A Hybrid FEM/DEM Approach to Estimating Rock Block Breakage due to Gravity Free-Fall
Bibliographic record
Abstract
Abstract An understanding of the fragmentation of rock blocks from gravity free-fall has applications to rock fall analysis as well as block cave mining. In the case of a rock fall, blocks splitting mechanics can reduce the overall block energy and therefore reduce the travel distance of the blocks. Conversely, splitting may result in a larger number of smaller fragments that may still pose a risk despite their reduced dimensions. In the case of block caving, splitting of blocks it is believed to aid the flow of the ore column, to reduce the potential of hang-ups, and to allow for easier extraction and processing of the ore. 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. In this paper we present 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. To date 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. We believe the results could provide useful insight into designing catchments for rock fall, as well as 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".