dfi journal · approach that simulates the entire pile, soil and hammer system. this methodology is...

Vol. 7, No. 2 December 2013

DFI JOURNALThe Journal of the Deep Foundations Institute

PAPERS:A Driveability Study of Precast Concrete Piles in Dense Sand – Paul Doherty, David Igoe [3]

Ant Colony Optimization Method for Design of Piled-Raft Foundations (DFI 2013 Student Paper Competition Winner) – Hessam Yazdani, Kianoosh Hatami, Elahe Khosravi [17]

Piezocone Penetration Testing in Florida High Pile Rebound Soils – Fauzi Jarushi, Paul J. Cosentino, Edward H. Kalajian [28]

Factors Affecting the Reliability of Augered Cast-In-Place Piles in Granular Soils at the Serviceability Limit State (DFI 2013 Young Professor Paper Competition Winner) – Armin W. Stuedlein, Seth C. Reddy [46]

A Review of the Design Formulations for Static Axial Response of Deep Foundations from CPT Data (DFI 2013 Student Paper Competition Runner-Up) – Fawad S. Niazi, Dr. Paul W. Mayne [58]

Deep Foundations Institute is the Industry Association of Individuals and Organizations Dedicated to Quality and Economy in the Design and Construction of Deep Foundations.

DFI JOURNAL Vol. 7 No. 2 December 2013 [1]

From the Editors and Publisher 2013 DFI Board of TrusteesPresident:Robert B. BittnerBittner-Shen ConsultingEngineers, Inc.Portland, OR USA

Vice President:Patrick BerminghamBermingham Foundation SolutionsHamilton, ON Canada

Secretary:Matthew JanesIsherwood AssociatesBurnaby, BC Canada

Treasurer:John R. WolosickHayward Baker Inc. Alpharetta, GA USA

Immediate Past President:James A. MorrisonKiewit Infrastructure Engineers Omaha, NE USA

Other Trustees:David BorgerSkyline Steel LLCParsippany, NJ USA

Maurice BottiauFranki Foundations BelgiumSaintes, Belgium

Dan BrownDan Brown and Associates, PLLCSequatchie, TN USA

Gianfranco Di CiccoGDConsulting LLCLake Worth, FL USA

Rudolph P FrizziLangan Engineering &Environmental Services Elmwood Park, NJ USA

Bernard H. HertleinGEI Consultants Inc. Libertyville, IL USA

James O. JohnsonCondon-Johnson & Associates, Inc.Oakland, CA USA

Douglas KellerRichard Goettle, Inc.Cincinnati, OH USA

Samuel J. KosaMonotube Pile CorporationCanton, OH USA

Kirk A. McIntoshAMEC Environment & Infrastructure, Inc.Jacksonville, FL USA

Raymond J. PolettoMueser Rutledge Consulting EngineersNew York, NY USA

Michael H. WysockeyThatcher Engineering Corp.Chicago, IL USA

Journal PublisherManuel A. Fine, B.A.Sc, P.Eng

Journal EditorsAli Porbaha, Ph.D., P.E. Central Valley Flood Protection Board Sacramento, CA, USADan A. Brown, Ph.D. Dan Brown and Associates, Sequatchie, TN, USAZia Zafir, Ph.D., P.E. Kleinfelder Sacramento, CA, USA

Associate EditorsLance A. Roberts, Ph.D., P.E.RESPEC Consulting & ServicesRapid City, SD USAThomas Weaver, Ph.D., P.E.Nuclear Regulatory CommissionRockville, MD USA

Published By Deep Foundations Institute

Copyright © 2013 Deep Foundations Institute. AII rights reserved.

Written permission must be obtained from DFI to reprint journal contents, in whole or in part.

ContactDFI, 326 Lafayette AvenueHawthorne, NJ 07506staff@dfi .org, www.dfi .org

DFI, its directors and offi cers, and journal editors assume no responsibility for the statements expressed by the journal’s authors. International Standard Serial Number (ISSN): 1937-5247

Mission/Scope The Journal of the Deep Foundations Institute publishes practice-oriented, high quality papers related to the broad area of “Deep Foundations Engineering”. Papers are welcome on topics of interest to the geo-professional community related to, all systems designed and constructed for the support of heavy structures and excavations, but not limited to, different piling systems, drilled shafts, ground improvement geosystems, soil nailing and anchors. Authors are also encouraged to submit papers on new and emerging topics related to innovative construction technologies, marine foundations, innovative retaining systems, cutoff wall systems, and seismic retrofit. Case histories, state of the practice reviews, and innovative applications are particularly welcomed and encouraged.

DFI JOURNAL

To quote Heraclitus, “There is nothing permanent except change.” Change is inevitable and essential in a growing organization, being preferable over remaining static. Following seven years of in-house production of this journal, there are going to be changes. Manny Fine is retiring from the Publisher position and DFI has contracted with Maney Publishing, (similarity of name is coincidental), an independent publishing company specializing in journals in materials science and engineering, the humanities, and health science. Maney is committed to publishing high quality journals in print and electronic formats that are international in scope and peer-reviewed. The partnership allows DFI to retain editorial control and ownership of the DFI Journal so that we can continue to produce a journal containing practice-oriented papers that are useful to our readers. We look forward to a successful relationship with our new publisher and all the developments this will entail. DFI members will continue to have online access to the journal as a perk of membership and previous issues, back to Vol. 1, will also be available online.

Additionally there will be changes to the editorial board with two of the three current editors, Dan Brown and Ali Porbaha, stepping down, along with associate editor Lance Roberts, while Zia Zafir, Ph.D., P.E., of Kleinfelder will remain on the editorial board to provide continuity during the transition. Two new co-editors were appointed by DFI president Robert Bittner; Anne Lemnitzer, Ph.D., Assistant Professor of Structural/Geotechnical Engineering at University of California and Timothy Siegel, P.E., G.E., D.GE, Principal Engineer at Dan Brown and Associates, LLC. Anne’s current research work focuses on soil-structure interaction of various bridge foundation systems and lead to significant changes in California bridge design (Caltran’s Seismic Design Criteria). She serves as reviewer for multiple geotechnical journals and conferences and is an active committee member of DFI, EERI and ASCE. Tim began his career as a Geotechnical Consultant with S&ME, Inc. from 1993-2005 and then at Berkel & Company Contractors for the following 5 years, joining DBA in 2010. He also has served as Adjunct Faculty at The University of Tennessee since 2003. Additional members of the editorial board will be Antonio Marinucci, Ph.D., P.E., Director of Sales and Research at American Equipment and Fabricating as well as Thomas Weaver, Ph.D., P.E. of the Nuclear Regulatory Commission, previously associate editor. The editorial board will be rounded out in 2014 to ensure coverage of all technical areas of expertise.

This edition includes a variety of papers, both as to subject matter and as to geographic location of the authors. This diversity of papers illustrates both the technical and geographic success of the DFI Journal in attracting papers of interest to our membership.


INTRODUCTIONPile driveability analysis is used to assess whether piles can be installed to a target depth in a reasonable timeframe without overstressing the pile material or generating excessive fatigue damage. Most pile driving analysis procedures adopt a wave equation approach that simulates the entire pile, soil and hammer system. This methodology is routinely implemented for offshore projects where pile installation is deemed to be a significant risk due to the high cost associated with vessel delays (Toolan & Fox, 1977). Pile driveability analysis is also employed for onshore projects and is particularly important where there is a specific concern over the installation stresses, soil resistance, or hammer capabilities. In 1976, Goble et al. presented an alternative procedure for predicting pile installation performance, which was deemed superior to the empirical hammer-pile ratios employed at the time. The recommendations included undertaking wave equation predictions and also measuring the force and acceleration of the pile throughout installation. It is worth noting,

that Goble, et al. (1976) cautioned that wave equation analysis is “meaningful only if the driving system and soil conditions, as modelled in the computer, realistically reflect the actual conditions”. To date, accurately capturing the site specific soil-pile behaviour remains one of the most challenging aspects of undertaking a driveability analysis. This paper describes a case study where precast concrete piles were installed at a very dense sand test site, where the soil relative density approached 100%, and driveability was a concern from the outset. The initial driving analysis are discussed and compared to the measured installation performance.

BACKGROUND TO PILE DRIVEABILITYThe procedure for undertaking pile driveability analysis is illustrated schematically in Fig. 1 below. The analysis undertaken for this project used one-dimensional wave equation software to model the energy transfer from the hammer into the pile-soil system.

A Driveability Study of Precast Concrete Piles in Dense Sand Paul Doherty, Post-Doctoral Researcher, University College Dublin, Ireland, [email protected]

David Igoe, Post-Doctoral Researcher, University College Dublin, Ireland

ABSTRACTA research study was recently completed by University College Dublin to examine the performance of various pile types including open steel tubular piles, concrete precast piles, and helical piers. At the outset of this project, one of the key risks identified was that the concrete piles could not be installed to the target depth due to (i) insufficient energy from the available hammer and (ii) the onset of pile material damage. In order to mitigate this risk a detailed pile driveability analysis was completed to predict the installation performance during driving. Selecting an appropriate model for predicting the Static Resistance to Driving (SRD) was seen as a critical component of the driveability process in order to predict reasonable stresses and blow counts. This paper describes the procedures adopted for a base case driveability analysis and the outcome of the pile installations. A comparison of the SRD using other models (including the API and IC-05 methods) was conducted and the results were compared to SRD profiles derived from dynamic pile monitoring conducted on one of the concrete piles. The base case driveability analysis indicated that the piles could be installed with the available hammer equipment, however it was noted that the driving stresses were relatively high and approached the failure stress of the concrete as the pile approached the target penetration. While hard driving was observed in the field, all of the piles reached their design depth of 7m (23 ft) with the exception of one pile which refused due to structural failure near the pile head. The driveability analysis and the measured stresses were interpreted to identify the cause of failure for the single pile, which was linked to the material properties of that specific pile.

[4] DFI JOURNAL Vol. 7 No. 2 December 2013

The main inputs for the analysis include the soil resistance, the pile properties and the hammer properties. The pile properties include the geometry, the material type and relevant mechanical parameters such as modulus of elasticity. The hammer properties are used to determine the energy imparted to the pile head (enthru energy) and therefore the hammer model, anvil mass, efficiency and drop heights are all important parameters in the analysis. The ability of the hammer to advance the pile into the ground will depend on both the hammer energy during impact and also the contact time over which the energy is transferred from the hammer into the pile. In this regard, the cushion properties are also a critical input parameter, as a piling cushion can be used to decrease the peak stresses in the pile while simultaneously increasing the contact duration and consequently the pile penetration rate.

[FIG. 1] Driveability Process

The soil driving resistance consists of two parts, the static component (SRD) and a velocity dependent dynamic component, termed damping. The damping factors implemented in the wave equation model account for inertial and viscous rate effects. The static component is similar to the static capacity determined from traditional pile capacity formulas. The

main differences relate to ageing and partial consolidation effects. To overcome these differences, specific procedures have been developed to predict the static resistance to driving (SRD). The baseline SRD calculation method considered in this study was formulated by Alm & Hamre (2001).

SELECTING A CONSERVATIVE SRD MODEL FOR PILE DRIVEABILITY ANALYSISThe objective of a pile drivability analysis is to ensure that a pile can be safely installed to a target penetration with the available equipment without overstressing the pile material. This paper focuses on pile driveability analyses and is not considering the long-term pile behaviour. The SRD model selected for input into the pile drivability analysis should assume an upper bound profile for the soil resistance, such that the hammer and pile are analysed against the hardest driving conditions that may occur at site. A conservative SRD model would typically over predict the average in-situ soil resistance to ensure that the hammer is capable of delivering sufficient energy to overcome the maximum possible ground resistance that may be encountered. By contrast, a non-conservative SRD model would under predict the driving resistance and as a result the pile may experience premature refusal due to the hammer being undersized for the given installation conditions. An upper bound SRD model will also yield the highest driving stresses and therefore increasing the SRD values is a conservative approach with respect to material damage. When specifically considering pile driveability analysis, the concept of conservatism is reversed in comparison to static capacity predictions.

For this study, the A&H method (Alm & Hamre, 2001) was preferred because of (i) its calibration against North Sea pile driving records, (ii) the direct use of the CPT test and (iii) consideration of friction fatigue effects in assessing the shaft friction acting on the pile.

The friction degradation (or friction fatigue) concept was first introduced for clay by Heerema (1980), and a complete SRD model that considers friction fatigue effects was described by Heerema (1981),. Friction fatigue has also been documented in static load tests undertaken by Chow (1997), and others. Alm


& Hamre (1998) highlighted the limitations of the Heerema SRD approach and proposed an alternative method that explicitly considered friction fatigue in dense sands. This method subsequently evolved into the A&H approach, which considers that the model should define an initial friction value and a residual static friction, along with a shape function describing the relative friction fatigue reduction. This resulted in the general formula for shaft shear stress (fs) defined by equation 1, below:

[1]

Where fres

is the residual shaft shear stress after complete degradation; fs

i is the initial

shaft shear stress value; k is a shape factor controlling the rate of degradation; d is the depth and p is the pile tip penetration. The residual shaft shear stress is defined by:

[2]

Where qT is the CPT cone tip resistance and σ'

v0

is the vertical effective stress.

The initial shaft friction is defined by the traditional static formula:

[3]

[4]

Where δ is the mobilised friction angle at failure, and pa is atmospheric pressure.

Calibration of this approach against a database of piling records in the North Sea yielded a shape factor defined below:

[5]

This shape factor suggests that rapid degradation will occur for dense deposits with high cone tip resistances.

The A&H formula for unit base resistance acting on a pile in sand was derived as:

[6]

Equation 6 yields base stress values that increase with sand relative density from 0.35 to 0.55 as the sand density changes from loose to very dense.

For application in driveability analysis, Alm & Hamre (2001) suggest using an upper bound prediction of the SRD by multiplying the resistance by a factor of 1.25 to provide an upper bound resistance.

For this paper, soil profiles calculated from the models were incremented to give approximately 100 elements for input into the wave equation software. Issues arose due to friction fatigue, as the distribution of shaft friction varied with pile tip penetration. Schneider & Harmon (2010) found that the shape of the shaft friction distribution had little effect on the resultant bearing graph and as a result the incremental change in shaft capacity as the pile penetrates a distance ∆L could be used to calculate the pseudo average shaft friction (∆τ

f,avg):

[7]

where ∑QS,L

is the cumulative shaft resistance at tip depth; ∑Q

S,L-1 is the cumulative shaft

resistance at the previous depth increment; and D is the pile diameter. The pseudo average values can then be used to generate a synthetic soil shear stress profile with depth that can be implemented within the wave equation software.

It is usual for an SRD model to be accompanied by a set of damping factors and quake values. Quake is defined as the displacement required to mobilise the soil-pile resistance at a yield condition. The parameters used in this paper were those associated with the original A&H model. Quake values were taken as 2.5 mm (0.1 in) for both side and tip yield, and soil damping constants of 0.5 s/m and 0.25 s/m (0.15 s/ft and 0.08 s/ft) were used for the tip and the side respectively.

SITE CONDITIONS AND GROUND MODELThe UCD Blessington test site is an over-consolidated, glacially deposited, very dense fine sand bed with a CPT q

c resistance in the

range of 15- 20 MPa (2,175 – 2,900 psi) and small strain shear stiffness in the range of 100 to 150 MPa (14.5 to 21.8 ksi). The water table is approximately 13m (42.7 ft) below ground level. Details of the deposit have been provided in Gavin & O’Kelly (2007), Gavin, et al. (2009), Doherty, et al., (2012) and others. Eight CPTs were conducted in the vicinity of the piling


location and are shown in Fig. 2. Laboratory testing was used to supplement the in-situ site investigation and establish basic soil properties. Particle size distributions conducted on the Blessington sand classified the material as fine to medium grained, with a mean particle size varying from 0.1 - 0.15 mm (.004 - .006 in). The moisture content increased with depth from 8% at ground surface to approximately 12% at 4.5 m (15 ft) depth.

[FIG. 2] CPT Cone Resistances from Blessington

Experimental Piling ProgrammeThe pile tests described in this paper were part of an extensive investigation of displacement pile behaviour in sand including; driveability, ageing, cyclic loading, dynamic loading and lateral loading. In total, five 275 mm (10.8 in) square precast concrete piles and seven 340 mm (13.4 in) diameter open-ended steel piles were driven into a dense sand deposit in Blessington, Ireland. This paper will focus on the driveability of the precast concrete piles, which were 8m (26.2 ft) long and had a target penetration of 7m (23 ft) below ground level. The piles were made from grade C50/60 concrete with 28-day cube strengths varying

from 52 – 85 MPa (7,540 psi – 12,300 psi) depending on the concrete batch. Details of the concrete mix are provided in Table 1. The frequency distribution of 28-day cube strengths from concrete batches taken over the course of a year are shown in Fig. 3. The average 28-day strength of the concrete cubes was 64 MPa (9,300 psi) but significant variability in concrete strength is evident. Pile reinforcement consisted of eight H12 (0.47 in) helical cold drawn rebars, with H5 (0.20 in) shear links spaced every 55 mm (2.2 in) in the bottom and top 1m (3.3 ft) and spaced every 150mm (5.9 in) in the middle section.

[TABLE 1] Typical Concrete Mix Design for Precast Piles

MaterialQuantity (kg/m3)

Cement 42.5N

PFA

Water

Admixture (Sika)

14mm Aggregate

10mm Aggregate

Concrete sand

Water/Cement Ratio = 0.4

400

80

160

3.3

670

300

850

[FIG. 3] Variability of Concrete Cube Strength from 84 Samples

The piling rig used to drive the piles at Blessington was a 4 tonne (4.4 ton) Junttan hammer (see Fig. 4), with a maximum rated energy of 47kJ (34,700 ft lb) for a ram fall height 1.2 meters (3.94 ft). For the concrete piles a 75mm (3 in) thick beech pile cushion was also used to manage the applied stresses to the concrete pile.


[FIG. 4] Driving Concrete Piles

BEST ESTIMATE PREDICTION OF PILE DRIVEABILITYThe pile, soil and hammer model was implemented in a one dimensional wave equation programme as described in the previous sections. The A&H SRD values and the synthetic shear stress profile implemented in the one dimensional wave equation software are plotted in Figs. 5 and 6 respectively. The SRD value is seen to increase from 0.6MN (67 ton) for initial penetration to approximately 1.3MN (146 ton) when the pile tip reaches 7m (23.0 ft) depth. The base resistance accounts for nearly 100% of the initial SRD with the proportion of shaft resistance increasing to 44% of the total SRD at the end of installation.

The resultant installation response predicted by the base case driveability analysis is shown in Fig. 7. The initial analysis adopted the maximum hammer drop heights to assess whether sufficient energy was available to overcome the soil resistance. The analysis yielded blow counts that ranged from 10 blows/250 mm (10 blows/10 in) at shallow depths to approximately 20 blows/250 mm (20 blows /10 in) at the target penetration. To ensure a

reasonable driving time and to minimise fatigue damage, practical refusal was defined as a penetration rate of less than 250 mm (10 in) per 100 blows. The blow counts predicted for this site were well below the design limits and therefore the hammer was deemed appropriate for driving the piles. However, the compressive stresses imparted to the pile material were relatively constant with depth at approximately 42MPa (6,100 psi) and were an area of concern.

[FIG. 5] SRD predictions using A&H method

[FIG. 6] Synthetic shear stress profi le for A&H method


[FIG. 7] Driving Response for Hammer Operating at Maximum Energy

The analysis was repeated for various drop heights, as shown in Fig. 8, where increasing the drop height increases the compressive stress but reduces the blow counts. Both the standard and upper bound A&H approaches were used to predict the driving behaviour. To achieve the optimum installation performance it was decided to maintain the hammer drop height between 400mm and 600mm (16 in and 24 in) (i.e. less than 50% of the potential hammer energy). By restricting the hammer drop heights the analysis showed that it was possible to maintain the compressive stresses at an acceptable level, allowing the piles to be installed without damage.

PILE INSTALLATION PERFORMANCEThe five concrete piles were installed in the dense sand in June 2012. The applied drop heights were limited to 600 mm (24 in), which allowed four of the piles to be installed to the target depth without any issues. This procedure allowed the blow counts to be maintained below 100 blows/250 mm (100 blows/10 in) as shown in Fig. 9, with the cumulative blows typically ranging between 1000 and 1500 to reach the 7m (23.0 ft) penetration target. The range in

blow counts is due to the varying height of the hammer impacts. PC5 was consistently struck using lower hammer drop heights of 400mm (16 in), whereas several of the other piles used drop heights up to 600mm (24 in), which resulted in lower blow counts.

One of the precast piles (PC3) installed at the Blessington test site, exhibited early flaking of the concrete near the pile head, which during subsequent driving resulted in vertical cracks extending several meters down the pile shaft and ultimately led to complete failure of the concrete (as shown in Fig. 10). The material failure was sufficient to reduce the cross section to 65% of the original value, exposing

[FIG. 8] Driveability analysis to determine hammer drop heights


the longitudinal reinforcement and the shear links. The damage was sufficient to force premature refusal, with the pile tip terminating approximately 1.3 meters (4.3 ft) above the target penetration as seen in Fig. 11.

It is worth noting that due to the uniformity of the underlying ground conditions, all of the piles were expected to exhibit similar driving behaviour. However despite this,

some variations in the hammer operation between piles led to a spread in the measured blowcounts (see Fig. 9). As the enthru energy from the hammer to the piles was not recorded for every precast pile installed, this paper focuses primarily on the single pile for which detailed driving measurements are available. The lack of monitoring on all five piles was purely due to budgetary and time constraints and the authors recommend that for further field studies every effort should be made to dynamically monitor every pile installed.

[FIG. 9] Blowcount Records

[FIG. 10] Pile damage on PC3

[FIG. 11] Piles installed to target depth


MEASURED RESPONSE FROM PILE DRIVING ANALYZER RESULTS Due to practical and financial constraints, only one of the concrete piles (PC4) was monitored throughout driving, using a pair of strain gauges and accelerometers placed on opposite faces of the pile. The strain and acceleration generated by every hammer blow was measured throughout driving and simultaneously recorded on an on-site dynamic data acquisition system. As the strains and accelerations were recorded for every blow during installation, it was possible to derive a continuous estimate of the SRD during installation for comparison with the original design assumptions. The measured SRD values are shown in Fig. 12. The continuous profiles represent CASE method estimates of the pile resistance (RMX and RX5). The CASE estimates are determined from the force and velocity inputs at times corresponding to the ground and toe wave arrivals from the hammer impact. The RMX value is determined for the maximum total resistance during driving, whereas RX5 assumes a damping factor, J of 0.5. Selected blows at 3m, 5m and 7m (10.0 ft, 16.4 ft and 23.0 ft) penetration have also been extracted for further study using a signal matching process. CAPWAP (CAse Pile Wave Analysis Program) analysis allowed more accurate estimates of the pile resistance during driving to be derived. In this instance, the CAPWAP estimates are seen to provide excellent agreement with the more rudimentary CASE method capacities. The CAPWAP resistance estimates are seen to increase from 1000kN (112 ton) at 3m (10 ft) depth to approximately 1450kN (163 ton) at 7m (23.0 ft) depth. It is worth noting that these values are slightly higher than the range predicted by the original A&H SRD model, which estimated values that would increase from 700kN (78.7 ton) to 1200kN over the same penetration interval. Furthermore, the factor of 1.25 for the A&H UB approaches the measured values, indicating that the driving resistance are a close approximation to the upper bound design predictions. It is worth noting that the A&H methodology was derived for the specific case of coring pipe piles and the full-displacement nature of the concrete piles and the increased surface roughness may have yielded the higher resistances recorded.

The CAPWAP signal matching process can be used to determine an estimate of the shear stress distribution along the pile shaft. The shaft stress profile derived for each of the tip penetrations at 3, 5 and 7m (10.0 ft, 16.4 ft and 23.0 ft) is shown in Fig. 13, where friction fatigue is evident between each of the profiles. For example, at 4m (13 ft) depth, the shaft shear stress reduces from 140kPa (20.3 psi) when the pile tip is at 5m (16.4 ft) to less than

[FIG. 12] Measured Static Resistance to Driving (SRD) on Pile PC4

[FIG. 13] Shaft Shear Stresses Derived from CAPWAP


30kPa (4.4 psi) when the pile tip advances to 7m (23.0 ft) depth. In short, continuous driving can cause a serious reduction in the shear strength at any given depth as the pile penetrates further into the ground. This implies that a suitable SRD model should capture this effect, standing to the applicability of the A&H approach which has been developed to incorporate this effect.

The CAPWAP analysis can also be used to derive the normal stress acting on the base of the concrete pile, as shown in Fig. 14. This stress increases slightly with depth from 10500 kPa (1,523 psi) to 12000 kPa (1,740 psi) over the depth range from 3m to 7m (10.0 ft to 23.0 ft). This stress represents between 0.6 and 0.7 times the CPT tip resistance, q

c.

[FIG. 14] Measured Base Stress During Driving Pile PC4

PARAMETRIC SRD STUDYA parametric study was completed to determine the impact of various SRD models on the pile installation response to determine the worst case scenario and to identify the upper bound to the installation risk profile.

Three static capacity approaches were considered, including recent CPT based methods that incorporate friction fatigue, to assess which of the methods are most appropriate for use as an SRD model for driven concrete piles.

Design methods for estimating pile capacity in sands have been developed by the offshore

industry and are summarised in the American Petroleum Institute API-RP2A (2007) design guidelines. The API method has traditionally been based on the earth pressure approach which relates the local unit shaft friction, τ

f,

to the vertical effective stress, σ′v0

, using an empirical factor β:

[8]

where β ranges from 0.36 for medium-dense deposits to 0.7 for very dense sand (for full displacement piles). The API method calculates the unit base resistance using traditional bearing capacity theory where:

[9]

where values of Nq range from 12 – 50 for

medium-dense to very dense sands. Several studies, Chow (1997), Lehane, et al. (2005) and others have shown the API method has poor reliability, typically underestimating the shaft resistance of piles in dense sand and overestimating the resistance in loose sand. In recent API editions, the method is precluded from use in loose sands as the pile lengths obtained were noted to be un-conservative (API-2007).

Dennis & Olson, (1983), Lehane & Jardine, (1994) and others have shown that the local radial effective stress at failure, σ′

rf, which

controls τf can be described using the Coulomb

failure criteria:

[10]

where δf is the interface friction angle at failure.

