Application of the GeoSequencing Module to ensure optimised underground mine schedules with reduced geotechnical risk
Bibliographic record
Abstract
Conventionally, engineers will impose stope sequencing rules in order to ensure the stability of excavations as mining progresses. Typical rules include fixed direction, primary-secondary and chevron. Using a mine planning software suite, a mine layout is designed, and a set of stope-to-stope precedence links are created, enforcing the applicable rule. The mine planner produces a schedule that meets these and other constraints, in an effort to achieve a high net present value (NPV) for the operation. This time-consuming process limits the planner’s ability to assess alternative mining strategies. Further, the process tends to over-constrain the mine schedule, since it is often the case that an alternative set of precedence constraints can enforce the same rule. By overconstraining the schedule, and by not assessing alternative strategies, significant value may be lost. The GeoSequencing Module is software that facilitates the assessment of stope sequencing strategies, through integration with the Schedule Optimization Tool (SOT). The GeoSequencing Module rapidly generates multiple alternative sets of stope-to-stope precedence links that enforce stope sequencing rules selected by the user. Each set of precedence links is referred to as a geosequencing scenario. For each geosequencing scenario, SOT optimises the life-of-mine schedule, allowing the planner to identify the scenario that supports the highest value for the mining operation. Furthermore, the software has been integrated with a boundary element solver, facilitating assessment of the geotechnical stability of the optimised schedules. A case study for an underground mine has been conducted to validate this methodology. The study demonstrates that multiple mining strategies can be conveniently assessed to determine which scenario yields the most desirable outcome for the mining operation in terms of both geotechnical stability and financial assessment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".