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A Bayesian network approach to assess underwater scour around bridge foundations

Maroni, Andrea and Tubaldi, Enrico and Douglas, John and Ferguson, Neil and Zonta, Daniele and McDonald, Hazel and Greenoak, Euan and Walker, Douglas and Green, Christopher (2018) A Bayesian network approach to assess underwater scour around bridge foundations. In: 9th European Workshop on Structural Health Monitoring Series (EWSHM), 2018-07-10 - 2018-07-13, Hilton Manchester Deansgate.

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    Abstract

    Flood-induced scour is by far the leading cause of bridge failures, resulting in fatalities, traffic disruption and significant economic losses. In Scotland, there are around 2,000 structures, considering both road and railway bridges, susceptible to scour. Scour assessments are currently based on visual inspections, which are expensive and time-consuming. The two main transport agencies in Scotland, Transport Scotland (TS) and Network Rail (NR), spend £2m and £0.4m per annum, respectively, in routine inspections. 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 sustainable. This paper proposes a methodology overcoming this limitation, based on the installation of monitoring systems at critical locations, and the use a probabilistic approach to extend this information to the entire population of assets. The state of the bridge stock is represented through a set of random variables, and ad-hoc Bayesian networks (BNs) are used to describe their conditional dependencies. The BN can estimate, and continuously update, the present and future scour depth at bridge foundations using real-time information provided by the monitored scour depth and river flow characteristics. In the occurrence of a flood, monitoring observations are used to infer probabilistically the posterior distribution of the state variables, giving the real-time best estimate of the total scour depth. Bias, systematic and model uncertainties are modelled as nodes of the BN in such a way that the accuracy of predictions can be updated when information from scour monitoring systems is incorporated into the BN. The functioning and capabilities of the BN is illustrated by considering a small network of bridges managed by TS in south-west Scotland. They cross the same river (River Nith) and only one of them is instrumented with a scour monitoring system.