Advances in Hazard Identification Systems for Large Scale Construction Operations

Hazard identification remains a fundamental component of safety management in large-scale construction operations, where complex workflows, heavy machinery, dynamic environments, and multi-contractor coordination significantly increase the likelihood of accidents. Recent technological developments have transformed traditional hazard detection approaches, shifting from manual inspections and static risk assessments toward intelligent, data-driven systems capable of real-time monitoring and predictive analysis. This review examines recent advances in hazard identification systems designed for large construction environments, focusing on digital sensing technologies, computer vision–based monitoring, Internet of Things (IoT) safety networks, wearable safety devices, and artificial intelligence–driven risk prediction models. The study synthesizes current research on automated hazard recognition using machine learning algorithms, including convolutional neural networks for visual risk detection and predictive analytics for identifying unsafe behaviors and environmental conditions. In addition, the review evaluates the integration of Building Information Modeling (BIM), digital twins, and cloud-based safety platforms that enable continuous data exchange and proactive risk management across distributed construction sites. Particular attention is given to system architecture, data acquisition methods, model training strategies, and implementation challenges such as sensor reliability, data interoperability, and worker privacy concerns. By critically analyzing existing hazard identification frameworks and emerging intelligent safety technologies, this paper highlights their effectiveness in improving early hazard detection, reducing accident rates, and supporting safety decision-making in large-scale infrastructure projects. The findings provide insights into future research directions and practical strategies for deploying integrated, technology-enabled hazard identification systems within modern construction safety management programs.