Electronic performance monitoring framework to quantify unhealthy and unsafe on-site behaviours

• Published in: Procedia computer science 2024, Vol.232, p.274-283
• Main Authors: Calvetti, Diego, Mêda, Pedro, de Sousa, Hipólito
• Format: Article
• Language: English
• Subjects: Accident Triangle; Behaviour-Based Safety; Construction 4.0; H&S; Worker 4.0
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2024.01.027

Health and Safety (H&S) should be a significant concern in construction projects. The quantification of the work injury accidents started with work A. W. Heinrich (1931), passing per Frank E. Bird Jr. (1969) to a study by ConocoPhillips Marine (2003). These theories, also known as the Accident triangle, Heinrich's triangle, or Bird's triangle, are a base for industrial accident prevention applied in practice by the Dupont STOPTM methodology. Over this methodology, the number of unhealthy and unsafe behaviours is classified and accounted for to diagnose the potential workers' recordable injuries, lost workdays, and fatalities. The At-risk behaviours and the Near Misses are the most challenging safety measurement approach as they depend on the human-observation notes usually conducted in a safety audit. Electronic Performance Monitoring (EPM) concerns the use of innovative hardware and software to assess on-site safety and productivity. The electronic monitoring of construction workers can contribute to evaluating unsafe behaviours. At-risk behaviours could be measured based on location, trajectory, and motions. Also, with the monitoring of the equipment and vehicles, it is possible to measure Near Misses. The Information Technologies integration allows for collecting information about Recordable Injuries, Lost workdays, and Fatalities. This work discusses and presents a framework connecting EPM deployment to data acquisition of unhealthy and unsafe on-site behaviours. The contributions in the theoretical and practical fields concern the opportunities of using new technological assumptions to increase data acquisition and improve the accuracy and effectiveness of quantitative safety analysis.