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Predicting the resilience of transport infrastructure to a natural disaster using Cox’s proportional hazards regression model

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Abstract

Transport infrastructure is at significant risk of direct damage from extreme climate events such as flooding, where the cost implications of delayed recovery are generally significant. Previous research in this regard has focused on the technical and engineering aspects of infrastructure construction. The risk management of resilient transport infrastructure is poorly considered, and little has been done to quantify the capacity of transport infrastructure to recover from the impact of natural disasters under varying conditions. This paper applies Cox’s proportional hazards regression model to determine the rate of recovery and cumulative probability that recovery occurs for transport infrastructure across regional areas in New South Wales, Australia. Data for post-disaster reconstruction projects over the period 1992–2012 are used to analyze recovery rate against geographic region, natural disaster type and post-disaster transport infrastructure reconstruction cost. Results demonstrate that transport infrastructure recovered slowest when the failure is the result of a flood rather than bushfire or storm, and in regions with a riverine geography. To validate the accuracy of the model, a bootstrap resampling technique is used. The bootstrap result confirms that the model is robust and reasonable.

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Authors and Affiliations

  1. Red Centre, Faculty of Built Environment, Kensington Campus, The University of New South Wales, Room 3006, Sydney, NSW, 2052, Australia

    Mohammad Mojtahedi & Sidney Newton

  2. School of Architecture and Built Environment, The University of New Castle, Callaghan, Australia

    Jason Von Meding

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  1. Mohammad Mojtahedi
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  2. Sidney Newton
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  3. Jason Von Meding
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Correspondence to Mohammad Mojtahedi.

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Mojtahedi, M., Newton, S. & Von Meding, J. Predicting the resilience of transport infrastructure to a natural disaster using Cox’s proportional hazards regression model. Nat Hazards 85, 1119–1133 (2017). https://doi.org/10.1007/s11069-016-2624-2

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