飓风易损社区建筑改造策略的多灾害概率风险评估与公平多目标优化

IF 8.5 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Abdullah M. Braik, Himadri Sen Gupta, Maria Koliou, Andrés D. González
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引用次数: 0

摘要

沿海社区越来越容易受到飓风的影响,飓风每年通过风力、风暴潮和洪水造成数十亿美元的损失。缓解工作对于减少这些影响至关重要,但面临重大挑战,包括灾害预测、损害估计和恢复成本方面的不确定性。资源限制和社会经济弱势群体承担的不成比例的负担使改造战略进一步复杂化。本研究提出了一种概率方法,通过综合危害分析、建筑脆弱性和经济损失评估来评估和减轻飓风风险。该方法使用风险知情、以股票为中心的方法来优先调整策略。多目标优化平衡了成本效益和降低风险,同时促进了社会经济群体之间资源的公平分配。本研究的新颖之处在于通过多目标优化直接将公平作为资源配置的目标,全面考虑多灾害风险,在成本评估中包括直接和间接损失,并使用概率风险分析来考虑不同的时间范围。加尔维斯顿试验台的案例研究证明了该方法在减少损害和促进公平弹性方面的潜力。对预算情景和成本与公平之间权衡的分析强调了在缓解和复原力规划中全面损失评估和公平考虑的重要性。主要研究结果强调了在不同预算和时间范围内改造战略的不同有效性,解决直接和间接损失的必要性,以及对准确风险评估进行多灾害考虑的重要性。多目标优化强调,即使在预算有限的情况下,也可以实现公平的解决方案。超过某一点,实现公平并不一定会增加预期损失,这表明可以在不损害总体成本效益的情况下实施更公平的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-hazard probabilistic risk assessment and equitable multi-objective optimization of building retrofit strategies in hurricane-vulnerable communities

Coastal communities are increasingly vulnerable to hurricanes, which cause billions of dollars in damage annually through wind, storm surge, and flooding. Mitigation efforts are essential to reduce these impacts but face significant challenges, including uncertainties in hazard prediction, damage estimation, and recovery costs. Resource constraints and the disproportionate burden borne by socioeconomically vulnerable groups further complicate retrofitting strategies. This study presents a probabilistic methodology to assess and mitigate hurricane risks by integrating hazard analysis, building fragility, and economic loss assessment. The methodology prioritizes retrofitting strategies using a risk-informed, equity-focused approach. Multi-objective optimization balances cost-effectiveness and risk reduction while promoting fair resource allocation among socioeconomic groups. The novelty of this study lies in its direct integration of equity as an objective in resource allocation through multi-objective optimization, its comprehensive consideration of multi-hazard risks, its inclusion of both direct and indirect losses in cost assessments, and its use of probabilistic hazard analysis to incorporate varying time horizons. A case study of the Galveston testbed demonstrates the methodology's potential to minimize damage and foster equitable resilience. Analysis of budget scenarios and trade-offs between cost and equity underscores the importance of comprehensive loss assessments and equity considerations in mitigation and resilience planning. Key findings highlight the varied effectiveness of retrofitting strategies across different budgets and time horizons, the necessity of addressing both direct and indirect losses, and the importance of multi-hazard considerations for accurate risk assessments. Multi-objective optimization underscores that equitable solutions are achievable even under constrained budgets. Beyond a certain point, achieving equity does not necessarily increase expected losses, demonstrating that more equitable solutions can be implemented without compromising overall cost-effectiveness.

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来源期刊
CiteScore
17.60
自引率
19.80%
发文量
146
审稿时长
1 months
期刊介绍: Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.
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