An SHM-based classification system for risk management of bridge scour

Abstract

Flood-induced scour is the principal cause of bridge failure worldwide. Nevertheless, bridge scour risk assessment is still based on visual inspections, which may be affected by human errors and cannot be performed during flood peaks. This problem, together with the simplifications in scour estimation, might cause misclassification of the bridge scour risk, unnecessary bridge closures or recourse to avoidable scour mitigation measures. Structural health monitoring systems allow overcoming these issues, providing bridge managers with more accurate information about scour, thus supporting them in taking optimal management decisions. This paper illustrates the development of an SHM- and event-based classification system for bridge scour management, which extends and complements current risk rating procedures by incorporating the various sources of uncertainty characterising the scour estimation, and information from different sensors. The proposed system is based on a probabilistic framework for scour risk estimation and can be used to provide transport agencies with a real-time scour risk classification of bridges under a heavy flood event. The system is applied to a bridge network located in South-West Scotland under a heavy flood scenario and information from heterogeneous sources are considered for updating the knowledge of scour. It is shown that integrating scour monitoring data leads to an overall uncertainty reduction that is reflected in a more accurate scour risk classification, thus helping transport agencies in prioritising bridge inspections and risk mitigation actions.