Highlights Jointly optimizing inspection, maintenance, and reconstruction for pavement systems. The framework is defined as a Partially Observable Markov Decision Process. A bottom-up approach is proposed to consider facility-specific heterogeneity. We use a statistical learning method for belief updates to reduce model uncertainty. Condition-based strategies are found and validated through a numerical example.
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.
Condition-based pavement management systems accounting for model uncertainty and facility heterogeneity with belief updates
2023-01-30
Article (Journal)
Electronic Resource
English
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