑝(𝑝1 − 𝑝2) − 𝐶𝑒𝑝𝑝2 − 𝑞2 = 𝑑𝑉2𝑑𝑡

+ 𝑉2𝛽𝑒

𝑑𝑝2𝑑𝑡

(10) where:

βe is the effective bulk modulus for both gas and liquid contained in the chamber.

V1 is the actual oil volume of forward chamber.

V2 is the actual oil volume of return chamber.


56

Considering a symmetrical actuator with a chamber volume of 𝑉0 when piston is centered, we

have:

𝑉0 = 𝑉01 = 𝑉02 (11) 𝑉1 = 𝑉0 + 𝐴𝑝𝑥𝑝 (12) 𝑉2 = 𝑉0 − 𝐴𝑝𝑥𝑝 (13)

Then (9) and (10) give:

𝑞𝐿 = 𝐴𝑝𝑝 + 𝐶𝑡𝑝𝑝𝐿 + 𝑉𝑡4𝛽𝑒

𝐿 + 𝐴𝑝𝑥𝑝2𝛽𝑒

(1 + 2) (14)

𝐶𝑡𝑝 = 𝐶𝑖𝑝 + 12𝐶𝑒𝑝, global leakage coefficient of the actuator.

The last term on the right of equation (14) is neglected to linearize the equation around the

centered position of the piston and equation (14) is written:

𝑄𝐿 = 𝐴𝑝𝑝 + 𝐶𝑡𝑝𝑃𝐿 + 𝑉𝑡4𝛽𝑒

𝐿 (15)

2.6.1.1.3 Final Merritt model equations

From Figure 2 , we can write the resulting force equation applied to the piston:

𝐹𝑔 = 𝐴𝑝𝑃𝐿 = 𝑀𝑡𝑠2𝑋𝑝 + 𝐵𝑝𝑠𝑋𝑝 + 𝐾𝑋𝑝 + 𝐹𝐿 (16)

with:

Fg, force generated or developed by the piston,

Mt, total mass of piston and load referred to piston,

Bp, viscous damping coefficient of piston and load,

K, load spring gradient,

FL, arbitrary load force on piston.

Equations (8), (15) and (16) lead to:


57

𝑋𝑝 =𝐾𝑞𝐴𝑝𝑋𝑣−

𝐾𝑐𝑒𝐴𝑝

21+𝑉𝑡

4𝛽𝑒𝐾𝑐𝑒𝑠𝐹𝑙

𝐾𝑐𝑒𝐾𝐴𝑝2

+1+𝐵𝑝𝐾𝑐𝑒𝐴𝑝2

+ 𝐾𝑉𝑡4𝛽𝑒𝐴𝑝2

𝑠+𝐾𝑐𝑒𝑀𝑡𝐴𝑝2

+𝐵𝑝𝑉𝑡

4𝛽𝑒𝐴𝑝2𝑠2+ 𝑉𝑡𝑀𝑡

4𝛽𝑒𝐴𝑝2𝑠3

(17)

where 𝐾𝑐𝑒 = 𝐾𝑐 + 𝐶𝑡𝑝 is the total flow-pressure coefficient.

2.6.1.2 The modified Actuator Model

The model presented here is the Merritt model modified in the CEA/TAMARIS laboratory to

make it more suitable to realtime hybrid tests. Indeed, the accuracy of the Merritt model is good

enough for actuator design but should be improved for control.

First, the transfer function of the servovalve will be used as it is well known that the servovalve

dynamic has a large influence on the actuator dynamic response.

Second, friction forces have been introduced, to take into account non linearities in particular.

Third, for some hybrid test setup, the actuator should work on his total stroke. We will make a

theorical evaluation of the oil column stiffness variation along the stroke.

One other main evolution is to keep out the tested structure from the model. The system has been

reduced to the actuator.


58

2.6.1.2.1 Flows decomposition for actuator modeling

The following figure presents leakage flow, compression flow and supply flow in the 2 chambers

of the actuator:

Fig. 2. 24 Flows in actuator

The principle of the method is based on the decomposition of flows in the actuator chambers:

𝑞1 − 𝑞𝑙𝑒𝑎𝑘1 = 𝑞𝑐𝑖𝑛1 + 𝑞𝑐𝑜𝑚𝑝1 (18)

𝑞𝑙𝑒𝑎𝑘2 − 𝑞2 = 𝑞𝑐𝑖𝑛2 + 𝑞𝑐𝑜𝑚𝑝2 (19)

with:

qi, servovalve flow in chamber I,

qleaki, flow due to leakage in chamber I,

qcompi, flow due to oil stiffness in chamber I,

qcini, flow due to piston displacement in chamber i.

This leads to the global continuity equation:

𝑞𝐿 − 𝑞𝑙𝑒𝑎𝑘 = 𝑞𝑐𝑖𝑛 + 𝑞𝑐𝑜𝑚𝑝 (20) with:

qL = q1+q22

, average flow from servovalve,

qleak = CtppL, average flow due to leakage,

qcomp = qcomp1−qcomp2

2, average flow due to oil stiffness,

qcin = Apxp, flow due to piston displacement.


59

Compression flow 𝐐𝐜𝐨𝐦𝐩

Merritt linearized the system equation around the centered position of the piston. This

assumption could make the model unusable for a whole stroke compatible modeling.

To make the model compatible with the whole stroke of the piston, we will take into account the

last term of the equation (14).

To make the development of the equation easier, we will use the concept of virtual compression

displacement which has been presented in (Ahmadizadeh, 2007).

A flow can be written as:

𝑞𝑖 = 𝑑𝑉𝑖𝑑𝑡

= 𝐴𝑝𝑖 (21)

where 𝑥𝑖 is a virtual displacement associated with the flow 𝑞𝑖.

Using this, we can write the compression flow in each chamber of the actuator as:

𝑞𝑐𝑜𝑚𝑝1 = 𝑉1𝛽𝑒

𝑑𝑝1𝑑𝑡

= 𝐴𝑝𝑐𝑜𝑚𝑝1 (22)

𝑞𝑐𝑜𝑚𝑝2 = 𝑉2𝛽𝑒

𝑑𝑝2𝑑𝑡

= 𝐴𝑝𝑐𝑜𝑚𝑝2 (23)

It gives:

1 = 𝐴𝑝𝛽𝑒𝑉1𝑐𝑜𝑚𝑝1 = 𝐴𝑝𝛽𝑒

𝑉0+𝐴𝑝𝑥𝑝𝑐𝑜𝑚𝑝1 (24)

2 = 𝐴𝑝𝛽𝑒𝑉2𝑐𝑜𝑚𝑝2 = − 𝐴𝑝𝛽𝑒

𝑉0−𝐴𝑝𝑥𝑝𝑐𝑜𝑚𝑝2 (25)

We will assume that the compressed volume in chamber 1 is the same as the dilated volume in

chamber 2 but in opposite direction, then:

𝑞𝑐𝑜𝑚𝑝1 = −𝑞𝑐𝑜𝑚𝑝2 ⇔ 𝑥𝑐𝑜𝑚𝑝1 = −𝑥𝑐𝑜𝑚𝑝2 (26) Then:

𝑞𝑐𝑜𝑚𝑝 = 𝑞𝑐𝑜𝑚𝑝1−𝑞𝑐𝑜𝑚𝑝2

2= 𝐴𝑝

𝑐𝑜𝑚𝑝1−𝑐𝑜𝑚𝑝22

= 𝐴𝑝𝑐𝑜𝑚𝑝


60

Finally, we obtain:

𝐴𝑝(1 − 2) = 𝐴𝑝𝐿 = 2𝛽𝑒𝑉0𝐴𝑝2

𝑉02−𝐴𝑝2𝑥𝑝2𝐾ℎ𝑥𝑝

𝑐𝑜𝑚𝑝 (27)

This leads to:

𝑞𝑐𝑜𝑚𝑝 = 𝐴𝑝2𝐿𝐾ℎ𝑥𝑝

(28)

where 𝐾ℎ𝑥𝑝 = 2𝛽𝑒𝑉0𝐴𝑝2

𝑉02−𝐴𝑝2𝑥𝑝2 is the oil stiffness, varying in function of the piston position

(Fig. 2. 22):

Fig. 2. 25 Stiffness and pusation normalized variations

This curve is also explained in (Spinnler, 1997) (with a mechanical approach) and in (Jellali and

Kroll, 2003). The oil stiffness is a non linear parameter.

We can replace 𝑞𝑐𝑜𝑚𝑝 in (20) to obtain:

𝑞𝐿 = 𝐴𝑝𝑝 + 𝐶𝑡𝑝𝑝𝐿 + 𝐴𝑝2𝐿𝐾ℎ𝑥𝑝

(29)


61

2.6.1.2.2 Force equation on Piston

The following figure presents main forces applied to the piston:

Fig. 2. 26 Forces acting on the piston

with Mp, mass of the piston.

Forces on the piston are coming from the structure and internal damping. An interesting

approach of forces on piston was made in (Jellali and Kroll, 2003).

The same approach is used, adding forces coming from leakage area.

Stribeck friction force

The Stribeck friction force model (Stribeck, 1902, Jacobson, 2002) is commonly used to take

into account friction in systems (Figure 6). The purpose is to model static friction 𝐟𝐬𝐭𝐚𝐭, Coulomb

friction 𝐟𝐜𝐨𝐮𝐥 and viscous friction 𝐟𝐯𝐢𝐬𝐜 depending on velocity:

𝑓𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛𝑝 = 𝑓𝑠𝑡𝑎𝑡 + 𝑓𝑐𝑜𝑢𝑙 + 𝑓𝑣𝑖𝑠𝑐 (30)

with:

fstat = fs0 signxpexp−cs|xp|fcoul = fc0 signxpfvisc = dvxp

dv, viscous damping force gain.


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Fig. 2. 27 Stribeck model curve for friction forces variation depending on velocity (Jellali and Kroll, 2003)

Stribeck force is a non linear force around zero velocity.

Forces from structure

One commun problem in control techniques is the determination of the structure model. Indeed,

as we are testing it, we do not know it very well. Then, generally, estimation of structure

parameters have to be done before the test (shaking table tests) and during the test (adaptative

control). In this model, to create an independancy of the model in front of the structure, force has

to be measured. The force is “coming from” the structure. Taking into account the force will

consequently take into account the structure. In hybrid tests, force is generally already measured

with a load cell or at least with the pressure sensor.

We will use a global expression of external force 𝑓𝑠𝑡𝑟𝑢𝑐 to include them in the system equation,

in the same way as (Jellali and Kroll, 2003).

Forces from oil leakage

Leakage is a inevitable phenomenon in actuators. The leakage has the advantage of increasing

the damping by generating a natural force feeback. Assuming the flow as proportionnal to

differential pressure 𝑝𝐿, we model forces from leakage by the following expression:


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𝑓𝑙𝑒𝑎𝑘 = 𝑑𝑓𝑝𝐿 (31) df, leakage force gain.

Equation of force

From Figure 5 and previous considerations, we can write the resulting force equation applied to

the piston :

𝑀𝑝𝑝 + 𝑑𝑣𝑝 + 𝑓𝑐𝑜𝑢𝑙 + 𝑓𝑠𝑡𝑎𝑡 + 𝑓𝑠𝑡𝑟𝑢𝑐 = 𝐴𝑝 − 𝑑𝑓𝑝𝐿 (32)

2.6.1.2.3 Servovalve equations

The Merritt servovalve model doesn't take into account any of the systems dynamics. In (Thayer,

1958), a second order dynamic model of the servovalve is presented. We can write the transfert

function between the drive current 𝐼 sent to the servovalve and the spool displacement:

𝑋𝑣 = 𝐾1𝐼

1+2𝜉𝑆𝜔0𝑆

𝑠+ 1𝜔0𝑆2 𝑠2

(33)

where 𝜉𝑠 and 𝜔0𝑠 are the servovalve apparent damping and pulsation.

We obtain from equations (8) and (33):

𝑞𝐿 = 𝐾𝑞𝐾1𝐼


𝑠+ 1𝜔0𝑆2 𝑠2

− 𝐾𝑐𝑃𝐿 (34)

𝐾1 is known to be non linear ( as reported by Moog in (Thayer, 1958). So, the equation (34) is

only valid for a small variation of 𝐾𝐼 = 𝐾𝑞𝐾1.

𝑞𝐿 = 𝐾𝐼𝐼


𝑠+ 1𝜔0𝑆2 𝑠2

− 𝐾𝑐𝑃𝐿 (35)

𝐾𝐼: no load flow gain.


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2.6.1.2.4 governing equations of the modified actuator model

For small variation of 𝐾𝐼 and 𝐾ℎ (a small variation of the system around an initial configuration

at 𝑡 = 𝑡𝑖), we can write the equations of the system:

𝑞𝐿 = 𝐴𝑝𝑠𝑋𝑝 + 𝐶𝑡𝑝 + 𝐴𝑝2

𝐾ℎ𝑠 𝑃𝐿 (36)

𝑞𝐿 = 𝐾𝐼I


𝑠+ 1𝜔0𝑆2 𝑠2

− 𝐾𝑐𝑃𝐿 (37)

𝑀𝑝𝑠2𝑋𝑝 + 𝑑𝑣𝑠𝑋𝑝 + 𝐹𝑐𝑜𝑢𝑙 + 𝐹𝑠𝑡𝑎𝑡 + 𝐹𝑠𝑡𝑟𝑢𝑐 = 𝐴𝑝 − 𝑑𝑓𝑃𝐿 (38)

For small variations in the system, parameters can be considered as constant, and above

equations give:

𝑠𝑋𝑝 = 𝛼𝑁𝐹𝑠𝑡𝑟𝑢𝑐+𝐹𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛+𝛽𝑁𝐼𝜀𝐷𝑠4+𝛿𝐷𝑠3+𝛾𝐷𝑠2+𝛽𝐷𝑠1+𝛼𝐷

(39)

The servohydraulic system is controled by a fourth order transfert function in velocity (see

equation 39). The system has two resonance frequencies. One frequency and damping of the

system is generated by the servovalve (ω0, ζ). For a given servovalve, this frequency and

damping is constant (equation 33). The other frequency is generated by the oil column (equation

36) and unfortunately, it does not only depends on the geometry of the actuator. Indeed, this

frequency depends on the pressure pL wich is varying with the tested structure, the velocity and

the acceleration (equation 39). Damping of the oil column is coming from velocity (𝑑𝑣𝑠𝑥𝑝) and

force (𝐹𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛). Actuators are flow equipements which means velocity equipements, with a

natural feedback in force and velocity.

The assumption of small variation is not very hard in regard to nowdays realtime computer clock

speed (> 2 kHz) and relatively low frequencies of structures and actuators (< 100 Hz).

2.6.2 Experimental setup

An experimental campaign (TT2) has been done at CEA (France) in the experimental part

(TAMARIS) of the Seismic Mechanic Studies Laboratory (http://www-tamaris.cea.fr).

The target of this experimental campaign was to:

http://www-tamaris.cea.fr/


65

• Identify the 13 parameters of the model.

• validate the model by a comparison between test and simulation.

The 13 needed parameters are:

• 𝐴𝑝, area of piston.

• df, leakage force gain.

• 𝐶𝑡𝑝, global leakage coefficient.

• 𝐾ℎ𝑥𝑝 is the oil stiffness.

• 𝜉𝑠 and 𝜔0𝑠 are the servovalve apparent damping and pulsation.

• Mp, mass of the piston.

• dv, viscous damping force gain.

• fstat, static friction.

• fcoul, Coulomb friction.

• KI, no load flow gain.

• 𝐾𝑐, valve flow-pressure gain.

2 parameters are known to be non-linear: 𝐾ℎ and 𝐾𝐼 . Other parameters are supposed to be

constant. This range of assumptions has to be verified.

The experimental setup is a little shaking table (1 dof) composed by:

• a 2.5 kN hydraulic symetric actuator with 2 servo valves (2 stages),

• a steel table mounted on a low friction ball bearing rail,

• a rigid iron corner and bracket to support the actuator and the table,

• a hydraulic supply manifold with one supply gas accumulator and one output gas

accumulator,

• a real-time hybrid controller,

• a fiber optic communication system,

• a conditioning, acquistion and filtering system,

• 9 sensors.


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The next general picture describes this setup:

Fig. 2. 28 Experimental setup

The position of the different element of the experimental system is shown on Fig. 2. 26:

Fig. 2. 29 Drawing of the experimental servo-hydraulic setup

From equations (36), (37) and (38), the following measurement sensors have been installed:

• qL = q1+q22

, average flow: a flow turbine measuring oil flow from 0 to 300 l/min.

• pL = p1 − p2 , differential pressure between chambers: a differential pressure

sensor inside the piston.

• sXp, velocity of the piston: a LVT sensor.

• s2xp, acceleration of the piston: an acceleration sensor.

• Fstruc , force of the structure: a load cell (+/- 35 daN).


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The following additional sensors have also been installed:

• Supply pressure sensor to verify the constant pressure supply assumption,

• Output pressure sensor to verify the constant pressure output assumption,

• Temperature sensor to verify the constant temperature assumption,

• Displacement sensor (LVDT) to measure the displacement Xp of the piston.

The drive current I sent to each servo valves is also recorded in the real-time controller.

The acquisition frequency of each sensor was 2048 Hz.

The next picture shows some of the sensors:

Fig. 2. 30 Sensors of the actuator

To identify the 13 parameters of the model, three identification tests have been performed. Each

one gives the oportunity to nullify some of the variable of the system. Finally, a reference test

has been performed to compare the response of the test and the model.

Fo acce

velocity Oil flow

temperature

Supply & output pressure


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2.6.2.1 Identification tests

2.6.2.1.1 Identification test n° 1: no velocity test

Description

The aim of this test is to simplify equation with annoying all cinematic terms. This identification

test is performed at very low velocity and large range of force. The piston is blocked with a rigid

assembly. The force goes from minimum to maximum load capacity (+/- 25 kN). The actuator is

controled with a closed loop in force.

Fig. 2. 31 Variations of displacement, drive and pressure depending on time are not significant

Equations

The test conditions of the no velocity test give:

• s. xp = 0 and s². xp = 0 : velocity and acceleration are null. Indeed, the

displacement is nearly constant around initial position (standard deviation

𝜎 = 0.396𝑚𝑚 for a total stroke value of 250𝑚𝑚) and the average velocity is

around 0.06 𝑚𝑚. 𝑠−1.

• 𝑓𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛 = 0: the “no velocity” hypothesis includes the fact that dry friction force

is null.

• s. pL = 0: the pressure variation depending on time is 25𝑏𝑎𝑟𝑠/𝑠 which make it

negligible.

• 𝑞𝐿 = 𝐾𝐼I − 𝐾𝑐𝑝𝐿: the drive variation depending on time is about 2𝑚𝑉/𝑠 which

avoids the dynamic effect of the servo valve negligible.


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For the no velocity test, equations of the model are :

𝐾𝐼𝐼 − 𝐾𝑐𝑝𝐿 = 𝐶𝑡𝑝𝑝𝐿 (40)

𝑓𝑠𝑡𝑟𝑢𝑐 = 𝐴𝑝 − 𝑑𝑓𝑝𝐿 (41)

From this test, two important parameters are identifed : the effective piston area factor (𝐴𝑝 − 𝑑𝑓)

and the charge loss factor 𝐾𝑐𝑒 = 𝐾𝑐 + 𝐶𝑡𝑝.

Determination of the effective area 𝐀𝐩 − 𝐝𝐟

From equation (41), the coefficient (𝐴𝑝 − 𝑑𝑓) is given by a linear fitting of the curve

representing 𝑓𝑠𝑡𝑟𝑢𝑐 depending on 𝑝𝐿 (Fig. 2. 29).

Fig. 2. 32 Force (from load cell) depending on differential pressure

The linear interpolation gives the value 𝐀𝐩 − 𝐝𝐟 = 𝟏𝟏𝟕𝟓𝐦𝐦𝟐 (with a correlation coefficient

𝑟 = 1).


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Determination of the charge loss (total flow-pressure coefficient) 𝐊𝐜𝐞

From equation (40), the coefficient 𝐾𝑐𝑒 = 𝐾𝑐 + 𝐶𝑡𝑝 is given by plotting the no load flow 𝐾𝐼𝐼

depending on 𝑝𝐿 (Fig. 2. 30).

Fig. 2. 33 No velocity flow depending on pressure

𝐾𝑐𝑒 is not a constant parameter.

In order to obtain Kce coefficent, the no-velocity-flow curve is fitted with a 9 𝑡ℎ order polynomial

function (Fig. 2. 31):

Fig. 2. 34 No velocity flow experimental and fitted curves

The obtained 𝐾𝑐𝑒 value depending on pressure is on Fig. 2. 32:


71

Fig. 2. 35 Charge loss coefficient 𝑲𝒄𝒆

2.6.2.1.2 Identification test n° 2: no load flow test

Description

The test is made with a free piston (no load), at a very low acceleration and for the full range of

velocity. The maximum velocity of the actuator is 1.6 m/s (flow limitation of the servo valves).

For this test, the actuator was controled in open loop.

Equations

The test conditions of the no load flow test give:

• s2xp = 0: velocity is piecewise constant, so acceleration is null.

• 𝑞𝐿 = 𝐾𝐼I − 𝐾𝑐𝑝𝐿 : the motion of the servovalve spool is constant at each cycle

(square drive current). We can then neglect the dynamic effects of servovalve.

• 𝐹𝑠𝑡𝑟𝑢𝑐𝑡 = 0: no structure is attached to the actuator.

• 𝐾𝑐. pL = 0 and 𝐶𝑡𝑝 + 𝐴𝑝2

𝐾ℎ𝑠 𝑝𝐿 = 0 : the differential pressure value is small.

Indeed, 𝑃𝐿 is varying between −10𝑏𝑎𝑟𝑠 and 14𝑏𝑎𝑟𝑠 during the test.

For the no load flow test, equations of the model are:

𝐾𝐼𝐼 = 𝐴𝑝𝑠𝑥𝑝 (42)


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𝑑𝑣𝑠𝑥𝑝 + 𝑓𝑐𝑜𝑢𝑙 + 𝑓𝑠𝑡𝑎𝑡 = 𝐴𝑝 − 𝑑𝑓𝑝𝐿 (43)

Determination of the piston area 𝐀𝐩

The piston area comes from equation (42). A linear fitting of entering flow depending on

velocity allows to find 𝐴𝑝 = 1255𝑚𝑚2 (Fig. 2. 33).

Fig. 2. 36 Flow depending onpiston velocity

Ap − df has already been measured in the no-velocity test. This means 𝐝𝐟 = 𝟖𝟎𝐦𝐦𝟐. Determination of the friction force 𝐟𝐟𝐫𝐢𝐜𝐭𝐢𝐨𝐧

Friction forces in the actuator comes out with equation (43). By plotting differential pressure,

multiplied by the effective area (obtained with the no-velocity test), we obtain the value of

friction force in the actuator varying with velocity.


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Fig. 2. 37 Friction force depending on velocity

On Fig. 2. 34, we notice that friction forces are actually a Stribeck friction force.

