Probabilistic Behavior of Pavements

Abstract

Since the AASHO road test of 1962, tremendous efforts have been devoted to improve the methodologies of pavement performance prediction. The successful implementation of the Network Optimization System (NOS) in the Arizona Department of Transportation (ADOT), in the 1980s, represented an advancement in the prediction methodology by using Markov‐process‐based transition‐probability matrices (TPMs) to define the transition process of pavement conditions. This paper addresses some of the inadequacies of the original NOS prediction model. Two approaches were used to evaluate the transition probability matrices. First, the current pavement performance data base was used to develop new TPMs. Second, the Chapman‐Kolmogorov method was used to examine the logical extension of the transition probability matrices from a single step to longterm pavement behavior. As a result, the concept of pavement probabilistic behavior curves (PBC) was established. The newly generated TPMs were modified with accessibility rules to improve the prediction of pavement performance. More importantly, it was demonstrated that Markovian prediction satisfactorily models actual pavement behavior. The developments presented in this paper improve the reliability of the microcomputer‐based NOS.