AI-AUGMENTED SAFETY MANAGEMENT IN CONSTRUCTION

The construction sector continues to be one of the world's most hazardous, with high rates of accidents fuelled by multicomponent site dynamics, extensive use of heavy equipment, and unstable human behaviour. Conventional safety management methods, although essential, are generally reactive and fall short in offering real-time hazard perception or forecasting risk assessment. Recent advancements in Artificial Intelligence (AI) provide revolutionary opportunities to enhance safety performance through anticipatory, automated, and evidence-based decision-making. This article explains how AI techniques—ranging from computer vision for PPE detection and unsafe behaviour recognition, to wearable sensor analysis for fatigue and stress monitoring, to predictive machine learning models for incident prediction—can significantly enhance construction safety management. Furthermore, the combination of AI with Building Information Modelling (BIM) and digital twin technology allows for real-time hazard mapping, safety scenarios through simulation, and end-to-end synchronization between the virtual and physical worlds. This paper proposes a complete AI-based safety paradigm that harmonizes multimodal data sources, edge analytics, and interpretable predictive models to close the risk mitigation gap with worker privacy and trust. Data quality anomalies, model generalization, alert fatigue, and surveillance implications in terms of ethics are also addressed with responsible deployment practices. AI will eventually be able to shift construction safety from reactive compliance to preventive intervention, reducing incidents and safer conditions.