A monitoring-based classification system for risk management of bridge scour. Issue 2 (June 2022)

• Record Type: Journal Article
• Authors: Maroni, Andrea; Tubaldi, Enrico; McDonald, Hazel; Zonta, Daniele
• Is Part Of: Proceedings of the Institution of Civil Engineers. Volume 175:Issue 2(2022)
• Page Start: 1
• Page End: 11
• Publication Date: 2022-06
• Subjects: bridges -- risk & probability analysis -- sensors; Construction industry -- Technological innovations -- Periodicals; Construction industry -- Information technology -- Periodicals; Building -- Technological innovations -- Periodicals; Smart structures -- Periodicals; Smart materials in design -- Periodicals; 624.0285
• DOI: 10.1680/jsmic.21.00016
• Languages: English
• ISSNs: 2397-8759

Abstract

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 (SHM) 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 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 in a heavy-flood scenario, and information from heterogeneous sources is 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 more accurate scour risk classification, thus helping transport agencies in prioritising bridge inspections and risk mitigation actions.