Random Forest Analysis of Occupational Accident Reports among Roofing Contractors
Publication: Construction Research Congress 2022
ABSTRACT
One of the trades most susceptible to occupational hazards in the construction industry is roofing contractors, whose projects inherently involve exposure to falls from height. In response, this study used a random forest data mining technique to analyze the impacts of accidents’ contributing factors on roofers’ injuries. The analysis examined over 600 incidents obtained from the Occupational Safety and Health Administration’s (OSHA’s) database of fatal and nonfatal accident reports. Some of the contributing factors considered include source of injury, cause of injury, project cost, project end use, project type, injured body part, and day of injury. The results of validated random forest model revealed that the most important factor for predicting the nature of injury is injured body part followed by source of injury. The presented results can be used by managers, policymakers, and safety professionals to reduce the frequency and severity of incidents.
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Construction Research Congress 2022
Pages: 561 - 570
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Published online: Mar 7, 2022
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- Ikechukwu Sylvester Onuchukwu, Behzad Esmaeili, Sébastien Hélie, Support Vector Machine Analysis of Construction Workers’ Automatic Behavior and Visual Attention, Construction Research Congress 2024, 10.1061/9780784485293.086, (865-874), (2024).