Bayesian network-based human error reliability assessment of derailments

Dindar, S, Sakdirat, K and An, M ORCID: https://orcid.org/0000-0002-1069-7492 2020, 'Bayesian network-based human error reliability assessment of derailments' , Reliability Engineering & System Safety, 197 , p. 106825.

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Abstract

The knowledge acquired in relation to failures associated with components has made significant contributions to the development of components with increased reliability, as well as a reduction in the number of rail incidents caused by certain system defects. These new systems have led to innovative developments in both the operations and technology of rail networks. Hence, rail employees must now function in conditions that have high complexity that are hard to comprehend. The risk of failure caused by human error (such as by dispatchers, train crews and track engineers) has developed into a significant safety problem. This study is the world first to provide novel insights into better understanding human errors, which result in derailments at rail turnouts. A most- to-least-critical importance ranking of these errors is established throughout a novel risk management technique. Moreover, the new findings and recommendations of this research study have a strong potential for industry to improve the reliability of rail operation, and avoid safety concerns regarding train derailments at rail turnouts.

Item Type: Article
Schools: Schools > School of the Built Environment > Centre for Urban Processes, Resilient Infrastructures & Sustainable Environments
Journal or Publication Title: Reliability Engineering & System Safety
Publisher: Elsevier
ISSN: 0951-8320
Related URLs:
Funders: British Department for Transport (DfT) for Transport, the European Commission for the financial sponsorship of the H2020-RISE
Depositing User: Professor Min An
Date Deposited: 18 Apr 2020 11:39
Last Modified: 18 Apr 2020 11:45
URI: http://usir.salford.ac.uk/id/eprint/56847

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