Mitigating geotechnical challenges in deep mining: lessons learned from shaft station excavations at extreme depths
Bibliographic record
Abstract
This paper presents the geotechnical challenges experienced while excavating two shaft stations at the depths of 2490 and 2550 m in highly stressed grounds, along with engineering solutions that effectively reduce exposure and risk associated with the excavations and help improve safety. Although proactive measures were implemented to ensure the successful construction of the 2490 shaft station, adverse stress conditions and a localized geological fault resulted in damage to the shaft concrete liner and significant overbreak of the station floor, leading to the bowling of the station floor. These unanticipated conditions resulted in project delays and increased risk for the operators working at the face. Based on detailed observations made while excavating the 2490 shaft station, instrumentation, and microseismic monitoring data, the methodology for excavating the 2550 shaft station was modified to mitigate the risk and increase safety. Detailed numerical modelling analysis was completed to determine zones of stress concentration, and the findings were used to better prepare seismic re-entry and enhanced ground support considerations. The numerical model was calibrated using measured and observed depths of failure and rock mass response to mining during the excavation of the 2490 shaft station. This modelling work emphasized the need for additional engineering controls in subsequent shaft station development. The modifications include reinforcing the shaft concrete liner, reducing the size of the initial shaft station blast, and pre-sinking and supporting the shaft below the station floor before the excavation of the shaft station, leading to cost savings and valuable insights into the geotechnical intricacies and excavation strategies essential to improving the safety of deep mining projects.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".