Interpretation of the limits in shear strength in binary granular mixtures
Bibliographic record
Abstract
Many natural slopes and rockfill structures are made of a mixture of rock fragments and sand-size particles. To analyze the stability of such natural slopes and rockfills, a knowledge of how rocksand mixtures develop their shear strength is needed. Laboratory tests conducted on mixtures of glass beads of two different sizes (5 and 0.4 mm) have indicated that their shear strength depends upon the relative concentration by weight of the large and small beads in the mixtures. If the concentration by weight of the large beads is greater than 70%, the shear strength of the mixtures is controlled by the frictional resistance of the large beads. If the concentration of the large beads is less than 40%, the shear strength of the mixtures is controlled by the frictional resistance of the small beads. If the concentration of the large beads is between 40 and 70%, the shear strength of the mixture is partially controlled by the frictional resistance provided by the large beads in the mixtures. These limits are very similar to those reported for rocksand mixtures. To date, no explanation has been put forward to account for why these limits exist. This study presents an explanation for their existence. The explanation is based on the porosity developed by the mixtures and the type of structural support provided by the coarse and fine grains.Key words: shear strength, granular mixtures, porosity, fabric, compaction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".