AI-DRIVEN SAFETY COMMUNICATION, RISK MANAGEMENT, AND PRODUCTIVITY OPTIMIZATION IN MEGA CONSTRUCTION PROJECTS OF PAKISTAN

Abstract

management systems capable of reducing workplace accidents and improving project performance. This study examined the effect of AI-Driven Safety Communication on Productivity Optimization in Pakistan's mega construction projects, with Risk Management serving as the mediating variable. A quantitative, cross-sectional research design was adopted, and data were collected from 400 project managers, site engineers, safety officers, contractors, and construction professionals working on major infrastructure projects across Pakistan. The proposed research model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that AI-driven safety communication significantly enhanced risk management practices and directly improved productivity optimization. Furthermore, risk management significantly mediated the relationship between AI-driven safety communication and productivity optimization, indicating that intelligent safety communication improves construction performance by strengthening hazard identification, risk mitigation, and operational efficiency. The study concludes that integrating artificial intelligence into construction safety communication provides an effective strategy for improving occupational safety, minimizing project delays, enhancing workforce efficiency, and optimizing productivity in Pakistan's mega construction industry. The findings offer valuable implications for construction managers, policymakers, and industry practitioners seeking to accelerate digital transformation and sustainable infrastructure development through AI-enabled safety management systems.