Determination of the “no load flow” 𝐊𝐈𝐈

With equation (42), it is possible to obtain the no load flow:

Fig. 2. 38 No load flow depending on drive


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Both non linearities due to first and second servovalve are observable by zooming on Fig. 2. 36:

Fig. 2. 39 Non-linearities appearing on first servovalve (left) with an overlap and the second servovalve (right) with an underlap

2.6.2.1.3 Identification test n° 3: Sine sweep test

The values of the manufacturer have been used for the pulsation 𝜔0𝑠 and the damping ξs of the

servovalve.

The mass of the piston Mp has been estimated using the volume of the piston.

The last parameter is the oil stiffness Khxp. A sine sweep test has been used to evaluate this

stiffness.

Description

The test is made with a rigid mass fixed to the piston. The rigid mass is the empty shaking table

(Figure 19). The mass of the shaking table is 295 kg. A linear sine sweep test is performed

between 0.1 Hz to 200 Hz. For this test, the actuator was controled in open loop.


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Fig. 2. 40 Actuator with a rigid mass

The response of the actuator to the sine sweep drive signal is presented in the next figure (green

color):

Fig. 2. 41 Oil stiffness evaluation, sine sweep test

Determination of 𝐊𝐡𝐱𝐩

The rigid mass is large enough to create the amplification at a quite low frequency (oil column

frequency). The stiffness 𝐾ℎ𝑥𝑝 has then been calculated using this frequency and the mass of

the table.


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2.6.2.2 Results: model vs test

The model has been implemented in Matlab Simulink software. The model validation was done

by comparing the velocity between the test and the model. Indeed, for comparison, the

displacement of the piston is not large enough for frequency up to approximatly 20 Hz while

comparison has been done up to 120 Hz.

A reference test has then been performed with a “not stiff” structure. For this test, the actuator

was controled in open loop. 3 types of drive signal were sent to the actuator:

A step, a linear sine sweep from 0.1Hz to 120 Hz and finally a white noise. Next figure shows

the drive signal of the reference test:

Fig. 2. 42 Drive signal of the reference test

Note: the electrical current drive signal is a percentage of the maximum opening of the servo

valve.

The comparisons between the velocity of the model and the velocity of the reference test is

presented on the following figure:

Fig. 2. 43 Model vs test velocity, reference test


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2.6.2.2.1 Step test

Comparisons between the velocity of the model and the velocity of the step test are presented on

the following figures:

Fig. 2. 44 Model vs test velocity, step test

Fig. 2. 45 Model vs test velocity, step test, zoom

2.6.2.2.2 Sine sweep test

Comparisons between the velocity of the model and the velocity of the sine sweep test are

presented, for some frequency from 0.1 Hz to 120 Hz, on the following figures:

Fig. 2. 46 Model vs test velocity, 0.1 Hz and 10 Hz sinus test


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Fig. 2. 47 Model vs test velocity, 18 Hz and 40 Hz sinus test



The delay (phase) between the model and the test is closed to zero between 0.1 Hz to 80 Hz.

Amplitude of the model is also quite closed to the real one between 0.1 Hz to 30 Hz.


79

2.6.2.2.3 White noise test

Comparisons between the velocity of the model and the velocity of the white noise test are

presented on the following figures:

Fig. 2. 50 Model vs test velocity, white noise test

Fig. 2. 51 Model vs test acceleration, 15 Hz sinus test, zoom

2.6.2.2.4 Conclusion

Comparisons between the model and the test have been done for a large range of frequency and

various excitation signals. The delay (phase) between the model and the test is closed to zero

between 0.1 Hz to 80 Hz. Amplitude of the model is also quite closed to the real one between 0.1

Hz to 30 Hz.

Up to 30 Hz, the servovalve non linearities create distorsions on the model. Indeed, the overlap

and underlap are odd functions. For a given excitation frequency “f”, these odd funtions create

distorsions at “f+2nf” frequencies (n, integer). In the test, up to 30 Hz, these distorsions slowly

disappear (but not in the model). Figure 35 shows the acceleration of the test compared to the


80

acceleration of the model. The non linearity acts like a shock. When can observe that the

amplification due to the servovalve non linearity is larger for the model. Nevertheless, in the

model, the main frequency of this shock is higher than 500 Hz.

2.7 CONCLUSIONS

In this section, some basic ideas of inverse dynamics control and ccombined inverse-dynamics

and adaptive control method are introduced. From this brief overview, the advantages of the

combined method, such as easiness of implementations and better performances, are shown.

Finally, an analytical non linear hydraulic actuator model is developed. Parameters of the model

are identified within TT2 tests performed at CEA. A comparison between the model and the test

gives a good accuracy of the model for a large range of frequency. This model permited to better

understand physical phenomena in actuators. It will also permit to numerically test algorithms

and will give the oportunity to control and compensate the actuator dynamics.


81

2.8 ERROR ASSESSMENT

Up to now, researches on the hybrid test or Hardware-in-the-loop mainly focus on the integration

schemes, control strategies and test validations. However, to what extent are test results reliable?

This question must be replied before test methods are widely used. Due to the complixity of the

question, few researches worked on this topic. This section will concentrate on influences of

errors, resources of errors and approaches to error assessment.

2.8.1 Influences of errors

It is well known that displacement response delay due to actuator dynamics, which is of one type

of control error, adds negative damping to the system in a fast or real-time hybrid test on a spring

specimen. The negative damping can result in the artificial instability of the system when it is

greater than the actual damping. Even if the test is stable, it is not enough compared to our

objective to evaluate the structural dynamical response by means of the test results, since the

accumulative errors may render experimental results greatly different from the actual ones.

Except delay, Ahmadizadeh and Mosqueda (2009) showed that measurement errors introduce

energy in a hybrid test. Therefore, before experimental results are utilized for some objectives,

errors or error bounds should be provided.

2.8.2 Resources of errors

In the hybrid test with an explicit integration method, errors are mainly resulted from the actuator

control and measurements. Usually control errors are of systematic type whilst the latter ones are

of random type (Shing et al 1990). For a hybrid test based on an implicit integration method,

there is another error type: convergence error (Shing et al 1991), owing to the unbalanced force

generated when solving the nonlinear equation introduced by a time integration. In addition to

these errors, one other type of error should be mentioned, which is introduced by the numerical

simulation of the numerical substructure, called integration error. For a pseudodynamic test, the

integration error is not serious while for a hybrid test with physical damper or mass considered

the error is greater owing to discrepancies between the movement quantity targets and the actual

response of a numerical substructure.


82

2.8.3 Complication of error assessment

In the general case of a hybrid test, the physical substructure should contain physical springs,

dampers and masses. Therefore, the control errors can be classified as follows: displacement

control errors, velocity control errors and acceleration control errors. Before the assessment of

the velocity and acceleration control error, the velocity and acceleration targets and responses

should be known. However, it is not so easy to obtain these quantities. On the hand, the tangent

stiffness, damping coefficient and mass of the physical substructure are needed to calculate the

force errors owing to control errors. The stiffness and damping coefficient are often not available

online for a specimen that enters into the nonlinear regime.

With regard to measurement errors, they could be assessed with probability theories. However, it

is hard or impossible to find the error distribution during testing, because many data and much

time are needed to evaluate distribution parameters. Another scheme to assess the error may

adopt interval arithmetic, but the method has the trends to obtain larger bounds. In practice, these

errors are neglected compared with the control errors when suitable measures are applied to

reduce the environment noise.

Like error propagation and accumulation in an integration scheme, errors grow in the hybrid test.

Then different integration schemes, different delay compensators and different correction

schemes will results in different properties of the errors. All these factors increase the complexity

of error assessments in hybrid simulations.

2.8.4 Approaches to assess errors

Up to now, some researches have been published about error assessments in a pseudo-dynamic

test or Hardware in the loop, which are introduced and discussed herein.

Error amplification factors are used by several researchers to assess the influence of errors in

hybrid tests. Shing (1991) showed that his correction has a smaller error amplification factor than

Peek’s scheme. In simulations, the response amplitude is found to keep increasing owing to the

error accumulation with Peek’s correction. This factor doesn’t find the phenomenon, since the

method’s assumption misses some information of importance.

Energy-based error indicator, proposed by Mosqueda et al (2007a), is another scheme to assess

errors. The idea of the method is that both control errors and measurement noises change the

system energy. The unbalanced energy indicates the error. Simulations and actual hybrid test on


83

a spring specimen were conducted (Mosqueda et al 2007a, b). The results showed that the

indicator can monitor the error change. Ahmadizadeh (2009) expanded the method to monitor

the integration error. But weaknesses of the method are obvious: (1) the unbalanced energy at

some step is the local energy error, not the total energy. The sum of all unbalanced energies is

still not the total energy error or the energy error bounds. The error propagation is not clearly

shown by the method; (2) the relationship between the displacement error and the energy error is

demonstrated to be linear, but is not quantitatively given. In fact, it’s hard, since it depends on

the specimen characteristics. So the method just monitors the error qualitatively, not

quantitatively.

Ren et al (2007) proposed an accuracy evaluation scheme in power Hardware-in-the-loop

simulation. They considered the control errors and measurement errors through the interface

transfer function perturbation and the interface noise perturbation. The error function derivation

and the limits are given. The method is suitable to study the effects of errors of a linear system.

For a hybrid test on the nonlinear specimen, it is not so easy to represent the specimen with a

linear element, which may change the dynamic characteristics of the system.

2.8.5 Conclusions

Even though the error assessment in hybrid tests is of the great importance, the state of the art

researches are not yet satisfactory. Several suggested methods are either not so general or

obviously weak. At the same time, they are suitable to evaluate the effect of errors but not to

assess errors online. Therefore, further researches should be conducted, even for some specific

tests in order to accumulate experience.

2.9 SENSORS IN PRESENCE OF LINEAR ELECTRO-MAGNETIC ACTUATORS – INITIAL ANALYSIS

2.9.1 Dynamic seating deck - outline of project

As part of an EPSRC research project the Department of Engineering Science at Oxford

University built a 15-seat grandstand simulator. The aim of the project was to measure the

forces that spectators apply to seating decks. Several modes of testing were conducted, some

app:ds:qualitative%20analysis


84

when the spectators were just standing (i.e. passive) and others when they were more actively

engaged by jumping or bobbing up and down either to a metronome beat or to music. The

seating deck could be set in two configurations, the first fixed firmly to the ground and the other

free to move under the control of a set of electro-magnetic actuators. There were particular

problems associated with controlling the motion of the deck. The original design and description

of the actuation system and the control strategy have been reported elsewhere (Williams, Comer

& Blakeborough 2010). In the section below the control and monitoring signals are analysed in

more detail since this use of electro-magnetic actuators is novel.

2.9.2 Description of seating deck

The design of the seating deck reproduced the seating arrangement found in modern stadia. The

overall geometry of the structure (without seats) is shown in Figure 3.7.1.

Fig. 2. 52 Schematic of seating deck frame with actuators and air springs

The 15 seats were arranged in three rows of five. Structurally each row was carried by a beam

spanning between the two main beams on the sides. At each spectator position there was a load

cell which measured the vertical force from the feet of each spectator. When the deck was

controlled it was supported on four Firestone 116 single convolution air springs which carried

the dead weight. The pressure was separately adjusted in each spring and controlled to set the

deck level, whatever the static load distribution on the deck.


85

The deck was actuated by four Oswald® type LIN-S132A synchronous linear electric motors.

The motors are water cooled and had a load rating of 5.4kN continuous and 10.9kN 2s peak with

a stroke of 120mm. Each actuator was powered by a 3-phase, 415 volt Danaher® S640 inverter

drive unit which switches a 600V DC bus to the motors at 16 kHz. The motor supply cables were

routed carefully from the drive cabinet to reduce electrical interference on the load cells and

other measuring equipment. A spacing of at least 250 mm was kept between power and signal

cables and all power-signal cable crossings are at 90°. Nevertheless, there was electrical

interference on the signal lines especially from those instruments close to the motors.

The precise position of the actuator is required for the commutation of the separate phase wirings

of the motor. This required the absolute position of the actuator to be known. Each motor was

fitted with a Heidenhain® LC182 sealed linear encoder, which produces both digital absolute and

analogue incremental positions on the EnDat® interface. Normally the actuator position is

determined by homing the actuator – moving it so that it can pick up an absolute position point

but this was not possible because the motors were permanently connected to the deck and the

homing procedure can only be done with no load on the actuator. The encoder’s absolute

position measurement was useful in this case because the drive polled the encoder for the

absolute position. As well as providing the commutation, the encoder also supplied the feedback

signal for the position control system in the inverter.

2.9.3 Control system

The overall control of the seating deck was achieved by using xPC Target® which ran

Mathworks® Simulink® models compiled with Real Time Workshop. This system was employed

to start and stop the test, control the electric motors and the safety features of the rig, as well as

collecting the individual load cell signals from the test subjects. One of the advantages of using

xPC is that the target machine can contain a large amount of RAM which can store large

quantities of data, which can be transferred from the RAM on the target PC to the host PC hard-

drive the end of a test for post processing.

The data was collected using a PowerDAQ II PD2-MF-64-333/16H, which has 64 of 16-bit A/D

channels. The A/D inputs were configures in the differential mode to reduce the effects of

electrical interference. The control outputs signals were supplied by a Measurement Computing

PCI-DDA08/16 board which had 8 off 16-bit D/A channels. In tests to commission the system,


86

the measured time taken to write 4 channels of output to the D/As and reading 50 A/D channels

was 773μs. The target PC was a Intel PII machine with a 400MHz clock. Running a Simulink®

model with 620 blocks and 100 continuous states took 134μs which meant that a time step of

1ms was achievable with a small amount of headroom.

Initially, it was intended to use the position controller in the inverter drives to control the linear

motors, and combine this is with a force feedback from the seating deck in the standard

displacement-controlled hybrid test loop. In practice, it was not possible to achieve a stable and

responsive position control system using the PI/P and P/PI controllers supplied in the motor

inverters. Each motor was individually controlled and they quickly became unstable when

optimising the control parameters. Although great care had been taken in routing and screening

the signal cables there was interference on the actuator force load cell signals (see below). These

effects precluded using the standard displacement controlled hybrid control loop.

Several control strategies were tried but responsive and stable motor control was only obtainable

by directly controlling the current and therefore the force in each actuator. Basing the hybrid

loop on force control required the actuator displacements as feedback signal. The displacement

of each actuator was available as a voltage output from the inverter, but it was also heavily

contaminated by noise. The solution was to tap into the sin-cos signal from the Heidenhain®

encoders.

The encoder output has a 1Vp-p sine and cosine component with a wavelength corresponding to

20μm of actuator displacement. The sine signals were converted into RS422 differential square

wave quadrature signals by a sine wave interpolator (Deva® 018 single axis) with an

interpolation rate of 10 per cycle giving a resolution of 2μm. The digital pulses were counted by

a Measurement Computing® PCI QUAD04 PCI card located in the target PC.

With this equipment in place the seating deck could be controlled in effect making the

combination of actuators behave as global springs. This was achieved quite simply using the

method shown in Figure 3.7.2. The displacements of the actuators from the linear encoders were

converted into global coordinates (heave – vertical displacement, pitch – forwards rotation, and

roll – sideways rotation). These displacements were then multiplied by a factor representing the

stiffness of the springs. A derivative component was also added. This served as a compensator

signal (a time constant for the spring/damper combination of about 10ms was required for

stability), but also set the equivalent damping of the free oscillations of the seating deck. Some


87

tests required the deck to be moved. This was achieved by feeding in the desired displacement

just before the vertical spring element. 4

Lin

ear

Enc

oder

s15

Indi

vidu

al

Load

Cel

ls

Convert to global displacements

Convert to global force and moments

Heave

Pitch

Roll

Vertical force

PD

Tg

Fg

PD

PD

Controllers

-

+Tg

-1

4 L

inea

r A

ctua

tors

Vertical force, pitch and roll moments

Kff

++ +

+

Desired vertical displacement

Fig. 2. 53 Control loops for motion of seating deck

2.9.4 Investigation into the transducer signals

2.9.4.1 Encoder

There were two major problems with the transducer outputs. The first was from the encoders,

and was unexpected. The nature of the problem can be seen in the traces in figure 4.3. The test

was executing a sine wave vertical displacement of the deck with an amplitude or 3mm at 2Hz.

Just before 75s into the test the trace shows a spike in the signal. The lower trace is an expanded

plot around the spike, which can be seen to be limited to a single reading. The spike in the

displacement signal is considerable (>100mm) and did cause a transient response in the deck.

Because of its size the spike is easily removed by monitoring the input and rejecting any reading

that has an unfeasible increment from the last value, which can be substituted for the erroneous

reading. All the encoder channels exhibited this behaviour, which appeared to occur randomly.

There was no correlation with time through the test.

It was not possible to determine exactly where the spike was introduced. There was never any

spikes recorded on the displacement signal fed back into the inverters – it was possible to

monitor these separately, but not log the data – so the encoders were probably working as


88

expected. The link to the xPC computer was incremental, the displacement was inferred by

counting pulses from the interpolator between timesteps and adding the count to the previous

position. If the error had been in the generation of the pulses then the induced error in the

position would have been permanent. The displacement signal from the encoder input blocks in

the Simulink model did not show a step but a spike, which implies the error was connected with

the encoder block and how it was read. Despite investigation it was not possible to resolve this

issue. Nevertheless, apart from the glitches, the encoders provided a very clean feedback signal

with no evidence of electro-magnetic interference despite being used in a demanding location.

Fig. 2. 54 Encoder displacement of an actuator on the grandstand (upper), detail showing data points and glitch (lower)

2.9.4.2 Load cells

Strain gauge load cells were used to measure the actuator load and the forces exerted by the

‘spectators’ on the seating deck. Examples from each for the same test can be seen in the figures

below. The load cells connected to the actuators were 10kN RDP universal load cells driven by

RDP S7DC transducer amplifiers. The spectator loads were measured using 500kg Tedea-

73 73.5 74 74.5 75 75.5 76 76.5 77-6

-4

-2

0

2

Time (s)

Dis

plac

emen

t mm

Encoder displacement (mm)

74.94 74.95 74.96 74.97 74.98 74.99 75 75.01 75.02

-100

-50

0

Time (s)

Dis

plac

emen

t mm


89

Huntleigh single point load cells load cells driven by the same type of RDP transducer

amplifiers. Fig. 3.7.4 shows the load cell trace from one of the actuator load cells for the whole

of a test. The interference starts when the actuators are switched on just after the start and this

rises when the motion starts at 30s. Fig. 3.7.5 shows the power spectral density of the signal. For

comparison, Fig. 3.7.6 shows the output from the ‘spectator’ load cell at the position nearest the

actuator and shows the effectiveness of the measures to reduce interference. There was no

spectator at that location and the main variation is from the inertial load from the mass connected

to the load cell as it is accelerated. There is however a considerable noise signal, although in

absolute terms is small compared with the loads measured when the spectator is present. At high

frequencies there is a single peak at 300 Hz.

Fig. 2. 55 Load cell output for actuator load cell – whole trace (upper), detail (lower)

0 10 20 30 40 50 60 70 80 90 100-1000

-500

0

500

1000

Time (s)

Forc

e (N

)

Actuator load cell

37 38 39 40 41 42-400-200

0200400600800

Time (s)

Forc

e (N

)

Detail


90

Fig. 2. 56 Power spectral density of load cell signal

Fig. 2. 57 Load cell signal from spectator cell – detail trace (upper) and psd (lower)

0 50 100 150 200 250 300 350 400 450 5000

5

10

15

20

25

30

35

40

45

50

Frequency (Hz)

Pow

er/fr

eque

ncy

(dB

/Hz)

Welch Power Spectral Density Estimate

41 42 43 44 45 46 47 48 49 50 51

-20

-10

0

10

Time (s)

Forc

e (N

)

Load @ A3

0 50 100 150 200 250 300 350 400 450 500-40

-20

0

20

Frequency (Hz)

Pow

er/fr

eque

ncy

(dB

/Hz)

Welch Power Spectral Density Estimate


91

The significant feature to note is the difference between the power spectral densities. Both

signals have components at frequencies below 30Hz which are to do with the resonances in the

stand, but the significant difference to note is the peaks at multiples of 100Hz. In the actuator

load cell there are several and originate in the bursts that can be seen in the lower trace of figure

3.7.4. These peaks in the spectator load cell signal are fewer and of a different nature. Whereas

the ‘spectator’ load cell has small noise peaks centred at 50, 100 and 300Hz the actuator load cell

has double sideband with a bandwidth of ~25Hz around the centre frequencies of multiples of

100Hz. The low frequency component also has a bandwidth of 25Hz so the high frequency peaks

have the appearance of an amplitude modulated signal carrying the low frequency signal. This

indicates a significant interaction between the main signal and these carrier frequencies. The

exact mechanism has not yet been identified, but is most likely to be the electro-magnetic field

from the actuator coils interacting with the amplifier, which makes the entire transducer signal

suspect. Investigations and possible remedies are being investigated.


93

3 JRA 2.2 Sensing and Verification Tests for Measuring Structural and Foundation Performance

3.1 INTRODUCTION

The task- JRA 2.2 aims at the thorough assessment of sensor arrays that can be effectively

implemented in civil engineering structures and in the soil under earthquake and dynamic loads.

The development of vision systems is another goal of this task where special attention will be

paid to sensor dynamics, grade and spatial resolution, on the basis of the most recent digital

cameras equipped with complementary metal oxide semiconductor (CMOS) and charge coupled

device (CCD) sensors. A complete set of guidelines for sensors and actuators are to be

developed, covering implementation and application.

In this report, the state-of the-art scenario of different types of sensors and vision systems has

been presented. The usability of these sensors in civil engineering applications has been

discussed and recent developments and future research trends have been reported.

3.2 FIBRE OPTIC SENSORS

An optical fibre is a glass or plastic fibre that carries light along its length. Fibre optics is the

overlap of applied science and engineering concerned with the design and application of optical

fibres.

Fibres have many uses in remote sensing. In some applications, the sensor is itself an optical

fibre. In other cases, fibre is used to connect a non-fibre optic sensor to a measurement system.

Depending on the application, fibre may be used because of its small size, or the fact that no

http://en.wikipedia.org/wiki/Glass

http://en.wikipedia.org/wiki/Plastic

http://en.wikipedia.org/wiki/Light

http://en.wikipedia.org/wiki/Applied_science

http://en.wikipedia.org/wiki/Engineering


94

electrical power is needed at the remote location, or because many sensors can be multiplexed

along the length of a fibre by using different wavelengths of light for each sensor, or by sensing

the time delay as light passes along the fibre through each sensor. Time delay can be determined

using a device such as an optical time-domain reflectometer.

Optical fibres can be used as sensors to measure strain, temperature, pressure and other quantities

by modifying a fibre so that the quantity to be measured modulates the intensity, phase,

polarization, wavelength or transit time of light in the fibre. Sensors that vary the intensity of

light are the simplest, since only a simple source and detector are required. A particularly useful

feature of such fibre optic sensors is that they can, if required, provide distributed sensing over

distances of up to one meter.

Fibre optic sensing is one of today’s fastest developing technologies. One reason for this is that

the costs of fibre sensors have been dropping steadily (in large part due to exceptional advances

in fibre telecommunications technologies) and this trend will continue. Further, measurement

capabilities and system configurations (such as wavelength multiplexed, quasi-distributed sensor

arrays) that are not feasible with conventional technologies, are now possible with fibre sensors,

enabling previously unobtainable information on structures to be acquired.

