A decision support system for scour management of road and railway bridges based on Bayesian networks

Andrea Maroni, Enrico Tubaldi, Dimitry Val, Hazel McDonald, Stewart Lothian, Oliver Riches, Daniele Zonta

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

Abstract

Flood-induced scour is the excavation of material around bridge foundations due to the erosive action of flowing water and it is by far the leading cause of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fail every year whereas in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Monitoring an entire infrastructure network against scour is not economically feasible. A way to overcome this limitation is to install scour monitoring systems (SMSs) at critical bridge locations, and then extend information gained to the entire asset through a probabilistic approach. In this paper, we propose a Decision Support System (DSS) for road and railway 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 random variables involved. It allows estimating the present and future scour depth distribution using real-time 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. The idea is to use this information to update the scour threshold after which bridges are closed. A case study consisting of several road bridges in Scotland is built to demonstrate the functioning of the DDS. They cross the same river and only one of them is instrumented with a SMS. The BN is found to estimate accurately the scour depth at unmonitored bridges and the decision model provides higher values of scour threshold compared to the ones implicitly chosen by transport agencies.

Original languageEnglish
Title of host publicationStructural Health Monitoring 2019
Subtitle of host publicationEnabling Intelligent Life-Cycle Health Management for Industry Internet of Things
EditorsFu-Kuo Chang, Alfredo Guemes, Fotis Kopsaftopoulos
PublisherDEStech Publications Inc.
Pages2394-2401
Number of pages8
ISBN (Electronic)9781605956015
Publication statusPublished - 2019
Event12th International Workshop on Structural Health Monitoring 2019 - Stanford, United States
Duration: 10 Sep 201912 Sep 2019

Conference

Conference12th International Workshop on Structural Health Monitoring 2019
Abbreviated titleIWSHM 2019
CountryUnited States
CityStanford
Period10/09/1912/09/19

Fingerprint

Management Decision Support Systems
Structure Collapse
Scour
Bayesian networks
Decision support systems
Rivers
Information Management
Decision Support Techniques
Scotland
Water
Monitoring

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Information Management

Cite this

Maroni, A., Tubaldi, E., Val, D., McDonald, H., Lothian, S., Riches, O., & Zonta, D. (2019). A decision support system for scour management of road and railway bridges based on Bayesian networks. In F-K. Chang, A. Guemes, & F. Kopsaftopoulos (Eds.), Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (pp. 2394-2401). DEStech Publications Inc..
Maroni, Andrea ; Tubaldi, Enrico ; Val, Dimitry ; McDonald, Hazel ; Lothian, Stewart ; Riches, Oliver ; Zonta, Daniele. / A decision support system for scour management of road and railway bridges based on Bayesian networks. Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things. editor / Fu-Kuo Chang ; Alfredo Guemes ; Fotis Kopsaftopoulos. DEStech Publications Inc., 2019. pp. 2394-2401
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title = "A decision support system for scour management of road and railway bridges based on Bayesian networks",
abstract = "Flood-induced scour is the excavation of material around bridge foundations due to the erosive action of flowing water and it is by far the leading cause of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fail every year whereas in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Monitoring an entire infrastructure network against scour is not economically feasible. A way to overcome this limitation is to install scour monitoring systems (SMSs) at critical bridge locations, and then extend information gained to the entire asset through a probabilistic approach. In this paper, we propose a Decision Support System (DSS) for road and railway 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 random variables involved. It allows estimating the present and future scour depth distribution using real-time 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. The idea is to use this information to update the scour threshold after which bridges are closed. A case study consisting of several road bridges in Scotland is built to demonstrate the functioning of the DDS. They cross the same river and only one of them is instrumented with a SMS. The BN is found to estimate accurately the scour depth at unmonitored bridges and the decision model provides higher values of scour threshold compared to the ones implicitly chosen by transport agencies.",
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Maroni, A, Tubaldi, E, Val, D, McDonald, H, Lothian, S, Riches, O & Zonta, D 2019, A decision support system for scour management of road and railway bridges based on Bayesian networks. in F-K Chang, A Guemes & F Kopsaftopoulos (eds), Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things. DEStech Publications Inc., pp. 2394-2401, 12th International Workshop on Structural Health Monitoring 2019, Stanford, United States, 10/09/19.

A decision support system for scour management of road and railway bridges based on Bayesian networks. / Maroni, Andrea; Tubaldi, Enrico; Val, Dimitry; McDonald, Hazel; Lothian, Stewart; Riches, Oliver; Zonta, Daniele.

Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things. ed. / Fu-Kuo Chang; Alfredo Guemes; Fotis Kopsaftopoulos. DEStech Publications Inc., 2019. p. 2394-2401.

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

TY - GEN

T1 - A decision support system for scour management of road and railway bridges based on Bayesian networks

AU - Maroni, Andrea

AU - Tubaldi, Enrico

AU - Val, Dimitry

AU - McDonald, Hazel

AU - Lothian, Stewart

AU - Riches, Oliver

AU - Zonta, Daniele

PY - 2019

Y1 - 2019

N2 - Flood-induced scour is the excavation of material around bridge foundations due to the erosive action of flowing water and it is by far the leading cause of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fail every year whereas in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Monitoring an entire infrastructure network against scour is not economically feasible. A way to overcome this limitation is to install scour monitoring systems (SMSs) at critical bridge locations, and then extend information gained to the entire asset through a probabilistic approach. In this paper, we propose a Decision Support System (DSS) for road and railway 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 random variables involved. It allows estimating the present and future scour depth distribution using real-time 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. The idea is to use this information to update the scour threshold after which bridges are closed. A case study consisting of several road bridges in Scotland is built to demonstrate the functioning of the DDS. They cross the same river and only one of them is instrumented with a SMS. The BN is found to estimate accurately the scour depth at unmonitored bridges and the decision model provides higher values of scour threshold compared to the ones implicitly chosen by transport agencies.

AB - Flood-induced scour is the excavation of material around bridge foundations due to the erosive action of flowing water and it is by far the leading cause of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fail every year whereas in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Monitoring an entire infrastructure network against scour is not economically feasible. A way to overcome this limitation is to install scour monitoring systems (SMSs) at critical bridge locations, and then extend information gained to the entire asset through a probabilistic approach. In this paper, we propose a Decision Support System (DSS) for road and railway 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 random variables involved. It allows estimating the present and future scour depth distribution using real-time 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. The idea is to use this information to update the scour threshold after which bridges are closed. A case study consisting of several road bridges in Scotland is built to demonstrate the functioning of the DDS. They cross the same river and only one of them is instrumented with a SMS. The BN is found to estimate accurately the scour depth at unmonitored bridges and the decision model provides higher values of scour threshold compared to the ones implicitly chosen by transport agencies.

UR - http://www.scopus.com/inward/record.url?scp=85074260275&partnerID=8YFLogxK

M3 - Conference contribution

SP - 2394

EP - 2401

BT - Structural Health Monitoring 2019

A2 - Chang, Fu-Kuo

A2 - Guemes, Alfredo

A2 - Kopsaftopoulos, Fotis

PB - DEStech Publications Inc.

ER -

Maroni A, Tubaldi E, Val D, McDonald H, Lothian S, Riches O et al. A decision support system for scour management of road and railway bridges based on Bayesian networks. In Chang F-K, Guemes A, Kopsaftopoulos F, editors, Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things. DEStech Publications Inc. 2019. p. 2394-2401