Evaluation of construction projects based on the safe work behavior of co-employees through a neural network model

Abstract

The safety climate of an organization influences employees’ safe work behavior. It is essential for project management to be aware of any unsafe work behavior of their workers on a project and to take any necessary remedial measures to reduce the likelihood of accidents. This study aims to predict and evaluate the work behavior of employees on construction projects using the constructs of the safety climate. Because of the prevailing nonlinear and complex interrelationships among these determinants, an artificial neural network (ANN) is employed to develop the model. Ten important safety climate features are used as inputs, and co-employees’ safe work behavior is taken as the output. A total of 240 responses from different construction projects across India were collected through a questionnaire in a two-stage process to train, test, and validate the model. A three-layer feedforward backpropagation neural network architecture 10-17-1 was found to be a suitable model. Through this model, outlier projects have been determined based on the project efficiency score and the Anderson-Darling (AD) statistics. The study advocates the practice of safe work behavior as reported by co-employees, rather than workers’ own reported safe work behavior, as the output variable. The significant constructs of safe work behavior are presented based on the sensitivity analysis. This model will be helpful to evaluate, predict, and monitor the safety performance of construction projects.