Abstract

In civil infrastructure maintenance planning, prevention of severe accidents is essential. Toward that aim, it is necessary to consider the probability of rare events, and risk indices such as value at risk (VaR) and conditional value at risk (CVaR) are frequently employed. However, these indices are not time-consistent. A policy that was initially regarded as optimal may therefore not be optimal when one wants to evaluate the maintenance policy to reduce the probability of tail-risk events, considering policy changes in the future. To eliminate the effect of inconsistency, this study proposes a risk quantification scheme that accounts for the dynamic characteristics of infrastructure maintenance by exploiting the formulation of iterated risk measures as a framework for conventional risk indices such as CVaR. The estimation of the iterated risk measure using a Monte Carlo simulation is costly because it requires a great many samples. To mitigate this problem, the samples are divided into clusters, which enables an efficient identification of samples corresponding to tail-risk events. The performance of the proposed scheme is verified through numerical simulations of road pavement maintenance. The results show that the proposed scheme can evaluate the maintenance policy to reduce the probability of tail-risk events, with consideration for time-consistency.

抽象的

在民用基础设施维护计划中，防止严重事故至关重要。为了实现该目标，有必要考虑罕见事件的可能性，并且经常采用诸如风险价值（VaR）和条件风险价值（CVaR）之类的风险指标。但是，这些索引不是时间一致的。因此，当一个人想要评估维护策略以减少发生尾部风险事件的可能性时，考虑到将来的策略更改，那么最初被认为是最优的策略可能不是最优的。为了消除不一致的影响，本研究提出了一种风险量化方案，该方案通过利用迭代风险度量的公式化作为传统风险指标（例如CVaR）的框架来说明基础设施维护的动态特征。由于需要大量样本，因此使用蒙特卡洛模拟对迭代风险度量进行估计是昂贵的。为了缓解此问题，将样本分为几类，从而可以有效识别与尾部风险事件相对应的样本。通过道路养护的数值模拟验证了所提方案的性能。结果表明，该方案可以在考虑时间一致性的前提下，对维护策略进行评估，以降低发生尾部风险事件的可能性。通过道路养护的数值模拟验证了所提方案的性能。结果表明，该方案可以在考虑时间一致性的前提下，对维护策略进行评估，以降低发生尾部风险事件的可能性。通过道路养护的数值模拟验证了所提方案的性能。结果表明，该方案可以在考虑时间一致性的前提下，对维护策略进行评估，以降低发生尾部风险事件的可能性。