A thermo-mechanical model for saturated and unsaturated soil–structure interfaces
Bibliographic record
Abstract
Shear behaviour of soil–structure interfaces greatly affects the performance of geotechnical structures. The soil–structure interfaces in geothermal structures (e.g., energy pile and energy wall) are often subjected to varying temperature and suction conditions. However, there is no constitutive model to simulate the coupled effects of suction and temperature on the shear behaviour of soil–structure interfaces. In this study, a thermo-mechanical model was newly developed based on the bounding surface plasticity framework to predict the thermo-mechanical behaviour of saturated and unsaturated interfaces. A power function was used to calculate the degree of saturation at the interface and improve the evaluation of suction effects on interface shear strength. A linear relationship between temperature and interface critical state friction angle was proposed to incorporate thermal effects. New equations were also proposed to describe the critical state lines (CSLs) in the void ratio versus stress plane ( e-[Formula: see text]) and to model the shearing-induced deformation at various temperatures and suctions. The experimental data from different interfaces in the literature were used to evaluate the model capability. Comparisons between measured and computed results suggest that this model can well capture the coupled effects of temperature, suction, and net normal stress on the shear behaviour of interfaces.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".