One area where the above advanced technology can have an immediate impact in construction is

in improving the current state-of-practice of structural monitoring systems. The importance of

structural monitoring is growing due to a shift from construction costs to life cycle costs and

lifetime performance including safety and use. This holistic approach in addition to monitoring

technologies includes assessment methods.

Fibre optic sensor technologies are finding growing application in the area of monitoring of civil

structures. This has resulted in a growing field of fibre optic structural sensing in construction

that is highly competitive and composed exclusively of SMEs.

In fact, a good number of articles are available on the applications and developments of fiber

optic sensors (e.g. Hong-Nan et al, 2004, Hans Poisel, 2008, Christine Connolly, 2009 etc.) for

structural monitoring.

http://en.wikipedia.org/wiki/Electrical_power

http://en.wikipedia.org/wiki/Multiplexing

http://en.wikipedia.org/wiki/Optical_time-domain_reflectometer

http://en.wikipedia.org/wiki/Deformation_(mechanics)

http://en.wikipedia.org/wiki/Temperature

http://en.wikipedia.org/wiki/Pressure

http://en.wikipedia.org/wiki/Intensity_(physics)

http://en.wikipedia.org/wiki/Phase_(waves)

http://en.wikipedia.org/wiki/Polarization

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95

Daniele Inaudi et al. (1999) have presented the use of fibre optic sensors for structural

monitoring adopting the SOFO monitoring system. This system has been applied to a large

number of new and existing bridges as well as to other civil structures in order to monitor their

short and long-term behavior.

In 2000, Daniele Inaudi (2000) has described different types of fibre optic sensors and their

applications in structural monitoring systems in Europe. The different types of sensors described

are SOFO DISPLACEMENT SENSORS, MICROBEND DISPLACEMENT SENSORS,

BRAGG GRATING STRAIN SENSORS, FABRY`PEROT STRAIN SENSORS, RAMAN-

DISTRIBUTED TEMPERATURE SENSORS, BRILLOUIN-DISTRIBUTED

TEMPERATURE SENSORS and HYDROGEL-DISTRIBUTED HUMIDITY SENSORS.

These sensors have been successfully applied for the structural monitoring systems.

Joan R. Casas et al. (2003), in their state of the art report ‘‘Fiber Optic Sensors for Bridge

Monitoring’’, have stated that that it is possible to apply the fiber optic monitoring system in the

field of long-term monitoring of bridges.

Inaudi and Glisic (2008) have presented the application of fiber optics for structural health

monitoring, in which they described the application of SOFO Displacement Sensors and

Brillouin Distributed Temperature sensors for the monitoring of different structural systems.

The University of Trento, Italy investigated the capability of fibre optic sensors to

produce reliable data under the Project – ‘MONICO’ (Bursi et all, 2011). The goal of the Work-

package 6 of this project was to experimentally evaluate the acquisition system for the

assessment of the seismic structural reliability of monitored cross sections in a tunnel lining

cross-cross section subject to seismic loads. Fibre optic sensors were used in five cyclic tests.

Both optic Fiber Bragg Grating (FBG, by AOS) and Brillouin fibers by Sensornet were

employed. It is clear that some of the results of this EU project obtained by UNITN can be useful

for the WP13/JRA2 as well. In this respect, UNITN split the experimental campaign into two

parts: i) six tests on substructures; ii) one full scale test on a tunnel ring.


96

The substructure specimens were made of C25/30 concrete and 7+7 Φ16 B450C steel bars; they

were three meter long with a rectangular cross section. Fig. 3. 1 shows the specimen cross-

section used in the test N. 4, labelled CF2. It is the only one commented here.

Fig. 3. 1 cyclic test N. 4: specimen cross-section (dimensions in mm)

This specimen was equipped with two types of AOS fibres: i) embedded in the concrete; ii)

external to the concrete. Fiber pre-straining was about 0.84 per cent. Also a standard sensor

equipment was adopted. Fig. 3. 2 shows the testing equipment employed to load each specimen

via a four-point bending test.

Fig. 3. 2 Four load points scheme (dimensions in mm)

Fig. 3.3 reports the data acquired from the top fiber. It can be highlighted how the external fibre

measured values higher than the internal ones -at least before the failure of the internal one-. It

externaloptical fibre

welded plate on transverse bar

20

externaloptical fibre

3 strain gauges (120 mm)

transducersA2 A4

optical fibres installedon rebar pieces

inclinometersinc0 inc1 inc2

G2

inclinometersinc3 inc4

transducersA1 A3

3 strain gauges (120 mm)

100

50

1000

G4

specimendywidag rods

400 12252850

1225


97

reached a maximum value of about 0.8 per cent against internal fibers that approached 1.2 per

cent at collapse. Fig. 3.4 shows fiber data in the bottom side. The aforementioned trend can be

observed. External fibers measured values up to a maximum strain of 0.9 per cent whilst the

internal one approached the 1.4 per cent. Other results can be found in Fig. 3.5 and 3.6,

respectively.

Fig. 3. 3 Cyclic test N.4: top side internal vs external fiber data

Fig. 3. 4 Cyclic test N.4: bottom side internal vs external fiber data

-4.000

-2.000

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

15:23:31 15:30:43 15:37:55 15:45:07 15:52:19 15:59:31 16:06:43 16:13:55

ε [µm/m]

time

external up strain internal up strain

-4000

-2000

0

2000

4000

6000

8000

10000

12000

14000

16000

15:23:31 15:30:43 15:37:55 15:45:07 15:52:19 15:59:31 16:06:43 16:13:55

ε [µm/m]

time

external bottom strain internal bottom strain


98

Fig. 3. 5 Cyclic test N.4: moment-rotation curve

Fig. 3. 6 Cyclic test N.4: Comparison between AEPs, strain gauges and fiber optic sensors.

In particular the sketch of Fig. 3.6 based on the assumption that plane sections remain plane,

illustrates the favourable performance of external fibers. In summary, results of substructure

tests confirm that the fibre optic system externally installed allowed the plastic moment to be

estimated measuring strains up to 1 per cent, thus detecting a neat hysteretic behaviour.

The design of a full scale test was performed considering the dimensions of a real,

seismic vulnerable metro tunnel. In detail, a circular section with the exterior pipe diameter equal

to 4.8 m, thickness equal to 0.2 m and the tunnel axis depth equal to 20 m was chosen. As far as

the seismic condition is concerned, the worst case for the structural safety of a lining is that in

which the direction is inclined at 45°, because maxima of seismic actions are summed to maxima

of static loads. In this way the maximum moment is reached at 0°, 90°, 180° and 270°,

-200

-150

-100

-50

0

50

100

150

200

-50 -40 -30 -20 -10 0 10 20 30 40 50

M [kNm]

θ [mrad]

EXT. FIBERS

STRAIN GAUGES

INT. FIBERS


99

respectively. This was the choice for the full scale test whose sketch is reported in Fig. 3.7. In

detail, the chosen application of an axial force by means of steel ropes on a cylindrical bearings

system proved to be the most efficient solution, with respect to friction losses; the ovalling of the

section by two hydraulic actuators allowed for a good representation of the stress state predicted

by the Penzien-Wu method (1998). Both the specimen and the testing equipment with sensors

are shown in Fig. 3.7 and 3.8, respectively.

Fig. 3. 7 Full scale test set-up of the tunnel ring (dimensions in cm)

934

1123


100

Fig. 3. 8 Full scale test set-up of the tunnel ring (dimensions in cm)

It was decided to apply the ECCS procedure (Technical Committee 1, TWG 1.3, 1986) both for

the substructure specimens and the final test. The loading protocol was proportional to a

conventional displacement δy which represents the elastic-plastic transition of the cross section.

This parameter was estimated to be about 60 mm, because a monotonic test was not possible.

The displacement history exerted by Actuator N. 1 is depicted in Fig. 3.9.

Fig. 3. 9 Comparison between actuator inner displacement and wire 2-6

The pre-straining values recorded in Section 1 –see Fig. 3.8- by external unbounded AOS fibers

are highlighted in Fig. 3.10. An average value of -120 μm/m was imposed.


101

Fig. 3. 10 External unbounded AOS fiber data in Section 1 for the pre-straining phase.

Typical recorded values both for bonded and unbounded AOS fibers are shown in Fig. 3.11 and

3.12, respectively. The vertical line indicates the failure of Section 8.

Fig. 3. 11 Inner bonded AOS fiber data in Section 2 during the ECCS phase.


102

Fig. 3. 12 External unbounded AOS fiber data in Section 8 for the ECCS phase.

In order to offer a result overview, maximum values of deformation for each instrumented

section and comparison with the value of εy of longitudinal reinforcing bars were gathered in

Table 3.1.

Table 3. 1 Maximum deformations for each instrumented section and comparison with εy of longitudinal reinforcing bars.

Section εmax AOS fiber inside ring

Comparison with εy of

reinforcing bar

εmax AOS fiber outside ring

Comparison with εy of

reinforcing bar

1 0.1209% < 0,3% 0.0532% < 0,3%

2 1.1880% > 0,3% 0.4630% > 0,3%

3 0.0212% < 0,3% 0.0129% < 0,3%

4 0.8724% > 0,3% 0.5939% > 0,3%

5 0.0480% < 0,3% 0.0226% < 0,3%

6 0.5380% > 0,3% 0.2146% > 0,3%

7 0.1751% < 0,3% 0.0334% < 0,3%

8 0.6308% > 0,3% 0.5014% > 0,3%


103

As expected, fibers measured higher deformations in the Sections 2,4,6 and 8 where plastic

hinges formed. In detail, external AOS fibers approached maximum values of about 0.6 per cent

in Section 8, whilst inner AOS fibers reached maximum values of about 1.0 per cent in Section

2.

Inner or embedded AOS fibers showed readings less disturbed than the ones provided by AOS

external fibers located in sections without plastic hinges, i.e. Sections 1-3-5 and 7. Also Brillouin

fibers showed measurements in agreement with those of AOS fibers until the failure of Section 8.

However, relevant signals were disturbed by the presence of a persistent noise. The

measurements of temperature, provided by the AOS fibers N. 2i, 2o, 3i, 3o, 4i, 4o, 6i and 6o

indicated variations of temperature between 19,05 °C and 21,51 °C. The relevant variation of

about 2 °C for the 4 hours test, was consistent with its duration.

There are a number of commercially available fibre-optic interrogation systems which are based

on amplitude measurement with a microbending sensor (OSMOS) or spectrum measurement

with a fibre Bragg grating (Micron Optics, Insensys, Smart Fibres, Blue Road Research). There

also exist measurement systems which utilize interferometry, such as the Fabry-Perot system

(Fiso Technologies, Roctest), or a low coherence interferometer (Smartec, Fox-Tek, Fogale

Nanotech). Furthermore, some systems are based on measuring frequency changes in Brillouin

backscattered radiation by means of Brillouin optical time domain reflectometry (BOTDR,

Yokogawa Electric) or Brillouin optical time domain analysis (BOTDA, Omnisens).

Concluding Remarks

The use of fibre optic technology has been a common practice in structural monitoring systems.

There are many articles available on the successful applications of this technology and on its

development. The commercial fiber optic sensors stated above have been proved to be successful

in structural monitoring, and they can reliably be employed in this respect.


104

3.3 MICROELECTROMECHANICAL SYSTEMS

Micro-Electro-Mechanical Systems (MEMS) is the integration of mechanical elements, sensors,

actuators, and electronics on a common silicon substrate through microfabrication technology.

While the electronics are fabricated using integrated circuit (IC) process sequences (e.g., CMOS,

Bipolar, or BICMOS processes), the micromechanical components are fabricated using

compatible "micromachining" processes that selectively etch away parts of the silicon wafer or

add new structural layers to form the mechanical and electromechanical devices.

MEMS are made up of components between 1 to 100 micrometers in size (i.e. 0.001 to 0.1 mm)

and MEMS devices generally range in size from 20 micrometers to a millimeter. They usually

consist of a central unit that processes data, the microprocessor and several components that

interact with the outside such as microsensors. The distinctive features are: miniaturization,

micro-electronics and multiplicity.

MEMS technology will have an impact on engineering in the following ways (Nii O. Attoh-

Okine, 2002):

• By causing orders of magnitude increase in the number of sensors and actuators. • By enabling the use of very large-scale integration (VLSI) as a design and synthesis approach

for electromagnetic. • By becoming a driver for multiple, mixed and emerging technology integration.

• By being both a beneficiary of and a driver for information systems.

In addition to the potential economic benefits MEMS has the ability to integrate mechanical (or

chemical, biological and environmental) functions, It also allows for consideration of concepts

such as the highly distributed networks for the condition monitoring of large civil infrastructure

systems.

Today, a number of MEMS sensors are available off-the-shelf, including transducers

accelerometers, pressure gauges, load cells, gyroscopes, and chemical gauges (Schenk et al.,

2001). Due to their low cost and small dimension, MEMS are likely to radically change the

philosophy of instrumenting test pieces during laboratory tests, as they allows a much more


105

refined distribution throughout the investigated structure. Moreover, they can be wireless, i.e.

they need power, but no cables for signals (Jung et al., 2001).

MEMS application in civil engineering is theoretically feasible, even if few applications can be

found in the literature (Lynch et al., 2003). The synergistic combination of MEMS technology

and Opto-electronics has recently evolved into a class of integrated micro systems, expected to

create important new application opportunities. The component areas of MEMS are categorised

as micro-machines, micro-integrated-circuits, micro-optics and diffractive optics. The latter two

are often called MOEMS technologies. In the MOEMS sector, low-cost miniature spectrometers

are key components in the realisation of small-sensor solution for application such as colour

measurement or industrial process control (Grueger et al. 2003).

The high costs associated with commercial monitoring systems can be eradicated through the

adoption of MEMS sensors (C.U. Grosse et al., 2006). MEMS accelerometers are much smaller,

more functional, lighter, more reliable, and are produced for a fraction of the cost of the

conventional macroscale accelerometer elements. Various MEMS-based accelerometers are

commercially available that are mechanically similar to traditional accelerometers but fabricated

on a micrometer scale. An additional advantage of MEMS sensors is their ability to

monolithically fabricated signal conditioning circuity on the same die, resulting in improved

sensor performance and reduced sensor cost in the case of mass-volume production (J. W. Judy

et al., 2001).

In June 2009, Adam Pascale reported the use of MEMS accelerometer for earthquake monitoring.

In this report, he stated that apart from the significant cost saving over traditional force-balance

accelerometers, due to the nature of their design micro-electromechanical systems sensors have a

much better high frequency response. Where most earthquake accelerometers are specified as

having a frequency response of DC to 50Hz, 100Hz or in some cases 200Hz, the seismic-

oriented MEMS sensors have a much higher frequency range. For example, the Silicon Designs

units used in the ESS-1221 sensor have a frequency response of DC to 400Hz, and the Colibrys

SF3000L MEMS sensors extend to 1000Hz.

Rafael Aguilar et al. (2009) have presented the use of MEMS for structural dynamic monitoring


106

in historical masonry structures. This new technology offers great advantages such as economy,

time saving and simplicity for the dynamic monitoring systems.

Concluding Remarks

Microelectromechanical system is a very attractive choice for structural monitoring systems.

Although this is relatively newer technology and much remains to be developed, some recent

applications (e.g. Adam Pascale, 2009, Rafael Aguilar et al., 2009) show the potential of MEMS

sensors to be readily used for structural monitoring purposes. Crossbow Technology

(www.xbow.com), which is a reliable company for sensors, produces MEMS sensor systems.

Desired sensors can be provided by them.

3.4 WIRELESS SENSORS AND SENSOR NETWORKS

3.4.1 Introduction

In recent years, there has been an increasing interest in the adoption of emerging sensing

technologies for instrumentation within a variety of structural systems. Wireless sensors and

sensor networks have begun to be considered as the substitution of the traditional tethered

monitoring systems in structural engineering. Wireless sensor networks are inexpensive to

install, they can play greater roles in the processing of structural response data and can even

actuate the actuators.

In structural monitoring systems, because of the high cost of wires, only a few numbers of

sensors (10-20 sensors) are installed in a single structure. Such small numbers of sensors are not

very effective in structural monitoring systems as they are poorly scaled to the localized

behaviour of the structure, often rendering global-based behaviour detection difficult to

implement. With potentially hundreds of wireless sensors installed in a single structure, the

wireless monitoring system is better equipped to screen for structural behaviour by monitoring

the behaviour of critical structural components.

Wireless sensors are autonomous data acquisition nodes to which traditional structural sensors

(e.g. strain gages, accelerometers, linear voltage displacement transducers, inclinometers, among

others) can be attached. Perhaps the greatest attribute of the wireless sensor is its collocation of

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computational resources with the sensor. Already, many data processing algorithms have been

embedded in wireless sensors for autonomous execution.

Recognizing power consumption to be a major limitation of wireless sensors operating on

batteries, some researchers are exploring the development of power-free wireless sensors known

as radio-frequency identification (RFID) sensors. RFID sensors are a passive radio technology,

which capture radio energy emanated from a remote reader so that it can communicate its

measurement back. There are RFID sensors explicitly developed for structural monitoring

systems.

3.4.2 Hardware Design of Wireless Sensors

The fundamental building block of any wireless sensor network is the wireless sensor. As shown

in Fig. 3.3, all wireless sensors can generally have their designs delineated into three or four

functional subsystems: sensing interface, computational core, wireless transceiver and, for some,

an actuation interface.

Wireless sensors must contain an interface to which sensing transducers can be connected. The

sensing interface is largely responsible for converting the analog output of sensors into a digital

representation that can be understood and processed by digital electronics. Selection of an

appropriate sensing interface must be done in consultation with the needs of the monitoring

application. Ordinarily, low sampling rates (e.g. less than 500 Hz) are adequate for global based

structural monitoring.

Once measurement data have been collected by the sensing interface, the computational core

takes responsibility of the data, where they are stored, processed, and readied for

communication. To accomplish these tasks, the computational core is represented by a

microcontroller that can store measurement data in random access memory (RAM) and data

interrogation programs in read only memory (ROM). A broad assortment of microcontrollers is

commercially available. A major classifier for microcontrollers is the size (in bits) of their

internal data bus with most microcontrollers classified as 8, 16 or 32 bits. While larger data

buses suggest higher processing throughput, both cost and power consumption of these


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microcontrollers are also higher (Gadre, 2001). Again as the speed of these microcontrollers

Increases, there is linear increase in power consumed.

To have capability to interact with other wireless sensors and to transfer data to remote data

repositories, a wireless transceiver is an integral element of the wireless sensors design. A radio

transceiver is an electrical component that can be used for both the transmission and reception of

data. Similar to microcontroller, a plethora of radios are readily available for integration with a

wireless sensor. Thus far, the majority of wireless sensors proposed for use in structural

monitoring have operated on unlicensed radio frequencies. There exist two types of wireless

signals that can be sent upon a selected radio and: narrow band and spread spectrum signals.

Strong consideration must be given to the communication range of the wireless transceiver. The

range of the wireless transceiver is directly correlated to the amount of power the transceiver

consumes. As wireless signal radiates from an antenna in open space, it loses power in

proportion to the wavelength of the radio band and inversely proportional to the square of the

distance from the transmitter (Rappaport, 2002).


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Fig. 3. 13 Functional elements of a wireless sensor for structural monitoring applications

The last subsystem of a wireless sensor would be the actuation interface. Actuation provides a

wireless sensor with the capability to interact directly with the physical system in which it is

installed. Actuators and active sensors (e.g. piezoelectric elements) can both be commanded by

an actuation interface. The core element of the actuation interface is the digital-to-analog

converter (DAC) which converts digital data generated by the microcontroller into a continuous

analog voltage output (which can be used to excite the structure).

3.4.3 State of the art of Academic Wireless Sensing Unit Prototype

Realizing the need to reduce the costs associated with wired structural monitoring systems,

Straser and Kiremidjian (1998) have proposed the design of a low-cost wireless modular

monitoring system (WiMMS) for civil structures. Using commercial off-the-shelf (COTS)

components, a low-cost wireless sensor approximately 12 × 12 × 10 cm3 is produced. To control

the remote wireless sensing unit, the Motorola 68HC11 microprocessor is chosen for its large

number of on-chip hardware peripheral and the availability of high-level programming language

(e.g. C) for embedding software.

Bennett et al. (1999) have proposed the design of a wireless sensing unit intended for embedment

in flexible asphalt highway surface.

Recognizing the importance of decentralized data processing in wireless structural monitoring

systems, Lynch et al. (2001, 2002a, 2002b) have proposed a wireless sensor prototype that


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emphasizes the design of a powerful computational core. Setting the goal to minimize the entire

sensing unit design, the 8-bit Atmel AVR AT90S8515 enhanced RISC (reduced instruction set

computer) microcontroller is selected, which is capable of handling 8 million instructions per

second (MIPS).

Mitchell et al. (2002) have proposed a two-tier structural health monitoring (SHM) architecture

using wireless sensors (as shown in Fig. 3. 4c). In their system, a compact (footprint size of 4

×7.5 cm2) wireless sensor using a powerful Cygnal 8051F006 microcontroller is proposed for

data collection. Capable of 25 MIPS, the microcontroller only consumes 50 mW of battery

power and provides 2 KB of RAM for data storage. A key element of this two-tiered wireless

SHM system architecture proposed by Mitchell et al. (2002) is its seamless interface to internet.

Fig. 3. 14 Wireless network typologies for wireless sensor networks

(a) star; (b) peer-to-peer ; (c) two-tier network topologies

Kottapalli et al. (2003) have presented a wireless sensor network architecture that is intended to

overcome the major challenges associated with the time synchronization and limited power

availability in wireless SHM systems powered by batteries. While Mitchell et al. (2002) and

Kottapalli et al. (2003) have proposed attainment of an overall low-power wireless SHM system

by partitioning functionality upon multiple network tiers, Lynch et al. (2003a, 2004a, 2004e)

focus upon the design of a low power but computationally rich wireless sensing unit. In their

design, each component of the wireless sensor is selected such that minimal power is required.

Aoki et al. (2003) have proposed a novel wireless sensing unit prototype, which they call the

Remote Intelligent Monitoring System (RIMS). Designed for the purpose of bridge and

infrastructure SHM, each hardware component included in their design is carefully chosen to

reduce the cost and size of the prototype while achieving adequate performance standards.


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Casciati et al. (2003) presented the design of a wireless sensing unit intended for SHM of historic

landmark in which wired monitoring system would be too obtrusive. Again a two-tier approach

to the design of the wireless structural monitoring system is proposed. Upon the second-tier of

the hybrid wireless monitoring system architecture proposed by Casciati et al. (2003, 2004), are

wireless computational units where data streams originating from the lower tier wireless sensing

units are aggregated and locally processed.

Basheer et al. (2003) have proposed the design of a wireless sensor whose hardware design has

been optimized for collaborative data processing (such as damage detection) between wireless

sensors. The wireless sensors proposed from building blocks of a self-organising sensor called

the Redundant Link Network (RLN). Basheer et al. (2003) called their wireless sensor ISC-

iBlue. The design of ISC-iBlue is divided into four main components: communication,

processing, sensing, and power subsystems.

