Simulation of fragmental rockfalls detected using terrestrial laser scans from rock slopes in south-central British Columbia, Canada
Bibliographic record
Abstract
Rockfall presents an ongoing challenge to the safe operation of transportation infrastructure, creating hazardous conditions which can result in damage to roads and railways, as well as loss of life. Rockfall risk assessment frameworks often involve the determination of rockfall runout in an attempt to understand the likelihood that rockfall debris will reach an element at risk. Rockfall modelling programs which simulate the trajectory of rockfall material are one method commonly used to assess potential runout. This study aims to demonstrate the effectiveness of a rockfall simulation prototype which uses the Unity 3D game engine. The technique is capable of simulating rockfall events comprised of many mobile fragments, a limitation of many industry standard rockfall modelling programs. Five fragmental rockfalls were simulated using the technique, with slope and rockfall geometries constructed from high-resolution terrestrial laser scans. Simulated change detection was produced for each of the events and compared to the actual change detection results for each rockfall as a basis for testing model performance. In each case the simulated change detection results aligned well with the actual observed change in terms of location and magnitude. An example of how the technique could be used to support the design of rockfall catchment ditches is shown. Suggestions are made for future development of the simulation technique with a focus on better informing simulated rockfall fragment size and the timing of fragmentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".