Quantifying component importance for disaster resilience of communities with interdependent civil infrastructure systems. (December 2022)

• Record Type: Journal Article
• Authors: Blagojević, Nikola; Didier, Max; Stojadinović, Božidar
• Is Part Of: Reliability engineering & system safety. Volume 228(2022)
• Publication Date: 2022-12
• Subjects: Interdependent civil infrastructure systems -- Sobol' indices -- Disaster resilience -- Sensitivity analysis -- Component importance -- iRe-CoDeS; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108747
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: Communities and their supporting civil infrastructure systems can be viewed as an assembly of, often numerous, interacting, interdependent components. Tools that can identify and rank the components relevant for community disaster resilience can help efficiently allocate limited resources to reach community resilience goals. We propose a method based on Sobol' indices and a heuristic upper and lower bound search to measure the importance of vulnerability and recoverability of components for disaster resilience of communities, quantified using the iRe-CoDeS framework, and demonstrate it in two Case Studies. An important feature of the proposed method is that no prior knowledge of component's vulnerability and recoverability is necessary to perform the initial component importance analysis. The first Case Study confirms the ability of the proposed method to recognize components important for meeting housing resilience goals. The second Case Study illustrates the effectiveness of the proposed method. Namely, almost half of the components of the considered system are identified as irrelevant for meeting the set infrastructure resilience goal. Therefore, the proposed method makes it possible to rationally reduce the number of components considered in community resilience assessment, as well as to avoid redundant component-level modeling and data gathering efforts. Highlights: Rank component importance for community resilience using Sobol' indices. Ranking is possible without information on components' vulnerability and recoverability. Community resilience metrics bounds obtained without information on sensitive components. Reduce the data collection and modeling effort of community resilience assessment.