The purpose of this paper is to propose a novel approach to assess the safety climate level of different groups of workers in a construction company and predict safety performance and implement targeted improvement measures.
This paper utilizes the BP neural network and random forest algorithm to establish a weight learning mechanism for calculating the weights of safety climate evaluation criteria. The cloud model is employed to construct the decision matrix for different groups under the evaluation criteria. Meanwhile, the paper utilizes the COPRAS method to compare the safety climate of different groups.
The findings show the accuracy of the CM-COPRAS model is assessed by comparing it with the other methods. The three models are almost consistent in assessing the safety climate for working age groups, accident experience groups, and work type groups, with slight differences in the evaluation results for the education groups. The consistency of the computational results of the CM-COPRAS model with the results of the existing research, i.e., that the education level is positively proportional to the safety climate supports the reasonableness and validity of the CM-COPRAS model.
The paper proposes a hybrid MCDM method that integrates the Combined weighting method, Cloud model, and COPRAS for safety climate level evaluation in different construction worker groups. A case study is presented to demonstrate the applicability of the proposed method and to compare it with other methods to validate the effectiveness of the present method.