关键词: 灰狼优化算法(GWO), 随机森林(RF), 建筑施工安全事故, 预测模型, 关联规则

Abstract:

In order to reduce the occurrence of building construction safety accidents, association rules were used to reveal the mechanism of accident association, and the optimized RF was fused to predict the occurrence of accidents. First, the causal factors of 388 case reports of construction safety accidents were extracted using 24Model as the theoretical basis. Then, Apriori algorithm was used to excavate the interrelated action paths between the accident causal factors. Finally, hyper-parameters of RF were optimized using GWO algorithm, and the GWO-RF prediction model of construction safety accidents was constructed. And the accident causal factors were the characteristic importance ranking was carried out. The results show that: unsafe behavior, safety ability of organization members, safety management system and safety culture elements constitute a combination of strong correlation conditions. GWO can effectively optimize the hyper-parameters of RF, and prediction accuracy of the optimized GWO-RF model is as high as 93.2%. The characteristic importance ranking shows that: safety education and training have the greatest influence on the prediction of construction safety accidents, with a weighting of 10.5% and a weighting of 10.5%. The importance ranking of features shows that: safety education and training has the greatest influence on the prediction of building construction safety accidents, with a weight of 10.5%. And safety integration management, safety production rules and regulations, and safety production responsibility system are the important factors affecting the prediction of building construction safety accidents, with weights of 7.5%, 7%, and 6%, in that order.

Key words: gray wolf optimization (GWO), random forest (RF), construction safety accidents, prediction model, association rules