Wang et al. (2003a) have proposed the design of a wireless sensor specifically intended to report

displacement and strain reading from a polyvinylidene fluoride (PVDF) thin film sensor. Their

wireless sensor is similar to that proposed by Casciati et al. (2003) in that the wireless sensor

design is based upon an Analog Device ADuC832 microsystem.

Extending upon the design of the wireless sensing unit proposed by Kottopalli et al. (2003),

Mastroleon et al. (2004) have attained greater power efficiency by upgrading many of the unit’s

original hardware components. Drawing from previous experience with commercial wireless

sensor platforms, Ou et al. (2004) have described the design of a new low-power academic

wireless prototype for structural monitoring.

In recent years, a new wireless communication standard IEEE802.15.4 has been developed

explicitly for wireless sensor networks (Institute of Electrical and Electronics Engineers, 2003).

The wireless standard is intended for use in energy-constrained wireless sensor networks because

of its extreme power efficiency.

Allen (2004) and Farrar et al. (2005) have proposed a design strategy, in which they emphasised

on providing ample computational power to perform a broad array of damage detection

algorithms within a wireless SHM system. In close collaboration with Motorola Labs, Farrar et


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al. (2005) have described the design of a wireless sensor designed to have seamless interaction

with DIAMOND II, an existing damage detection package written in Java. As such, the overall

design of the wireless sensor is based on the powerful computational core needed to execute

DIAMOND II-based damage detection routines.

Using the latest commercially available embedded system components, Wang et al. (2005) have

proposed a wireless sensing unit with multitasking capabilities. In particular, a low-power

wireless sensor that can sample measurement data simultaneous to wirelessly transmitting data

with other wireless devices is proposed.

3.4.4 Commercial Wireless Sensor Platforms

A number of commercial wireless sensor platforms have emerged in recent years that are well

suited for use in SHM application. The advantages associated with employing a commercial

wireless sensor system include immediate out-of-the-box operation, availability of technical

support from the platform manufacturer, and low unit costs. For this reason many academic and

industrial research teams have begun to explore these generic wireless sensors for use within

SHM systems. In particular, the structural engineering community has focused their attention on

the Mote wireless sensor platform initially developed at the University of California-Berkley and

subsequently commercialised by Crossbow (www.xbow.com) (Zhao and Guibas, 2004).

Crossbow has received a number of awards for these products, including a ‘‘best of Sensors

Expo Gold 2006’’ and the BP Helios Award. In 2008, Crossbow Japan released NeoMote

System that monitors energy usage in a building and provides an enhanced visual display that

allows for easy viewing of data collection enabling quick and intelligent decision making for

smart energy saving.

Tanner et al. (2002, 2003) have presented the adoption of the Crossbow Rene2Mote in a system.

During this study, the authors report their experience of interfacing two types of MEMS

accelerometers with the Mote: the Analog Devices ADXL202 and Silicon Devices SD-1221.

Glaser (2004) has evaluated the suitability of the hardware elements of the Crossbow Rene Mote

during monitoring performed in the laboratory and field.


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To provide more program and data storage and to improve the flexibility of the wireless

communication channel Crossbow released the MICA Mote wireless sensor in early 2002 as the

successor to the Rene 2. Ruitz-Sandoval et al. (2003) have reported their experiences using the

MICA Mote wireless sensing platform for structural monitoring. They proposed (2004) a new

sensor board to replace the existing one of the MICA Mote to address a limitation of this sensor.

In 2003, MICA was modified to improve the reliability of the communication channel.

Since the MICA2 Mote is unable to measure structural strain, Nagayama et al. (2004) implement

a new integrated strain sensor board for the MICA2 Mote that accommodates strain gages.

Pakzad and Fenves (2004) described a study where a novel prototype accelerometer sensor board

is integrated with a MICA2.

Close research collaboration between the University of California-Berkley and the Intel Research

Berkley Laboratory has resulted in a next-new generation Mote Platform called iMote. iMote

employ a highly modular construction allowing sensing interfaces fabricated as separate boards

to be snapped onto the iMote circuit board.

3.4.5 ZigBee and 802.15.4 Overview

The ZigBee Alliance [ZIG05] is an association of companies working together to develop

standards (and products) for reliable, cost-effective, low-power wireless networking and it is

foreseen that ZigBee technology will be embedded in a wide range of products and applications

across consumer, commercial, industrial and government markets worldwide.

ZigBee builds upon the IEEE 802.15.4 standard which defines the physical and MAC layers for

low cost, low rate personal area networks. It defines the network layer specifications, handling

star and peer-to-peer network topologies, and provides a framework for application programming

in the application layer.

3.4.6 IEEE 802.15.4 Standard

The IEEE 802.15.4 standard defines the characteristics of the physical and MAC layers for Low-

Rate Wireless Personal Area Networks (LR-WPAN). The advantages of an LR-WPAN are ease


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of installation, reliable data transfer, short-range operation, extremely low cost, and a reasonable

battery life, while maintaining a simple and flexible protocol stack.

3.4.7 Field Deployment of Wireless Sensors in Civil Infrastructure Systems

The deployment of wireless sensors and sensor networks in actual civil structures is perhaps the

best approach to assessing the merits and limitations of this nascent technology. In particular,

bridges and buildings provide complex environments in which wireless sensors can be

thoroughly tested. The transition of wireless monitoring systems from the laboratory to the field

has been demonstrated by a number of research studies. In all of these studies, the goal of the

researchers has been to assess the performance of a variety of wireless sensor platforms for the

accurate measurement of structural acceleration and strain responses. Common to most of the

studies reported, the sensitivity and accuracy of the wireless monitoring systems are compared to

that of traditional cable based monitoring systems which have been installed alongside their

wireless counterparts.

Perhaps the earliest field validation of wireless telemetry for monitoring the performance of

highway bridges was described by Maser et al. (1996). Their wireless monitoring system, called

the Wireless Global Bridge Evaluation and Monitoring System (WGBEMS), consists of two

levels of wireless communication. After completing the design of their academic wireless sensor prototype, Straser and Kiremidjian

(1998) utilized the Alamosa Canyon Bridge to validate its performance. Comparing the

acceleration response of the bridge measured by the wireless sensors and the tethered monitoring

system, the time-history response records showed in strong agreement. Using the same bridge as

Straser and Kiremidjian (1998), the performance of the wireless sensing prototype developed by

Lynch et al. (2003a) was validated in the field.

Galbreath et al. (2003) demonstrate the use of a wireless sensor network to monitor the

performance of a steel girder composite deck highway bridge spanning the LaPlatte River in

Shelburne, Vermont. They select the Microstrain SG-Link wireless sensor platform to measure

flexural stain on the bottom surface of the bridge girders.


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Aoki et al. (2003) have outlined the validation of their Remote Intelligent Monitoring System

(RIMS) wireless sensor platform. To test the accuracy of their wireless monitoring system, field

tests are performed using a flexible light pole mounted to the surface of the Tokyo Rainbow

Bridge, Japan. With fatigue failure common in light poles subjected to frequent excitation, the

study is intended to illustrate the potential of the RIMS wireless monitoring system to monitor

the long-term health of non-structural components on bridges.

Chung et al. (2004a, 2004b) have described a detailed study taken to validate the performance of

their DuraNode wireless sensing unit prototype. Using two different MEMS accelerometers

(Analog Devices ADXL210 and Silicon Design SD1221) interfaced to the wireless sensing unit,

the ambient and forced response of a 30 m long steel truss bridge is recorded. To compare the

accuracy of the wireless monitoring system, a traditional cable-based monitoring system is also

installed; the cable-based system uses piezoelectric PCB 393C accelerometers as its primary

sensing transducer. Results from the field study show very strong agreement in the acceleration

time histories recorded by both the wireless and cable-based monitoring systems.

Binns (2004) has presented a wireless sensor system developed by researchers at the University

of Dayton, Ohio for bridge monitoring. The wireless monitoring system, called WISE (Wireless

InfraStructure Evaluation System), can perform wireless monitoring of bridge structures using

any type of analog sensor.

Lynch et al. (2005) have installed 14 wireless sensing unit prototypes to monitor the forced

vibration response of the Geumdang Bridge in Korea. The Geumdang Bridgeis a newly

constructed concrete box girder bridge continuously spanning 122 m. The vertical acceleration of

the bridge is measured by the wireless sensing units using PCB 3801 capacitive accelerometers

mounted on the interior spaces of the box girder. In tandem with the wireless monitoring system

is a cable-based monitoring system with PCB 393C piezoelectric accelerometers mounted

adjacent to the wireless sensing unit accelerometers. The stated goals of the field validation study

are to assess the measurement accuracy of the wireless sensing units, to determine the ability of a

central data repository to time synchronize the wireless sensor network, and to use the wireless

sensors to calculate the Fourier amplitude spectra from the recorded acceleration records.

Comparing the recorded time histories of the bridge using both monitoring systems (wireless and

cable-based), the accuracy of the wireless sensing units is confirmed. In addition, the time


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synchronization procedure implemented by Wang et al. (2005) is shown to be perfect for almost

all of the wireless sensing units.

Jin-Song Pei et al. (2007) have carried out an experimental study to investigate the reliability

issue of applying wireless sensing to structural health monitoring. They have developed a

wireless unit by using an off-the-shelf microcontroller and radio components; software has been

developed to capture the loss of data using a flexible payload scheme when transmitting

vibration data from a shake table through various building materials.

Kincho et al. (2008) have performed laboratory experiments that are designed to assess the

viability of decentralised wireless structural control using a six-story scaled structure. Multiple

centralized/decentralized control architectures based on different communication and

information processing schemes are investigated. The results indicate that decentralized control

strategies may provide equivalent or even superior control performance, given that their

centralized counterparts could suffer longer feedback time delay due to wireless communication

latencies.

Lynch et al. (2008) have proposed a wireless sensor prototype capable of data acquisition,

computational analysis and actuation for use in a real-time structural control system. The

performance of a wireless control system is illustrated using a full-scale structure controlled by a

semi-active magnetorheological (MR) damper and a network of wireless sensors. The wireless

control system proves effective in reducing the inter-storey drifts of each floor during seismic

excitation. Particularly for the case of acceleration feedback control, the wireless control system

performs at a level of performance equivalent to a baseline wired control system for both far- and

near-field seismic excitations.

Jian-Huang Weng (2008) have presented two modal identification methods that extract dynamic

characteristics from output-only data sets collected by a low-cost and rapid-to-deploy wireless

structural monitoring system installed upon a long-span cable-stayed bridge. The use of wireless

sensors has been very effective and during data collection, the wireless monitoring system

experienced no data loss as a result of a highly-robust communication protocol.


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Kung-Chun Lu et al. (2008) have designed a wireless sensing system for application to structural

monitoring and damage detection applications. To validate the performance of the proposed

wireless monitoring and damage detection system, two near full scale single-story RC-frames,

with and without brick wall system, are instrumented with the wireless monitoring system for

real time damage detection during shaking table tests. The accuracy and sensitivity of the

MEMS-based wireless sensors employed are also verified through comparison to data recorded

using a traditional wired monitoring system.

3.4.8 Reliability assessment of wireless sensors in the University of Trento

In the University of Trento, a project named “MEMSCON” (Pozzi et al., 2009) had a task of

assessing the accuracy and reliability of the wireless sensing system in condition similar to that

experienced in field during a seismic event. Under this task the performance of the sensors has

been tested by mounting them on a shaking table, back to back with high precision, wired piezo-

electrical seismic accelerometers, instrumented with traditional accelerometers and drivable with

harmonic or random excitations.

Each sensing node used in the tests (MOTE unit) is packaged in a plastic box of dimensions

11x8x4cm, endowed with a 19cm high antenna. The weight of a node is 150g, and it contains a

tri-axial accelerometer, permitting the following performances:

Sampling rate: 100Hz

Resolution: 18mg (=0.18m/s2)

Range: from -2g to +2g (= from -20m/s2 to+ 20m/s2)

Sampling period: up to 30 seconds

Fig. 3. 5 shows an overview of the system base station and a node that have been used during the

laboratory tests. A single base station, connected via USB to a standard PC, is able to acquire

vibration measures from many nodes in a range of dozens of meters, even inside a building.


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Fig. 3. 15 A Base Station and a MOTE Unit

Testing scheme reflects the aim to acquire data independently and simultaneously from reference

and wireless sensors, subjected to the same excitation generated by the shaking table.

Fig. 3. 16 Testing Scheme


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Fig. 3. 17 Laboratory Test Layout

Two types of tests have been performed:

1) Calibration tests on the wireless sensors, for the 3 different axes and for different frequency.

2) Simulation of an earthquake scenario, in order to study the behaviour of the sensors during an

earthquake.

Calibration Tests

During the calibration tests, a small shaking table was driven using harmonic wave forms

established by the operator via a function generator. For each direction of the sensors (X, Y, Z),

tests at 1, 2, 4, 8, 16 Hz were carried out. Each testing frequency was repeated twice at different

wave amplitude, obtaining acceleration peaks about of 1 m/s2 or 4 m/s2 (test called “Low

Amplitude” and “High Amplitude”, respectively). In summary, 33 calibration tests were carried

out. The sensor arrangements in X, Y and Z directions are shown in Fig. 3.18


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Fig. 3.18 Sensors arrangements; (a) Tests on X axis, (b) Tests on Y axis, (c) Tests on Z axis

The calibration was performed by using the “back to back” mounting scheme, by a direct

comparison between the reference accelerometer and the accelerometer to be tested. Two

parameters, acceleration factor and frequency factor were measured. In particular, the

“Frequency Factor” is a parameter which quantifies the effective sampling rate of the wireless

accelerometer comparing that with the rate of the wired system, while “Acceleration Factor” is

a parameter which permits to know the sensitivity of the instrument. For the three wireless

sensors used (WL1, WL2, WL3), the mean values of acceleration factors and frequency factors

are presented below.

WL1 WL2 WL3

Acceleration Factor 1.0327 1.0147 0.9927

Frequency Factor 1.0075 0.9748 0.9921

The time histories and the spectra of the wireless accelerometer WL1 and the reference wired accelerometer B12-1 using these parameters and after pre-processing and fitting are shown in Fig. 3.19. These results show good agreement between the signals acquired by the two types of accelerometer.


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Fig. 3.19 (a) Fitted time histories of the sample test using test’s parameters;

(b) FFT of the fitted time histories of sample test applying test’s parameters

Earthquake Simulations

Besides the calibration tests, additional tests were carried out with the aim of better

understanding the behaviour of wireless sensors during a seismic event. A 2-storey

steel/aluminum frame was mounted on a shaking table. By installing wireless and wired

instruments back to back on the frame, three different time histories were induced: one on the

table and two on the frame floors, correlated by the mechanical properties of the structure. In

particular, 2 modulating frequency tests (“SWEEP TEST”) and 1 random input test with

frequency of the shaking table arranged at about 2 Hz (resonance frequency of the first vibration

mode of the frame) were carried out. The most important physical properties of the frame are

listed here:

- weight: about 8 Kg per floor slab

- natural frequencies: 2.1 Hz (first mode) 5.2 Hz (second mode)

- damping factor: about 1%


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The frame placed on the shaking table with the reference and wireless accelerometers are

presented in Fig. 3.20.

Fig. 3.20 Steel/aluminium frame placed on the shaking table instrumented with

accelerometers

The parameters obtained through the calibration tests were applied to the time histories of the

earthquake simulations. The Fitted and synchronized time histories are reported in Fig. 3.21. One

can clearly notice a good agreement between the time histories acquired by the wireless

accelerometers (WL1, WL2 and WL3) and the reference accelerometers (B31, B12-1 and B12-

2).


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Fig. 3.21 Earthquake simulation fitted and synchronized time histories

3.4.8.1 Tests with wireless strain gauges

Under the same European project, MEMSCON, UniTn also tested some wireless strain gauges in

order to evaluate their performances. Some wireless strain gauges along with some wired strain

gauges were mounted in some bare bars and in some bars in concrete. The outcomes of these two

types of strain gauge were assessed. The wireless nodes to be tested are shown in Fig. 3.22. The

test scheme is shown in Fig. 3.23 and the bare bar and bar in the concrete mounted with the

strain gauges are presented in Fig. 3.24.


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Fig. 3.22 The wireless nodes to be tested (the nodes are packaged in plastic boxes of

dimension 11x8x4cm, a 19cm high antenna; the weight of a sensor is 150g).

Fig. 3.23 Testing scheme with the wired and wireless strain gauges.

Fig. 3.24 (a) Strain gauges mounted in the bare bars; (b) strain gauges mounted in the bar

in the concrete.

on-off switch

female connectorfor coaxial cable

antenna

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The test results showed a very good performance of the wireless strain gauges.

- the initialization procedure was very easy and the functioning reliable (no problem of

communication).

- after calibration, the strain recorded by the wireless strain gauges followed that of the wired

ones with a precision (30με) is in the order of the resolution (20με).

The outcome of the test on the bare rebar is presented in Fig. 3.25. Close agreement can be noted

between the wireless (WL10 and WL12) and wired strain gauges (SG1 and SG2).

Fig. 3.25 Strain measured by wired and wireless strain gauges

3.4.9 Concluding Remark

Wireless sensors and sensor networks were proved to be very effective in structural monitoring

systems. Many research works are continuously being carried out for further development of

these sensors. However, wireless sensors and sensor networks have been developed enough to be

employed for the sensing and monitoring of structures. In particular, the current wireless sensing

approaches described by Lynch et al. (2005, 2008), Jin-Song Pei et al. (2007), Kincho et al.

(2008), Jian-Huang Weng (2008) and Kung-Chun Lu et al. (2008) can be adopted. The

‘Crossbow Technology’ (www.xbow.com) is a reliable company that supplies Wireless Sensor

Networks. Necessary sensor systems can be purchased from them.

0 500 1000 1500 2000 25000

1000

2000

3000

4000

Time [sec]

Stra

in [ µ

ε]

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3.5 SENSORS AND TECHNIQUES FOR VISION SYSTEMS

3.5.1 Introduction

Today’s practice, consisting of classical few point/local measurements in seismic testing, cannot

serve the current needs of performance-based earthquake engineering which emphasizes on

structural damage. New sensor technologies for array, field and 3D measurements are necessary,

to efficiently detect, locate and quantify global and local damage on test specimens. Remote,

non-contact measurements should be investigated and advanced, for use when direct contact with

the specimen should be avoided, e.g., for safety of the instrumentation at specimen collapse.

The development of vision systems in the context of civil engineering laboratory brings extended

information on the displacement/strain field of structures under test, and on the boundary

conditions of their loading. Obvious problems may arise from the span of characteristic scales of

one experiment, which may extend from several meters (building size) to cm (structural detail)

or mm (bed joints, cracks). This could be solved by playing with the number of cameras (rising

the complexity of handling image data), by increasing the camera resolution –to some optical

limits- or by merely reducing the zone on which measurements must be done. However, one of

the interests of vision techniques is their “latent potentiality”, which means that an extensive –

photogrammetric- coverage of the experiment does not mean that all the data should be

immediately exploited. Indeed, as long as a complete calibration of the system has been done, a

correct archiving is possible and post processing is always feasible at any time, on any portion of

the specimen. In this way, unforeseen phenomena could be documented and measured, sudden

interest to one experimental detail could be satisfied afterwards or spurious effects removed from

classical measurement system.

The scope of this review is to investigate and assess the performance of related efforts so far,

which have been using optical systems to record displacement phenomena, while at the same

time quantitatively determining their magnitude with adequate accuracy and reliability.

In what follows, a description of the available equipment for performing photogrammetric

measurements will be given, with the various calibration and tracking techniques. Some

examples will be given to illustrate the possibilities of this kind of optical measurements in PsD

experiments performed at JRC, or in shake table experiments made at CEA.


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3.5.2 Photogrammetric principles

Since the advent of photography, various scientists have strived to exploit image recording in

order to perform measurements of any kind. This developed to what is called Photogrammetry, a

complex but elegant science, art and technique (McGlone 2004), which interprets geometrically

the imaging action of a camera and thus it is able to determine 3D co-ordinates of all imaged

points in space at very high speeds and at very high accuracies.

The geometric model of Central Projection is the key for modeling the performance of the

cameras and the resulting images. Provided the internal geometry of the camera is known,

modeling of the imaging process in order to reconstruct the bundle of rays may be performed

with great reliability. As a result the direction, and in fact the actual position, of all rays in 3D

space may be determined, enabling thus the calculation of the 3D position of all points imaged in

at least two optical systems (e.g. cameras) as intersections of the corresponding rays from –at

least- two different vantage points.

Photogrammetric methodology today is mainly used for cartographic purposes, but also for

industrial quality assessment, for monument recording, for medical applications and practically

for every application which requires large volume of determined points and allows the object to

be imaged.

Applying photogrammetry to experiments serving to assess vulnerability of buildings and civil

infrastructures implies to sample thousands of frames per experiment on each camera. The

equipment will strictly depend on the experimental methodology: PSD technique will provide

“plenty of time” to make acquisitions, and allow to use high dynamics, high resolution camera

having a low number of frame per second (FPS), while shaking table experiments require higher

FPS, at the expense of dynamic and resolution (centrifuge have even more stringent conditions).

In the field of monitoring high frequency movements and assessing the resulting deformations or

displacements, specialized equipment should be employed. Technological advances in recent

years have significantly boosted this area of application and several in-house and commercial

systems have been developed for this very purpose. But in any experimental case the calibration

(for free camera positioning) must be made beforehand with the same methodology, and tracking


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procedures are common to all cases and may be accomplished in post-processing Optical

Methods.

In the related literature, various vision based methods are being reported to be able to collect

three dimensional data. These methods include photogrammetric and non-photogrammetric ones

and consist of, but are not limited to the following:

Single cameras: a camera may be used on its own to measure and monitor planar displacement

(Capéran 2007), using photogrammetric techniques. This method has the advantage of a simpler

hardware and software setup, but the limitation of monitoring displacement only in two

dimensions on a single plane.

Two or multiple camera setup: By using two or more cameras simultaneously to record the

subject, measurements can take place in three-dimensional space using photogrammetric

triangulation. In previous research, various systems have been developed in order to monitor

seismic induced motions in three dimensions by using two or more cameras. Those systems

differ in terms of hardware cost and complexity, sampling frequency and spatial resolution.

Low-cost digital consumer cameras have been used to monitor structures undergoing destructive

tests with promising results (Lathuilière and Capéran 2007), though neither the resolution nor the

sampling frequency fulfil the needs of monitoring the effects of the shake table.

Range cameras are recording systems which produce pixel distance maps, based on a phase-

measuring time-of-flight (TOF) principle. Although their actual characteristics prevent their use

for our purpose, we will briefly describe their principle below, as it is a technique which will

refine and mature in the next years and may become efficient in our configuration.

Laser scanners use a controlled laser beam and by measuring -usually- its time-of-flight (TOF),

are able to produce detailed three dimensional models, and reflectance. However, the time

needed to complete the scans ranges from seconds to minutes, since mechanical movement of the

laser beam is required to cover the object’s area, making the method useless for monitoring high

frequency dynamic phenomena such as seismic induced motions.

