Micro- and Macro-Mechanical Analysis of Rev in Sheared Polydisperse Granular Samples
Bibliographic record
Abstract
Abstract A representative elementary volume (REV) is key to ensuring accurate estimations of the mechanical behaviour of granular materials. Guidelines given by testing standards and laboratory equipment restrictions define limits on the maximum particle size (𝑑 max ) in a triaxial test. The recommended aspect ratio (𝛼 = 𝐷/𝑑 max ) varies from 5 to 20. However, particle size polydispersity is often ignored in the standards, and its effects on REV estimation are poorly understood. This work studies the combined effects of α and particle size distribution (PSD) on the critical shear strength of granular materials through drained triaxial tests DEM simulations. We found that shear strength increases with α to a maximum value and it stabilizes, indicating a REV. However, better-graded samples stabilize earlier. At the microscopic level, the fraction of particles contributing to stress transmission ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msubsup> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>*</mml:mo> </mml:mrow> </mml:msubsup> </mml:mrow> </mml:math> ) shows an effect on shear strength. Finally, for our samples, when α > 12.5 and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msubsup> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>*</mml:mo> </mml:mrow> </mml:msubsup> </mml:mrow> </mml:math> > 3000, a REV state is achieved regardless of gradation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".