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Identification of appropriate risk analysis techniques for railway turnout systems

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  • Serdar Dindar
  • Sakdirat Kaewunruen
  • Min An

Abstract

The construction of railway turnout entails a complex geometry and multi-disciplinary engineering science, which makes it one of the most critical railway infrastructures. As such, these characteristics pose various risks in rail operation. A considerable number of derailment incidents at the turnouts are reported annually worldwide. Not only do these incidents cause operational downtime and financial loss, they also give rise to casualties and sometimes loss of life. One of the fundamental reasons for this may well be the fact that the railway industry pays little attention to the risk elements of railway turnouts. The paper provides an integrated approach of how to deal with the many different risks arising from various sources in railway turnout systems by suitably identifying the multi-disciplinary risk analysis methods for the complex systems. In order to do so, available open literatures are critically analysed by virtue of comparison, industry experiences and deductions. As a result, various qualitative- and quantitative-based risk analysis methods are proposed to fully understand a number of technical phenomena, e.g. ageing, degradation and signalling faults, in a railway turnout system.

Suggested Citation

  • Serdar Dindar & Sakdirat Kaewunruen & Min An, 2018. "Identification of appropriate risk analysis techniques for railway turnout systems," Journal of Risk Research, Taylor & Francis Journals, vol. 21(8), pages 974-995, August.
    • Handle: RePEc:taf:jriskr:v:21:y:2018:i:8:p:974-995
    DOI: 10.1080/13669877.2016.1264452
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    Cited by:

    1. Dindar, Serdar & Kaewunruen, Sakdirat & An, Min, 2020. "Bayesian network-based human error reliability assessment of derailments," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    2. Regina Lamedica & Alessandro Ruvio & Laura Palagi & Nicola Mortelliti, 2020. "Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line," Energies, MDPI, vol. 13(23), pages 1-22, November.
    3. Zhao, Na, 2019. "Managing interactive collaborative mega project supply chains under infectious risks," International Journal of Production Economics, Elsevier, vol. 218(C), pages 275-286.

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