Model for Predicting the Hydraulic Conductivity of Frozen Soils Using the Soil Freezing Characteristic Curve
Bibliographic record
Abstract
Abstract Frost heave and thaw settlement driven by freeze‐thaw cycles in seasonally frozen soils of cold regions are strongly related to water migration. A key parameter controlling water migration in frozen soils is the hydraulic conductivity, which predominantly governs the movement of water under thermal and hydraulic gradients. The direct measurement of hydraulic conductivity in frozen soils requires extensive laboratory equipment, is time‐intensive and hence expensive. To address these challenges, numerous prediction models have been proposed in the literature utilizing the Soil Freezing Characteristic Curve (SFCC). However, many of these models suffer from limitations associated with computationally intensive integral formulations that only address capillary water flow and ignore the contribution of film water flow—a critical mechanism driving frost heave. In this study, a novel closed‐form model is proposed for predicting the hydraulic conductivity in frozen soils based on a theoretical framework using the capillary bundle model and the SFCC. Validation against published experimental data for a variety of soil types demonstrates the strong predictive capability of the proposed model. The model is robust and can be used for estimating water movement in frozen soils, offering significant advantages for use in the numerical simulations of frost heave, artificial ground freezing, and other cold region engineering applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".