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Proportional hazards models of infrastructure system recovery

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  • Barker, Kash
  • Baroud, Hiba

Abstract

As emphasis is being placed on a system's ability to withstand and to recover from a disruptive event, collectively referred to as dynamic resilience, there exists a need to quantify a system's ability to bounce back after a disruptive event. This work applies a statistical technique from biostatistics, the proportional hazards model, to describe (i) the instantaneous rate of recovery of an infrastructure system and (ii) the likelihood that recovery occurs prior to a given point in time. A major benefit of the proportional hazards model is its ability to describe a recovery event as a function of time as well as covariates describing the infrastructure system or disruptive event, among others, which can also vary with time. The proportional hazards approach is illustrated with a publicly available electric power outage data set.

Suggested Citation

  • Barker, Kash & Baroud, Hiba, 2014. "Proportional hazards models of infrastructure system recovery," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 201-206.
    • Handle: RePEc:eee:reensy:v:124:y:2014:i:c:p:201-206
    DOI: 10.1016/j.ress.2013.12.004
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    References listed on IDEAS

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    Cited by:

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    2. Mohammad Mojtahedi & Sidney Newton & Jason Meding, 2017. "Predicting the resilience of transport infrastructure to a natural disaster using Cox’s proportional hazards regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 1119-1133, January.
    3. Rezgar Zaki & Abbas Barabadi & Javad Barabady & Ali Nouri Qarahasanlou, 2022. "Observed and unobserved heterogeneity in failure data analysis," Journal of Risk and Reliability, , vol. 236(1), pages 194-207, February.
    4. Ali Nouri Qarahasanlou & Ali Zamani & Abbas Barabadi & Mahdi Mokhberdoran, 2021. "Resilience Assessment: A Performance-Based Importance Measure," Energies, MDPI, vol. 14(22), pages 1-16, November.
    5. Monsalve, Mauricio & de la Llera, Juan Carlos, 2019. "Data-driven estimation of interdependencies and restoration of infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 167-180.
    6. Lijiao Yang & Yishuang Qi & Xinyu Jiang, 2021. "An Investigation of the Initial Recovery Time of Chinese Enterprises Affected by COVID-19 Using an Accelerated Failure Time Model," IJERPH, MDPI, vol. 18(22), pages 1-16, November.
    7. Lijiao Yang & Yoshio Kajitani & Hirokazu Tatano & Xinyu Jiang, 2016. "A methodology for estimating business interruption loss caused by flood disasters: insights from business surveys after Tokai Heavy Rain in Japan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 411-430, November.
    8. Barabadi, A. & Ayele, Y.Z., 2018. "Post-disaster infrastructure recovery: Prediction of recovery rate using historical data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 209-223.
    9. Caputo, Antonio C. & Kalemi, Bledar & Paolacci, Fabrizio & Corritore, Daniele, 2020. "Computing resilience of process plants under Na-Tech events: Methodology and application to sesmic loading scenarios," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    10. Payuna Uday & Karen Marais, 2015. "Designing Resilient Systems‐of‐Systems: A Survey of Metrics, Methods, and Challenges," Systems Engineering, John Wiley & Sons, vol. 18(5), pages 491-510, October.
    11. XiaoFei, Lu & Min, Liu, 2014. "Hazard rate function in dynamic environment," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 50-60.

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