| description abstract | Serious injuries and fatalities (SIF) continue to be an enigmatic problem in the construction industry. Researchers have begun to explore new ways of preventing these incidents by developing and testing leading indicators, precursor analysis, and risk assessment supported by data analytics. These recent themes suggest a new paradigm in safety prediction. Aligned with this trajectory, the objectives of this study were to (1) identify a comprehensive list of potential predictors of SIFs in construction, including business factors, project characteristics, and crew demographics; (2) quantitatively prioritize potential predictors; and (3) develop a rank-ordered list of factors that could be tested for predictive validity and practically deployed on site. An expert panel of 22 industry practitioners generated 254 potential predictors of construction SIFs through structured brainstorming. To prioritize these potential predictors, the expert panel rated the extent to which each is measurable, predictive, simple, and actionable. Finally, a weighted sum method and a maximin approach was used to identify the predictors with the greatest practical potential for predicting SIF events, including brand-new concepts that have not yet been considered in the associated safety literature. Most previous research has focused on one specific form of safety prediction at a time (e.g., leading indicators), whereas this research effort is a first step toward a unified model with high feasibility and practical relevance. | |