Tests performed using the closed-ended Imperial College Pile (ICP) by Lehane (1992) and Chow (1997) showed that σ′

rf comprised

two discrete components, namely σ′rc, the

radial effective stress after pile installation and equalisation and ∆σ′

rd the increase in stress due

to dilation during loading.

[11]

Jardine, et al. (2005) propose a direct correlation between σ′

rc and q

c known as the IC-05 method.

The effects of friction fatigue (discussed earlier) are considered through a geometric term, h/R (where h is the distance from the pile tip to the point under consideration and R is the pile radius).


[12]

where pref

=100 kPa (14.5 psi). Lehane et al. (2005) suggest an alternative expression for σ′

rc

known as the UWA-05 method:

[13]

where D is the pile diameter. Both IC-05 and UWA-05 approaches include expressions to predict the stress increase ∆σ′

rd caused by

dilation. Both are based on the work of Lehane & Jardine (1994), who demonstrated through cavity expansion theory that ∆σ′

rd is inversely

proportional to the pile diameter pile diameter.

[14]

where G is the operational shear modulus of the soil (which can be correlated with CPT q

c)

and ∆y is the radial displacement during pile loading.

Both the IC and UWA design methods use notably different base resistance calculations. In the IC-05 method the base resistance is considered as a direct function of the average CPT q

c and the pile diameter. The CPT cone

resistance is averaged over ±1.5 diameters above and below the pile tip (q

c,avg) and the base

capacity is calculated as follows:

[15]

Where DCPT

= 0.036m (0.118 ft). The UWA-05 design method uses a more complex Dutch CPT averaging technique and assumes q

b is only a

function of qc,dutch

as follows:

[16]

DISCUSSION – SRD MODELSThe three different static capacity methods described above were used to determine the ultimate pile resistance values, which are illustrated in Fig. 15 and documented in Table 2. The API method is seen to predict the lowest resistance (0.82MN or 92.2 ton), while the UWA approach predicts the highest value (1.69MN or 190 ton). It should be noted that in contrast to the A&H method, the UWA, API and IC approaches have been developed to calculate static pile capacity; and therefore

this study is focussing on their applicability for use as SRD models within a drivability framework. The static capacity approaches have been designed to predict a long-term static capacity that accounts for some degree of pile aging /setup, where the resistance will increase over time. As a result the red dots that depict the end of installation (EOI) resistance in Fig. 15 are likely to migrate closer to the UWA prediction over time, suggesting that the UWA method may be the most accurate approach for long term resistance, while simultaneously providing a conservative SRD estimate for driving analysis. By sharp contrast, the API approach is seen to grossly underestimate the installation resistance which would yield a non-conservative SRD for input into a driveability analysis. For long-term capacity predictions the API values would provide a conservative

[FIG. 15] Comparison of SRD Methods

[TABLE 2] SRD Predictions at Final Depth

SRD ModelQT

[MN]

Qs

[MN]

Qb

[MN]

A&H 1.19 0.5 0.69

A&H UB 1.49 0.63 0.86

API 0.82 0.3 0.52

UWA05 1.69 0.79 0.90

IC-05 1.68 0.76 0.92


estimate of the pile failure loads; however this may not result in an efficient pile design. As the pile capacity increases over time, the measured pile resistance will migrate further from the API predictions, leading to an even greater inaccuracy in the predicted API values. Direct use of simplified static capacity methods such as the API model for driveability predictions was recognised by Stevens et al. (1982) to provide a non-conservative driveability prediction, whereas the UWA method appears to provide a more conservative SRD prediction for implanting in driveability analysis. The IC-05 method shows reasonable correlation with the measured values, whereas the Alm and Hamre method slightly under predicts the driving resistance.

The relative conservatism of the different design methodologies is shown in Fig. 16, which plots the maximum predicted blow counts and compressive stresses for the different design SRD models. The API method results in the lowest values while the UWA method predicts higher stresses and blow counts. Employing the UWA approach within a driveability analysis for piles driven in dense sand will yield more conservative estimates of the driving behaviour and should allow a more appropriate driving system to be selected without damaging the pile.

[FIG. 16] Stresses and Blow Counts for Different SRD Models

DISCUSSION – PILE PC3 FAILUREAfter failure of pile PC3 during driving, it was discovered that this pile had been cast from a different concrete batch than the remaining precast concrete piles. Table 3 shows a summary of the concrete casting and pile driving dates. PC4 and the rest of the piles had

been cast 156 days before driving compared with 66 days for pile PC3. Concrete cubes taken during pile casting from the two different concrete batches are compared in Fig.17. Seven-day and 28-day cube tests from the PC3 batch indicated strength values of 72 and 76 MPa (10,440 and 11,020 psi) respectively. Further cube tests from the PC4 batch indicated higher strengths varying from 90 – 96 MPa (13,050 – 13,920 psi) although these were first tested 157 days after casting (the day after driving).

[TABLE 3] Concrete Casting Times

PileCasting

Date

Driving

DateAge (days)

PC4+other 23/11/10 29/04/11 156

PC3 21/02/11 29/04/11 66

[FIG. 17] Concrete Cube Strengths from the two concrete batches

A further investigation into the concrete properties of PC3 was conducted by cutting a 0.5m (1.64 ft) vertical section from the top of pile PC3. Three horizontal drilled cores, 100mm (4 in) in diameter and 200mm (8 in) in length were taken from the cut off section and compression strength tests were conducted in a Controls Automax 5 concrete compression machine. The cores from pile PC3 indicated peak strengths of between 42 – 46 MPa (6,090 – 6,670 psi), significantly below the cast cube


strength. Bungey & Millard (1996) suggest that concrete cubes may have compression strengths between 1.25 – 1.65 times higher than equivalent cut cores (with an L/D=2), depending on the curing, which may explain this discrepancy.

Cast cylinder strengths, which are more typical in the United States, were suggested to be ≈15% lower than cut cores, due to the weaker top surface zone of cast cylinders (Bungey & Millard, 1996). Traditional US design practice recommends that driving stress not exceed 85% of the concrete cylinder strength minus any effective pre-stressing (Goble & Hussein, 2000). Using the measured core strength values this design practice would limit the allowable compressive stress during driving on pile PC3 to less than 31 MPa (4,500 psi). The measured stresses on Pile PC4 are shown in Fig. 18, where CSX is the average compressive stress at the pile head; CSB is the average compression stress on the pile base; and CS1 is the maximum compressive stress recorded from the individual strain gauges. The CS1 values are typically 15 to 20% higher than the CSX values indicating some stress eccentricity and non-uniform load transfer. This stress eccentricity was not considered within the wave equation simulations of the driving system. The applied compression stresses are seen to exceed 31MPa (4,500 psi) and approach the failure strength of the concrete in pile PC3. The other pre-cast piles (including PC4), which had measured cube strengths approximately 25% higher than pile PC3, did not fail during driving. The predicted stress range from 25 to 27 MPa (3,635 to 3,915 psi) (as shown in Fig. 8) are seen to provide a reasonable match to the average CSX stresses but are slightly lower than the maximum measured stresses due to the slightly higher SRD encountered in the field and the stress eccentricity recorded.

It is worth noting that typical construction practice often uses piles that have cured for significantly shorter durations than used for this project, with driving often taking place 7 days after casting. For relatively fresh piles, the concrete is unlikely to have reached full compressive strength and concrete crushing can be a significant risk. This case history highlights the benefit of performing a pile driveability analysis before pile installation. The driveability study performed as part of this paper, indicated

that the concrete strength would be an issue and this was verified by the failure of pile PC3. In cases where a driveability analysis indicates hard driving and high concrete stresses are to

{FIG. 18] Measured Static Resistance to Driving (SRD) on Pile PC4

[FIG. 19] Concrete Cores from pile PC3


be expected, it may be preferable to specify a higher grade of concrete, or longer curing times to allow the concrete to achieve the required strength.

CONCLUSIONSThis paper provides an interesting case study into the driveability of concrete piles in dense sands. Five precast concrete piles were driven into the dense sand deposit in Blessington, Ireland. An original driveability study was undertaken to analyse the performance of the hammer-soil-pile system. The measured response showed that the Alm and Hamre SRD model provided reasonable estimates of the installation resistance. By contrast, the UWA and IC-05 pile capacity models were relatively conservative yielding higher SRD values, which were more in line with long-term static values. The traditional API model was the least accurate and non-conservative of all models considered in the analysis (when used for SRD calculations). Friction fatigue was also measured in the field and therefore should be incorporated in any driveability approach considered.

One pile (PC3) experienced concrete cracking and failed during driving. Analysis of the pile indicated that the pile that failed was cast from a different concrete batch and cores tested after driving indicated the pile had a lower concrete strength. Using the measured core strength values, traditional design practice would limit the compressive stress during driving to less than 31 MPa (4,500 psi). The measured stresses on an adjacent pile showed the stresses to exceed this value, pointing toward the most likely cause of failure. The other precast piles, which had 25% higher cube strengths, all survived driving and reached their target penetration.

ACKNOWLEDGMENTSThe authors would like to acknowledge the financial support received from Science Foundation Ireland, Enterprise Ireland and IRCSET to undertake field testing at Blessington. Furthermore, the first author would like to thank our enterprise partner, Mainstream Renewable Power, for supporting this research initiative. Technical assistance received from Dr. Kenneth Gavin is gratefully acknowledged.

REFERENCES1. Alm, T. & Hamre, L. (1998), "Soil model

for driveability calculations", OTC 8835, Offshore Technology Conference, paper No. OTC 8835. Houston, TX, 4-7 May, 1998.

2. Alm, T. & Hamre, L. (2001), "Soil model for pile driveability predictions based on CPT interpretations", Proceedings, The XV-th International Conference. on Soil Mechanics and Geotechnical Engineering, Istanbul, Vol.3, pp. 1297-1302.

3. API 2007, "Recommended Practice for Planning, Designing and Constructing Fixed Offshore Platforms – Working Stress Design", API RP2A. Washington, D.C., American Petroleum Institute.

4. Bungey, JH. & Millard, SG. (1996), "Testing of concrete in structures, 3rd Edition", Blackie Academic & Professional, Cambridge.

5. Chow, F.C. (1997), "Investigations into the behaviour of displacement piles for offshore foundations", PhD thesis, University of London (Imperial College).

6. Dennis, N. D. & Olson, R. E. (1983), "Axial capacity of steel pipe piles in sand", Geotechnical Practice in Offshore Engineering. pp. 389–402.

7. Doherty, P., Kirwan, L., Gavin, K.; Igoe, D.; Tyrrell, S.; Ward, D. & O'Kelly, B., (2012). "Soil properties at the UCD geotechnical research site at Blessington", Bridge and Concrete Research in Ireland 2012, Dublin, Irl, 6-7 September 2012, 499-504.

8. Gavin, K, Adekunte, A & O’Kelly, B. (2009), "A field investigation of vertical footing response on sand", Proceedings of the ICE - Geotechnical Engineering, 162 (5) 2009-10, pp.257-267.

9. Gavin, K. & O'Kelly, B. (2007), "Effect of friction fatigue on pile capacity in dense sand", Journal of Geotechnical and Geoenvironmental Engineering, 133(1), pp. 63-71.

10. Goble, G. G., Fricke, K. E. & Likins, G. E. (1976), “Driving Stresses in Concrete Piles”, Prestressed Concrete Institute Journal, Vol.21(1) pp. 1-20.


11. Goble, G. G., Hussein, M.H., (2000). “Potential for HPC in Driven Pile Foundations“, Proceedings of the PCI/FHWA/FIB International Symposium on High Performance Concrete: Orlando, FL;pp. 608-615.

12. Heerema, E.P, (1980), "Predicting pile driveability: heather as an illustration of the friction fatigue theory", Ground Engineering, 13(3), pp. 15-37.

13. Heerema, E., (1981), "Dynamic point resistance in sand and in clay, for pile driveability analysis", Ground Engineering, 1 4(6), pp. 30-46.

14. Jardine R, Chow, F, Overy, R & Standing, J (2005), "ICP design methods for driven piles in sands and clays", London: Imperial College London

15. Lehane, B. M. (1992), "Experimental investigations of pile behaviour using instrumented field piles", PhD thesis, Imperial College, University of London.

16. Lehane B.M. & Jardine R.J. (1994), "Shaft capacity of driven piles in sand: a new design approach", Proceedings of Conference on the Behaviour of Offshore Structures, Boston, 1 pp. 23- 36.

17. Lehane, B.M., Schneider, J.A., and Xu, X. (2005). "The UWA-05 method for prediction of axial capacity of driven piles in sand", Proceedings, International Symposium, Frontiers Offshore Geomechanics. ISFOG, Perth, 683-689.

18. Schneider, J. A. & Harmon, I. A., (2010). "Analyzing Drivability of Open Ended Piles in Very Dense Sands", DFI Journal, IV(1), pp. 3-15.

19. Stevens, R.F., Wiltsie, E.A., & Turton, T.H. (1982), "Evaluating Pile Driveability for Hard Clay, Very Dense Sand and Rock", Proceedings, 14 Offshore technology Conference, Houston, Vol 1, pp 465-479

20. Toolan, F.E. & Fox, D.A. (1977), "Geotechnical planning of piled foundations for offshore platforms", Proceedings Institution of Civil Engineers, May 1977 62(1) pp. 22 -244


Ant Colony Optimization Method for Design of Piled-Raft Foundations (DFI 2013 Student Paper Competition Winner)Hessam Yazdani, PhD Candidate, School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA; (405) 325-5218; [email protected]

Kianoosh Hatami, Associate Professor, School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA

Elahe Khosravi, Graduate Student, School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA

ABSTRACTIn comparison to conventional piled foundations, piled-raft foundations provide a more economical solution to support high-rise buildings constructed on compressible soils. In this type of foundation, the bearing capacity of the underlying soil is taken into account in supporting the superstructure loads, and the piles are placed to control both the total and differential movements of the superstructure. Currently, there are no universally accepted methods to design piled-raft foundations including the selection of the piles locations and dimensions. Most piled-raft foundation designs are based on empirical methods and the experience of designers. However, piled-raft foundations are massive and expensive. Therefore, developing methodologies for their optimal design could significantly help minimize their otherwise high construction costs and would make them more feasible and common practice. This paper examines the capability of the ant colony optimization (ACO) algorithm to optimize piled-raft foundations. The soil-pile interactions are taken into account by modeling the side and tip capacities of the piles using the nonlinear p-y, t-z, and Q-z springs in the OpenSees platform. The soil-raft interaction is taken into consideration using the Winkler springs beneath the raft. The objective of the optimization problem is to minimize the volume of the foundation by taking the number, configuration, and penetration depth of the piles, as well as the thickness of the raft, as design variables. The side and tip forces of the piles, the pressure applied on the underlying soil, and the total and differential movements of the foundation under the serviceability limit state are the constraints adopted for the optimization problem. Results indicate that the ACO algorithm is a suitable method for optimal design of piled-raft foundations. Findings of the study also indicate that including soil nonlinearity in the analysis (as opposed to a linear elastic soil model) can lead to a more economical design for these foundation systems.

INTRODUCTIONPiled foundations are best suited for sites where a shallow foundation may incur excessive movements (settlements) and may not provide adequate bearing capacity to carry structural loads. Current design guidelines primarily require that the piles should carry the entire structural load of a piled foundation and transfer it to deeper and more competent layers (de Sanctis and Mandolini, 2006; Sales et al., 2010). However, field monitoring of several piled foundations has revealed that the raft could significantly increase the overall bearing capacity of a piled-raft foundation system (Kakurai, 2003). Consequently, designing a piled foundation merely as a pile group to meet the

required factors of safety within the framework of the allowable stress design could often lead to overly conservative, and hence costly solutions (Poulos and Davids, 2005).

Piled-raft foundations are an economical alternative to the conventional piled foundations when competent soil strata exist immediately beneath the raft. In contrast to piled foundations, structural loads supported by piled-raft foundations are mostly carried by the raft (Burland et al., 1977). The piles, known as the settlement-reducing piles, are therefore located strategically to enhance the bearing capacity of the raft besides controlling or minimizing the total and differential movements that may cause distortion and


cracking of the superstructure (Randolph, 1994; Momeni et al., 2012; Yazdani et al., 2013). Such a design approach can significantly reduce the cost of the foundation without jeopardizing the safety and performance of the superstructure (Sales et al., 2010).

Optimal design of piled-raft foundations could significantly help minimize their construction costs. The optimal design of piled-raft foundations includes the selection of type, number, configuration and penetration depth of the piles in addition to the thickness of the raft in conformance with the existing design and construction standards (Gates and Scarpa, 1984; Prakoso and Kulhawy, 2001). Several factors influence the design of piled-raft foundations including the structural loads, the properties of foundation soil and the negative skin friction on piles associated with long-term settlements of a foundation underlain by soft ground (Poulos and Davis, 1980).

Optimization of piled and piled-raft foundations has been the subject of a few past studies. Chow and Thevendran (1987) carried out an optimization analysis on pile groups and concluded that the central and peripheral piles of a pile group need to be designed with different degrees of rigidity in order to minimize the differential movements of a group under a flexible pile cap, or the load differentials among piles under a rigid cap. Valliappan et al. (1999) used a theoretical optimization approach together with the finite element method to analyze and optimize piled-raft foundations. They investigated two cases of uniform and non-uniform pile lengths and concluded that using non-uniform pile lengths increases the contribution of the raft in supporting the structural load. Kim et al. (2001) used recursive quadratic programming to find an optimum configuration for the piles which would minimize differential movements of piled-raft foundations. However, their approach did not fully account for the interactions among the elements of the foundations (i.e. the raft, piles and the underlying soil). Wang et al. (2002) introduced an analytical method for the optimal design of piled foundations in nonlinear soils. However, their procedure included only a limited number of design parameters.

Metaheuristic algorithms are approximate but efficient algorithms that can be used to explore a search space for optimal solutions by

combining basic heuristic methods in higher level frameworks (Blum et al., 2011). This class of algorithms includes swarm intelligence (imitating the processes of decentralized, self-organized systems such as ant colony optimization - ACO and particle swarm optimization - PSO), evolutionary computation approaches (which are non-gradient, population-based algorithms such as genetic algorithms - GAs and evolutionary programming - EP), simulated annealing (SA), and tabu search (TS), among others. The efficiency and robustness of an optimization approach depend on the problem in hand, and therefore, no globally accepted approach has been proven to best fit all engineering optimization problems (Rajeev and Krishnamoorthy, 1992).

Geotechnical systems in general and foundations in particular have not so far benefitted significantly from the existing metaheuristic algorithms for optimal design (Cheng et al., 2007). Chan et al. (2009) proposed a modified genetic algorithm, which showed promise in optimizing a series of pile groups. Khajehzadeh et al. (2011) proposed a modified particle swarm optimization technique for optimal design of spread footings and retaining walls. In this paper, an ACO algorithm is developed and used to optimize piled-raft foundations. The ACO approach was selected because it retains the memory of the entire colony from all generations to approach the optimal solution, as opposed to GA-based methods in which the search information is contained only in the current generation of a GA (Camp and Bichon, 2004).

ANT COLONY OPTIMIZATION TECHNIQUE Inspired by the foraging behavior of blind animals such as ants (see Deneubourg et al., 1990), Dorigo et al. (1996) proposed a population-based metaheuristic approach, called ant colony optimization (ACO), in which a colony of artificial ants is used to construct solutions guided by pheromone intensities and heuristic information. Pheromone is a substance ants deposit on the ground when carrying food from sources to the nest. Ants use this medium to indirectly communicate information regarding the shortest paths between feeding sources and the nest and use the intensity of pheromone to evaluate the potential of marked


paths. In other words, the probability of a path to be chosen by an isolated ant moving essentially at random, searching for food, is proportional to the pheromone intensity of the path. The greater the pheromone intensity marking the path (i.e., the greater the number of ants selecting the path), the stronger the stimulus for an ant to follow that path, thus reinforcing the trail with its own pheromone and increasing the probability that subsequent ants will follow this path (Blum et al., 2011).

In the ACO technique, several terms are adopted from the graph theory. Graphs are mathematical structures used to model pairwise relations between objects. The objects in a graph are represented by vertices and the pairwise relations are established using edges connecting the vertices. In ACO, the optimization problem is projected on a graph, where the shortest path determines the optimal solution of the problem. Consider a combinatorial optimization problem projected to a multigraph (a graph which is permitted to have multiple edges, also known as pseudograph), defined over a set x = {x

i | i = 1,

…, n} of design variables, where n is the number of design variables (vertices), and a set E = { e

ij

| i = 1, …, n; j = 1, …, Ji} of options (potential

values) for variables, where Ji is the number of

options for the ith design variable (number of edges departing from the ith vertex when moving forward). A subset ψ of edges represents a solution of the problem. Since each edge has the possibility of being selected or rejected, there are 2J feasible solutions for each problem,

where1

n

ii

J J=

= Let C = { cij | i = 1, …, n; j = 1, …,

Ji} is the set of costs (weights) corresponding

to the potential values for variables. Given this, the major steps in an ACO algorithm, shown in Fig. 1, are as follows (Maniezzo et al., 2004; Uğur and Aydin, 2009; Moeini and Afshar, 2012):

Initialization

The size of the colony (number of ants), m, is chosen and a proper initial intensity of pheromone, τ

0, is assigned to all options, e

ij.

Increasing the size of the colony can improve the quality of the final results achieved by ACO. However, colony size should be chosen such that a good tradeoff between solution quality and computational effort is guaranteed (Viana et al., 2008). Initial intensity of pheromone is usually assumed to be:

01

nnnLτ = [1]

where Lnn

is the length of the tour between n cities created by the nearest neighbor heuristic (i.e., the minimum value of the optimization problem which is obtained by assigning the smallest options to n design variables - Camp and Bichon, 2004). It does not matter whether this assignment satisfies the design constraints or not (Rajasekaran and Chitra, 2009; Aydoğdu and Saka, 2012).

Construction:

A set of concurrent and asynchronous agents (a colony of ants) travel between vertices and construct solutions to the problem. Starting from an arbitrary or pre-selected design variable, i, ant k applies a stochastic local decision policy to select one of the J

i available

options for the design variable. Known as random proportional transition rule (also known as pseudorandom proportional rule - Maniezzo et al., 2004; Dorigo et al., 2006), this decision policy is governed by two parameters, namely visibility (also known as attractiveness of the move) and trail level (also known as pheromone intensity). Visibility indicates a priori desirability of a move and is an artificial sight for selecting the shortest path among the options without experience or observation. Trail level, in contrast, can be interpreted as an adaptive artificial memory, and indicates a posteriori, the desirability of the move and reflects the experience acquired by the ants at this stage. The transition rule used by ant k to select one of the options is:

[FIG. 1] ACO Algorithm


1

( )( , )

( )i

ij ijij J

ij ijj

tp k t

t

α β

α β

τ η

τ η=

= [2]

where pij(k,t) is the probability that ant k selects

option j of the ith decision point, eij, at iteration

t; τij is the trail level on option e

ij at iteration t;

ijη is the visibility of the ant representing the local cost of choosing option j at the ith decision point ( ijη =1/c

ij ); and α and β are two

parameters regulating the relative importance of trail level versus visibility. An iteration comes to an end when m moves are carried out by m ants, each making one move, in the time interval (t, t + 1).

After each iteration, a local update rule is applied to reduce the trail level relative to the options most recently chosen for design variables (the paths chosen by ants) in order to prevent premature convergence to a sub-optimal solution. When an ant travels from city i to city j, an optimization rule is applied to adjust the intensity of trail on the path connecting these two cities by:

( ) 0( ) 1 ( )ij ijt tτ τ τ= − + [3]

where (0 ≤ ≤ 1) is the coefficient of decay (Uğur and Aydin, 2009), representing the persistence of the trail. Ants incrementally construct m solutions for the problem by choosing paths to travel between decision points, visiting each point once, until all points have been visited and they arrive back at their starting points. When they return to their point of origin, the ants have completed a tour, and each has constructed its own trial solution, ψk. A cycle is complete when m ants complete their tours and construct m trial solutions.

Evaluation:

The objective function is evaluated for the trial solutions and their fitness values (global scores) are calculated.

Modification:

Once m ants have completed a tour and the objective function has been evaluated for the solutions constructed, a global update rule is applied to the options selected for design variables. Typically, the global update rule has a dual function in communication among the colony agents. First, it implements a

positive feedback from the ants constructing satisfactory solutions by reinforcing the trail level on the paths they have selected. Secondly, a negative feedback mechanism reduces the trail level on the selected paths to promote exploration of the search space. The rule corresponds to the evaporation of the substance in nature and helps prevent early stagnation, an undesirable situation in which all ants repeatedly construct the same solutions making any further exploration in the search space impossible. The amount of this trail reduction is kept low to guarantee overall solution convergence.

ACO algorithms are different from each other with respect to the techniques adopted to update the pheromone level and to implement the random proportional transition rule. Interested readers are referred to a summary provided by Uğur and Aydin (2009). A variation of the ACO approach, called the ranked-based ant system (RBAS - Bullnheimer et al., 1997) is used in this study. In the RBAS, only λ top ranked ants, having the best designs, are selected in the global update scheme. As positive feedback, the RBAS increases the trail level by ijτ +Δ , corresponding to the solution found by the elite ant, ψ

+, as:

1( )ij f

τψ

++Δ = [4]

where ( )f ψ + is the value of the objective function associated with the solution ψ

+. The

change in the trail level of the path i-j, if chosen by the ant ranked μ (1 ≤ μ ≤ λ), is given by:

( )ijR

fμ

μτψ

Δ = [5]

where ( )f μψ is the fitness value of the solution made by the ant ranked μ, and R is a quantity regulating the contribution of the top ranked ants called pheromone reward factor (Moeini and Afshar, 2012). Camp and Bichon (2004) used R = λ - μ in their study, meaning the contribution of a top ranked solution is linearly proportional to its ranking. The path i-j may be selected by more than one ant. Therefore, the total increase in the trail level of the path is given by:

1

1

rij ij

λμ

μτ τ

−

=

Δ = Δ [6]


Therefore, the updated trail levels at the end of a cycle (at time t + n) are a function of the trail levels at the beginning of the cycle, the tour constructed by the elitist ant, and the tours made by the top ranked ants, and they are calculated as:

( ) (1 ) ( ) rij ij ij ijt n tτ ρ τ λ τ τ++ = − + Δ + Δ [7]

where ρ is an adjustable parameter in the range 0 ≤ ρ ≤ 1 so that (1 – ρ) represents the evaporation rate (Camp and Bichon, 2004).

