Next Article in Journal
The Associations between Dietary Patterns and Short Sleep Duration in Polish Adults (LifeStyle Study)
Previous Article in Journal
Teamwork and Safety Climate in Homecare: A Mixed Method Study
Previous Article in Special Issue
PARS: Using Augmented 360-Degree Panoramas of Reality for Construction Safety Training
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Int. J. Environ. Res. Public Health 2018, 15(11), 2496; https://doi.org/10.3390/ijerph15112496

A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work

1
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
2
School of Civil Engineering and Built Environment, Queensland University of Technology, 2 George St., Brisbane, QLD 4001, Australia
*
Author to whom correspondence should be addressed.
Received: 1 September 2018 / Revised: 26 October 2018 / Accepted: 6 November 2018 / Published: 8 November 2018
(This article belongs to the Special Issue Improving Safety, Health, and Wellbeing in Construction)
Full-Text   |   PDF [1765 KB, uploaded 8 November 2018]   |  

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. View Full-Text
Keywords: electrical and mechanical (E&M) works; accident analysis; Bayesian Networks; safety management electrical and mechanical (E&M) works; accident analysis; Bayesian Networks; safety management
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top