1. Pavement Thickness Evaluation Using Ground Penetrating Radar Dwayne Harris Dwayne Harris Presented for Final Exam

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

3. Introduction  Background on pavement thickness evaluation  Literature review

4. Significance 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  INDOT Major Moves $138,483,477 budgeted for 2006 resurfacing  Thickness of uppermost surface course needed for mill and Fill resurfacing projects.  Pavement thickness is needed for project level FWD structural analysis

5. National Pavement Rehabilitation Year Urban Interstates Rural Interstat Rural Road Expenditure 19988.69%Poor3.25%Poor1.42%Poor$36.3Billion 20037.62%Poor1.64%Poor0.76%Poor$49.3Billion Change1.07%1.61%0.66%36% [Hartegen, 2005]

6. 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

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

8. Fundamentals  GPR trace and waveforms and data presentations  Mathematical model

10. GPR Data B-scan

13. EM Wave Propagation Velocity

14. Principles of GPR Interface Interpretation Principles of GPR Interface Interpretation An interface is defined as the anomaly in GPR data occurring when the reflected waveforms from a physical pavement boundary are contiguous for a group of sequential traces An interface is defined as the anomaly in GPR data occurring when the reflected waveforms from a physical pavement boundary are contiguous for a group of sequential traces  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.

16. Two Interface Case A

17. Two Interface Case B

18. Methodologies for Thickness Evaluation (regional M1)  Top layer methodology –Discontinuities are located in data –Interfaces are identified 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

19. Interface Selection

20. Regional Dielectric Constants

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

22. Multiple Layer Methodology (M2)  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

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

25. Improper Waveform Selection I-65 Study Area 13 Inches HMA Over PCC

26. Interface Selection Interface Selection

28. Difference in Dielectric Constant and Thickness Positive Phase Dielectric constant Negative Phase Dielectric Constant Positive Phase Thickness Negative Phase Thickness

29. Error Omitted Pavement Layers

30. Omitted Pavement Layers Thickness (Layers Omitted) Thickness (All Layers)

31. Travel Time Systematic Error

33. Velocity Systematic Error

34. Random Error

35. Error Summary  Improperly selecting waveforms is a significant blunder source  Utilizing automated interface selection algorithm increased the likelihood of this blunder  Omitting pavement layers introduces errors  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

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

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

38. Statistical Analysis (M2 vs TERRA)  Population Intersection  Split into 50 or 25 foot subsections  Normality, F test, and T-test analysis  Explanation of T-test results

39. Normality Analysis of Sub Section Populations H 0 =Population Normally Distributed Alpha=95%

40. Equality of Means and Variance Analysis of Sub Section Populations H0=Populations Have Same Variance Alpha=95% H0=Populations Have Same Means Alpha=99%

41. Best Case Worst Case I-65 T-test 8% Rejected

42. Best CaseWorst Case I-74F T-test 72% Rejected

44. T-test Explanation

45. 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

46. Methodology Comparisons  Effect of sample size  Effect of using regional dielectric constant

49. Network Thickness Evaluation Over 1,600 Miles Evaluated Uppermost Surface Course Thickness Evaluated with GPR Using Regional M1 Method Pavement Structure Thickness Evaluated with FWD

50. 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

51. Thickness Variation SectionNumberMeanSTDCV I-6525,6724.620.449.45% I-6941,1086.480.578.72% I-74A16,5876.670.548.10% I-74B8,8103.740.4010.67% I-74C15,7044.970.346.93% I-74D14,2507.270.587.94% I-74F21,4276.900.547.81% SR-4732,2605.700.396.78% SR-2136,2336.180.477.65% SR-2820,6706.661.3620.49% Average9.45% Average*8.23%

52. Published CV values StudyCV LTPP HMA 6.83% to 12.66% LTPP PCC 2.36% to 5.19% NCDOT HMA 25% to 38%

53. Reported Accuracies of GPR Thickness Estimates REPORTAccuracy Kansas DOT 7.5% - 10% SHRP8% Minnesota DOT 3% - 6.5% Missouri DOT 4% - 11.3% Kentucky DOT 5.82% - 165.04%

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

55. Accuracy/CV Results  Study CV (8.23%) within published range of 2.36% to 38%  Study absolute accuracy range (2% to 16.6%) in within published range of 3% to 23.4%

56. Conclusions  M1 provides efficient acceptable thicknesses for the uppermost pavement surface course  M2 provides accurate pavement thicknesses for multilayer pavements  The expanded visualization tools of M2 help prevent interface interpretation blunders

57. Conclusions Continued  Likelihood of interface interpretation blunders increases when automated interface selection and tracking algorithm  The process of evaluating pavement thickness with GPR has not progressed to the point of eliminating a trained GPR interpreter  Study absolute accuracy range (2% to 16.6%) within published range of 3% to 23.4%