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On the component resilience importance measures for infrastructure systems

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  • Li, Ruiying
  • Gao, Ying

Abstract

For an infrastructure system, resilience can measure its performance degradation and recovery behavior after the disturbance. To reasonably allocate the limited resources and fully consider the effect of component resilience on the overall system, this paper proposes seven component resilience importance measures, i.e., Birnbaum resilience importance, resilience improvement/reduction potential importance, equal-improvement/reduction-based resilience importance, and equal-improvement/reduction-ratio-based resilience importance. A Monte-Carlo-based method is proposed to analyze the above resilience importance measures, and a transportation network is used as a case to verify the effectiveness of our method. Resilience behaviors of both links and the transportation network, and the resilience importance of links are analyzed in detail.

Suggested Citation

  • Li, Ruiying & Gao, Ying, 2022. "On the component resilience importance measures for infrastructure systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 36(C).
    • Handle: RePEc:eee:ijocip:v:36:y:2022:i:c:s1874548221000676
    DOI: 10.1016/j.ijcip.2021.100481
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    References listed on IDEAS

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    1. Ruiying Li & Xiaoyu Tian & Li Yu & Rui Kang, 2019. "A Systematic Disturbance Analysis Method for Resilience Evaluation: A Case Study in Material Handling Systems," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
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    4. Ouyang, Min, 2014. "Review on modeling and simulation of interdependent critical infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 43-60.
    5. Henry, Devanandham & Emmanuel Ramirez-Marquez, Jose, 2012. "Generic metrics and quantitative approaches for system resilience as a function of time," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 114-122.
    6. Natvig, Bent, 1979. "A suggestion of a new measure of importance of system components," Stochastic Processes and their Applications, Elsevier, vol. 9(3), pages 319-330, December.
    7. Liu, Xing & Ferrario, Elisa & Zio, Enrico, 2019. "Identifying resilient-important elements in interdependent critical infrastructures by sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 423-434.
    8. Barker, Kash & Ramirez-Marquez, Jose Emmanuel & Rocco, Claudio M., 2013. "Resilience-based network component importance measures," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 89-97.
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    Cited by:

    1. Tibor Sipos & Zsombor Szabó & Mohammed Obaid & Árpád Török, 2023. "Disaster Risk Assessment Scheme—A Road System Survey for Budapest," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    2. Dui, Hongyan & Wei, Xuan & Xing, Liudong, 2023. "A new multi-criteria importance measure and its applications to risk reduction and safety enhancement," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Jian Zhou & Weijian Zheng & Dali Wang & David W. Coit, 2024. "A resilient network recovery framework against cascading failures with deep graph learning," Journal of Risk and Reliability, , vol. 238(1), pages 193-203, February.
    4. Wang, Feng & Tian, Jin & Shi, Chenli & Ling, Jiamu & Chen, Zian & Xu, Zhengguo, 2024. "A multi-stage quantitative resilience analysis and optimization framework considering dynamic decisions for urban infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Wang, Nanxi & Yuen, Kum Fai, 2022. "Resilience assessment of waterway transportation systems: Combining system performance and recovery cost," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    6. Wang, Shuliang & Dong, Qiqi, 2023. "A multi-source power grid's resilience enhancement strategy based on subnet division and power dispatch," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).

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