A Bayesian Belief Network method for bridge deterioration detection

Vagnoli, Matteo; Remenyte-Prescott, Rasa; Andrews, John

Abstract

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.

Citation

Vagnoli, M., Remenyte-Prescott, R., & Andrews, J. (2021). A Bayesian Belief Network method for bridge deterioration detection. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 235(3), 338-355. https://doi.org/10.1177/1748006X20979225

• Journal Article Type: Article
• Acceptance Date: Nov 16, 2020
• Online Publication Date: Dec 15, 2020
• Publication Date: 2021-06
• Deposit Date: Dec 16, 2020
• Publicly Available Date: Dec 16, 2020
• Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
• Print ISSN: 1748-006X
• Electronic ISSN: 1748-0078
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 235
• Issue: 3
• Pages: 338-355
• DOI: https://doi.org/10.1177/1748006X20979225
• Keywords: Bayesian Belief Network; bridge deterioration; detection and diagnostics; structural health monitoring 2
• Public URL: https://nottingham-repository.worktribe.com/output/5153616
• Publisher URL: https://journals.sagepub.com/doi/full/10.1177/1748006X20979225