In addition, several other alternative methods have been researched all over the world, e.g. the

use of fibre optics in an integrated system comprising GPS, accelerometer, and optical fibre

sensors, which has been proposed to monitor structural deformation in order to assess the

integrity of a structure (Li et al. 2003). The GPS and accelerometer sensors have been installed

on a 108m tall steel tower, and data have been collected during Typhoon No. 21 on 1 October


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2002, at 10Hz and 20Hz rates respectively. The wind induced deformation has been analysed in

both time and frequency domains. In the frequency domain, both the GPS and accelerometer

results show strong peaks at 0.57Hz, although GPS measurements are noisy in the low frequency

end. On the other hand, the result of a series of indoor experiments shows that the optical fibre

Bragg grating (FBG) sensors have demonstrated excellent performance with respect to

sensitivity, linearity, repeatability and dynamic range.

3.5.3 Optical components, data collection and calibration

As the first stage of the measurement chain, the various types of optical components will be

briefly described in what follows. We will only deal with digital camera.

3.5.3.1 Camera sensor

In order to clearly describe the related information, this part is divided into several subsections

herein.

(1) History and main characteristics of CCD and CMOS sensors

The essential part of a digital camera is indeed its optical sensor, which is composed of an array

of photosensitive regions and their associated electronics able to convert charges generated by

the photoelectric effect into digital numbers. The present sensors belong mainly to two classes

that are CCD or CMOS sensors, and are building from silicon wafer.

CCD stands for Charge-Coupled Device, it comes from an invention by Boyle and Smith in 1969

(for which they received the 2009 Nobel Prize), on an efficient technique of storing and

transferring charge packets in the interior of a semiconductor. Their aim was to produce a new

type of sequential memory, which was based on coupling between MOS capacitors and surface

electrodes, so that charge packets trapped in a line of such capacitors could be safely and swiftly

shifted by manipulating the electrodes signals. An array of such trapping sites could be read by

shifting charge packets column after column to a collecting row that would convey them to a

charge amplifier and an a/d convertor (Full-Frame CCD architecture). Now if this array of such

MOS capacitors, that acts as a storage medium, is fed from its surface by corresponding


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photosensitive regions (10 μm-thick layer of dopped Si), this sequential memory acts as a

photographic sensors, of which each “picture element” -coined “pixel”- are the MOS capacitors.

The amount of charge in each “pixel” is proportional to the amount of photons impinging the

corresponding photosensitive site. In its recent paper on the invention of CCD, Smith

(GeorgeE.Smith, 2009) insists on the fact that: “the basic unit of information in the device was a

discrete packet of charge and not the voltages and currents of circuit-based devices. The CCD is

indeed a functional device and not a collection of individual devices connected by wires”.

This naturally introduces the second type of sensors, which are based on the Complementary

MOS (transistors) and were elaborated contemporaneously to the CCD. In this architecture, each

photosensitive region has its own transistor circuitry integrated on the pixel surface, to convert

charge to voltage. These circuits reduce the photosensitive site area and thus decrease the overall

sensitivity (fill factor). Up to now, CMOS has been widely used in consumer product that would

not require a high image quality. In this area, it benefitted from its low power consumption, its

ease of integration (low cost) and its high frame rate. CMOS dramatically improved their

performance in the past years and seems to be actually a serious alternative to CCD in the field

of scientific imagery. However, the scientific CMOS have about the same cost than CCD

equivalent, because custom, sophisticated processes of production had to be invented to reach the

quality level of CCD.

Both technologies compete to takeover a market spanning from cell phones to high ends

scientific applications (e.g. Hubble telescope). Reviews of the respective evolution of these two

types of sensors can be found in (Litwiller, 2001) and (Litwiller, 2005). Janesick wrote an

exhaustive book on CCD (Janesick, 2001), before completely devoting his research to CMOS

technology (Janesick 2002, 2003, 2008). It is worthwhile noting that sensors are built from

silicon wafers, on which circuitry and active components are build on the ‘front’ side, creating

photosensitive ‘dead zones’. The photons can reach the photosensitive zone directly on the front-

side, or from the backside, passing through the wafer (if it has been correctly thinned, a delicate

and expensive process). These sensors design are respectively called front and back side

illuminated. Critical parameters for photogrammetric measurements are the resolution of the

sensor (number of pixel in both direction), dynamic range of the camera and frequency of frame


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sampling. At this stage, we will not develop any further on various sensor architecture, like

multiple taps CCD, full frame or interline CCD, etc.

Following Janesick (2002) 4 successive stages of the image sampling can be identified, which

are: the charge generation, the charge collection, the charge transfer and finally its measurement.

These various functions are shortly described in what follows.

(2) Successive stages of a frame’s elaboration

Charge Generation: this corresponds to the ability of the sensor to intercept photons and

generate electrons, and is characterized by the ‘quantum efficiency’ (QE). The first factor

influencing QE is the ‘fill factor’ that qualifies the percentage of optically active surface on the

total surface of the pixel (e.g. transistor surface on the pixel surface for CMOS, electrodes for

CCD, front or back side design). The other factors influencing QE are the photons reflection and

transmission, which depends on the physical properties of silicon. As a result, QE depends on the

wavelength and on the angle of the incident ray on the sensor. To increase the QE, one could

build micro-lens on each pixel, in order to focus light in a favourable way and collect on a larger

surface (see the specifications of KAF-8300 full frame Kodak sensor, of 5.4 micron pixel size,

(Kodak, 2008)). In this way, QE can be increased from 30% to 50% at a wavelength of 600 nm

(~orange) and efficient incident angle can be enlarged. For CMOS, the ‘fill factor’ can be

increased using an hybrid CCD-CMOS technology by grouping pixels and transferring their

charge to a common transistor, thus freeing pixels from the transistor dead zone. Among other,

this technique has been used for KODAK KAC-05020 Image Sensor that has 1.4 micron pixel

size, only two to three times the wavelength of visible light. For pixel larger than 10 microns, QE

performance are about the same for CMOS and CCD. As already mentioned, CCD and then

CMOS have been developed as “thinned back-illuminated sensor”, where it is the back of the

sensor that, when properly etched in order to reduce sensor’s thickness, will be used as photon

exposed surface. In this way, sensor’s circuitry does not occlude any more the photon arrival.

Charge collection: the electrons that are provided by the photoelectric effect must be efficiently

collected, before they recombine. In fact they must be trapped in the potential well created inside

the semi-conductor, and each of these traps can contain up to a certain number of carriers, called


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the ‘full well capacity’ (FWC), above which they escape to adjacent pixels (blooming effect on

CCD). The FWC depends mainly on the pixel surface. Let’s take one pixel excited by a beam of

photons of its size. The “charge collection efficiency’ (CCE) expresses the number of charges

effectively collected in the pixel to the number of charges freed by photons into this pixel. The

major causes of loss are recombination, and collection by adjacent pixels. In fact, under thermal

diffusion, electrons could wander to adjacent pixels, creating a cross-talk effect: the result of this

is a smearing of the image. Cross-talk could be reduced by having strong electric fields to

efficiently confine the electrons (this is in favour of CCD that work at high voltages, in contrast

to CMOS). The last parameter qualifying charge collection is the “fixed pattern noise” (FPN),

that comes mainly from geometrical variations in the pixels, inducing slight sensitivity changes

from pixel to pixel. The FPN depends on the ability of the manufacturers and is in general of the

order of 1% for both technology.

Charge transfer: Charge transfer is critical for CCD technology, as the charges collected in one

pixel must be sequentially transferred, over distances that may be thousands pixel’s size, to the

output amplifier. This means that the elemental transfer must be highly efficient, in fact it is of

the order of 99.9999%, thanks to the high electric fields produced in CCD technology. CMOS

have not such problem since they have their conversion to voltage done directly on the pixel site.

However, as noted earlier, hybrid CCD-CMOS groups pixels with a common transistor, and the

transfer efficiency in this case is reduced since CMOS work at low-voltage.

Charge measurement: CCD or CMOS convert charge to voltage in the same way. The difference

is that CCD have space to integrate sophisticated analogous circuits to reduce the readout noise,

while CMOS cannot afford that on each pixel: While CCD reject white noise by reducing the

electrical bandwidth, CMOS are working in open bandwidth. CMOS have usually a greater

readout noise.

(3) On some varieties of sensors and pixel

Various architectures are used for CCD:

- Full-Frame CCD has been already described, columns are simultaneously shifted down

by one pixel to a serial line register that will be read, this operation being repeated until


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every sensor’s line has been read. This architecture requires a mechanical shutter to

sample all the frame simultaneously before reading it.

- Frame-Transfer the frame is first completely transferred to a storage array (a CCD

insulated from light) so that the storage zone could be read while the light sensitive CCD

could be exposed. The numerous transfer operations increase the image smear and this

type of sensor, integrating in fact two CCD, one being light sensitive and the other not, is

more expensive than the full-frame architecture.

- Interline CCD consists into adding to each CCD column a storage column. At the end of

each exposure, the sensitive columns contents are quickly transferred to the storage

column, the array of storage column being read as for a full-frame, while the sensitive

columns are re-exposed. This architecture reduce the smear respectively to the frame-

transfer one. However, the presence of storage column between sensitive column reduces

the “fill-factor”, thus the sensitivity of this type of sensor. This could be partly

compensated by micro-lenses. The interline CCD is more complex than the full-frame,

and thus more expensive.

As was previously said, CCD have a frame sampling frequency usually smaller than CMOS. To

increase the FPS, keeping the noise at a fair level, the CCD could be divided into four quadrant

that are simultaneously read with their own amplifiers. This kind of CCD are called “multiple

taps CCD”.

Various types of pixels are distinguished for the CMOS sensors, they appeared successively as

the integration technology was reducing its scales:

- “Passive pixels” have only one transistor integrated on their surface, switching the

charges at the end of the time exposure to the charge amplifier common to each pixel

row.

- “Active pixels” have an extra charge amplifier integrated on their surface, and finally.

- “Digital pixel” have their own A/D converter!

These evolutions were driven by the need to drastically reduce noise in CMOS (El Gamal 2005).

Their improving effect is counterbalanced by the correlative reduced proportion of optically

sensitive surface on the pixel: this has necessitated the integration of micro-lenses on top of each

pixel, in order to focus light on the useful part of the pixel.


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(4) Black and white or colour cameras?

Colour cameras have filters in front of each pixel, so that red, green and blue intensities would be

sampled on 3 intertwined grids (sometime a 4th, panchromatic grid, is added to keep a sufficient

amount of light). Various type of colour disposition could be used, among other the “Bayer

pattern”. The 2 missing colours on each pixel are interpolated from neighbours, in a process

coined “demosaicing”. This process gives rise to “ghost” colours, for instance the interpolation

of a black and white discontinuity (e.g. a photography of black characters on a white

background) will produce parasitic colours, depending on the positioning of the three colour

grids respective to the discontinuity (www.foveon.com/files/Color_Alias_White_Paper_

FinalHiRes.pdf). Thus by comparison to black and white camera, the colour ones, when based on

intertwined filtering grids, have a reduced spatial resolution since two third of the information is

reconstituted in all pixels.

To palliate this problem, a first solution is to separate the three colours by an adapted optic

system and project each beam on three separate sensors. The cost of such a system must be

obviously higher, and the mechanical set-up is quite sophisticated. An other solution is proposed

by Foveon, that produces CMOS sensors in which each pixel is able to collect charges at various

depths in the semi-conductor: based on the fact that photon’s depth of penetration is linked to its

wavelength, one can reconstitute the light colour component on each pixel. While these two

solutions, 3 sensors camera or Foveon’s sensor, have been brought to the market, an other

solution is still in the limbo and needs to be developed: it was recently patented by Nikon. The

incident light on each individual “pixel” would be condensed to an opening in the sensor, colour-

separated by two successive ad-hoc dichroic mirrors, and each of the resulting coloured beams

directed to photosensitive elements.

It is easily understandable that solutions based on filter patterns, doing already information

reconstitution, may give problems when one wants to accurately track deformations and

displacement on an image. But to be fair, most of the still cameras proposed by the providers

have such an high number of pixels that one could consider sacrificing part of the spatial

resolution. Still, the interpolation programs are frequently proprietary, and the frame per second

http://www.foveon.com/files/Color_Alias_White_Paper_%20FinalHiRes.pdf

http://www.foveon.com/files/Color_Alias_White_Paper_%20FinalHiRes.pdf


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attainable by these still cameras quite low, but adapted to quasi-static experiments. Other

solution based on true measurements on each pixel may be more satisfying for our purpose.

Although tracking programs need only black and white information, the adding of colour

information could give help in some case, but most of the time black and white is sufficient.

(5) Sensors’ Noise sources

If one wants to use a camera as a measuring instrument, the noise problem must be in some way

dealt with. In what follows, the main noise sources are shortly described, more information can

be found in Janesick, J, 2001, 2007, Reibel 2003.

Dark Current:

A semi-conductor in thermodynamic equilibrium has a continuous, random generation and

recombination of electron-hole pairs, and the generation-recombination number increases with

temperature. Electrons freed in this process may eventually recombine, or may be trapped in the

pixel potential well and be a source of random noise: this is known as “Dark Current”(DC).

Moreover, the generation rate may vary over the pixel array. Special designs have been made to

avoid the main sources of dark current in CCD, that are the interfaces (e.g. Multi-phase pinned –

MPP- devices that reduce the DC to 10 pA/cm2 at 300 K). Custom design are currently under

refining to obtain the same results for CMOS.


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Photon shot noise:

Light is not continuous as it is a finite number of photons that arrive on a given surface per unit

time. Even under “constant” illumination, the photon arrival will follow a Poisson statistic, with

a standard deviation equal to the square root of the average number. Considering a pixel of area

A with a quantum efficiency η, submitted to an average of n photons per unit time, the signal

value is n A η t and the photon shot noise would be (n A η t)1/2 where t is the exposure time.

Read noise:

The read-out amplifier has is own noise, as previously discussed. It may increases with the

reading frequency. The pixel reset before charge accumulation has its own noise, that can be

included in read out noise.

Quantisation noise:

Round off errors are introduced during the analog to digital conversion.

Amplifier glow:

This is given here just for its peculiarity, as when the read-out amplifier is working, it emits

infra-red radiation that induces free charges in the adjacent pixels, thus provoking a glow on the

sensor zone in the vicinity of the amplifier.

Fixed Pattern Noise (FPN):

Electronics properties and geometry of pixels are varying over all the chip surface. This non-

uniformity of the pixels response, that gives systematic errors, has been called Fixed Pattern

Noise. It may be removed by measuring the flat-field response of the sensor, that is obtained by

illuminating the sensor with a uniform light. Average of multiple samples of the flat field will

attenuate the effect of other noise sources. The FPN bias will then be removed by dividing pixel

to pixel the frame value by the normalised flat-field response. It is noteworthy that other parasitic

effects like dust or deposits on the sensor or optics could be assimilated as an addition to basic

FPN, to be treated by flat-field. This should be done when the camera is positioned and its optics

properly set to the experimental conditions.


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(6) Sensors’ noise parameters

Dynamic range:

The dynamic range of the sensor is given by the ratio of full well capacity to sensor rms noise in

the dark. It may be expressed in decibels. The number of quantification levels of the signal

should be in harmony with the dynamic range.

Signal to noise ratio (SNR):

The SNR is given by the ratio of signal electrons to noise electrons. If the signal has been

correctly cleaned from FPN, and when DC of sensor and read-out noise are negligible in front of

shot noise, the SNR varies as (n A η t)1/2 , while for effective DC and read-out noise it would

behave as n A η t (constant read-out noise).

(7) Present status of CCD and CMOS sensors

CMOS are getting to the quality of CCD, but the cost of development was bigger than expected.

The advantage of CMOS is that it can attain higher frame rate (frames per second FPS) than

CCD, and has a lower consumption. Manufacturers are presently increasing the CCD’s FPS, but

the read-out noise becomes a difficulty.

Recent news from Fairchild and PCO about a new concept of scientific CMOS gives hope to

those willing to reach high FPS, keeping with the high quality of CCD sensor. This needs to be

confirmed by the market.

(8) Future of CCD and CMOS sensors

The CCD technology seems to be mature, while the CMOS technology benefits from evolution

in semi-conductor industry, whether on the level of planar integration or in the new domain of

3D integration.

Sensor Planar Technology: the advent of new integration techniques will dramatically modify the

imaging sensor properties. Techniques are developed to create permanent electric field in the

semi-conductor (by gradient of enrichment) so that the potential well would be more extended

and charge trapping better stabilised (Bogaert, 2007). It is even possible to cut trenches around


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pixels and fill them with ad-hoc compound, so that stabilising electric field would be permanent

on the border of the pixel. These new design will strongly reduces the cross-talk of CMOS

devices. These new sensors are first developed for space application, where there is a need to

replace the usual CCD by CMOS, that are less sensitive to radiations.

New techniques of 3D integration: the 2D, planar concept of the integrated circuit that prevailed

up to now in the semiconductor industry is superseded in recent years by the 3D concept. This

new technology will touch every electronic component from CMOS image sensor, or DRAM to

CPU. Basically, the various functions of a sensor could be integrated in successive packed up

tiers. For example, the CMOS pixel surface can be freed from the readout electronics, in a 3D

design where the first tier will correspond to the photosensitive surface, and the second tier to the

read-out electronics. Both layers are electronically interconnected by “Through Silicon Via”

perpendicular to the sensor plane. Besides the increased fill-factor, many advantages result from

3D design: all the interconnections are reduced, the photosensitive substrate can be fully adapted

to the spectral range of the sensor, the new design will favour parallel processing (e.g. direct

connection to an FPGA tier). Last but not least, the introduction of the third connecting

dimension frees the borders of the chip and renders it 4-edges buttable, which means that larger

sensors can be build by paving of identical smaller sensors (see Ziptronix, among others).

3.5.3.2 Time of flight sensors

The Time of flight (TOF) sensors consist in a pulsating near infra-red source (usually LED

source) associated with an imaging sensor (sensitive to the given IR range). Under usual constant

IR light, the sensor’s pixels would give an image of the scene IR reflectance. When the IR light

is modulated (with a few tens of MHz), each pixel registers a signal having a phase shift

proportional to the distance of its corresponding object point. Thus, a TOF sensor is able to give

the scene reflectance and distance in each pixel (Oggier et al. 2007). Phase folding is limiting the

range of distance as function of modulating frequency. The main noise source of these sensors is

the shot-noise, this results in some uncertainties on the measured distance, of the order of ± 10

mm at 6 m for a bright surface (it can be of ± 74 mm at 5 m for an “obscure” surface).


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Fig. 3. 26 The Swissranger® SR4000 range camera

This type of sensor has been first conceived by CSEM in 1998, and brought to market in 2001

under the name SwissRanger, since 2006, the SwissRanger is commercialised by a spin-off of

CSEM, and numerous companies have started producing their own product, based on the same

principle (Kolb et Al. 2010). It seems that the depth accuracy can be improved with special

processing, but it is still rather low, hence it does not comply with the resolution standards

necessary for the task at hand.. Although these sensors are presently limited to a resolution of at

most 2042 pixels, their use in conjunction with a stereo system may be useful.

3.5.3.3 Optical Calibration

The calibration of optical system is an essential stage for obtaining accurate results, but is not

such an easy task in a civil engineering lab, because the optical system has to adapt to many

different experimental situations.

This step will be illustrated with the calibration of a stereo rig made during a recent experiment

that was performed at ELSA (FUTURE-Bridge project), on a fibre reinforced composite bridge

beam. On the general view of the experiment shown in Fig. 3. 9, the half-part of the bridge that

was observed is the most distant one. The 2 cameras were disposed on the ground, 4 meters apart

from each other and 9 meters from the bridge (the direction of observation is indicated by a red

arrow). These cameras are based on monochrome KAF1602 Kodak CCD image sensors that

have a pixel resolution of 1536 x 1024 (with a pixel size of 9 μm). Each camera delivers


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monochrome image on 12 bits, with an effective dynamic of 68 dB, and is equipped with AF

Nikkor 24-50 mm lenses.

The common field of view of the stereo rig corresponds to a span of ~6 m on the bridge. The

photo of the bridge as seen by the right camera is shown in Fig. 3. 9.c. In this figure, the

perspective effect gives an approximate scale –along the bridge- varying from 3.6 mm/pixel (on

the right side) to 4.35 mm (on the left side of the photo). Indeed these scales are reversed for a

left view and if we take as example a point on the bridge on the right side, a reduction of size of

20% will be seen passing from right to left view –plus some distortion- the matching process will

have to compensate these changes of scale in order to accurately associate the pixel pair, as will

be seen later.

The calibration of the stereo rig was accomplished with a mobile chessboard -Fig. 3. 9.b- that

was successively located in a dense network of positions -Fig. 3. 9.a & d-, in the space between

bridge and cameras. On each position of the chessboard, a sequence of 20 frames was shot, in

order to reduce the noise by averaging. The points at cross-lines of the chessboard were

recognised and referred to the proper coordinates system of the chessboard. It is noteworthy that

in many positions the chessboard was only partly in the common field of view of both cameras.

The correspondence between points in the left and right views was resolved, giving two sets of

singletons corresponding to points exclusively appearing on left or right view, and a set of

coupled pixel points corresponding to matched points (in the common field of view). All this

work was automatically done with an in house program. Singleton sets, and pair sets were then

processed with the method of Bouguet (Bouguet, 2007), in order to obtain the intrinsic

parameters of each camera and the extrinsic parameters related to their mutual positioning in

space.


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Fig. 3. 27 Calibration of the stereo rig

a) top view of the set of chessboard positions, the frame has its origin on the left camera and its

Y axis is aligned with the optical axis of this camera. b) mobile chessboard, c) right view of the

beam, d) side view of the set of chessboard positions. The side part of the bridge’s beam is

shown in green in a and d, and the corresponding zone is shown in c

In Fig. 3. 9.a, the position of each object is drawn in the frame of the left camera, that have its

origin at the centre of the CCD sensor, with the first axis parallel to the pixel lines (1536 pixels)

and the second axis parallel to the pixel columns (1024 pixels). The third axis is along the optical

axis of this camera. Both cameras have been set on the same zone of interest of the bridge, thus

their optical axes were convergent to a point in the centre of this zone. The laboratory vertical

axis is obtained by averaging on the set of local vertical vectors of the chessboard. Both cameras

were disposed on the floor, so that the sight “under the bridge” would be feasible as far as

possible, in consequence, their optical axis are not horizontal. This is clearly illustrated in Fig. 3.

9.d, furthermore the lower limits of the chessboards pertain to an horizontal plane at 30 cm from

the floor, giving the origin of laboratory vertical axis.


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Fig. 3. 28 Optical distortion of the right camera

In a is shown the distortion effect, amplified by a factor 10, in order to reveal the characteristic

pincushion distortion. In b the error modulus is exhibited, with a maximum of 18 pixel on the

border

A plot of the optical distortion of the right camera is shown in Fig. 3. 10, with a 10 time

amplification to show the pincushion distortion. In b, the modulus of distortion displacement is

shown as a surface, as function of pixel coordinates. The distortion could go up to 20 pixels in

the corner. Indeed, this error cannot be neglected in the 3D reconstruction and every pixel

coordinates has been corrected for distortion in what follows.

3.5.4 Tracking methods

Up to now many tasks like monitoring of building deformations or displacements were solved by

means of artificial targets on the objects of interest. The extraction of "interesting points" from

the object surface (e.g. window corner), which can replace the artificial targets is another

interesting and developing optical method for monitoring seismic movements (Reiterer et al.

