Stochastic prediction of road network degradation based on field monitoring data

Asset management of pavement network requires understanding of pavement deterioration rate for cost-effective maintenance and adequate budget allocation. The pavement industry has recognized the challenge of uncertainty or variation in deterioration processes that could not be captured by deterministic deterioration models. This study investigated the stochastic Markov chain theory for modeling deterioration of pavement network. The discrete condition data for the Markov model is obtained by a proposed maintenance-related condition rating scheme (MRCR) that combines three commonly inspected pavement distresses including cracking, rutting and roughness. The Markov model is calibrated by the proven Bayesian Markov chain Monte Carlo simulation method, and the statistical Chi-square test is used for testing model fitness. A case study with time series data of pavement distresses collected from regular inspection of a highway network is used in this study. Various influential factors to pavement deterioration are also investigated in this study to understand their impact on the deterioration rate of highways. The results on the case study show that the Markov model is suitable for modeling deterioration of highway network, and there are significant differences in deterioration rates of highways among influential factors including traffic volume, rainfall amount, demographic location, and prioritized maintenance. The outcomes of this study provide more understanding of pavement deterioration of road networks and demonstrate the forecasting of maintenance budget by the deterioration prediction of Markov model for supporting asset management of pavement network.