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 conferencePaper

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. 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.

Conference

Conference9th International Conference on Structural Health Monitoring of Intelligent Infrastructure
Abbreviated titleSHMII 2019
CountryUnited States
CitySt. Louis
Period4/08/197/08/19

Fingerprint

Scour
Decision support systems
Bayesian networks
Monitoring
Random variables
Rivers

Keywords

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

Cite this

Maroni, A., Tubaldi, E., Val, D., McDonald, H., Lothian, S., Riches, O., & Zonta, D. (2019). SHM-based decision support system for bridge scour management. Paper presented at 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure, St. Louis, United States.
Maroni, A. ; Tubaldi, Enrico ; Val, Dimitri ; McDonald, Hazel ; Lothian, Stewart ; Riches, Oliver ; Zonta, Daniele. / SHM-based decision support system for bridge scour management. Paper presented at 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure, St. Louis, United States.6 p.
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Maroni, A, Tubaldi, E, Val, D, McDonald, H, Lothian, S, Riches, O & Zonta, D 2019, 'SHM-based decision support system for bridge scour management' Paper presented at 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure, St. Louis, United States, 4/08/19 - 7/08/19, .

SHM-based decision support system for bridge scour management. / Maroni, A.; Tubaldi, Enrico; Val, Dimitri; McDonald, Hazel; Lothian, Stewart; Riches, Oliver; Zonta, Daniele.

2019. Paper presented at 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure, St. Louis, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - SHM-based decision support system for bridge scour management

AU - Maroni, A.

AU - Tubaldi, Enrico

AU - Val, Dimitri

AU - McDonald, Hazel

AU - Lothian, Stewart

AU - Riches, Oliver

AU - Zonta, Daniele

PY - 2019/8/7

Y1 - 2019/8/7

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. 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.

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. 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.

KW - scour

KW - road and rail bridges

KW - structural health monitoring

KW - Bayesian network

KW - Decision Support System

UR - https://shmii-9.mst.edu/

M3 - Paper

ER -

Maroni A, Tubaldi E, Val D, McDonald H, Lothian S, Riches O et al. SHM-based decision support system for bridge scour management. 2019. Paper presented at 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure, St. Louis, United States.