ABSTRACT: Rockfall poses serious hazards to safety and infrastructure along both excavated and natural rock slopes. Significant progress has been made in forecasting time-to-failure for large-scale, progressive slope failures using full-spatial, real-time monitoring techniques. However, small-scale, brittle rockfall events typically occur with little to no detectable precursory movement, highlighting the value of identifying and characterizing the triggers for these events as a potential predictive approach. There is broad consensus within the geotechnical community that meteorological factors, such as heavy/cumulative rainfall and freeze/thaw, contribute to rockfall occurrence. However, quantitative documentation of these relationships has been limited by the lack of real-time rockfall monitoring. Quantifying the relationship between meteorological forces and rockfall events could be a critical first step towards higher confidence predictions of rockfall occurrence to support risk management. The University of Arizona's Geotechnical Center of Excellence has collected a unique dataset of observed rockfall events captured using thermal video from two mine sites in North America. These events were identified and evaluated alongside the concurrent meteorological data. Here, we present the results of preliminary predictive rockfall models developed for one of the two study sites with the goal of improving on-site safety and reducing economic losses from rockfall-related work interruptions.
Ortmann et al. (Sun,) studied this question.