{"title":"评估易受灾社区基础设施组成部分重要性的系统方法","authors":"C. Nicholson , M.H. Tehrani , A. Ghasemkhani","doi":"10.1016/j.ijdrr.2024.104880","DOIUrl":null,"url":null,"abstract":"<div><div>Investing in pre-event disaster mitigation interventions for physical infrastructure, such as structural retrofits and enhancements, can be costly due to limited resources. To prioritize investments, infrastructure components are ranked by their criticality within the overall system. To be effective in real-world deployment, this approach must account for the complex interactions between components, as failures can occur simultaneously across large geographical areas due to the hazard footprint. As a result, hazard-specific uncertainties and spatial correlations may lead to distinctive failure patterns. In this study, we propose a novel data-driven framework leveraging Monte Carlo simulation, that harnesses the individual realizations to capture and model realistic component damage patterns under a specified hazard scenario. This framework addresses a gap in literature by moving beyond traditional methods that often treat component failures as independent events. By capturing the interdependence between bridges, primarily through failure interactions, and system-wide effects, our method provides a more comprehensive criticality assessment. The simulation data provides a foundation for the framework, which applies to a wide variety of infrastructure networks and performance metrics. To demonstrate the method's effectiveness, a simplified transportation network from Shelby County, TN subjected to an earthquake event is analyzed. The proposed framework provides an effective approach for component ranking, suitable for decision-making where human intuition and simple methods are insufficient. Its broad applicability suggests a potential for large-scale and interdependent network problems.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"113 ","pages":"Article 104880"},"PeriodicalIF":4.2000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A systemic approach for assessing infrastructure component importance in hazard-prone communities\",\"authors\":\"C. Nicholson , M.H. Tehrani , A. Ghasemkhani\",\"doi\":\"10.1016/j.ijdrr.2024.104880\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Investing in pre-event disaster mitigation interventions for physical infrastructure, such as structural retrofits and enhancements, can be costly due to limited resources. To prioritize investments, infrastructure components are ranked by their criticality within the overall system. To be effective in real-world deployment, this approach must account for the complex interactions between components, as failures can occur simultaneously across large geographical areas due to the hazard footprint. As a result, hazard-specific uncertainties and spatial correlations may lead to distinctive failure patterns. In this study, we propose a novel data-driven framework leveraging Monte Carlo simulation, that harnesses the individual realizations to capture and model realistic component damage patterns under a specified hazard scenario. This framework addresses a gap in literature by moving beyond traditional methods that often treat component failures as independent events. By capturing the interdependence between bridges, primarily through failure interactions, and system-wide effects, our method provides a more comprehensive criticality assessment. The simulation data provides a foundation for the framework, which applies to a wide variety of infrastructure networks and performance metrics. To demonstrate the method's effectiveness, a simplified transportation network from Shelby County, TN subjected to an earthquake event is analyzed. The proposed framework provides an effective approach for component ranking, suitable for decision-making where human intuition and simple methods are insufficient. Its broad applicability suggests a potential for large-scale and interdependent network problems.</div></div>\",\"PeriodicalId\":13915,\"journal\":{\"name\":\"International journal of disaster risk reduction\",\"volume\":\"113 \",\"pages\":\"Article 104880\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of disaster risk reduction\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212420924006423\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of disaster risk reduction","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212420924006423","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
A systemic approach for assessing infrastructure component importance in hazard-prone communities
Investing in pre-event disaster mitigation interventions for physical infrastructure, such as structural retrofits and enhancements, can be costly due to limited resources. To prioritize investments, infrastructure components are ranked by their criticality within the overall system. To be effective in real-world deployment, this approach must account for the complex interactions between components, as failures can occur simultaneously across large geographical areas due to the hazard footprint. As a result, hazard-specific uncertainties and spatial correlations may lead to distinctive failure patterns. In this study, we propose a novel data-driven framework leveraging Monte Carlo simulation, that harnesses the individual realizations to capture and model realistic component damage patterns under a specified hazard scenario. This framework addresses a gap in literature by moving beyond traditional methods that often treat component failures as independent events. By capturing the interdependence between bridges, primarily through failure interactions, and system-wide effects, our method provides a more comprehensive criticality assessment. The simulation data provides a foundation for the framework, which applies to a wide variety of infrastructure networks and performance metrics. To demonstrate the method's effectiveness, a simplified transportation network from Shelby County, TN subjected to an earthquake event is analyzed. The proposed framework provides an effective approach for component ranking, suitable for decision-making where human intuition and simple methods are insufficient. Its broad applicability suggests a potential for large-scale and interdependent network problems.
期刊介绍:
The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international.
Key topics:-
-multifaceted disaster and cascading disasters
-the development of disaster risk reduction strategies and techniques
-discussion and development of effective warning and educational systems for risk management at all levels
-disasters associated with climate change
-vulnerability analysis and vulnerability trends
-emerging risks
-resilience against disasters.
The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.