Empirical and numerical assessment of two extended stopes for dilution estimation in an underground mine
Bibliographic record
Abstract
Over the decades, several empirical and analytical approaches have been developed to assess the stability of underground excavations. For stope design, the stability graph method is commonly used for preliminary sizing assessments. The method has been modified by multiple authors over time using extensive databases to adjust the three factors and boundary limits for the stability zones. Based on field observations, the adjustments were made to improve the qualitative representation of rock mass stability and associated risks. Moreover, different applications such as the dilution graph have been developed based on the stability graph method. The overbreak prediction for open stope footwalls and hanging walls can be quantified with this graph. Numerical modelling is another important tool in rock engineering, commonly utilised in conducting complex analyses in mining. The model consists of numerous elements or zones that discretise the rock mass, requiring initial calibration to predict future results. In the stability graph method, the estimation of induced stress for the stability Factor A purely depends on numerical analysis techniques. In the present study, an assessment is developed for the stability of two extended stopes that were extracted in an underground mine. The stope designs were evaluated using the stability graph method and two versions of the dilution graph. Advanced 3D models were constructed for determining Factor A and for assessing the stability of stope surfaces based on a numerical approach. Finally, a comparison was made between the empirical results of the stability graph method and dilution graph, the numerical models, and actual field observations and cavity monitoring survey (CMS) measurements after the extraction of each stope at the mine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".