U.S. Department of Transportation
Federal Highway Administration
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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
| REPORT |
| This report is an archived publication and may contain dated technical, contact, and link information |
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| Publication Number: FHWA-HRT-14-092 Date: February 2015 |
Publication Number: FHWA-HRT-14-092 Date: February 2015 |
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This report documents the development of the Long-Term Pavement Performance (LTPP) automated faulting measurement (AFM) algorithm to identify transverse joint locations on jointed plain concrete pavements and compute faulting at these locations using the profile data collected by LTPP high-speed inertial profilers. The LTPP AFM algorithm is intended to replace traditional manual faulting surveys that entail traffic control and significant survey time from State departments of transportation and highway agencies. The software program developed based on this algorithm will serve as an automated tool for highway engineers to significantly increase their productivity when detecting transverse joint locations with acceptable accuracy.
Jorge E. Pagán-Ortiz
Director, Office of Infrastructure
Research and Development
Notice
This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. This report does not constitute a standard, specification, or regulation.
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Technical Report Documentation Page
| 1. Report No.
FHWA-HRT-14-092 |
2. Government Accession No. | 3 Recipient's Catalog No. | ||
| 4. Title and Subtitle
Long-Term Pavement Performance
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5. Report Date February 2015 |
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| 6. Performing Organization Code | ||||
| 7. Author(s)
Mahesh Agurla and Sean Lin |
8. Performing Organization Report No.
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9. Performing Organization Name and Address Engineering & Software Consultants, Inc. |
10. Work Unit No. (TRAIS) |
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11. Contract or Grant No. DTFH61-12-C-00002 |
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| 12. Sponsoring Agency Name and Address
Office of Infrastructure Research and Development |
13. Type of Report and Period Covered
Research Report |
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14. Sponsoring Agency Code
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15. Supplementary Notes Contracting Officer's Representative (COR): Aramis Lopez; Task Manager: Larry Wiser |
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| 16. Abstract
This study focused on identifying transverse joint locations on jointed plain concrete pavements using an automated joint detection algorithm and computing faulting at these locations using Long-Term Pavement Performance (LTPP) Program profile data collected by the program's high-speed inertial profilers (HSIP). This study evaluated two existing American Association of State Highway and Transportation Officials R 36-12 automated faulting measurement (AFM) models: ProVAL (Method-A) and Florida Department of Transportation (FDOT) PaveSuite (Method-B). A new LTPP AFM was developed using LTPP profile data. The LTPP AFM is an automated algorithm to identify joint locations where faulting is also computed for each joint identified to replicate the manually collected faulting data using the Georgia Faultmeter (GFM), which has been used on LTPP test sections since the program's inception. |
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| 17. Key Words
Automated faulting measurement, LTPP high-speed inertial profiler data, Georgia Faultmeter, jointed plain concrete pavement, joint faulting measurement, transverse joint location detection |
18. Distribution Statement
No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161. |
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19. Security Classification Unclassified |
20. Security Classification Unclassified |
21. No. of Pages 36 |
22. Price | |
| Form DOT F 1700.7 | Reproduction of completed page authorized |
SI* (Modern Metric) Conversion Factors
Figure 1. Diagram. MFM using the GFM
Figure 2. Equation. Moving average smoothing filter
Figure 3. Graph. Anti-smoothed profile with 1.25-m base length
Figure 4. Graph. Anti-smoothed profile with 0.3-m base length
Figure 5. Equation. RMS
Figure 6. Flowchart. Peakdet algorithm flow chart
Figure 7. Graph. A 4-m moving window (0-4 m) using the Peakdet algorithm to detect the first transverse joint
Figure 8. Graph. A 4-m moving window (6.025-10.025 m) using the Peakdet algorithm to detect the second transverse joint
Figure 9. Graph. Detected true positive transverse joints identified by circles
Figure 10. Equation. Two-point slope formula
Figure 11. Graph. The slope method to determine P1 on approach slab and P2 on leave slab
Table 1. LTPP AFM joint detection results using LTPP profiler data
Table 2. ProVAL AFM joint detection results using LTPP profiler data
Table 3. FDOT and LTPP AFM joint detection results using FDOT HSIP
Table 4. LTPP AFM faulting results (slope method) using LTPP profiler data
Table 5. ProVAL AFM faulting results using LTPP profiler data
Table 6. LTPP AFM faulting results (AASHTO Method-A) using LTPP profiler data
Table 7. Joint faulting results using FDOT HSIP data
| AASHTO | American Association of State Highway and Transportation Officials | |
| AFM | Automated faulting measurement | |
| ANN | Artificial neural network | |
| CEP | Current elevation point | |
| CEPos | Current elevation position | |
| CMaE | Current maximum elevation | |
| CMaEP | Current maximum elevation position | |
| CMiE | Current minimum elevation | |
| CMiEP | Current minimum elevation position | |
| ERD | Engineering Research Division | |
| FDOT | Florida Department of Transportation | |
| GFM | Georgia Faultmeter | |
| HSIP | High-speed inertial profiler | |
| ICC | International Cybernetics Corporation | |
| Inf | Infinity | |
| IRI | International Roughness Index | |
| JDR | Joint detection rate | |
| JPCP | Jointed plain concrete pavements | |
| KDOT | Kansas Department of Transportation | |
| LTPP | Long-Term Pavement Performance | |
| LVDT | Linear Variable Differential Transformer | |
| MFM | Manual faulting measurement | |
| MLR | Multivariate linear regression | |
| NaN | Not a number | |
| PPDB | Pavement performance database | |
| RMS | Root mean square |
