弹性(材料科学)
相互依存
过程(计算)
计算机科学
风险分析(工程)
比例(比率)
危害
环境资源管理
运筹学
环境科学
业务
工程类
地理
化学
物理
地图学
有机化学
政治学
法学
热力学
操作系统
作者
Neetesh Sharma,Armin Tabandeh,Paolo Gardoni
摘要
Abstract Reducing hazard‐induced disruptions to infrastructure functionality is cardinal to regional resilience. Specifically, effective strategies to enhance regional resilience require: (a) developing mathematical models for infrastructure recovery; (b) quantifying resilience associated with the developed recovery process; and (c) developing a computationally manageable approach for resilience optimization. This paper proposes a rigorous mathematical formulation to model recovery, quantify resilience, and optimize the resilience of large‐scale infrastructure. Specifically, a multiscale model of the recovery process is proposed that significantly reduces the computational cost, while favoring practical and easily manageable recovery schedules. To quantify regional resilience, resilience metrics are proposed that capture the temporal and spatial variations of the recovery process. The paper then formulates a multiobjective optimization problem that aims to improve regional resilience in terms of the proposed metrics, while minimizing the recovery cost. Finally, the paper illustrates the proposed formulation by considering interdependent infrastructure in Shelby County, Tennessee, United States.
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