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A probabilistic model of the economic risk to Britain’s railway network from bridge scour during floods

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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  • Rob Lamb
  • Paige Garside
  • Raghav Pant
  • Jim Hall
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<mark>Journal publication date</mark>1/11/2019
<mark>Journal</mark>Risk Analysis
Issue number11
Volume39
Number of pages22
Pages (from-to)2457-2478
Publication StatusPublished
Early online date18/07/19
<mark>Original language</mark>English

Abstract

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system-wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.