1. Pavement Thickness Evaluation Using Ground Penetrating Radar
Dwayne Harris P.E. L.P.G
Presented for Final Exam

2. OUTLINE Introduction Fundamentals of GPR
Methodologies for Thickness Evaluation
Acquisition and Interpretation of GPR data
GPR Data Quality
Validation of Methodologies

3. Introduction Background on Pavement Thickness Determination
Literature Review

4. Why Use GPR? Why is Pavement Thickness Information Useful?
What are the Current Methods for Obtaining Thickness Information?
What are the Advantages of Using GPR for thickness Evaluation?

5. Importance of Thickness Information
Pavement Management
Pavement performance and remaining life estimates require knowledge of pavement thickness
Setting maintenance and rehabilitation priorities
Main input in overlay design

6. National Rehabilitation
Year
Urban Interstates
Rural Interstates
Rural Roads
Expenditures
1998
8.69%
Poor
3.25%
1.42%
$36.3
Billion
2003
7.62%
1.64%
0.76%
$49.3
Change
1.07%
1.61%
0.66%
36%
[Hartegen, 2005]

7. INDOT INDOT Major Moves $138,483,477 budgeted for 2006 resurfacing
Large percentage Mill and Fill rehabilitation where thickness of uppermost surface course important
Pavement thickness is needed for project level FWD structural analysis

8. Technologies Used for Pavement Thickness Evaluation
Core
Costly
Destructive
Provides a good ground truth record.
Falling Weight Deflectometer (FWD)
None Destructive
Ground Penetrating Radar
Non Destructive
Collected at Highway Speed
Dense Coverage
Heavy Post Processing

9. Related Work Thickness Evaluation
[Berge et al, 1986] initial pavement thickness studies
[Livneh and Siddiqui, 1992] mathematical model presented
[Fernando, 2000; Scullion and Saarenketo, 2002] automated interface identification
[Al-Quadi et al, 2005] model expanded to three or more layers

10. Literature Summary
There are multiple models available for pavement thickness evaluation
The model selected for this study is utilized for a large majority of the studies
Current literature suggests using semi-automatic data interpretation methodologies

11. Fundamentals GPR trace and waveforms and data presentations
Mathematical model

14. GPR Data B-scan

16. Simple GPR Thickness Model

17. EM Wave Propagation Velocity

18. Dielectric Calculation

21. Principles of GPR Interface Interpretation
The radar (EM) wave must propagate, to the interface and back.
The radar wave must reflect off the interface with enough energy to be recorded.
The interface must be identified in the GPR record.
Definition of interface

23. Two Interface Case A

24. Two Interface Case B

25. Methodologies for Thickness Evaluation
Top layer methodology
Interfaces are identified in the data
Discontinuities are located in the data
Regional dielectric constants are determined
Thickness values are calculated for each mile
Enhanced to calculate thickness using dielectric constants from individual traces
Multiple Layer Methodology

26. Interface Selection

27. Regional Dielectric Constants

28. Thickness Calculation
Every thickness pick is assigned the respective regional dielectric value.
New Thickness Values Calculated.
Average value calculated for each mile.

29. Multiple Layer Methodology
Determine the layers to be modeled
Form data set of possible interfaces
Select interfaces to be modeled
Calculate thickness values
Present the thicknesses in a visually acute format allowing for proper interpretation

32. Quality of GPR Data Blunders Systematic errors Random errors
Improper waveform selection
Omitted pavement layers
Systematic errors
Travel time systematic error
Velocity systematic error
Random errors
Error propagation

33. I65 Study Area

34. 13 Inches HMA Over PCC

35. TERRA Interface Selection

38. Difference in Dielectric Constant and Thickness

39. Blunders
Improper waveform selection
Omitted pavement layers

41. Omitted Pavement Layers

42. Blunder Summary
Improperly selecting waveforms is a significant blunder source
Utilizing automated interface selection algorithm increased the likelihood of this blunder
Blunders are introduced when using the top layer methodology to evaluate thickness of pavement composed of multiple layers

43. Systematic Error: Travel Time

46. Velocity Systematic Error

47. Random Error Propagation

48. Random Error Propagation

49. Systematic and Random Error Summary
Channel 1 data not used due to large systematic error is travel time
Velocity systematic errors propagate into thickness error
Amplitude random error propagates to about 1% relative thickness error

50. Validation of Methodologies
Comparison with 3rd party Software
Comparison of methodologies developed
Thickness variation
GPR thickness evaluation accuracy
Network thickness study

51. Thickness Comparisons
Seven pavement sections of three interstates.
Pavement sections of three state roads
Five pavement sections of two interstates used for 3rd party comparison

52. Statistical Analysis (TERRA & M2)
Population Intersection
Split into 50 foot subsections some populations split into 25 foot subsections
Normality and T-test analysis all 50 foot subsections containing at least 10 samples
Explanation of T-test results

58. T-test Explanation

59. Summary M2 TERRA Comparison
90% of the M2 and TERRA populations have the same variance (alpha=95%)
98% of the M2 and TERRA populations for I-65 have the same mean (alpha=99%)
28% of the M2 and TERRA populations for I-74F have the same mean

60. Methodology Comparisons
Difference in sample size
Difference in velocity calculation by use of regional dielectric constant

63. Thickness Variation Section Number Mean STD CV I-65 25,672 4.62 0.44
9.45%
I-69
41,108
6.48
0.57
8.72%
I-74A
16,587
6.67
0.54
8.10%
I-74B
8,810
3.74
0.40
10.67%
I-74C
15,704
4.97
0.34
6.93%
I-74D
14,250
7.27
0.58
7.94%
I-74F
21,427
6.90
7.81%
SR-47
32,260
5.70
0.39
6.78%
SR-213
6,233
6.18
0.47
7.65%
SR-28
20,670
6.66
1.36
20.49%
Average
Average*
8.23%

64. Reported Accuracies of GPR Thickness Estimates
Accuracy
Kansas DOT
7.5% - 10%
SHRP 8%
Minnesota DOT
3% - 6.5%
Missouri DOT
4% %
Kentucky DOT
5.82% %

65. Case Study Results Study Accuracy I-65 12 Inch Concrete 4.5%
13 Inch HMA
2.0%
7.5 Inch HMA
13.2%
US41 North
HMA
8.8%, 5.2%
Concrete
8.8%
SR32E
16.6%

66. Reported Accuracies of GPR Thickness Estimates
Accuracy
Kansas DOT
7.5% - 10%
SHRP 8%
Minnesota DOT
3% - 6.5%
Missouri DOT
4% %
Kentucky DOT
5.82% %

67. Accuracy/CV Results
Study CV (8.23%) with published range of 2.36% to 38%
Study absolute accuracy range (2% to 16.6%) in published range of

68. Network Thickness Evaluation
A majority of the INDOT interstate system is 25 inches thick with an uppermost surface course thickness of 5 to 7 inches of HMA.
GPR provided reasonable estimates of the uppermost surface course thickness
FWD provided reasonable estimates of the pavement structure thickness

69. Conclusions