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Stress tests for a road network using fragility functions and functional capacity loss functions

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  • Lam, Juan Carlos
  • Adey, Bryan T.
  • Heitzler, Magnus
  • Hackl, Jürgen
  • Gehl, Pierre
  • van Erp, Noel
  • D'Ayala, Dina
  • van Gelder, Pieter
  • Hurni, Lorenz

Abstract

A quantitative approach to conduct a specific type of stress test on road networks is presented in this article. The objective is to help network managers determine whether their networks would perform adequately during and after the occurrence of hazard events. Conducting a stress test requires (i) modifying an existing risk model (i.e., a model to estimate the probable consequences of hazard events) by representing at least one uncertainty in the model with values that are considerably worse than median or mean values, and (ii) developing criteria to conclude if the network has an adequate post-hazard performance. Specifically, the stress test conducted in this work is focused on the uncertain behavior of individual objects that are part of a network when these are subjected to hazard loads. Here, the relationships between object behavior and hazard load are modeled using fragility functions and functional capacity loss functions. To illustrate the quantitative approach, a stress test is conducted for an example road network in Switzerland, which is affected by floods and rainfall-triggered mudflows. Beyond the focus of the stress test, this work highlights the importance of using a probabilistic approach when conducting stress tests for temporal and spatially distributed networks.

Suggested Citation

  • Lam, Juan Carlos & Adey, Bryan T. & Heitzler, Magnus & Hackl, Jürgen & Gehl, Pierre & van Erp, Noel & D'Ayala, Dina & van Gelder, Pieter & Hurni, Lorenz, 2018. "Stress tests for a road network using fragility functions and functional capacity loss functions," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 78-93.
  • Handle: RePEc:eee:reensy:v:173:y:2018:i:c:p:78-93
    DOI: 10.1016/j.ress.2018.01.015
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    References listed on IDEAS

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    1. Haowen You & Elizabeth B. Connelly & James H. Lambert & Andres F. Clarens, 2014. "Climate and other scenarios disrupt priorities in several management perspectives," Environment Systems and Decisions, Springer, vol. 34(4), pages 540-554, December.
    2. J. David Cummins & Olivier Mahul, 2009. "Catastrophe Risk Financing in Developing Countries : Principles for Public Intervention," World Bank Publications - Books, The World Bank Group, number 6289.
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    Cited by:

    1. Lan, Meng & Zhu, Jiping & Lo, Siuming, 2021. "Hybrid Bayesian network-based landslide risk assessment method for modeling risk for industrial facilities subjected to landslides," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Argyroudis, Sotirios A. & Mitoulis, Stergios Α. & Winter, Mike G. & Kaynia, Amir M., 2019. "Fragility of transport assets exposed to multiple hazards: State-of-the-art review toward infrastructural resilience," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    3. Pagliara, Francesca & Zingone, Marco, 2023. "Providing resilience due to adverse weather events: A cost-benefit analysis for the case of the Milan Malpensa airport in Italy," Journal of Air Transport Management, Elsevier, vol. 113(C).
    4. Yu, Yun-Chi & Gardoni, Paolo, 2022. "Predicting road blockage due to building damage following earthquakes," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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