gpr presentation italy 2006 - siiv.net · presentation overview ... h1 =ct1 2 εr1 modified cmp...
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1
Ground Penetrating Radar Theory and Applications
I. L. Al-QadiS. Lahouar
Presentation Overview• Background
– Nondestructive Testing– GPR Systems– Electromagnetic Theory – GPR Data Analysis
• GPR Applications– Flexible Pavements– Rigid Pavements– Rail Road
Introduction• US transportation infrastructure is deteriorating:
• 2005 ASCE Report card for American Transportation Infrastructure gave an overall grade of “D” – estimated $1.3 trillion investment needed for improvements
• Transportation agencies are shifting efforts from building new to assessing and rehabilitating existing structures
2
What is NDE?Non-destructive evaluation
Detect defects, internal distresses, measure dimensions, etc. w/o damaging the material
X-ray radiograph
• Ultrasonic waves• X-ray/ CAT scans• RADAR scanning• Thermal imaging• etc
What Are Waves?Propagation of a disturbance through a medium
(mass is not transported in propagation direction)
Direction of Travelof Wave Front
Excitation
Direction of Particle Disturbance
Important parameters: wavelength (λ), period (Τ) and frequency (f)
Ultrasonic Testing (UT)Flaw detection: wave echoes from air-filled defects such as cracks and voids
crack
wavepath
wavesource
Time
Signal amplitude
Time
Signal amplitude
Back surface echoes
crack echoes
3
Ultrasonic Pulse Velocity (UPV)
www.cnsfarnell.co.uk
d
Vp = d/t
t
Vp related to in-place material strength or presence of internal voiding and cracking (Frequency 20-200MHz)
Crack No Crack
Approach Illustration
Impact Echo Principle (ASTM C1383)
Reflected waves set up a resonance condition having a characteristic frequency (like a bell)
4
Concrete Element Inspection
2- and 3-D maps of backscatter intensity (courtesy of BAM)
• Ground Penetrating Radar (GPR):special kind of RADAR
• GPR usage:1. Detect buried targets2. Estimate their depths
• GPR applications:– Geophysics: estimate structure of earth sediments– Archeology: locate buried archeological structures– Safety tool: locate landmines– Civil Engineering: evaluate performance of civil
structures (buildings, bridges, pavements…)
GPR Applications
GPR History• Early 1900’s: RADAR Principle Was Used
to Detect Airborne Objects• Hulsenberg (1926): Detection of Buried
Objects• Austria (1929): Depth of Glacier• Late 1950’s: US Air force plane crashed
in Greenland because of wrong altitude from Radar
⇒ Ability of Radar to See Into Subsurface
5
GPR History• 1960’s: Moon Surface Characterization by
NASA– Ground Probing Radar Systems Were Used in
Geological Applications• Vietnam War: “Combat Radar” Was Used to
Locate Mines, Tunnels, and Bunkers• 1970’s: Locating Sewer Lines and Cables,
Measuring Ice Thickness, Profiling Bottom of Lakes and Rivers
• 1980’s-now: Different Applications Related to Pavements and Bridge Decks
EM Properties of MaterialsInteraction of a Material with Applied Electric (E) and Magnetic (H) Fields:
– Polarization– Conductivity– Magnetic Permeability – Permittivity/ Dielectric Constant
"'rrr jεεε −=
Polarization• Electronic
Polarization:
• Ionic Polarization:
• Molecular Polarization:
• Interfacial Polarization:
++
--
---- --
------ --
++
E = 0 E ≠ 0
++--++ ++--
++E = 0 E ≠ 0
++--
++
++
--++++
--++
++--
++E = 0 E ≠ 0
------ ++--++++
++++---- ++ ++ ++ ++ ++++++
-- -- -- -- -- --E = 0 E ≠ 0
6
DIELECTRIC PROPERTIES OF CONCRETE
0.00
50.00
100.00
150.00
0.001 0.010 0.100 1.000 10.000
Frequency (GHz)
Com
plex
Die
lect
ric C
onst
ant
Parallel Plate Capacitor Coaxial T. Line TEM Antenna
Real Part
Loss Component
Typical Dielectric Constant Values
3-5Dry Sand
5-40Clays5-30Silts20-30Saturated Sand
4-6Granite5-9Limestone
3-10HMA3-18Concrete81Water1Air
Dielectric ConstantMaterial
GPR Types
Three main types of GPR systems:
• Frequency modulated GPR
• Synthetic pulse GPR
• Pulsed (or impulse) GPR
7
Frequency Modulated GPR• Change frequency of
transmitted signal linearly between two limits
• Frequency difference (transmitted - reflected) proportional to the layer depth
Time
f1
f2
Freq
uenc
y
Transmitted Layer 1
Layer 2
fd1
fd2td1
td2
Synthetic Pulse GPR• Change frequency of transmitted signal
between two limits in discrete steps• Amplitude and phase of reflected signal at
each frequency step are recorded• Reflected signal reconstructed in the time
domain using an IFFT algorithm
Pulsed or Impulse GPR
• Most common type of GPR systems
• Transmitted signal is a short pulse (1 ns or less)
• Principle: Transmit a short pulse and record the reflected pulses from layer interfaces
1ns
8
GPR Antennas• Ground-coupled: the antenna is in
contact with the ground surface• Air-coupled: the antenna is 0.5m
above surface• Monostatic: one antenna used as Tx
and Rx• Bistatic: one antenna is used for Tx
and another one for Rx
GPR: How does it work?
