Influencing Factors and Prewarning of Unsafe Status of Construction Workers Based on BP Neural Network

Abstract

Scholars have paid considerable attention to the factors that affect the safety states of construction workers. However, only a few studies have focused on the safety assessment and security alerts of individual workers. In this study, the term ‘frequency statistics’ refers to the factors considered by domestic and foreign experts and scholars. The statistical results were combined with the interpretation of these factors to determine 22 factors that negatively influence the safety status of construction workers, which were used as the research object. The initial weight of the research results was integrated into the BackPropagation neural network, using the improved analytic hierarchy process to establish an early warning model for the unsafe status of construction workers. The mean squared error meets the requirements of the model and the prediction accuracy meets the requirements of the sample. The model can effectively provide an early warning and correct the initial weighting of the results. The early warning model was then applied to a project that involved the construction of a primary school in Suzhou. The follow-up results show that the safety status of the workers significantly improved. These results show that the early warning model was successfully used in the safety assessment to provide security alerts to individual workers. The data obtained by comprehensively considering both workers and experts are universal, unlike those obtained by considering only one of these two groups. Among the indicators, safety awareness, protection measures, and team cohesion most strongly negatively affected the safety statuses of the construction workers. The results of the early warning model combined with the sensitivity analysis are targeted and applicable in the practice of safety monitoring.