弹性(材料科学)
背景(考古学)
脆弱性(计算)
风险分析(工程)
自然灾害
蒙特卡罗方法
计算机科学
关键基础设施
系统工程
网络规划与设计
环境资源管理
脆弱性评估
工程类
过程管理
适应(眼睛)
选择(遗传算法)
运筹学
可靠性工程
风险管理
管理科学
应急管理
作者
Kimia Bagheri-Lotfabad,Sayyed Vahid Faghihi,Seyed Hossein Hosseini Nourzad
出处
期刊:International Journal of Disaster Resilience in The Built Environment
[Emerald Publishing Limited]
日期:2026-04-16
卷期号:: 1-33
标识
DOI:10.1108/ijdrbe-06-2025-0072
摘要
Purpose This study aims to address the challenges in evaluating and enhancing health-care infrastructure resilience during the design and preconstruction phases. By presenting a novel approach based on Monte Carlo simulation to facilitate this process. Design/methodology/approach The study uses a Monte Carlo simulation-based simulation aims to enhance the resilience of infrastructural systems against natural disasters in the context of locating new hospital infrastructural. This approach analyzes the vulnerability of the transportation network based on the OpenStreetMap network graph and uses Monte Carlo simulation to assess damage to the network and its impact on the hospital infrastructure network. It evaluates resilience based on the proposed location optimization framework. Findings The results demonstrate the model’s capability to effectively assess the impact of natural disasters on infrastructure networks across various scenarios. This approach enables designers to make more informed decisions about resilience characteristics during the crucial design and preconstruction phases. The research underscores the importance of incorporating resilience evaluation early in the project lifecycle and illustrates how the proposed model can facilitate this process, potentially leading to more resilient infrastructure designs. Originality/value It focuses on the application of Monte Carlo simulation to evaluate resilience during the feasibility study phase, a stage often overlooked in resilience planning. The findings underscore the potential of guiding designers toward improving project resilience, supporting them in selecting options favorable to network resilience.
科研通智能强力驱动
Strongly Powered by AbleSci AI