Automatic intrusion detection and warning method in the hoisting scenario integrated BIM and GPS

Monitoring of intrusion at construction sites is crucial to ensure personnel safety. However, current systems struggle to automatically determine the spatial extents of hoisting areas and reliably assess worker movements in dynamic construction environments. This study proposes a novel three-dimensional (3D) automatic intrusion detection method that uniquely integrates Building Information Modeling (BIM) data with Real-Time Kinematic (RTK)-enhanced Global Positioning System (GPS) data. The proposed methodology automatically extracts BIM parameters to compute dynamic spatial boundaries of hoisting areas and converts geographic coordinates into a unified 3D virtual environment. The study’s key novelty lies in its rule-based approach that considers both worker location and movement direction to minimize false alarms, addressing a critical limitation in existing position-only detection systems. A dual alert mechanism is implemented, facilitating real-time warnings through intelligent safety helmets for field workers and a comprehensive web-based management interface for supervisors. Validation tests demonstrate substantial improvement in detection accuracy. Proposed rule-based algorithms, which incorporate both spatial position and movement direction analysis, achieved a mean error rate of 8.9 % compared to 42.8 % for traditional position-only methods tested under identical conditions. This represents a 79.2 % reduction in false alarms compared to traditional position-based methods. This scalable solution offers significant potential for enhancing personnel safety management across diverse construction sites and can be extended to monitor multiple workers simultaneously. The system’s integration capabilities make it suitable for widespread adoption in construction safety practices. However, the current implementation is limited to outdoor environments and single-worker scenarios, with future research needed to address indoor applications and multi-worker detection scenarios.