Pedram Omidian , Naser Khaji , Ali Akbar Aghakouchak
{"title":"An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework","authors":"Pedram Omidian , Naser Khaji , Ali Akbar Aghakouchak","doi":"10.1016/j.rcns.2024.12.002","DOIUrl":null,"url":null,"abstract":"<div><div>Bridge networks are essential components of civil infrastructure, supporting communities by delivering vital services and facilitating economic activities. However, bridges are vulnerable to natural disasters, particularly earthquakes. To develop an effective disaster management strategy, it is critical to identify reliable, robust, and efficient indicators. In this regard, Life-Cycle Cost (LCC) and Resilience (R) serve as key indicators to assist decision-makers in selecting the most effective disaster risk reduction plans. This study proposes an innovative LCC–R optimization framework to identify the most optimal retrofit strategies for bridge networks facing hazardous events during their lifespan. The proposed framework employs both single- and multi-objective optimization techniques to identify retrofit strategies that maximize the R index while minimizing the LCC for the under-study bridge networks. The considered retrofit strategies include various options such as different materials (steel, CFRP, and GFRP), thicknesses, arrangements, and timing of retrofitting actions. The first step in the proposed framework involves constructing fragility curves by performing a series of nonlinear time-history incremental dynamic analyses for each case. In the subsequent step, the seismic resilience surfaces are calculated using the obtained fragility curves and assuming a recovery function. Next, the LCC is evaluated according to the proposed formulation for multiple seismic occurrences, which incorporates the effects of complete and incomplete repair actions resulting from previous multiple seismic events. For optimization purposes, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) evolutionary algorithm efficiently identifies the Pareto front to represent the optimal set of solutions. The study presents the most effective retrofit strategies for an illustrative bridge network, providing a comprehensive discussion and insights into the resulting tactical approaches. The findings underscore that the methodologies employed lead to logical and actionable retrofit strategies, paving the way for enhanced resilience and cost-effectiveness in bridge network management against seismic hazards.</div></div>","PeriodicalId":101077,"journal":{"name":"Resilient Cities and Structures","volume":"4 1","pages":"Pages 16-40"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Resilient Cities and Structures","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772741624000693","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Bridge networks are essential components of civil infrastructure, supporting communities by delivering vital services and facilitating economic activities. However, bridges are vulnerable to natural disasters, particularly earthquakes. To develop an effective disaster management strategy, it is critical to identify reliable, robust, and efficient indicators. In this regard, Life-Cycle Cost (LCC) and Resilience (R) serve as key indicators to assist decision-makers in selecting the most effective disaster risk reduction plans. This study proposes an innovative LCC–R optimization framework to identify the most optimal retrofit strategies for bridge networks facing hazardous events during their lifespan. The proposed framework employs both single- and multi-objective optimization techniques to identify retrofit strategies that maximize the R index while minimizing the LCC for the under-study bridge networks. The considered retrofit strategies include various options such as different materials (steel, CFRP, and GFRP), thicknesses, arrangements, and timing of retrofitting actions. The first step in the proposed framework involves constructing fragility curves by performing a series of nonlinear time-history incremental dynamic analyses for each case. In the subsequent step, the seismic resilience surfaces are calculated using the obtained fragility curves and assuming a recovery function. Next, the LCC is evaluated according to the proposed formulation for multiple seismic occurrences, which incorporates the effects of complete and incomplete repair actions resulting from previous multiple seismic events. For optimization purposes, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) evolutionary algorithm efficiently identifies the Pareto front to represent the optimal set of solutions. The study presents the most effective retrofit strategies for an illustrative bridge network, providing a comprehensive discussion and insights into the resulting tactical approaches. The findings underscore that the methodologies employed lead to logical and actionable retrofit strategies, paving the way for enhanced resilience and cost-effectiveness in bridge network management against seismic hazards.