Uneven frost heave deformation in water conveyance channels in cold-arid regions: Experimental evidence and thermo-hydro-mechanical coupling analysis
Bibliographic record
Abstract
Uneven frost heave deformation can shorten the operational lifespan of foundation engineering. Clarifying the mechanisms of uneven frost heave facilitates the targeted mitigation of frost damage. This study focused on a water conveyance channel in Jilin Province, northern China, and found after monitoring that the frost heave at the channel bottom lining exceeded that at the crest by 44.5 mm, with the freezing temperature at the bottom being over 2 °C lower than that at the crest. Soil columns with an initial gravimetric moisture content of 12%, 16%, 18%, and 20% were then prepared. The effects of temperature and moisture content on frost heave were analyzed under two freezing conditions (−5 °C and −10 °C) through unidirectional freezing tests. A coupled thermo-hydro-mechanical (THM) frost heave model, validated by the test results, was further established. In the soil with an initial moisture content of 20%, the formation of ice lenses associated with substantial water migration contributed to a large temperature gradient, which can jointly induce frost heave. Under the −10 °C condition, the temperature gradient in the soil column with a 20% initial moisture content reached 0.84 °C/cm, the total water migration reached 10.72%, and the frost heave deformation was 1.86 mm. The THM coupling results indicated that, under the interaction of a large temperature gradient and moisture accumulation, the volumetric ice content remained high in the bottom soil during freezing and peaked at 0.36. The frost damage to the bottom soil was severe, and the maximum deformation reached 57 mm. • The latent heat from phase change limits the formation of large temperature gradients. • The moisture content threshold required for frost heave in channel slope soil is 20%. • A large temperature gradient and high moisture content jointly promote frost heave.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".