A Ground Support Strategy for Deep Underground Mines Subjected to Dynamic-loading Conditions
Bibliographic record
Abstract
In deep and high stress mines, managing the seismic risk is a very important technical challenge. Large-magnitude seismic events that result in damage to excavations and ground support systems are referred to as “rockbursts”. Under dynamic-loading conditions, ground support design is influenced, to a degree, by a lack of full understanding of the mechanisms of action and interaction of reinforcement and surface support elements. Consequently ground control engineers rely mostly on empirical experience to select appropriate ground support systems. These decisions are often based on limited field and technical data. This thesis proposes a ground support design strategy, supported by high-quality field data, for underground mines subjected to dynamic-loading conditions. Comprehensive data were collected on rockbursts that occurred over 8 to 14 years at Creighton, Copper Cliff, and Coleman mines, in the Sudbury Basin. The majority of pertinent information was obtained through on-site field assessments, seismic system records, and numerical elastic stress modeling. A database was constructed and validated comprising of 324 events documenting the performance of ground support during rockbursts. A multi-variate statistical analysis was conducted to characterize the dynamic-load demand on ground support systems. Mine excavations susceptible to rockbursts were identified in time and space based on the anticipated magnitudes, the radius of influence of seismic events, and the mining-induced stress conditions. The undertaken analysis resulted in the development of a transition strategy from static to dynamic ground support requirements in seismically active mines. It was observed that strong and stiff support systems, enhanced by the application of shotcrete, were capable of mitigating relatively small magnitude strain burst events up to a magnitude of 1.5 Nuttli. Beyond this magnitude threshold, or if shotcrete is not applied for either logistic or economic reasons, the potential dynamic-load demand on the support must be quantified in terms of the design magnitude and distance from the seismic event. Support performance thresholds, quantified using the peak particle velocity, were presented in order to assist in the selection of appropriate ground support systems to mitigate damage. Ground support selection guidelines were developed for a range of excavation dimensions and stress conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".