Assessing the impact of eye-tracking features on construction hazard identification skills using virtual reality and machine learning

Purpose Identifying hazards in construction sites is essential for ensuring safety. This study aims to propose a machine learning-based approach to identify the most effective set of eye-tracking features for assessing hazard identification skills on construction sites. By determining which features best contribute to hazard identification across various hazard types, the approach seeks to improve workplace safety. Design/methodology/approach Four hazard types and 11 eye-tracking features were identified, and 18 strategies were developed to evaluate hazard identification skills. Support vector machine (SVM) and artificial neural network (ANN) models were applied to assess these strategies. Virtual reality simulations were used to gather eye-tracking data and evaluate hazard identification. Findings The SVM and ANN models effectively identified the most impactful set of eye-tracking features for each hazard type individually and all hazard types together. The results indicated that the proposed approach can accurately assess hazard identification skills in construction, offering a data-driven means of reducing injuries. Research limitations/implications This study advances safety research by identifying the most relevant eye-tracking features linked to different types of construction hazards using a machine learning approach. The results can help develop personalized VR-based training and evaluation systems that improve workers’ visual attention for hazard identification, leading to safer construction practices. Originality/value While previous studies have explored eye-tracking features for assessing hazard identification skills, the effectiveness of certain features has been unclear. This study clarifies which set of eye-tracking features is most effective for assessing hazard identification skills based on specific hazard types, contributing to enhanced workplace safety.