Rockfall rebound: comparison of detailed field experiments and alternative modelling approaches
Bibliographic record
Abstract
ABSTRACT The accuracy of rockfall trajectory simulations mainly rests on the calculation of the rebound of fragments following their impact on the slope. This paper is dedicated to the comparative analysis of two rebound modelling approaches currently used in rockfall simulation using field experiments of single rebounds. The two approaches consist in either modelling the rock as a single material point (lumped mass approach) or in explicitly accounting for the fragment shape (rigid body approach). A lumped mass model accounting for the coupling between translational and rotational velocities and introducing a slope perturbation angle was used. A rigid body approach modelling the rocks as rigid locally deformable (in the vicinity of the contact surface) assemblies of spheres was chosen. The comparative analysis of the rebound models shows that both of them are efficient with only a few parameters. The main limitation of each approach are the calibration of the value of the slope perturbation (‘roughness’) angle, for the lumped mass approach, and the estimation of the rock length and height from field geological and historical analyses, for the rigid body approach. Finally, both rebound models require being improved in a pragmatic manner to better predict the rotational velocities distribution. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".