Frost heave and thaw consolidation modelling. Part 2: One-dimensional thermohydromechanical (THM) framework
Bibliographic record
Abstract
This paper presents a thermohydromechanical framework to model frost heave and (thaw) consolidation simultaneously, in which effective and total stresses are taken as the stress variables for unfrozen and frozen soils, respectively. “Effective (total) stresses – void ratio – permeability” relations are proposed to interpret the frost heave behavior of soil in different cooling modes, (thaw) consolidation processes, and changes in key parameters induced by freeze–thaw cycles. The water flux function proposed by Yu et al. in a companion paper is used to calculate frost heave in the frozen zone and to determine the moving boundary of the unfrozen zone during thaw consolidation. Compared with conventional methods, two other modifications are made to characterize the effect of residual stress and the influence of freeze–thaw cycling on permeability in the thaw consolidation analysis. After the governing equations developed in Lagrangian coordinates are implemented in a finite-element system, the framework is firstly verified by a comparison with both small- and large-strain thaw consolidation theories, in terms of simulating a semi-infinite thaw consolidation case, and is then examined with a focus on the three modifications one-by-one. Following that, the framework is assessed by two numerical examples that reasonably reproduce the freeze–thaw cycling processes in both seasonal frost and permafrost regions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".