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Bayesian network-based probability analysis of train derailments caused by various extreme weather patterns on railway turnouts

Dindar, S; Kaewunruen, S; An, M; Sussman, JM

Authors

S Dindar

S Kaewunruen

JM Sussman



Abstract

Since multiple failure events associated with derailments could not be identified and derailment probability could not be reached quantitatively by event tree and fault tree analysis for safety assessment in railway systems, applications of Bayesian network (BN) were introduced over the last few years. The applications were often aimed at understanding safety and reliability of railway systems through various basic principles and unique inference algorithms focusing on particular railway infrastructures. One of the most critical engineering infrastructure, railway turnouts (RTs) have been investigated and analysed critically in order to develop a new BN-based model with unique algorithm. This unprecedented study reveals the causal relations between primary causes and the subsystem failures, resulting in derailment, as a result of extreme weather-related conditions. In addition, the model, which is designed for rare events, has been proposed to identify the probability and underlying root cause of derailment. Consequently, it is expected that various weather-related causes of derailment at RTs, one such undesirable event, which can result, albeit rarely, damaging rolling stock, railway infrastructure and disrupting service, and having the potential to cause casualties and even loss of life, are identified to allow for smooth railway operation by rail industry itself. The insight into this weather-derailment will help the industry to better manage railway operation under climate uncertainty.

Citation

Dindar, S., Kaewunruen, S., An, M., & Sussman, J. (2018). Bayesian network-based probability analysis of train derailments caused by various extreme weather patterns on railway turnouts. Safety Science, 110(Part B), 20-30. https://doi.org/10.1016/j.ssci.2017.12.028

Journal Article Type Article
Acceptance Date Dec 22, 2017
Online Publication Date Dec 29, 2017
Publication Date Dec 1, 2018
Deposit Date Nov 2, 2018
Journal Safety Science
Print ISSN 0925-7535
Publisher Elsevier
Volume 110
Issue Part B
Pages 20-30
DOI https://doi.org/10.1016/j.ssci.2017.12.028
Publisher URL https://doi.org/10.1016/j.ssci.2017.12.028
Related Public URLs https://www.journals.elsevier.com/safety-science/
Additional Information Funders : European Commission
Projects : RISEN: Rail Infrastructure Systems Engineering Network
Grant Number: H2020-RISE
Grant Number: 691135