A SHM-based decision support system for risk management of bridge scour

Flood-induced scour, the erosion of material around bridge foundations due to flowing water ,is by far the leading cause of bridge failures worldwide. Although recent developments in sensor technology have resulted in more structures being monitored, practical applications of scour monitoring systems are limited. Thus, there is a need of quantifying the benefits stemming from the use of Structural Health Monitoring (SHM) systems for bridge scour assessment and of methodologies supporting the decision-making process of transport agencies responsible for scour risk management with information from scour sensors.;This thesis presents a probabilistic framework and a Decision Support System (DSS) for scour risk management of road and railways bridges, aiming to extend current procedures by incorporating (i) the various sources of uncertainty characterising the scour estimation, and (ii) information from scour sensors. The probabilistic framework for the estimation of bridge scour depth is based on a Bayesian network approach that exploits information from scour sensors to achieve a more precise estimate of the scour depth at unmonitored bridges. The DSS for bridge scour management is an SHM- and event-based decision model producing measurement-informed scour thresholds triggering bridge closure to traffic under heavy floods.;The functioning of the DSS is illustrated by considering as case study a network of road bridges crossing the same river in Scotland, under a heavy flood scenario. Only one of these bridges is instrumented with a scour monitoring system. The probabilistic framework demonstrates that the limited data from the scour sensors allow a significant reduction of uncertainty in the scour estimates at unmonitored bridge piers. This reduction is in the order of 70%, leading to a more precise classification of the bridge scour risk and to an increase of about 10% of the scour thresholds that trigger bridge closures compared to the ones chosen by transport agencies in their decision plans.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral-pg

Grantor dc:publisher.institution

University of Strathclyde

Year dc:date.issued

2020

Author dc:creator

• Maroni, Andrea

Advisor dc:contributor.advisor

• Tubaldi, Enrico

DOI

10.48730/m5bw-xg47

Identifier

T15694

Author Identifier

201655935

OAI identifier oai:identifier

oai:strathclyde:c247ds08x