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Condition-based pavement management systems accounting for model uncertainty and facility heterogeneity with belief updates
Transportation Research Part C: Emerging Technologies ( IF 8.3 ) Pub Date : 2023-02-09 , DOI: 10.1016/j.trc.2023.104054
Le Zhang , Weihua Gu , Young-Ji Byon , Jinwoo Lee

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.



中文翻译:

基于条件的路面管理系统通过信念更新来解释模型不确定性和设施异质性

由于路面管理预算紧张,检查活动的时间表应与路面系统的维护和重建 (M&R) 计划共同优化。每年都进行检查是不必要的,并且会减少 M&R 活动的预算,而不经常检查可能会由于缺乏准确的信息而导致 M&R 计划不理想。本文提出了一种联合优化路面系统检查调度和 M&R 规划的方法,同时考虑模型不确定性和设施特定的异质性。该问题被定义为部分可观察的马尔可夫决策过程 (POMDP) 模型,考虑了信息价值和检查成本之间的权衡。而且,统计学习方法用于使用收集的检查数据更新路面状况的预测,并最终改进基于状况的决策。随着数据集大小的增加,这种“信念更新”过程可以逐渐降低模型的不确定性。我们通过一个包含 50 个异质路面设施的系统的数值示例,在检查和 M&R 活动的综合预算下展示了所提出的随机优化框架。讨论了一些管理见解和影响。例如,最佳检查频率对预算不太敏感;当预算有限时,该机构应该进行更少的重建和更多的修复。随着数据集大小的增加,这种“信念更新”过程可以逐渐降低模型的不确定性。我们通过一个包含 50 个异质路面设施的系统的数值示例,在检查和 M&R 活动的综合预算下展示了所提出的随机优化框架。讨论了一些管理见解和影响。例如,最佳检查频率对预算不太敏感;当预算有限时,该机构应该进行更少的重建和更多的修复。随着数据集大小的增加,这种“信念更新”过程可以逐渐降低模型的不确定性。我们通过一个包含 50 个异质路面设施的系统的数值示例,在检查和 M&R 活动的综合预算下展示了所提出的随机优化框架。讨论了一些管理见解和影响。例如,最佳检查频率对预算不太敏感;当预算有限时,该机构应该进行更少的重建和更多的修复。讨论了一些管理见解和影响。例如,最佳检查频率对预算不太敏感;当预算有限时,该机构应该进行更少的重建和更多的修复。讨论了一些管理见解和影响。例如,最佳检查频率对预算不太敏感;当预算有限时,该机构应该进行更少的重建和更多的修复。

更新日期:2023-02-10
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