A Bayesian network approach to assess underwater scour around bridge foundations

Andrea Maroni, Enrico Tubaldi, John Douglas, Neil Ferguson, Daniele Zonta, Hazel McDonald, Euan Greenoak, Douglas Walker, Christopher Green

Research output: Contribution to conferencePaper

Abstract

Flood-induced scour is by far the leading cause of bridge failures, resulting in fatalities, traffic disruption and significant economic losses. In Scotland, there are around 2,000 structures, considering both road and railway bridges, susceptible to scour. Scour assessments are currently based on visual inspections, which are expensive and time-consuming. The two main transport agencies in Scotland, Transport Scotland (TS) and Network Rail (NR), spend £2m and £0.4m per annum, respectively, in routine inspections. Nowadays, sensor and communication technologies offer the possibility to assess in real-time the scour depth at critical bridge locations; yet monitoring an entire infrastructure network is not economically sustainable. This paper proposes a methodology overcoming this limitation, based on the installation of monitoring systems at critical locations, and the use a probabilistic approach to extend this information to the entire population of assets. The state of the bridge stock is represented through a set of random variables, and ad-hoc Bayesian networks (BNs) are used to describe their conditional dependencies. The BN can estimate, and continuously update, the present and future scour depth at bridge foundations using real-time information provided by the monitored scour depth and river flow characteristics. In the occurrence of a flood, monitoring observations are used to infer probabilistically the posterior distribution of the state variables, giving the real-time best estimate of the total scour depth. Bias, systematic and model uncertainties are modelled as nodes of the BN in such a way that the accuracy of predictions can be updated when information from scour monitoring systems is incorporated into the BN. The functioning and capabilities of the BN is illustrated by considering a small network of bridges managed by TS in south-west Scotland. They cross the same river (River Nith) and only one of them is instrumented with a scour monitoring system.

Conference

Conference9th European Workshop on Structural Health Monitoring Series (EWSHM)
Abbreviated titleEWSHM
CountryUnited Kingdom
CityManchester
Period10/07/1813/07/18
Internet address

Fingerprint

Scour
Bayesian networks
Monitoring
Rivers
Inspection
Random variables
Rails
Economics
Communication
Sensors

Keywords

  • scour
  • road and rail bridges
  • structural health monitoring
  • Bayesian network

Cite this

Maroni, A., Tubaldi, E., Douglas, J., Ferguson, N., Zonta, D., McDonald, H., ... Green, C. (2018). A Bayesian network approach to assess underwater scour around bridge foundations. Paper presented at 9th European Workshop on Structural Health Monitoring Series (EWSHM) , Manchester, United Kingdom.
Maroni, Andrea ; Tubaldi, Enrico ; Douglas, John ; Ferguson, Neil ; Zonta, Daniele ; McDonald, Hazel ; Greenoak, Euan ; Walker, Douglas ; Green, Christopher. / A Bayesian network approach to assess underwater scour around bridge foundations. Paper presented at 9th European Workshop on Structural Health Monitoring Series (EWSHM) , Manchester, United Kingdom.12 p.
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abstract = "Flood-induced scour is by far the leading cause of bridge failures, resulting in fatalities, traffic disruption and significant economic losses. In Scotland, there are around 2,000 structures, considering both road and railway bridges, susceptible to scour. Scour assessments are currently based on visual inspections, which are expensive and time-consuming. The two main transport agencies in Scotland, Transport Scotland (TS) and Network Rail (NR), spend £2m and £0.4m per annum, respectively, in routine inspections. Nowadays, sensor and communication technologies offer the possibility to assess in real-time the scour depth at critical bridge locations; yet monitoring an entire infrastructure network is not economically sustainable. This paper proposes a methodology overcoming this limitation, based on the installation of monitoring systems at critical locations, and the use a probabilistic approach to extend this information to the entire population of assets. The state of the bridge stock is represented through a set of random variables, and ad-hoc Bayesian networks (BNs) are used to describe their conditional dependencies. The BN can estimate, and continuously update, the present and future scour depth at bridge foundations using real-time information provided by the monitored scour depth and river flow characteristics. In the occurrence of a flood, monitoring observations are used to infer probabilistically the posterior distribution of the state variables, giving the real-time best estimate of the total scour depth. Bias, systematic and model uncertainties are modelled as nodes of the BN in such a way that the accuracy of predictions can be updated when information from scour monitoring systems is incorporated into the BN. The functioning and capabilities of the BN is illustrated by considering a small network of bridges managed by TS in south-west Scotland. They cross the same river (River Nith) and only one of them is instrumented with a scour monitoring system.",
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note = "9th European Workshop on Structural Health Monitoring Series (EWSHM) : EWSHM 2018, EWSHM ; Conference date: 10-07-2018 Through 13-07-2018",
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Maroni, A, Tubaldi, E, Douglas, J, Ferguson, N, Zonta, D, McDonald, H, Greenoak, E, Walker, D & Green, C 2018, 'A Bayesian network approach to assess underwater scour around bridge foundations' Paper presented at 9th European Workshop on Structural Health Monitoring Series (EWSHM) , Manchester, United Kingdom, 10/07/18 - 13/07/18, .

