Three-dimensional modelling of de-stressed rock mass using classification systems
Bibliographic record
Abstract
De-stressing and preconditioning blasts have been used in the past 70 years at underground mines on all continents. The main design approach for implementing the technique has been empirical in nature and an engineering assessment methodology was developed only at the turn of the century. As mining progresses to increasing depths, additional tools are required to evaluate the impact of various de-stressing techniques and designs. Numerical modelling has been adopted since the early 1970s to simulate de-stressed rock masses and to aid in the assessment of the resulting stress distributions. In most cases, a percentage reduction (often by an arbitrary quantity) in the deformation modulus Erm has been used to represent the de-stressed regions. Rock fragmentation and stress dissipation factors have constituted another approach used in modelling the affected areas. In this paper, the rock mass rating (RMR) classification scheme is used as the basis for determining model input properties for de-stressed regions. Based on an extensive literature review, it is shown that the two main mechanisms responsible for de-stressing are the creation of new fractures or slippage of blocks on preexisting ones. The RMR allocates 20 points each to the rock quality designation (RQD) and spacing of discontinuities, which can be incrementally reduced to account for the first mechanism. The conditions of discontinuities constitute another 30 points that can be used to adequately represent the second mechanism. Using a simplified 3D model of a typical mine in the Canadian Shield, the Erm is calculated at an underground drift and crosscut system based on relative reductions in the RMR corresponding to both destressing mechanisms. Not only is the new approach found to be suitable for design purposes by validating it against reported field measurements, but the changes in the rock mass classification represent physical modifications that can be observed and that make sense from a rock mechanics perspective.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".