Automated hazard identification and energy-based risk assessment for tower crane lifting operations using RTK-GNSS positioning and tracking

Tower cranes are critical for lifting heavy elements in prefabricated and modular construction, but their operations pose significant safety risks. This study proposes a data-driven method using Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) that tracks the crane trolley and workers movements and assesses the potential struck-by hazards from lifted payloads. By analyzing the trolley’s velocity and vertical displacement, the novelty in the method respectively detects the lifting stages and estimates the payload weight, while pairing its real to the planned placement location in the Industry Foundation Classes (IFC) model. An energy-based hazard assessment computes the intensity of detected incidents when workers’ RTK-GNSS wearables are inside of hazardous crane swing zones. The severity is evaluated in the form of a statistical analysis and density map. Unprecedented safety-relevant information becomes available to practitioners that can use it in responsible decision-making. Compared to computationally intensive systems such as cameras, the proposed method provides an alternative cost-effective, scalable solution for automating the monitoring of hazardous outdoor workspaces.