Pore Ice Content and Unfrozen Water Content Coexistence in Partially Frozen Soils: A State-of-the-Art Review of Mechanisms, Measurement Technology and Modeling Methods
Bibliographic record
Abstract
Partially frozen soil (PFS) is comprises of coexisting unfrozen water and ice within its pores at subzero temperatures. The review paper examines how unfrozen water content (UWC) and pore ice content interact during phase changes under near-freezing conditions, governed by microscopic thermodynamic equilibrium. Key theories describing why UWC persists (premelting, disjoining pressure) and the soil freezing characteristic curve (SFCC), along with measurement techniques, including the gravimetric approach to advanced nuclear magnetic resonance for characterization of water content. The influence of the water–ice phase composition on mechanical behavior is discussed, signifying pore pressure and effective stress. Various modelling approaches categorized into empirical SFCC, physio-empirical estimations, and emerging machine learning and molecular simulations are evaluated for capturing predictions in PFS behavior. The relevance of PFS to infrastructure foundation, tailings dams, permafrost slope stability, and climate change impacts on cold regions’ environmental geotechnics is also highlighted as a challenges in practical application. Hence, understanding pore pressure dynamics and effective stress in PFS is critical when assessing frost heave, thaw weakening, and the overall performance of geotechnical structures in cold regions. By combining micro-scale phase interaction mechanisms and macro-scale engineering observations, this review paper provides a theoretical understanding of the underlying concepts vital for future research and practical engineering in cold 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".