The trail level update rule is followed by a feasibility analysis of the solutions constructed. An analysis is carried out for each solution. If any constraints are violated, a penalty is applied to the objective function corresponding to the solution. The value of the penalty is proportional to the extent of the violation of constraints (Camp et al., 1998). Many constraint-handling techniques for evolutionary algorithms have been proposed in the literature (e.g., static penalties, dynamic penalties, adaptive penalties). Interested readers are referred to a comprehensive survey by Coello (2002). In this study, objective functions that violate the imposed constraints are penalized using a static penalty approach as follows:

[ ]( ) ( ) 1k kpf f εψ ψ= + Φ [8]

where ( )kpf ψ is the penalized objective function of the kth ant, Φ is the total penalty and the summation of the force and deflection penalties (described in “Formulation of the Design Problem”), and ε is a positive penalty exponent, which remains constant in the static penalty approach and is adjusted proportionally to the extent of the violation of the constraints in adaptive penalties (Camp and Bichon, 2004; Rajasekaran and Chitra, 2009; Aydoğdu and Saka, 2012).

Termination:

The ant decision mechanism, steps 2-4, continues until either a maximum number of cycles has been completed or all ants construct the same solutions.

Production:

The outputs of the optimization process are obtained.

ANALYSIS OF PILED-RAFT FOUNDATIONSIn this study, the OpenSees (Open Source for Earthquake Engineering Simulation) finite element platform was used for three-dimensional analysis of piled-raft foundations. The piles were assumed to be drilled piles, where the pile capacity is provided by a combination of soil-pile friction and end bearing resistance at the pile tip (Brown et al., 2010). In practice, the geological and local soil conditions govern the depth of the piles. However, the underlying soil was assumed to be a homogenous, dry medium dense sand for simplicity. The analysis was carried out using two different assumptions of linear and nonlinear material behavior. Piles were modeled using nonlinear beam-column elements. The soil-pile interactions were modeled using the nonlinear p-y, t-z, and Q-z springs (Fig. 2). Vertical nonlinear springs (t-z) were used to describe the relationship between mobilized soil-pile shear transfer and local pile deflection at any depth. The relationship between the lateral soil resistance and pile deflection was modeled using p-y springs (Boulanger et al., 1999; API, 2000). The load-displacement response of the tip resistance of the piles was modeled using vertical Q-z springs. The beam on nonlinear Winkler foundation (BNWF) framework (Raychowdhury and Hutchinson, 2010) was adopted to model the raft resting on the soil. BNWF includes a fine mesh of independently distributed, nonlinear inelastic springs placed vertically beneath the raft to capture its rocking and settlement movements. The mechanical response of the springs was assumed as linear elastic in the linear analysis.

[FIG. 2] Soil-Pile-Raft Interaction Model


Table 1 summarizes the soil and other input parameters used in the analysis.

[TABLE 1] Soil Properties and other Input Parameters

Model Model Parameters Value

Initial unit weight (kN/m3)

Peak friction angle, ϕp (°)

Void ratio, e

18.0

35

0.55

p-y

(API, 2000)

Initial modulus of subgrade reaction, k, (MN/m3)

43.0

t-z

(API, 2000)

The friction angle of soil-pile interface, δ (°)

(z/D)max

(1)

20

0.01

Q-z

(pile tip)

(API, 2000)

(z/D)max

(2) 0.1

Q-z (raft)

(Raychowdhury and Hutchinson, 2010)

Elastic modulus of soil,

Es (MPa)

Stiffness ratio, Rk

35.0

1

1 z: Local pile deflection, D: Pile diameter2 z: Axial tip deflection, D: Pile diameter

FORMULATION OF THE DESIGN PROBLEM

Objective Function and Design Variables

In this study, an ACO approach was used to minimize the material cost of a piled-raft foundation and meet design requirements. The objective function is formulated as:

Minimize 2

1( )

4

pn

p pp

V d L BLtπ=

= +x [9]

where x is a vector containing n design variables; d

p and L

p are the diameter and length

of the pth pile (np piles overall); and B, L and t

are the breadth, length, and thickness of the raft, respectively. For ease of construction as well as to reduce the pile-pile interaction, the piles were assumed to be located on the nodes of a regular latticework with a constant spacing of three times the average piles diameter (Brown et al., 2010). The total number of piles, their configuration and penetration depth and the thickness of the raft were taken as design variables. Details of the steel reinforcement

design were not included in the objective function because they can be determined using pertinent design guidelines after the design variables are determined numerically using the optimization algorithm.

Design Constraints

The structural and geotechnical capacities of the piles, and the total and differential movements of the foundation under the serviceability limit state were considered as the constraints for the optimization problem in this study. Depending on their type, the ultimate structural capacities of piles in compression and tension, c

s uP − and ts uP − , are governed by the

compressive strength of concrete, yield strength of steel, and the contributions of concrete, steel reinforcing bars, and steel casing in the cross-sectional area of the piles. The ultimate capacity of piles in compression, c

g uP − , is the sum of the tip and side resistances mobilized at failure. In contrast, the ultimate uplift capacity of piles, t

g uP − , is governed by the self-weight of the piles and their side resistances, neglecting the weight of the wedge of the soil around the piles accompanying them in tension (Brown et al., 2010). The allowable values of the piles vertical movement, v aδ − , and pile-head lateral displacement, h aδ − , were set to 60 mm and 25 mm (2.4 in and 1.0 in), respectively (Budhu, 2007). The allowable differential movement of the piles is expressed in terms of angular distortion of the raft (defined as the ratio of differential movement between two adjacent columns/piles to the distance between them), β

a, which was set to 1/500 (Chan et al., 2009).

The piles are allowed to operate at 100% of their ultimate load capacity (Randolph, 1994). However, a margin of safety was applied on the ultimate bearing capacity of the raft, σ

u,

using a safety factor of 3 (σa = σ

u/3, where σ

a

is the allowable bearing capacity of the raft). Finally, the constraints of the problem and their associated penalties as well as the constraints-handling technique used in this study are as given below:

• The structural force penalty for pile p, pP s−Φ , is calculated as (where compressive

forces are taken to be positive):

,

,or

0

c tt c p i s u

i s u i s u P s c ts u

t c ps u i s u P s

P PP P P PP

P P P

−− − −

−

− − −

−< > Φ =

≤ ≤ Φ =

[10]


• The geotechnical force penalty for each pile p,

pP g−Φ , is calculated as:

,

,or

0

c ti g ut c p

i g u i g u P g c tg u

t c pg u i g u P g

P PP P P P

P

P P P

−− − −

−

− − −

−< > Φ =

≤ ≤ Φ =

[11]

Hence, the total force penalty for the solution generated by ant k is:

1

pnk p pP P s P g

p− −

=

Φ = Φ + Φ [12]

• For pile p, the vertical movement penalty, v

pδΦ , and the pile-head lateral displacement

penalty, h

pδΦ , are stated as:

0

v

v

pp p v v av v a

v a

p pv v a

δ

δ

δ δδ δδ

δ δ

−−

−

−

−> Φ =

≤ Φ = [13]

0

h

h

pp p h h ah h a

h a

p ph h a

δ

δ

δ δδ δδ

δ δ

−−

−

−

−> Φ =

≤ Φ =

[14]

• The angular distortion penalty between piles p and q,

,p qβΦ , is:

,,,

,, ,

,

0;

p q ap qp q a

a

p qp q v v

p q a p qp qL

β

β

β ββ β

β

δ δβ β β

−> Φ =

−≤ Φ = =

[15]

where Lp,q is the distance between piles p and q.

Therefore, the total deflection penalty for the solution generated by ant k is:

,

1 1 1

p p p

v h

n n nk p p p q

p p qq p

δ δ δ β= = =

≠

Φ = Φ + Φ + Φ [16]

• For the solution generated by ant k, if the bearing pressure of the raft, σ, exceeds its allowable bearing capacity, σ

max, the follow-

ing penalty is applied:

maxmax

max

max 0

k

k

σ

σ

σ σσ σσ

σ σ

−> Φ =

≤ Φ = [17]

Therefore, the total penalty for ant k, kΦ , in Equation 8 is given by:

k k k kP δ σΦ = Φ + Φ + Φ [18]

A static constraints-handling technique was considered in this study in which a constant positive penalty exponent of ε = 2 was used in Equation 8.

RESULTS AND DISCUSSIONSThe capability of the ACO algorithm to optimize the piled-raft foundations is demonstrated using the following example. The optimization task was carried out using a mutual communication between MATLAB® and OpenSees. The algorithm was executed in MATLAB environment and the foundations generated by ants were analyzed in OpenSees platform. OpenSees results were imported in MATLAB to evaluate the objective function, fitness value, and penalties corresponding to each solution. This information was used to direct the next ant colony towards the optimal solution. The process continued until either a maximum number of cycles was met or all ants constructed the same solutions.

Example Problem

Consider a high-rise building with the structural loads shown in Fig. 3. A 20 m × 20 m (66 ft × 66 ft) reinforced concrete raft has been proposed to safely transfer the building loads to the underlying sandy soil, with the properties as given in Table 1.

Assuming that the groundwater level is well below the ground surface, and the embedment depth of the raft is 1 m, the ultimate net bearing capacity of the raft using Meyerhof’s equation is calculated as (Buhdu, 2007):

[FIG. 3] Structural Loads on Raft


( )1 0.5 'u f q q qD N s d B N s dγ γ γσ γ γ= − + [19]

where Nq and Nγ are bearing capacity factors

that are functions of the peak friction angle, Φ

p; s

q and sγ are shape factors; d

q and dγ are

embedment depth factors; and B' is the equivalent footing breadth. After the ultimate net bearing capacity is determined, the allowable bearing capacity can be calculated as:

maxu

fDFSσσ γ= + [20]

Therefore,

18 1 32.2 1.7 10.5 18 20 37.1 0.6 1 4,991kPa

16 20,000 800kPa20 204991800 18 1 6.4

u

FSFS

σ

σ

= × × × ×+ × × × × × =

×= =×

= + × =

The vertical elastic settlement of the raft is given by:

2(1 )

0.62ln( ) 1.12

v s

s

B IELIB

σ νδ −=

= + [21]

where σ is the surface stress and Is is a

settlement influence factor. The settlement is calculated as:

2

0.62ln(1) 1.12 1.12

800 20 (1 0.3 ) 1.12 485mm30,000

s

v

I

δ

= + =

× × −= × =

Therefore, the raft provides a bearing capacity adequate to carry the working loads with a large safety factor. However, the raft will experience excessive settlements from the serviceability point of view. Therefore, settlement-reducing piles are required to control the building settlements, and to reduce the bending moments in the raft.

The ACO algorithm is used to determine the optimal design of the foundation. The values used for the algorithm parameters are summarized in Table 2. A set of candidate values for the design variables are given in Table 3.

[TABLE 2] Input Parameters for the ACO Algorithm

Parameter Value

Size of the colony, m

Evaporation rate, ρCoefficient of decay, φαβ λ

100

0.1

0.1

5

0.2

10

[TABLE 3] Potential Values for Design Variables

Parameter Value

Pile diameter, dp (m)

Pile length, Lp (m)

Raft thickness, t (m)

Grid spacing (m)

0.8, 1.0, 1.2

20, 25, 30

1.0, 1.2, 1.4

3

Fig. 4 shows the convergence history of the foundation design represented by the raft volume. The optimization process is initiated with a design generated by randomly chosen values for the design variables and evolves to an optimal design. Results in Fig. 4 indicate that the ACO algorithm yields an optimum solution in approximately 25 cycles using linear analysis and 31 cycles using nonlinear analysis. However, including the soil nonlinearity in the analysis results in a 5% more economical design based on the volume of the foundation raft. The 5% reduction in the volume of the raft could prove significant, or even critical, in cases where physical obstacles are present for the construction of the foundation.

[FIG. 4] Foundation Design’s Convergence History


The influence of soil nonlinearity on the optimum configuration of piles is depicted in Fig. 5. It can be observed that assuming a linear elastic behavior for the soil increases the load concentration of the peripheral piles and results in an overdesigned foundation system. In contrast, taking soil nonlinearity in the analysis reduces the share of the load carried by the peripheral piles and results in a redistribution of loads among the load carrying elements. As a result, it helps achieve a more favorable design in which all piles carry comparable loads as the load applied on the foundation increases. Results shown in Fig. 5 are in conformity with those reported in the literature (e.g., Basile, 2003).

CONCLUSIONSThe capability of the ACO algorithm for optimizing piled-raft foundations was investigated. Soil-pile-raft interactions were included in the analysis using nonlinear p-y, t-z and Q-z Winkler springs in the OpenSees platform. The analysis of a piled-raft foundation system indicated that including soil nonlinearity in the analysis results in a more uniform distribution of predicted loads among the piles and hence a more economical design. Results shown in this paper illustrated that the ACO algorithm developed in this study is capable of finding an optimal solution for piled-raft foundation systems. Further work is underway to compare the performance of the ACO algorithm with other metaheuristic algorithms in order to identify faster and more effective methods for optimal design of foundations.

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INTRODUCTION AND OBJECTIVE At numerous sites throughout Florida, the Florida Department of Transportation (FDOT) contractors and engineers have experienced serious pile installation problems while driving high displacement 18, 24 and 30 inch (457, 610 and 762 mm) square prestressed concrete piles (PCPs) using diesel and air hammers. During these installations, excessive rebound per hammer blow occurred, followed by either small or no permanent-set. Excessive or high pile rebound (HPR) may stop the pile driving and result in limited pile capacity. In some cases, rebound leads to pile damage, delaying the construction project, and foundation redesign (Hussein et al., 2006; Cosentino et al., 2010).

Pile rebound is defined as the upward elastic pile displacement that occurs during a hammer blow. FDOT considers that excessive rebound takes place when it is greater than 0.25 inch (6 mm) per hammer blow (FDOT Road and Bridge Construction Specification 455, 2010) or when the blow count exceeds 20 blows per inch (25 mm). HPR is the condition where the set (i.e., plastic soil deformation) represents a small portion of the maximum displacement and the rebound (i.e., recovered elastic deformation) constitutes the majority of the displacement.

The design-phase geotechnical investigations at HPR sites did not produce any unusual soil properties. The research objective was to develop geotechnical-testing protocol that would identify HPR.

Piezocone Penetration Testing in Florida High Pile Rebound Soils Fauzi Jarushi, Ph.D., Department of Civil Engineering, Tripoli University, Tripoli, Libya, [email protected]

Paul J. Cosentino, Ph.D., P.E., Professor of Civil Engineering, Florida Institute of Technology, Melbourne, FL, USA, cosentin@fi t.edu

Edward H. Kalajian, Ph.D., P.E., Professor of Civil Engineering and Associate Dean, College of Engineering, Florida Institute of Technology, Melbourne, FL, USA, kalajian@fi t.edu

ABSTRACTContractors and engineers have experienced pile installation problems while driving high displacement piles with single-acting diesel hammers at Florida Department of Transportation (FDOT) construction sites located throughout the Central and Panhandle regions of Florida. At certain depths during pile driving in saturated soils, rebound exceeding 1 inch (25 mm) was experienced, followed by a small permanent-set during each hammer blow.

High pile rebound (HPR) may cause false refusal to occur, stopping the pile driving and resulting in a limited pile capacity. In some cases, rebound leads to pile damage, delaying of the construction project and foundation redesign. In this paper, the response of HPR is investigated using cone penetrometer testing (CPT) and a pile driving analyzer (PDA). PDA data, which yielded the amount and the depth where rebound occurred, produced the pile movement per blow, Nineteen Piezocone soundings were performed at seven FDOT sites in Florida, of which five sites experienced a rebound greater than 0.6 inches (15 mm), one site yielded rebound of 0.35 inches (9 mm), and one site’s rebound was less than the FDOT limit of 0.25 inches (6 mm).

In order to improve the knowledge about the soil types producing HPR, a traditional geotechnical investigation on grain-size distribution and soil plasticity allowing for classification using Unified Soil Classification System (USCS) was conducted. Piezocone data were interpreted using the CPT and CPTu soil behavior type (SBT) charts proposed by Schmertmann (1978), Robertson (1990) (i.e., Q-F

r, Q-R

f, and

Q-Bq), Eslami and Fellenius (2004), and Schneider et al. (2008). Comparison with classification data from

laboratory tests was in excellent agreement with the CPT soil type, indicating that the CPT is a useful tool in evaluation of HPR or “large quake” soils. Correlations between rebound and CPTu data were developed showing that rebound is a direct function of both friction ratio R

f and pore pressure u

2.


BACKGROUNDThe background will show a series of case studies describing HPR followed by studies about the soil and excess pore pressures associated with HPR. The common variable associated with HPR is excess pore pressure; while the common variable associated with soil type is the percent fines, particularly silt.

Murrell et al. (2008) presented a case history of HPR, which occurred during the construction of a new ferry terminal in coastal North Carolina. Twenty inch (508 mm) square, 70 ft (21.3 m) long PCPs, were designed to support an over water structure. The authors describe the high rebound using the term “bounce” which was observed at an overburden depth of 53 ft (16.2 m) (elevation -43ft or -13 m) as the piles penetrated into saturated, firm to stiff, fine-grained marine soils along the southeastern coast of the United States.

During the geotechnical investigation at this site, excess pore water pressures greater than 20 tsf (1915 kPa) were measured during CPTu testing, at the u

2 position behind the cone tip,

at the depth where bouncing occurred. When the blow counts during pile driving were at 303 blows per foot (bpf) (248 blows per 250 mm) , the pile displacement became zero. The driving process was then stopped for two hours and restrike was conducted. However, an additional 2.5 ft (0.76 m) of pile length was driven with blow counts of 73 bpf (60 blows per 250 mm), 112 bpf (92 blows per 250 mm), and finally 87 blows per 6 inches (150 mm). Again the driving was halted when large rebound resulted in near zero set. In order to achieve the pile capacity and overcome pile rebound, the pile was driven after four days using a hammer with a larger ram and a shorter stroke.

Hussein et al., (2006) presented a case study related to HPR during driving of PCP’s for the State Road 528 Bridge over the Indian River in Florida. A group of 30-inch (762-mm) square, hollow core PCP’s with a length of 115 ft (35 m) were used to support the bridge. The piles rebounded when they penetrated into hard soils consisting of saturated medium dense sand with silt (SP-SM) to fine silty sand (SM) to clayey sands and sandy clays (SC). The authors believe that the incompressible water in the soil near and below the pile tip produced excess pore pressure during the driving process which created an upward force on the pile, thereby

causing rebound. However, no analytical proof of this conclusion is available.

Likins (1983) analyzed three sites with HPR between 0.4 and 1 in (10 and 25 mm). He determined that the only common geotechnical parameter observed at each site was the fully saturated soils. Preliminary analysis using the basic wave equation was conducted for each site. The author then modified the results to match field data acquired by CAPWAP (Case Pile Wave Analysis Program). Likins proposed that the only reasonable cause of HPR was the buildup of excess pore pressure beneath the pile tip. It was also clear through testing, that pile capacities decreased when high quake/rebound occurred. Findings from the work indicated that high quake lowers the pile capacity by a factor of 3. Field observations often led to a false interpretation that the hammer is not large enough for the pile. In these cases where the hammer size is increased, the pile could be damaged. Likins (1983) concludes that alternative pile designs, such as hollow piles, should be considered as an effective way to avoid high soil quake.

Jarushi et al., (2013) studied the effect of fines content (material passing # 200 sieve) and uncorrected standard penetration test (SPT) blow count (N

SPT) on HPR at a group of Central

Florida sites. The study showed that permanent-set and rebound were a direct function of N

SPT

and fines content of the soil at the pile tip. The authors reported that when N

SPT was less than

15 bpf (12 blows per 250 mm) and the fines content was less than 25 percent, the rebound was less than 0.25 inches (6 mm), yielding an acceptable permanent-set of up to 3 in (76 mm). For N

SPT values between 15 and 40 bpf (12

and 33 blows per 250 mm) and a fines content of 25 to 40 percent, the pile rebound varied between 0.25 and 0.6 in (6 and 15 mm), yielding acceptable permanent-set values. For cases where the N

SPT exceeded 40 bpf (33 blows

per 250 mm) with a fines content greater than 40 percent, pile rebound was greater than 0.6 in (15 mm) and was accompanied by an unacceptable permanent-set.

Baziar et al., (2011) studied pore pressure generation during the cyclic loading of silty sands. The results showed that the magnitude of the excess pore water pressures during loading of silty sand with 15% to 30% silt was similar to the pore pressure behavior observed


in clean sand. However, as the silt content increased past 30%, the buildup of excess pore pressure was much faster.

Bingjian (2011) studied excess pore pressure generated during pile driving. The pore pressure under the tip of the pile was equivalent to 1.25 of the effective stress, and the soil disturbance was obvious. The excess pore pressure extended a distance of 5 to 6 pile diameters around the pile.

Moayed (2006) reported that as the silt content exceeded 30%, the time for 50% of the excess pore water pressure dissipation increased. Results from a laboratory study on pore pressure generation by Erten et al., (1995), showed that pore pressures increased when the silt content exceeded 30% at a constant void ratio.

Studies by Jefferies et al., (2006) of California’s 1918 Calaveras Dam failure showed that as the fines content of the granular soils approached 30%, the fines appeared to fill the void space between the sand particles. The soils then acted similar to a fine-grained material with the coarse grains being surrounded by the silt (Baziar et al., 2006).

TESTING PROGRAM

Dynamic Testing

During FDOT construction at seven sites (see Fig. 1), pile driving was monitored with Pile Driving Analyzer (PDA) accelerometer and strain gage equipment. Only site number 5 was outside of the Central Florida region. Ramsey Branch Bridge is located in Florida’s Panhandle. These sites were accessible and allowed the research team to further evaluate the soils using field-testing equipment.

HPR was categorized using two levels: Unacceptable rebound with minimal or near zero set per blow, and acceptable rebound with sets large enough that continual pile penetration occurred (Fig. 1). Five of the seven sites had unacceptable HPR (i.e., > 0.25 in (6 mm) with minimal set) while two of the seven produced HPR but with acceptable set (i.e., > 0.25 in (6 mm) with enough set to allow driving until completion). As part of this research cone penetration testing either with (CPTu) or without (CPT) pore pressure readings was completed.

Figure 2 shows typical HPR PDA data from one pile blow at a FDOT site. The plot, with

displacement in inches on the vertical axis and time in milliseconds on the horizontal axis, shows a maximum displacement (DMX) of 1 in (25 mm), an inspector permanent-set (iSet) of 0.11 in (2.8 mm) and a digital set (DFN or dSet) of 0.27 in (7 mm). The dSet was not presented here because it is currently industry practice to use iSet values. The dSet is recorded in 200 milliseconds, while the iSet is based on the total time between hammer blows. Jarushi (2013) used both dSet and iSet data and concluded that the iSet produced more reliable correlations. The number of hammer blows per foot is used to produce an average inspector set per blow. The maximum displacement and iSet were subtracted to determine the rebound per hammer blow (i.e., DMX-iSet =Rebound), yielding a rebound of 0.89 in (23 mm).

CPTu Testing

As shown in Fig. 1, nineteen CPT and CPTu soundings were performed at the seven FDOT sites. CPTu testing pore water measurements were performed by hydraulically advancing the cone penetrometer while signals were digitally recorded using the Hogentogler® standard recording system. The CPTu soundings were conducted using 10-cm2 (1.55 sq in) piezocones; with type 2 porous filter elements located at shoulder (i.e., u

2). CPTu testing followed ASTM

D 5778 "Electronic Friction Cone and Piezocone Penetration Testing." During testing, data was collected digitally producing the cone resistance q

c, sleeve friction f

s, inclination, and pore water

pressure every 2 in (51 mm) of penetration. The rod insertion speed was 0.75 in/sec (19 mm/sec). Tests were conducted until refusal of the CPTu or until the desired depth was met.

HIGH PILE REBOUND SITESThe seven FDOT sites, summarized in both Fig. 1 and Table 1, are discussed in this paper. Six sites were along I-4 near Orlando, and one site was located in Florida’s panhandle region. The results from an additional HPR site located in North Carolina as presented by Murrell et al., (2008), were also analyzed and compared to data from the FDOT sites.

A summary of pile driving information obtained from the case histories is continued in Table 1. Although not shown in the table, all HPR piles were set into predrilled holes about 25 ft (7.6 m) deep. In all but one instance the piles were square PCP’s (SPCP’s), however, due to


construction problems at FDOT’s Anderson Street Overpass H-Piles were used. This table includes site description, pile description, pile spacing, hammer characteristics, driving blow counts, rebound and elevations.

The common characteristics among the HPR sites are as follows:

• Piles were concrete displacement piles ranging from 18 to 24 in (457 to 610 mm) in diameter.

• Both the PDA test piles and production piles were longer than 70 ft (21.3 m).

• Pile spacings were 6 to 11 ft (1.8 to 3.4 m) or 2.5 to 5.5 diameters.

• Piles were set into predrilled holes.

• The majority of pile driving hammers were single-acting diesel hammers.

• Rebound occurred at depths from 50 to 80 ft (15 to 24 m).

• Average pile driving blow counts in the rebound layers were greater than 105 bpf (86 blows per 250 mm).

Data analysis of the hammer type, pile size, and spacings did not produce any clear correlations to HPR Jarushi (2013).

OVERVIEW OF CPT SOIL PROFILING LITERATUREBegemann (1965) developed the first CPT soil-profiling chart based on q

c and f

s. Soils with

FDOT Site Descriptions

Rebound Level

TypeNumber of Soundings

1Anderson Street Overpass

UnacceptableCPT

u3

CPT 2

2 SR 50 over SR 436 Unacceptable CPTu

2

3 I-4/US192 Unacceptable CPTu

3

4 I-4 / Osceola Parkway Unacceptable CPTu

1

5 Ramsey Branch Bridge Unacceptable CPTu

2

6I-4/ SR 408

(Ramp B)Acceptable CPT

u3

7SR 417 /International Parkway

Acceptable CPTu

3

[FIG. 1] HPR Site Description, Location according to FDOT District, Rebound Level, and CPT tests conducted

FIG. 2] Typical PDA Pile Top Displacement versus Time Diagram from One Hammer Blow for FDOT HPR Site


higher qc and f

s were considered coarse-grained

while soils with low qc and f

s are fine-grained.

Begemann (1965) showed that the soil types are a function of the friction ratio (R

f).

