Panel Destressing Strategies for Remnant Pillar Extraction
Bibliographic record
Abstract
Large-scale panel destressing is a rockburst mitigation technique employed in deep hard rock mines during remnant pillar extraction. Panels are choke blasted in the pillar footwall to cutoff the far-field major stress in the mining area and deviate them around the pillar. In this study, the effects of panel geometry and far-field stress magnitude are investigated. Destress blast performance is assessed by measuring change to the energy release rate (ERR) of all mining steps during the extraction of a simplified remnant pillar due to destressing. It is demonstrated that the energy release rate (ERR) of critical stopes is reduced by 30% with the base panel geometry. The panel thickness is shown to have the most influence on the efficiency of destressing, followed by the stand-off distance between the panel and the pillar and the overhang length of the panel. The effect of far-field stress magnitude on the ERR is also investigated, and the destress blast performance is expressed as an equivalent major principal stress reduction. It is shown that with the base panel geometry, the destressing program offers the same ERR reduction as a 9.6 MPa reduction in the far-field stress for the most critical stopes. Finally, the Copper Cliff Mine (CCM) panel destressing program is presented as a case study. The ore at risk and ERR are calculated over the extraction and destressing sequence in the pillar with a pillar-wide numerical model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".