Regional seismic risk and resilience assessment: Methodological development, applicability, and future research needs – An earthquake engineering perspective

地震风险 脆弱性 风险分析(工程) 弹性(材料科学) 地震灾害 地震情景 风险评估 危害 背景(考古学) 概率逻辑 工程类 计算机科学 土木工程 地理 业务 热力学 物理 计算机安全 物理化学 人工智能 考古 有机化学 化学
作者
Ao Du,Xiaowei Wang,Yazhou Xie,You Dong
出处
期刊:Reliability Engineering & System Safety [Elsevier BV]
卷期号:233: 109104-109104 被引量:125
标识
DOI:10.1016/j.ress.2023.109104
摘要

Given the devastating losses incurred by past major earthquake events together with the ever-increasing global seismic exposures due to population growth and urbanization, strategic decision-support tools are required to help stakeholders make more contemplated decisions that promote seismic resilience of the built environment. Such decision-support is enabled by regional seismic risk and resilience assessment, which holistically incorporates the various underlying physics processes and uncertainties for quantitative and probabilistic assessment of regional seismic hazard impacts, and has thereby attracted increasing research focus over the past twenty years. As a significant departure from the traditional site-specific assessment, where only an individual structure and site-specific seismic hazard are of interest, such regional-level assessment introduces additional dimensions and complexity. To date, there is a lack of review studies summarizing the related research advancements in seismic risk and resilience assessment from a regional-level perspective in the context of earthquake engineering. This study fills this gap by conducting a systematic review covering: the methodological development of regional seismic risk assessment (RSRiA) across its key modules, including hazard analysis, exposure modeling, fragility assessment, and consequence evaluation, as well as the associated uncertainty quantification and propagation; the development of resilience metrics, restoration modeling and planning in regional seismic resilience assessment (RSReA); and the applicability of existing computational workflows. Insights into the features, applicability, compatibility, and limitations of existing models and tools are provided. This study also highlights the challenges and future directions toward further advancing the research frontiers of RSRiA and RSReA.
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