Tortuosity—A Novel Approach to Quantifying Variability of Rockfall Paths
Rockfall poses a significant hazard in steep terrain, where complex ground interactions cause falling boulders to deviate from straight-line paths. While lateral dispersion is commonly used to describe the distribution of deposited boulders from rockfall events, it does not provide any insight into...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Geotechnics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-7094/5/2/36 |
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| Summary: | Rockfall poses a significant hazard in steep terrain, where complex ground interactions cause falling boulders to deviate from straight-line paths. While lateral dispersion is commonly used to describe the distribution of deposited boulders from rockfall events, it does not provide any insight into the complexity of boulder trajectories while in motion. This study introduces tortuosity, a metric typically applied in porous media hydraulic analysis, as a novel approach for quantifying the deviation of rockfall paths from linearity. Using high-resolution UAV-based LiDAR data and RocFall3 (Version 1.017) simulation software, this research investigates the effects of terrain model resolution, boulder shape, and boulder mass on tortuosity values for 20,000 simulated rockfalls on a columnar jointed basalt slope in Boise, ID, USA. Results show that increasing terrain resolution leads to higher tortuosity values due to the increased presence of terrain asperities. Spherical boulders exhibited higher tortuosity than hexagonal ones, and tortuosity decreased with increasing mass for spheres, likely due to their momentum overcoming minor terrain features. Hexagonal boulders, constrained by their angular shape, showed less variability in tortuosity across resolutions and sizes. These findings emphasize the limitations of low-resolution publicly available LiDAR data and highlight the critical influence of accurate boulder representation in simulation models. |
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| ISSN: | 2673-7094 |