A possible elasticplastic framework for unsaturated soils with high-plasticity
Bibliographic record
Abstract
The paper proposes a new elasticplastic framework for unsaturated, high-plasticity, clayey soils and sandclay mixtures. The framework considers possible coupling of stress- and suction-induced hardening, leading to a yield surface that is closed or "capped" as suctions increase. This produces a stress state boundary surface in three-dimensional pqs stress space (where p is the net mean stress, q is the deviator stress, and s is the matric suction) which differs from that of other conceptual models of its kind. Yielding, a hardening law, and failure criteria for saturated soils are incorporated into the stress state boundary surface. Two parameters, equivalent pressure pe and stress ratio ηs, are introduced to form the basis of the proposed elasticplastic framework for highly plastic soils with high suctions. This provides an alternative for the stress variables net mean stress and matric suction that are commonly used in modeling unsaturated soils with lower plasticity and lower suctions. This framework has allowed results of experiments on an unsaturated sandbentonite mixture to be successfully described using elastoplasticity. Yield and failure envelopes associated with the proposed state boundary surface in pqs space can be normalized using pe and ηs in such a way that they agree with a comparable envelope for saturated specimens. Key words: unsaturated, elasticplastic, triaxial, matric suction, state boundary surface, sand-bentonite.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".