Layer 1
Layer 2
Control Unit
Transceiver
Antenna DMI
Reflection & TransmissionAt normal incidence:• Reflection coefficient:
• Transmission coefficient:21
21
12
12
rr
rr
ηηηηγ
εεεε
+
−=
+−
=
21
1
21
2 22
rr
r
ηηητ
εεε
+=
+=
x
z
yεr 1
εr 2
EiHi
βi Er
βr
Hr
Et
βt
Ht
Incident Reflected
Transmitted
9
EM Scattering• EM scattering: occurs when there is a
discontinuity in the dielectric properties of a medium
• For pavements a discontinuity can be:– Layer interface – Distress within the layer
• At a discontinuity: – Reflection – Transmission
Typical GPR Response (scan)
HMA
Base
Subgrade
t1
t2
A1
A2
-8000
-6000
-4000
-2000 0
2000
4000
6000
8000
10000
12000
02
46
810
1214
16
Tim
e (ns)
Amplitude
HM
AB
aseSubgrade
A0
Typical Raw GPR Data
HMA
Base
Subgrade
HMA
Base
Subgrade
10
GPR Applications• Layer thickness estimation
– QC/QA: new pavements– Structure evaluation: in service pavements
• Subsurface Moisture/ Distress detection• Rebar/ Dowel Localization• Rebar Cover Depth Estimation• Railroad Evaluation (Ballast)
Typical Problems with GPR Data Interpretation
• GPR doesn’t provide “real” images of the subsurface
• Dielectric properties unknown– Dielectric properties change with depth
(especially in the presence of moisture)• Thin pavement layers are difficult to resolve• Extensive amount of data• GPR results are operator dependent
Layer Thickness Estimation
2
12
1
2
0
12
1
2
0
1,,
1
1
⎟⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜⎜
⎝
⎛
−+⎟⎟⎠
⎞⎜⎜⎝
⎛−
++⎟⎟⎠
⎞⎜⎜⎝
⎛−
=−
−
=
−−
=
∑
∑
p
nn
i p
ii
p
p
nn
i p
ii
pn-rnr
AA
AAγ
AA
AA
AAγ
AA
εε
Thickness of i th layer:HMA
Base
Subgrade
t1, d1
t2, d2
A0
A1
A2
εr,1
εr,2
εr,3
1,,
1,,
+
+
+
−=
irir
iriri εε
εεγ
where: ,2
1, ⎟⎟⎠
⎞⎜⎜⎝
⎛
+
−=
op
opr AA
AAε
ir
ii
ctd,2 ε
=
11
BM-25.0
GPR: EMBEDDED COPPER PLATES
1
2
34
Subgrade/21B
21B/21A
OGDL/21A
BM-25.0/OGDL
Fi ρ
1+ρ
(1+ρ)*T −(1+ρ)*T
−(1+ρ)*T2
−(1−ρ2)*T2
ρ∗(1+ρ)*T2
ρ∗(1+ρ)*T3
−ρ∗(1+ρ)*T3
−ρ∗(1+ρ)*T4
−ρ(1−ρ2)*T4
ρ2∗(1+ρ)*T4
ρ2∗(1+ρ)*T5
−ρ2∗(1+ρ)*T5
−ρ2∗(1+ρ)*T6
−ρ2∗(1−ρ2)*T6
d
Copper Plate
Multiple Reflection Model
WS Dielectric Constant
*r
*r
1
-1
ε+
ε=ρ
ddρ+1ρ−ω
−εω
−==
1c
j*rc
jeeT
∑=
2222
+222 ρΤρ−Τ − ρ=
ρΤ−ρΤ−
