Immersive virtual reality as an empirical research tool: exploring the capability of a machine learning model for predicting construction workers’ safety behaviour

In recent years, research has found that people have stable predispositions to engage in certain behavioural patterns to work safely or unsafely, which vary among individuals as a function of their personality features. In this regard, an innovative machine learning model has been recently developed to predict workers’ behavioural tendency based on personality factors. This paper presents an empirical evaluation of the model’s prediction performance (i.e. the degree to which the model can generate similar results compared to reality) to address the issue of the model’s usability before it is implemented in real situations. As virtual reality allows a good grip on fidelity resembling real-world situations, it can stimulate more natural behaviour responses from participants to increase ecological validity of experimental results. Thus, we implemented a virtual reality experimentation environment to assess workers’ safety behaviour. The model’s prediction capability was then evaluated by comparing the model prediction results and workers’ safety behaviour as assessed in virtual reality. The comparison results showed that the model predictions on two dimensions of workers’ safety behaviour (i.e. task and contextual performance) were in good agreement with the virtual reality experimental results, with Spearman correlation coefficients of 79.7% and 87.8%, respectively. The machine learning model thus proved to have good prediction capability, which allows the model to help identify vulnerable workers who are prone to undertake unsafe behaviours. The findings also suggest that virtual reality is a promising method for measuring workers’ safety behaviour as it can provide a realistic and safe environment for experimentation.