Research on Deterioration Mechanism of Sulphate Saline Soil Strength Under Freeze-Thaw Cycles in Xining Area
Bibliographic record
Abstract
In China’s seasonal permafrost regions, widespread saline soils are prone to frost heave, salt heave, and uneven thaw settlement, mainly driven by freeze–thaw cycles and salt content. This study investigates the mechanical behavior and strength degradation of sulfate saline soils in the Xining area under freeze–thaw cycles through field investigations, laboratory tests, theoretical analysis, and establishes a corresponding strength damage model. The results indicate that sulfate saline soils exhibit a thick diffuse double layer and high particle dispersibility. Repeated salt frost heave–thaw settlement leads to significant structural degradation. With increasing freeze–thaw cycles, the abundance distribution proportion of structural units within the range of 0.8–1.0 has increased. Microstructural units become more equiaxed, pores elongate, particle contact area decreases. Macroscopically, the soil undergoes periodic volume changes and progressive strength deterioration. The decrease in cohesion and internal friction angle is 26%–30% and 32%–44%, respectively. Moreover, increasing salt content enhances the heterogeneity in the morphology and orientation of structural units, aggravating structural damage. At the macroscopic level, the amplitude of alternating “salt frost heave–thaw settlement” deformation increases, and the salt heave rate rises to about 11 times that of frost heave, accounting for more than 90% of the total deformation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 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".