 Energy-based safety risk assessment: does magnitude and intensity of energy predict injury severity?Matthew R. Hallowell, Dillon Alexander and John A. Gambatese Construction Management and Economics, 2017, vol. 35, issue 1-2, 64-77 Abstract: Although the quantity and quality of safety risk data have improved in recent years, available data do not link directly to natural principles and are, therefore, limited in their application and scientific extension. The present study aims to test the hypothesis that the quantity and intensity of energy observable prior to an incident predicts the severity of the incident. The hypothesis is built upon the theory that energy is translated to an injury through uncontrolled release of the energy, transfer of the energy to the human body and the vulnerability of the body and associated protective equipment. To test the hypothesis, a multi-phase experiment was conducted. First, over 500 injury reports were gathered from national databases and private companies. For each report, the leading information describing the work operations and environment and the lagging information describing the injury were extracted, separated and isolated. Second, the magnitude of the energy was estimated using only leading information. Once energy magnitude was quantified, the distribution was compared across injury severity levels using analysis of variance tests. As hypothesized, energy magnitude is a strong predictor of injury severity. Although computationally intensive, energy intensity, defined as the magnitude of energy divided by the area of contact between an object and the human body, showed strong predictive validity. This research indicates that energy-based safety risk analysis has predictive validity and is a promising line of scientific inquiry with the potential to increase our understanding of the natural phenomena that contribute to injuries. Date: 2017 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:conmgt:v:35:y:2017:i:1-2:p:64-77 Ordering information: This journal article can be ordered from DOI: 10.1080/01446193.2016.1274418 Access Statistics for this article Construction Management and Economics is currently edited by Will Hughes More articles in Construction Management and Economics from Taylor & Francis Journals |
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