Construction worker safety prediction and active warning based on computer vision and the gray absolute decision analysis method

Although a great deal of worker safety risks analysis has been conducted, safety accidents continue to occur and recur at regular intervals. On construction sites, various activities have different levels of accident probability and severity, and existing methods are limited because they involve the use of the same weights to assess accident probability and accident severity. In addition, uncertainties that occur over time result in rapidly changing risks. The majority of existing approaches provide postevent warnings, so workers may not have sufficient time to prevent accidents once they are warned about the possibility. Thus, there is a need for mechanisms that can be used to assess safety risk regularity, predict worker risk levels, and provide proactive warnings based on the comprehensive consideration of the impacts of worker behaviors and environments. To address these issues, a safety prediction model was proposed for use, and an active warning mechanism was constructed for construction workers. The prediction model performs accident potential regularity analysis based on attribute-based safety risk analysis and precursor analysis. Further, it quantifies worker risk levels using decision matrix risk assessment (DMRA) and the gray absolute decision analysis (GADA) method. The model overcomes the limitation of using the same weights in DMRA to assess accident probability and severity. An active warning mechanism for construction workers was created to validate the efficacy of the safety prediction model, and the proposed safety prediction model is embedded in the mechanism. The mechanism mainly consists of three modules: (1) a data collection module that mainly includes expert knowledge and dynamic safety information from surveillance cameras; (2) a data analysis module that mainly uses the proposed safety prediction model to predict individual worker risk levels; and (3) an early warning module that displays the predicted risk levels, dynamically ranks risk indicators, and provides corresponding early warning measures. Finally, the feasibility and operability of the proposed active warning mechanism are demonstrated through a practical case study.