城市基础设施系统的级联故障:灾害链机制的综合综述

Zheng Lu , Deyu Yan , Huanjun Jiang , Hongjing Xue , Zhao-Dong Xu
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引用次数: 0

摘要

城市工程系统(UESs)是高度互联的,形成了复杂的依赖关系,使它们在灾难期间容易发生级联故障。虽然现有的研究已经探索了UESs灾害链的具体方面,但了解其相互依赖性、数据获取挑战和方法局限性的综合框架仍然不发达。本文从不同学术视角对灾害链的定义、城市灾害链的常见类型,即地震、洪水、火灾、冰冻和地面沉降灾害链,以及城市灾害链之间的相互依存关系等方面对UES灾害链进行了系统回顾,以弥补这一空白。总结了基于历史灾害数据、专家经验和自然语言处理(NLP)的三种灾害链识别方法。总结了基于贝叶斯网络、复杂网络、数值模拟、场景模拟和遥感的五种灾害链分析方法,比较了它们的适用性、优势、局限性和复杂性。每种方法的优点和缺点都清楚地说明了。本文最后讨论了现有文献的局限性,并建议未来的研究可以利用新技术来促进数据分析过程,进行跨区域研究,并将重点放在整合社会经济因素以支持灾害相关决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cascading failures in urban infrastructure systems: A comprehensive review of disaster chain mechanisms
Urban engineering systems (UESs) are highly interconnected, forming complex dependencies that render them vulnerable to cascading failures during disasters. While existing studies have explored specific aspects of disaster chains in UESs, a synthesized framework for understanding their interdependencies, data acquisition challenges, and methodological limitations remains underdeveloped. This paper addresses this gap by conducting a systematic review of UES disaster chains, beginning with the definitions of disaster chains from different academic perspectives, common types of urban disaster chains, namely earthquake, flood, fire, freezing and ground subsidence disaster chains, as well as the interdependency of UES. Furthermore, three identification methods of disaster chains are summarized, namely based on historical disaster data, expert experience, and natural language processing (NLP). Moreover, five analysis methods of disaster chains are summarized, including those based on Bayesian networks, complex networks, numerical simulation, scenario simulation and remote sensing, with comparison of their applicability, advantages, limitations and complexity. The benefits and drawbacks of each approach are clearly illustrated. The paper concludes by discussing the limitations in the current literature and suggests that future research may utilize new technologies to facilitate data analyzing process, conduct cross-regional studies, and focus on integrating socio-economic factors for disaster-related decision-making support.
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