Robertson et al., (1986) developed the first Soil Behavior Type (SBT) chart taking into account the effect of pore pressure u

2, and presented

a refinement to a normalized chart, which considered overburden stress. Robertson (1990) developed SBT’s with cone tip resistance as Q in terms of Q vs R

f, Q

tn vs F

r and Q

tnvs B

q. The

Bq ratio, friction ratio, R

f, normalized friction

ratio Fr, and normalized cone resistance are be

defined as follows:

, qc) 100%,

,

and

Where:

Bq = pore pressure ratio

Fr = normalized friction ratio

Rf = friction ratio, percent

Qtn = normalized cone resistanceQ —

u2 = pore pressure at shoulder

uo = hydrostatic pore pressure

qc = cone resistance

σv = total overburden stress

σv ́ = effective stress

fs = sleeve friction

pa = atmospheric pressure

n = stress exponent

Eslami and Fellenius (2004) presented a CPTu approach for soil classification. This approach, where the soil type depends on u

2, is based on

the effective cone resistance (qE) where: q

E=q

c-u

2.

In soils where u2 is very high, q

E can be smaller

SiteDescription

Pile size and type

Pile Length

(ft)

Pile Spacing

(ft)

aHammerModel Type

bAverage BL

(blows/ft)

ReboundElevation

(ft)

MAXRebound

(in)

1Anderson Street

Overpass

24-in SPCP

124 7Delmag D62-22

135 15 to -10 1.4

HP(14 x 89)

120 NA ICE I-30 NA No Rebound

2 SR 50/SR 436 overpass24-in SPCP

105 8APE

D62-42143 26 to 17 1.1

3I-4/

US.192

Pier 624-in SPCP

106 7ICE

120 S220 35 to 25 0.6

Pier 724-in SPCP

112 9ICE

120 S140 35 to 20 0.6

Pier 824-in SPCP

100 6ICE

120 S111 30 to 15 1.25

4 I-4/ Osceola Parkway24-in SPCP

95 6ICE

120 S105 15 to 8 0.9

5 Ramsey Branch Bridge18-in SPCP

100 10 & 20Vulcan512 Air

110 -28 to -70 1.2

6I-4/ SR 408(Ramp B)

18-in SPCP

100 NADelmag D36-32

50 30 to 0 0.5

7SR 417 /International

Parkway24-in SPCP

100 6APE

D46-4242 5 to 0 0.25

[TABLE 1] Pile driving information summary at selected FDOT sites

SPCP=square prestressed concrete pile; asingle acting; baverage driving blow counts at HPR layer; NA= not available


than qc. The authors profile chart contains five

main soil type: (1) Sensitive and Collapsible Clay and/or Silt, (2) Clay and/or Silt, (3) Silty Clay and/or Clayey Silt, (4) Sandy Silt and/or Silty Sand, (5) Sand and/or Sandy Gravel.

RESULTS AND DISCUSSIONS

Index Properties of HPR Soils

Disturbed samples obtained during Standard Penetration Testing were used to determine the USCS symbol using the procedure outlined in ASTM D-2488. The soils at the HPR sites were mainly sand with varying percentages of silts and clays. Table 2 is a summary of the soil classification, Atterberg limits, natural moisture content and average fines content of the soils. Most HPR layers had high fines content with the natural moisture content less than the liquid limit. The soils plotted near the A-line on the Cassagrande plasticity chart. These soils displayed an olive green to light green color with visual descriptions ranging from clayey and silty fine sands, to highly plastic clays. Direct correlations between Atterberg limits, natural moisture and rebound content were developed and examined from the HPR sites. Consequently, the data analyses of the water content did not provide any clear findings, which could be used to predict HPR soils. At all sites, the water table was located near the surface and well above the HPR elevation. Sand was the predominate material, consistently

representing over 50% of the soil. The fines content at these sites increased at HPR layers to over 30%.

Using the results from SPT borings, PDA, and the CPT testing, plots, shown in Figs. 3 through 11, for each of the case studies were developed relating soil descriptions to elevations of iSet, rebound and CPT output. The elevations associated with the start of PDA collection, corresponded to the depth at which pile driving commenced. This elevation was below the ground surface, since the piles were set into predrilled holes.

GEOTECHNICAL INVESTIGATION RESULTSThe soils at site 1 (Anderson Street Overpass), consisting of an 18 ft (5.5 m) thick HPR zone (Fig. 3a), included a greenish-gray silty clayey fine-sand (SM-SC), and dark greenish-gray clayey fine-sand (SC). The bottom soil layer in this HPR zone was greenish-gray clay (CH) with a trace of phosphate and shell.

The soils at site 2 (SR50/SR436 Overpass), shown in Fig. 4a, consisted of mainly medium dense sands with varying percentages of silts and clays to elevation 30 ft (9 m); while classifying as SP, SP-SM, and SM. The soils within the HPR zone from elevations 28 to 17 ft (8.5 to 5.2 m), generally were dense to very dense or stiff green silty fine-sand (SM) to green clay (CH) with a trace of phosphate.

[TABLE 2] Summary of Soil Properties at Elevations where HPR soils were Encountered

NOTE: aNAVD elevations; USCS=Unified Soil Classification System; FC= fine content; wn= natural

moisture content; LL= Liquid Limit; PI =Plasticity index; NA=not available; NP=non plastic.

Site Site NameReboundElevationa

(ft)

MaximumRebound(in)

USCS FC (%) wn (%) LL (%) PI (%)

1Anderson Street Overpass

15 to -10 1.4SM-SC, SC

CL & CH>40 30-50

40

86

13

42

2 SR50/SR436 overpass 26 to 17 1.1 CH >40 63 155 110

3 I-4/ US.192

Pier 6 35-25 0.6 SM >30 31 NP NP

Pier 7 35-20 0.6 SM 30 NA NA NA

Pier 8 35-15 1.25 SM >40 47 NP NP

4 I-4/Osceola Parkway 15-8 0.9 SM 25 NA NA NA

5 Ramsey Branch Bridge -28 to -70 1.2 SP-SC& SC >40 38 45 25

6 I-4/ SR 408 (Ramp B) 30-0 0.5 SC 20 23 NA NA

7SR417/International Parkway

5 to 0 0.25SP-SM & SM

2031

41

NP

48

NP

17


(a) (b) (c) (d) (e) [FIG. 3] (a) USCS Soil Profi le, (b) PDA output (c) Cone Resistance, (d) Friction Ratio and (e) Pore Pressure for Site 1: Anderson Street Overpass

[FIG. 4] (a) USCS Soil Type, (b) PDA output, (c) Cone Resistance, (d) Friction Ratio and (e) Pore Pressure for Site 2: SR50/SR436

1

EE(f

10

20

30

40

50

60

70

80

90

00

lev. GSE 99ft ft)

Sa

Sil

FinSilCla

SilFinSil

Fin

Sil

SPFinSilFinSil

USCS Type

ndy Fat Clay (CH)

ty Fine Sand (SM)

ne Sand (SP)ty Fine Sand (SM)ay (CH)

ty Fine Sand (SM)ne Sand (SP)ty Fine Sand (SM)

ne Sand (SP)

ty Fine Sand (SM)

P-SM)ne Sand (SP)ty Fine Sand (SM)

ne Sand (SP)ty Fine Sand (SM)

(a)

0 1 2

EB 4 WB

EB 4 WB

0.25 inch

PredrilleDepth

Inspector sRebound

)

3 4

B Pile 5 Rebound

B Pile 5 iSet

h Rebound

ledth

set and d (in)

0

(b)

300 600

qc(tsf)

(c)

0 2 4

Rf (%)

(d)

6 0 10 2

U2 (

20 30 40

tsf)

CPT-1 (U2)

CPT-5b (U2)

(e)


The three soil profiles at I-4/US.192 (site 3), shown in Figs. 5a, 6a, and 7a, included loose to medium dense brown fine-sand (SP), fine-sand with silt (SP-SM), gray silty fine-sand (SM) with abundant shell and a trace of phosphate, plus gray clayey fine-sand (SC). HPR occurred when the piles encountered very dense silty sand (SM) between elevations 25 and 0 ft (7.6 and 0 m).

The soils encountered at site 4 (I-4/ Osceola Parkway), shown in Fig. 8a, consisted of loose to medium dense fine sand (SP) and silty sand (SM) with a lense of gray silt with sand (ML) to elevation 40 ft. The rebound layer elevation from 15 to 0 ft (4.6 to 0 m) is medium dense gray silty fine sand (SM), some shell, and trace phosphate, which is underlain by tan sandy weathered limestone, with a trace phosphate to elevation -30 ft (-9.1 m).

11

EEle(ft

(a)

10

20

30

40

50

60

70

80

90

100

Silty S

Fine S

Sand w(SP-SM

Sand wSand (S

ev. GSE 106ftt)

and (SM)

Sand (SP)

with Silt M)

with silt to Silty M)

t USCS Type

(b)

0 0.25 0.5 0

Inspector sRebound

Pier 6 Pile Pier 6 Pile 0.25 inch R

PredrillDepth

(c

0.75 1

set and d (in)

16 Rebound16 iSet

Rebound

ledh

0

qc

)

300 600

c (tsf)

0

(d)

2 4 6

Rf (%)

(e)

0 10 20 30

U2 (tsf)

CPTu-4

0

[FIG. 5] (a) USCS Soil Type, (b) PDA output, (c) Cone Resistance, (d) Friction Ratio and (e) Pore Pressure for Site 3: I-4/US192 Pier 6

[FIG. 6] (a) USCS Soil Type, (b) PDA output (c) Cone Resistance, (d) Friction Ratio and (e) Pore Pressure for Site 3: I-4/US192 Pier 7

[FIG

El(f

1

Ele(ft

G 6] ( ) USCS S

10

20

30

40

50

60

70

80

90

00

Silty S

Fine

Sand (SP-S

Sand Sand (S

v. GSE 106f)

0

10

20

30

40

50

60

70

80

90

100

Silty S

(SP-SM

Fine S

Sand w(SP-SM

Silty S

(SP-SMSilty S

(SP-SM

ev. GSE 106ftt)

(a) S il T (b) P

and (SM)

and (SP)

ith Silt )

ith silt to Silty M)

USCS Type

Sand (SM)

M)

Sand (SP)

with Silt M)

Sand (SM)

M)Sand (SM)

M)

t USCS Type

DA t t ( ) C

0 0.25 0.5

Inspector Reboun

Pier 6 Pile Pier 6 Pile 0.25 inch

PredrilDept

0 0.25 0

InspectorReboun

Pier 7 Pile

Pier 7 Pile

0.25 inch

PredDe

(b) C R i t

.75 1

et and (in)

16 Rebound16 iSetebound

ed

0

q

0.5 0.75

r set and nd (in)

e 10 Rebound

e 10 iSet

Rebound

drilledepth

0

(c) (d) F i ti R

300 600

(tsf)

0

200 400

qc (tsf)

(d) ti d ( ) P

2 4 6

Rf (%)

0 2 4

Rf (%)

(e) P f S

0 10 20 3

U2 (tsf)

CPTu-4

6 0 20

U2(tsf)

CPTu

Sit 3 I 4/US19

40

-3

2 Pi 7


[FIG

--

Ele(ft)

-

El(ft

G 7] ( ) USCS S

10

0

10

20

30

40

50

60

70

80

90

Silty

Sandy

(SP-SFine

SaSilt

Fin

ev. GSE 90ft US)

10

0

10

20

0

0

0

0

0

0

0

v. GSE 92ft

Tan Sa

Silty

ClayeSilty SSilt w

Sand (SP-S

Fine

Silty

(SP-S

(a) S il T (b) PD

y Sand (SM)

y Limestone

SM)Sand (SP)

nd with t(SP-SM)

ne Sand (SP)

SCS Type

USCS Type

ndy Limestone

and (SM)

Sand (SC)nd (SM)th sand (ML)

ith Silt )

and (SP)

and (SM)

)

DA t t ( ) C

0 0.25 0.5 0.7

InspectoRebou

Pier 8 Pile

Pier 8 Pile

0.25 inch

0 0.5 1

Inspector Reboun

Pier 2 Pile

Pier 2 Pile

0.25 inch

(b) R i t

5 1 1.25

r set and und(in)

e 11 Rebound

e 11 iSet

Rebound

0

1.5

et and (in)

Rebound

iSet

ebound

0

(c) (d) F i ti R

200 400

qc (tsf)

300 600

qc (tsf)

(d) ti d ( ) P

0 2 4 6

Rf (%)

2 4 6

Rf (%)

(e) P f S

6 0 10 20 30

U2(tsf)

CPTu

0 10 20

U2 (tsf)

CPTu

Sit 3

0 40

-2

30

1

[FIG. 7] (a) USCS Soil Type, (b) PDA output (c) Cone Resistance, (d) Friction Ratio and (e) Pore Pressure for Site 3: I-4/US192 Pier 8

[FIG. 8] (a) USCS Soil Type, (b) PDA output, (c) Cone Resistance, (d) Friction Ratio and (e) Pore Pressure for Site 4: I-4/Ocela Parkway

[FIG

-

El(ft

-

2

3

4

5

6

7

8

9

Ele(ft)

G 8] ( ) USCS S

10

0

10

20

30

40

50

60

70

80

90

Silt

Sand

(SP-Fine

SaSil

v. GSE 90ft U

10

0

10

20

30

40

50

60

70

80

90

ev. GSE 92ft )

Tan Sa

Silty S

ClayeySilty SaSilt wi

Sand w(SP-SM

Fine S

Silty S

(SP-SM

(a) S il T (b) PD

Sand (SM)

Limestone

M)Sand (SP)

nd with (SP-SM)

CS Type

USCS Type

ndy Limestone

Sand (SM)

y Sand (SC)and (SM)ith sand (ML)

with Silt M)

Sand (SP)

Sand (SM)

M)

(bDA t t ( ) C

0 0.25 0.5 0.7

InspectoRebo

Pier 8 Pil

Pier 8 Pil

0.25 inch

0 0.5 1

Inspector sRebound

Pier 2 Pile 8

Pier 2 Pile 8

0.25 inch R

b) C R i t

5 1 1.25

r set and nd(in)

11 Rebound

11 iSet

Rebound

0

1.5

set and d (in)

8 Rebound

8 iSet

Rebound

0

(c) (d) F i ti R

200 400

qc (tsf)

300 600

qc (tsf)

0

(d) ti d ( ) P

0 2 4

Rf (%)

0 2 4 6

Rf (%)

(e) P f S

0 10 20 3

U2(tsf)

CPTu

0 10 20

U2 (tsf)

CPTu-

Sit

40

-2

30

-1


Fig. 9a shows the soil profile for site 5 (Ramsey Branch Bridge). In the HPR zone below elevation -28 ft (-8.5 m), the soils include greenish-gray clayey sand (SC) and greenish-gray sand with clay (SP-SC). A trace of shell was found throughout soils in the HPR zone.

The soil profile at site 6, along I-4/SR408 (Ramp B), is shown in Fig. 10a and consisted generally of three layers. The first was an upper stratum of loose to medium dense sand with silt and clay (SP-SM & SP-SC) to silty sand (SM) to elevation 60 ft (18.3 ft). This stratum was

underlain by very soft sandy clay and clayey fine-sand (SC) to elevation 45 ft (13.7 ft). This layer was underlain by the HPR soil, a medium dense clayey fine-sand and silty clayey fine-sand (SC) to elevation 9 ft (2.7 m).

The soils at site 7 (I-4/SR417), shown in Fig. 11a, varied between silty fine-sand (SM) and clayey fine-sand (SC), and included thin layers of silt (MH) and sandy fat clay (CH). Minor rebound less than the FDOT limits of 0.25 in (6 mm) occurred between elevation 20 and -2 ft (6 m and -0.6 m), where the soils were silty fine-sand (SM).

--80

-70

-60

-50

-40

-30

-20

-10

0

C

S

Elev. GSE 7 ft (ft)

0

10

20

30

40

50

60

70

80

90

100

Elev. GSE 1((ft)

C

C

S

(

(

F

(a)

Cemented Sandwith Limestone(SP)

Clayey Sand (SC)

Sand with Clay(SP-SC)

(SP-SM)

Sand with Silt to Silty Sand (SP-S

Sand with Silt (SP-SM)

Silty Sand (SM)

USCS Type

5ft USCS Type

layey Fine Sand (

ayey Fine Sand (S

ay (CH)

nd With Clay SP-

P-SM)

P-SC)

ne Sand(SP)

ilty Fin Sand (SM)

M)

0 0.5

InspeReb

EB

EB

0.2

InRe

C)

)

C)

0 0.5

EB

EB

0.

PredDe

InspectReb

(b)

1 1.5

ctor set and bound (in)

B 3 Pile 11 Rebound

B 3 Pile 11 iSet

25 inch Rebound

nspector noebound Soils

1 1.5

1 Pile 2 Rebound

1 Pile 2 iSet

5 inch Rebound

illedt

or set andund (in)

(c)

0 150 30

qc(tsf)

0 150 30

qc(tsf)

00 0 2 4 6

Rf (%)

0 2

Rf (%)

(d)

8 0 10 20

U2 (t

6 0 5

(e)

0 30 40

tsf)

CPTu1CPTu2

10 15 20

CPTu-B109

CPTu-B118

CPTu -A1-105A

2 (tsf)

[FIG. 9] (a) USCS Soil Type, (b) PDA output, (c) Cone Resistance, (d) Friction Ratio and (e) Pore Pressure for Site 5: Ramsey Branch bridge


(

-80

-70

-60

-50

-40

-30

-20

-10

0

Elev. GSE 7 ft (ft)

0

10

20

30

40

50

60

70

80

90

100

Elev. GSE 10((ft)

C

Cl

Cl

Sa

(S

(S

Fi

S

(a)

emented Sandwith Limestone(SP)

Clayey Sand (SC)

and with Clay(SP-SC)

(SP-SM)

Sand with Silt to Silty Sand (SP-

Sand with Silt (SP-SM)

Silty Sand (SM)

USCS Type

05ft USCS Type

Clayey Fine Sand (S

layey Fine Sand (SC

lay (CH)

and With Clay SP-S

SP-SM)

SP-SC)

ine Sand(SP)

Silty Fin Sand (SM)

(

M)

0 0.5

InspeRe

E

E

0.

IR

SC)

C)

SC)

0 0.5

EB

EB

0.2

PredrDep

InspectRebo

(b)

1 1.5

ctor set and ound (in)

3 Pile 11 Rebound

3 Pile 11 iSet

5 inch Rebound

spector nobound Soils

1 1.5

1 Pile 2 Rebound

1 Pile 2 iSet

25 inch Rebound

rilledpth

or set andound (in)

0

0 150 3

qc(tsf)

0 150 300

qc(tsf)

(c)

0 0 2 4 6

Rf (%)

0 0 2 4

Rf (%)

(d)

8 0 10 2

U2 (

4 6 0 5

U

(e)

30 40

sf)

CPTu1CPTu2

10 15 20

CPTu-B109

CPTu-B118

CPTu -A1-105A

U2 (tsf)

)

[FIG. 10] (a) USCS Soil Type, (b) PDA output, (c) Cone Resistance, (d) Friction Ratio and (e) Pore Pressure for Site 6: I-4/SR408 Ramp B

[FIG. 11] (a) USCS Soil Type, (b) PDA output, (c) Cone Resistance, (d) Friction Ratio and (e) Pore Pressure for Site 7: I-4/SR417

--10

0

10

20

30

40

50

60

70

Elev. GSE 75ft(ft)

S

(S

Si

(SE

Sa

(S

(S

SiFi

(

t USCS Type

Silty Fine Sand (SM

SC)

lty Sand (SM)

SP- SM)lastic silt (MH)

andy Fat Clay (CH)

SP- SM)

SP- SM)

lty Sand (SM)ne Sand (SP)

(a)

M)

0 0.5

EB 2

EB 2

0.25

PredDe

InspectRebo

1 1.5 2

2 Pile 5 Rebound

2 Pile 5 iSet

5 inch Rebound

drilledepth

tor set and ound (in)

(b)

0 200 4

qc (tsf)

(c)

400 0 2 4

Rf (%)

4 6

)

0 2

U2

(d)

4 6

(tsf)

CPTu1

CPTu2

CPTu3

(e)


PDA AND CPT PROFILES Typical piezocone profiles from the seven sites, which include cone resistance q

c, friction ratio

Rf, and pore pressure u

2, are shown in Figs. 3

to 11. The rebound, shown as a red line and iSet shown as a green line, were plotted versus elevation. A vertical dashed line at 0.25-in (6-mm) was included to differentiate between high and acceptable rebound.

At site one (Anderson Street Overpass) HPR problems that occurred during installation of 24-inch (610 mm) PCP, caused construction delays and required the redesign of the foundations and replaced with low displacement steel H-piles (HP 14 x 89 or HP 360 x 132). As shown in Fig. 3b, the PDA rebound ranged between 0.25 and 1 in (6 and 25 mm) beginning at elevation 15 ft (4.6 m) to the end of driving at elevation -7 ft (-2.1 m). In this HPR lower zone, the CPT pore pressure increased to 30 tsf (2873 kPa) and friction ratio increased to 5 percent.

Consistent behavior was observed for soils that produced HPR (Figs. 3 to 9); pore water pressure u

2 is typically greater than 20 tsf (1915 kPa) and

Rf is greater than 2%. At several locations shown

in Figs. (5e, 6e, 7e, and 8e), u2 decreased in the

layers below where rebound occurred. The pore water pressure at the tip increased throughout the driving process and as the pile tip passed through the intact soil and advances down, excess pore water pressure still building up due to large soil deformation, allowing rebound for continue.

Lower qc observed at these soils is typically

indicative that these soils are more fine-grained soils than coarse-grained. This is in good agreement with laboratory results that HPR soils had fines content of greater than 30%.

For non-HPR soils at sites where minor rebound was observed followed by an acceptable set (Figs. 10 and 11), the pore pressure u

2 exhibited

values of less than 17 tsf (1628 kPa) and Rf

of less than 2%. The non-HPR soils behaved more like coarse soils than fine soils, as these soils exhibited higher q

c and lower u

2. Sand

is representing over 70% of the non-HPR soils, which confirmed that pore pressure in soils with low fines content may dissipate very quickly.

SOIL BEHAVIOR TYPE (SBT) CHART CORRELATIONSTwo CPT and four CPTu SBT charts were used to study HPR and non-HPR soil characteristics.

(1) Schmertmanns’ (1978) CPT chart.

(2) Eslami and Fellenius (2004) qE-f

s CPTu Chart.

(3) Robertson (1990) Q-Rf CPT Chart.

(4) Robertson (1990), Q-Fr, and Q-B

q CPTu Chart.

(5) Schneider et al. (2008) Q-∆u2

⁄ σvo

CPTu Chart.

In general, these SBT charts show coarse-grained soils (sand and gravel) to have a higher q

c and

a lower Rf, while fine-grained soils produce a

lower qc and a higher R

f (Lunne et al. 1997).

Fig. 12a illustrates the CPT data plotted on the Schmertmann (1978) chart, which relates cone tip to cone friction. The soils change from cohesive at low q

c and high f

s values (i.e.

lower right portion) to sands with higher qc and

lower fs values (i.e., upper left portion). The

HPR soils from this study are shown in red and plot mostly in the dense sand region and also the stiff or very stiff zones near the cohesive-sand boundary. The non-HPR soils from this study are shown in blue and generally plot in the loose sand regions, with some data also in the soft to medium cohesive zones. Data from Murrell et al., (2008) were added to this chart and generally plotted near the blue-red border of loose to dense sands. N

SPT in the HPR soils

were greater than 35 bpf (28 blows per 250 mm) (i.e., dense to very dense or hard soils), while those in non-HPR soils were less than 30 bpf (25 blows per 250 mm) (loose to medium dense soils). Just below f

s values of 1 tsf (96 kPa) there

is a clear delineation between HPR and non-HPR soils in Schmertmanns’ (1978) CPT chart.

CPTu data from HPR and non-HPR soils were also plotted on the Eslami and Fellenius (2004) chart. As shown in Fig. 12b, HPR soils plotted in silty sandy to silty clay zones with f

s values

greater than 1 tsf (96 kPa), while Non-HPR soils plotted in the sandy silt and sensitive-collapsible clay-silt or clay zone with a f

s values

less than 1 tsf (96 kPa). This trend generally matches the trend from Schmertmanns’ (1978) chart.

The HPR and non-HPR data was plotted on Robertson’s (1990) Q-R

f charts (Fig. 13). Fig. 13a

shows the HPR soils as sand mixtures in zone 5 (Silt mixtures: silty sand to sandy silt). The majority of these soils have R

f values greater


than 1.5%. Although the trends are not as clear as those shown in Fig. 12, the non-HPR soils, shown in Fig. 13b, plot from zones 4 (silt mixtures clayey silt & silty clay) to 6 (sands: clean sands to silty sand) and a large percentage have R

f values less than 1%. The data from the

North Carolina site presented by Murrell et al., (2008) is shown in Fig. 13a and generally plots in Zone 6.

HPR soils were plotted as sand mixture zone 4 (clayey silt to silty clay) on the normalized Robertson (1990) Q-F

r chart (Fig. 14a), where

atmospheric pressure was used to normalize each axis. These plots (shown on the left side) are similar to those shown in Fig. 13. In this case most HPR soils plotted with F

r values

greater than 1.0%, while most non-HPR soils plot below 1% between zones 3 and 6 or clays: clay to silty clay to sands: clean sand to silty sand. On the Q-B

q charts HPR soils (Fig. 14a),

produced pore pressures of greater than 20 tsf (1915 kPa), which led to a B

q ratio of 0.4, while

non-HPR soils (Fig. 14b) had a Bq ratio of near

zero when u2 was small or approached the

hydrostatic pressure.

HPR soils exhibited excess pore pressures (i.e., u

2-u

0) greater than 20 tsf (1915 kPa). The CPTu

data from the HPR sites was plotted on the two SBT charts proposed by Schneider et al., (2008). The excess pore pressures were denoted as ∆u

2,

and were normalized by dividing them by the effective overburden stress (σ

v). As shown in

both parts of Fig. 15a, HPR soils behave as clays or silts (Zones 1a or 1b). These soils produced a ∆u

2 ⁄ σ

v ratio greater than 1 and a ratio of

[FIG. 12] Location of HPR and Non-HPR soils on (a) Schmertmann (1978) Chart, (b) Eslami and Fellenius (2004) Chart

[FIG. 13] Location of (a) HPR soils and (b) Non-HPR soils on Q-Rf classifi cation approaches proposed by Roberston (1990)

(a)

(b)

(a) (b)


net cone point resistance to effective stress (q

cnet ⁄ σ

v) greater than 10. Non-HPR soils, shown

in Fig. 15b, plotted well to the left of the HPR soils in Zone 3 which is the transitional zone between drained and undrained behavior. The normalized pore pressures are consistently below 1.

