Attribute-based safety risk assessment. I: Analysis at the fundamental level

Quantifying safety risks and performing comparative analyses is an emerging research field. Unfortunately, current risk assessment strategies are problematic because they require every new infrastructure feature and construction method to be individually evaluated using laborious research processes. To enhance the current construction safety management methods, an attribute-based risk identification and analysis method is presented that helps designers and preconstruction planners identify and model safety risk independently of specific activities or building components. The inspiration for this new risk management technique was derived from the Human Genome Project, which implies that while there are billions of people around the world, their vulnerability towards specific kinds of disease can be explained by a limited number of genes. This concept for attribute-based risk assessment was adapted by testing the hypothesis that injuries and fatalities in construction result from a finite number of hazardous attributes of the work environment. The research reported in this paper includes content of large, representative, and reliable national database of 1,812 injury reports of struck-by incidents. The combined manual and automated content analysis procedure was created for this specific application to overcome the challenges associated with a large and complex dataset. In total, 22 safety risk attributes that lead to struck-by incidents were identified and their relative risks were quantified. The results can be used by practitioners to integrate robust safety risk data into technological models and management systems, thereby facilitating proactive safety management. The contribution of fundamental and empirical attribute-based safety risk data fills a knowledge gap that has long prevented the integration of empirical safety data with technological models. It is expected that this new knowledge will serve as a catalyst for proactive safety management in emerging technologies.