Preparation of Joint Faulting Data for the Local Calibration of AASHTO Pavement Mechanistic

Abstract

Louisiana utilized performance data from the pavement management system (PMS) to evaluate and calibrate the AASHTO Pavement Mechanistic–Empirical (ME) Design. Analysis of the PMS faulting data revealed that there were no records between 0 and 0.2 in. (5 mm); others over 0.2 in. (5 mm) appeared to be much greater than would be expected based on engineering experience. Therefore, several tasks were completed to validate the PMS faulting data and prepare them for local calibration. This paper presents details of the problem, approach, results, and lessons learned. First, faulting data from the PMS and Long-Term Pavement Performance database were analyzed to have an overview of the common range of joint faulting. To validate the PMS faulting data, 43 representative projects across Louisiana were selected for further analysis. Longitudinal profiles were collected with high-speed profilers and analyzed with the AASHTO R36 automated faulting measurement (AFM) algorithms. Manual measurements were also conducted during site visits. The comparison of faulting from different methods showed that the PMS data extremely overestimated faulting compared with the AFM estimation or the manual measurement. Results from the AFM algorithm were much closer (in the same magnitude) to the manual measurements. Therefore, faulting data from the AFM algorithm were used, and the faulting model was successfully calibrated. It is recommended to evaluate PMS faulting data carefully before applying them to calibrate the AASHTO Pavement ME Design software. Automated faulting measurement based on high-speed profiles is a feasible approach.

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Preparation of Joint Faulting Data for the Local Calibration of AASHTO Pavement Mechanistic–Empirical Design in Louisiana

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