System-Reliability-Based Disaster Resilience Analysis of Infrastructure Networks and Causality-Based Importance Measure

Youngjun Kwon, Junho Song
{"title":"System-Reliability-Based Disaster Resilience Analysis of Infrastructure Networks and Causality-Based Importance Measure","authors":"Youngjun Kwon, Junho Song","doi":"10.1115/1.4062682","DOIUrl":null,"url":null,"abstract":"\n Civil infrastructure systems become highly complex and thus get more vulnerable to disasters. The concept of disaster resilience, the overall capability of a system to manage risks posed by catastrophic events, is emerging to address the challenge. Recently, a system-reliability-based disaster resilience analysis framework was proposed for a holistic assessment of the components' reliability, the system's redundancy, and the society's ability to recover the system functionality. The proposed framework was applied to individual structures to produce diagrams visualizing the pairs of the reliability index (β) and the redundancy index (p) defined to quantify the likelihood of each initial disruption scenario and the corresponding system-level failure probability, respectively. This paper develops methods to apply the β-p analysis framework to infrastructure networks and demonstrates its capability to evaluate the disaster resilience of networks from a system reliability viewpoint. We also propose a new causality-based importance measure of network components based on the β-p analysis and a causal diagram model that can consider the causality mechanism of the system failure. Compared with importance measures in the literature, the proposed measure can evaluate a component's relative importance through a well-balanced consideration of network topology and reliability. The proposed measure is expected to provide helpful guidelines for making optimal decisions to secure the disaster resilience of infrastructure networks.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4062682","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Civil infrastructure systems become highly complex and thus get more vulnerable to disasters. The concept of disaster resilience, the overall capability of a system to manage risks posed by catastrophic events, is emerging to address the challenge. Recently, a system-reliability-based disaster resilience analysis framework was proposed for a holistic assessment of the components' reliability, the system's redundancy, and the society's ability to recover the system functionality. The proposed framework was applied to individual structures to produce diagrams visualizing the pairs of the reliability index (β) and the redundancy index (p) defined to quantify the likelihood of each initial disruption scenario and the corresponding system-level failure probability, respectively. This paper develops methods to apply the β-p analysis framework to infrastructure networks and demonstrates its capability to evaluate the disaster resilience of networks from a system reliability viewpoint. We also propose a new causality-based importance measure of network components based on the β-p analysis and a causal diagram model that can consider the causality mechanism of the system failure. Compared with importance measures in the literature, the proposed measure can evaluate a component's relative importance through a well-balanced consideration of network topology and reliability. The proposed measure is expected to provide helpful guidelines for making optimal decisions to secure the disaster resilience of infrastructure networks.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于系统可靠性的基础设施网络抗灾能力分析及基于因果关系的重要性测度
民用基础设施系统变得高度复杂,因此更容易受到灾害的影响。为了应对这一挑战,灾害恢复力的概念应运而生,即系统管理灾难性事件带来的风险的整体能力。近年来,提出了一种基于系统可靠性的灾害恢复分析框架,以全面评估组件的可靠性、系统的冗余以及社会对系统功能的恢复能力。将提出的框架应用于单个结构,生成可视化的图表,显示可靠性指数(β)和冗余指数(p)对,这些指数分别用于量化每个初始中断场景的可能性和相应的系统级故障概率。本文开发了将β-p分析框架应用于基础设施网络的方法,并从系统可靠性的角度论证了其评估网络抗灾能力的能力。在β-p分析的基础上,提出了一种新的基于因果关系的网络组件重要性度量方法,并建立了考虑系统失效因果机制的因果图模型。与文献中的重要性度量相比,本文提出的度量可以通过平衡考虑网络拓扑和可靠性来评估组件的相对重要性。拟议的措施有望为制定最佳决策提供有用的指导方针,以确保基础设施网络的抗灾能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
5.20
自引率
13.60%
发文量
34
期刊最新文献
Verification and Validation of Rotating Machinery Using Digital Twin Risk Approach Based On the Fram Model for Vessel Traffic Management A Fault Detection Framework Based On Data-driven Digital Shadows Domain Adaptation Of Population-Based Of Bolted Joint Structures For Loss Detection Of Tightening Torque Human-Comfort Evaluation for A Patient-Transfer Robot through A Human-Robot Mechanical Model
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1