A Bayesian Belief Network method for bridge deterioration detection

Bridges are one of the most important assets of transportation networks. A closure of a bridge can increase the vulnerability of the geographic area served by such networks, as it reduces the number of available routes. Condition monitoring and deterioration detection methods can be used to monitor...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part O, Journal of risk and reliability Journal of risk and reliability, 2021-06, Vol.235 (3), p.338-355
Main Authors: Vagnoli, Matteo, Remenyte-Prescott, Rasa, Andrews, John
Format: Article
Language:English
Subjects:
Belief networks
Concrete bridges
Condition monitoring
Conditional probability
Deterioration
Finite element method
Mathematical models
Post-tensioned concrete
Post-tensioning
Steel bridges
Structural health monitoring
Transportation networks
Truss bridges
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Summary:Bridges are one of the most important assets of transportation networks. A closure of a bridge can increase the vulnerability of the geographic area served by such networks, as it reduces the number of available routes. Condition monitoring and deterioration detection methods can be used to monitor the health state of a bridge and enable detection of early signs of deterioration. In this paper, a novel Bayesian Belief Network (BBN) methodology for bridge deterioration detection is proposed. A method to build a BBN structure and to define the Conditional Probability Tables (CPTs) is presented first. Then evidence of the bridge behaviour (such as bridge displacement or acceleration due to traffic) is used as an input to the BBN model, the probability of the health state of whole bridge and its elements is updated and the levels of deterioration are detected. The methodology is illustrated using a Finite Element Model (FEM) of a steel truss bridge, and for an in-field post-tensioned concrete bridge.
ISSN:1748-006X
1748-0078
DOI:10.1177/1748006X20979225