ρ−Τ − ρ===Γn
1i
i1n
i
r
i
r )()1(1
)(1)1(FF
)FT(Y)FT(Y
0FF
1)(1)1((f
i
r1n
=−ρΤ−
ρΤ−ρ−Τ − ρ=ρ)
2
+222
Input Reflection Coefficient
with: and
To find εr, Solve:
12
Multiple Solutions
0
4
8
12
16
20
0.5 0.75 1 1.25 1.5 1.75 2f (GHz)
-1
0
1
2
3
4
5
0.5 0.75 1 1.25 1.5 1.75 2f (GHz)
Real Solutions
Imaginary Solutions
02468
101214
0.5 0.75 1 1.25 1.5 1.75 2f (GHz)
Real Imaginary
Dielectric Constant for SM-12.5D
Physical Solution
13
Thickness Estimation Using CMP Technique (more accurate)
Common midpoint (CMP) technique (or common-depth point, CDP) is used as follows:
T/RT R
x
t1
t2
P
HMAεr1
h
ν : EM velocity in the layer ( )
2
21
22
2
xttc
r−
=εhvt 21 =
22
2 22 ⎟
⎠⎞
⎜⎝⎛+=
xhvt
21
22rε tt
xcv−
==
⇒
Thickness Estimation Using Modified CMP Technique
h1
T/R
x0
t1
t2
P
HMAεr1
h0
airεr0=1
x1
T R
θi
θt
21
22
1
ttxv−
=
trir θsinεθsinε 10 =
01i0 θtan2 xxh =+
1
1
1
1t 2θtan
vtx
hx
==
Snell’s law of refraction:
Using the figure:
(1)
(2)
(3)
(4)
Thickness Estimation Using Modified CMP Technique Algorithm:
1. Measure the reflection times t1 and t2
2. Calculate the transmission angle θt using:3. Find the angle θi by solving numerically
4. Solve for εr1 using:
5. Compute HMA thickness using t1 and εr1
1
21
22
tθtant
tt −=
021
22
i
ti0 θsin
θsinθtan2 xttch =−+
2
t
i1 θsin
θsin⎟⎟⎠
⎞⎜⎜⎝
⎛=rε
111 2 rcth ε=
Modified CMP Setup
14
Automated GPR Data Analysis
Raw GPR Data
Layer Interface Detection
Dielectric Properties Estimation
Layer Thicknesses
Preprocessing
Layer Interface/ Distress
Separation
PreprocessingEnhance signal quality:• Noise filtering• Coupling pulse removal
-8000-6000-4000-2000
02000400060008000
1000012000
0 5 10 15 20
Time (ns)
Am
plitu
de
Pavement Surface
Noise
Coupling
Noise Filtering• GPR susceptible to EM noise: CB radios,
cell phones, cell phone towers, etc.
Noise due to CB radio
Noise due to cell phone towers
15
Noise FilteringElliptic low-pass filter: most efficient filter• Lowest order fast to run on data• Lowest transition bandwidth high performance
-10000
-5000
0
5000
10000
15000
0 2 4 6 8 10 12 14Time (ns)
Am
plitu
de
Original Signal
Filtered Signal
Coupling Pulse Removal: Makes Analysis Easier!