In summary, the proposed CPT SBT charts from all four sources show that the HPR soils plot in

different zones than non-HPR soils. The charts proposed by Schmertmann (1978) Eslami and Fellenius (2004) and Schneider et al., (2008) produced the clearest delineations between the two. Both the CPT friction and CPTu excess pore pressure highlighted these differences. HPR soils classified as dense according to Schmertmann (1978), silty sand to silty clay according to Eslami and Fellenius (2004), sandy

(a)

(b)

[FIG. 14] Location of (a) HPR soils and (b) Non-HPR soils on Q-Fr and Q-Bq classifi cation approaches proposed by Roberston (1990)


silt to silty sand acording to Robertson (1990), and silt or clay according to Schneider et al., (2008). Non-HPR soils behave more like sandy soils than fine-grained soils.

Based on SPT and laboratory tests, HPR soils were classified as one of the following USCS groups: dense to very dense or hard: SM, SC, SM-SC, and CH. Conversely, non-HPR soils were classified as: loose to medium dense SP, SP-SM,

SC, SM-SC. There was close agreement between the USCS method and the CPT soils type.

CORRELATING REBOUND TO Rf, qc, AND u2

Correlations between the pile rebound, Rf, q

c,

and u2 were developed and examined. These

correlations, shown in Fig. 16a and 16b, were developed based on averages obtained within

(a)

(b)

[FIG. 15] (a) HPR soils and (b) Non-HPR soils on the Schneider et al. (2008) chart


HPR zones (rebound > 0.25 in or 6 mm) and non-HPR zones (rebound < 0.25 inches or 6 mm). The rebound versus friction ratio (R

f)

(Fig. 16a) produced an increasing linear trend with a regression coefficient R2 of 0.80 while an R

f of 1% was associated with 0.25 in (6 mm)

of rebound. The rebound versus qc did not

produce desirable correlations (i.e., R2=0.1) and are not shown (Jarushi, 2013). Rebound versus pore pressure u

2 (Fig. 16b) produced a linearly

increasing trend. Soils with u2 of 5 tsf (479 kPa)

or less produced an acceptable rebound of less than 0.25 in (6 mm). As u

2 exceeded 20 tsf

(1915 kPa), the rebound increased to over 0.60 inches. The CPTu data R

f data from Murrell

et al. (2008) did not agree with the results from this study. The pore pressure data from Murrell et al., (2008) did plot near the data from this study.

Statistical analysis using SPSS (Statistical Package for the Social Sciences) software was carried out on the CPT data from the seven sites. An analysis of variance (ANOVA) was included to determine if there was any significant relationship between rebound and R

f, q

c, and u

2. The ANOVA result produced

very strong correlations from both Rf and u

2.

A design equation to predict pile rebound was developed. It included both R

f and u

2, but as

did the previous correlations excluded qc due to

poor correlations. These results were further validated via examination of the experimental residuals, which were normally distributed and showed no patterns that would cause concern.

Equation 1 is presented to relate HPR rebound to CPTu parameters R

f and u

2:

R = 0.25Rf + 0.005u

2 + 0.05 [1]

Where: R= rebound (inches);

Rf= Friction Ratio (%);

u2= CPTu pore pressure at

shoulder (tsf).

The applicability of Eqn. 1 was evaluated by plotting its predicted rebound versus actual rebound, using the data from the seven sites used in this study plus the one site presented by Murrell et al., (2008). As shown in Fig. 17, the equation produced R2 values of 0.80, showing an ability to predict rebound using R

f and u

2.

Two of the three data points from the Murrell et al., (2008) study do not agree with data from this study.produced R2 values of 0.80, showing an ability to predict rebound using R

f and u

2.

Two of the three data points from the Murrell et al., (2008) study do not agree with data from this study.

[FIG. 17] Predicted Rebound using Rf and u2 versus Actual Rebound from Equation 1

00

0.5

1

1.5

0

Pred

icte

d Re

boun

d (in

)

0.5Actual

R² = 0

1Rebound (in)

0.75

1.5

[FIG. 16] Correlation between Rebound and (a) Rf and (b) u2

(b)(a)

00

0.4

0.8

1.2

1.6

0 1 2 3 4 5

Rebo

und

(in)

Excludes Murrell, R² = 0.80

Friction Ratio, Rf (%)

0 5 10 15 20 25 30 35

Excludes Murrell, R² = 0.80

Pore Pressure, U2 (tsf)


SUMMARYLarge displacement piles at numerous Florida locations experienced HPR during driving into saturated soils. The overburden depth at which HPR occurred was typically greater than 50 ft (15 m). There was a large increase in the CPTu pore pressure u

2 from negative or near

zero to high positive values whenever HPR was identified.

CONCLUSIONSBased on SPT blow counts, HPR soils were very dense or hard while non-HPR soils were loose to medium dense. HPR soils classified as one of the following groups: SC, SM-SC, SM, CL, SP-SM, and CH; while non-HPR soils as SP, SP-SM, SC, and SM-SC.

An equation for predicting rebound, based on Rf

and u2, was developed, and the results showed

the ability to predict pile rebound during design if large displacement piles are to be driven.

Three of the four SBT charts evaluated (i.e., Schmertmann’(1990), Eslami and Fellenus (2004) and Schneider et al., (2008)) would enable geotechnical engineers to predict the behavior of HPR soils based on clear limits from either CPT friction or CPTu pore pressures. Engineers may encounter HPR problems if soils plot on the:

• Schmertmann (1978) SBT chart as dense or stiff soils silts with f

s values greater than

1 tsf (96 kPa)

• Eslami and Fellenius (2004) SBT chart as silty sand or silty clay to clay silts with f

s

values greater than 1 tsf (96 kPa)

• Schneider et al., (2008) SBT chart as 1a, and 1b, (silt and clays) with ∆u

2 ⁄ σ

vo values

greater than 1

• Robertson (1990) (Q-Rf) SBT chart as silty

sand to sandy silt where qc/p

a values

are greater than 20 and Rf greater than

0.8 percent.

• Robertson (1990) (Qtn-F

r) SBT chart where

most HPR soils have Fr values greater than

0.80 percent.

RECOMMENDATIONSIt is recommended that engineers use the three most promising SBT charts when large diameter, high displacement piles are to be driven into saturated very-dense or hard silty or clayey fine sands. Measured rebound should

be checked with the predicted rebound from Equation 1 to help validate its accuracy.

REFERENCES 1. ASTM D2488, Standard Practice for

Description and Identification of Soils.

2. ASTM D5778, Standard Test Method for Performing Electronic Friction Cone and Piezocone Penetration Testing of Soils.

3. Baziar, Mohammad H., and Reza Z. Moayed. (2006), “Evaluation of cone penetration resistance in loose silty sand using calibration chamber”, International Journal of Civil Engineering 4.2, pp106-119.

4. Baziar, Mohammad H., Habib Shahnazari, and Hassan Sharafi. (2011), “A laboratory study on the pore pressure generation model for firouzkooh silty sands using hollow torsional test”, International Journal of Civil Engineering vol 9(2), pp126-34.

5. Begemann, H. K. S. (1965), “The friction jacket cone as an aid in determining the soil profile”, Proceedings 6th ICSMFE 1, pp17-20.

6. Bingjian, Zhu. (2011), “Study of the pore water pressure variation rule in saturated soft soil caused by prestressed concrete pile penetration”, Electric Technology and Civil Engineering Conference, 22-24 April 2011, Taizhou, China, pp.756-59.

7. Cosentino, P. Kalajian, E. Misilo, T, Chin Fong, Y. Davis, K., Jarushi F., Bleakley A., Hussein M. H., and Bates, Z. (2010), “Design phase identification of high pile rebound soils”, Technical report, Contract BDK81 Work Order 977-01, Florida Department of Transportation.

8. Erten, D. and Maher, M. H. (1995), “Cyclic undrained behavior of silty sand”, Journal of Soil Dynamics and Earthquake Engineering, 14(2), pp 115-123.

9. Eslami, A. & Fellenius, B.H. (2004), “CPT and CPTu data for soil profile interpretations; review of methods and a proposed new approach”, Iran Journal of Science and Technology 28(B1), pp 69-86.

10. FDOT. (2010), Standard Specification for Road and Bridges Section 455.

11. GeoLogismiki. (Released 2007), CPeT-IT, Version 1.70. GeoLogismiki.


12. Hussein, M.H., Woerner, W. A., Sharp, M. and Hwang, C. (2006), “Pile driveability and bearing capacity in high-rebound soils”, American Society of Civil Engineers GEO Congress CD-ROM, Atlanta, GA.

13. Jarushi, F., (2013), “Evaluating geotechnical engineering properties associated with high pile rebound”, Ph.D. Dissertation, Florida Institute of Technology, Melbourne Florida.

14. Jarushi, F , Cosentino, P.J, and Kalajian E.H., (2013), “Using fines content and uncorrected SPT blow counts of soils to predict high pile rebound”, Journal of the Transportation Research Board: Paper No. 13-2880.

15. Jefferies, Mike, and Ken Been. (2006), “Soil Liquefaction: A critical state approach”, London: Taylor & Francis.

16. Likins, Garland E. (1983), “Pile installation difficulties in soils with large quakes”, In G.G. Globe, editors, Proceedings of Symposium 6 at the 1983 ASCE Convention, Philadelphia, May 18, 1983. ASCE Geotechnical Engineering Division.

17. Lunne, T., Robertson, P.K., and Powell, J.J.M. (1997), “Cone penetration testing in geotechnical practice”, Blakie Academic and Professional, Melbourne, 312.

18. Moayed, R.Z. (2006), “Evaluation of the fine contents of silty sands using CPTU results”, Proceedings of the 10th IAEG International Congress, Nottingham, United Kingda; publication #506.

19. Murrell, Kyle L., Canivan, Gregory J., Camp, William M. III. (2008), “High and low strain testing of bouncing piles”, Proceedings of the 33rd Annual and 11th International Conference on Deep Foundations.article #1603; publication #85

20. Robertson, P.K. (1990), “Soil Classification using the cone penetration test”, Canadian Geotechnical Journal, 27(1), pp151-8.

21. Robertson. P.K., Campanella, R.G., Gillespie, D., and Greig, J., (1986), “Use of Piezometer Cone data”, In-Situ’86 Use of In-situ testing in Geotechnical Engineering, GSP 6 , ASCE, Reston, VA, Specialty Publication, pp 1263-1280.

22. Schmertmann, J.H. (1978), “Guidelines for cone penetration test, performance and design”, Report No. FHWA-TS-78-209, U.S. Department of Transportation, Washington, D.C., pp. 145.

23. Schneider, J.A., Randolph, M.F., Mayne, P.W., and Ramsey, N. (2008), “Analysis of factors influencing soil classification using normalized piezocone tip resistance and pore pressure parameters”, ASCE Journal of Geotechnical and Geoenvironmental Engineering, 134(11), pp1569-1586.


Factors Affecting the Reliability of Augered Cast-In-Place Piles in Granular Soils at the Serviceability Limit State (DFI 2013 Young Professor Paper Competition Winner)Armin W. Stuedlein, Ph.D., P.E., Assistant Professor and Loosley Faculty Fellow, School of Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA; (541) 737-3111, [email protected]

Seth C. Reddy, E.I., Graduate Research Asst., School of Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA, [email protected]

ABSTRACTOwing to an increasing demand to manage risk and maximize cost-effectiveness, preference for reliability-based design (RBD) over traditional deterministic design procedures has increased for deep foundation elements. In this study, factors affecting the reliability of augered cast-in-place (ACIP) piles under axial compression at the serviceability limit state (SLS) are addressed using a simple probabilistic hyperbolic model and a database of static loading tests conducted on ACIP piles in cohesionless soils. The aleatory and model uncertainty in a selected two-parameter load-displacement model is statistically characterized for use in reliability simulations. Reliability simulations incorporating the correlated bivariate model parameter distribution were generated using a statistical translational model and various parametric and non-parametric correlation coefficients to assess the effect of correlation coefficient type on the reliability simulations. The first-order reliability method (FORM) was used to determine the effect of sample size on the stability and uncertainty of the serviceability limit state reliability index. Sample sizes greater than about 40 provided relatively consistent estimates of the reliability index; however, its uncertainty continued to decrease with increasing sample sizes. A parametric study was conducted in order to determine the variables (i.e. allowable displacement, predicted pile capacity, slenderness ratio) which govern reliability. In general, the uncertainty in the model used to predict pile capacity had a more significant impact on foundation reliability compared to the uncertainty in allowable displacement; this finding illustrates one advantage of having an accurate capacity prediction model. The slenderness ratio had the largest effect on foundation reliability at the SLS, and illustrates the importance for accounting for the pile geometry in reliability assessments.

INTRODUCTIONIt is widely recognized that many geotechnical design parameters exhibit uncertainties; these have been historically accounted for in a deterministic framework using a global safety factor based on experience or general rules of thumb. This approach, however, does not recognize the uncertainty in the individual underlying variables nor their potential correlation. Reliability-based design (RBD) can overcome many of these limitations, and can provide a means for estimating the risk or probability of exceeding a particular design limit state. The demand for managing risk by explicitly recognizing and mitigating sources of

uncertainty is increasing. Consequently, RBD methods are replacing traditional deterministic design procedures (e.g. allowable stress design [ASD]) for many deep foundation alternatives. Augered cast-in-place (ACIP) piles have generally received limited attention; however, Stuedlein et al. (2012) present one example of a probabilistic ACIP-specific design model intended for the ultimate limit state (ULS).

Although the reliability of the serviceability limit state (SLS) is not as well understood as that of the ULS, it is often the governing design criterion (Uzielli and Mayne 2011). Phoon et al. (2006) discussed key factors impeding RBD for the SLS and indicated the lack of


model statistics for pile displacement. Phoon (2006) recognized that the uncertainty in foundation displacement will have a significant impact on the reliability of a foundation and proposed a simple probabilistic hyperbolic model that captures the uncertainty in the entire load-displacement relationship. The applied load was normalized by an interpreted failure load and the remaining uncertainty in the load-displacement curve was modeled using a bivariate random vector consisting of hyperbolic curve-fitting parameters that were found to be correlated and non-normally distributed. Phoon and Kulhawy (2008) describe two methods to incorporate the correlated random variables comprising the load-displacement model, and point to their performance in consideration of a database of 40 loading tests on ACIP piles.

This paper examines certain aspects of RBD for ACIP piles at the SLS using an expanded load test database. Contrary to previous work, dependence between the load-displacement model parameters and pile slenderness ratio was observed; this correlation was eliminated through the use of two transformed load-displacement model parameters, which were then used to assess foundation reliability for various pile geometries using the translation model described in Phoon and Kulhawy (2008) and the first-order reliability method (FORM). Because the correlation coefficient used in the translation model is not suitable for highly correlated model parameters, random pairs of model parameters were sampled from the database in order to estimate the minimum sample size needed to use the translation model. The effect of sample size on the distribution of computed reliability indices was assessed for a variety of correlation coefficients and a range of slenderness ratios in order to assess the effect of correlation modeling on the reliability of the SLS design model. By varying the mean and uncertainty of allowable displacement, uncertainty of the predicted resistance, and slenderness ratio, a parametric study was conducted to determine the variables that govern ACIP pile foundation reliability. Because slenderness ratio was found to be the most critical parameter, additional investigations were undertaken to better quantify the influence of slenderness ratio on foundation reliability at the SLS.

MODELING OF THE SERVICEABILITY LIMIT STATE Ideally, RBD principles should be used to estimate the likelihood of failure at both the ULS and SLS. Reliability of foundations for the ULS is associated with the probability that a single value of pile capacity (i.e. the ultimate resistance) will be less than the applied load. If pile capacity is determined using a consistent criterion, a model factor (i.e., the bias), defined as the ratio of the measured to predicted capacity can be used to assess the uncertainty in the design model. The SLS, however, is defined by one or more pre-defined allowable displacements, which are related to corresponding allowable loads. Due to the nonlinearity of the load-displacement curve, the model factor distribution must be evaluated for each specified allowable displacement. Efficient RBD analyses for the SLS can consider the entire load-displacement curve in which a range of allowable displacements may be specified (Uzielli and Mayne 2011).

Multiple sources of uncertainty influence the behavior of the load-displacement relationship of a deep foundation element. Development of a load-displacement model from a database allows the aleatory uncertainty resulting from the uniqueness of each test, as a consequence of inherent soil variability, and the epistemic model uncertainty to be captured (albeit indirectly) and statistically characterized. Epistemic uncertainties resulting from errors in measurement and observation of a loading test contribute to the variability in the load-displacement behavior of a pile, and this effect is lumped into the model uncertainty.

Although any appropriate nonlinear function can be used to model load-displacement data, Phoon (2006) showed that a hyperbolic curve provided an adequate fit to the ACIP pile load test data compiled in Chen (1998) and Kulhawy and Chen (2005). This model was adopted herein for the purpose of identifying factors controlling the determination of the reliability index, β. Hyperbolic load-displacement curves were fitted to the observed load-displacement using the applied load, Q, normalized by the slope-tangent capacity, Q

STC, given by:

1 2

a

STC a

yQQ k k y

=+ ⋅

[1]


where y is the pile head displacement, and k1

and k2 are the curve-fitting parameters that

describe the shape of the load-displacement curve. The slope-tangent capacity, similar to the Davisson offset load but uses the initial load-displacement curve slope rather than the pile elastic compression line and described in detail by Kulhawy (2004), is not necessarily the ultimate pile resistance but a simple reference capacity that can be derived from most load-displacement curves and is used to reduce the scatter in the observed test results through normalization.

The hyperbolic model parameters are physically meaningful: the reciprocal of k

1

and k2 equal the initial slope and asymptotic

(or ultimate) resistance, respectively. The curve fitting parameters from the Chen (1998) and Kulhawy and Chen (2005) database were obtained directly from Phoon and Kulhawy (2008); ordinary least squares regression was performed to determine the model parameters for the newly added load test data.

LOAD TEST DATABASE The database compiled herein consisted of 87 load tests performed on ACIP piles installed in predominately cohesionless soils: 40 loading tests were compiled by Chen (1998) and Kulhawy and Chen (2005), 23 were compiled by McCarthy (2008), ten were presented in local ASCE Chapter meetings and were reported by Stuedlein et al. (2012), ten were compiled by Park et al. (2012), three from load tests were conducted by Mandolini et al. (2002), and one load test was reported by O’Neill et al. (1999). The diameter, B, depth, D, and slenderness ratio, D/B, ranged from 300 to 800 mm (11.8 to 31.5 in), 7.5 to 29.0 m (24.6 to 95.1 ft), and 20 to 68.5 m (65.6 to 224.7 ft), respectively. The database compiled by Chen (1998) and Kulhawy and Chen (2005) included load tests on short piles (D/B < 20), but these were not assessed herein. Kulhawy and Chen (2005) observed that shaft resistance constituted a small portion of the total resistance for short piles, and consequently, the load-displacement behavior is different from other piles in the database. In order to assess the effect of sample size on reliability and make direct comparisons to previously reported studies, the expanded database is limited to slenderness ratios greater than 20.

RANDOMNESS OF THE HYPERBOLIC MODEL PARAMETERS In order to use traditional statistical methods and be considered random, k

1 and k

2 must not

be statistically correlated to soil strength or geometrical parameters (e.g., SPT-N and D/B). The non-parametric Kendall’s Tau test (Daniel 1990) for correlation between k

1 and average

SPT-N and k2 and average SPT-N yielded p-values

= 0.81 and 0.93, respectively, indicating no correlation at a 5 percent level of significance. Conversely, there was convincing evidence (p-values < 0.05) that suggested neither k

1 nor

k2 were independent of D/B. Considering only

the Phoon and Kulhawy (2008) dataset, the Kendall Tau test indicated k

1 and D/B were

correlated (p-value = 0.02), whereas k2 and

D/B were independent (p-value = 0.40). Fig. 1 shows that the potential correlation between the model parameters and the slenderness ratio is strengthened by the addition of the new data. A positive correlation between k

1 and

D/B is physically intuitive; that is, a larger k1

represents a less stiff pile, corresponding to a larger slenderness ratio. Similarly, a larger k

2

indicates less ultimate resistance which likely corresponds to a smaller embedment depth since the range of B in the database is relatively small. These findings indicate one pitfall associated with the use of a limited dataset for reliability calculations.

To assess the likelihood that k1 and k

2 were

actually correlated with D/B, the new database was randomly sampled using sample sizes, n, ranging from 5 to 85, and the p-values associated with the Kendall’s Tau correlation test were calculated. One million subsets of k

1,

k2, and D/B were generated using Monte Carlo

simulations for each sample size evaluated. Monte Carlos simulations, in this case repetitive deterministic calculations that incorporate samples of independent random variables from their source distributions, provide a convenient framework for estimated probabilistic outcomes based on limited and/or uncertain datasets. Fig. 2 shows the relationship between the mean p-value and sample size, as well as the probability that the p-value is less than the selected significance level, α of 0.05, Pr(p-value < 0.05). The coefficient of variation (COV), defined as the standard deviation divided by the mean, was used to assess the degree of scatter of the simulated p-values, which ranged


from 54 to 350 percent as a function of sample size. Because p-values cannot be used directly to determine the strength of the likelihood of correlation between two variables, and due to the large amount of scatter associated with the simulated p-values, a curve representing the probability of rejecting the null hypothesis (i.e. the probability of obtaining a p-value < 0.05), was constructed. Fig. 2 indicates that the mean p-value is less than 0.05 for sample sizes equal to 19 and 23 for k

1 and k

2,

respectively. However, the probability that k1

and k2 are correlated with D/B is approximately

73 percent. At n = 40, Pr(p-value < 0.05) = 99.89 and 99.04 percent for k

1 and k

2, respectively;

whereas Pr(p-value < 0.05) = 100 percent at n = 55 and 60 for k

1 and k

2, respectively. Therefore,

direct use of k1 and k

2 should not be permitted

for reliability simulations as they are very likely to be correlated to slenderness ratio.

0

20

40

60

80

100

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0 10 20 30 40 50 60 70 80

Pr(p

-val

ue <

0.0

5)

Mea

n p-

valu

e

Sample Size, n

5% Significance Level

Uncorrelated

Correlated

p-value (k1-D/B)p-value (k2-D/B)Pr(p-value<0.05) (k1-D/B)Pr(p-value<0.05) (k2-D/B)

[FIG. 2] Average p-values from the Kendall’s tau correlation test and the probability of obtaining a p-value less than 0.05 as a function of sample size using Monte Carlo simulations from the expanded database

Considering the typical ACIP pile response, a small initial slope of the load-displacement curve (i.e., a large k

1 value) implies a slowly

decaying curve, and is generally associated with a less well-defined and larger asymptote (i.e. smaller k

2), and vice versa. Therefore,

it is expected that k1 and k

2 will be inversely

correlated to some degree. The magnitude of the dependence between normally distributed k

1 and k

2 can be characterized using the Pearson

product-moment correlation coefficient, ρ, as shown in Fig. 3:

( )( )

( ) ( )

1, 1 2, 21

2 2

1, 1 2, 21 1

n

i ii

n n

i ii i

k k k k

k k k kρ =

= =

− −=

− ⋅ − [2]

where k1- and k

2- equal the mean value of the

corresponding hyperbolic model parameter. Fig. 3a shows that the new model parameters agree with those reported by Phoon and Kulhawy (2008), but indicates that the strong inverse correlation between k

1 and

k2 is significantly stronger than previously

characterized. To perform accurate and unbiased reliability analyses at the SLS, the correlation between model parameters and D/B must be considered. The dependence of k

1

and k2 on D/B was eliminated for the purposes

of simulation by transforming the model parameters:

1, 1tBk kD

= ⋅ [3]

[FIG. 1] Correlation between slenderness ratio D/B and hyperbolic parameters k1 (a) and k2 (b) from Phoon and Kulhawy (2008) and new data

0

5

10

15

20

0 20 40 60 80

k 1

D / B

New Data

Phoon and Kulhawy (2008)

(a)0.0

0.5

1.0

1.5

0 20 40 60 80

k 2

D / B

(b)


2, 2tDk kB

= [4]

The Kendall’s Tau correlation test between k1,t

and D/B, k

2,t and D/B, k

1,t and SPT-N, and k

2,t

and SPT-N yielded p-values = 0.78, 0.56, 0.37, and 0.96, respectively, indicating no correlation for α = 5 percent. Fig. 3b illustrates that the hyperbolic model parameters remain correlated to one another once transformed using Eqns. 3 and 4, respectively.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 5 10 15 20

k 2

k1

New Data

Phoon and Kulhawy (2008)

= -0.80 (Expanded dataset)

= -0.67 (Phoon and Kulhawy 2008 dataset)

(a)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0 0.2 0.4 0.6

k 2,t

k1,t

= -0.73 (Expanded dataset)

= -0.69 (Phoon and Kulhawy 2008 dataset)

(b)

[FIG. 3] Correlation between model parameters (a) k1 and k2 and (b) k1,t and k2,t from Phoon and Kulhawy (2008) and the new data

The statistical distributions of k1,t

and k2,t

must be determined in order to accurately simulate the uncertainty in the observed load-displacement curves. The empirical, fitted normal, and fitted lognormal marginal cumulative distribution functions (CDF) for k

1,t and k

2,t are shown in Fig. 4a and 4b,

respectively. The Anderson-Darling goodness-of-fit test (Anderson and Darling 1952) provided no evidence to reject the null hypothesis of

lognormality for k1,t

and k2,t

at α = 5 percent. Although the distribution of k

2,t could also be

considered normal based on the Anderson-Darling test, the null hypothesis of normality for k

1,t was rejected (p-value < 0.05). In addition

to the evidence provided by the Anderson-Darling test, a lognormal marginal distribution was adopted for k

1,t and k

2,t because it

appeared to fit the data better than the normal distribution, and the lognormal distribution is confined to positive real values.

