An ISM-MICMAC-based study for identification and classification of preventable safety risk mitigation factors in mass...

Construction operation is among the most high-risk sectors in terms of work-related accident, making it highly challenging to surveil the safety of such projects. In construction projects, failure to observe safety represents a leading cause of fatal accidents, not to mention the losses incurred by such accidents to national assets of the country. Accordingly, recent decades have witnessed the emergence of modern techniques for improving the occupational safety of construction projects. The main purpose of the present research is to identify and classify different preventable risk mitigation factors in mass housing projects following a building information modeling (BIM) approach. The research methodology included interviews with relevant experts and elites followed by analysis of the data on the 12 identified-as-significant variables for mitigating the preventable risk factors in mass house construction projects by means of the inferential - structural modeling (ISM) in MICMAC software. In order to explore the relationships among and succession of different criteria and further classify them at different levels, ISM was implemented, with the MICMAC software used to analyze the direct and indirect influences, develop influence/dependence maps, and judge about the role of each criterion. Findings of the present research showed that the mutual relations (H3), the reward system (H6), the reporting system (H7), and the supervisors' supervision (H8) are autonomous variables and hence impose the smallest contributions to the system. Accordingly, they can be eliminated from the model though their effects may not be completely ignored. On the other hand, the employees' empowering (H4), the safety management system (H5), the teamwork (H9), the self-efficiency (H10), and the knowledge and awareness (H11) were identified as the linkage variables that fill in the gap between the safety and occupational accident reduction in the mass house construction projects. Further, the continuous improvement (H2) and the safe behavior (H12) were identified as dependent variables, implying that they exhibit the weakest influence coupled with highest dependence on any change in the conditions of the system. Last but not the least, the management commitment (H1) was identified as the only dependent variable which deserves lots of attention. This information can be helpful to safety decision-makers, end users, research organizations, and academic institutes who work to reduce the preventable risk factors in mass house construction projects.