A hierarchical Bayesian-based model for hazard analysis of climate effect on failures of railway turnout components

Dindar, S; Kaewunruen, S; An, M

Abstract

There has been a considerable increase in derailment investigations, in particular at railway turnouts (RTs), as the majority of derailments lead to lengthy disruptions to the appropriate rail operation and catastrophic consequences, being potentially severely hazardous to human safety and health, as well as rail equipment. This paper investigates the impact of climates with different features across the US on the derailments to light up a scientific way for understanding importance of climatic impact. To achieve this, official derailment reports over the last five years are examined in detail. By means of geographic segmentation associated with spatial analysis, different exposure levels of various regions have been identified and implemented into a Bayesian hierarchical model using samples by the M–H algorithm. As a result, the paper reaches interesting scientific findings of climate behaviour on turnout-related component failures resulting in derailments. The findings show extreme climate patterns impact considerably the component failures of rail turnouts. Therefore, it is indicated that turnout-related failure estimates on a large-scale region with extreme cold and hot zones could be investigated when the suggested methodology of this paper is considered.

Citation

Dindar, S., Kaewunruen, S., & An, M. (2022). A hierarchical Bayesian-based model for hazard analysis of climate effect on failures of railway turnout components. Reliability Engineering and System Safety, 218(Part A), 108130. https://doi.org/10.1016/j.ress.2021.108130

• Journal Article Type: Article
• Acceptance Date: Oct 9, 2021
• Online Publication Date: Oct 16, 2021
• Publication Date: Feb 1, 2022
• Deposit Date: Oct 25, 2021
• Publicly Available Date: Oct 16, 2022
• Journal: Reliability Engineering & System Safety
• Print ISSN: 0951-8320
• Publisher: Elsevier
• Volume: 218
• Issue: Part A
• Pages: 108130
• DOI: https://doi.org/10.1016/j.ress.2021.108130
• Publisher URL: https://doi.org/10.1016/j.ress.2021.108130
• Related Public URLs: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety/vol/218/part/PA
• Additional Information: Funders : British Department for Transport (DfT);The European Commission; Projects : Transport-Technology Research Innovations Grant Scheme;H2020-RISE RISEN: Rail Infrastructure Systems Engineering Network;H2020-Shift2Rail; Grant Number: RCS15/0233; Grant Number: 691135; Grant Number: 730849