Multi-Scale Study of Specimen Size Effect on Shear Strength of Polydisperse Granular Materials Using DEM
Bibliographic record
Abstract
In soil shear strength characterization, particle size is usually much smaller than the testing device size. However, when particle size is similar to the size of the apparatus (e.g., for coarse granular materials), the mechanical response becomes less reliable. To address this, international standards prescribe minimum sample scales based on maximal particle and device sizes. Nevertheless, the influence of the sample scale on the mechanical response is still not well understood. This topic is studied through simple shear simulations in the frame of the discrete-element method, covering a wide range of sample scales and particle size distributions. Micromechanical analyses of force and contact configurations reveal that the stability of parameters is linked to the formation of local rigid structures that carry forces significantly higher than the average. When the height of these structures becomes comparable to that of the sample, macroscopic and microscopic parameters deviate from those found under larger sample scales. Although these findings require further validation, this work suggests that the standards may need to be re-evaluated for an effective material characterization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".