Abstract
Occupational safety hazards and risk management in construction industries are major worldwide concerns due to the unique dynamic nature of their working environment. Safety-related documentations such as injury reports, physical demand analyses (PDAs), and standard operating procedures (SOPs) contain valuable information to support the risk prevention algorithms. This paper presents a blended study on occupational risk assessment, in perspective with both attribute-based and data mining approaches. The study explored a strategy of attribute-based identification and degree of risk classification by applying distinctive quantitative analysis on the level of injuries. Data mining analysis further exposed the causality links of potentially hazardous activities and injuries. A conceptual digital transformation framework is proposed to assess digital mapping and future predictive measurements for up-to-date risk factor evaluation. The blended analysis is expected to help the construction industry identify relationships on causes of safety hazards, key safety attributes, as well as their corresponding risk controls.
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