Investigation into the Rock Mass Response to Pillar Extraction in a Hard Rock Tabular Mine
Bibliographic record
Abstract
ABSTRACT: Historically, secondary pillar extraction has been successful in the soft rock mining environment. Several hard rock platinum mines have conducted selective remnant extraction with backfill as a regional support measure. A hard rock tabular platinum mine instigated the extraction within a non-yield pillar layout without backfill support. The mine is situated within the Great Dyke of Zimbabwe in a shallow depth environment of approximately 100m. Pillar extraction without backfill in a hard rock mine is a novel mining method. A study was carried out to make a quantitative comparison between the various design parameters and the rock mass response measurements. The primary mining utilised the mechanised room and pillar mining method to extract ore from the wide tabular reef. Similar, low profile mechanised equipment with additional automated systems were adopted for ore extraction and transportation to the surface crusher. The risks associated with the extraction of pillars include large localised falls of ground due to wide spans, pillar run, high severity injuries due to windblasts and large regional collapses through to the surface. The pre-feasibility study for the mine layout design used MAP3D to assess the expected displacement and stress limits post-extraction. MAP3D uses the boundary element method of analysis and has an in-built CAD system for stress analysis and 3-dimensional visualisation of models. A monitoring strategy consisting of displacement, deformation, stress change, ground motion and groundwater level measurements was put in place to record the variations resulting from the pillar extraction. The rock mass response analysis aimed at trending the monitoring results, conducting a comparison to the design parameters and previous pillar collapse trends. Minor stress, strain, and displacement changes have been recorded within a period of one year since the project began with no visible deformation noted on accessible pillars. The current system was identified as including the hazard identification and first-pass monitoring stages. Real-time monitoring systems with a higher sensitivity are required to ensure long-term data retrieval and timeous emergency response.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".