Particle damage observed in ring shear tests on sands
Bibliographic record
Abstract
In this paper, particle damage of three test sands with different mineralogical compositions is studied using stress–displacement response measured in ring shear tests, particle-size distributions of the original sand prior to shear and from the shear band after shear, and by examining particle shape changes determined by scanning electron microscope. Particle damage during shearing produced a wider particle-size distribution, and damage typically continued until the normal stress was small (about 28 kPa) in constant volume ring shear tests and the internal stresses were distributed among sufficient particle contacts such that damage practically ceased. The dominant damage mechanism (typically either particle abrasion and shearing-off asperities or particle splitting) depended strongly on the soil response (i.e., contraction or dilation), particle hardness, and particle-size distribution, but both mechanisms produced particles that were more angular and rougher than the original sand particles. The magnitude of particle damage observed in the ring shear tests was influenced by the consolidation normal stress, shear displacement, particle mineralogy, particle-size distribution, drainage conditions, and soil fabric (in constant volume tests). Lastly, the influence of particle damage on engineering properties including hydraulic conductivity, liquefaction resistance, stress–strain response, friction angle, and critical state are briefly discussed.
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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 | 0.000 |
| 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".