A hybrid finite element/discrete element method of modelling fracturing of brittle rock blocks with applications to block cave fragmentation
Bibliographic record
Abstract
An understanding of the fragmentation of rock blocks is an important parameter in the planning and development of a block cave mine. Traditional methods for block fragmentation analysis use empirical relationships and rule-based approaches that heavily rely on the block geometry, rather than block-to-block interactions. This thesis presents a study of fragmentation processes using a hybrid finite-element/discrete-element method (FEM/DEM). The approach is capable to account for the numerical instability generally associated with the simulation of high-velocity surface interactions and subsequent fracturing. The analysis has focused on the simulation of free-falling blocks onto a fixed surface. The initial and final block breakage has been compared against parameters including the roughness and curvature of the impacted surface, and the rock block orientation in space during free fall. The fractal relationship between resulting fragments was also explored to observe if a size invariable relationship exists and could be used for block breakage prediction. The results show that the curvature of the impacted surface reduces the fragmentation of the rock block regardless of whether the surface is concave or convex. In addition, the angle at which the rock block contacts the impacted surface is critical with more eccentric angles resulting in less final fragmentation. Little to no correlation with the numeric JRC characterisation of surface roughness is seen. The fractal relationships show promising results for size invariability of fragmentation with some variability noticed possibly due to the mechanism of fracturing. These results could provide an increased understanding of the complexities of secondary fragmentation estimates and the range of fragmentation that could be expected in block caving.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".