Forecast of post-overlay flexible pavement roughness progression: a random coefficient linear regression with autocorrelation model

Abstract

Pavement roughness progression model is an essential component in the pavement management system. Asphalt overlay is a typical pavement maintenance and rehabilitation (M&R) technique. To schedule asphalt overlay timely, the relationship between asphalt overlay design and post-overlay roughness progression is required. However, due to difficulty in data compilation and model development, the effect of endogenous overlay design and continuous variable of asphalt overlay thickness on post-overlay roughness progression is not documented. This study aims to develop a comprehensive post-overlay flexible pavement roughness model with the long-term pavement performance (LTPP) data. The asphalt overlay projects from the LTPP SPS-3, SPS-5, and GPS-6 programs were incorporated for data analysis. A random coefficient linear regression with autocorrelation (RCLRA) model is proposed to simultaneously address endogenous overlay design issue, between-section heterogeneity issue, and within-section serial correlation issue in post-overlay roughness progression. By addressing the within-section serial correlation, the proposed post-overlay roughness model can reduce the mean absolute percentage error (MAPE) from 21.26% to 2.19%. The model estimation results provide some new insights into the relationship between post-overlay roughness and asphalt overlay design factors. Endogenous overlay design indicator, continuous variable of asphalt overlay thickness, relative fatigue cracking area, and severe rutting indicator are firstly identified to have significant effects on post-overlay roughness progression.