A comprehensive numerical investigation of multi-scale particle shape effects on small-strain stiffness of sands
Bibliographic record
Abstract
The effects of multi-scale particle shape characteristics on the small-strain stiffness of granular soils remain controversial. This study revisits this topic using well-calibrated three-dimensional discrete-element simulations incorporating a particle roughness–embedded contact model and particles of realistic shape. Based on the numerical simulation results, the multi-scale particle shape effects on the small-strain stiffness of sands and the magnitude of Hardin's equation parameters are systematically investigated, and the underlying micro-mechanisms are also thoroughly explored. Results indicate that the small-strain stiffness increases with the increase of particles’ overall irregularity due to the increased mechanical coordination number, but decreases with the increase of particle surface roughness because of the decreased contact normal stiffness. In addition, the constant term parameter and void ratio term parameter of Hardin's equation increases and decreases linearly, respectively, with the particles’ overall regularity, but reduces and grows with the particle surface roughness, respectively. Furthermore, the stress exponent is almost unchanged with the particles’ overall regularity, but increases with the particle surface roughness, which determines the relative proportions of contacts under asperity-dominated, transitional and Hertzian stages. The study helps to advance the cross-scale understanding of multi-scale particle shape information in relation to small-strain stiffness of sands.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".