Managing bridge scour risk using structural health monitoring

Scour is the leading cause of bridge failures worldwide. In the United States, 22 bridges fail every year, whereas in the UK scour contributed significantly to the 138 bridge collapses recorded in the last century. In Scotland, there are around 2,000 bridges susceptible to scour. Scour assessments are currently based on visual inspections, which are expensive, time-consuming, and the information collected is qualitative. However, monitoring an entire infrastructure network against scour is not economically feasible. A way to overcome this limitation is to install monitoring systems at critical locations, and then extend the pieces of information gained to the entire asset through a probabilistic approach. This paper proposes a Decision Support System (DSS) for bridge scour management that exploits 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 (BN) is used to describe conditional dependencies among the involved random variables. The BN allows estimating, and updating, the scour depth distributions using information from monitoring of scour depth and river flow characteristics. Data collected by the monitoring system and BN's outcomes are then used to inform a decision model and thus support transport agencies' decision frameworks. A case study consisting of several road bridges in Scotland is considered to demonstrate the functioning of the DSS. The BN is found to estimate accurately the scour depth at unmonitored bridges, and the decision model provides higher values of scour thresholds compared to the ones implicitly chosen by the transport agencies.