Predictive analytics for construction project risk management: Leveraging AI for proactive mitigation strategies

Jayakannan, Sai Manoj (2025) Predictive analytics for construction project risk management: Leveraging AI for proactive mitigation strategies. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 2204-2209. ISSN 2582-8266

The construction industry confronts significant challenges related to risk management, with traditional approaches often failing to prevent costly delays, budget overruns, and safety incidents. Artificial intelligence and machine learning technologies present transformative opportunities for shifting from reactive to proactive risk management in construction projects. Predictive analytics leverages historical data patterns, real-time monitoring, and sophisticated algorithms to forecast potential issues before they materialize and impact project performance. This comprehensive examination explores the current state of construction risk management, the fundamental applications of AI-driven predictive analytics, implementation frameworks, and empirical evidence from industry applications. The integration of predictive analytics with Building Information Modeling and Internet of Things technologies creates powerful ecosystems for comprehensive risk surveillance. Case studies from pioneering organizations demonstrate significant improvements in project outcomes, including reductions in recordable incidents, cost overruns, and schedule delays. Despite implementation challenges related to data fragmentation, algorithm transparency, and organizational change management, predictive analytics offers substantial benefits for construction risk management across financial, schedule, safety, quality, and environmental domains.