An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework
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 crit...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Resilient Cities and Structures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772741624000693 |
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| author | Pedram Omidian Naser Khaji Ali Akbar Aghakouchak |
| author_facet | Pedram Omidian Naser Khaji Ali Akbar Aghakouchak |
| author_sort | Pedram Omidian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a87f327265524e27a8d4ec5042db69f0 |
| institution | Kabale University |
| issn | 2772-7416 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Resilient Cities and Structures |
| spelling | doaj-art-a87f327265524e27a8d4ec5042db69f02025-01-08T04:53:51ZengElsevierResilient Cities and Structures2772-74162025-03-01411640An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based frameworkPedram Omidian0Naser Khaji1Ali Akbar Aghakouchak2Faculty of Civil and Environmental Engineering, Tarbiat Modares University, P.O. Box 14115–397, Tehran, IranCivil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan; Corresponding author.Faculty of Civil and Environmental Engineering, Tarbiat Modares University, P.O. Box 14115–397, Tehran, IranBridge 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.http://www.sciencedirect.com/science/article/pii/S2772741624000693Bridge networkInfrastructures managementDecision-making frameworkResilienceLife-cycle cost |
| spellingShingle | Pedram Omidian Naser Khaji Ali Akbar Aghakouchak An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework Resilient Cities and Structures Bridge network Infrastructures management Decision-making framework Resilience Life-cycle cost |
| title | An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework |
| title_full | An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework |
| title_fullStr | An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework |
| title_full_unstemmed | An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework |
| title_short | An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework |
| title_sort | integrated decision making approach to resilience lcc bridge network retrofitting using a genetic algorithm based framework |
| topic | Bridge network Infrastructures management Decision-making framework Resilience Life-cycle cost |
| url | http://www.sciencedirect.com/science/article/pii/S2772741624000693 |
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