0.00.10.20.30.40.50.60.70.80.91.0

0 0.1 0.2 0.3 0.4 0.5

Cum

ulat

ive

Prob

abili

ty D

ensi

ty

k1,t

Lognormal

Normal

Mean = 0.16St. Dev. = 0.08

COV = 48.7n = 87

(a)

0.00.10.20.30.40.50.60.70.80.91.0

0 2 4 6

Cum

ulat

ive

Prob

abili

ty D

ensi

ty

k2,t

Mean = 3.40St. Dev. = 0.80

COV = 23.4n = 87

(b)

[FIG. 4] Empirical, lognormal, and normal marginal cumulative distributions for the hyperbolic model parameters: (a) k1,t, and (b) k2,t.

In order to adequately represent the uncertainty associated with the SLS model, load-displacement curves were simulated using randomly generated, lognormally distributed, correlated pairs of k

1,t and k

2,t. The simulated

curves were then compared to the normalized


empirical curves from the database to assess the appropriateness of the simulations. The simulation of k

1,t and k

2,t using a translational

model requires generating uncorrelated standard normal random variables Z

1 and Z

2

(mean = 0, standard deviation = 1) which are then transformed into correlated random variables X

1 and X

2 (Phoon and Kulhawy 2008):

1 1X Z= [5a]

22 1 ln 2 ln1X Z Zρ ρ= ⋅ + − [5b]

where ρln = an equivalent-normal correlation

coefficient and is given by:

( )( )2 21, 2,

ln1, 2,

ln 1 1 1t t

t t

e eζ ζρρ

ζ ζ

⋅ − − +=

⋅

[6]

Where λ1,t

, ζ1,t

and λ2,t

, ζ2,t

are the approximate lognormal mean and standard deviation of k

1,t

and k2,t

, respectively. These second moment descriptors were calculated from the sample mean, k

i,t, and standard deviation, σ

i,t:

2 2, , ,ln(1 / )i t i t i tkζ σ= + [7a]

2, , ,ln( ) 0.5i t i t i tkλ ζ= − ⋅ [7b]

Lognormally distributed k1,t

and k2,t

simulations were then calculated by:

( )1, 1 1,1,

t tXtk e ζ λ⋅ += [8a]

( )2, 2 2,2,

t tXtk e ζ λ⋅ += [8b]

Because ρln is unstable for highly correlated

variables and small sample sizes, the appropriateness of two non-parametric correlation coefficients for use in Eqn. 5b, including Kendall’s Tau, ρτ, and Spearman’s rank, ρ

s, for simulation of load-displacement

curves was investigated. Substituting ρs for

ρln in Eqn. 5b is equivalent to the rank model

described by Phoon and Kulhawy (2008). Spearman’s rank coefficient is calculated by applying Eqn. 2 to the ranked distributions of k

1,t and k

2,t, whereas ρτ is based purely on the

number of concordant and discordant pairs of k

1,t and k

2,t and thus provides an unbiased

estimate of the population parameter. Table 1 shows each correlation coefficient investigated

herein (ρln, ρ

s, and ρτ) for k

1 and k

2, and k

1,t

and k2,t

.

To verify that the translational model described above can adequately reproduce the uncertainty in the observed load-displacement curves, k

1,t and k

2,t were first simulated using Eqns.

8a-b and then back-transformed into k1 and

k2 using deterministic values of D/B because

the uncertainty associated with the individual pile geometry due to construction error could not be evaluated and the distribution of D/B in the database was relatively uniform. Fig. 5 compares the observed and simulated model parameters and corresponding load-displacement curves using ρ

ln and D/B = 25, 45,

and 65. The slenderness ratios in Fig. 5 were selected in order to represent the distribution of observed values in the database and illustrate the difference in the range of simulated model parameters for different D/B.

In general, the simulated model parameters in Fig. 5 represent the scatter of the observed values well. Owing to the values of D/B used to back-transform k

1,t and k

2,t, the simulated

model parameters in Fig. 5 are associated with different sections of the observed k

1-k

2

relationship. Smaller slenderness ratios are associated with smaller and larger k

1 and k

2,

respectively, whereas the opposite holds for larger D/B. For each independent average D/B in Fig. 5 (i.e. 25, 45, 65), the correlation coefficient of the back-transformed simulated model parameters is equal to ρ

ln(k

1,t,k

2,t) = -0.82

as shown in Table 1; the combined coefficient of the back-transformed simulated model parameters should be approximately equal to ρ

ln(k

1,k

2) = -0.92 in Table 1) but will depend on

the number and values of D/B selected.

In order to validate the use of a non-parametric correlation coefficient in place of ρ

ln in Eqn. 5b

and compare the degree of scatter produced in the simulated load-displacement curves, 5,000 simulations of k

1,t and k

2,t were generated

using Eqns. 8a and 8b, respectively, and the correlation coefficients shown in Table 1. The simulated, transformed model parameters were then converted to k

1 and k

2 for D/B = 25,

35, 45, 55, and 65 in order to provide a higher resolution of simulated load-displacement curves.


[TABLE 1] Correlation coeffi cients for original and transformed model parameters

Correlation Coefficient

k1, k

2k

1,t, k

2,t

pln

-0.919 -0.819

ps

-0.850 -0.824

pτ -0.702 -0.648

Fig. 6 shows the simulated and observed distribution of k

1 and k

2 and the corresponding

load-displacement curves using ρln, ρ

s, and

ρτ. In general, the observed and simulated correlation coefficients are in good agreement, where ρ

ln(Simulated), ρ

s(Simulated), and

ρτ(Simulated) are calculated with Eqn. 6 in order to be consistent with the methodology used to simulate k

1,t and k

2,t. Ideally, the combined

correlation coefficient of the back-transformed simulated model parameters should equal that of the observed model parameters. The degree of scatter in the simulated k

1 and k

2 values is

inversely proportional to the magnitude of the correlation coefficient, where the scatter in Figs. 6a and 6b is smaller compared to Figs. 6e and 6f since | ρ

ln | > | ρτ |. Based on the simulated

load-displacement curves, it appears that the use of ρτ provides the most appropriate k

1 - k

2

correlation.

INVESTIGATION OF RBD FOR THE SERVICEABILITY LIMIT STATE The serviceability limit state (SLS) occurs when the foundation settlement, y, equals or exceeds a predetermined allowable settlement, y

a. In

terms of load, the SLS takes place when the

applied load, Q, equals or exceeds an allowable load, Q

a, which can be related to y

a through

the use of a selected load-displacement model. The assessment of the SLS can be evaluated quantitatively through the use of a performance function, P, defined here as (Phoon and Kulhawy 2008):

( ) ( )a a aP y y Q Q y Q= − = − [9]

which is equal to or less than zero for the SLS. If Q is assumed to be deterministic, y remains a random variable because of the uncertainty present in the load-displacement model (Phoon 2006). Similarly, Q

a is a random variable even

if ya is assumed to be deterministic. The

probability of exceeding the limit state (i.e., failure), p

f, can be defined as:

( )Pr 0fp P= < [10]

Eqns. 1 and 9 were substituted into Eqn. 10 and rearranged in terms of a deterministic mean factor of safety, FS, to determine the probability of failure (Phoon and Kulhawy 2008):

1 2

1Pr af

a p

y Qpk k y FS Q

′= < ⋅

′+ ⋅ [11]

where Q’ and Q’p are unit mean random

variables associated with the applied load and predicted pile capacity, respectively, and FS is associated with the ULS and equivalent to a global value adopted in current practice (Phoon 2006). The inverse standard normal function, Φ-1, maps the probability of failure to the reliability index, β, in order to provide an estimate of the foundation reliability at the SLS:

( )1fpβ −= −Φ [12]

0 5 10 15 200.0

0.2

0.4

0.6

0.8

1.0

1.2

k1

k 2

ρln(Observed) = -0.92ρln(Simulated) = -0.91

(a)

Simulated (D/B=25)Simulated (D/B=45)Simulated (D/B=65)Observed

0 10 20 30 40 500.0

0.5

1.0

1.5

2.0

2.5

Q/QSTC

y

(b)

[FIG. 5] (a) Observed and simulated hyperbolic model parameters, k1 and k2, and (b) load displacement curves for pln and D/B = 25, 45, and 65


defined as the number of standard deviations between the mean of the multivariate resistance distribution and the limit state surface. To accurately assess foundation reliability at the SLS, the uncertainty in y and y

a must be

characterized (Zhang and Phoon 2006). In this probabilistic framework, the uncertainty in y is captured by the bivariate distribution of k

1

and k2 that is associated with the hyperbolic

model, whereas ya in Eqn. 11 is treated as a

random variable with a pre-defined magnitude of uncertainty.

Model Statistics

Second-moment model statistics (i.e. mean and COV) and the type of distribution must

be known (or assumed) for all variables contributing to foundation reliability to perform RBD simulations. Model statistics for k

1,t and

k2,t

were obtained from the database herein and found to be correlated and lognormally distributed, as described earlier. Table 2 summarizes the statistics for the other random variables in Eqn. 11, selected for comparison to a previous study by Phoon and Kulhawy (2008). The model statistics for Q’

p given in

Table 2 were associated with the Meyerhof method for shaft resistance that was modified by Kulhawy and Chen (2005) for use with ACIP piles. The model statistics for Q’ are consistent with that recommended for live loads by Paikowsky et al. (2004). Phoon and Kulhawy

[FIG. 6] Observed and simulated hyperbolic curve-fi tting parameters, k1 and k2, and load-displacement curves for: (a) and (b) pln, (c) and (d) ps, and (e) and (f) pτ .


(2008) recommended a mean ya of 25 mm (1 in)

based on Skempton and McDonald (1956), and a COV of 60 percent based on probabilistic distributions of limiting tolerable displacements reported by Zhang and Ng (2005). Each random variable in Table 2 was modeled using a lognormal distribution.

[TABLE 2] Statistics for the transformed model parameters, k1,t and k2,t from the new database and model statistics from Phoon and Kulhawy (2008)

Random Variable

Mean COV %

k1

0.16 48.7

k2

3.40 23.4

ya

25 60

Q'p

1 50

Q' 1 20

FS 3 -

First-order Reliability Method

Although closed-form solutions exist for estimating β at the SLS, the distribution of the model factor has to be evaluated each time a new allowable displacement is specified (Phoon, 2006). The first-order reliability method (FORM) is therefore a more suitable approach as y

a is

treated as a random variable. In FORM, each random variable in the limit state function (k

1,

k2, y

a, Q’, Q’

p) is transformed into a standard

normal variable such that the differences in magnitude of the random variables are eliminated (Hasofer and Lind 1974). The random variables in Eqn. 11 are represented by a multivariate probability distribution that is traversed by the performance function. The portion of the multi-dimensional distribution beneath the hyper-surface where P ≤ 0 is equal to the probability of failure. FORM assumes the limit state function is linear at the most probable failure point, so the probability of failure can be calculated by evaluating the standard normal CDF as a function of β. This approach may not be applicable for strongly nonlinear limit state functions (Chan and Low 2011) or when probabilities of failure are very large, though the latter case is typically avoided in geotechnical applications.

Effect of Sample Size and Slenderness Ratio on Reliability

In order to study the effect of sample size on the distribution of the reliability index, simulations that randomly sampled the new k

1,t and k

2,t data set were performed. Fifty-

thousand subsets of k1,t

and k2,t

with sample size n ranging from 5 to 85 were generated using Monte Carlo simulations. The Spearman rank, Kendall’s Tau, and equivalent normal correlation coefficients (ρ

s, ρτ, and ρ

ln) and the

corresponding β were calculated for each subset using the model statistics for y

a, Q’, Q’

p, and FS

in Table 2.

Figs. 7a-c show the effect of sample size on the mean and COV of β for the SLS at y = 25 mm (1 in) assuming D/B = 65, 45, and 25, respectively. Although the mean β stabilizes rapidly, the uncertainty in β continues to decrease with increasing sample size for each D/B and correlation coefficient. However, the COV(β) remains relatively small compared to typical geotechnical design parameters, and falls below 2 and 1 percent between n = 25 and 58, and 55 and 78, respectively, for the range of D/B and the correlation coefficients investigated. In general, ρ

s and ρ

ln produced

similar β for larger sample sizes. Though the use of ρ

ln and ρ

s consistently produces larger β

compared to ρτ, the difference is relatively small (e.g., 0.024 at n = 85 and D/B = 25). Thus, β is relatively insensitive to the type of correlation coefficient and the sample size for n ≥ 40.

Effect of Design Variables on Pile Foundation Reliability at the SLS

In order to determine which design variables most strongly influence foundation reliability at the SLS, reliability indices were calculated for different values of the mean and uncertainty of allowable displacement, the uncertainty of predicted resistance, and slenderness ratio. Each random variable in the performance function (Eqn. 11) was presumed to be lognormally distributed, while the statistics for k

1,t and k

2,t (i.e. mean, COV) were obtained

directly from the database herein. For allowable displacement, the mean and COV was varied from 10 to 50 mm (0.4 to 2.0 in) and 5 to 85 percent, respectively. The uncertainty of predicted resistance, which relates to different capacity prediction methodologies with an associated amount of uncertainty, ranged


from 5 to 85 percent. In order to investigate the effect of pile geometry on β, slenderness ratios of 25 and 65 were chosen based on the approximate upper and lower bounds observed in the database. The statistics for the other variables in the performance function (Q’ and FS) are listed in Table 2.

[FIG. 8] The effect of COV(ya), COV(Q’p), and D/B on ββ for mean ya = 10 (a), 20 (b), 30 (c), 40(d), and 50 mm (e) using the fi rst-order statistics of k1,t and k2,t (Table 2)

1.74

1.76

1.78

1.80

1.82

1.84

0

2

4

6

8

10

12

0 20 40 60 80

Mea

n R

elia

bilit

y In

dex,

CO

V (

), %

COV (Kendall Tau)COV (Spearman rank)COV (Eqv. Normal)

(Spearman rank) (Kendall Tau) (Eqv. Normal)

D/B = 65

(a)

2.04

2.05

2.06

2.07

2.08

2.09

0

2

4

6

8

0 20 40 60 80

Mea

n R

elia

bilit

y In

dex,

CO

V (

), %

D/B = 45

(b)

2.17

2.18

2.19

2.20

2.21

2.22

0

1

2

3

4

5

6

0 20 40 60 80

Mea

n R

elia

bilit

y In

dex,

CO

V (

), %

Sample Size, n

D/B = 25

(c)

[FIG. 7] The relationship between the mean and COV of reliability index and sample size using 50,000 realizations for pln, ps, and pτ and D/B = (a) 65, (b) 45, and (c) 25


The effect of varying the mean and COV of ya,

COV of Q’p, and D/B on β is shown in Figs. 8a-e

for allowable pile head displacements ranging from 10 to 50 mm (0.4 to 2.0 in). In general, decreasing the uncertainty in Q’

p and y

a, and

increasing the mean ya results in a larger

reliability index and a smaller probability of exceeding y

a. When the uncertainties in Q’

p and

ya are relatively small (5-45 percent), foundation

reliability decreases rapidly with increasing uncertainty in Q’

p; this trend is more prominent

for smaller slenderness ratios and larger mean allowable displacements. As the uncertainties for Q’

p and y

a become larger, β continues to

decrease though at a slower rate. For each mean y

a and slenderness ratio investigated

herein, the uncertainty in Q’p had a larger

effect on β as compared to the uncertainty in y

a. For larger allowable displacements and

smaller slenderness ratios, β was observed to be insensitive to the level of uncertainty in y

a,

compared to Q’p; this demonstrates the benefit

of an accurate ACIP design methodology. As mean y

a increases and its associated uncertainty

decreases, β approaches an upper bound limit regardless of level of uncertainty in Q’

p. Overall,

the slenderness ratio had a considerable impact on foundation reliability, especially at lower mean allowable displacements. Fig. 9 shows the dependence of β on slenderness ratio; for example β = 2.21 and 1.76 (p

f = 1.34 and 3.90

percent) for D/B = 25 and 65, respectively. The contrast in the level of reliability is directly due to the difference in the statistical parameters of k

1 and k

2 that describe the characteristic

behavior in the load-displacement relationship for different D/B. As a result, taking into account the correlation between model parameters and slenderness ratio is essential when assessing the reliability of ACIP piles.

SUMMARY AND CONCLUSIONS This paper investigated the effect of various factors in reliability-based serviceability limit state (SLS) design using an expanded database consisting of ACIP piles installed in cohesionless soils. The hyperbolic model parameters, k

1 and k

2, used to model the load-

displacement data were shown to be more strongly correlated than previously reported. Additionally, these model parameters were found to be correlated to the slenderness ratio (D/B). After removing the dependence from D/B, the effect of sample size on the

uncertainty in the computed reliability index, β, was assessed for an equivalent normal and two different non-parametric correlation coefficients (Spearman rank, Kendall Tau) by generating random subsets of the transformed load-displacement model parameters from the ACIP pile database. The mean reliability index rapidly stabilized with sample size, n, and was shown to be relatively insensitive for n > 40. Although β is fairly stable with relatively low database sample sizes, this work shows that the accurate characterization of the degree of correlation among bivariate random variables requires as many samples as possible. A parametric study was conducted in order to identify the factors which govern foundation reliability at the SLS. The uncertainty in the capacity prediction model was found to have a larger effect on β compared to the uncertainty in allowable displacement; this finding illustrates one benefit of an accurate capacity prediction model. Owing to the dependence of the model parameters on pile stiffness and geometry, β was found to be very sensitive to D/B, and this effect must be accounted for in the reliability-based serviceability limit state design of deep foundations.

REFERENCES1. Anderson, T.W. and Darling, D.A. (1952)

“Asymptotic Theory of Certain Goodness-of-Fit Criteria Based on Stochastic Processes”, The Annals of Mathematical Statistics, 23(2), pp. 193-212.

1.7

1.8

1.9

2.0

2.1

2.2

2.3

20 30 40 50 60 70

Mea

n R

elia

bilit

y In

dex,

Slenderness Ratio, D/B

Spearman RankEqv. NormalKendall Tau

Correlation Coefficient

[FIG. 9] The relationship between the mean reliability index and D/B using pln, ps, and pτ .


2. Chan, C.L. and Low, B.K. (2011) “Practical Second-order Reliability Analysis Applied to Foundation Engineering”, International Journal for Numerical & Analytical Methods in Geomechanics, DOI: 10.1002/nag.1057.

3. Chen, J.R. (1998) “Case History Evaluation of Axial Behavior of Augered Cast-in-Place Piles and Pressure-Injected Footings”, MS Thesis, Cornell University, Ithaca, NY.

4. Daniel, W.W. (1990) “Applied Nonparametric Statistics”, 2nd Edition, PWS-Kent, Boston.

5. Hasofer, A.M. and Lind, N. (1974) “An Exact and Invariant First-Order Reliability Format”, Journal of Engineering Mechanics, ASCE, 100(1), pp. 111-121.

6. Kulhawy, F.H. (2004) “On the Axial Behavior of Drilled Foundations”, Geo-Support 2004: Drilled Shafts, Micropiling, Deep Mixing, Remedial Methods, and Specialty Foundation Systems, GSP No. 124, ASCE, Reston, VA, 18 pp.

7. Kulhawy, F.H. and Chen, J.R. (2005) “Axial Compression Behavior of Augered Cast-in-Place (ACIP) Piles in Cohesionless Soils”, Advances in Designing & Testing Deep Foundations, ASCE, pp. 275-289

8. Mandolini, A., Ramondini, M., Russo, G., and Viggiani, C. (2002) “Full Scale Loading Tests on Instrumented CFA Piles”, Proceedings, Deep Foundations 2002, GSP No. 116, pp. 1088-1097.

9. McCarthy, D.J. (2008) “Empirical Relationships Between Load Test Data and Predicted Compression Capacity of Augered Cast-In-Place Piles In Predominately Cohesionless Soils”, M.S. Thesis, University of Central Florida, Orlando, 209 pp.

10. O’Neill, M.W., Vipulanandan, C., Ata, A., and Tan, F. (1999) “Axial Performance of Continuous Flight Auger Piles for Bearing”, Project Report No. 7-3940-2, Texas Department of Transportation, 253 pp.

11. Park, S., Roberts, L.A., and Misra, A. (2012) “Design Methodology for Axially Loaded Auger Cast-in-Place (ACIP) and Drilled Displacement (DD) Piles”, Journal of Geotechnical & Geoenvironmental Engineering, 138(12), pp. 1431-1441.

12. Paikowsky, S.G., with contributions from Birgisson, B., McVay, M., Nguyen, T., Kuo, C., Baecher, G., Ayyab, B., Stenersen, K., O’Malley, K., Chernauskas, L., and O'Neill, M. (2004) “Load and Resistance Factor Design (LRFD) for Deep Foundations”, NCHRP Report 507, Transportation Research Board, Washington, DC, 126 pp.

13. Phoon, K.K. (2006) “Serviceability Limit State Reliability-based Design”, International Symposium on New Generation Design Codes for Geotechnical Engineering Practice, November 2-3, Taipei, Taiwan, 18 pp.

14. Phoon, K.K., Chen, J.R., and Kulhawy, F.H. (2006) “Characterization of Model Uncertainties for Auger Cast-in-Place (ACIP) Piles under Axial Compression”, Foundation Analysis & Design: Inn. Meth. GSP No. 153, ASCE, pp. 82–89.

15. Phoon, K.K. and Kulhawy, F.H. (2008) “Serviceability Limit State Reliability-based Design”, Reliability-Based Design in Geotechnical Engineering – Comp. & App., London, UK: pp. 344-384.

16. Skempton, A.W. and McDonald, D.H. (1956) “The Allowable Settlement of Buildings”, Proceedings, Institute of Civil Engineering, Part 3(5), pp. 727-768.

17. Stuedlein, A.W., Neely, W.J., and Gurtowski, T.M. (2012) “Reliability-Based Design of Augered Cast-in-Place Piles in Granular Soils”, Journal of Geotechnical & Geoenvironmental Engineering, 138(6), pp. 709-717.

18. Uzielli, M. and Mayne, P.W. (2011) “Statistical Characterization and Stochastic Simulation of Load-displacement Behavior of Shallow Footings”, Geotechnical Risk Assessment & Management, GSP 224, ASCE, pp. 672-679.

19. Zhang, L.M. and Ng, A.M.Y. (2005) “Probabilistic Limiting Tolerable Displacements for Serviceability Limit State Design of Foundations”, Geotechnique, 55(2), pp. 151-161.

20. Zhang, L.M. and Phoon, K.K. (2006) “Serviceability Considerations in Reliability-based Design”, Foundation. Analysis & Design: Inn. Meth. GSP No. 153, pp. 127–136.


INTRODUCTIONWith the advancement of engineering knowledge, considerable improvements have been made in the piling industry in terms of materials, installation methods, and the sizes of these foundations. In the contemporary history of deep foundations technology, the pace of change has been particularly rapid. Until recently it was possible to categorize deep foundations according to their methods of installation as driven piles or drilled shafts. This, however, does not satisfactorily cope with the many different forms of piles now in use. The newer pile designs are dictated by such factors as economy, durability, time schedules, and heavier loads due to the size/scale of the modern state-of-the-art projects including multi-span bridges, high-rise towers, and offshore structures. Fig. 1 shows common applications of deep foundations in the modern times and the basic concept of pile-to-ground load transfer.

Thorough geotechnical site investigations have become far more vital prerequisite to making learned decisions for selection of reliable and economical designs and choice of construction methods. Conventional investigations (e.g., boring and sampling) for subsurface characterization of soil layers affecting the performance of deep foundations are time consuming, expensive, and tedious. Laboratory tests are conducted on disturbed samples, or costly undisturbed samples obtained from selected depths. The results are highly dependent on the tools employed in samples’ retrieval and skills and expertise of individuals assigned to the task. In-situ geotechnical investigation tools offer quick and economical alternatives. Their measurements can be employed in a wide variety of interpretive schemes and simulation models to match with the full-scale axial pile response (see Fig. 2).

In the realm of in-situ geotechnical methods, cone penetration testing (CPT) stands out as

A Review of the Design Formulations for Static Axial Response of Deep Foundations from CPT Data (DFI 2013 Student Paper Competition Runner-Up)Fawad S. Niazi, Graduate Research Assistant, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA, [email protected]

Research Advisor: Dr. Paul W. Mayne, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA, [email protected]

ABSTRACTAxial capacity analysis of deep foundations has been a topic of great interest in the soil-structure interaction problems. Soil behavior is governed by a series of complex stress-strain changes that occur during pile installation and subsequent loading. Owing to the difficulties and uncertainties on the basis of the soil strength-deformation characteristics, one of the most frequently followed design practice is to refer to the formulae correlating directly the pile axial capacity components of unit base resistance (q

b) and unit shaft resistance (f

p) to the data collected from cone penetration test (CPT). The

elementary basis for such formulations has been the idea of considering cone penetrometer as a mini-pile foundation. This has resulted in plethora of correlative relationships in the past over 60 years. Such correlations, although empirical, have been worked out on the basis of load test results from both instrumented and un-instrumented full scale piles and are able to accommodate many important variables. A quick review of the evolution process and development of such design formulations is presented. An existing method is refined and modified to bring more convenience in extended applications. Few recommendations are proposed for future research directions, where the latest version of CPT i.e., seismic piezocone test (SCPTu) can be used to advance from capacity singularity to the complete axial pile load – displacement (Q – w) response.


a modern, expedient, economical and reliable means of obtaining detailed subsurface profiles. In CPT, an electronic steel probe is hydraulically pushed into the ground to collect multiple continuous readings throughout the depth of investigation in a much shorter period of time. The data can be simultaneously logged and post-processed in a field computer to evaluate the geostratigraphy and engineering parameters of the geomaterials on-site, thereby offering quick and preliminary conclusions for final design parameters and analysis. The seismic piezocone penetration test (SCPTu), a newer version of CPT, is a hybrid geotechnical- geophysical in-situ device. It provides downhole measurements of shear wave velocity (V

s) at

every 1-m (3.3 ft) depth interval in addition to the penetration test parameters [tip stress (q

c)

or more proper corrected tip stress (qt), sleeve

friction (fs), and tip or mid-face porewater

pressure (u1) and/or shoulder porewater

pressure (u2)] at every 10 to 50 mm (0.4 to

2.0 in) depth interval from a single vertical sounding. Here, q

t = q

c + (1 – a

n)u

2, where a

n =

net area ratio of the particular penetrometer determined through calibration in a triaxial chamber. Fig. 3 shows a schematic illustration of implementing SCPTu.

PILE CAPACITY FROM CPT DATAEver since the first use of CPT in geotechnical investigations, research efforts have advanced the very elementary idea of considering it as mini-pile. This has resulted in plethora of correlations between CPT readings and pile capacity components of unit shaft resistance (f

p) and unit base resistance (q

b) on the basis

of load test results from instrumented and un-instrumented piles. These correlations are able to accommodate many important variables.

