Using Bayesian networks for the assessment of underwater scour for road and railway bridges

Andrea Maroni, Enrico Tubaldi, Dimitri V. Val, Hazel McDonald, Daniele Zonta

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Flood-induced scour is among the most common external causes of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fails every year whereas, in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Scour assessments are currently based on visual inspections, which are time-consuming and expensive. Nowadays, sensor and communication technologies offer the possibility to assess in real-time the scour depth at critical bridge locations; yet monitoring an entire infrastructure network is not economically feasible. A way to overcome this limitation is to install scour monitoring systems at critical bridge locations, and then extend the piece of information gained to the other assets exploiting the correlations present in the system. In this paper, we propose a scour hazard model for road and railway bridge scour management that utilises information from a limited number of scour monitoring systems to achieve a more confined estimate of the scour risk for a bridge network. A Bayesian network is used to describe the conditional dependencies among the involved random variables and to update the scour depth distribution using data from monitoring of scour and river flow characteristics. This study constitutes the first application of Bayesian networks to bridge scour risk assessment. The proposed probabilistic framework is applied to a case study consisting of several road bridges in Scotland. The bridges cross the same river, and only one of them is instrumented with a scour monitoring system. It is demonstrated how the Bayesian network approach allows to significantly reduce the uncertainty in the scour depth at unmonitored bridges.
Original languageEnglish
Number of pages21
JournalStructural Health Monitoring
Publication statusAccepted/In press - 13 Aug 2020


  • underwater scour
  • structural health monitoring
  • Bayesian inference
  • Bayesian network
  • bridge management

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