A framework for assessing the effectiveness of resilience enhancement strategies for interdependent infrastructure systems

Shafieezadeh, Abdollah; Leelardcharoen, Kanoknart; Dueñas‐Osorio, Leonardo

doi:10.1201/b16387-85

Cited by 1 publication

(1 citation statement)

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“…Subsequently, some studies focused on the vulnerability of interdependent networks (Buldyrev et al., ; Vespignani, ; Gao et al., ; Hernandez‐Fajardo and Dueñas‐Osorio, ) and later, on reducing that vulnerability (Brummitt et al., ). Also, some studies started quantifying the recoverability and resilience of these interdependent networked systems (Vugrin et al., ; Pant et al., ; Ouyang, ; Shafieezadeh et al., ; Baroud et al., ; Ouyang and Wang, ), as well as developing recovery strategies for such systems (Lee et al., ; Cavdaroglu et al., ; González et al., ). Nevertheless, these papers emphasize the complexity associated with generating recovery strategies and the difficulty of doing so via computationally efficient algorithms, thus the importance of developing new tools.…”

Section: Introductionmentioning

confidence: 99%

Efficient Infrastructure Restoration Strategies Using the Recovery Operator

González

Chapman

Dueñas‐Osorio

et al. 2017

Computer aided Civil Eng

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Infrastructure systems are critical for society's resilience, government operation, and overall defense. Thereby, it is imperative to develop informative and computationally efficient analysis methods for infrastructure systems, which reveal system vulnerabilities and recoverability. To capture practical constraints in systems analyses, various layers of complexity play a role, including limited element capacities, restoration resources, and the presence of interdependence among systems. High‐fidelity modeling such as mixed integer programming and physics‐based modeling can often be computationally expensive, making time‐sensitive analyses challenging. Furthermore, the complexity of recovery solutions can reduce analysis transparency. An alternative, presented in this work, is a reduced‐order representation, dubbed a recovery operator, of a high‐fidelity time‐dependent recovery model of a system of interdependent networks. The form of the operator is assumed to be a time‐invariant linear dynamic model apt for infrastructure restoration. The recovery operator is generated by applying system identification techniques to numerous disaster and recovery scenarios. The proposed compact representation provides simple yet powerful information regarding systemic recovery dynamics, and enables generating fast suboptimal recovery policies in time‐critical applications.

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