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Critical Infrastructure Vulnerability to Spatially Localized Failures with Applications to Chinese Railway System

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  • Min Ouyang
  • Hui Tian
  • Zhenghua Wang
  • Liu Hong
  • Zijun Mao

Abstract

This article studies a general type of initiating events in critical infrastructures, called spatially localized failures (SLFs), which are defined as the failure of a set of infrastructure components distributed in a spatially localized area due to damage sustained, while other components outside the area do not directly fail. These failures can be regarded as a special type of intentional attack, such as bomb or explosive assault, or a generalized modeling of the impact of localized natural hazards on large‐scale systems. This article introduces three SLFs models: node centered SLFs, district‐based SLFs, and circle‐shaped SLFs, and proposes a SLFs‐induced vulnerability analysis method from three aspects: identification of critical locations, comparisons of infrastructure vulnerability to random failures, topologically localized failures and SLFs, and quantification of infrastructure information value. The proposed SLFs‐induced vulnerability analysis method is finally applied to the Chinese railway system and can be also easily adapted to analyze other critical infrastructures for valuable protection suggestions.

Suggested Citation

  • Min Ouyang & Hui Tian & Zhenghua Wang & Liu Hong & Zijun Mao, 2019. "Critical Infrastructure Vulnerability to Spatially Localized Failures with Applications to Chinese Railway System," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 180-194, January.
    • Handle: RePEc:wly:riskan:v:39:y:2019:i:1:p:180-194
    DOI: 10.1111/risa.12708
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    Cited by:

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    2. Yiming Cao & Hengxing Lan & Langping Li, 2023. "Disaster Risk Assessment for Railways: Challenges and a Sustainable Promising Solution Based on BIM+GIS," Sustainability, MDPI, vol. 15(24), pages 1-27, December.
    3. Chao Fang & Piao Dong & Yi-Ping Fang & Enrico Zio, 2020. "Vulnerability analysis of critical infrastructure under disruptions: An application to China Railway High-speed," Journal of Risk and Reliability, , vol. 234(2), pages 235-245, April.
    4. Ouyang, Min & Liu, Chuang & Wu, Shengyu, 2020. "Worst-case vulnerability assessment and mitigation model of urban utility tunnels," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    5. Pan, Shouzheng & Yan, Hai & He, Jia & He, Zhengbing, 2021. "Vulnerability and resilience of transportation systems: A recent literature review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    6. Li, Tao & Rong, Lili & Yan, Kesheng, 2019. "Vulnerability analysis and critical area identification of public transport system: A case of high-speed rail and air transport coupling system in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 55-70.
    7. Li, Tao & Rong, Lili, 2021. "Impacts of service feature on vulnerability analysis of high-speed rail network," Transport Policy, Elsevier, vol. 110(C), pages 238-253.
    8. Caroline A Johnson & Allison C Reilly & Roger Flage & Seth D Guikema, 2021. "Characterizing the robustness of power-law networks that experience spatially-correlated failures," Journal of Risk and Reliability, , vol. 235(3), pages 403-415, June.
    9. Nikolaos Argyris & Valentina Ferretti & Simon French & Seth Guikema & Gilberto Montibeller, 2019. "Advances in Spatial Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 1-8, January.
    10. Wang, Shuliang & Gu, Xifeng & Luan, Shengyang & Zhao, Mingwei, 2021. "Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    11. Li, Tao & Rong, Lili, 2020. "A comprehensive method for the robustness assessment of high-speed rail network with operation data: A case in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 666-681.

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