Automated Collection, Identification, Localization, and Analysis of Worker-Related Proximity Hazard Events in Heavy Construction Equipment Operation

Abstract

The construction industry measures worker safety performance through lagging indicators such as counting numbers of illnesses, injuries, and fatalities. Active leading indicators, such as capturing hazardous proximity situations between ground workers and heavy construction equipment, provide an additional metric for construction site personnel safety performance without incurring worker accidents. This article presents an algorithm for recording, identifying, and analyzing interactive hazardous proximity situations between ground workers and heavy construction equipment. Spatialtemporal GPS data of ground worker and heavy equipment movements are analyzed to automatically measure the frequency and duration of identified hazardous proximity situations. Individual periodic ground worker and equipment operator safety performance with regards to exposure to hazardous proximity situations is reported in detail. The results are integrated with previous research on blind spots and other safety deficiencies of the equipment. By measuring and analyzing leading indicator data of ground workers and heavy equipment, safety managers can identify hazardous situations that may otherwise lead to incidents. Knowledge generated about hazardous proximity issues are disseminated to construction personnel through enhanced safety training and education. Mitigation measures can also be taken for safer construction equipment operation.