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A platform for research: civil engineering, architecture and urbanism
Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory
Abstract In this paper, we present a methodological framework for resilience analysis of interdependent critical infrastructure systems and use artificial interdependent power and gas network as an example. We use deep learning to identify network topology attributes and analyze the vulnerability process of interdependent infrastructure systems to different failure scenarios and coupling modes under structural perspective. Then, functional model of the interdependent network is constructed, and the vulnerability process based on functional characteristics is analyzed. At last, we propose different recovery strategies and use a resilience triangle to study the restoration process, and the optimal resilience improvement strategy is acquired from both structural and functional perspectives. The method proposed in this paper can help decision makers develop mitigation techniques and optimal protection strategies.
Highlights A methodological framework to study resilience of the interdependent network from multiple perspectives is developed. A novel network attribute classification method utilizing deep learning is proposed. Failure-spreading stage and vulnerability process of interdependent network to different failure scenarios and coupling modes are analyzed. Optimal recovery strategies under both structural and functional perspective are acquired through simulations of failure-recovering stage.
Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory
Abstract In this paper, we present a methodological framework for resilience analysis of interdependent critical infrastructure systems and use artificial interdependent power and gas network as an example. We use deep learning to identify network topology attributes and analyze the vulnerability process of interdependent infrastructure systems to different failure scenarios and coupling modes under structural perspective. Then, functional model of the interdependent network is constructed, and the vulnerability process based on functional characteristics is analyzed. At last, we propose different recovery strategies and use a resilience triangle to study the restoration process, and the optimal resilience improvement strategy is acquired from both structural and functional perspectives. The method proposed in this paper can help decision makers develop mitigation techniques and optimal protection strategies.
Highlights A methodological framework to study resilience of the interdependent network from multiple perspectives is developed. A novel network attribute classification method utilizing deep learning is proposed. Failure-spreading stage and vulnerability process of interdependent network to different failure scenarios and coupling modes are analyzed. Optimal recovery strategies under both structural and functional perspective are acquired through simulations of failure-recovering stage.
Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory
Wang, Shuliang (author) / Gu, Xifeng (author) / Luan, Shengyang (author) / Zhao, Mingwei (author)
2021-06-21
Article (Journal)
Electronic Resource
English
Deep Learning for Critical Infrastructure Resilience
| ASCE | 2019
Resilience Strategies for Interdependent Multiscale Lifeline Infrastructure Networks
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