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Railway bridge fault detection using Bayesian belief network

Vagnoli, M.; Remenyte-Prescott, R.; Andrews, J.


M. Vagnoli

Professor of Infrastructure Asset Management


Bridges are one of the most critical structures of the railway system. External loads may affect the bridge health state, and consequently their safety, availability and reliability can be improved by monitoring their condition and planning maintenance accordingly. In this paper, a Bayesian Belief Network (BBN) fault detection methodology for a truss steel railway bridge is proposed. The BBN is developed to assess the health state of the whole bridge using evidence about the behaviour of the bridge. In this initial study, the evidence is provided in terms of the values of displacement computed by a Finite Element model.


Vagnoli, M., Remenyte-Prescott, R., & Andrews, J. (2017). Railway bridge fault detection using Bayesian belief network.

Conference Name Stephenson Conference: Research for Railways
End Date Apr 2, 2017
Acceptance Date Feb 10, 2017
Publication Date Apr 25, 2017
Deposit Date Mar 8, 2017
Peer Reviewed Peer Reviewed
Public URL