Tx Rx
Et
EcEr
Tx/Rx
Et Er
EerEt: TransmittedEr: Reflected Ec: CouplingEer: End Reflection
-8000-6000-4000-2000
02000400060008000
1000012000
0 5 10 15Time (ns)
Am
plitu
tde
Coupling pulse
Surface reflectionSubsurface reflections
Air
Coupling Pulse Removal Procedure
-0.6-0.4-0.2
00.20.40.60.8
1
-60 -40 -20 0 20 40 60Lag
Nor
mal
ized
Am
plitu
de
lm
Cross-correlation
-8000-6000-4000-2000
02000400060008000
10000
0 100 200Time (samples)
Am
plitu
de
yc(t)yr(t)
Original signals
-8000-6000-4000-2000
02000400060008000
10000
0 50 100 150 200 250Time (samples)
Am
plitu
de
yc(t)yr(t)
Shifted signals
-8000-6000-4000-2000
02000400060008000
10000
0 50 100 150 200 250Time (samples)
Am
plitu
de
Removed coupling pulseCoupling pulse, yc(t),
measurement
16
Preprocessing Results
Noise due to CB radio
Noise due to cell phone towers
Raw GPR Data
After Filtering & Coupling Removal
GPR Depth Resolution
-6000-4000-2000
02000400060008000
1000012000
0 5 10 15Time (ns)
Am
plitu
de
-8000-6000-4000-2000
02000400060008000
1000012000
0 5 10 15Time (ns)
Am
plitu
de
Measured Signal, VA Smart Road
Synthesized Signal
Surface Reflection
OGDL/Base Reflection
WS/BM-25.0 Reflection
BM-25.0/OGDL Reflection
Base/Subgrade Reflection
Reflection Overlap
Surface Reflection HMA/Base
Reflection
Base/Subgrade Reflection
Base
OGDL
WS
BM-25.0
GPR Depth Resolution
r
cTdε2
=ΔDepth resolution:• T: Pulse width• εr: Dielectric Constant
020406080
100120140160180
0 5 10 15 20Dielectric Constant
Dep
th R
esol
utio
n (m
m)
T = 0.6nsT = 1.0nsT = 1.1ns
HMA
Limestone aggregate
Concrete
17
GPR Detection Limitations
Copper Plate Reflections at the (1) 21B/Subgrade, (2) 21A/21B, (3) OGDL/21A, (4) BM-25.0/OGDL, and (5) WS/BM-
25.0 Interfaces; Section A
1
2
34
5
Depth
Pavement Surface
OGDL/21A Interface
Spurious interface reflection
21A
OGDL
WS
BM-25.0
21B
Subgrade
Depth Resolution Enhancement
• Inverse filtering• Predictive deconvolution• Pulse spiking• Pulse shaping• Homomorphic deconvolution
Wiener Filter
Inverse Filtering• Implemented as a Wiener filter• Desired output: impulse at time t=0 ⇒ δ(t)• Incident signal assumed unknown• Filter designed according to:
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
=
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
−
−−−
− 0
001
)0()1(
)3()0()2()2()1()0()1()1()2()1()0(
1
2
1
0
MM
LLL
MOM
L
L
Nyyyy
yyyyyy
yyyyyyyy
yyyyyyyy
a
aaa
rNr
NrrrNrrrrNrrrr
18
Predictive Deconvolution• Implemented as a Wiener filter• Desired output: reflected signal
advanced by α samples ⇒ y(t+ α)• Incident signal assumed unknown• Filter designed according to:
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
−+
++
=
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
−
−−−
− )1(
)2()1(
)(
)0()1(
)3()0()2()2()1()0()1()1()2()1()0(
1
2
1
0
Nr
rr
r
a
aaa
rNr
NrrrNrrrrNrrrr
yy
yy
yy
yy
Nyyyy
yyyyyy
yyyyyyyy
yyyyyyyy
α
αα
α
MM
LLL
MOM
L
L
Pulse Spiking• Implemented as a Wiener filter• Desired output: an impulse (or spike) at
a lag l• Incident signal should be known• Filter designed according to:
⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡−
=
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
−
−−−
−
0
0)0(
)1()(
)0()1(
)3()0()2()2()1()0()1()1()2()1()0(
1
2
1
0
M
M
M
LLL
MOM
L
L
x
lxlx
a
aaa
rNr
NrrrNrrrrNrrrr
Nxxxx
xxxxxx
xxxxxxxx
xxxxxxxx
Pulse Shaping• Implemented as a Wiener filter• Desired output: a pulse with a fixed
shape s(t) at a given lag l• Incident signal should be known• Filter designed according to:
