Occupational Hazards Evaluation in Residential Construction Projects: Novel Sorting Methods Based on q-Rung Orthopair Fuzzy Choquet Integral

Abstract

Despite multiple efforts to improve safety in construction, insufficient hazard identification remains a significant concern. Failure to address these hazards can lead to severe safety incidents that harm workers and a firm’s reputation. This problem is especially prevalent in construction small and medium enterprises (SMEs) due to their limited resources, reliance on manual labor, and lack of technical expertise regarding safety concerns. Thus, this study addresses the gap by offering a computational framework that provides a comprehensive evaluation of occupational hazards, considering multiple factors, such as severity, frequency of occurrence, and the likelihood of detection, which are risk dimensions of failure mode effect analysis (FMEA). Notwithstanding the FMEA-based evaluation methods for safety evaluation in the construction sector, drawbacks attributed to the interdependencies of the risk dimensions and the handling of judgment uncertainties are evident. In this work, an extension of the FMEA is developed that assigns an occupational hazard to a risk category under a holistic framework that better addresses the current limitations of the FMEA. In particular, the study offers a two-fold contribution: (1) putting forward the proposed Choquet–FMEA–Sort methods under a q-rung orthopair fuzzy set (q-ROFS) environment and (2) demonstrating an actual case study in the Philippines that comprehensively evaluates occupational hazards in construction SMEs. Results of a demonstrative case of residential construction projects show that out of the 26 identified occupational hazards, 18 pose a high risk to workers, while the remaining eight pose a moderate risk. High-risk occupational hazards require more attention for mitigation efforts, especially in residential construction SMEs facing resource constraints. The computational framework offered in this work aids decision-makers in identifying high-risk occupational hazards in a more systematic approach. The robustness and stability of the proposed methods were tested using layers of sensitivity and comparative analyses.