{"title":"以最终用户为导向、动态量化单个设施灾后恢复能力的新方法。","authors":"Gemma Cremen","doi":"10.1111/risa.17637","DOIUrl":null,"url":null,"abstract":"<p><p>Community recovery from a disaster is a complex process, in which the importance of different types of infrastructure functionality can change over time. Most of the myriad of metrics available for measuring disaster resilience do not capture the dynamic importance of functionality explicitly, however. This means that very different recovery trajectories of a given infrastructure can correspond to the same resilience value, regardless of variations in its utility over time. While some efforts have been made to integrate features of time dependency into individual facility resilience quantification, the resulting metrics either capture only a limited set of temporal instances throughout the post-disaster phase or do not offer a way to prioritize time steps in line with variations in the importance of facility functionality. This study proposes a novel, straightforward metric for component-level post-disaster resilience quantification that overcomes the aforementioned limitations. The metric involves a dynamic weighting component that enables stakeholders to place varying emphasis on different temporal points throughout the recovery process. The end-user-centered approach to resilience quantification facilitated by the metric allows for flexible, context-specific interpretations of infrastructure functionality importance that may vary across different communities. The metric is demonstrated through a hypothetical case study of infrastructure facilities with varying degrees of importance across the post-disaster recovery period, which showcases its versatility relative to a previously well-established measurement of component-level resilience. The proposed metric has significant potential for use in stakeholder-driven approaches to decision making on critical infrastructure (as well as other types of built environment) recovery and resilience.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A new end-user-oriented and dynamic approach to post-disaster resilience quantification for individual facilities.\",\"authors\":\"Gemma Cremen\",\"doi\":\"10.1111/risa.17637\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Community recovery from a disaster is a complex process, in which the importance of different types of infrastructure functionality can change over time. Most of the myriad of metrics available for measuring disaster resilience do not capture the dynamic importance of functionality explicitly, however. This means that very different recovery trajectories of a given infrastructure can correspond to the same resilience value, regardless of variations in its utility over time. While some efforts have been made to integrate features of time dependency into individual facility resilience quantification, the resulting metrics either capture only a limited set of temporal instances throughout the post-disaster phase or do not offer a way to prioritize time steps in line with variations in the importance of facility functionality. This study proposes a novel, straightforward metric for component-level post-disaster resilience quantification that overcomes the aforementioned limitations. The metric involves a dynamic weighting component that enables stakeholders to place varying emphasis on different temporal points throughout the recovery process. The end-user-centered approach to resilience quantification facilitated by the metric allows for flexible, context-specific interpretations of infrastructure functionality importance that may vary across different communities. The metric is demonstrated through a hypothetical case study of infrastructure facilities with varying degrees of importance across the post-disaster recovery period, which showcases its versatility relative to a previously well-established measurement of component-level resilience. The proposed metric has significant potential for use in stakeholder-driven approaches to decision making on critical infrastructure (as well as other types of built environment) recovery and resilience.</p>\",\"PeriodicalId\":21472,\"journal\":{\"name\":\"Risk Analysis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Analysis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/risa.17637\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Analysis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/risa.17637","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A new end-user-oriented and dynamic approach to post-disaster resilience quantification for individual facilities.
Community recovery from a disaster is a complex process, in which the importance of different types of infrastructure functionality can change over time. Most of the myriad of metrics available for measuring disaster resilience do not capture the dynamic importance of functionality explicitly, however. This means that very different recovery trajectories of a given infrastructure can correspond to the same resilience value, regardless of variations in its utility over time. While some efforts have been made to integrate features of time dependency into individual facility resilience quantification, the resulting metrics either capture only a limited set of temporal instances throughout the post-disaster phase or do not offer a way to prioritize time steps in line with variations in the importance of facility functionality. This study proposes a novel, straightforward metric for component-level post-disaster resilience quantification that overcomes the aforementioned limitations. The metric involves a dynamic weighting component that enables stakeholders to place varying emphasis on different temporal points throughout the recovery process. The end-user-centered approach to resilience quantification facilitated by the metric allows for flexible, context-specific interpretations of infrastructure functionality importance that may vary across different communities. The metric is demonstrated through a hypothetical case study of infrastructure facilities with varying degrees of importance across the post-disaster recovery period, which showcases its versatility relative to a previously well-established measurement of component-level resilience. The proposed metric has significant potential for use in stakeholder-driven approaches to decision making on critical infrastructure (as well as other types of built environment) recovery and resilience.
期刊介绍:
Published on behalf of the Society for Risk Analysis, Risk Analysis is ranked among the top 10 journals in the ISI Journal Citation Reports under the social sciences, mathematical methods category, and provides a focal point for new developments in the field of risk analysis. This international peer-reviewed journal is committed to publishing critical empirical research and commentaries dealing with risk issues. The topics covered include:
• Human health and safety risks
• Microbial risks
• Engineering
• Mathematical modeling
• Risk characterization
• Risk communication
• Risk management and decision-making
• Risk perception, acceptability, and ethics
• Laws and regulatory policy
• Ecological risks.