Some aspects of the mechanics of arching in backfilled stopes
Bibliographic record
Abstract
In current underground mining using “open stoping” methods, it is common to backfill mined-out voids (“stopes”) using hydraulically placed backfill, which is commonly composed of tailings, to which cement is often added. Knowledge of the stress state within a backfilled stope is required for safe design of drawpoint barricades and for other operational reasons. This stress state depends, inter alia, on the degree of “arching” that occurs, resulting from the development of shear stress between the fill and the stope walls. This paper presents a numerical modelling study of aspects of the arching phenomenon, carried using the computer code Plaxis. The backfill is characterized using the Mohr–Coulomb soil model, and both dry backfill and saturated backfill are considered to completely cover the full spectrum of backfill types that are used in practice. It is shown that even with dry backfill, the behaviour is governed by a complex interaction between the soil parameters. The behaviour is more complex with saturated backfill, with a key parameter being the permeability of the backfill relative to the rate of filling — i.e., whether the backfilling operation can be considered to be “drained” or “undrained” or somewhere between these two extremes.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".