2008). The method uses learning-based object recognition techniques to search for relevant areas

to collect robust interest point candidates to be long-term tracked to provide a deformation

database. The task of deformation analysis is on one hand based on a traditional geodetic

deformation analysis process and on the other hand on a new developed procedure called


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deformation assessment. The main goal of this development is to measure, analyse and interpret

object deformations by means of a highly automated process.

An alternative is to use natural texture on the object (Capéran, 2007b), or a mix of artificial

texture and targets (Anthoine, 2008), as in what follows.

3.5.4.1 Targets networks and artificial texture on the bridge

The beam and the concrete slab were painted with a random texture, and a loose mesh of targets

was superimposed on it. A high definition view of the artificial texture of the bridge is displayed

in Fig. 3. 11.a, with no perspective effect. In b) and c) are shown the same zone as seen by the

left and right cameras. The smearing of detail, due to the averaging on each pixel, is evidenced.

However, it appears that this is sufficient to follow material points with an accuracy better than

0.1 mm.

Fig. 3. 29 a) close-up view of the random texture of the bridge, b) corresponding window on left camera c) corresponding window on right camera


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Fig. 3. 30 Synopsis of the tracking method


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3.5.4.2 Tracking method and image matching

Fig. 3. 31 Illustration of the matching method

between left WOI (a) and right WOI (b) that is the C3 analytical template. This is compared to a

reduced left WOI (c) by way of their difference (not squared here, in d). The analytical template,


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sampled on the red cross network (see (e)), is interpolated on the distorted blue network until the

–squared- difference has reached its minimum (final direct difference is exhibited in (f))

The matching between two initial frames -e.g. matching from right to left view- consists in

associating each pixel of the right frame to its position –expressed with sub-pixel accuracy- in

the left view. In practice, an ensemble of Windows Of Interest (WOI) is chosen in the right view,

and these WOI are matched through adequate transformations to their corresponding stereo-

paired windows in the left view. This could be done taking into account the epipolar lines that

are associated to each right view pixels in the left view frame, but this is not the case here.

The tracking consists in choosing some WOIs (with a side ~ 11 pixels) in the initial reference

frame for one camera, and to follow these WOI on the successive frames of the run (of the same

camera). These WOIs could correspond indifferently to targets or texture. Indeed the technique

used for matching or tracking is identical.

This technique is described in (Capéran, 2007), and is of the type first described by (Lucas &

Kanade, 1981) –see the synopsis in Fig. 3. 12-. It can adapt to deformation and translation of the

reference WOI (reference template), under varying light conditions. The transformation of the

initial, reference template is modelled by 8 parameters: 2 parameters for the translation, 4

parameters for linear deformation, and 2 parameters for the lighting condition. The cost function,

which is the squared difference between the template and the current image, is minimized with

respect to these 8 parameters. As the reference template is interpolated by C3 thin plate splines,

the cost function has an analytical expression as function of transformation parameters, and

classical Newton-Raphson technique could be used to find a minimisation at each step. The

gradient and the Hessian matrix involved in this optimisation process are straightforwardly

derived, at any parameter point, from the interpolated cost function, and sub pixel approximation

is naturally introduced in this way.

An illustration of this matching technique is given in Fig. 3. 13, where a right WOI (b) has been

sampled and used as a reference to be matched on the left initial image (a). The reduced

comparison domain is shown in (c). In (e) are shown the red mesh corresponding to data-sites on

which the template (b) is known. The red dots network corresponds to the data site of the


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reduced comparison domain (c), and the distorted and translated blue network corresponds to

interpolation points of the template, before subtraction with the left reduced window (c). Note

that passing from red dots to blue dots expresses the linear transformation from (c) to central part

of (b), this corresponds to an expansion and a shear –with translation- , as can be seen by

comparing (a) and (b). The initial difference is shown in (d), and the final difference in (f), when

it remains only noise.

The initial 3D model of the beam seen as a green surface in Fig. 3. 9, comes from a complete

matching of the green rectangle in the right view, followed by an ad-hoc triangulation (with

compensation of optical deformation), to reconstruct the 3D geometry. The use of the points

tracking at successive times, combined with the matching of the initial stereo pair, allows to

follow material points on the bridge during all the experiment.

To explain the benefits one can expect from vision measurements in civil engineering, some

results obtained on the “Future Bridge” tests will be exposed. They are partly extracted from the

report on vision system made during the “Future Bridge” project. These measurements are done

in stereo view, a technique that was already tested in our laboratory with low cost cameras

(Lathuilière & Capéran, 2007). Then we will expose a real time tracking on a testing loop.


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3.5.5 PsD methodology: an example of stereo-vision measurements on the Future Bridge Project

3.5.5.1 Description of the experiment

Fig. 3. 32 Perspective view of the bridge

A section is shown on the right, with the FRP shell, the sandwich board in between FRP and

concrete slab. A detail on the left shows the connection between FRP, sandwich and concrete

slab through shear studs

A perspective view of the “Future Bridge” experiment is shown in Fig. 3. 14. The direction of

observation of the stereo rig is given by the red arrow. A section of the bridge is given in the

right low corner of the image: it can be seen that the bridge is basically composed of three

elements that are the composite shell in red, the sandwich panel, in green, and the concrete slab,

in blue. The composite shell, hereafter named FRP shell, is reinforced with diaphragms, as can

be seen at the nearest extremity in Fig. 3. 14. A detail of the connections between concrete slab,

sandwich and FRP is given is the upper left corner of the image. The concrete slab has steel


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reinforcement (not shown here), and connection between the three constituents is ensured by

shear studs going through FRP shell and sandwich panel, and anchored to the concrete slab

during its casting. Two types of shear studs were disposed on the 14 m long bridge, type 1 in the

central part of the bridge on a length of 6 m, and type 2 on the rest of the bridge (some

measurements related to shear studs will be exposed below). The section in the upper left corner

of Fig. 3. 14 shows only one stud, in fact they were disposed in two parallel lines, alternatively,

with a constant period.

The setting-up of a proper reference frame linked to the bridge was accomplished by first

extracting a mean vertical direction from the set of chessboards positions (and thus a horizontal

plane). The longitudinal axis of the bridge was found by fitting a plane to the surface of the beam

(appearing in green in Fig. 3. 9, deduced from a method described in the following) and

computing its intersection with the horizontal plane. The third direction was constructed from

vertical and beam longitudinal directions to obtain a direct system of reference. The origin of the

frame depends of the type of study made on the bridge, e.g. if the zone of interest would be the

slab, the origin would be chosen on one of the targets toward the centre of the bridge.

Thus after calibration, the surface of the bridge at initial position can be deduced, from which a

reference system linked to the initial geometry of the bridge can be deduced. The first coordinate

relates to the direction perpendicular to the bridge and horizontal in the laboratory, the second

relates to the longitudinal axis of the bridge and the third relates to the vertical direction of the

laboratory.

For the run considered here, the bridge was disposed on a knife edge support at the nearest

extremity in Fig. 3. 14. At its other end, in the zone under vision system observation, it was

disposed on a roller support. Both steel supports were disposed on reinforced concrete block and

the interface between steel supports and composite beam was made by means of rubber mats.

The bridge was loaded in its centre using four actuators anchored to the strong floor

(see Fig. 3. 14).


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The first observation that can be made would be on the boundary conditions of the experiment.

More precisely, they are made on the strong floor that resists to the pull up of the actuators and

reacts to the push down of both concrete blocks.

3.5.5.2 Strong floor displacements

The Fig. 3. 15 shows the first right view of the run, with numbered points of interest. The sub-

windows on which tracking is made are coloured in green.

Points on the bridge, corresponding to LVDT attachment (1, 11, and 9) and one point on the shell

(41) used to get the origin along bridge axis (this point is 30 mm from the shell end on the right).

Points considered as fixed, corresponding to targets stuck on iron masses disposed on the ground

(13, 15, 17, and 19) or on the concrete block sustaining the bridge. One point (23) corresponds to

an iron mass disposed nearby the LVDT 26 attachment on the ground.

Indeed, all these points corresponds to stereo couples, thus the tracking has been done jointly on

the right and the left views. Note that the point 23 is in a very dark region (in Fig. 3. 15). The

vertical displacements obtained from 3D optical measurements are shown in Fig. 3. 16. The

ground has some vertical displacement (no more than 0.3 mm for point 23). The interesting fact

is the consistency of these curves. As points 13 and 15 are proximate to the zone of the floor

where the actuators have been anchored, the floor level is going up –pulled by the actuators’

anchors- as the bridge is pushed down at the same time (e.g. points 9, 11). In a few words, points

13 and 15 are in anti-phase with the bridge vertical displacement. In contrary, points 19, 25 and

23 are in phase with the bridge displacement: they are in the neighbourhoods of, or on the

concrete block supporting the bridge. The loading of the actuator is also applied to the floor

through the bridge and the block, thus the floor must go down in the vicinity of the support. Point

17 is more or less in a “neutral” position where uplift and down-lift motion compensate each

other.

Notice the high level of noise on point23, in contrast to a low level of noise in point 25, while

signals are almost identical (except for some discrepancy at the last cycle). The first point is in a

shadowed zone, as was previously said, in contrast with the second point that is on the concrete


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block in a bright and contrasted zone. This illustrates the importance of having a good dynamic

for the camera. In the present case, it is possible to detect variation of less than 0.1 mm, even

0.05 mm (signal of point 15, first cycle). As the scale for a pixel is about 4 mm, this gives a

resolution of 1/80 pixel.

The slope of the floor can be evaluated from the 3D measurements, as the iron masses would

incline with the floor, the target longitudinal displacement on top of the iron masse divided by

the target distance to the ground would give an approximation of the floor angle. This is given

for points 13 and 17 in Fig. 3. 17. The variation of slope at the “neutral” point 17, behaving as

the oscillation node, is clearly put in evidence.

Fig. 3. 33 Right view of the beam, with some measurement points and LVDT available for comparison


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Fig. 3. 34 Evidence of the floor displacement


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Point 13 that is near by the actuator, is in anti-phase with point 9, on the bridge. Point 17 is

neutral while point 25 is in phase. See text for detail

Fig. 3. 35 Evolution of the slope of the floor at points 13 and 17

3.5.5.3 General drift of the beam

An important parasitic effect has been sensed by the vision system, as the bridge had an

unexpected drift. This drift could not be monitored with classical LVDT sensors linking a

“reference” point to the bridge, on the contrary, it polluted their measurements with spurious

effect. The photogrammetry permitted the correction of this parasitic effect, by providing the

drift on the LVDT points. The longitudinal and lateral drifts are shown in Fig. 3. 18, for points 1,

9, 11 and 41. The analysis of drift curves in Fig. 3. 18, taking into account the position of the

points on the bridge, reveals a mean uniform longitudinal drift, combined with a small rotation.

A more exhaustive investigation made on the general displacement of the slab has shown that

this rotation was of the order of 0.88 mrad.


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It has been shown that not only a longitudinal drift (as large as 50 mm at most) took place, but

also a lateral drift of 10 mm, to which a slight rotation is superimposed.

Fig. 3. 36 Drifting of the bridge longitudinal to its axis (a) and perpendicular to it (b) for points 1, 41 9 and 11

3.5.5.4 Opening and sliding between slab and sandwich

Fig. 3. 37 right view of the concrete slab with targets indicated by red crosses. Cyan crosses correspond to sandwich and green ones to FRP

The connection between slab and beam is important to monitor since sliding and opening could

appear at their interface. This is constrained by the shear studs, and a minute observation of the

respective displacement of slab and sandwich panel is interesting. This study has been done on

the target network deposited on these two elements (Fig. 3. 19). First a comparison with classical

sensor measurements will be done, then profiles of sliding and opening will be exhibited, and

finally a qualitative analysis will corroborate the quantitative information.


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(1) Comparison with LVDT 22

Fig. 3. 38 Left and right views of the LVDT 22. The profile of the lever is delineated on the left view

Fig. 3. 39 Signal of the LVDT 22, compared to distance between targets 77 and 569, on its extremities. The green curves corresponds to sliding as measured from target 417


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The red rectangle in Fig. 3. 19 delineates the close-up views of LVDT22, which are presented in

Fig. 3. 20. These left and right views show the installation of the LVDT22, used to measure the

sliding between the FRP shell and the slab. A lever -indicated by a cyan line in the left view- was

anchored by its lower end to the FRP’s edge. LVDT22 joins the upper extremity of this lever –

point 569- to an anchorage to the slab –point 77-, this sensor appears as a faint shadow between

these two points, and its cable connection is clearly visible in front of the lever. As targets were

stuck on both extremities of the LVDT22, its length can be monitored with vision system. The

comparison between optical measurements (red line) and LVDT22 (black line) are shown in Fig.

3. 21. The maximum variation of length is 4 mm, approximately the mean pixel scale. The

agreement between both signals is quite good for the three first cycles, it is not so satisfying for

the two last ones. The noise on the optical signal is low, if one considers that this measurement

results from operations implying a difference between two 3D points. The pair of material points

that were followed shifted in space by 40 mm (for the first three cycles), which means that they

swept on both photography a zone of approximately 10 pixels, but the tracking kept its accuracy.

The green curve in Fig. 3. 21corresponds to the relative horizontal displacement between slab –

point 77- and FRP –point 417-, this can be considered as a first approximation to the sliding

between these two components as inclination to the horizontal of the bridge did not exceed 20

mrad (measurements given by inclinometers). While the successive maxima –corresponding to

end of cycles- are in good correlation between green and black curves, a large discrepancy

occurs in between. This shows that the LVDT22 sensor does not gives the pure sliding but a

composite effect of opening, sliding,and possible lever amplification effect.

(2) Sliding and opening obtained from optical method

To complement the LVDT22 measurements, the monitoring of points on the slab (lower line of

red crosses in Fig. 3. 19) and their corresponding points on the sandwich panel edge (line of cyan

crosses in Fig. 3. 19) was performed. As already noticed the inclination of the deck with respect

to its initial state did not exceed 20 mrad, thus a good approximation of sliding is given by the

horizontal displacement difference, and of opening by the vertical displacement difference. In

this way, profiles of sliding and opening can be measured. The results are shown in Fig. 3. 22, as

profiles at successive loading maxima. The two vertical lines on these figures materialise the

transition zone between the first (central) and second type of shear studs. For high loadings,


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opening is high in the central zone (left of the vertical lines) whereas sliding slightly dominates

on the other zone. In the studs transition zone, sliding peaks are appearing for low loading and

opening peaks at high loading. Unfortunately, no texture was deposited on the edges of FRP and

sandwich panel, so that the profiles are based on a sparse number of points. It is noteworthy that

the same study made on profiles between FRP and slab gave the same behaviour. To illustrate

these observations, a qualitative study will be now presented.

Fig. 3. 40 In a is exhibited the sliding profile of the concrete slab with respect to sandwich panel, at the successive loading maxima. In b is shown the corresponding opening

(3) Qualitative observation of the sliding and opening

The window of interest (WOI) delineated in green in Fig. 3. 19 is shown in a close-up view in Fig. 3.

23 b, at the initial state: it is disposed in the middle of a) and c) for ease of comparison. Fig. 3. 23 a and

c represent the WOI at the instant of maximum load, but these two frames have been processed in a

different way. As history of displacement (in pixel) is known for all the targets, this WOI can be

followed as function of time. The top frame a) is the instantaneous follow-up of the initial zone, by

correcting for the mean translation of this zone. The slight tilting of the bridge can be observed. This

frame can be transformed so that the slab part is put in coincidence with its initial position. This is

exhibited in the bottom frame c). Left sliding of the sandwich and FRP elements can be seen with help

of the superimposed red grid. Opening is also easy to distinguish on the left part of the figure, and a gap

is indicated by a red arrow. The left limit of these frames correspond to the origin of profiles in Fig. 3.

22, and targets are approximately distant by 200 mm, so that the frames presented in Fig. 3. 23

correspond to the ‘central zone’ of the bridge.


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Fig. 3. 41 Close up view of the green rectangle in Fig. 3. 9 (right view), for b) initial time, to be compared with a) and c). For c) the concrete slab has been registered to its initial state,

so that relative displacement of targets on Sandwich panel and FRP are evidenced


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3.5.5.5 Shell buckling

The aim of this study was to put in evidence the occurrence of shell buckling. The effect of this

could be observed on some displacements monitored with the optical technique, that exhibited

discontinuities at time step 2070. Thus a zone of the beam was selected (see green rectangle in

Fig. 3. 9.c) on the initial –reference- right view, and the matching of each pixel of this reference

zone was done with the left and right views at time steps 1, 2069 and 2071. In this way, material

surfaces of the beam were obtained for the initial state and for time steps on each side of the

observed discontinuity. These material surfaces are structured sets of points in 3D space, each of

these points corresponding to the same material point of the reference state surface.

The surface of the reference state is almost planar within ±10 mm, it has random irregularities

superimposed on structural shape (e.g. the diaphragms impose their shape on the shell, while it is

“free” in between). The processing was made as follow:

- A best fitting “mean plane” was found for each of the three surfaces. To do that, some

parts of the selected zones were removed prior to the fitting, namely the upper and lower

parts of the beam where the profile is curved.

- These 3 planes were used as reference for each of the 3 surfaces, and a material point

corresponding to the lower right corner of the studied zone (see Fig. 3. 9.c) was chosen as

origin for each plane (it is the most proximate to the support). The intersection of the

laboratory horizontal plane with each “mean planes” permits to build a local reference

frame on each plane (with its normal completing it in 3D space).

- Considering a given surface, its “material points” were projected on the associated plane

and on the normal, respectively giving their in plane and out of plane coordinates.

- The use of the reference surface permits to compute the in plane and out of plane

displacements.

Perspective views of the meshes representing the same material points at time step 1 (black) and

2069 (red) are presented in Fig. 3. 24. The bending of the beam at step 2069 is easily seen. Some

bulge and declivity, relative to the reference state, are already present on the surface at time step

2069.


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Fig. 3. 42 Perspective views of the surface of reference (black) and of its displacement at time step 2069 (red). A bulge and a declivity can be seen on the red surface, with respect to

the reference one


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Fig. 3. 43 The difference between out of plane displacement for time steps 2071 and 2069 reveals the shell buckling

The difference between out of plane displacements at time steps 2071 and 2069, plotted on the

basis of the reference mesh, is shown in Fig. 3. 25. This reveals the buckling occurring at time

step 2070, with an amplitude of ±15 mm. As was mentioned previously, some bulge and

declivity zones pre-exist to the buckling, in fact this event corresponds to a sudden extension of

these zones. This effect may results from a sudden weakening of the link between diaphragm and

shell. Only vision system was able to fairly quantify this phenomenon.

3.5.6 On some real time displacement measurements

Some real-time measurements were done during a a sub-structured experiment on a damper (see

Fig. 3. 26). The damper is put in sandwich between the floor and a square plate with tensioned

Dividag at its four corners, so that its vertical load can be controlled. The horizontal loading is

made through an actuator, and control of displacement is made by Heidenhain along the actuator

axis. The camera is aimed at a target stuck on the actuator head, and frames of 400 x 200 pixels

are sampled synchronously with the basic frequency of the experiment, between 1 and 2 Hertz.

The target is tracked in real time with a normalised correlation program, and Heidenhain curves

are plotted together with optical results in real time (see Fig. 3. 27). The results are satisfying

and, as usual, more information can be extracted from the optical signal, as a transversal

displacement can be measured that could not be sensed with the Heidenhain (see Fig. 3. 28).


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Fig. 3. 44 a) Experimental set-up, the actuator loading the damper is clearly visible on the right side of the photo. The camera on the left partially hide the damper in the back-

ground, that is vertically loaded by a square plate and 4 Dividags. b) A detail of the piston on which the tracked target is stuck

Fig. 3. 45 a) comparison of optical results (green) with Heidenhain (red) and Temposonics

(blue); b) difference between Heidenhain and optical methods

a) b)


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Fig. 3. 46 a) longitudinal and lateral displacements; b) cycles

3.5.7 Shake table methodology: recent Research Efforts in using photogrammetry

The use of high speed cameras able to capture 200 frames per second in previous research (Fujita

et al. 2005) allows for an accurate capture of the movements of the shake table. The limited

maximum resolution of the cameras used (504x242) allows for a very small area to be monitored

by each pair of cameras in order to achieve the desired accuracy in X, Y and Z axes.

Furthermore, the use of custom luminous LED markers requires some possibly undesired

physical contact with the subject being tested.

A prototype with a camera rig consisting of four 640x480 cameras capable of capturing 5 frames

per second, with a future upgrade to a system capable of 500 FPS test the concept of

videogrammetry in the monitoring of civil engineering structures (Tait et al. 2007). The accuracy

of the calibrated system using signalized points was tested and found to be of the same order as a

simulated design for the system based on constraints of test object dimension and available

stand-off distance from the object. Synchronizing the cameras remains the biggest issue facing


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the use of such a system. Nevertheless, the advantages to be derived from this non-contact, three

dimensional, full-field method have been shown to be possible to implement.

On another research, (Doerr et al. 2005) an image-based capture system consists of four high-

speed charged-couple-device (CCD) cameras, connected to a server style PC with extended

storage and networking capabilities. All cameras operate at a resolution of 658x494 pixels and

are capable of acquiring images at 80 frames per second. The camera synchronization problem

has been adequately solved with an appropriate software solution. The experimental evaluation

of the system demonstrates that the required data transfer capabilities can be achieved on a server

style PC and that commodity hardware is sufficient to acquire, archive and process sensor data in

real-time. Sample waveforms were extracted utilizing pixel-based algorithms applied to images

collected with the array of high speed, high-resolution charged-couple-device (CCD) cameras

and presented a reasonable match with data provided by traditional accelerometers.

On the other hand, videogrammetry is a useful tool for determining such deformations. As early

as in 1995 efforts have been underway for implementing stereoscopic video sequences for such

applications (Georgopoulos et al. 1995). An own developed stereoscopic system has served to

monitor, re-observe and measure the seismic experiment on the shake table (Georgopoulos &

Tournas 1999 and 2001, Tournas 1999).

3.5.8 Commercial Integrated Systems

Ready built commercial systems that fit the project’s needs do exist mainly as a hardware-

software bundle. However those products mostly aim at production industries, and are balanced

mostly towards accuracy versus sampling frequency, while at the same time they also raise the

cost far above the current project’s limitations. The most important and fitting to the present

project ones are presented in the following.

V-Stars/M by Geodetic Systems Inc, (http://www.geodetic.com/) is a 3D coordinate measuring

system that uses two or more INCA cameras to make fast, accurate, real-time measurements. The

V-STARS/M system is based on the single-camera V-STARS/S system. V-STARS/M is capable

of real-time measurements of targeted points or probes. The 3D data is reported to the system

http://www.geodetic.com/


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laptop. V-STARS/M is also immune to vibration and -most importantly- portable. The system

can operate in stable mode, which provides for a quick set up, but relies on the cameras

remaining unmoved during the measurement period. Alternatively a non-stable mode is available

that allows vibration or movement of the cameras to occur without loss of accuracy. This mode

locates the cameras each and every time a set of pictures is taken using a stable field of control

points on the object. Thus, movement of the cameras is fully accounted for and of no

consequence. Typically, the control field is established by a quick single-camera measurement.

