Risk modelling techniques for occupational safety and health management in construction: A case study of physical industrial assets maintenance

The construction industry is considered one of the most significant contributors to the buoyancy of most economies globally. Additionally, typical construction operations are both capital and labour intensive due to the application of heavy earth-moving assets. However, statistics related to occupational safety and health (OSH) within this industry have not been very encouraging, and a significant proportion of the reported OSH-related incidents can be attributed to major maintenance activities. Studies have also shown that a significant number of work-related injuries and illnesses are associated with the maintenance of large earth-moving assets such as those used on construction sites. It would therefore be logical to prioritise such high-hazard activities through real-time knowledge-sharing initiatives between owners of similarly-configured physical industrial assets (PIAs) within the construction industry. The concept of an industrial Internet of Things or Industry 4.0 has been widely advocated to possess the capability to adequately handle such real-time knowledge-sharing capabilities through its ability to enhance consistency and repeatability of critical operations, including maintenance. However, one of the major limiters to its full deployment across industries is the lack of a robust approach for managing the big data that will be generated as a result of routine condition monitoring and fault-diagnosis activities. The prioritisation of assets based on their criticalities through well-established approaches such as failure modes effects and criticality analysis (FMECA) have served as a conventional means of rationalising data requirements, but these approaches are highly subjective, and outcomes could be skewed based on the perception of the lead individual. Therefore, this study applied a multi-criteria FMECA that is based on the grey-complex proportional assessment (COPRAS-G) method to assess the criticalities of the failure modes (FMs) so as to overcome drawbacks of conventional risk priority numbers (RPN) methods and improve FMs ranking orders. The analysis shows that the most critical FMs are shell wear due to excessive abrasion and atmospheric moisture. Crack/fracture deformation failure of the motor coupling-shaft of the main drive system due to excessive loading/fatigue/misalignment/vibration is the least critical FM.