Theory and calibration of the Pierre 2 stochastic rock fall dynamics simulation program
Bibliographic record
Abstract
The use of computer models to determine rock fall hazards is increasingly common, with increasingly complex models being developed. In most practical applications, slopes potentially affected by rock falls are characterized in general terms only, thus a simpler model is desirable to reduce the parameter uncertainty. The model presented here utilizes a lumped-mass representation of the rocks. Key features are the stochastic roughness angle to represent contact geometry variability, hyperbolic restitution factors, and a stochastic shape factor, which have been developed considering impact mechanics theory. Together, these features can yield realistic results for linear and angular velocity, bounce height, runout distance, and normal restitution factors greater than one while still being easy to calibrate. The model calibration has been carried out using detailed, full-scale experiments from a talus slope in France, a hard rock quarry in Austria, and a weak bedrock and talus slope in Japan. An observed rock fall event in British Columbia was modeled as a pseudoforward analysis to demonstrate the model validity. The usefulness of the model as a design tool has been demonstrated by using the simulation results as inputs for a hypothetical barrier design application, and calculating the reliability of the design values.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".