A bounding surface constitutive model for simulating repeated freeze–thaw cycles of saturated soil
Bibliographic record
Abstract
Repeated cycles of frost and thawing actions of soil can cause serviceability issues in soil structures. Many theoretical models have been developed to account for soil deformation resulting solely from either freezing or thawing processes. They fail to adequately capture the plastic strain accumulated through repeated cycles of pore water/ice phase transitions. A new bounding surface constitutive model is developed using two stress state variables (Bishop’s effective stress and cryogenic suction), enabling simulation of soil stress state under repeated cycles of freezing and thawing processes. A new freeze–thaw state parameter is introduced to consider the effects of soil’s stress history on a soil’s residual state after infinite freeze–thaw cycles. By incorporating this new parameter and bounding surface plasticity, the model is capable of predicting plastic deformation during hardening and softening induced by freeze–thaw cycles, even when the stress state of soil remains within the yield surface. It is shown that the model can capture the accumulated volumetric response due to freeze–thaw cycles. Moreover, by implementing the freeze–thaw state parameter into the hardening law, the newly developed model can also capture the effects of OCR on freeze–thaw-induced volume changes, particularly the irreversible heave observed in heavily over-consolidated soils.
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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 | 0.000 |
| 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 | 0.000 |
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".