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
−+
++
=
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
−
−−−
− )1(
)2()1(
)(
)0()1(
)3()0()2()2()1()0()1()1()2()1()0(
1
2
1
0
Nlr
lrlr
lr
a
aaa
rNr
NrrrNrrrrNrrrr
sx
sx
sx
sx
Nxxxx
xxxxxx
xxxxxxxx
xxxxxxxx
MM
LLL
MOM
L
L
19
Homomorphic Deconvolution• Power cepstrum of signal y(t):
• For GPR reflected signals:
• Term inside brackets periodic function of time delays ⇒ Power cepstrum composed of pulses corresponding to time-delays
{ }[ ]{ }22)(log)( tyFFqC y =
[ ] [ ]2
1
1 10
20 )2cos(2log)(log)(
⎭⎬⎫
⎩⎨⎧
∑++Φ≈ ∑−
= =
N
n
n
ii
nxy tf
AA
AfFqC π
Typical Results
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0 5 10 15 20Time (ns)
Am
plitu
de
Surface
OGDL/BaseWS/BM-25.0
BM-25.0/OGDL
Base/Subgrade
-6000
-4000
-2000
0
2000
4000
6000
8000
10000
0 5 10 15 20Time (ns)
Am
plitu
de
Surface
OGDL/BaseWS/BM-25.0
BM-25.0/OGDL Base/Subgrade0
500
1000
1500
2000
2500
0 2 4 6 8 10 12 14 16 18 20Time (ns)
Am
plitu
de
Surface
OGDL/BaseWS/BM-25.0
Base/Subgrade
-8000-6000-4000-2000
02000400060008000
1000012000
0 5 10 15Time (ns)
Am
plitu
de
HomomorphicPulse Spiking
Inverse FilteringOriginal
Typical Results: Inverse Filtering
HMA Layers
Base LayerMultiple Reflections from Copper Plates
WSBM-25.0
OGDL
Raw Data,VA. Smart Road, Section A
Processed Data
20
Resolving Thin Layers (Deconvolution)
21A
OGDL
WS
BM-25.0
21B1
2
34
5
Depth
Pavement Surface
OGDL/21A Interface
Spurious Interface Reflection
1
2
34
5
Depth
Pavement Surface
OGDL/21A Interface
WS
BM-25.0
OGDL
Base
Layer Interface Detection
• Purpose:• Detect layer interface reflections• Estimate layer interface reflection time-
delays
-8000-6000-4000-2000
02000400060008000
1000012000
0 5 10 15Time (ns)
Am
plitu
de)()()(1
0 0tnttxAty
N
i
i
jjir +∑−= ∑
−
= =
TD reflected signal:
x(t): Incident signaln(t): Additive noise
Detection of Multiple Layers
Combineddetection/estimation
Detect strongest pulse in yr(t)
Least-squaresFitting ⇒ yrs(t)
yr(t) = yr(t)-yrs(t)
More pulsesin yr(t) yes
no
Exit
21
Detected Layer Interfaces
Raw GPR Data
Detected Time Delays
Layer 1
Layer 2
Layer 3
Layer 1Layer 2
Layer 3
Dielectric Constant Results
Layer Thickness Results
Dielectric Constant/ Thickness
Layer 1
Layer 2
GPRan: GPR Data Analysis Software
Channel 1
Channel 2
Channel 1
Channel 2
22
GPRan: Data Analysis Window
GPRan: Data Analysis ResultsRaw Reflections Layer Interfaces
Dielectric Constants Layer Thicknesses
HMA
Base
SubgradeHMA
Reinforcement
Concrete
Raw GPR Data: Flexible Section
Raw GPR Data Composite Section
Case Studies: I-81
23
BridgePavementPavement
Concrete section
Concrete section
Full depth repair
Case Studies: I-81
Raw GPR Data
0
100
200
300
400
500
600
146.00 146.14 146.28 146.42 146.57 146.72 146.85
Distance (mile)
0
100
200
300
400
500
600
147.00 147.13 147.27 147.41 147.57 147.71 147.85 148.00
Distance (mile)
Bridge
Flexible Section (1mile) Composite Section (1mile)
HMA Thickness from GPR, I-81
HMA Thickness Error, I-81
250
275
300
325
350
375
400
250 275 300 325 350 375 400
Core Thickness (mm)
Line of Equality
10% Over Prediction
10% Under Prediction
24
QA/QC New Pavement (VA Rt. 288)
21-B aggregate base
IM-19.0 (1) w/ PG 64-22
BM-25.0 w/ PG 64-22
SM-12.5 w/ PG 76-22
IM-19.0 (2) w/ PG 76-22
150mm (6in)
300mm (12in)
100mm (4in)
75mm (3in)
65mm (2.6in)
50mm (2in)
Lime stabilized subgrade
Pavement design:
