Evaluating Machine Learning and AHP Tools for the Pre-Qualification of Construction Contractors Based on Occupational Health and Safety Criteria

Workers in the construction industry remain exposed to different accidents and hazardous environments. Yet, occupational health and safety (OHS) factors are the least addressed in contractors' pre-qualification. As such, this paper presents two decision-making tools to evaluate contractors' performance based on OHS-related criteria. Particularly, it presents the status of OHS-related orders in Alberta, Canada, and the most prominent criteria to be integrated in pre-qualification. Based on the identified criteria, a machine learning-based clustering model and an AHP model were formulated to assist in the pre-qualification process. Data related to construction OHS performance was obtained from the Workers Compensation Board between 2016 and 2020 for more than 1,500 contractors with more than 7,000 OHS-related orders issued. Orders related to (1) falls, (2) hazard assessment, (3) safeguards, and (4) entrances, walkways, stairways, and ladders were the most common ones. Both developed approaches showed the potential in supporting the contractor's pre-qualification process.