SHM-based decision support system for bridge scour management

A. Maroni, Enrico Tubaldi, Dimitri Val, Hazel McDonald, Stewart Lothian, Oliver Riches, Daniele Zonta

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)
13 Downloads (Pure)


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. Monitoring an entire infrastructure network against scour is not economically feasible. This limitation can be overcome by installing 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 (SMSs) 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, and it allows estimating the scour depth distributions using information from monitoring of scour depth and river flow characteristics. Data collected by SMSs 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.
Original languageEnglish
Number of pages6
Publication statusPublished - 7 Aug 2019
Event9th International Conference on Structural Health Monitoring of Intelligent Infrastructure - Missouri University of Science and Technology, St. Louis, United States
Duration: 4 Aug 20197 Aug 2019


Conference9th International Conference on Structural Health Monitoring of Intelligent Infrastructure
Abbreviated titleSHMII 2019
Country/TerritoryUnited States
CitySt. Louis


  • scour
  • road and rail bridges
  • structural health monitoring
  • Bayesian network
  • Decision Support System


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