Incorporating local environmental factors into railway bridge asset management

Yianni, Panayioti C.; Neves, Lu�s C.; Rama, Dovile; Andrews, John D.; Dean, Robert

Abstract

© 2016 Elsevier Ltd A novel approach to comparing bridge deterioration rates under different environmental conditions is employed using a network analysis approach. This approach uses a matrix condition scoring system utilised by Network Rail (NR). It does not require any conversion factors which can introduce subjectivity. A number of different factors were analysed to ascertain if they have an effect on bridge deterioration. The key factors were identified and their deterioration profiles incorporated into a probabilistic Petri-Net (PN) model, calibrated with historical data. From these, comparative model outputs pinpointing which factors affect bridge deterioration the most can be computed. Finally, simulations were carried out on the PN model to evaluate which of the factors would have the most financial effect for a transport agency. This allows a bridge manager to categorise bridges in different deterioration sets allowing the definition of different optimal inspection and maintenance strategies for each set.

Citation

Yianni, P. C., Neves, L. C., Rama, D., Andrews, J. D., & Dean, R. (2016). Incorporating local environmental factors into railway bridge asset management. Engineering Structures, 128, 362-373. https://doi.org/10.1016/j.engstruct.2016.09.038

• Journal Article Type: Article
• Acceptance Date: Sep 19, 2016
• Online Publication Date: Oct 5, 2016
• Publication Date: Dec 1, 2016
• Deposit Date: Nov 29, 2016
• Publicly Available Date: Oct 6, 2018
• Journal: Engineering Structures
• Print ISSN: 0141-0296
• Electronic ISSN: 1873-7323
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 128
• Pages: 362-373
• DOI: https://doi.org/10.1016/j.engstruct.2016.09.038
• Keywords: Rail; Bridges; Asset management; Maintainability; Environmental factors; Petri-Net
• Public URL: https://nottingham-repository.worktribe.com/output/824686
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0141029616306745
• Contract Date: Nov 29, 2016