Towards a real-time Structural Health Monitoring of railway bridges
More than 350,000 railway bridges are present on the European railway network, making them a key infrastructure of the whole railway network. Railway bridges are continuously exposed to changing environmental threats, such as wind, floods and traffic load, which can affect safety and reliability of...
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| Format: | Conference or Workshop Item |
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2017
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| Online Access: | https://eprints.nottingham.ac.uk/46918/ |
| _version_ | 1848797427603079168 |
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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 350,000 railway bridges are present on the European railway network, making them a key infrastructure of the whole railway network. Railway bridges are continuously exposed to changing environmental threats, such as wind, floods and traffic load, which can affect safety and reliability of the bridge. Furthermore, a problem on a bridge can affect the whole railway network by increasing the vulnerability of the geographic area, served by the railway network. In this paper a Bayesian Belief Network (BBN) method is presented in order to move from visual inspection towards a real time Structural Health Monitoring (SHM) of the bridge. It is proposed that the health state of a steel truss bridge is continuously monitored by taking account of the health state of each bridge element. In this way, levels of bridge deterioration can be identified before they become critical, the risk of direct and indirect economic losses can be reduced by defining optimal bridge maintenance works, and the reliability of the bridge can be improved by identifying possible hidden vulnerabilities among different bridge elements. |
| first_indexed | 2025-11-14T20:03:42Z |
| format | Conference or Workshop Item |
| id | nottingham-46918 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:03:42Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-469182020-05-04T18:48:38Z https://eprints.nottingham.ac.uk/46918/ Towards a real-time Structural Health Monitoring of railway bridges Vagnoli, Matteo Remenyte-Prescott, Rasa Andrews, John More than 350,000 railway bridges are present on the European railway network, making them a key infrastructure of the whole railway network. Railway bridges are continuously exposed to changing environmental threats, such as wind, floods and traffic load, which can affect safety and reliability of the bridge. Furthermore, a problem on a bridge can affect the whole railway network by increasing the vulnerability of the geographic area, served by the railway network. In this paper a Bayesian Belief Network (BBN) method is presented in order to move from visual inspection towards a real time Structural Health Monitoring (SHM) of the bridge. It is proposed that the health state of a steel truss bridge is continuously monitored by taking account of the health state of each bridge element. In this way, levels of bridge deterioration can be identified before they become critical, the risk of direct and indirect economic losses can be reduced by defining optimal bridge maintenance works, and the reliability of the bridge can be improved by identifying possible hidden vulnerabilities among different bridge elements. 2017-05-31 Conference or Workshop Item PeerReviewed Vagnoli, Matteo, Remenyte-Prescott, Rasa and Andrews, John (2017) Towards a real-time Structural Health Monitoring of railway bridges. In: 52nd ESReDA Seminar on Critical Infrastructures Enhancing Preparedness & Resilience for the security of citizens and services supply continuity., 30-31 May 2017, Kaunas, Lithuania. Real-time monitoring; Structural Health Monitoring; Bayesian Belief Networks; Steel truss bridge |
| spellingShingle | Real-time monitoring; Structural Health Monitoring; Bayesian Belief Networks; Steel truss bridge Vagnoli, Matteo Remenyte-Prescott, Rasa Andrews, John Towards a real-time Structural Health Monitoring of railway bridges |
| title | Towards a real-time Structural Health Monitoring of railway bridges |
| title_full | Towards a real-time Structural Health Monitoring of railway bridges |
| title_fullStr | Towards a real-time Structural Health Monitoring of railway bridges |
| title_full_unstemmed | Towards a real-time Structural Health Monitoring of railway bridges |
| title_short | Towards a real-time Structural Health Monitoring of railway bridges |
| title_sort | towards a real-time structural health monitoring of railway bridges |
| topic | Real-time monitoring; Structural Health Monitoring; Bayesian Belief Networks; Steel truss bridge |
| url | https://eprints.nottingham.ac.uk/46918/ |