Pavement service life estimation and condition prediction

Remaining service life estimation and pavement condition prediction are two essential functions of Pavement Management Systems. Survival curves are often developed to obtain remaining life of a pavement family at network level. Regression equations are often developed to predict future pavement condition at project level. The two objectives of this study are: (1) To develop the Cox Proportional Hazards model to analyze the effects of influential factors on pavement remaining life; (2) To develop linear mixed effects prediction model to improve the condition prediction accuracy for individual pavements.; In this study, by specifying pavement condition rating (PCR) of 70 as the terminal pavement status, survival curves were developed based on historical PCR data using Cox Proportional Hazards method. Further, the estimated service lives of pavements were obtained from these survival curves. As an example, the survival data of asphalt overlays on flexible pavements in Ohio were analyzed for this study. The effects of influential factors such as structure thickness, climate, traffic loading, and pavement conditions prior to repair on pavement service life, were assessed. The results show that the Cox Proportional Hazards model is applicable in estimating the effects of influential factors on pavement service life. The service life obtained from this study can be used to assist in pavement rehabilitation decision-making, overlay design, and budget allocation.; Condition prediction of individual pavement is usually required in project-level management and is often based on adjusting corresponding pavement family deterioration trend. This study proposes using the Linear Mixed Effects Model (LMEM) method to predict future conditions of a specific pavement section by a weighted combination of the deterioration trends of the family average and that of the specific pavement. The weights are determined by the number of historical condition measurements available and the variations of the measured historical conditions of the specific pavement. The results of the LMEM showed significantly better accuracy in predicting specific pavement conditions compared with two commonly used adjustment methods, which use the latest condition measurement to adjust family model for individual pavement. The findings in this study show that the LMEM is useful for project level pavement condition prediction.