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Title: A Bayesian Network Model for reducing accident rates of Electrical and Mechanical (E&M) work
Authors: Chan, APC 
Wong, FKW 
Hon, CKH
Choi, TNY 
Keywords: Accident analysis
Bayesian Networks
Electrical and mechanical (E&
M) works
Safety management
Issue Date: 2018
Publisher: Molecular Diversity Preservation International (MDPI)
Source: International journal of environmental research and public health, 2018, v. 15, no. 11 How to cite?
Journal: International journal of environmental research and public health 
Abstract: Accidents in Repair, Maintenance, Alteration, and Addition (RMAA) work have become a growing concern, in recent years. The repair and maintenance works of electrical and mechanical (E&M) installations involves a variety of trades, a large number of practitioners and a series of high-risk activities. The uniqueness of E&M work, in the RMAA sector, requires a discrete and specific research to improve its safety performance. Understanding the causal relationships between safety factors and the number of accidents becomes crucial to develop a more effective safety management strategy. The Bayesian Network (BN) model is proposed to establish a probabilistic relational network between the causal factors, including both safety climate factors and personal experience factors that have influences on the number of accidents related to E&M RMAA work. The data were collected using a survey questionnaire, involving a hundred and fifty-five E&M practitioners. The BN results demonstrated that safety attitude and safety procedures were the most important factors to reduce the number of accidents. The proposed BN provides the ability to find out the most effective strategy with the best utilization of resources, to reduce the chance of a high number of E&M accidents, by controlling a single factor or simultaneously controlling, both, the safety climate and personal factors, to improve safety performance.
ISSN: 1661-7827
EISSN: 1660-4601
DOI: 10.3390/ijerph15112496
Rights: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication: Chan, A.P.C.; Wong, F.K.W.; Hon, C.K.H.; Choi, T.N.Y. A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work. Int. J. Environ. Res. Public Health 2018, 15, 2496 is available at
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