Construction accidents pose significant risks to workers and the public, affecting industry productivity and reputation. While several reviews have discussed risk assessment methods, recent advancements in artificial intelligence (AI), big data analytics, and real-time decision support systems have created a need for an updated synthesis of the quantitative methodologies applied in construction safety. This study systematically reviews the literature from the past decade, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thorough search identified studies utilizing statistical analysis, mathematical modeling, simulation, and artificial intelligence (AI). These methods were categorized and analyzed based on their effectiveness and limitations. Statistical approaches, such as correlation analysis, examined relationships between variables, while mathematical models, like factor analysis, quantified risk factors. Simulation methods, such as Monte Carlo simulations, explored risk dynamics and AI techniques, including machine learning, enhanced predictive modeling, and decision making in construction safety. This review highlighted the strengths of handling large datasets and improving accuracy, but also noted challenges like data quality and methodological limitations. Future research directions are suggested to address these gaps. This study contributes to construction safety management by offering an overview of best practices and opportunities for advancing quantitative risk assessment methodologies.

In the construction industry, workers are exposed to hazardous emissions, such as dust, and various diseases, such as chronic obstructive pulmonary disease (COPD), which affect workers. There is, however, a lack of studies that evaluate the dust that workers are exposed to, taking into account different factors of dust. Therefore, this study aims to estimate the amount of dust construction workers are exposed to by considering different factors of dust emission and to assess the health and economic impact of dust emissions. This study is conducted in three steps: (1) scope definition, (2) definition of worker dust exposure, and (3) health impact assessment. As a result, dust concentrations from the energy used, the atmosphere, and during construction activities were 1.01 × 10⁻⁵ µg/m³, 37.50 µg/m³, and 1.33 × 10⁴ µg/m³ respectively. Earthwork had the highest dust concentration of 3.85 × 10³ µg/m³. The total added number of disability-adjusted life years (DALY) of workers was 0.0542a with an economic cost of $13,691.00. The contributions of this study are the accurate assessment of the amount of dust workers are exposed to and the development of policies to help compensate construction workers suffering from dust emission-related diseases.