Modelling the thermo-mechanical behavior of saturated fine-grained soils
Bibliographic record
Abstract
This paper presents a simple thermo-elasto-plastic constitutive model for saturated fine-grained soils, addressing thermal volume change, excess pore pressure, and shear strength. The model incorporates a novel temperature-dependent plastic modulus formulation that attributes the thermoplastic strain to an internal state variable representing the thermal stabilization of soils due to cyclic thermal loading. It can capture the accumulative volume expansion of highly overconsolidated (OC) soils, and the accumulative contraction of normally consolidated (NC) and slight OC soils after several heating–cooling cycles. A thermally induced pore pressure formula is derived with consideration of thermo-elastic expansion of pore water and soil particles, thermo-plasticity of soil skeleton as well as the elastic unloading due to the decrease of effective stress under undrained heating. The effect of temperature on the shear strength was emphasized. An insight into the evolution of shear strength with temperature is provided. The consolidated stress history and stress path play a vital role in the thermal effect on the shear strength. The proposed model comprises nine parameters, which can be easily calibrated by element tests (triaxial tests and oedometer tests). The adequacy of the proposed model has been verified with experimental results from fine-grained soils documented in the literature.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".