Experimental behaviour and modelling of an unsaturated compacted soil
Bibliographic record
Abstract
This paper reports the experimental study and modelling of the mechanical response of a silty sand used in the core of the Metramo dam, Italy. Specimens were prepared by compacting the soil at optimum water content conditions using the modified Proctor technique. Tests were performed under suction-controlled conditions by a stress path triaxial cell and an oedometer. The experimental program consists of 23 tests carried out in the suction range of 0-400 kPa. The findings indicate the strong influence of suction on compressibility, stiffness, and shear strength. The mechanical properties of the soil improve with suction following an exponential law with decreasing gradient. Furthermore, the soil exhibited collapsible behaviour upon wetting even at low stress levels. Interesting results were also achieved in elastoplastic modelling as well. The results led to characterization of soil behaviour with reference to widely accepted modelling criteria for unsaturated soils, providing noteworthy suggestions about their applicability for granular materials with a non-negligible fine component. Finally, some remarks are made for the extension under unsaturated conditions of the "Nor sand" model for saturated granular soils. The proposed approach yields improved predictions of deviator soil response of the tested soil when Cambridge-type frameworks prove invalid.Key words: unsaturated soils, stress state variables, triaxial tests, oedometer tests, constitutive model.
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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".