Human error risk management methodology for rail crack incidents

Wang, Zhiru, Su, Guofeng, Skitmore, Martin, Chen, Jianguo, Chan, Albert P.C., & Xia, Bo (2015) Human error risk management methodology for rail crack incidents. Urban Rail Transit, 1(4), pp. 257-265.

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The paper presents an innovative approach to modelling the causal relationships of human errors in rail crack incidents (RCI) from a managerial perspective. A Bayesian belief network is developed to model RCI by considering the human errors of designers, manufactures, operators and maintainers (DMOM) and the causal relationships involved. A set of dependent variables whose combinations express the relevant functions performed by each DMOM participant is used to model the causal relationships. A total of 14 RCI on Hong Kong’s mass transit railway (MTR) from 2008 to 2011 are used to illustrate the application of the model. Bayesian inference is used to conduct an importance analysis to assess the impact of the participants’ errors. Sensitivity analysis is then employed to gauge the effect the increased probability of occurrence of human errors on RCI. Finally, strategies for human error identification and mitigation of RCI are proposed. The identification of ability of maintainer in the case study as the most important factor influencing the probability of RCI implies the priority need to strengthen the maintenance management of the MTR system and that improving the inspection ability of the maintainer is likely to be an effective strategy for RCI risk mitigation.

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ID Code: 93707
Item Type: Journal Article
Refereed: Yes
Keywords: Bayesian Network, human error, Hong Kong, importance analysis, Rail Crack Incidents, sensitivity analysis
DOI: 10.1007/s40864-016-0032-2
ISSN: 2199-6679
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: The Author(s) 2016.
Copyright Statement: This article is published with open access at
Deposited On: 15 Mar 2016 04:03
Last Modified: 18 Mar 2016 17:39

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