Stiffness and shear strength of unsaturated soils in relation to soil-water characteristic curve
Bibliographic record
Abstract
This paper introduces a methodology to predict the non-linear stiffness–suction and shear strength–suction relationships for unsaturated soils within the lower suction range from the non-linearity of the soil-water characteristic curve (SWCC), using a normalised function formulated with ‘suction times exponential degree of saturation’. The information required in this methodology includes (a) measurements of the shear strength or stiffness properties at saturation condition and one unsaturated condition and (b) the SWCC. Published experimental data on the stiffness and shear strength properties and the SWCC obtained from 25 different soils varying from coarse-grained sands to expansive clays are used to validate the proposed normalised function and to calibrate the exponent value. It is found that the normalised function, using exponent values of 1·0 and 2·0, respectively, for cohesionless and cohesive soils, provides reasonable predictions of the stiffness–suction and shear strength–suction relationships for all the soils used in this study, taking account of various influencing factors including external stress, soil structure, anisotropy, hydraulic hysteresis and testing technique.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".