• GPR surveys were performed on each layer after its construction
• Three surveys were performed per lane and per layer; in total, 36 surveys were performed
• DMI set at 10 scans/m• For HMA layers, static
measurements were taken near core locations
9.8m 9m 8.2m 6.2m 5.4m 4.6m 2.6m 1.8m 1m
HMA Base CoresHMA Intermediate 1 CoresHMA Intermediate 2 Cores
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
S1S2
QA/QC1
QA/QC2
QA/QC3
QA/QC4
QA/QC5
QA/QC6
Station 189 + 80
Station 186 + 10
Survey Direction
Offset
Right Lane Center Lane Left Lane
GPR Data Collection (Rt. 288)
Typical GPR Response (Rt. 288)
-8000-6000-4000-2000
02000400060008000
1000012000
0 1 2 3 4 5 6 7 8 9 10 11 12
Time (ns)
Am
plitu
de
21B Coupling Surface
Air
GPR Scan over 21-B Layer
GPR Scan over HMA Intermediate 2
-8000-6000-4000-2000
02000400060008000
1000012000
0 1 2 3 4 5 6 7 8 9 10 11 12
Time (ns)
Am
plitu
de 21BHMA
25
21-B Thickness (Rt 288)
0
50
100
150
200
250
300
350
0 20 40 60 80 100 120 140 160 180
Distance (m)
Thic
knes
s (m
m)
Offset 6.2mOffset 5.4mOffset 4.6mDesign
0
50
100
150
200
250
300
350
184 204 224 244 264 284 304 324 344 364
Distance (m)
Thic
knes
s (m
m)
Offset 6.2mOffset 5.4mOffset 4.6mDesign
Center Lane, 0-184 m
Center Lane, 184-370 m
Offset values are with respect to left edge of left lane
HMA Intermediate-2 Thickness (Rt 288)
Center Lane, 0-184 m
Center Lane, 184-370 m
0
50
100
150
200
250
300
350
0 20 40 60 80 100 120 140 160 180
Distance (m)
Thic
knes
s (m
m)
Offset 6.2mOffset 5.4mOffset 4.6mDesign
0
50
100
150
200
250
300
350
184 204 224 244 264 284 304 324 344 364
Distance (m)
Thic
knes
s (m
m)
Offset 6.2mOffset 5.4mOffset 4.6mDesign
Offset values are with respect to left edge of left lane
Overall Thickness (Rt 288)
050
100150200250300350400450500
40 42.5 45 47.5 50 52.5 55 57.5 60Distance (m)
Dep
th (m
m)
HMA Base
HMA Design Base Design
26
020406080
100120140160180200220
9.8 9 8.2 6.2 5.4 4.6 2.6 1.8 1Offset (m)
Offset values are with respect to left edge of left lane
Average Thickness, 21-B Layer
Average Thickness, HMA Base Layer
Right
02 04 06 08 01 00
1 2 01 4 01 6 01 8 02 002 2 0
2 4 02 6 02 8 03 003 2 0
9 . 8 9 8 .2 6 .2 5 .4 4 .6 2 .6 1 . 8 1O ffse t (m)
Thick. 0-184mThick. 184-370mStd. Dev. 0-184mStd. Dev. 184-370m
Center Left
0
20
40
60
80
100
120
140
160
9.8 9 8.2 6.2 5.4 4.6 2.6 1.8 1Offset (m)
Right Center Left
GPR Accuracy (Rt 288)
Case Study: 18 Premium Pavement Sites
• Sites located mainly on highways • Different types of pavements were tested:
– Full-depth flexible pavement– Rigid (jointed plain concrete pavement
(JPCP)– Continuously reinforced concrete
(CRCP)– Composite pavement
• Each section was 0.8 km (0.5 mile) long
27
Test Sites (Part)Site # County Route Direction Milepost*
Pavement Type Age (yrs)
1 Amherst 29 South 7.80-7.30 Flexible 15-Oct
2 Albemarle 64 East 12.99-13.37
Comp. CRC
(rehab)>20
(Surf.>10)
3 Louisa 64 West 9.91-9.41 Flexible>20
(Surf.<10)4 Louisa 64 West 2.28-1.78 CRC 15-Oct
7 York 64 West 22.23-24.71 JPCP 0-5
8 Suffolk 58 East 25.50-26.00Comp. (new)
>20 (Surf.<10)
9Washingt
on 81 South 1.50-1.00 Flexible 0-5
Data Collection: 18 Prem. Sites• GPR data were taken in the traveling lane at two
transverse locations (center and right wheel path)
• GPR data acquisition frequency:– Flexible sections: 1scan/0.3m (1ft) using air-coupled
antenna– JPCP and composite sections (without rebar):
1scan/0.3m (1ft) using air-coupled and ground-coupled antennae.
