A Big Data Based Safety Risk Classification Model of Construction Workers for Construction Site Safety ManagementAuthor/Presenter: Chang, Hsien Kuan; Yu, Wen Der; Hsiao, Wen Ta; Bulgakov, Alexey
The construction industry has been blamed for her high accident occurrence rate, compared with any other industries. The construction safety has drawn major attention of the government world-wide. The application of Artificial Intelligence (AI), especially in computer image recognition, has enjoyed successful applications for automatic monitoring of construction safety in the past decade. However, the probability to cause construction accidents may differ due to different personal attributes such as age, professional experience, expertise, etc. Such attributes are not distinguishable by existing AI techniques. This study utilizes the big data accumulated in a national database—the Construction Industry Accident Knowledge Platform (CIAKP)—developed by the Institute of Labor, Occupational Safety and Health (ILOSH), Ministry of Labor, Taiwan, to determine the safety risk levels of construction workers based on his/her attributes, e.g., age, professional experience, expertise, project type, cost of project, size of company, etc. By integrating with other intelligent computer visualization techniques, the construction personnel safety risk classification method proposed in this research can pay attention to the construction worker’s according to different risk levels. With the application framework of the construction safety automatic monitoring system proposed by this research, it will assist site managers to focus their attention on workers who may encounter real-time risks. As a result, it will help reduce the occurrence of construction accidents on job sites.