Effects of particle size–strength and size–shape correlations on parallel grading scaling
Bibliographic record
Abstract
Waste mining rock and rockfill materials contain coarse clasts that could easily reach more than 1 m in size. Small scaling techniques for mechanical testing on such coarse materials require altering the particle-size distribution (PSD), by reducing maximum grain size to be able to fit a sample in a laboratory device. To capture the stress−strain behaviour, it is assumed that the reduced PSD has to be parallel to the prototype grading. However, individual grain properties could change with particle size, such as shape and crushing strength. Parallel scaling techniques have been widely applied in rockfill materials, however, the effects of particle-size correlations have been rarely considered, and its effects remain not well understood. This paper presents experimental data on particle-size correlations with both particle shape and particle strength, together with triaxial tests on parallel graded samples of a shale rockfill material. The results show that inverse particle-size−strength correlation results in decreasing particle crushing in finer samples, while particle size−shape correlation could contribute to increase particle crushing in finer samples comprising more elongated grains. Depending on characteristic particle size, one of these opposed trends will prevail and control the material behaviour.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".