Rockfall risk management using a pre-failure deformation database
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Terrestrial laser scanning (TLS) monitoring has been used to estimate the location, volume, and kinematics of a variety of small magnitude rockfalls before failure (1–1000 m3 range), and in some cases, potential failure time has been assessed through the application of inverse velocity methods. However, our current understanding of rock slope pre-failure behavior for this magnitude range and prediction ability is based on observations of a small number of failure case histories. In this study, a pre-failure deformation database was constructed for rockfall volumes exceeding 0.1 m3, observed over a 1252-day study interval at the Goldpan rock slope, British Columbia, Canada, in order to better understand the pre-failure behavior of rock slopes and provide an empirical means of estimating temporal failure ranges. Repeated TLS datasets were acquired at an average scanning interval of 2–3 months. A total of 90 rockfall events were recorded at this site, during this time period, of which 64 (71%) exhibited measurable deformation prior to failure. Classification of rockfalls by volume suggests that a scale dependency may exist, as deformation was detected for a greater proportion of rockfalls > 5 m3 (92%) than for smaller rockfalls in the range of 0.1–0.5 m3 (61%). A lower rate of pre-failure deformation detection was also reported for planar sliding failures as compared with wedge or toppling failures, suggesting that deformation was less easily detected for these failure types. This study proposes and implements a framework for rockfall assessment and forecasting that does not require continuous monitoring of deformation.
KeywordsLiDAR Rockfall Deformation Monitoring Risk management Terrestrial laser scanning Database
We thank David Bonneau, Richard Carter, Matthew Ondercin, and Megan van Veen of the Geomechanics Group at Queen’s University for aid in field data collection.
The authors would like to thank CN Rail for their generous technical and financial contributions as well as assistance offered during data collection campaigns. The overall funding for this project is provided by NSERC with financial support by CN Rail and Canadian Pacific. Last author also acknowledges funding from H2020 program of the European Commission [MSCA-EF 705215].
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