[FIG. 1] Examples of modern applications of pile foundations and the load transfer concept (Niazi, 2014)


There are two main approaches to accomplish axial pile capacity analysis from CPT data: (a) indirect (or rational) methods, and (b) direct methods. The rational methods require a two-step approach. As a first step, CPT data are used to provide assessments of stress history [overconsolidation ratio (OCR)], in-situ radial stress coefficient (K

o), undrained shear strength

(su), relative density (D

r), effective stress

strength (φ'), total unit weight (γt), fundamental

soil stiffness [intial shear modulus (Gmax

), or initial Young’s Modulus (E

max)], interface friction

between soil and pile material (δ), and bearing capacity coefficients (N

c, N

q). Some of the

pertinent relationships have previously been summarized by Niazi and Mayne (2010), and Niazi et el. (2010). Utilizing these input values of geoparameters, the second step enables assessments of f

p and q

b components of pile

capacity within a selected analytical framework.

The pile fp can be evaluated using total stress

analysis (α-method) for clays that relates fp to

su via adhesion factor (α), or effective stress

analysis (β-method) for sands and clays that relates effective overburden (σ

vo') via empirical

parameter β. The fundamental formulations of the two approaches for f

p are shown below:

α-Method: fp ≈ α ∙s

u [1]

β-Method: fp ≈ β ∙σ

vo' ≈ K

o∙σ

vo'∙tanδ [2]

For pile foundations, an important factor of relevance to q

b is the likely strain compatibility

differences between the unmatched mobilization of f

p and q

b components during

pile loading. For undrained loading (in clays and cohesive silts) beneath the base, q

b can fully

mobilize within tolerable limits of settlements, usually taken as w/d = 0.10, where w = pile settlement, and d = base diameter. In the case of drained loading (in sands and granular materials) it is impractical to assume full mobilization of end bearing resistance for the range of tolerable settlements.

[FIG. 2] Alternatives to interpret axial pile response from in-situ geotechnical investigations (Niazi, 2014)


To achieve a settlement ratio corresponding to w/d = 0.10, it is customary to use an operational value of q

b, reduced from the theoretical value

[qb = 0.1q

b(theory)]. In sands and slow loading

(long-term analysis) in clays and silts, drained conditions are assumed (use bearing capacity coefficient for overburden, N

q, corresponding to

effective stress analysis). In clays, silts, and soils with low permeability (assume φ' = 0 for rapid loading), undrained conditions are evaluated (use bearing capacity term for cohesion, N

c

corresponding to total stress analysis). Here qb

is evaluated using relevant coefficients to relate to s

u or σ

vo' (q

b ≈ N

c∙s

u for undrained loading,

and qb ≈ 0.1N

q∙σ

vo' for drained loading). N

c ≈ 9

for deep foundations, while Nq is function of φ'.

Doherty and Gavin (2011), Jamiolkowski (2003), Patrizi and Burland (2001), and Karlsrud (2012) have effectively reviewed significant relevant contributions.

In this paper, no detailed discussions are included on the indirect methods. Yet, a summary list of the various factors considered by different researchers in their respective studies for their CPT-based rational evaluations has been included (see Tables 1 and 2).

The direct CPT methods use measured penetrometer readings by scaling algorithms to directly evaluate f

p and q

b for full-size pilings.

Fig. 4 presents various paths to evaluate the two components of q

b and f

p from CPT readings. As

shown in this tree chart, fp and q

b can also be

estimated using semi-empirical direct methods, in which few additional parameters are also

used besides the direct use of CPT readings. Herein, such semi-empirical direct methods have been grouped with the direct methods.

EVOLUTION OF CPT-BASED DIRECT PILE DESIGN METHODSAccording to Begemann (1969), the earliest research on the use of CPT-pile relationship was focused on estimating the driving depth of piles. Plantema (1948) concluded simple one-to-one correspondence between q

b and q

c for

jacked square concrete piles in dense sand. Huizinga (1951) introduced the concept of computing q

b from average q

c

in the soil zone influenced by pile base, and noted that the total shaft friction (Q

s) was

about twice the value computed by assuming f

p = f

s. Meyerhof (1951) identified the scale

effect on qb vs. q

c due to the difference in

pile and cone diameters. Van der Veen and Boersma (1957) further explored this scale effect relationship on concrete piles and CPT soundings, concluding that q

b equals average q

c

over the influence zone (L – 3.75d to L + 1.0d, where L and d are pile length and diameter, respectively). Menzenbach (1961) identified the type and strength of soil as additional factors affecting the relationship between q

b and q

c.

During the following six decades CPT-based direct pile design methods have considerably evolved. Various researchers extended the scope of investigations in their respective efforts to make improvements in the predictive reliability of these methods. They also extended such design applications to larger varieties of piles and soils by evaluating the influence of following factors, parameters and variables (also see Table 3, where applicable piles and soils are also listed):

• Pile embedment through varying strata

• Pile length and diameter (and slenderness ratio, L/d)

• Pile load application procedure [e.g., slow maintained load test (SMLT), quick maintained load test (QMLT), constant rate of penetration test (CRPT)]

[FIG. 3] A conceptual scheme of acquiring continuous multiple SCPTu readings (Niazi, 2014)


• Penetrometer-to-pile scale effects

• Pile-to-soil interface friction (δ)

• Sand Dr, K

o and stress history

• Thin seams of weak or stiff soil layers, leading to extreme values of CPT q

c profiles

(being non-influential for large piles)

• Definition of influence zone around the pile base for correlating q

b with q

c (or q

t)

• qc averaging procedures in the influence

zone

• Uplift (tension) vs. compression capacity

• Pile fp to penetrometer f

s correlations

• Pile installation methods: non-displacement piles (bored piles, drilled shafts), driven and jacked displacement piles [closed-ended (CE) steel piles, square or circular precast concrete piles etc.] and piles with small displacement [driven and jacked open-ended (OE) steel piles, steel H piles etc.]

• Pile end conditions (OE vs. CE)

• Base plugging for OE piles, separating qb

into plug resistance (qplug

) and annulus resistance (q

ann) and correlating both with q

c

(or qt)

• Diameter to wall thickness (d/t) for OE piles

• Relative position from pile base (h/r*), where r* is the modified radius of OE pile

• Different combinations of soil types including clays, silts, sands and mixed type

• Difference of CPT readings in mechanical vs. electrical friction sleeve penetrometers

• Piles in offshore environment

• Use of excess porewater pressure (u2)

readings in pile-CPT correlations

• Overburden stress (σvo

), and use of qt(net)

= qt

– σvo

instead of qt

• Tapered piles

• Spatial correlation between qc (or q

t) values

of different soil layers

Of particular interest amongst the direct methods are following design formulations that utilize multiple readings from CPTu (i.e., maximum data from cone penetration tests):

• Norwegian Geotechnical Institute – Building Research Establishment (NGI–BRE) Method

• UniCone Method

• Kajima Technical Research Institute, Japan (KTRI) Method

Direct Methods

Indirect Methods

CPT Based Pile Capacity Evaluations

fp via α-Methods:(fine grained soils)

fctn(su, σvo', OCR, Ip, L, plugging, progressive failure)

fp via β-Methods:(coarse and fine grained soils)fctn(σr, δ, φ', OCR, K, σvo', L, d,

su, Dr, St, Ip)

qb for undrained loading(fine grained soils): fctn(su)

qb for drained loading(coarse grained soils and slow loading

in fine grained soils):fctn(φ', σvo', L, d, Dr)

Pure Empirical Methods:Evaluate fp and qb directly using

qc (or qt), and/or fs and/or u2

Semi-Empirical Methods:Evaluate fp and qb using qc (or qt),

and/or fs with additional geoparameters(σr, δ, φ', K, σvo', L, d, su, Dr, plugging)

Total Stress Approach Effective Stress Approach

[FIG. 4] Alternative paths for CPT-based evaluations of fp and qb components of pile capacity (adapted from Niazi and Mayne, 2013)


Method/Reference Length effect Stress history Ip

su σ

vo' φ' Progressive failure Plugging effect

Tomlinson (1957) x x x √ x x x x

Peck (1958) x x x √ x x x x

Skempton (1959) x x x √ x x x x

Woodward et al. (1961) x x x √ x x x x

Kerisel (1965) x x x √ x x x x

API (1969) x √ x x √ x x x

McClelland (1974) x √ x √ x x x x

Vijayvergiya and Focht (1972)

√ x x √ √ x x x

Vesić (1977) x x x √ x x x x

Drewry et al. (1977) x x x √ x x x x

API (1975; 1976) x √ √ √ √ x x x

Kraft et al. (1981) √ x x √ √ x √ x

Dennis and Olson (1983) √ x x √ x x x x

Randolph (1983) √ x x √ x x √ x

Semple and Rigden (1984) √ √ x √ √ x x x

Randolph and Murphy (1985)

x √ x √ √ x x x

API (1987) x √ x √ √ x x x

API (1993) x √ x √ √ x x x

Karlsrud et al. (1993) x x √ √ √ x x x

Chen and Kulhawy (1994) x x x √ x x x x

Kolk and van der Velde (1996)

√ √ x √ √ x x x

Miller and Lutenegger (1997)

x x x x x x x √

O'Neill and Reese (1999) x x x √ x x x x

Jamiolkowski (2003) x √ x √ √ x x x

Goh et al. (2005) x √ x √ √ x x x

NGI-05 (Karlsrud et al., 2005)

x √ √ √ √ x x x

Salgado (2006; 2008; 2010)

x √ x √ x x x x

German Method (Kempfert and Becker, 2010)

x x x √ x x x

Karlsrud (2012) x √ √ √ √ x x x

Chakraborty et al. (2013) x x x √ √ √ x x

[TABLE 1] Factors considered in the total stress approach (α-methods) for estimating pile unit shaft resistance (fp)

Notes: Ip = plasticity index; s

u = undrained shear strength; σ

vo' = effective overburden stress;

φ' = friction angle; √ = factor considered in the design formulation; x = factor not considered in the design formulation.


Method/Reference σr' δ φ' OCR K σ

vo' L d s

uD

rS

tIp

Chandler (1968); Burland (1973); Pelletier and Doyle (1982)

√ √ √ x √ √ x x x x x x

Meyerhof (1976) x √ √ √ x √ x x x x x x

Flaate and Selnes (1977) x x x √ x √ √ x x x x x

Coyle and Castello (1981) x x √ √ x x √ √ x x x x

Kulhawy et al. (1983) √ √ √ x √ √ x x x x x x

Twine (1987); Patel (1989) x x x x x √ x x x x x x

Reese and O'Neill (1988) x x x x x √ √ x x x x x

Fleming et al. (1992) √ √ x x √ √ x x x x x x

Burland (1993) x x x √ x √ x x √ x x x

de Nicola and Randolph (1999) x x x x x √ x x x √ x x

Patrizi and Burland (2001) x x x √ x √ x x √ x x x

ICP-05 Method (Jardine et al., 2005) √ √ x √ √ x √ √ √ x √ √

Karlsrud (2012) x x x √ x √ x x x x x √

[TABLE 2] Factors considered in the effective stress approach (β-method) for estimating pile unit shaft resistance (fp)

Notes: σr' = radial effective stress; δ = soil-pile interface friction angle; φ' = effective friction angle; OCR

(= σp/σ

vo') = overconsolidation ratio; σ

p = preconsolidation stress; K (= σ

r'/σ

v') = radial effective stress

coefficient; σvo

' = effective vertical overburden stress; L = pile length; d = pile diameter; su = undrained

shear strength of clay; Dr = relative density of sand; S

t = sensitivity of clayey soils; I

p = plasticity index;

√ = factor considered in the design formulation; x = factor not considered in the design formulation.

• Fugro V-K Method

• UWA'13 Method

The respective design equations of these five methods are presented in Table 4. Some of these apply to specific pile and soil types, while others were based on wider varieties of piles installed in different geomaterials. Selected observations specific to each method are appended below:

• NGI-BRE method applies to driven and jacked piles in clays. Majority of piles analyzed were tested in tension (f

p is

the main component of pile capacity). Correlation for q

b was based on fewer

compression tests. The fs readings of CPTu

were not utilized.

• UniCone method developed from a large pile and soil database, applies to a wide variety of situations. Eslami and Fellenius (1997) noted that the number of cases was limited, and newer field experience should result in modifications of the situations. Eslami and Fellenius (1997) noted that the number of cases was limited, and newer field experience should result in modifications of the correlation coefficients (C

se and C

te).

A soil classification chart is used to select relevant values of C

se (see Fig. 5), making it

a two-step method. The classification chart presents five soil zones delineated by sharp boundaries. The recommended C

se values

show abrupt variations in any two adjacent zones, rather than gradual transitional values for intermediate soil types.

• KTRI method was derived by comparing CPT f

s with pile f

p over a range of applicable ∆u

2

of a small local database. Here, it may be noted that f

s is the weakest CPTu readings.

The most consistent qt reading was not

used.

• Fugro V-K method was developed from a database of driven offshore piles in clays, with limited applicability. Like the NGI-BRE method, f

s readings of CPTu were not

explored. For over one-third of the database, u

2 readings were not available, and were,

thus, derived from correlations. For almost 50% of the database, an was not available, in which case a value of 0.75 was assumed.

• UWA method, which was developed from 53 previous and 22 newer load tests, concerns driven and jacked piles in clayey soils only,


Method/reference

CPT readings, pile information and soil parameters used in the design equations

Pile unit shaft resistance (fp) Pile unit end bearing (q

b)

Bogdanovic (1961)(driven and jacked concrete piles in dense sand)

fs, d, and d

CPTq

ca(tip)

Begemann (1963; 1965; 1969)(driven piles in sand)

qc, f

s and pile type q

ca(tip)

Meyerhof (1976; 1983)(driven piles and drilled shafts in sand)

fs and pile type, or alternatively q

c

and pile typeq

ca(tip), d, z

d, and pile and soil types

Aoki and Velloso (1975)(piles in all soils)

qca(side)

, and pile and soil types qca(tip)

, and pile type

Nottingham (1975);Schmertmann (1978)(driven concrete, steel, timber piles and drilled shafts in all soils)

fs, pile and soil types, penetrometer

types, and relative depth (z/d)q

ca(tip), and soil and penetrometer

types

Penpile Method(Clisby et al., 1978)(piles in all soils)

fs

qca(tip)

, and soil type

Dutch Method(de Ruiter and Beringen 1979)(offshore piles in all soils)

qca(side)

, soil types, OCR, and loading direction

qca(tip)

, and soil type

Philipponnat (1980)(for all pile types in all soil types)

qca(side)

, and pile and soil types qca(tip)

, and soil type

LCPC or French Method(Bustamante and Gianeselli, 1982; Bustamante and Frank, 1997)(all piles in all soils)

qca(side)

, pile and soil types, and installation procedure

qca(tip)

, and pile and soil types

Cone-m Method(Tumay and Fakhroo, 1982)(piles in clay)

fsa

qca(tip)

Price and Wardle (1982) (driven and jacked piles, and drilled shafts in stiff clay)

fs, and pile type q

ca(tip), and pile type

Gwizdala (1984)(drilled shafts in sand)

qca(side)

, and soil type qca(tip)

, d, and pile type

Kulhawy and Phoon (1993); Kulhawy (2004); Lunne et al. (1997)(drilled shafts in clay)

qca(net)

, and σatm

qca(net)

Alsamman (1995)(drilled shafts in all soils)

qca(side)

, and soil and penetrometer types

qca(tip)

, and soil and penetrometer types

NGI-BRE Method(Almeida et al., 1996; Powell et al., 2001; Powell and Quarterman, 1988)(driven and jacked piles in clay)

qt(net)

, and σvo

' qt(net)

, clay type

Politecnico di Torino Method(Fioravante, 1994; Fioravante et al., 1995) (drilled shafts in sands)

qca(side)

qca(tip)

, wt, d, and soil type

Lee and Salgado (1999) (piles in sands)

This method does not indicate a means for evaluating f

p

qta(tip)

, wb, and d

UniCone Method(Eslami and Fellenius, 1997; Fellenius, 2002)(all piles in all soils)

qt(side)

, u2(side)

, and soil classification based on q

t(side), u

2(side) and f

s

qt(tip)

, u2(tip)

, and d

[TABLE 3] Summary of the CPT based direct pile design methods


Method/reference



b)

KTRI Method(Takesue et al., 1998)(all piles in all soils)

fs and ∆u

2(side)This method does not indicate a means for evaluating q

b

UWA-99 Method(De Nicola and Randolph, 1999)(driven pipe piles in medium - dense sand)

This method utilizes effective stress approach as a means for evaluating f

p

qca(tip)

, d, wb, σ

vo', pile end conditions,

and sand plugging

TCD-01 Method(Lehane and Gavin, 2001)(jacked pipe piles in loose sand; measured IFR)


p

qca(tip)

, pile end conditions, and sand plugging

TCD-03 Method(Gavin and Lehane, 2003; de Nicola and Randolph, 1999)(OE jacked pipe piles in loose sand; measured IFR)

qc(side)

, d, t, σrc', δ, σ

vo', q

c(tip), loading

direction, sand pluggingq

ca(tip), d, t, σ

vo', and sand plugging

Fugro-05 Method(Kolk et al., 2005)(driven piles in sands, mostly offshore piling)

qc(side)

, d, t, σvo

', loading direction, and pile shape

qca(tip)

, d, t, wb

UCD-05 Method(Gavin and Lehane, 2005)(driven and jacked pipe piles in dense sand)


p

qca(tip)

, t, pile end conditions, and sand plugging

ICP-05 Method(Jardine et al., 2005)(driven piles loaded first time via SML test around 10 days after driving in sand)

qc(side)

, d, t, σr', δ, σ

vo', loading

direction, and pile end conditionsq

ca(tip), d, t, d

CPT, D

r, pile end

conditions, and sand plugging

ICP-05 Method(Jardine et al., 2005)(driven piles in clay)

This method utilizes effective stress approach as a means for evaluating f

p

qca(tip)

, d, t, dCPT

, pile loading rate and end conditions, and soil plugging

UWA-05 Method(Lehane et al., 2005)(driven piles in sand)

qc(side)

, d, σr', δ, σ

vo', loading direction,

and sand pluggingq

ca(tip), d, t, and sand plugging

NGI-05 Method(Clausen et al., 2005)(driven piles in sand)

qc(side)

, Dr, σ

vo', loading direction, and

pile material and end conditionsq

ca(tip), D

r, σ

vo', L, d, t, pile end

conditions, and sand plugging

Cambridge-05 Method(White and Bolton, 2005)(CE pipe piles, Franki piles, PCC piles jacked or driven through soft into hard sand layer)


p

qca(tip)

, L, d, and zd

Togliani (2008)(cylindrical and tapered driven piles and drilled shafts in all soils)

qc(side)

, L, d, and pile type and shape qca(tip)

, L, dtip

, and pile type

German Method(Kempfert and Becker, 2010) (piles in sand)

qc(side)

qca(tip)

UCD-11 Method(Igoe et al., 2010; 2011) (OE piles in sand)

qc(side)

, σr', δ, d, t, σ

vo', and sand

plugging and densityThis method does not indicate a means for evaluating q

b

Fugro V-K Method (Van Dijk and Kolk, 2011)(offshore piles in clay)

qt(net)

, and depth qt(net)


Notes: d = pile diameter; dCPT

= penetrometer diameter; qca

and qta = averages of q

c and q

t values

(respectively) in the applicable zone/soil layers that depend on the method; zd = embedment depth

in dense sand layer (in m); z = depth below ground surface; OCR = overconsolidation ratio; fsa

= Ft/L

= average layer friction, Ft = total sleeve friction determined for pile penetration length (L) in the

layer; qc(net)

= qc – σ

vo; q

t(net) = q

t – σ

vo; σ

vo' = effective vertical overburden stress = σ

vo – u

o; σ

vo = vertical

overburden stress; wt and w

b are displacements at pile head and base, respectively; u

2 = porewater

pressure measured at cone shoulder; ∆u2 = excess pore water pressure = u

2 – u

o; u

o = hydrostatic

pore water pressure; σr' = radial effective stress; δ = pile-soil interface friction angle; t = pile wall

thickness; Dr = relative density; OE = open-ended; CE = close ended; SML = slow maintained load; IFR = incremental filling ratio.

Method/reference



b)

SEU Method (Cai et al., 2011; 2012)(driven or jacked PCC thin-wall high-strength caissons, cement fly- ash grave pile in clay)

fs, and ∆u

2 Similar to Unicone Method

HKU Method(Yu and Yang, 2012)(OE steel pipe piles in sand)


p

qca(tip)

, soil plugging

UWA-13 Method(Lehane et al., 2013)(for shaft capacity of driven and jacked piles in clays)

qt, d, t, and depth This method does not indicate a

means for evaluating qb

[TABLE 4] Design formulations of CPTu based direct pile design methods

Method/Reference Design Equations


b)

NGI-BRE Method

(Almeida et al., 1996; Powell et al., 2001)

(for driven/jacked piles in clays)

fp = q

t(net)/k

1

k1 = 10.5 + 13.3log[q

t(net)/σ

vo']

qb = q

t(net),avg/k

2

k2 = N

kt/9; N

kt = 15 (soft – firm intact

clays), 25 to 35 (fissured to hard clays)

UniCone Method

(Eslami and Fellenius, 1997; Fellenius, 2002)

(for all pile and soil types)

fp = C

se∙q

E

qE = q

t – u

2

(see Fig. 5 for Cse)

qb = C

te∙q

Eg

Cte is generally taken as 1; for pile

diameter d > 0.4 m, Cte = 1/(3d)

KTRI Method

(Takesue et al., 1998)

(for all pile and soil types)

For ∆u2 > 300 kPa: f

p /f

s = ∆u

2/200

– 0.5

For ∆u2 < 300 kPa: f

p /f

s = ∆u

2/1250

+ 0.76

This method does not indicate a means for evaluating q

b

Fugro V-K Method

(Van Dijk and Kolk, 2011)

(for offshore driven piles in clays)

fp = k

s(z)∙q

t(net),z

ks(z)

= 0.16(h/uL)–0.3[Qt(z)

]–0.4 < 0.08

uL = 1.0 m (=3.3 feet)Q

t(z) = q

t(net),z/σ

vo' at z

qb = 0.7q

t(net),avg

UWA Method

(Lehane et al. 2013)

(for driven/jacked piles in clays)

fp = 0.055 qt [max(h/r*, 1)]–0.2 This method does not indicate a

means for evaluating qb

Notes: qt(net)

= qt – σ

vo; σ

vo = vertical overburden stress; σ

vo' = effective vertical overburden stress = σ

vo –

uo; u

o = hydrostatic pore water pressure; q

t(net),avg = q

t(net) averaged + 1.5d over pile toe level; N

kt = cone

factor; Cse = shaft correlation coefficient from soil classification chart (from q

t; f

s and u

2); C

te = toe


and therefore, it has limited applicability. This method does not indicate a means for evaluating q

b.

A REFLECTION ON CPT-BASED DIRECT METHODS FOR AXIAL PILE DESIGN In order to select and implement any suitable CPT-based direct method detailed above, following factors warrant deliberations:

Measured vs. Corrected Cone Tip Resistance

The methods that utilize tip stress do not commonly account for correction of u

2 acting

on unequal tip area of the cone to obtain qt. In

clean sands and dense granular soils, it may be reasonable to assume q

t ≈ q

c because u

2 remains

nearly hydrostatic (uo). In soft to stiff intact clays and silts considerable Du

2 are generated

during cone penetration, warranting significant corrections to the measured q

c in order to

obtain qt (Mayne, 2007).

1

2

4

3

5

0.1

1

10

100

1 10 100 1000

q E=

q t-u

2(M

Pa)

Sleeve Friction, fs (kPa)

fp = Cse qE

where Cse = shaft correlation coefficient

Zone No. Soil Type Cse

1 Soft sensitive clay 0.082 Soft clay and silt 0.053 Stiff clay and silt 0.0254 Silty sandy mix 0.015 Sand 0.004

[FIG. 5] UniCone chart for zone numbers and soil types (after Eslami and Fellenius, 1997)

Dependence on CPT Data and Additional Measurements

From within the two main groups of CPT-based direct methods, for pure-empirical category, simple direct relationships have been suggested between CPT readings and f

p and/or

qb components of pile capacity. Semi-empirical

methods require additional parameters,

besides simple reliance on CPT readings. Certain methods falling in the category of semi-empirical methods take the basic concept from either total or effective stress approach, while also establishing direct correlations with CPT readings.

From the list of CPT-based direct methods, 26 methods provide estimates for both f

p and q

b,

while the remaining 9 methods account for either of the two. Most of the direct methods draw purely on cone tip stress readings. Only a few rely on multiple CPTu parameters. Fig. 6 provides an overview of the dependence of these methods on various combinations of CPT readings and additional measurements.

Soil Influence Zone at Pile Tip

Direct methods relate unit base resistance to the cone tip resistance (q

c or q

t). Here, the

penetrometer readings are averaged over the depth interval of "influence zone" around the pile base (or toe). Different methods provide different definitions of the influence zone to account for the rupture path around to the pile toe. Various considerations that have led to these recommendations include: (1) the trend of cone tip resistance values around the pile toe, (2) extent of soil variability around the pile toe, (3) pile diameter, (4) pile embedment depth into the bearing stratum, (5) existence of weak layer beneath the bearing layer, and (6) soil compressibility.

Averaging Procedure for qc

The CPT readings typically display squiggly profiles of random peaks and troughs representing thin seams of variable soils. For large piles, inclusion of these readings in averaging can result in non-representative q

b

values. Readings may be filtered out applying a "minimum path" rule (e.g., Begemann, 1963), or by simply removing the peaks and troughs from the records (e.g., Bustamante and Gianeselli, 1982). Eslami and Fellenius (1997) recommended use of geometric average, as it results in filtered representation of readings.

correlation coefficient; qEg

is the geometric average of qE values over the influence zone (from 4d below

pile toe to 8d above pile toe if pile is installed from weak soil into dense soil, and from 4d below pile toe to 2d above pile toe when pile is installed from dense soil into weak soil) after correction for u

2 and

adjustment to σvo

'; d = pile diameter; ∆u2 = excess pore water pressure = u

2 – u

o; z = depth below the

surface; h = distance between pile toe level and z; uL = unit length to render expression dimensionless; r* = modified radius term for OE piles = (r2 – r

i2)0.5; r = external radius; r

i = internal radius.


