Prediction and follow-up of failure and fallouts in footwall drifts in the Kiirunavaara mine
Bibliographic record
Abstract
A likely result of changes in rock stresses due to progressing mining is an increased number of compressive stress–induced failures. This paper presents the results from numerical analysis and observations of stress-induced fallouts in footwall drifts in the Kiirunavaara underground mine. A brittle-plastic “cohesion-softening friction-hardening” (CSFH) material model was used for simulating brittle fallouts. To account for mining-induced stress changes, the local model stress boundary conditions were extracted from a global model. The rock mass properties were based on field observations in the footwall drifts as well as on results from laboratory testing. A multi-stage analysis was carried out to gradually change the stresses to simulate mining progress. A parametric study was conducted in which strength properties, location, and shape of the footwall drift were varied. Yielded elements and maximum shear strain were used as damage and fallout indicators. The modelling results were sensitive to the shape of the drift. The location of the predicted fallouts was in good agreement with the location of observed fallouts for the case in which the drift roof was simulated flatter than the theoretical cross section. The results indicate that the true shape of the drift is different from the planned one.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".