Workers' unsafe behaviors are one of the main causes for construction accidents. Fully understanding the causes of unsafe behaviors on site will help to prevent them, thus reducing construction accidents. The accurate and timely identification of site workers' unsafe behaviors can aid in the analysis of the causes of unsafe behaviors and prevention of construction accidents. However, the traditional methods (e.g., site observation) of behavior data collection on site is neither efficient nor comprehensive. This paper develops a skeleton-based real-time identification method by combining image-based technologies, construction safety knowledge, and ergonomic theory. The proposed method recognizes unsafe behaviors by simplifying dynamic motions into static postures, which can be described by a few parameters. Three basic modules are involved: an unsafe behavior database, real-time data collection module, and behavior judgement module. A laboratory test demonstrated the feasibility, efficiency, and accuracy of the method. The method has the potential to improve construction safety management by providing comprehensive data for the systematic identification of the causes to workers' unsafe behaviors, such as inappropriate management methods, measures or decisions, personal characteristics, work space and time, as well as warning workers identified as behaving unsafely, if necessary. Thus, this paper contributes to practice and the body of knowledge of construction safety management, as well as research and practice in image-based behavior recognition.
|Journal||Journal of Construction Engineering and Management|
|Publication status||Published - 1 Jun 2018|