ABSTRACT
Many studies indicated that the COVID-19 pandemic had significant impacts on construction safety. However, the studies had differing views on whether the pandemic increased or decreased construction safety performance. Furthermore, past studies did not adopt a comparative time series approach to evaluate the impact of the pandemic on construction safety. Thus, this study explores the differences in the impact of the COVID-19 pandemic on construction safety in China and the United States. This study used SciNet to forecast the number of construction accidents without the pandemic. Subsequently, the forecast was compared with the actual number of accidents since the outbreak, and the analysis showed a reduced number of construction accidents during the pandemic. However, there were minimal changes and even a slight worsening of fatality rates. Moreover, the correlation analyses showed that the effect of the pandemic on construction safety was weak and lagging. Construction safety was significantly affected by the pandemic in China, and the impact is relatively rapid. In comparison, outbreaks did not have a major impact on construction safety in the U.S. in the early stage. Since the pandemic is still raging worldwide, this research helps governments or project stakeholders formulate more targeted and data-driven safety countermeasures to improve construction safety during the crisis. The study also helps nations respond to future pandemics and crises to prevent adverse effects on construction safety.
ABSTRACT
The outbreak of Coronavirus Disease 2019 (COVID-19) poses a great threat to the world. One mandatory and efficient measure to prevent the spread of COVID-19 on construction sites is to ensure safe distancing during workers' daily activities. However, manual monitoring of safe distancing during construction activities can be toilsome and inconsistent. This study proposes a computer vision-based smart monitoring system to automatically detect worker breaching safe distancing rules. Our proposed system consists of three main modules: (1) worker detection module using CenterNet; (2) proximity determination module using Homography; and (3) warning alert and data collection module. To evaluate the system, it was implemented in a construction site as a case study. This study has two key contributions: (1) it is demonstrated that monitoring of safe distancing can be automated using our approach; and (2) CenterNet, an anchorless detection model, outperforms current state-of-the-art approaches in the real-time detection of workers.