Pavement segments are functionally interdependent under traffic equilibrium, leading to interdependent maintenance and rehabilitation (M&R) decisions for different segments, but it has not received significant attention in the pavement management community yet. This study developed a maintenance optimization model for interdependent pavement networks based on the simultaneous network optimization (SNO) framework and a multi-agent reinforcement learning algorithm. The established model was demonstrated on a highway pavement network in the real-world, compared to a previously built two-stage bottom-up (TSBU) model. The results showed that, compared to TSBU, SNO produced a 3.0% reduction in total costs and an average pavement performance improvement of up to 17.5%. It prefers concentrated M&R schedules and tends to take more frequent preventive maintenance to reduce costly rehabilitation. The results of this research are anticipated to provide practitioners with quantitative estimates of the possible impact of ignoring segment interdependencies in M&R planning.

中文翻译：

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用于相互依赖的公路路面网络维护优化的多智能体强化学习模型


在交通平衡下，路面路段在功能上是相互依赖的，导致不同路段的相互依赖的维护和修复（M＆R）决策，但尚未引起路面管理界的重视。本研究基于同时网络优化（SNO）框架和多智能体强化学习算法，开发了一种相互依赖的路面网络维护优化模型。与之前构建的两阶段自下而上（TSBU）模型相比，所建立的模型在现实世界的高速公路路面网络上进行了演示。结果表明，与 TSBU 相比，SNO 的总成本降低了 3.0%，平均路面性能提高高达 17.5%。它更喜欢集中的维护和修复计划，并倾向于采取更频繁的预防性维护，以减少昂贵的修复费用。这项研究的结果预计将为从业者提供对 M&R 规划中忽略部门相互依赖性可能产生的影响的定量估计。