Quantitative analysis of haulage system instability in deep hard rock mines using numerical modelling
Bibliographic record
Abstract
Haulage drifts and related infrastructure are crucial to the success of underground mining operations. In the sublevel stoping mining method, they are developed well before any extraction commences in a given section of the orebody. One of the more complex design parameters is the relative distance of a haulage drift from the orebody as it runs parallel to its strike. Opposing considerations from operational and ground control teams are balanced, with the former preferring a shorter distance for increased productivity and the latter requesting a further distance for safety and stability. Numerical codes are one of the analytical tools used frequently in making these decisions by providing mining-induced stress and displacement magnitudes with a properly calibrated model. In this study, a simplified model is constructed of a typical tabular orebody within the geological settings of the Canadian Shield, striking East-West and dipping steeply to the south. Three other formations with the same strike and dip are added to the model, along with two intrusive dykes at variable distances from the orebody and the drift. The rockmass properties for all formations are taken from a previous work on a case study mine in the Canadian Shield, and the model is calibrated based on in-situ stress measurements there. Two stope sequences comprising two simultaneous mining fronts are implemented and analyzed for the orebody; a diminishing pillar one that moves from both east and west to the middle, and a centre-out option that moves from its centre to the sides. In both cases, 24 mine-and-backfill stages – comprising 6 stopes each – are needed to completely extract the orebody. A quantitative assessment of instability around the drifts, crosscuts, and stopes is conducted for a single level at a depth of 1490 m for each stage. Three instability parameters – the brittle shear ratio (BSR), uniaxial compression, and tensile failure – are combined with volumetric analysis to obtain the quantity of potentially unstable rockmass. The relative proximity of the drift and stopes to the dykes is evaluated as well and observed to have an impact on the results. A combined numerical-volumetric approach is found to provide a useful tool for comparing different sequences and obtaining information on the type, location, volume, and timing of rockmass instability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".