Real-Time Rockfall Monitoring for Construction Safety Using Pulse-Doppler Radar at Grafenhöfe, Austria

The Grafenhöfe site in Innervillgraten, Austria, has been affected by ongoing rockfall activity following the destabilization of a steep rock slope due to storm-induced deforestation (storm Vaia in 2018) and subsequent mass movements. The slope, composed primarily of marble and mica schist, experienced initial failures in September 2023, leading to repeated rockfall events and the partial infill of the Grafenbach torrent. In January 2024, further destabilization resulted in large-scale rock detachments, prompting immediate safety measures, including the evacuation of a farmstead.

To enhance safety during the construction of protective dams, the Austrian Torrent and Avalanche Control (WLV) implemented a pulse-Doppler Radar system (IBTP Koschuch) for real-time rockfall detection. The system provided continuous high-resolution monitoring, triggering alarms within two seconds upon detecting rockfall movement. This allowed for rapid response and significantly reduced exposure of construction workers to hazards on-site. For monitoring the deformation of the detached rock mass, an automatic continuous terrestrial survey was installed on the opposite slope. This provided insight into the development of the slope's deformation, allowing for the avoidance of construction during a potential large-scale failure of the rock mass.

The radar system, which was specifically developed for alpine settings, detects Doppler spectra, ensuring reliable detection independent of weather and light conditions. Integrated with an automated alerting network, it facilitated direct communication with construction teams and authorities, enabling proactive safety management. Beyond immediate hazard mitigation, the radar data provided a valuable basis for refining WLV's safety strategy, as evidenced by the correlation of rockfall detections with rainfall data, revealing a strong dependency: all detected rockfalls coincided with precipitation events, while rainfall did not always led to rockfall. This enabled an optimised risk management approach, where construction activities were suspended during rainfall, effectively minimising exposure to potential rockfall events.

This study underscores the effectiveness of real-time monitoring for adaptive hazard mitigation during high-risk construction projects. Future work will focus on refining detection algorithms and integrating AI-based predictive models to enhance early warning capabilities for rockfall hazards.