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Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System

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  • Jingjing Kong
  • Slobodan P. Simonovic
  • Chao Zhang

Abstract

Coupled infrastructure systems and complicated multihazards result in a high level of complexity and make it difficult to assess and improve the infrastructure system resilience. With a case study of the Greater Toronto Area energy system (including electric, gas, and oil transmission networks), an approach to analysis of multihazard resilience of an interdependent infrastructure system is presented in the article. Integrating network theory, spatial and numerical analysis methods, the new approach deals with the complicated multihazard relations and complex infrastructure interdependencies as spatiotemporal impacts on infrastructure systems in order to assess the dynamic system resilience. The results confirm that the effects of sequential hazards on resilience of infrastructure (network) are more complicated than the sum of single hazards. The resilience depends on the magnitude of the hazards, their spatiotemporal relationship and dynamic combined impacts, and infrastructure interdependencies. The article presents a comparison between physical and functional resilience of an electric transmission network, and finds functional resilience is always higher than physical resilience. The multiple hazards resilience evaluation approach is applicable to any type of infrastructure and hazard and it can contribute to the improvement of infrastructure planning, design, and maintenance decision making.

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  • Jingjing Kong & Slobodan P. Simonovic & Chao Zhang, 2019. "Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System," Risk Analysis, John Wiley & Sons, vol. 39(5), pages 1141-1168, May.
    • Handle: RePEc:wly:riskan:v:39:y:2019:i:5:p:1141-1168
    DOI: 10.1111/risa.13222
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    Cited by:

    1. Trucco, Paolo & Petrenj, Boris, 2023. "Characterisation of resilience metrics in full-scale applications to interdependent infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Ilalokhoin, Ohis & Pant, Raghav & Hall, Jim W., 2023. "A model and methodology for resilience assessment of interdependent rail networks – Case study of Great Britain's rail network," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Jingjing Kong & Slobodan P. Simonovic & Chao Zhang, 2019. "Resilience Assessment of Interdependent Infrastructure Systems: A Case Study Based on Different Response Strategies," Sustainability, MDPI, vol. 11(23), pages 1-31, November.
    4. Das, Laya & Munikoti, Sai & Natarajan, Balasubramaniam & Srinivasan, Babji, 2020. "Measuring smart grid resilience: Methods, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    5. Kong, Jingjing & Zhang, Chao & Simonovic, Slobodan P., 2021. "Optimizing the resilience of interdependent infrastructures to regional natural hazards with combined improvement measures," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    6. Chen Wang & Chao Zhang & Ling Luo & Xiaoman Qi & Jingjing Kong, 2024. "Optimal Resilience and Risk-Driven Strategies for Pre-Disaster Protection of Electric Power Systems against Uncertain Disaster Scenarios," Energies, MDPI, vol. 17(15), pages 1-24, July.
    7. Jingjing Kong & Chao Zhang & Slobodan P. Simonovic, 2019. "A Two-Stage Restoration Resource Allocation Model for Enhancing the Resilience of Interdependent Infrastructure Systems," Sustainability, MDPI, vol. 11(19), pages 1-16, September.
    8. Adel Mottahedi & Farhang Sereshki & Mohammad Ataei & Ali Nouri Qarahasanlou & Abbas Barabadi, 2021. "The Resilience of Critical Infrastructure Systems: A Systematic Literature Review," Energies, MDPI, vol. 14(6), pages 1-32, March.
    9. Suo, Weilan & Wang, Lin & Li, Jianping, 2021. "Probabilistic risk assessment for interdependent critical infrastructures: A scenario-driven dynamic stochastic model," Reliability Engineering and System Safety, Elsevier, vol. 214(C).

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