基于遗传算法框架的lcc桥梁网络弹性改造综合决策方法

Resilient Cities and Structures Pub Date : 2025-03-01 Epub Date: 2025-01-07 DOI:10.1016/j.rcns.2024.12.002
Pedram Omidian , Naser Khaji , Ali Akbar Aghakouchak
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引用次数: 0

摘要

桥梁网络是民用基础设施的重要组成部分,通过提供重要服务和促进经济活动来支持社区。然而,桥梁容易受到自然灾害的影响,尤其是地震。为了制定有效的灾害管理战略,确定可靠、稳健和高效的指标至关重要。在这方面,生命周期成本(LCC)和恢复力(R)是帮助决策者选择最有效的减灾计划的关键指标。本研究提出了一个创新的lc - r优化框架,以确定在其生命周期内面临危险事件的桥梁网络的最优改造策略。所提出的框架采用单目标和多目标优化技术来确定改造策略,以最大化R指数,同时最小化所研究桥梁网络的LCC。考虑的改造策略包括各种选择,如不同的材料(钢、碳纤维增强塑料和玻璃钢)、厚度、安排和改造行动的时间。该框架的第一步涉及通过对每种情况进行一系列非线性时程增量动态分析来构建脆弱性曲线。在接下来的步骤中,使用得到的易损性曲线并假设恢复函数计算地震恢复面。接下来,根据多次地震发生的建议公式评估LCC,其中包括以前多次地震事件导致的完全和不完全修复行动的影响。基于优化目的,非支配排序遗传算法II (non - dominant Sorting Genetic Algorithm II, NSGA-II)进化算法能够有效地识别Pareto front来表示最优解集。本研究提出了一个示范性桥梁网络的最有效的改造策略,提供了对由此产生的战术方法的全面讨论和见解。研究结果强调,所采用的方法导致了逻辑和可操作的改造策略,为增强桥梁网络管理抵御地震灾害的弹性和成本效益铺平了道路。
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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 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.
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