Algorithm for quantitative analysis of close call events and personalized feedback in construction safety

Abstract In many of the developed countries about 15–25% of all fatal construction workplace accidents relate to a too close proximity of pedestrian workers to construction equipment or hazardous materials. Extracting knowledge from data on near hits (aka. close calls) might warrant better understanding on the root causes that lead to such incidents and eliminate them early in the risk mitigation process. While a close call is a subtle event where workers are in close proximity to a hazard, its frequency depends–among other factors–on poor site layout, a worker's willingness to take risks, limited safety education, and pure coincidence. For these reasons, pioneering organizations have recognized the potential of gathering and analyzing leading indicator data on close calls. However, mostly manual approaches are infrequently performed, subjective due to situational assessment, imprecise in level of detail, and importantly, reactive or inconsistent in effective or timely follow-ups by management. While existing predictive analytics research targets change at strategic levels in the hierarchy of organizations, personalized feedback to strengthen an individual worker's hazard recognition and avoidance skill set is yet missing. This study tackles the bottom of Heinrich's safety pyramid by providing an in-depth quantitative analysis of close calls. Modern positioning technology records trajectory data, whereas computational algorithms automatically generate previously unavailable details to close call events. The derived information is embedded in simplified geometric information models that users on a construction site can retrieve, easily understand, and adapt in existing preventative hazard recognition and control processes. Results from scientific and field experiments demonstrate that the developed system works successfully under the constraints of currently available positioning technology.