A Bayesian network approach to assess underwater scour around bridge foundations. / Maroni, Andrea; Tubaldi, Enrico; Douglas, John; Ferguson, Neil; Zonta, Daniele; McDonald, Hazel; Greenoak, Euan; Walker, Douglas; Green, Christopher.

2018. Paper presented at 9th European Workshop on Structural Health Monitoring Series (EWSHM) , Manchester, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - A Bayesian network approach to assess underwater scour around bridge foundations

AU - Maroni,Andrea

AU - Tubaldi,Enrico

AU - Douglas,John

AU - Ferguson,Neil

AU - Zonta,Daniele

AU - McDonald,Hazel

AU - Greenoak,Euan

AU - Walker,Douglas

AU - Green,Christopher

PY - 2018/7/10

Y1 - 2018/7/10

N2 - Flood-induced scour is by far the leading cause of bridge failures, resulting in fatalities, traffic disruption and significant economic losses. In Scotland, there are around 2,000 structures, considering both road and railway bridges, susceptible to scour. Scour assessments are currently based on visual inspections, which are expensive and time-consuming. The two main transport agencies in Scotland, Transport Scotland (TS) and Network Rail (NR), spend £2m and £0.4m per annum, respectively, in routine inspections. Nowadays, sensor and communication technologies offer the possibility to assess in real-time the scour depth at critical bridge locations; yet monitoring an entire infrastructure network is not economically sustainable. This paper proposes a methodology overcoming this limitation, based on the installation of monitoring systems at critical locations, and the use a probabilistic approach to extend this information to the entire population of assets. The state of the bridge stock is represented through a set of random variables, and ad-hoc Bayesian networks (BNs) are used to describe their conditional dependencies. The BN can estimate, and continuously update, the present and future scour depth at bridge foundations using real-time information provided by the monitored scour depth and river flow characteristics. In the occurrence of a flood, monitoring observations are used to infer probabilistically the posterior distribution of the state variables, giving the real-time best estimate of the total scour depth. Bias, systematic and model uncertainties are modelled as nodes of the BN in such a way that the accuracy of predictions can be updated when information from scour monitoring systems is incorporated into the BN. The functioning and capabilities of the BN is illustrated by considering a small network of bridges managed by TS in south-west Scotland. They cross the same river (River Nith) and only one of them is instrumented with a scour monitoring system.

AB - Flood-induced scour is by far the leading cause of bridge failures, resulting in fatalities, traffic disruption and significant economic losses. In Scotland, there are around 2,000 structures, considering both road and railway bridges, susceptible to scour. Scour assessments are currently based on visual inspections, which are expensive and time-consuming. The two main transport agencies in Scotland, Transport Scotland (TS) and Network Rail (NR), spend £2m and £0.4m per annum, respectively, in routine inspections. Nowadays, sensor and communication technologies offer the possibility to assess in real-time the scour depth at critical bridge locations; yet monitoring an entire infrastructure network is not economically sustainable. This paper proposes a methodology overcoming this limitation, based on the installation of monitoring systems at critical locations, and the use a probabilistic approach to extend this information to the entire population of assets. The state of the bridge stock is represented through a set of random variables, and ad-hoc Bayesian networks (BNs) are used to describe their conditional dependencies. The BN can estimate, and continuously update, the present and future scour depth at bridge foundations using real-time information provided by the monitored scour depth and river flow characteristics. In the occurrence of a flood, monitoring observations are used to infer probabilistically the posterior distribution of the state variables, giving the real-time best estimate of the total scour depth. Bias, systematic and model uncertainties are modelled as nodes of the BN in such a way that the accuracy of predictions can be updated when information from scour monitoring systems is incorporated into the BN. The functioning and capabilities of the BN is illustrated by considering a small network of bridges managed by TS in south-west Scotland. They cross the same river (River Nith) and only one of them is instrumented with a scour monitoring system.

KW - scour

KW - road and rail bridges

KW - structural health monitoring

KW - Bayesian network

UR - http://www.bindt.org/events/ewshm-2018/

M3 - Paper

ER -

Maroni A, Tubaldi E, Douglas J, Ferguson N, Zonta D, McDonald H et al. A Bayesian network approach to assess underwater scour around bridge foundations. 2018. Paper presented at 9th European Workshop on Structural Health Monitoring Series (EWSHM) , Manchester, United Kingdom.