Neural Network Approach for Safety Climate Prediction

Abstract

Safety climate is a snapshot of the safety culture of any organization and it is dynamic in nature. It reflects the employees’ perceptions and attitude towards the existing safety practices in construction projects. The main goal of this paper is to evaluate and differentiate construction projects based on their safety climate and to determine the significant constructs of a safety climate. This paper develops a model to predict safety climate in a construction project based on an artificial neural network (ANN). The important constructs for a safety climate have been determined through literature review. The constructs of safety climate are used as inputs and the safety climate of the project is used as an output for the ANN algorithm. This study collected a total of 250 responses through a questionnaire survey across the country. A three-layer feed-forward back-propagation neural network (10-18-1) has been utilized for the analysis. The developed model predicts the safety climate of a construction project reasonably well. Based on sensitivity analysis, commitment and supervisory environment are proposed as the most significant out of 10 constructs of safety climate. Moreover, projects are ranked with respect to their safety climate using this model and outlier projects could easily be identified through the normal probability technique. Thus, the model should prove to be helpful to clients and contractors to evaluate and predict safety climate, thereby managing safety effectively at construction projects.