– Composite sections (with rebar): 1scan/25mm (1in) using air-coupled and ground-coupled antennae
Raw GPR Data: 18 Prem. Sites
HMA
Base
Subgrade
28
GPR Results: 18 Prem. Sites (Flexible)
0
100
200
300
400
500
600
0 100 200 300 400 500 600 700 800 900Distance (m)
Dep
th (m
m)
SM-2B+S-5
Base
S-5
Subgrade
Core
GPR Results: 18 Prem. Sites (Rigid)
0
100
200
300
400
500
0 100 200 300 400 500 600 700 800 900Distance (m)
Dep
th (m
m)
CRC
Base
CRC RebarRebar
Core
GPR Accuracy (18 Prem. Sites)
200
210
220
230
240
250
260
270
280
290
300
200 210 220 230 240 250 260 270 280 290 300
Core Thickness (mm)
GPR
Thi
ckne
ss (m
m)
+3.7%
-3.7%
29
Average Error per Site (18 Prem. Sites)
3.7
6.0
4.7
1.8
3.2
8.2
2.3
3.2
5.3
3.1
4.14.8
5.6 5.4
8.4
4.64.0
0
1
2
3
4
5
6
7
8
9
1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18
Site #
GPR
Abs
olut
e Er
ror (
%)
Average Error per Age (18 Prem. Sites)
4.4 4.6 4.9
5.8
0
1
2
3
4
5
6
7
0-5 10 -- 15 >20 (Surf.<10) >20 (Surf.>10)
Pavement Age (years)
Ave
rage
GPR
Thi
ckne
ss E
rror
(%)
Average Error per Type (18 Prem. Sites)4.4
2.3
00.5
11.5
22.5
33.5
44.5
5
Flexible JPCP
Pavement Type
GPR
Thi
ckne
ss E
rror
(%)
3.0
4.2
00.5
11.5
22.5
33.5
44.5
CRC Flexible
Pavement Type
Ave
rage
GPR
Thi
ckne
ss E
rror
(%)
Pavement Age: 0-5 yrs
Pavement Age: 10-15 yrs
30
High Reflections ⇒ Moisture
Locating Subsurface Moisture
HMA
Base
HMA
Base
Ground-Coupled GPR Data, VA. Smart Road, Section J
Without geocompositeWith geocomposite
HMA
21B Aggregate
Copper Plates Subgrade
Geocomposite: Moisture Barrier?Moisture (high reflection)No Moisture
Locating Reinforcing Mesh
Section With Reinforcing Mesh
Section Without Reinforcing Mesh
Ground-Coupled Data, VA. Smart Road, Section L
31
Minor distortions detected in the mesh final position
QC of Mesh Installation
Ground-Coupled Data, VA. Smart Road, Section I
Slab/base interface
Transversal reinforcement
CRCP(ground-coupled)
Locating Reinforcement Using GPR
Locating Reinforcement (CRCP)
Copper Plate under Slab
Transversal Reinforcement
Concrete
Asphalt OGDL Longitudinal Reinforcement
32
Raw GPR Data
Surface
Transverse Rebar Signature
Ground-Coupled Data, JPCP, VA. Smart Road
Locating Dowels (JPCP)
Dowels
Concrete
Rebar Cover-Depth EstimationGPR antennas receive EM signals reflected by rebar at different locations
⇒ Parabolic Shape
Pavement Surface
Transverse RebarSignature
Bottom of Slab
33
GPR Data Collection
Concrete
Concrete
Rebar
Rebar Cover-Depth Estimation
GPR GPR GPR
Survey direction
Concrete slab εr
Transverse rebar
x
t0, d0 t, d
x0
Survey configuration on CRCP
Rebar Cover-Depth Estimation
From the previous configuration we have:
2
202
0202
22 484
vxtx
vxx
vt ++−=
r
cvε
=with:
34
Rebar Cover-Depth Estimation Algorithm• Measure the time t and the distance x
from GPR data (using image processing techniques)
• Estimate the parameters v, t0, and x0 by nonlinearly fitting t and x to the model
• Estimate the dielectric constant εr• Estimate the rebar cover-depth d0:
20
0vtd =
Parabolic Shape Extraction from GPR Data
Transverse rebar
Removal of unwanted reflections
Image Thresholding
Detected tand x
Threshold Image
Rebar Cover-Depth Results
0
50
100
150
200
250
10 20 30 40 50 60 70
Distance (m)
Reb
ar c
over
-dep
th (m
m) Estimated cover-depth
Design CRCP Section with cement OGDL
