Condition-based pavement management systems accounting for model uncertainty and facility heterogeneity with belief updates

Abstract

Due to the tighter budget for pavement management, schedules of inspection activities should be jointly optimized with the maintenance and reconstruction (M&R) plans for pavement systems. Conducting inspections every year is unnecessary and will decrease the budget for M&R activities, while infrequent inspections may lead to suboptimal M&R planning due to the lack of accurate information. This paper presents a methodology for jointly optimizing the inspection scheduling and M&R planning for pavement systems, considering model uncertainty and facility-specific heterogeneity. The problem is defined as a Partially Observable Markov Decision Process (POMDP) model, accounting for the tradeoff between the information value and inspection costs. Moreover, a statistical learning method is used to update the prediction of pavement conditions using the collected inspection data and, eventually, improve the condition-based decisions. This "belief update" process can gradually reduce the model uncertainty as the dataset size increases. We demonstrate the proposed stochastic optimization framework through a numerical example with a system of fifty heterogenous pavement facilities under a combined budget for inspection and M&R activities. Several managerial insights and implications are discussed. For example, the optimal inspection frequencies are less sensitive to the budget; and the agency should perform fewer reconstructions and more rehabilitations when the budget is limited.