Development and validation of a Novel rigid block modelling approach for support design in underground mining, enabling new avenues for improved safety and efficiency
Bibliographic record
Abstract
Support systems are vital for worker safety and to minimize production delays due to collapse incidents or the need for rehabilitation. The purchase of support elements and their installation also represent a significant cost for the operational budget of any mine. However, current standard practices rely on empirical methods developed several decades ago or on simplified kinematic models of the rock mass and its interaction with the support system based on wedge analysis and key-block theory. Experience shows that these lack the robustness required for today's mining depths and safety needs. The advent of faster computer processors and new numerical modelling tools allows us to move away from these over-simplified methodologies and embrace new approaches that can more accurately simulate the failure mechanisms encountered and interactions between the rock mass and the support elements. A new methodology using the 3D distinct-element modelling software PFC3D, combined with DFN simulations, was developed to evaluate support system strategies at the Raglan Mine. The joint strength properties were calibrated against overbreak data from lidar scans. The study demonstrated the effectiveness of the Rigid Block Modelling-DFN approach in simulating complex failure mechanisms in a gravitational stress environment. It provided valuable insights into the trade-offs between using PM12 and rebar #7 for supporting the backs of drifts, as well as determining the optimal timing for installing secondary long supports at intersections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".