A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work
Chan, A.P.C.; Wong, F.K.W.; Hon, C.K.H.; Choi, T.N.Y.
International Journal of Environmental Research and Public Health 15(11)
2018
ISSN/ISBN: 1660-4601 PMID: 30413061 DOI: 10.3390/ijerph15112496Document Number: 691308
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.