ShapeMonitor by ShapeQuest Inc. company (http://shapecapture.com/) is a turnkey system that

allows for measuring objects in real time. The system comprises of a high speed computer, frame

grabber and dual digital cameras for data acquisition and a target projector. The system enables

easy configuration for different camera types such as high speed cameras. The system has been

successfully used to monitor shake table tests (Robertson 2006) in a frequency range of 0-20Hz.

The resulting accuracy was a satisfactory 1mm on all axes.

GOM GmbH Optical Measurement Techniques (http://www.gom.com/) offers two products

capable of capturing high resolution images at high frame rates. Both PONTOS and ARAMIS

allow a minimum resolution of 1280x1024 and sampling rates that range from 15Hz up to 8

KHz. The software bundle offers numerous functionalities for optimized data acquisition,

evaluation and results visualization. Those systems could be used as a ready-to-use solution in

order to monitor shake table experiments in real time with high accuracy.

3.5.9 Hardware Components for photogrammetry on shake table experiments

In order to monitor seismic-inducted motions in 3D space using vision based techniques, a

stereoscopic image capture system is needed. Such a system usually consists of two video

cameras connected to a server style PC with extended storage and processing capabilities.

Specialized software for camera synchronization, image acquisition and time-stamping is also

needed.

The selection of the video camera equipment is critical for the application and mainly depends

on the frequency range of the seismic movements and the required measurement accuracy.

http://shapecapture.com/

http://www.gom.com/


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Assuming that the seismic frequency range will be between 0-50Hz, a sampling rate of at least

100Hz is necessary to capture the changes in motion without aliasing according to the Nyquist’s

sampling theorem. Concerning the accuracy requirements, coordinate measurement accuracy in

the order of 1 mm is satisfactory enough in most cases.

The first important aspect that should be taken into account is the sensor technology that will be

selected. Two different technologies for capturing images digitally are currently available on the

market: CCD (charge coupled device) and CMOS (complementary metal oxide semiconductor).

Each of them has unique strengths and weaknesses giving advantages in different applications:

• CCD sensors are more sensitive than CMOS and create high-quality, low-noise images. CMOS sensors are more susceptible to noise.

• CCD is much better for low contrast images. The light sensitivity of a CMOS sensor tends to be lower.

• CMOS have much lower power consumption. CCDs consume as much as 100 times more power than an equivalent CMOS sensor.

• CMOS are extremely inexpensive compared to CCD sensors. • CCD is more mature technology tending to have higher quality and more pixel resolution.

By examining the above differences, it is obvious that CCD sensors tend to be used in cameras

that focus on high-quality images with lots of pixels and excellent light sensitivity. This is the

case of photogrammetric measurements, where the quality of the images and the pixel resolution

play a predominant role.

When the cameras are used to capture a moving scene, the sharpness of a frozen image depends

on the technique used to render the video. Commercial video cameras usually create interlace

video images which are compatible to the common standards for transmitting video signals

between cameras and other devices such as TV monitors, video frame grabbers and video

players. Interlacing divides each image frame into odd and even rows and then alternately

refreshes them at 30 frames per second. The slight delay between odd and even row refreshes

creates some distortion or blurring. This is because only half the rows keep up with the moving

image while the other half waits to be refreshed. To avoid such blurred images a progressive

scan camera should be used. Progressive cameras read the entire entire image row by row within

the same scan and therefore no image blur is visible.


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The quality of the frozen images in a video sequence is also affected by the synchronization of

the image row acquisitions. Some CMOS sensors operate in "rolling shutter" mode, that means

that the rows start, and stop, exposing at different times. This type of shutter is not suitable for

moving subjects because this time difference causes the image to smear. To avoid this problem a

"global shutter" mode should be available. In this mode the camera starts and stops exposure of

all image rows simultaneously. An example of an image taken using a rolling shutter is below is

shown in Erreur ! Source du renvoi introuvable.. For seismic table monitoring applications where

images of fast-moving objects without smear or distortion must be captured, the operation in

global shutter mode is a 'must have'.

Fig. 3. 47 Rolling Shutter and global shutter video capture

The accuracy of the coordinate measurements mainly depends on two parameters: image

resolution and distance from the object. Higher image resolutions result in higher measurement

accuracy for the same object distance. Closest distances to the object result in better accuracy for

the same image resolution. Assuming a distance from the object of about 4m, images at

1024x1024 pixel resolution (= 1 Megapixel) are sufficient to obtain coordinate measurement

accuracy in the order of 1mm in Z direction and 0.6 mm in X, Y directions.

According to the above mentioned parameters, two CCD progressive scan cameras with

1 Megapixel resolution at a frame rate of 100 frames per second (fps), operating in global shutter

mode are adequate to monitoring seismic-inducted motions in space. Such a camera setup

produces 200 Mbytes of raw image data per second that have to be transferred to a computer

system and stored for further processing. Since the amount of data produced by each camera

reach the 100 Mbytes/sec, a high-bandwidth data transfer protocol should be employed. There

are four data transfer protocols currently available on the market:


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• IEEE-1394, (Firewire™), is a low-cost, high-bandwidth real-time data transfer standard.

It enables data transfer rates up to 50 M Bytes/sec. The new IEEE-1394b standard is a

high-speed revision of the original which allows for faster transfer rates of up to 800

Mbytes/sec.

• CameraLink™ is a high-speed data transfer protocol specifically designed for camera-

framegrabber interfacing. It significantly simplifies interconnection between camera and

framegrabber. CameraLink™ has a range of levels of compliance: base (300 Mbytes/sec),

medium (600 Mbytes/sec), and full (900 Mbytes/sec).

• USB-2 is the higher speed version of the USB interface commonly used to connect

computer peripherals. It enables transfer rates up to 60 Mbytes/sec.

• Gigabit Ethernet (GigE) is a high bandwidth development of the standard Ethernet

protocol used for PC and peripheral network connection. It enables transfer rates up to

125 Mbytes/sec. Using two Ethernet ports configured as a Link Aggregation Group

(LAG) on the same camera device a maximum data rate of 240 Mbytes/sec can be

obtained.

From the above mentioned protocols, IEEE-1394b, CameraLink and Gigabit Ethernet satisfy the

bandwidth requirements of 1MPixel cameras at 100 fps. Gigabit Ethernet is a very promising

solution that is employed by several companies, since it can be used to transfer large amounts of

data over long distances (up to 100 m). In addition, GigE cameras are reinforced with a packet

re-send mechanism than can eliminate the loss of transferred data. Furthermore, the overall cost

of a vision system can be reduced with these cameras, thanks to the availability of a variety of

low cost peripheral devices.

The problem with GigE cameras is that the Gigabit Ethernet may not always achieve its

125 MB/sec transfer rate. The problem is how the Gigabit Ethernet chip is connected to the

system. If it is connected to the standard PCI bus, it probably won’t achieve its full speed. PCI

bus works with a maximum transfer rate of 133 MB/s, while Gigabit Ethernet runs up to

125 MB/sec. By just observing these two numbers it seems that Gigabit Ethernet “fits” PCI bus.

The problem is that PCI bus is shared with several other components of the system, thus

lowering the available bandwidth. So, even though in theory Gigabit Ethernet can run fine on

PCI bus, it is just too close to the bandwidth limit of the bus. That is why a Link Aggregation

Group (LAG) connection is needed when transfer rates exceed 80 Mbytes/sec.


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In order to store the acquired image sequences, a high speed storage device should be available.

Currently available hard disk drives offer very sort access times, resulting fast read and write

speeds. Revolutions Per Minute (RPM) is usually used to help determine the access time on hard

disk. RPM is a measurement of how many complete revolutions a computer’s hard disk drive

makes in a single minute. The higher the RPM, the faster data will be accessed. The highest

RPMs that are currently available on the market are 10000 and 15000. Hard disks at 10000 RPM

can write sequential files at an average speed of 100 Mbytes/sec, while hard disks at 15000 RPM

can write sequential files at an average speed of 125 Mbytes/sec. Taking into account that 100

Mbytes/sec have to be stored for each camera, two 15000 RPM hard disk drives have to be used,

one for each camera. Alternatively, a compression scheme may be used in order to reduce the

data volume before storage. In this case only one hard disk drive may be sufficient.

Another solution to the storage problem is the use of solid state disks (SSDs) instead of

commonly used hard disk drives. SSD devices don’t have mechanical parts. They offer

sequential write speeds in the order of 200 Mbytes/sec, but there are more expensive compared

to the corresponding hard disks. Another problem of the SSD drives is that their read/write

performance degrades over time. A SSD drive with write speed of 200 Mbytes/sec may fall to

185 Mbytes/sec in a short period of time. Thus, whether two SSD drives have to be used in the

same way as previously described, or a minute use of the Intel SSD Optimizer (http://download.

intel.com/support/ssdc/hpssd/sb/intel_ssd_optimizer_white_paper_rev_2.pdf) must be made.

3.5.10 Photogrammetric System Configuration

Based on the above, a system for 3D stereoscopic video capture was developed at the Laboratory

for Earthquake Engineering (LEE) of NTUA. The system uses two high resolution CCD cameras

connected to a PC with enhanced communication and storage capabilities. The main

characteristics of the cameras used include:


170

Brand name Prosilica GX1660 (monochrome) Manufacture Allied Vision Technologies ( http://www.alliedvisiontec.com ) Resolution 1600x1200 pixels Type CCD Progressive Sensor Size 2/3” Cell Size 5.5 μm Frame Rate 66 frames per second at full resolution Bit Depth 8/12 bits (monochrome) Interface IEEE 802.3 1000baseT Lens mount C Additional Features auto exposure, auto gain, auto white balance, pixel binning,

region of interest readout, asynchronous external trigger and sync I/O, global shutter, video-type auto iris

Since the GX1660 cameras offered without lenses, two Fujinon HF12.5SA-1 (1:1.4/12.5mm)

lenses were attached to the camera devices.

Each GX1660 camera has two screw-captivated Gigabit Ethernet ports configured as a Link

Aggregation Group (LAG) to provide a sustained maximum data rate of 240 MB per second.

Two dual port network cards are used to connect the two cameras to the computer motherboard.

The storage system includes two Solid State Disks (SSDs) at 285 MB/sec write speed and 480

GB capacity in total. The employed computer system consists of the following components:

Motherboard Asus P7P55 WS SuperComputer Processor Intel® Core™ i5 CPU 760 @ 2.80GHz Memory (RAM) 4 GB Graphics Card nVidia Quadro FX 380 Storage 1 Seagate ST31000528AS, 1TB

2 Solid State Corsair Force 240GB Networking 2 Intel Pro/1000PT Dual Port (EXPI9402PT) Operating System Windows 7 Pro 64-bit

The two cameras are placed on a solid aluminum bar at a distance of about 0.70 m to each other.

A small 8” thatch screen device was also placed in the middle of the bar, allowing the control of

the two cameras without the need of additional input devices. The system configuration is shown

in Fig. 3. 30.

http://www.alliedvisiontec.com/


171

Fig. 3. 48 Configuration of vision system developed at LEE/NTUA

3.5.11 Software development

In order to monitor seismic motions in 3D space specialized software for camera

synchronization, image acquisition and time-stamping is needed. In addition, computer vision

software for camera calibration, exterior orientation, target tracking and photogrammetric

triangulation must be also available.

3.5.11.1 Stereoscopic video capture

Stereoscopic video capture from two independent cameras can be accomplished by using the

internal CPU clock of the Intel processor. Each incoming frame is time stamped when it is

transferred from the camera to the computer memory. The synchronization accuracy that can be

achieved varies between 0 and 1000/fps milliseconds. In case of a GX1660 camera the

synchronization accuracy may be between 0 and 16 msec. The video capture is driven by two

separate threading processes, one for each camera. The incoming frames are temporally stored in

a cyclic buffer and then transferred to the computer storage unit. The size of data arriving is

about 121 MB/sec (66 fps, 1600x1200 pixels, mono, uncompressed) for each camera. To

successfully save the incoming data 121 MB/sec disk throughput is necessary for each camera.


172

The software for stereoscopic video capture that was developed at LEE/NTUA is shown in

Fig. 3. 31. The two video streams from the left and right camera of the stereo ring are shown side

by side. The functions available to the user include:

Project definition

Each project has a unique name and may have several video streams. Each video stream is stored in a separate binary file. The name of a video stream is automatically generated by the software with extension “*.frm”. Video streams acquired simultaneously from the left and right camera have the same name (stored in different directories).

Connect Checking for the availability of the cameras and start streaming. The incoming images are shown at 25 fps, even if the acquisition frame rate is higher.

Disconnect Stop video streaming and close the cameras. Auto exposure Auto exposure control (ON/OFF) Auto gain Auto gain control (ON/OFF) – only for color

cameras White balance Auto white balance control (ON/OFF) – only for

color cameras Actual pixels By default, the video is show at 400x300 pixels

resolution. When actual pixels are activated only a small part of the center of the frame is shown at 1-by-1 screen resolution.

Large frames Change to 800x600 pixels resolution. Reset camera settings Auto exposure, gain and white balance control set

back to their initial values. Snap Take a single frame simultaneously from both

cameras. The names of the images are automatically generated. The images are saved in JPEG format.

Capture Start / Stop video capture.


173

Fig. 3. 49 Software for stereoscopic video capture developed at LEE/NTUA

Fig. 3. 50 Stereoscopic video play-back of the system developed at LEE/NTUA

3.5.11.2 Stereoscopic video play-back

The stereoscopic video play-back application that was developed at LEE/NTUA is shown in

Fig. 3. 32. The user selects a project file (created by stereoscopic video capture application) and

the first video stream is displayed on the screen. Left and right video images are shown side-by-

side. The current number of frame is shown in the bottom right corner in red. The time stamping

of the frame is shown in the bottom right corner in yellow. Since a project file may contain more


174

than one video stream, a drop down menu for video file selection is available from the main

menu. Play back, stop, step-forward and step-backward functionality is also included.

3.5.11.3 Camera calibration

Photogrammetry provides a variety of methods for determining the interior and exterior

orientation parameters of a camera, relating image measurements to scene coordinates of an

appropriate calibration field. Due to the high accuracy demands of the application the use of an

accurate calibration field is critical. Such a calibration field should satisfy two basic

requirements: automatic target recognition using well known image processing techniques and

absolute accuracy better than 1/5 mm.

In the implementation at LEE/NTUA, a calibration plate of a form of chessboard is used. The

calibration method adopted has been proposed by Zhang and is described in detail in Zhang

(1999). The calibration pattern consists of 9 x 12 black and white squares with cell size of 65x65

mm. Several photographs of the calibration plate were acquired from different positions and

orientations with a constant tilt angle of 45o (Fig. 3. 33). About 40 images were actually used for

camera calibration. The internal corners of the chessboard pattern were automatically identified

with sub-pixel accuracy and used as input measures for the calibration procedure. As a result the

extrinsic and intrinsic parameters of the camera calibration are estimated.

.

Fig. 3. 51 Indicative camera positions for camera calibration


175

Fig. 3. 52 Camera calibration software developed at LEE/NTUA

The software for camera calibration that was developed at LEE/NTUA is shown in Fig. 3. 34.

The main functions include automatic chessboard identification, coordinate measurement,

interior and exterior orientation. The software development is on the top of OpenCV 2.2, an open

library of programming functions for real time computer vision. According to the OpenCV

camera model, a scene view is formed by projecting 3D points into the image plane using the

following perspective transformation:

MtRAms ′=′ ]|[

(1) or

=

1100

00

1 3333231

2232221

1131211

ZYX

trrrtrrrtrrr

cfcf

vu

s yy

xx

(2)


176

where (X,Y,Z) are the coordinates of a 3D point in the world coordinate space, (u,v) are the

coordinates of the projection point in pixels; A is called a camera matrix, or a matrix of intrinsic

parameters; (cx,cy) is the principal point (that is usually at the image center); fx,fy are the focal

lengths in x,y direction expressed in pixel-related units. The joint rotation-translation matrix [R|t]

is called a matrix of extrinsic parameters. The transformation above is equivalent to the

following (when z ≠ 0):

yy

xx

cyfvcxfu

zyyzxx

tzYX

Rzyx

+′⋅=+′⋅=

=′=′

+

=

//

(3)

which is the well known co-linearity equation used in Photogrammetry. Real lenses usually have

some distortion, mostly radial distortion and slight tangential distortion. So, the above model is

extended as:

yy

xx

cyfvcxfu

yxrwhereyxpyrprkrkyy

xrpyxprkrkxxzyyzxx

tzYX

Rzyx

+′′⋅=+′′⋅=

′+′=

′′+′++++′=′′

′++′′+++′=′′

=′=′

+

=

2222

221

42

21

2221

42

21

2)2()1(

)2(2)1(//

(4)

where k1, k2, are radial distortion coefficients and p1, p2 are tangential distortion coefficients.

The estimated calibration parameters for the two GX1660 cameras used are:


177

Left Camera

Number of images 40 Focal length [fx fy] [ 2328.859 2330.606 ] ± [ 0.756 0.770 ] Principal point [cx cy] [ 781.335 592.573 ] ± [ 1.407 1.390 ] Distortion [k1 k2 p1 p2] [ -0.053450 0.143872 -0.001056 -0.001050 ]

± [ 0.001524 0.011191 0.000182 0.000191 ] Pixel error [sx sy] [ 0.158 0.169 ]

Right Camera

Number of images 40 Focal length [fx fy] [ 2318.414 2319.492 ] ± [ 0.786 0.793 ] Principal point [cx cy] [ 793.701 603.044 ] ± [ 1.461 1.440 ] Distortion [k1 k2 p1 p2]

[ -0.043840 0.056590 -0.000538 -0.000854 ] ± [ 0.001653 0.012107 0.000191 0.000197 ]

Pixel error [sx sy] [ 0.158 0.179 ]

3.5.11.4 Target tracking and Triangulation

Object coordinates in space are calculated by two-camera triangulation on the synchronized

video frames. To facilitate tracking and increase the accuracy of the computed coordinates

signalized targets of circular shape are used. Initial target coordinates are determined by using a

cross correlation algorithm that matches the observed targets with a predefined target template.

To improve the accuracy, a Least Squares Matching (LSM) algorithm is applied to estimate the

center of the circular targets with sub-pixel accuracy. In Fig. 3.35 a typical target template is

shown. The template target is matched to the actual target captured by the camera and initial

center coordinates are estimated at 1 pixel accuracy (fig. 7, red point). By applying the LSM the

estimation of the center coordinates is improved to sub-pixel accuracy (fig.7, green point).

Fig. 3. 53 Template and actual (captured) target

The object coordinates of a target in 3D space are calculated by solving the equation (2) for the

unknown coordinates X, Y, Z:


178

32233322322131

31133312321131

)()()()()()(

tyftfrryZfrryYfrryXtxftfrrxZfrrxYfrrxX

yyyy

xxxx

′′−=−′′+−′′+−′′′′−=−′′+−′′+−′′

(5)

where x”,y” the undistorted image coordinates (u,v). Each target observed on the image

introduces 2 observation equations. Since a target is visible on two images, four observation

equations are formed for 3 unknown parameters. The system is solved by employing a Least

Squares Adjustment.

The above methodology has been applied to monitoring the trajectory of a specimen placed on

the top of the shaking table of LEE/NTUA. Several circular targets were placed on the

specimen’s surface, as shown in Fig. 3.36. The shake table movement was in X direction. The

experiment was recorded from a distance of about 5 m. 16775 stereoscopic frames were captured

in about 4.5 minutes. The trajectory of a target in X direction is shown in Fig. 3.37.

The accuracy of the computed 3D coordinates was empirically tested by capturing the calibration

field from a distance of 5 m. 88 chessboard corners where automatically identified with sub-pixel

accuracy in both left and right camera frames. 3D object coordinates were calculated by

photogrammetric triangulation. To check the accuracy in X and Y direction the distance between

chessboard corners was calculated from the 3D object coordinates and compared to the actual

distance between the chessboard cells (65 mm). The minimum and maximum differences

observed were 0.20 mm and 0.89 mm, with a mean value of 0.52 mm. To check the accuracy in

Z direction the equation of a plan was fitted on the chessboard corners. The deviations of the

computed 3D coordinates from the chessboard plane do not exceed 0.82 mm.


179

Fig. 3. 54 Targets on specimen at LEE/NTUA

Fig. 3. 55 Trajectory along X axes (displacement in meters) for the experiment performed at LEE/NTUA


180

3.5.12 Shake table methodology: an example of photogrammetry on the CEA/AZALEE equipment

3.5.12.1 Presentation/Context

TAMARIS is the experimental part of CEA/EMSI laboratory (http://www-tamaris.cea.fr/).

Experimental capability is in constant improvement at TAMARIS experimental facility and

instrumentation is one of the main subjects which have seen a spectacular quality gap during last

years.

In particular, EMSI laboratory has recently bought a 3D displacement measurement system

based on target tracking. This system has been supplied by VIDEOMETRIC company

( http://www.videometric.com/ ) based near Clermont Ferrand, in France central area.

This company is operating mainly in 2D and 3D video techniques for displacement

measurements based on targets tracking and for strain field surface measurement. It also has an

offer in 3D surface digitization. VIDEOMETRIC proposes complete technical solutions and

services: hardware, software, on-demand adaptation, implementation on site, users training.

3.5.12.2 Equipment

The 3D displacement measurement system has been delivered in TAMARIS during September

2010. This system is based on target tracking by stereoscopic cameras technique. The original

aspect of the method proposed by VIDEOMETRIC (VDM) is based on a very accurate subpixel

detector presented in a previous article (Peuchot, B. 1988). The accuracy of displacement

measurement is so of 1/100 pixel.

The complete equipment which has been provided consists of:

2 CCD cameras (BAUMER TXG03 656x494 pixels) encapsulated in a 2 m long carbon

arm to limit relative displacement between cameras,

hardware with electric power control, ethernet card, trigger output,

a PC equipped with 2 ethernet cards and 2 high speed hard disks,

VDM software,

a dedicated lighting system.

http://www-tamaris.cea.fr/

http://www.videometric.com/


181

Fig. 3. 56 Carbon arm drawing

Carbon arm above test area Carbon arm end with camera window and

cooler

Carbon arm end with camera window and

mechanical arm plug One camera and mirror through window

Fig. 3. 57 Different pictures of the carbon arm

This equipment is built and calibrated by VIDEOMETRIC to answer user's needs. This means it

is not possible to modify camera positions or lenses in the system. It is so dedicated and

optimized for a particular set of applications as defined by user.

Connectors Rear side

Front side

Arm support (stuck) Camera cooler

Camera window Camera window

Camera cooler


182

For EMSI laboratory applications, VIDEOMETRIC system has been defined as following:

distance between cameras 1950 mm,

object / cameras distance 2,3 to 10 m,

scene area 1 m2 up to several m2 depends on object/camera distance,

measurement precision 1/50 000 of measurement field i.e. 0,02 mm for 1 m2,

image acquisition frequency 90 Hz.

These characteristics should allow the operators to carry on displacement measurements on

whole mock ups (typically concrete buildings of several m3) placed on AZALEE table (6x6 m2).

The targets used in VIDEOMETRIC system are ring

patterns in grey levels (at least 100 grey levels) which

metric diameter depends on the scene size (optimum

diameter is 14 to 20 pixels). VIDEOMETRIC software

algorithm is optimized for this kind of blurred targets.

