State parameter–based thermomechanical constitutive model for saturated fine-grained soils
Bibliographic record
Abstract
This paper presents a two-surface constitutive model for describing thermomechanical behaviour of saturated fine-grained soils at both normally consolidated and overconsolidated states. A thermal-dependent stress ratio-state parameter relation is adopted to account for the effects of temperature on the shape of the state boundary surface (SBS) of soils. In the model, both the size and the shape of the SBS are allowed to vary with temperature, which is evidenced by thermal variation of the mechanical yield loci and the shifts of the normal consolidation line (NCL) and the critical state line (CSL) upon heating and (or) cooling. A thermal yield surface is added for modelling the isotropic thermal deformation of soils more accurately, in particular at overconsolidated states. The mechanical and thermal yield mechanisms are coupled through the temperature-dependent preconsolidation pressure that is controlled by a volumetric hardening law. Based on experimental observations, a nonlinear relationship between the spacing ratio and temperature changes is defined and a simple thermal dependent non-associated flow rule is proposed. The model is validated against some selected experimental results of several soils tested under various mechanical and thermal paths such as drained isotropic heating and cooling, drained and undrained triaxial compression at non-isothermal conditions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".