Frost heave and thaw consolidation modelling. Part 1: A water flux function for frost heaving
Bibliographic record
Abstract
Although much effort has been made to develop various frost heave models in the past decades, a simple yet versatile model is still needed for engineering applications. This paper presents a method to estimate frost heave in frozen soil using a macroscopic water flux function that extends the segregation potential to make it applicable for both steady state and transient freezing and thawing states. The formation of an individual ice lens is modelled by combining previously developed stress and strain criteria. The water flux function, which includes various factors in accordance with the porosity rate function, can describe the growth of both new and old ice lenses. More importantly, every component of the water flux function is physically explained by the theory of pre-melting dynamics, where all the influencing factors are traced back to their impacts on the ice volume distribution. The performance of the model is demonstrated via simulations of one-dimensional freezing and thawing processes after the model is validated by a specific case from previous literature. Although adequate data are not available for a stricter experimental verification of the model, it is observed that the simulations predict the general course of events together with significant specific features that were identified in previous experimental studies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".