Effects of particle size‐shape correlations on steady shear strength of granular materials: The case of particle elongation
Bibliographic record
Abstract
Abstract Granular materials often present correlations between particle size and shape due to their geological formation and mechanisms of weathering and fragmentation. It is known that particle shape strongly affects shear strength. However, the effects of shape can be modified by the role the particle plays in a sample given its size. We explore the steady shear strength of samples composed of particles presenting size‐shape correlations and we focus on the case of particle elongation in two opposite scenarios: (A) large elongated grains with finer circular grains and (B) large circular grains with elongated finer grains. By means of numerical simulations, we probe the shear strength of samples of varying particle size span from mono to highly polydisperse and particle aspect ratios varying between 1 and 5. We find that the two correlations tested strongly impact the shear strength as particle size span evolves. Microstructural analyzes allow us to identify how each correlation affects connectivity and anisotropies linked to the orientation of the particles and load transmission. Decompositions of the stress tensor let us identify the sources of the different mechanical behavior in each correlation and determine the contributions of each particle shape to macroscopic shear strength. This study proves that common small‐scaling methods based on truncated or parallel particle size distributions can incur in under/over‐estimations of shear strength if particle shapes are not considered in the scaling process.
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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 | 0.001 | 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 | 0.000 |
| 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".