Shear strength and dilative characteristics of an unsaturated compacted completely decomposed granite soil
Bibliographic record
Abstract
Shear strength and dilative characteristics of a re-compacted completely decomposed granite (CDG) soil are studied by performing a series of single-stage consolidated drained direct shear tests under different matric suctions and net normal stresses. The axis-translation technique is applied to control the pore-water and pore-air pressures. A soil-water retention curve (SWRC) is obtained for the CDG soil from the equilibrium water content corresponding to each applied matric suction value for zero net normal stress using a modified direct shear apparatus. Shear strength increases with matric suction and net normal stress, and the failure envelope is observed to be linear. The apparent angle of internal friction and cohesion intercept increase with matric suction. A greater dilation angle is found at higher suctions with lower net normal stresses, while lower or zero dilation angles are observed under higher net normal stresses with lower suctions, also at a saturated condition. Experimental shear strength data are compared with the analytical shear strength results obtained from a previously modified model considering the SWRC, effective shear strength parameters, and analytical dilation angles. The experimental shear strength data are slightly higher than the analytical results under higher net normal stresses in a higher suction range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".