Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68962
Title: Human error risk management methodology for rail crack incidents
Authors: Wang, Z
Su, G
Skitmore, M
Chen, J
Chan, APC 
Xia, Bo
Keywords: Bayesian network
Human error
Hong Kong
Importance analysis
Rail crack incidents
Sensitivity analysis
Issue Date: 2015
Publisher: SpringerOpen
Source: Urban rail transit, 2015, v. 1, no. 4, p. 257-265 How to cite?
Journal: Urban rail transit 
Abstract: 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.
URI: http://hdl.handle.net/10397/68962
ISSN: 2199-6687
EISSN: 2199-6679
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