Effect of drainage and sequential filling on the behavior of backfill in mine stopes
Bibliographic record
Abstract
Underground backfilling offers significant economic and environmental advantages to mining operations. There is however a limited knowledge and understanding of how the backfill behaves within mine stopes, which creates some concern regarding the risk of accidents with potentially serious consequences. It is thus important to investigate further the response of backfill to ensure safe working conditions and optimize the filling sequence. This paper presents key results from a numerical study aimed at analyzing the hydrogeotechnical response of backfill in a narrow vertical stope. The simulations illustrate how stresses are influenced by stope geometry, water drainage, and filling rate. Three main cases are presented here to illustrate these effects; namely, (i) simulation of dry (or drained) backfill, (ii) a rapidly filled stope with progressive drainage and consolidation, and (iii) sequential backfill placement with different filling rates. The third case includes a simulation with evolving properties due to the binder added to the backfill. The results from the numerical analyses show that arching effects develop within narrow backfilled stopes because of the stiffness contrast between the rock and the fill material. This can produce a significant reduction of the stresses (horizontal and vertical) in comparison with the overburden pressure. The simulation results also show the development of excess pore-water pressures after the placement of the saturated backfill within the stope. Drainage tends to reduce these pressures and increase the frictional stresses along the rock walls. The sequentially filled stope simulations show that a rapid filling rate produces much higher total stresses and excess pore-water pressures, compared to slower rates. The simulation of the cemented backfill, with evolving properties, indicates that the progressive changes can have a significant effect on the total and effective stresses in the stope. A discussion follows on the implications of these results.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".