Development of Linear Mixed Effects Models for Predicting Individual Pavement Conditions

Abstract

Predicting future conditions of pavement plays an important role in pavement management. Prediction for a specific pavement is usually based on the deterioration trend of a group of pavements with similar characteristics, i.e., the same pavement family. This study proposes using the linear mixed effects model (LMEM) to predict future conditions of a specific pavement section by a weighted combination of the average deterioration trend of the family and the past conditions of the specific pavement. The relative weights are determined by the number of past condition measurements available and the degree of variations of the measured past conditions for the specific pavement. The results of the LMEM show significantly higher accuracy in predicting specific pavement conditions compared with two existing adjustment methods that use the last available condition measurement of the specific pavement to adjust the family trend prediction. The finding of this study shows that the LMEM can be used for project level pavement condition prediction or other types of infrastructure condition prediction, whereas future conditions of a specific entity are to be projected based on a combination of the average “family” trend, as well as the individual’s condition history.