Observations and Modelling of Compressive Failures of Hard Rock Masses
Bibliographic record
Abstract
Observations from five case studies of induced compressive brittle failure fallouts are compared with different numerical modelling approaches, to determine the best non-calibrated approach. All case studies are situated in hard rock masses that are massive or sparsely fractured, at depths of 630-965 m below surface. The observed fallouts are compared with the results from numerical models in Examine2D and Phase2D. The objective is to identify which of the five numerical modelling approaches gives the best agreement with respect to location, depth, shape, and extent of the observed fallouts. None of the studied approaches give perfect agreement with the actual fallouts. This study demonstrates that when rock mass strength parameters are defined by either the Hoek-Brown or the Mohr-Coulomb criterion, the elastic and elastic-perfectly plastic models predict reasonably well actual fallout depth, although the extent of fallout is exaggerated and the shape incorrect. Therefore, a precise estimation can not be expected. When using the m = 0 approach, the results were in poor agreement with the observed fallout. The fallouts are significantly overestimated when applying the brittle-plastic model using instantaneous softening by cohesion and friction or cohesion weakening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".