International Journal of Scientific Research and Engineering Development

Mega-infrastructure projects including highways, bridges, mass transit systems, and large energy facilities are inherently complex and operate under high levels of uncertainty related to cost, schedule, safety, and long-term structural performance. Conventional risk management and quality assurance practices depend heavily on periodic manual inspections, subjective expert judgment, and static forecasting models. These approaches are often inadequate for identifying rapidly evolving risks across the planning, construction, and operational phases of large-scale projects. With the emergence of Artificial Intelligence (AI), new opportunities have arisen to leverage data-driven analytics for real-time monitoring, predictive diagnostics, and automated quality verification. This study proposes an AI-based framework that integrates machine learning (ML), computer vision (CV), and sensor fusion techniques to enhance early risk detection and strengthen quality assurance in mega-infrastructure development. The framework utilizes historical datasets, site-generated sensor streams, and image-based inspections to forecast potential delays, detect material or structural anomalies, and evaluate construction performance with improved accuracy. Experimental findings and literature evidence demonstrate that AI-enabled systems can significantly reduce cost overruns, minimize schedule delays, and prevent safety hazards by providing continuous, automated oversight. Additionally, AI contributes to greater transparency, objective decision-making, and compliance with engineering standards. The paper concludes by discussing implementation challenges, such as data interoperability and workforce readiness, and provides recommendations for future research, including digital twins, real-time optimization models, and hybrid human AI inspection workflows. Overall, the adoption of AI has the potential to transform risk management and quality assurance practices in mega-infrastructure projects.