A network analytical framework to analyze infrastructure damage based on earthquake cascades: A study of earthquake cases in Japan

中间性中心性 关键基础设施 中心性 级联故障 相互依存的网络 计算机科学 概率逻辑 相互依存 地震学 地质学 复杂网络 计算机安全 物理 人工智能 数学 统计 万维网 政治学 功率(物理) 电力系统 量子力学 法学
作者
C. Y. Teresa Lam,Toshihiko Shimizu
出处
期刊:International journal of disaster risk reduction [Elsevier]
卷期号:54: 102025-102025 被引量:28
标识
DOI:10.1016/j.ijdrr.2020.102025
摘要

Earthquakes can result in disasters affecting infrastructure and assets that are exposed and vulnerable and thus prone to damage. Considering earthquake cascades, we can reveal the immediate and intermediate effects of earthquakes causing infrastructure damage. In this study, we propose a network analytical modeling approach to analyze earthquake cascades. The analytical framework integrates incident causation modeling and probabilistic network analysis. The linear effects from earthquakes are delineated in incident chains to represent the consequences of earthquakes to infrastructure; the incident chains are then remodeled topologically by the network theory to reveal the nonlinear cascading effects of earthquakes to infrastructure. The network of earthquakes’ effects and intermediate effects is then structurally analyzed by probabilistic network analysis that uncovers the unifying principles and statistical topology of earthquake cascades to infrastructure. The results show that earthquakes can directly cause significant damage to infrastructure and indirectly cascade the effects to infrastructure damage via intermediate effects; each effect or intermediate effects in earthquake cascades have their own role in leading to infrastructure damage. The critical infrastructure damage due to earthquakes and critical intermediate effects in propagating earthquake cascades are revealed in this study by the network analysis of network density, degree centrality, closeness centrality, betweenness centrality, and cascade quantification. To reduce infrastructure damage due to earthquake cascades, we suggest tackling the directed interdependent intermediate effects to minimize the risks associated with earthquakes to infrastructure damage. The network analytical framework in this study can be used to understand the propagation damage of earthquakes on infrastructure; thus, our work can provide valuable insight into earthquake preparedness and emergency management.
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