The Stress-Force-Fabric relation across shear bands
Bibliographic record
Abstract
The strength of granular materials is highly dependent on grain connectivity (fabric), force transmission, and frictional mobilization at the particle scale. Furthermore, these bulk properties are strongly dependent on the geometry and history of loading. It is well established that anisotropy in fabric and force transmission through a granular packing directly relates to the bulk scale strength of the packing via the Stress-Force-Fabric (SFF) relation. We have recently verified the validity of this framework for a broad variety of loading histories and geometries in experimental granular packings, using photoelastic disks to measure individual interparticle contact forces. By tracking both particle positions and interparticle contact force vectors, we mapped the anisotropy of the fabric and forces to the macroscale stress and strain and found excellent agreement between the anisotropic particle-scale measures and the macroscale responses in experiments. Here, we present an analysis of the effect of strong spatial gradients (shear bands) using the SFF framework in a sheared annular geometry, finding that there are strong variations in contact orientation depending on the location within or outside the shear band, even though the principal loading direction is uniform. This highlights that the fabric connectivity significantly changes across the shear band but does not contribute to the direct loading of the material. We disentangle the effects of packing fraction gradients and boundary constraints on the differences in fabric orientation.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".