Methodology for the design of dynamic rock supports in burst prone ground
Bibliographic record
Abstract
The depth at which underground mines operate has been increasing continuously which is particularly true in the case of hard rock mining. The stability issues associated with mining at great depth pose tough challenges to engineers and researchers alike. Long-term mine developments in deep hard rock mines such as haulage drifts need to be functional during the entire life of the mine plan without posing any major stability concerns, which will otherwise hamper the production and other logistics associated with mining operations. High convergence and rockburst hazards are the main problems due to high stress and mining-induced seismicity in deep hard rock mining. In such circumstances, the understanding of drift support behavior under static and dynamic conditions is crucial for mining engineers when dealing with drift stability in deep, hard rock mines. In this thesis, current design methods for selecting drift support systems are reviewed, which are mostly dependent on empirical approaches and are geared towards static support design. Based on this, the current research focuses on ground support analysis under both static and dynamic conditions to understand drift support behavior with respect to nearby mining. Numerical modeling of drift primary and secondary supports is performed by developing two models using the 2-dimensional FLAC code. Axial loads induced in the drift support system under static and dynamic conditions are estimated for the case study hard rock mine in Canada at a depth of 1500 m. The results of numerical modeling are obtained in terms of axial loads in the drift support system, wall damage due to tension under dynamic conditions, and the extent of rock mass yielding around the drift. It is found that mining on the same level is critical to drift stability under static conditions, and rock mass yielding in the south wall of the drift (towards the ore body) extends beyond the bolting horizon once this stage begins. The results also show that by providing secondary support before same level mining commences, drift stability is greatly enhanced. The static model is calibrated through the implementation of an in-situ monitoring program of axial loads induced at the head of the rockbolt. A new load monitoring device called U-cell is successfully used for this purpose. Measured and estimated axial loads are then compared and found to be in good agreement. The preliminary dynamic analysis shows that a peak particle velocity of 2.0 m/s at the periphery of the drift will cause wall damage more than 1.0 m when only primary supports are provided, and around 0.5 m when secondary supports are installed along with the primary ones, and when there is no nearby mining taking place. The effects of lower level and same level mining under dynamic conditions are also examined, and wall damage and rock mass yielding are estimated. The estimation of wall damage depth is crucial in designing dynamic rock supports. It is demonstrated that wall damage due to various levels of ground motion can be estimated by dynamic numerical modeling. Finally, a methodology for the design of dynamic rock supports is presented, which is based on the selection of yielding support type and pattern, the estimation of the ejection velocity, and the volume of wall damage as obtained from dynamic modeling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".