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Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

Author

Listed:
  • Pouya Gholizadeh

    (Sid and Reva Dewberry Department of Civil, Environmental and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA)

  • Ikechukwu S. Onuchukwu

    (Sid and Reva Dewberry Department of Civil, Environmental and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA)

  • Behzad Esmaeili

    (Sid and Reva Dewberry Department of Civil, Environmental and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA)

Abstract

This study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among electrical contracting enterprises, given an accident occurred. Accident reports were collected from the Occupational Safety and Health Administration’s fatality and catastrophe database. To ensure the reliability of the data, the team manually codified more than 600 incidents through a comprehensive content analysis using injury-classification standards. Inclusive of both fatal and non-fatal injuries, the results showed that most accidents happened in nonresidential buildings , new construction , and small projects (i.e., $50,000 or less). The main source of injuries manifested in parts and materials (46%), followed by tools, instruments, and equipment (19%), and structure and surfaces (16%). The most frequent types of injuries were fractures (31%), electrocutions (27%), and electrical burns (14%); the main injured body parts were upper extremities (25%), head (23%), and body system (18%). Among non-fatal cases, falls (37%), exposure to electricity (36%), and contact with objects (19%) caused most injuries; among fatal cases, exposure to electricity was the leading cause of death (50%), followed by falls (28%) and contact with objects (19%). The analysis also investigated the impact of several accident factors on the degree of injuries and found significant effects from such factors such as project type , source of injury , cause of injury , injured part of body , nature of injury , and event type . In other words, the statistical probability of a fatal accident—given an accident occurrence—changes significantly based on the degree of these factors. The results of this study, as depicted in the proposed decision tree model, revealed that the most important factor for predicting the nature of injury (electrical or non-electrical) is: whether the source of injury is parts and materials ; followed by whether the source of injury is tools, instruments, and equipment . In other words, in predicting (with a 94.31% accuracy) the nature of injury as electrical or non-electrical, whether the source of injury is parts and materials and whether the source of injury is tools, instruments, and equipment are very important. Seven decision rules were derived from the proposed decision tree model. Beyond these outcomes, the described methodology contributes to the accident-analysis body of knowledge by providing a framework for codifying data from accident reports to facilitate future analysis and modeling attempts to subsequently mitigate more injuries in other fields.

Suggested Citation

  • Pouya Gholizadeh & Ikechukwu S. Onuchukwu & Behzad Esmaeili, 2021. "Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013," IJERPH, MDPI, vol. 18(10), pages 1-24, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5126-:d:553030
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    References listed on IDEAS

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    1. Marhavilas, P.K. & Koulouriotis, D.E., 2012. "A combined usage of stochastic and quantitative risk assessment methods in the worksites: Application on an electric power provider," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 36-46.
    2. Qingwei Xu & Kaili Xu, 2021. "Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    3. Rivas, T. & Paz, M. & Martín, J.E. & Matías, J.M. & García, J.F. & Taboada, J., 2011. "Explaining and predicting workplace accidents using data-mining techniques," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 739-747.
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