As the backbone and the ‘blood vessel’ of modern cities, road networks provide critical support for community activities and economic growth, with their roles even more crucial due to the dramatic progress in urbanization. The service of road networks is subjected to the increasing frequency of high-consequence natural hazards such as earthquakes, floods, hurricanes, etc. Identifying resilient restoration sequences is essential to mitigate the disruption of such important infrastructure networks. This paper investigates a novel decision-support model to optimize post-disaster road network repair sequence. The model, named as GCN-DRL model, integrates the advantages of deep reinforced learning (DRL) with graph convolutional neural network (GCN), two emerging artificial intelligence (AI) techniques to achieve efficient recovery of road network service. The model is applied to analyze two cases of community road networks in the US that are subjected to different types of hazards, i.e., earthquakes and flooding. The performance of repair sequence by the GCN-DRL model is compared with two commonly used methods, i.e., repair sequence by the genetic algorithm and by prioritization based on graph importance with betweenness centrality. The results showed the decision sequence by GCN-DRL model consistently achieved superior performance in road network restoration than the conventional methods. The AI-based decision model also features high computational efficiency since the GCN-DRL model can be trained before the hazard. With a pre-trained GCN-DRL model, a close to optimal decision-making process can be made available rapidly for different types of new hazards, which is advantageous in efficiently responding to hazards when they happen. This study demonstrates the promise of a new AI-based decision support model to improve the resilience of road networks by enabling efficient post-hazards recovery.

中文翻译：

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地震或洪水灾害下弹性路网恢复的深度强化学习模型

道路网络作为现代城市的骨干和“血管”，为社区活动和经济增长提供了重要支撑，随着城市化进程的迅猛发展，道路网络的作用更加重要。道路网络的服务受到地震、洪水、飓风等后果严重的自然灾害频率越来越高的影响。确定有弹性的恢复序列对于减轻此类重要基础设施网络的破坏至关重要。本文研究了一种新的决策支持模型，以优化灾后路网修复顺序。该模型命名为GCN-DRL模型，结合了深度强化学习（DRL）和图卷积神经网络（GCN）的优点，两种新兴的人工智能 (AI) 技术可实现道路网络服务的高效恢复。该模型用于分析美国社区道路网络的两个案例，这些道路网络遭受不同类型的灾害，即地震和洪水。将GCN-DRL模型修复序列的性能与两种常用方法进行了比较，即通过遗传算法修复序列和基于具有中介中心性的图重要性进行优先排序。结果表明，GCN-DRL 模型的决策序列在路网恢复方面始终比传统方法具有更好的性能。基于 AI 的决策模型还具有高计算效率，因为 GCN-DRL 模型可以在危险发生之前进行训练。使用预训练的 GCN-DRL 模型，可以针对不同类型的新灾害快速提供接近最优的决策过程，有利于在灾害发生时高效应对​​。这项研究展示了一种新的基于 AI 的决策支持模型的前景，它可以通过实现高效的灾后恢复来提高道路网络的弹性。