Real-Time Safety Warning System for Lifting Operations in Construction Sites

Abstract

A construction site is an open and dynamic space. Construction accidents have been the top-ranked occupational accidents among all industries around the world. Due to the limited quality and quantity of occupational safety supervisors on construction sites, it is difficult to control or prevent risks in real-time. Therefore, a real-time safety warning system based on a deep learning technique (DL) is developed for lifting operations in building construction, called a portable lightweight lifting safety control station (PLSCS). Two modes can be switched manually by the supervisor. If the mode is switched to lifting control mode, PLSCS helps to ensure that nobody is in the hazardous area during lifting operations. The advantages and features of this system are as follows: (1) it warns of the potential safety hazards automatically during the operations; (2) it reduces the workload of occupational safety supervisors; (3) the system is self-powered and easy to carry and deploy. The system was tested and verified in the actual construction site. The results show that the system is useful for improving the safety of lifting operations.

Keywords

• machine learning
• pattern recognition
• construction safety
• lifting operation
• virtual fence