Given that roofing contractors in the construction industry have the highest fatality rate among specialty contractors, understanding the root cause of incidents among roofers is critical for improving safety outcomes. This study applied frequency analysis and decision tree data-mining techniques to analyze roofers’ fatal and non-fatal accident reports. The frequency analysis yielded insights into the leading cause of accidents, with fall to a lower level (83%) being the highest, followed by incidence sources relating to structures and surfaces (56%). The most common injuries experienced by roofing contractors were fractures (49%) and concussions (15%), especially for events occurring in residential buildings, maintenance and repair works, small projects (i.e., $50,000 or less), and on Mondays. According to the decision tree analysis, the most important factor for determining the nature of the injury is the nonfragile injured body part, followed by injury caused by coating works. The decision tree also produced decision rules that provide an easy interpretation of the underlying association between the factors leading to incidents. The decision tree models developed in this study can be used to predict the nature of potential injuries for strategically selecting the most effective injury-prevention strategies.
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Human Performance Best Practices in the Electrical Workplace
The construction industry is one of the most hazardous industries worldwide, and contact with electricity is a major cause of injury and death among construction workers. It is well known that unsafe acts resulting from human error are the primary cause for up to 80% of accidents across various industries, and some studies show that human performance tools may be functional in mitigating these incidents. Accordingly, this paper provides empirical evidence regarding the effectiveness of human performance tools as used to curb the frequency, probability, and severity of accidents. To achieve its objectives, this study first executed an extensive literature review to identify best practices related to human factors in mitigating the risk of electrical incidents. Then, the authors distributed an online questionnaire among various safety managers to determine the effectiveness of each practice in reducing the frequency, probability and severity of these incidents. The results and analysis show which human performance tools are recognized as most effective in helping safety managers mitigate human errors in electrical jobsites. The results of this study and paper will accelerate and transform current injury-prevention practices as well as overcome some of the barriers in the electrical workplace. An easy-to-use and effective set of human performance best practice solutions will be provided based on standards and industry experience.
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- Award ID(s):
- 1824238
- PAR ID:
- 10091752
- Date Published:
- Journal Name:
- 2019 IEEE IAS Electrical Safety Workshop (ESW)
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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