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A demand-based framework for resilience assessment of multistate networks under disruptions

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  • Geng, Sunyue
  • Liu, Sifeng
  • Fang, Zhigeng

Abstract

Multistate networks composed of many independent components are susceptible to natural disasters and intentional attacks. Resilience is described as the capability of keeping and enhancing the required functionality under disruptions, which is of great importance to design and analyze multistate networks. While the resilience engineering community makes great contributions to resilience assessment, approaches for measuring network performance are limited and the practical demand of users is neglected in multistate networks. This paper proposes a demand-based framework for resilience assessment under disruptions in order to address these issues. After specifying network structure with the help of reliability block diagram, component behavior is modeled as a Markov reward process. Both the probability of staying in a particular state and the cost of recovery actions can be calculated. Furthermore, network performance with multiple levels is quantified using universal generating function. Finally, network resilience is comprehensively evaluated from the perspectives of absorption, adaptation and recovery. Besides assessing overall resilience with the consideration of network demand, the proposed framework conducts quantitative analysis of multistate networks. A case study of the potable water network is used to demonstrate the applicability of the proposed framework.

Suggested Citation

  • Geng, Sunyue & Liu, Sifeng & Fang, Zhigeng, 2022. "A demand-based framework for resilience assessment of multistate networks under disruptions," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000928
    DOI: 10.1016/j.ress.2022.108423
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    2. Pan, Xing & Dang, Yuheng & Wang, Huixiong & Hong, Dongpao & Li, Yuehong & Deng, Hongxu, 2022. "Resilience model and recovery strategy of transportation network based on travel OD-grid analysis," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    3. Hui Xu & Shuxiu Li & Yongtao Tan & Bin Xing, 2022. "Comprehensive Resilience Assessment of Complex Urban Public Spaces: A Perspective of Promoting Sustainability," Land, MDPI, vol. 11(6), pages 1-23, June.
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    5. Liu, Meili & Qi, Xiaogang & Pan, Hao, 2022. "Optimizing communication network geodiversity for disaster resilience through shielding approach," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Ghosh, Puspendu & De, Mala, 2023. "A stochastic investment decision making method for distribution system resilience enhancement considering automation, hardening and distributed energy resources," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Sonal, & Ghosh, Debomita, 2022. "Impact of situational awareness attributes for resilience assessment of active distribution networks using hybrid dynamic Bayesian multi criteria decision-making approach," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    8. Dong, Shangjia & Gao, Xinyu & Mostafavi, Ali & Gao, Jianxi & Gangwal, Utkarsh, 2023. "Characterizing resilience of flood-disrupted dynamic transportation network through the lens of link reliability and stability," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    9. Cui, Hongjun & Wang, Fei & Ma, Xinwei & Zhu, Minqing, 2022. "A novel fixed-node unconnected subgraph method for calculating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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