Particle shape distribution effects on the critical strength of granular materials
Bibliographic record
Abstract
This study investigates the influence of correlations between particle morphology and gradation on the critical shear strength of three-dimensional granular assemblies via numerical simulations. While grain shape is acknowledged to play a central role in the mechanical behavior of granular media, only a few works have explored the combined effects of grain shapes varying with grain size. Employing three-dimensional discrete-element simulations, we explore the shear behavior of samples with diverse particle size distributions, where grain shapes (ranging from spheres to very angular polyhedra) are assigned based on the relative size of each particle. Using drained triaxial shear tests, we analyze the macroscopic behavior up to large deformation levels. Micro-mechanical analyses are also conducted to understand the underlying mechanisms governing the observed macroscopic behavior, highlighting the role of grain connectivity and force transmission. Surprisingly, well graded samples composed of coarse angular grains and fine rounded ones are not capable of developing higher shear strengths than uniform samples with the same diversity on grain shapes. Conversely, samples where coarser grains are rounded and fines are angular, show constant shear strength as the particle size distribution becomes broader. These findings underscore the importance of simultaneously considering grain size and shape distributions for the assessment of realistic granular soil behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".