Volumetric analysis of rock mass instability around haulage drifts in underground mines
Bibliographic record
Abstract
Haulage networks are vital to underground mining operations as they constitute the arteries through which blasted ore is transported to surface. In the sublevel stoping method and its variations, haulage drifts are excavated in advance near the ore block that will be mined out. Numerical modeling is a technique that is frequently employed to assess the redistribution of mining-induced stresses, and to compare the impact of different stope sequence scenarios on haulage network stability. In this study, typical geological settings in the Canadian Shield were replicated in a numerical model with a steeply-dipping tabular orebody striking EW. All other formations trended in the same direction except for two dykes on either side of the orebody with a WNW–ESE strike. Rock mass properties and in situ stress measurements from a case study mine were used to calibrate the model. Drifts and crosscuts were excavated in the footwall and two stope sequence scenarios – a diminishing pillar and a center-out one – were implemented in 24 mining stages. A combined volumetric-numerical analysis was conducted for two active levels by comparing the extent of unstable rock mass at each stage using shear, compressive, and tensile instability criteria. Comparisons were made between the orebody and the host rock, between the footwall and hanging wall, and between the two stope sequence scenarios. It was determined that in general, the center-out option provided a larger volume of instability with the shear criterion when compared to the diminishing pillar one (625,477 m3 compared to 586,774 m3 in the orebody; 588 m3 compared to 403 m3 in the host rock). However, the reverse was true for tensile (134,298 m3 compared to 128,834 m3 in the orebody; 91,347 m3 compared to 67,655 m3 in the host rock) instability where the diminishing pillar option had the more voluminous share.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".