0
50
100
150
200
250
0 10 20 30 40 50 60 70Distance (m)
Reb
ar c
over
-dep
th (m
m) Estimated cover-depth
Design
CRCP Section with asphalt OGDL
35
Rebar Cover-Depth Accuracy
Comparison of GPR estimated cover-depth to CRCP cores from the VA Smart Road
Core 1 145 141 2.8Core 2 121 118 2.5
2.6Average error (%)
Core cover-depth (mm)
GPR cover-depth (mm)
Error (%)
Supe
rstr
uctu
reSu
bstr
uctu
re
Rail
Bal
last
Ballast
Subballast
Fastening System
Vertical
Longitudinal
Crib
Tie
Ballast
Placed Soil (Fill)
Natural Ground (Formation)
Subgrade(Platform)
Railroad Structure
Clean and Fouled Ballast
• Clean ballast : made of uniformly graded aggregates ⇒ large air voids.
• Fouled ballast : fouled by fine-grained material that the fills air-voids.
Ballast fouling greatly influences its functions:
36
Dielectric Characteristics of Ballast
38.5Saturated, Spent Ballast
7.8Wet, Spent Ballast (5% water)
4.3Dry, Spent Ballast
3.5Wet, Clean Ballast (5% water)
3.0Dry, Clean Ballast
Dielectric ConstantRailroad Ballast
Dielectric constants of quartzite ballast
– Effect of moisture on fouled ballast is much greater than on clean ballast
Scattering Analysis• Pavement Materials:
• Scattering response is small• Reflected pulse has similar
shape as incident signal
• Railroad Ballast: • Scattering response is
dominant (heterogeneousityis high)
• Reflected pulse shape is influenced by scatter - air void
iE sE
Horn Antenna
Incident Field
ScatteredField+–
Surface
+
_
...
Scatter
EM Scattering from Air-Voids (cont.)
Ballast degradation ⇒ Less air-voids ⇒Less scattering ⇒ Less attenuation
2AN
s⋅
=α
E = E0 ⋅ e−α s ⋅L
N: number of particles per unit volume
Attenuation Coefficient:Transmittedsignal
Incidentsignal
air voids
Scattering
37
GPR Data Collection on RailroadVideo Camera
Horn Antenna: TR (Transmitter & Receiver)
TR TR
TRTR
GPS
Video Camera
Horn Antenna: TR (Transmitter & Receiver)
TR TR
TRTR
GPS
GPR Data ProcessingDigital Signal Processing performed during data collection:
– Vertical band-pass FIR filter to remove noise
– Horizontal band-pass filter to remove clutter from rails
– Gain to account for energy attenuation
Processed GPR Data
Raw GPR data
Processed GPR data
High-frequency clutter
Surface
Clean Ballast
Fouled Ballast
Surface
First pulse from surface and subsequent small pulses are mainly from scattering
38
Trapped Water Detection
Trapped water due to lack of lateral drainage
Road Crossing
2 GHz antenna is more sensitive to scattering pattern change than 1 GHz antenna.
2 GHz
1 GHz
Scattering Analysis of Ballast under Different Conditions
Threshold Image
(a) Clean (b) Moderately fouled (c) Fouled Ballast exhibiting various scattering patterns
GPR Data for different fouling conditions
Note: When ballast becomes fouled, interface between clean and fouled ballast is more difficult to find.
Ballast Breakdown
Ballast breakdown under sleepers.
Ballast BreakdownFouling
39
Summary• GPR is a NDE technique that can be
used effectively to evaluate various transportation structures
• GPR can provide quick and reliable information about the subsurface.
• GPR reliable applications include:– Layer thickness estimation– Subsurface Moisture/ Distress detection– Rebar cover depth estimation– Rail Road evaluation (Ballast)– …
Thank You!
Questions?