[FIG. 6] Dependence of different CPT-based direct methods on combinations of CPT readings and additional parameters (adapted from Niazi and Mayne, 2013)

Shaft and Base Areas for Pile Capacity Calculations

The pile Qs and Q

b calculations involve pile shaft

area (As) and base area (A

b), respectively. As is

simply given as the product of pile perimeter and length. For a circular pile, A

s = π∙d∙L.

For square and rectangular piles, equivalent diameter can be adopted: d = (2b + 2w)/ π, where b and w are the depth and width of pile cross-section, respectively. For OE piles, soil plug forms through the base during driving. It provides additional internal shaft resistance during loading, commonly considered as part of base resistance. For H piles, A

s contributing

to shaft capacity depends on the degree of soil that enters the flanges and remains attached during loading. Seo et al. (2009) recommended two options for A

s calculations

for H piles: (1) use average reduction factor of 0.65 when assuming full soil-pile interface contact perimeter; (2) assume outer perimeter in shaft capacity calculations to get comparable estimates.

For circular solid or CE pipe piles Ab is obtained

as π∙d2/4. For non-circular solid piles of square or rectangular cross-sections, equivalent base diameter is obtained from: d = (4 b∙w/π)0.5. For OE pipe piles, following the recommendations by Gavin and Lehane (2005), and Yu and Yang (2012), separate contributions from annulus and plug should be considered:

Qb = (π/4)∙[d2 q

plug + (d2 – d

i2) q

ann] [3]

where qplug

and qann

are unit plug resistance and unit annulus resistance of pile; d = pile outer diameter; d

i = pile inner diameter. For H

piles, Jardine et al. (2005) and Seo et al. (2009)

provided detailed recommendations for Ab

calculations, based on soil type, estimates of plugging and pile loading rate.

Capacity Criteria Definition

A single value of axial load on the entire load-displacement (Q – w) response curves is selected for design load purposes, commonly termed as "pile capacity." There are at least 45 different criteria defining the axial capacity. The capacities interpreted via different criteria span over a wide range (e.g., Niazi, 2011). Mostly, CPT-based methods do not explicitly refer to the specific capacity criterion on the basis of which the design equations were formulated, thus adding to the degree of uncertainty. Without reference to any specific criterion, the engineer in-charge of piling project has to rely on subjective judgment and apply Factors of Safety to strike a balance between economical and safe design.

Practicality of CPT-based Methods

Most of the research on pile-CPT correlations has addressed driven piles in sands. Drilled shafts in clays and silts have attracted lesser attention. Large amounts of latest database from the modern bi-directional Osterberg-cell (O-cell) type of proofing method have also been included in the inventory of load tests. Existing CPT-based methods focus purely on "pile capacity," without recourse to the behavior in terms of Q – w response. In particular, the V

s

component of the newer SCPTu can provide G

max = (γ

t/g)∙V

s2, where g = gravitational

acceleration. Gmax

can be utilized within an analytical framework (e.g., Randolph and Wroth, 1978) towards extension of pile design to "complete axial Q – w response." An appropriate modulus reduction scheme [e.g., G/G

max vs.

Q/Qmax

, or G/Gmax

vs. w/d (i.e., pseudo-strain)] may be adopted to account for non-linear response of settlements (w) corresponding to increasing loads. A schematic illustration of implementing such a model is shown in Fig. 7. The results of one such application has previously been shown in the bottom right portion of Fig. 2.

AN UNDATED DESIGN METHODIn a most recent effort, a much larger database, including the latest cases of pile load tests and CPTu soundings was collected (Niazi, 2014). The aim was to utilize all 3 CPTu readings to


refine the formulation of an existing method and to make it more convenient, and applicable to a larger variety of situations. This database includes 153 pile load tests from 52 sites at worldwide locations. The piles include drilled shafts, continuous flight auger (CFA) piles, driven and jacked OE and CE steel pipe piles, H-section steel piles, and square as well as circular precast concrete piles installed in variety of geomaterials. The pile L/d ratios of the database range from 3.9 to 142.0, while the load carrying capacities range from 10 kN to 75,500 kN (1.12 ton to 8487 ton).

From the penetrometer database at 52 sites, 46 sites provided CPTu data (i.e., penetrometer readings including porewater pressure data, u

2 or u

1, in addition to q

t and f

s), while for the

remaining 6 sites both qc and f

s readings were

present. In all these 6 cases, u2 readings were

assumed hydrostatic (i.e., u2 ≈ u

o) because of

the sandy soil profiles being encountered. Additionally, at 6 of the 46 sites with CPTu soundings, u

1 readings were measured instead

of u2, where following correlations were used

for appropriate conversions:

Chen and Kulhawy (1994): u2 = 0.742 (u

1) [4]

Peuchen et al. (2010): u2 = K (u

1 – u

o) + u

o [5a]

where

K = 0.91 exp(– 0.09 Qtn

0.47) {1/[1 + Fr (0.17

+ 0.061 (Qtn – 21.6)1/3)] – exp(–2 F

r)} [5b]

In calculations of K from Eqn. [5b], the parameters Q

tn and F

r relate to the CPT-based

soil behavior type (SBT) classification index, Ic.

Robertson (2009) presented SBT boundaries on Q

tn–F

r chart from the contours of I

c, leading to

the following:

Ic = [(3.47 – logQ

tn)2 + (logF

r + 1.22)2]0.5 [6a]

Qtn = [(q

t – σ

vo)/σ

atm] (σ

atm/σ'

vo)n [6b]

Fr = [f

s/(q

t – σ

vo)] 100% [6c]

n = 0.381 (Ic) + 0.05 (σ'

vo/σ

atm) – 0.15 [6d]

where σatm

is a reference stress = 100 kPa (14.5 psi).

The UniCone method was selected for refinement because it accommodates larger variety of piles and soils. The influence zone and averaging technique were adopted as recommended in this method.

The pile capacity components of fp for

respective layers through the pile embedded length and q

b were calculated from the peak

applied load for each test pile. In addition, from the Q – w curve of each case, the axial pile capacity was also calculated based on 3 different definitions: (1) Davisson's offset line criterion (Davisson 1972), (2) French criterion: w/d = 10% (Vesić 1977), and (3) Hyperbolic asymptote criterion by Chin-Kondner (Chin 1970; Kondner 1963). The goal was to associate these capacity criteria to the measured peak values of applied loads in the database, and so to the correlations that are developed based on the measured peak values.

For non-circular solid piles, equivalent pile diameters were adopted as per the recommendations given in the preceding section.

Shaft Correlation Coefficient

As a first step, the corresponding sets of mean values were plotted on the log-log q

E vs. f

s type

of soil classification diagram. Accordingly, new sets of boundaries and sub-boundaries were delineated on the UniCone type chart, thus proposing 11 soil sub-zones, in contrast to the 5 originally presented by Eslami and Fellenius (1997) (see Fig. 8). With f

p and q

b

obtained from the load test results, and their

[FIG. 7] Randolph analytical elastic model for evaluating pile load-displacement response (adapted from Niazi, 2011)


corresponding averaged piezocone readings available for different soil layers of each site, correlations were established between the two sets of the data. This followed uniting the correlation experience from the entire database to draw conclusive results based on the soil typology. The shaft correlation coefficients (i.e., C

se = f

p/q

E) and their relevant statistics

so obtained are shown in Fig. 8. These values, which present gradual transition, are the results of calibration experience from the extended database of this study.

For further simplification, the soil behavior type (SBT) classification index, I

c presented in Eqn. [6]

was considered. Thus, log-transformed values of C

se were plotted against I

c, giving a linear

relationship (see Fig. 9, where SBT boundaries are also shown). The data was separated on the basis of installation methods, the effects of load application procedure (MLT vs. CRPT), and the loading modes (compression vs. tension) to study their influence on the correlations trends. The most clearly discernible trend pertains to the loading mode, where it was found that C

se(t)/C

se(c) ≈ f

p(t)/f

p(c) ≈ 0.763. Here subscripts

t and c represent tension and compression, respectively. Following general expression was generated from the overall correlation experience:

Log[Cse(mean)

] = 0.732 (Ic) – 3.605 [7]

Cse(c)

= 1.11 [Cse(mean)

] [8]

Cse(t)

= 0.85 [Cse(mean)

] [9]

Reverse-transformation of the Cse values from

Eqn. [7] gives reasonable estimates of Cse. In

use of this correlation, attention must be paid to the applicable range of I

c from the database

shown in Fig. 9. The following two alternative correlation functions were also developed:

Hyperbolic Tangent Function:

Cse = 0.044 + 0.0416 tanh[1.51 (I

c) – 4.53] [10]

Modified Hyperbolic Function:

Cse = 0.1 – 0.097/[1 + (I

c/3.10)8.2] [11]

[FIG. 9] Variation of Log[Cse (= fp/qE)] with CPT SBT Ic

Use of any of the above 3 correlation expressions provides a direct approach to shaft capacity estimation from a continuous function, eliminating the need for use of Fig. 8.

Toe Correlation Coefficient

A similar methodology of back-analysis was employed for the toe correlation coefficients (C

te = q

b/q

E). The resulting trends plotted in

Fig. 10, with a generalized correlation given in Eqn. [12], apply to limited range of I

c

(i.e., 1.69 > Ic > 3.77).

Log[Cte(mean)

] = 0.325 (Ic) – 1.218 [12]

For base coefficient, the influence of additional factors like pile loading procedures (SMLT vs. QMLT vs. CRPT), and pile end conditions (CE vs. OE) were also investigated. Visibly, the data points plotted in Fig. 10 indicate much greater scatter in the C

te vs. I

c correlation than that

observed for Cse vs. I

c. This is besides the fact

that no clear distinction could be made either [FIG. 8] Modifi ed UniCone chart for zone numbers, soil types, and shaft correlation coeffi cients


due to the installation method, or the pile end condition. This study, however, clearly points to the fact that C

te cannot be simply assumed

equal to unity, or based solely on pile diameter, as proposed in the original UniCone method. Although, Eqn. [12] may provide a reasonable estimate for preliminary analysis and design, more focused studies on the influence of pile end conditions and loading procedures, coupled with further information from pile load tests, are warranted to further refine the proposed formulations.

Capacity Criteria

The correlations developed in this study were based on the measured peak loads [Q

max (meas.)].

For cautious application of the updated design formulations, the Q

max (meas.) from each load test

was associated to the pile capacities calculated from 3 different criteria in the arrangement of ratios: (1) Q

cap (Chin-Kondner)/Q

max (meas.), (2) Q

cap (w/d =

10%)/Q

max (meas.), and (3) Q

cap (Davisson)/Q

max (meas.). The

applicable statistics were calculated for each ratio, as shown in Table 5. Thus, in application of the proposed design formulations to estimate the response of a particular pile loading, these statistical results must be considered.

CONCLUSIONSA quick review of the CPT-based axial pile design formulations was presented. These designs are based largely on empirical methods

evolved through years of experience. Some methods are appropriate for wider variety of situations, while others are relevant to specific pile and soil types. In application of these methods, situations similar to the database used in their formulations may present expected results, but uncertainty becomes dominant when dealing with different structures or new types of geomaterials.

The direct CPTu-pile correlations offer a quick and convenient method for axial pile capacity from multiple penetrometer readings. Specifically, five CPTu based methods are discussed, along with respective observations concerning each. The UniCone method is selected for refinement that uses a 5-part soil classification system to assign pile side friction coefficients to each elevation of CPTu data. A case is made for extending this application based on new test piles and additional soil deposits. A database of 153 pile load tests from 52 sites is compiled, representing a considerable increase over the original study. The database includes diversity in terms of pile and soil types. The data selection process aimed at the availability of piezocone type of CPT testing.

Results of the derived coefficients Cse, and

Cte of the axial pile capacity from CPTu data

are presented. The UniCone type of log-log q

E vs. f

s soil classification chart is refined by

delineating 11 soil sub-zones along with their respective shaft coefficients, in contrast to the 5 zones originally proposed. Further, the CPT material index, I

c is used to establish direct

correlations between Cse and I

c, and C

te and I

c.

The results offer a continuous function for estimating the coefficients over a wide value of Ic, thus disregarding the need for use of the soil

classification chart and improving the reliability in the evaluations of f

p and q

b.

The analysis also reveals that the statistical reliability of estimating f

p is much superior to

that for qb.

[FIG. 10] Variation of Log[Cte (= qb/qE)] with CPT SBT Ic

Mean Standard Deviation

Variance Skewness Range Minimum Maximum

Qcap (Chin-Kondner)

/Qmax (meas.)

1.101 0.112 0.012 3.222 0.770 1.000 1.770

Qcap (w/d = 10%)

/Qmax (meas.)

0.986 0.084 0.007 -0.979 0.470 0.720 1.190

Qcap (Davisson)

/Qmax (meas.)

0.852 0.104 0.011 -0.847 0.520 0.470 0.990

[TABLE 5] Statistics of the ratios between capacity defi nitions and measured peak loads


RECOMMENDATIONS FOR FUTURE RESEARCHDifferent CPT-based methods were derived from diverse datasets of piles installed in different types of geomaterials. The reliability of no single method can be regarded as universal. The more recent methods that exploit maximum CPT parameters, and which were developed from larger and latest database of pile load tests may be considered for prediction analysis. However, the results must be checked against the estimates of other recent direct methods before finalizing the design. Following recommendations are offered towards future application and research on the topic:

• In design practice, caution must be exercised when applying empirical approaches to field situations. Engineering judgment must be exercised for interpretation of the data.

• Latest database of newer piles, top-down compression, top-up tension as well as the new O-cell load testing arrangements, and the modern multi-channel hybrid geophysical-geotechnical SCPTu testing records should form part of evolution process of pile-CPT correlations.

• Maximum use of SCPTu readings should be made for the complete axial pile response by utilizing three penetrometer readings (q

t, f

s, u

2) for axial capacity evaluations, and

geophysical Vs component for axial load-

displacement response within an analytical framework.

• Similar to the analysis presented herein, any future developments in this regard must explicitly indicate the measurement basis for f

p and q

b from their datasets, and the

average relationships thereof with some of the more frequently used capacity criteria.

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81. Peuchen, J., Vanden-Berghe, J. F., and Coulais, C., (2010), "Estimation of u

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82. Philipponnat, G., (1980), “Methode pratique de calcul d'un pieu isole a l'aide du penetrometre statique”, Revue Française de Géotechnique, Vol. 10, pp. 55–64.

83. Plantema, G., (1948), “Results of a special loading test on a reinforced concrete pile, a so-called pile sounding”, Proceedings of

the 2nd International Conference on Soil Mechanics and Foundation Engineering, Rotterdam, Vol. 2, 112 p.

84. Powell, J. J. M., Lunne. T., and Frank, R., (2001), “Semi-empirical design for axial pile capacity in clays”, Proceedings of 15th International Conference on Soil Mechanics and Geotechnical Engineering, Istanbul, Balkema, Rotterdam, The Netherlands, pp. 991–994.

85. Powell, J. J. M., and Quarterman, R. S. T., (1988), “The interpretation of cone penetration tests in clays with particular reference to rate effects”, Penetration Testing 1988, Orlando, FL, Balkema, Rotterdam, The Netherlands, Vol. 2, pp. 903–909.

86. Price, G., and Wardle, I. F., (1982), “A comparison between cone penetration test results and the performance of small diameter instrumented piles in stiff clay”, Proceedings of the 2nd European Symposium on Penetration Testing, Amsterdam, Vol. 2, pp. 775–780.

87. Randolph, M. F., (1983), “Design considerations for offshore piles”, Proceedings of Conference on Geotechnical Practice in Offshore Engineering, Austin, pp. 422–439.

88. Randolph, M. F., and Murphy, B. S., (1985), “Shaft capacity of driven piles in clay”, Proceedings of 17th Annual Offshore Technology Conference, Houston, Vol. 1, pp. 371–378.

89. Randolph, M. F., and Wroth, C. P., (1978), “Analysis of deformation of vertically-loaded piles”, Journal of Geotechnical Engineering Division, Vol. 104, GT12, pp. 1465–1488.

90. Reese, L. C., and O'Neill, M. W., (1988), “Drilled shafts: construction practices and design methods”, Publication No. FHWA-HI-88-042, Federal Highway Administration, Office of Implementation, Washington, D. C., 564 p.

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DFI Journal Paper Review Process

The peer review process for documents considered for publication in the DFI Journal is still evolving. The following is a description of the current process, however, the publication is still in its infancy and the review process is still in a state of flux. DFI reserves the right to alter the procedures as necessary.

Paper SubmittalPapers may be submitted at any time. Authors wishing to submit their papers for consideration of publication in the DFI Journal are invited to access www.edmgr.com/dfi. The website will ask for a login or, for new submitters, will ask for creation of an account. Once logged in the author must upload a full paper in MS Word format as well as any ancillary files such as figures, photos and other graphics which are included in the paper. The paper is then converted to a PDF file which the author must approve before the paper will be released to the publisher and journal editors for viewing. The journal editors preliminarily review the paper for relevancy to the Journal mission.

Paper Review The journal editors assign those papers deemed to be worthy of consideration for Journal publication to the appropriate editorial board member, which currently consists of DFI technical committee chairmen and other industry leaders, so that appropriate reviewers for the paper topic can be obtained. Reviewers are chosen based on their knowledge, areas of expertise, and qualifications to act as a reviewer on the particular subject matter of the paper in question. At least three reviewers will be assigned to each paper.

After the reviewers are selected, they are provided with instructions and a password for entry into the website where they can view the paper PDF and submit their evaluation. The criteria on which they base their review fall under two areas: technical content and quality of paper presentation. The criteria for technical content include relevancy, originality, appropriate references to support statements, significance of results and exclusion of personal opinion and commercialism. The criteria for paper presentation include quality of figures, quality of English language, paper organization and completeness. The reviewers enter their evaluation by responding to a number of questions rating the paper as well as entry of comments to authors. They are also required to make a recommendation to the journal editors of: accept as is; accept with mandatory changes; or reject. The author is advised by automatic email of the posting of reviews and he/she can access the reviews and respond and/or modify the paper to satisfy comments by the reviewers. A second round review can then take place if necessary, ultimately leading to second round reviewer recommendations. The publisher and editors, acting as a final review committee, make the decision, based on the reviewers’ recommendations, as to acceptance of the paper for publication in the next issue of the journal or in a subsequent issue.

Throughout the process, automatic emails are sent out to reviewers when papers are ready for their review and to the authors to keep them aware of the progress of their paper.

ContactFor enquires relating to the submission and status of articles please email [email protected]. Please quote the manuscript reference number (where possible) in all correspondence.

Paper Finalization Upon acceptance, the final paper submission by the author and all graphic files are downloaded by the publisher for processing and formatting for publication. The publisher is provided with proofs by the production house and these are edited to ensure acceptable layout, the absence of typos, clarity of figures, etc. In most cases the author(s) are provided with a final PDF for their review and approval.


DFI Journal Call for Papers

The Deep Foundations Institute compiles and publishes a Journal of practical and technically rigorous papers on a bi-annual schedule. The DFI Journal is distributed to ~3,000 DFI members plus non-member subscribers.

The DFI Journal content is subject to quality technical review, and must meet a standard in quality on practical subjects dealing with case studies, deep foundations history, design, construction, testing, innovations and research in the field.

Each journal consists of at least five documents collected from technical papers that are invited or selected from papers submitted by international industry members based on this call. Papers presented at the DFI Annual Conference and Specialty Seminars may be included if expanded to the Journal standard and review process.

The editors are herein sending out a call for original papers for consideration of inclusion in the upcoming journals. Full draft papers up to 15 pages in length are to be submitted to: www.edmgr.com/dfi for review. Authors will be required to create a login account and will be notified via email on the status of their submission.

Papers are solicited on the following topics:

• Case studies involving foundation systems with technical data support• Historical evolution of deep foundations• Relationship between use of design, construction and equipment• Quality control, quality assurance and non-destructive testing• Innovation in all aspects of deep foundations and earth retention• Practice-oriented research

The Publisher and the Journal Editorial Board will review submitted papers for acceptability for publication in the current or future issues of the Journal, subject to full peer reviews as described on the preceding page entitled "DFI Journal Paper Review Process". Authors of papers accepted for publication will be required to sign a copyright licence agreement.

Further information about the journal is available at www.maneyonline.com/dfi.

NEW PUBLISHER

From 2014, the DFI Journal will be published as part of Maney Publishing’s growing materialsscience and engineering collection. The Journal will continue to publish in print and onlineand will publish 2 issues per year, with the intention to increase in frequency in the future.

Maney is an independent publishing company with a diverse and fast-expanding portfolio of materials science and engineeringjournals, which the DFI Journal complements nicely. Maney’s publishing partners include the International Desalination Association, ASM International, the MetSoc, the Institute of Materials, Minerals and Mining, AusIMM and many others. For moreinformation on the titles in the Materials Science & Engineering Collection, visit www.maneyonline.com/matscieng.

With offices in Leeds and London in the UK, and Philadelphia in North America, Maney focuses on the publication and dissemination of high quality, peer-reviewed scientific research.

www.maneyonline.com/dfi

DFI Journal: The Journal of the Deep Foundations Institute

For more information, please visit the DFI Journal homepage: www.maneyonline.com/dfi

What does this mean for the journal?

For members

Members will be able to access allDFI Journal issues via the DFI website from 2014.

Content alerts and RSS feeds can beset up to inform you of new contentas soon as an issue is published online.

For subscribers

Maney will be contacting existingsubscribers directly concerning therenewal of subscriptions. For help orinformation, please email [email protected]

If you would like to subscribe to thisjournal, please visit the journal homepage: www.maneyonline.com/dfi

For authors

Authors will be able to submit theirpapers online using Editorial ManagerTM, an online submission,peer-review and tracking system.

For more information on how to submit, please see the instructionson the journal homepage:www.maneyonline.com/dfi

About Maney Publishing

www.dfi.org

New Publisher Ad DFI 2014 v2_USA A4 09/01/2014 13:53 Page 1

Maney Online, powered by Atypon’sLiteratum platform, brings greater functionality and flexibility to the online publication of the DFI Journal.

Members will continue to access the DFIJournal via their “myDFI” login and will benotified shortly. Subscribers should registeron Maney Online now and be ready to access the first issue of 2014.

What can you expect from Maney Online?

n straightforward access to content

n an intuitive user interface

n advanced search capabilities

n popular research at your fingertips

n ability to save your favorite articles

n export citations easily

n recommended articles tailored to you

Maney Publishing’s newonline journal platform

Have you tried reading the DFI Journal online?

ManeyOnline

All content published in the DFI Journal from 2014 will be available on a new journal platform, Maney Online.

www.maneyonline.com/dfiwww.maneyonline.com/dfi www.dfi.org

ManeyOnline DFI Ad 2014 v2_USA A4 09/01/2014 08:33 Page 1

Deep Foundations Institute was incorporated in 1976 in the State of New Jersey as a non-profit educational activity. DFI is a technical association of firms and individuals in the field of designing and constructing deep foundations and excavations. DFI covers the gamut of deep foundation construction and earth retention systems.

Although the bulk of the membership is in North America, the Institute is worldwide.

DFI’s strengths are:

• Communication of information concerning the state-of-the-art and state of the practice of deep foundation technologies

• Offering networking opportunities for our members

• Offering opportunities for members to improve the industry through publications produced by volunteer committees

• Offering educational conferences, seminars and workshops in the industry

The core strength of DFI is the broad spectrum of its membership. All disciplines participate on an equal footing, be they contractors, engineers, owners, academicians, equipment manufacturers and distributors or materials manufacturers and suppliers. All types of foundation systems are represented, whether installed by driving, drilling or other means. This diversity and openness without bias provides a forum for the free exchange of knowledge and a platform for the development of new technology and opportunity.

DFI is:

• An international network of heavy construction professionals dedicated to quality and economy in foundation design and construction

• A forum open to all construction professionals across disciplines and borders.

• A technological association devoted to gathering, storing and disseminating practical information

• A resource for identifying and locating the specialists and sources of expertise.

• An initiator and participant in research

Deep Foundations Institute Sustaining Members are Corporate Members of DFI who have voluntarily granted funding to the Institute for expanded support of the Industry. The fund is managed by the DFI Educational Trust.

DFI Sustaining MembersAECOM USA INC.AMEC - ENVIRONMENT & INFRASTRUCTUREAMERICAN EQUIPMENT & FABRICATING CORP.ANDERSON DRILLINGAPE/J&MBAUER FOUNDATION CORP.BAUER - PILECO INC.BEN C. GERWICK INC.BERKEL & COMPANY CONTRACTORS INC.BRASFOND FUNDAÇÕES ESPECIAIS S/ABRAYMAN CONSTRUCTION CORPORATIONCAJUN DEEP FOUNDATIONS LLCCASE FOUNDATION COMPANYCIPORT S.A.DEAN CONSTRUCTION CO. LTD.DEWITT CONSTRUCTION INC.FOUNDATION CONSTRUCTORS INC.FOUNDATION SUPPORTWORKS INC.FOUNDATION TECHNOLOGIES INC.GEOKON INC.GOETTLEHAYWARD BAKER INC.HJ FOUNDATION COMPANYKELLER FOUNDATIONS LTD.KIEWIT INFRASTRUCTURE ENGINEERS KLEINFELDERL.G. BARCUS & SONS INC.LANGAN ENGINEERING AND ENVIRONMENTAL SERVICESMCKINNEY DRILLING COMPANYMENARDMORETRENCHMUESER RUTLEDGE CONSULTING ENGINEERSNICHOLSON CONSTRUCTION COMPANYO.C.I. DIVISION / GLOBAL DRILLING SUPPLIERS INC.PND ENGINEERS INC.SAS STRESSTEEL INC.SCHNABEL FOUNDATION COMPANYTEI ROCK DRILLS INC.THATCHER ENGINEERING CORPORATIONURBAN FOUNDATION/ENGINEERING LLCWILLIAM F. LOFTUS ASSOCIATES FOUNDATION ENGINEERSWURSTER ENGINEERING & CONSTRUCTION INC.

DFI JOURNALThe Journal of the Deep Foundations Institute

Deep Foundations Institute326 Lafayette AvenueHawthorne, New Jersey 07506 USATel: 973-423-4030Fax: 973-423-4031www.dfi .org

International Standard Serial Number (ISSN): 1937-5247

dfi journal · approach that simulates the entire pile, soil and hammer system. this methodology is...

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