Long-Term Pavement Performance Automated Faulting Measurement
| dc.contributor | Virginia Tech Transportation Institute | en |
| dc.contributor | Engineering & Software Consultants | en |
| dc.contributor | Greenwood, Ian | en |
| dc.contributor.author | Agurla, Mahesh | en |
| dc.contributor.author | Lin, Sean | en |
| dc.date.accessed | 2015-07-06 | en |
| dc.date.accessioned | 2015-08-11T18:46:18Z | en |
| dc.date.available | 2015-08-11T18:46:18Z | en |
| dc.date.issued | 2015-06-04 | en |
| dc.description.abstract | This study focused on identifying transverse joint locations on jointed plain concrete pavements (JPCP) 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 (AASHTO) 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 devised an automated algorithm to identify joint locations where faulting is also computed for each joint identified in order 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. The study compared the LTPP manual faulting measurements collected using the GFM with the ProVAL AFM and the LTPP AFM using LTPP profile data. Similarly, the FDOT GFM measurements were compared to the FDOT PaveSuite AFM and the LTPP AFM using the same FDOT profile data. The initial results for six LTPP test sections show that the LTPP AFM can identify joint locations with a joint detection rate (JDR) ranging from 95 to 100 percent. ProVAL's JDR range is from 58 to 99 percent for the same six LTPP test sections. Similarly, for the one FDOT test section available, the LTPP AFM’s and FDOT PaveSuite's JDRs are approximately 96 percent. This study outlines the LTPP AFM algorithm, discusses the comparison of the three AFM results, and recommends future research needs in this area. | en |
| dc.description.notes | Presented during Session 5: Performance Prediction I, moderated by Raja Shekharan, at the 9th International Conference on Managing Pavement Assets (ICMPA9) in Alexandria, VA | en |
| dc.description.notes | Includes conference paper and PowerPoint slides. | en |
| dc.format.extent | 13 pages | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Agurla, M., & Lin, S. (2015, June). Long-term pavement performance automated faulting measurement. Paper presented at the 9th International Conference on Managing Pavement Assets, Alexandria, VA. Presentation retrieved from www.apps.vtti.vt.edu/PDFs/icmpa9/session5/Agurla.pdf | en |
| dc.identifier.uri | http://hdl.handle.net/10919/56367 | en |
| dc.identifier.url | www.apps.vtti.vt.edu/PDFs/icmpa9/session5/Agurla.pdf | en |
| dc.language.iso | en_US | en |
| dc.relation.ispartof | 9th International Conference on Managing Pavement Assets | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.title | Long-Term Pavement Performance Automated Faulting Measurement | en |
| dc.title.alternative | LTPP Automated Faulting Measurement | en |
| dc.type | Presentation | en |
| dc.type.dcmitype | Text | en |