Fig. 3. 58 VIDEOMETRIC target

First step is to detect the targets in the experimental scene twin images. Then, it is possible to put

some targets together in order to create some independent rigid objects. Three of them, at least,

are necessary to follow one object in the 6 degrees of freedom. The operator must, then, define

reference origin and axis in the scene with some sets of targets in 2 directions chosen in a

reference plane. The third axis is imposed to have a regular space reference.

When all targets are linked to objects and a space reference is set, it is possible to post process

images stored during test in order to calculate all displacements of objects.

The precision of the system is assured by the calibration process performed by VIDEOMETRIC.

It is based on pattern detection on a special grid mounted on a micrometric displacement bench

dedicated to calibration purpose. The accuracy of this process which takes the whole

measurement chain (fixed hardware i.e. with no adjustments + software) into account, guarantees

the intrinsic precision of the sensor.

All the hardware errors and inaccuracies (optical distortions, CCD sensor imperfections,

electronic noise...) are included in this process and quantify in a transfer function which is

characterising the measurement system.


183

To synchronize precisely the left and right images, some ‘synchronisation’ images are stored at

low frequency with their acquisition clock time before and after the test, in order to be able to

match precisely the images at the same acquisition time. This accurate process participates too in

the good quality of measurements.

Fig. 3. 59 Left and right images of stereovision system

The measurements are post processed on the whole set of images stored during test.

The system has, also, a high speed analyzing capability: 8 targets can be analyzed in real time

(with a standard computer, CPU 3GHz)

3.5.12.3 Stereovision system evaluation: test on a rocking and sliding block

Before buying this system, a testing campaign has been realized in TAMARIS facility to check

as far as possible the appropriateness of the system to the laboratory needs.

A rigid steel block used for "sliding and rocking structures" testing has been put freely on

VESUVE shaking table. So, it is able to move in the 6 degrees of freedom. Its height is 7 times

its base, that is:

Height 700 mm.

Base 100x100 mm2.

Displacement laser sensors and rotation gyroscopic sensors are put on the testing device. The

measurements of these sensors were used to check the VIDEOMETRIC measurements. Targets

are stuck on the steel block for video measurement. The dimension of the stereovision system

measuring area is about 1 m2. The distance between the cameras and the steel block is 4 m.


184

First, the targets have been used to construct, in VIDEOMETRIC system, a ‘virtual object’: Four

targets are linked together by cinematic equations.

Fig. 3. 60 Test rig for stereovision system evaluation

Two types of tests have been carried out with this experimental device:

Static tests for intrinsic system random errors evaluation or background noise in

measurement process.

Dynamic tests for comparison with EMSI conventional sensors.

The 'static tests' consist on capturing a measurement sequence of the experimental scene, without

any loading on the specimen. In other words the block is positioned still on the stopped shaking

table, but in experimental conditions: in the test hall with ambient light, with additional lighting

system, with implemented targets stuck on the block…. Each sequence counts 4000 images to be

able to post process some statistical evaluations.

The targets are detected and their positions in the 3D space are calculated in all these images.

Results along each axis are analysed by a statistical Gaussian analysis. Next drawing indicates

the probability of deviation from mean value µ for a given phenomenon described by this

Gaussian distribution. Peak value is maximum probability: πσ 2

1 .

Four targets fixed on the rigid block

Two targets fixed to the shaking table


185

Fig. 3. 61 A theoretical Gaussian distribution with µ, mean value and σ, standard deviation

Histograms of errors (that is, for each target and each axis, deviation of one position from the

mean value of the values set) are plotted: these show the distribution of errors, in other words the

number of errors for each error value.

Histogram along Ox Histogram along Oz

Histogram along Oy

Fig. 3. 62 Histogram “number of errors” versus “deviation from mean value” for 6 targets (error = deviation from mean value)


186

This statistical study shows that maximum possible errors but very unlikely (that is for 4 times

standard deviation σ) are around ±0,085 mm for in plane measurements (i.e. in plane parallel to

cameras plane) and around ±0,354 mm for out of plane measurements (i.e. perpendicular to

cameras plane). More likely errors are less than that, statistically.

These results are for an optimal implementation of the system in the test hall. For instance, the

influence of an appropriate lighting system has been evaluated: with only the ambient light,

errors are increased by about 50%.

Dynamic tests on the steel block permit to compare the coherence of the Videometric

measurements with standard sensors. During these tests, images are stored at 90 Hz frequency.

The post process of these sequences permits the displacements calculations of the different

targets in 3D. Some results examples are illustrated by the following figures for release test,

wavelet tests and seismic tests.

Top target displacement (mm) versus time for a release test

Top target displacement (mm) versus time for a seismic test

Block rotation (°) versus time for a seismic test

Fig. 3. 63 VIDEOMETRIC results for different check tests


187

The angle is measured by integration of rotation speed measured by a gyroscopic sensor. It is

compared to rotations evaluated from VIDEOMETRIC system. The results are presented on

following graphs. Comparison is very good between the 2 measurement methods.

Angle measurement versus time for a release test

Angle measurement versus time for a seismic test

Zoom on 2.5 s Zoom on 1.5 s

Fig. 3. 64 VIDEOMETRIC results for different tests

-4

-3

-2

-1

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20temps (s)

angl

e (°

)

capteur gyroscopiquecaméra

f(acq) capteur gyroscopique = 200 Hzf(acq) caméra = 90 Hz

p

-2

-1

0

1

2

14 19 24 29 34 39 44

temps (s)

rota

tion

(°)

caméracapteur gyroscopique

f (acq) caméra = 90 Hzf (acq) capteur gyroscopique = 200 Hz

-0,04

-0,03

-0,02

-0,01

0

0,01

0,02

0,03

0,04

0,05

17,6 17,8 18 18,2 18,4 18,6 18,8 19

temps (s)

angl

e (°

)

capteur gyroscopiquecaméra

f(acq) capteur gyroscopique = 200 Hzf(acq) caméra = 90 Hz

-2

-1

0

1

2

3

19 19,5 20 20,5 21 21,5temps (s)

rota

tion

(°)

caméracapteur gyroscopique

f (acq) = 75 Hz


188

Zoom on VIDEOMETRIC displacement measurements versus time permit to evaluate the measurement noise of the system.

In plane displacement measurements versus

time of the 4 targets

Out of plane displacement measurements

versus time of the 4 targets

Background noise for in plane displacements

measurements of the 4 targets

Background noise for out of plane

displacements measurements of the 4 targets

Fig. 3. 65 VIDEOMETRIC results quality (measurement noise)

These 2 zooms show that noise is about:

• ±0.01 mm for in plane displacements.

• ±0.1 mm for out of plane displacements.

These values can be compared to the one obtained at 4σ during static tests (±0,085 mm for in

plane measurements and ±0,354 mm for out of plane measurement).

These different results gave TAMARIS experimental team enough confidence into capability of

the VIDEOMETRIC system to perform good measurements during seismic tests. It has been thus

decided to equip the EMSI laboratory with a complete system.

The first experimental study suitable to use it has been seismic tests of metallic drums stacks

placed on AZALEE shaking table.

déplacements en Z(caméra) des 4 cibles objet

-20

-15

-10

-5

0

5

10

15

20

10 15 20 25 30 35 40 45

temps (s)dépl

acem

ent (

mm

)

Z m1Z m2Z m3Z m4

déplacements en Y(caméra) des 4 cibles objet

-15

-10

-5

0

5

10

15

10 15 20 25 30 35 40 45

temps (s)dépl

acem

ent (

mm

)

Y m1Y m2Y m3Y m4

déplacements en Z(caméra) des 4 cibles objet

-1,4

-1,2

-1

-0,8

-0,6

-0,4

-0,242 42,2 42,4 42,6 42,8 43 43,2 43,4 43,6 43,8 44

temps (s)

dépl

acem

ent (

mm

)

Z m1Z m2Z m3Z m4

déplacements en Y(caméra) des 4 cibles objet

0

0,2

0,4

0,6

0,8

1

1,2

42 42,2 42,4 42,6 42,8 43 43,2 43,4 43,6 43,8 44

temps (s)dépl

acem

ent (

mm

)

Y m1Y m2Y m3Y m4


189

3.5.12.4 Using the stereovision system during shaking table tests: drums stacked on AZALEE table

A first experimental application of the stereovision system has been carried out on a seismic

qualification testing campaign. Different configurations of standard drums stacked on pallets

have been put on AZALEE table in order to check their stability under different seismic

loadings.

AZALEE is the 6D shaking table of EMSI laboratory. It is 6 x 6 m2 and can support 100-ton mock up. It is activated by 8 1000 kN servo hydraulic actuators (4 in horizontal plane, 4 vertical). 4 pneumatic static supports are under the table to compensate part of the mass of the table + mock up system. The maximum displacements are +/- 125 mm in horizontal plane and +/- 100 mm in vertical direction.

The different drums stacks have been put on a concrete floor (3 x 3 x 0,2 m) covered with an epoxy coating (see figure after) as in the real industrial building which shall receive the drums. The stacks have been submitted to representative seismic signals.

Fig. 3. 66 Concrete floor with epoxy coating Fig. 3. 67 Drums stack on AZALEE table

(top view)

Drums stack Concrete floor (fitted on table by 8 bolts)


190

The different testing configurations are summarized in next table.

Drums stacks tests configurations Drum type Pallet number Stack type 100 litres

3 5 drums on each pallet

2 2 x 5 drums’ pallets, 1 x 4 drums’ pallet on top

200 litres


200 litres



24 tests have been performed, considering all the stacks configurations.


191

Set of accelerograms spectrums calculated (in grey) from theoretical spectrum (in green)

X direction Y direction

Z direction

Fig. 3. 68 Examples of accelerograms for drums stacks seismic tests

The main goals of the study were:

• First, to check that no drum is falling from the stack during seism.

• Second, to measure the maximum displacements of the drums during seism.

To protect testing device some metallic structures have been put around the stacks to prevent an

accidental collapse of the stack but at some distance in order not to interfere with the drums

during test. Due to the metallic beams size and number, the field view is quite reduced for the

cameras of VIDEOMETRIC system (Fig. 3. 51).


192

Fig. 3. 69 A typical 3 pallets and 3x4 drums on AZALEE

The whole mock up (shaking table, concrete floor, base pallet, top drums) have been

instrumented with various sensors (Fig. 3. 52):

• Actuators displacement sensors.

• Shaking table accelerometers.

• 2 cable displacement sensor on one top drum.

• Several targets for stereovision system, each of them permits the displacement

measurements in each axis X, Y, Z.

The conventional sensors are electrically processed and acquired by the PACIFIC

INSTRUMENT system (PI 660-6000) for conventional sensors and the stereovision pictures are

monitored and acquired by VIDEOMETRIC VDM-3D Acquisition module. These 2 systems

have a 100 Hz acquisition rate. The post process of stereovision pictures is carried out with

VIDEOMETRIC VDM-3D Analyser module.

AZALEE table

Concrete floor Metallic protection structure

Pallet

Drum


193

Fig. 3. 70 Drums stacks testing instrumentation

3 accelerometers (3D) on table next to

concrete floor 2 cable sensors on one top drum

Fig. 3. 71 Instrumentation implementation on drums stacks

More specifically, the ‘instrumentation’ for the stereovision system consists of targets fixed on

each part of the mock up as follows:

Concrete floor

x z

y

a

b c

d

e

a

d

c

c

d a

Floor 1

Floor 2

Floor 3

2 cable sensors

Accelerometers for 3D table control

AZALEE shaking table

Stereovision system


194

• 4 targets on the concrete floor. These are used to define the reference origin and axis. 3

are on ‘y’ axis and 2 on the ‘x’ one. The concrete floor is supposed to be perfectly linked

to the table.

• 1 or 2 targets on pallets depending on the field of view. These are put on mechanical parts

attached to the pallets in order to be seen by the cameras through the metallic structure.

• 1 on each top drum.

Fig. 3. 72 VIDEOMETRIC targets fixe on mock up

Colors as follows:

• Red targets reference axis on concrete floor

• Green targets pallet #1

• Blue targets pallet #2

• Magenta targets pallet #3

• Multicolored targets top drums

For all tests, no drum has fallen, that means stacks have remained globally in shape. The

maximum displacement of a top drum has been between 4.6 mm and 22.1 mm depending on

stack configuration. This last result has been directly post process from targets displacement

through VDM-3D Analyser module.

Ox

Oz Oy


195

Some comparisons have been carried out between the conventional displacement sensors and the

targets analysis. Overall comparisons are very good in both directions but noise is more

important in X axis.

The next 2 graphs show the comparisons between the LVDT sensors of the table horizontal

actuators and the relative displacement of the VIDEOMETRIC object (made with 4 targets on

the concrete floor) with the stereovision arm in reference.

Along Ox

Along Oy

Fig. 3. 73 Comparisons of VIDEOMETRIC and LVDT sensors measurements for shaking table

Comparisons between cable sensors on top drum and stereovision measurements (1 target) are

quite accurate too in both axis but noise is more important in Ox direction.


196

Displacement (mm) along Ox

Displacement (mm) along Oy

Fig. 3. 74 Comparisons of VIDEOMETRIC and LVDT sensors measurements for top drum

Measurement noise is greater on Ox axis because, there are only 2 targets to define Ox axis as a

reference whereas there are 3 targets for Oy axis (Fig. 3. 54), in other words Ox is geometrically

defined with less accuracy. This implies that there are more geometrical calculation errors in the

positioning of the other targets versus Ox axis than Oy axis, and so more measurement noise.

In the Ox case, measurement inaccuracy is about the maximum possible error determined during

still test (±0,085 mm).

Other displacement measurements with stereovision system are interesting to see the coherence

of the results.

Pallets displacements show that they increase with their height (pallet #1 is the bottom one and

#3 is the top one). This is qualitatively quite a logical result.


197

Pallets displacements along Ox

Pallets displacements along Oy

Fig. 3. 75 VIDEOMETRIC measurements for pallets

We can also compare the displacement of the 5 top drums. The measured displacements are well

in phase together. Once again qualitatively the results are quite relevant.


198

Top drums displacements along Ox

Top drums displacements along Oy

Fig. 3. 76 VIDEOMETRIC measurements for top drums

3.5.12.5 Conclusions

These different tests and results show that the stereovision system provided by the company

VIDEOMETRIC fulfils TAMARIS experimental needs. Some comments can be outlined from

these first experiments:

• This measurement technique is with no contact, that is with no interaction between

specimen and sensor.

• This is a robust displacement measurement technique to use in a test hall with triggering

capability (synchronization with other process) and dedicated lighting control.

• It is quite easy to implement and to use but care must be paid to the fulfilment of the

targets positioning, the choice of the reference axis and origin, the choice of different


199

independent objects if necessary. A ‘directions of use’ document must be written down

for the lab.

• The accuracy, in optimum implementation, is very good: 1/100th pixel.

• The acquisition frequency of 90 Hz is good enough for seismic testing.

• Analyzes are performed after test as many times as necessary.

In TAMARIS, next steps will be to use the same device on other test rigs on AZALEE table:

• Metallic structures.

• Concrete building.

Another system derived from this stereovision one will be provided by Videometric. It is based

on 3D stereo correlation technique and will be dedicated to the measurement of 3D displacement

maps on reduced area of concrete buildings, for instance. This system will be received, tested

and checked during 2011.

3.5.13 Conclusion

A review of the available vision sensors has been made, that has put in evidence the riches of this

technology, and its fast development, as it is linked to the prosperous branches of electronics

“submitted” to the well known Moore law. Indeed, the vision sensor will offer higher and higher

resolution, with decreasing pixel size. Their SNR will be probably kept at a fair level, giving a

true 12 bit output for medium quality CMOS sensors, and at frequency of frame sampling better

than 100 Hertz. The high end scientific CCD will have a very good SNR giving 14 to 16 bit

output with quite low sampling frequency, while scientific CMOS sensors are apparently on their

rise and should offer the same quality as CCD but at a higher frequency. This review has been

restricted to sensors working at frequency of the order of 100 hertz at most.

It has been also shown that, given a meticulous calibration of a stereo rig, it was possible to

extract important information on the behaviour of a large structure. The field of view was 6 m on

the bridge, and the pixel scale was varying from 3.6 to 4.4 mm on the frames, but it was possible

to clearly see vertical movement of the order of a tenth of a mm. Importance of vision system has

been demonstrated for checking boundary condition, for correcting unpredictable phenomenon


200

and indeed to give map of measurements, that would not be attainable with classical 2 points

distance sensors.

3.6 Stress and strain visualisation using thermal imaging

Thermal imaging provides a straightforward way to visualise stress distributions in metallic

elements deformed into their plastic range, and can provide more detailed information than

discrete sensors. There are many thermal imaging cameras available on the market; well-known

manufacturers include Agema and Flir. Using such a camera, is relatively straightforward to

obtain sequences of images of the temperature distribution across a surface, such as in Fig. 1.

Hotspots can be clearly seen, giving an excellent visual indication of areas of high plasticity and

incipient failure.

Fig. 3. 77 Thermal images from a fatigue test to failure on a yielding shear panel dissipative device

However, converting these to accurate numerical values of temperature, energy, stress and strain

requires considerable care and analysis. This section provides a brief summary of the steps

involved, and the potential pitfalls, based on experiments at Oxford University. The key stages

are:

1. Calibration of temperature data.

2. Transformation of images for deformed specimen to a fixed reference frame.


201

3. Conversion of temperatures to energy densities.

4. Conversion of energy density to stress and strain.

3.6.1 Calibration of temperature data

Thermal cameras measure infra-red radiation from surfaces within their field of vision, and

convert this to a temperature distribution. Typical cameras are able to scan at rates of the order of

tens of Hz and advertised accuracies are typically of the order of 1% or 1°C. Unfortunately, the

temperature measured from a surface is not necessarily equal to the actual temperature of the

surface. Accuracy can be affected by:

Reflections: a cool but reflective surface close to a heat source will show a high temperature due

to reflected infra-red radiation. This should be avoided by careful design of the test set-up as far

as possible.

Emissivity: different surface finishes radiate heat at different rates, and the thermal imaging data

need to be calibrated to account for this. In our experiments this was done by scaling the

camera’s temperature values by an emissivity ratio between 0 and 1. Appropriate values of the

ratio were chosen by comparing camera data with direct temperature sensors attached at discrete

points in calibration tests. As a general rule, dull, dark surfaces have high emissivity (ratio close

to 1) and shiny or polished surfaces have lower emissivity. There is an obvious benefit in having

a specimen with a uniform surface finish, so that emissivity scaling does not need to be varied

over the sample.

Many evaluation tests were performed on steel samples, often with a lightly oxidised surface;

this gave an emissivity ratio close to 1 for our camera system. However, at large plastic

deformations, parts of the oxidised surface sometimes flaked away, leaving a shinier surface with

lower emissivity. This then appeared as an apparent cold spot in the thermal imaging, which

needed to be ignored or adjusted.


202

3.6.2 Transformation of images to a fixed reference frame

Since, in a dynamic test, the test specimen is moving and deforming, successive thermal images

cannot simply be superposed. Instead it is necessary to track the movement of a point in the

specimen in order to extract the time variation of its thermal energy from the images. This is

most easily done by applying a transform to images of the deformed specimen so as to map each

point back to its initial, undeformed position. Once the images have been transformed in this

way, a point in the specimen can be assumed to lie at the same co-ordinates in all the images,

greatly simplifying the subsequent processing.

To perform the transformation, it is necessary to track the motion of a set of key points on the

specimen, and then apply an appropriate order of interpolation between them. In many instances

the specimen will include some suitable points. For example, in Fig. 2a) – c), tests were

performed on steel dissipators with webs perforated by circular holes; it was a simple matter to

track the position of the centre of each circle. A suitable interpolation function could then be

fitted to these points. Fig. 2d) – f) show thermal images for specimens with fewer obvious

features. In these tests, easily identifiable dark spots were introduced in the form of small

rectangular rubber pads, which were refrigerated until the test was ready and then attached to the

specimen. The choice of transformation method may vary depending on the complexity of the

specimen and its deformation pattern. For simple geometries, linear interpolation is likely to be

sufficiently accurate. For more complex deformations, more generally formulated tracking

strategies developed in other field of image analysis can be used (e.g. Marias et al., 2000).


203

Fig. 3. 78 Thermal images from tests on short beam sections

Images a) – c) are for beams weakened by perforating the webs with circular holes. Images d) –

f) are for I-section beams with stiffeners; the black rectangles are rubber pads. The circled area in

e) indicates where the surface has flaked off, giving an apparent change in temperature (Clement,

2002).

3.6.3 Conversion of temperatures to energy densities

A simple mathematical process can be followed to convert temperature distributions to plastic

energies. By manipulation of the heat diffusion equation, the power density p (i.e. energy

released per unit volume per second) can be related to the temperature u by:


204

(1)

where cp is specific heat, ρ is density and k thermal conductivity. If the temperature changes by

∆u over a timestep ∆t then the increment of energy per unit volume is

(2)

If the temperature is known over a regular 2D grid of points (i, j) spaced at ∆x in each direction,

the Laplacian is given by:

(3)

In the case of a thermal imaging camera, the grid points are represented by the pixel centres.

3.6.4 Conversion of energy density to stress and strain

Once a distribution of plastic energy has been achieved, this can be converted to stress and strain

distributions by applying plasticity theory. The energy density can be expressed in terms of the

stress σ and the plastic strain εp according to

(4)

To relate the stress to the plastic strain requires the definition of a yield criterion, a hardening

rule describing the work hardening of the material and flow rule relating the plastic strain

increment to the yield surface. For steel it is reasonable to use a von Mises yield surface with a

normal flow rule, in which the direction of the strain increment is always normal to the yield

surface. An example result is shown in Fig. 3, which shows two snapshots of plastic strain

distributions for the same element as pictured in Fig. 2e) and f).


205

Fig. 3. 79 Plastic strain distributions deduced from thermal images for the beam pictured in Fig. 3. 60

3.6.5 Conclusion

Thermal imaging has been shown to provide a viable way of developing visualisations of stress

and strain fields in metallic specimens during dynamic tests. However, the technique requires

careful implementation and remains prone to some uncertainty. The tests described here could be

improved by improved surface preparation of specimens, and more thorough calibration of their

thermal emissivity.


206

4 Summary

This report covered the research activities both of Task JRA2.1 and of Task JRA2.2,

respectively. In greater detail the state of the art as well as the implementation and application of

new types of sensors, time-integration and control techniques, visualisation and device modelling

tools capable of enhancing the measurement of the response of test specimens and of improving

the quality of test control were summarized.

To achieve the objectives of the aforementioned tasks, selected partners made extensive

use of testing and calibration of instrumented specimens. In particular, the following test types,

with relevant specimens were employed:

– test Type 1 (TT1): a testing bench comprising four – instead of two - electro-

magnetic actuators designed to control 4- instead of 2-DoF linear/non-linear systems with or

without substructuring.

– Test Type 2 (TT2): an actuator calibration bench including a 2.5 kN hydraulic

actuator with 2 servo valves, a steel table mounted on a low friction ball bearing rail, a real-time

hybrid controller with a fiber optic communication system.

Dissemination of time-integration techniques, control techniques and vision systems to

partner infrastructures not directly involved in the above-mentioned development and/or

application will follow.


207

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