Managing bridge scour risk using structural health monitoring

A. Maroni, E. Tubaldi, J. Douglas, N. Ferguson, D. Val, H. McDonald, S. Lothian, A. Chisholm, O. Riches, D. Walker, E. Greenoak, C. Green, D. Zonta

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

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.
LanguageEnglish
Title of host publicationInternational Conference on Smart Infrastructure and Construction 2019 (ICSIC)
Subtitle of host publicationDriving Data-Informed Decision-Making
Place of PublicationLondon
Pages77-84
Number of pages8
DOIs
Publication statusPublished - 5 Jul 2019
EventInternational Conference on Smart Infrastructure and Construction 2019 (ICSIC) - Churchill College, University of Cambridge, Cambridge, United Kingdom
Duration: 8 Jul 201910 Jul 2019

Conference

ConferenceInternational Conference on Smart Infrastructure and Construction 2019 (ICSIC)
Abbreviated titleICSIC
CountryUnited Kingdom
CityCambridge
Period8/07/1910/07/19

Fingerprint

Scour
Structural health monitoring
Bayesian networks
Monitoring
Decision support systems
Random variables
Inspection
Rivers

Keywords

  • scour
  • road and rail bridges
  • structural health monitoring
  • Bayesian network
  • decision support system

Cite this

Maroni, A., Tubaldi, E., Douglas, J., Ferguson, N., Val, D., McDonald, H., ... Zonta, D. (2019). Managing bridge scour risk using structural health monitoring. In International Conference on Smart Infrastructure and Construction 2019 (ICSIC): Driving Data-Informed Decision-Making (pp. 77-84). London. https://doi.org/10.1680/icsic.64669.077
Maroni, A. ; Tubaldi, E. ; Douglas, J. ; Ferguson, N. ; Val, D. ; McDonald, H. ; Lothian, S. ; Chisholm, A. ; Riches, O. ; Walker, D. ; Greenoak, E. ; Green, C. ; Zonta, D. / Managing bridge scour risk using structural health monitoring. International Conference on Smart Infrastructure and Construction 2019 (ICSIC): Driving Data-Informed Decision-Making. London, 2019. pp. 77-84
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Maroni, A, Tubaldi, E, Douglas, J, Ferguson, N, Val, D, McDonald, H, Lothian, S, Chisholm, A, Riches, O, Walker, D, Greenoak, E, Green, C & Zonta, D 2019, Managing bridge scour risk using structural health monitoring. in International Conference on Smart Infrastructure and Construction 2019 (ICSIC): Driving Data-Informed Decision-Making. London, pp. 77-84, International Conference on Smart Infrastructure and Construction 2019 (ICSIC), Cambridge, United Kingdom, 8/07/19. https://doi.org/10.1680/icsic.64669.077

Managing bridge scour risk using structural health monitoring. / Maroni, A.; Tubaldi, E.; Douglas, J.; Ferguson, N.; Val, D.; McDonald, H.; Lothian, S.; Chisholm, A.; Riches, O.; Walker, D.; Greenoak, E.; Green, C.; Zonta, D.

International Conference on Smart Infrastructure and Construction 2019 (ICSIC): Driving Data-Informed Decision-Making. London, 2019. p. 77-84.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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AU - Tubaldi, E.

AU - Douglas, J.

AU - Ferguson, N.

AU - Val, D.

AU - McDonald, H.

AU - Lothian, S.

AU - Chisholm, A.

AU - Riches, O.

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N2 - 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.

AB - 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.

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KW - road and rail bridges

KW - structural health monitoring

KW - Bayesian network

KW - decision support system

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U2 - 10.1680/icsic.64669.077

DO - 10.1680/icsic.64669.077

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SN - 9780727764669

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Maroni A, Tubaldi E, Douglas J, Ferguson N, Val D, McDonald H et al. Managing bridge scour risk using structural health monitoring. In International Conference on Smart Infrastructure and Construction 2019 (ICSIC): Driving Data-Informed Decision-Making. London. 2019. p. 77-84 https://doi.org/10.1680/icsic.64669.077