A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection
More than 35% of the European railway bridges are over 100 years old and the increasing traffic loads are pushing the railway infrastructure to its limits. Bridge condition-monitoring strategies can help the railway industry to improve safety, availability and reliability of the network. In this pap...
| Main Authors: | , , |
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| Format: | Book Section |
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CRC Press
2017
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| Online Access: | https://eprints.nottingham.ac.uk/41104/ |
| _version_ | 1848796197538496512 |
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| author | Vagnoli, Matteo Remenyte-Prescott, Rasa Andrews, John |
| author_facet | Vagnoli, Matteo Remenyte-Prescott, Rasa Andrews, John |
| author_sort | Vagnoli, Matteo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | More than 35% of the European railway bridges are over 100 years old and the increasing traffic loads are pushing the railway infrastructure to its limits. Bridge condition-monitoring strategies can help the railway industry to improve safety, availability and reliability of the network. In this paper, a Bayesian Belief Network method for condition monitoring and fault detection of a truss steel railway bridge is proposed by relying on a fuzzy analytical hierarchy process of expert knowledge. The BBN method is proposed for obtaining the bridge health state and identifying the most degraded bridge elements. A Finite Element model is developed for simulating the bridge behaviour and studying a degradation mechanism. The proposed approach originally captures the interactions existing between the health state of different bridge elements and, furthermore, when the evidence about the displacement is introduced in the BBN, the health state of the bridge is updated. |
| first_indexed | 2025-11-14T19:44:09Z |
| format | Book Section |
| id | nottingham-41104 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:44:09Z |
| publishDate | 2017 |
| publisher | CRC Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-411042020-05-04T18:47:09Z https://eprints.nottingham.ac.uk/41104/ A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection Vagnoli, Matteo Remenyte-Prescott, Rasa Andrews, John More than 35% of the European railway bridges are over 100 years old and the increasing traffic loads are pushing the railway infrastructure to its limits. Bridge condition-monitoring strategies can help the railway industry to improve safety, availability and reliability of the network. In this paper, a Bayesian Belief Network method for condition monitoring and fault detection of a truss steel railway bridge is proposed by relying on a fuzzy analytical hierarchy process of expert knowledge. The BBN method is proposed for obtaining the bridge health state and identifying the most degraded bridge elements. A Finite Element model is developed for simulating the bridge behaviour and studying a degradation mechanism. The proposed approach originally captures the interactions existing between the health state of different bridge elements and, furthermore, when the evidence about the displacement is introduced in the BBN, the health state of the bridge is updated. CRC Press 2017-05-25 Book Section PeerReviewed Vagnoli, Matteo, Remenyte-Prescott, Rasa and Andrews, John (2017) A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection. In: Safety and Reliability – Theory and Application: ESREL 2017. CRC Press. ISBN 9781138629370 https://www.crcpress.com/ESREL-2017-Portoroz-Slovenia-18-22-June-2017/Cepin-Bris/p/book/9781138629370 |
| spellingShingle | Vagnoli, Matteo Remenyte-Prescott, Rasa Andrews, John A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection |
| title | A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection |
| title_full | A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection |
| title_fullStr | A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection |
| title_full_unstemmed | A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection |
| title_short | A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection |
| title_sort | fuzzy-based bayesian belief network approach for railway bridge condition monitoring and fault detection |
| url | https://eprints.nottingham.ac.uk/41104/ https://eprints.nottingham.ac.uk/41104/ |