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Infrastructure resilience assessment through control design

Author

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  • Eric D. Vugrin
  • R. Chris Camphouse

Abstract

Infrastructure resilience is a priority for homeland security in many nations around the globe. This paper describes a new approach for quantitatively assessing the resilience of critical infrastructure systems. The mathematics of optimal control design provides the theoretical foundation for this methodology. This foundation enables the inclusion of recovery costs within the resilience assessment approach, a unique capability for quantitative resilience assessment techniques. This paper describes the formulation of the optimal control problem for a set of representative infrastructure models. This example demonstrates the importance of recovery costs in quantitative resilience analysis, and the increased capability provided by this approach's ability to discern between varying levels of resilience.

Suggested Citation

  • Eric D. Vugrin & R. Chris Camphouse, 2011. "Infrastructure resilience assessment through control design," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 7(3), pages 243-260.
    • Handle: RePEc:ids:ijcist:v:7:y:2011:i:3:p:243-260
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    Citations

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    Cited by:

    1. Morshedlou, Nazanin & González, Andrés D. & Barker, Kash, 2018. "Work crew routing problem for infrastructure network restoration," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 66-89.
    2. Jing Song & Weifeng Li, 2019. "Linkage Between the Environment and Individual Resilience to Urban Flooding: A Case Study of Shenzhen, China," IJERPH, MDPI, vol. 16(14), pages 1-15, July.
    3. Hosseini, Seyedmohsen & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "A review of definitions and measures of system resilience," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 47-61.
    4. Eldosouky, AbdelRahman & Saad, Walid & Mandayam, Narayan, 2021. "Resilient critical infrastructure: Bayesian network analysis and contract-Based optimization," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. Geng, Sunyue & Yang, Ming & Mitici, Mihaela & Liu, Sifeng, 2023. "A resilience assessment framework for complex engineered systems using graphical evaluation and review technique (GERT)," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    6. He, Peijun & Ng, Tsan Sheng & Su, Bin, 2017. "Energy-economic recovery resilience with Input-Output linear programming models," Energy Economics, Elsevier, vol. 68(C), pages 177-191.
    7. Mark Turnquist & Eric Vugrin, 2013. "Design for resilience in infrastructure distribution networks," Environment Systems and Decisions, Springer, vol. 33(1), pages 104-120, March.
    8. Kameshwar, Sabarethinam & Cox, Daniel T. & Barbosa, Andre R. & Farokhnia, Karim & Park, Hyoungsu & Alam, Mohammad S. & van de Lindt, John W., 2019. "Probabilistic decision-support framework for community resilience: Incorporating multi-hazards, infrastructure interdependencies, and resilience goals in a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Yu, Soonyoung & Kim, Sung-Wook & Oh, Chang-Whan & An, Hyunuk & Kim, Jin-Man, 2015. "Quantitative assessment of disaster resilience: An empirical study on the importance of post-disaster recovery costs," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 6-17.
    10. Darayi, Mohamad & Barker, Kash & Nicholson, Charles D., 2019. "A multi-industry economic impact perspective on adaptive capacity planning in a freight transportation network," International Journal of Production Economics, Elsevier, vol. 208(C), pages 356-368.

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