Rabi, MusabAlanazi, Fars2025-08-182025https://hdl.handle.net/20.500.14154/76171This study investigated the role of Artificial Intelligence (AI) in hazard risk mitigation and worker protection within green building projects in the Kingdom of Saudi Arabia, a sector that gained momentum under the country’s Vision 2030 sustainability goals. Green construction projects introduced unique safety risks due to the use of renewable energy systems, smart materials, and modular designs, which traditional safety protocols often failed to address effectively. The research examined whether AI-based technologies could offer proactive and adaptive safety management strategies in this evolving context. The study focused on five AI-enabled components: (1) AI- powered hazard detection, (2) AI-driven risk prediction and alert systems, (3) wearable smart safety devices, (4) AI-based worker training programs, and (5) AI-supported regulatory compliance tools. Six core research questions guided the analysis, each exploring how these technologies contributed to risk mitigation and safety enhancement. A quantitative descriptive- analytical method was employed using a structured questionnaire distributed to 350 employees working in green construction projects across Saudi Arabia. Data were analyzed using SPSS for descriptive statistics, t-tests, and preliminary regressions, while Structural Equation Modeling (SEM) via AMOS was used for model validation. The results showed that all AI components had a statistically significant positive effect on improving occupational safety, with wearable AI devices demonstrating the highest influence (R² = 0.297), followed by AI-based training systems (R² = 0.222) and risk prediction models (R² = 0.246). Model fit indices (CFI = 0.954, RMSEA = 0.041) indicated strong alignment between the conceptual framework and empirical data. Although the study focused on green construction, the proposed AI-driven safety strategies such as real-time risk prediction, biometric monitoring, and automated compliance reporting were found to be highly transferable to conventional construction environments. Their implementation in traditional projects could similarly improve safety outcomes, especially in high-risk or large-scale developments. The study concluded by emphasizing the value of integrating AI technologies into construction safety practices to enhance predictive risk management, regulatory alignment, and incident prevention. It recommended broader adoption of AI tools, particularly wearable technologies and predictive models, across both green and traditional construction sectors. The study also called for institutional support through training and policy reforms to accelerate AI adoption in line with Saudi Arabia’s sustainable development agenda.129enKeywords: Artificial IntelligenceGreen BuildingConstruction SafetyHazard MitigationWorker ProtectionPredictive AnalyticsWearable DevicesSmart ConstructionRegulatory ComplianceThesis