Influence of fine content on the behavior of sandy soils undergoing artificial freezing in triaxial conditions
Bibliographic record
Abstract
Artificial ground freezing (AGF) is used to stabilize loose soils and fractured rocks during tunnel and shaft excavation, ensuring temporary ground stabilization and groundwater control. It is particularly relevant for intermediate soils, where the influence of granulometry on freezing is crucial for effective design. However, existing studies often focus on site-specific materials, limiting broader applicability. This research investigates how fine content influences the freezing behavior of intermediate soils. Sandy soils with varying kaolin content (0% to 15%) are tested under different confining pressures representative of AGF applications. A modified triaxial apparatus simulated field conditions, applying radial thermal loading to replicate freezing around a pipe. Results demonstrated a pronounced dependence on kaolin content. Samples with 15% kaolin exhibited significant swelling, whereas soils with lower kaolin content were classified as non-frost-susceptible. Swelling was mitigated by increasing confining pressure. Water drainage was observed during the freezing process, governed by two mechanisms: (1) the expulsion of liquid water from the freezing front as water turned into ice and (2) cryogenic suction, which drew water toward the freezing front. These findings contribute to a more generalized and quantitative understanding of soil behavior with varying fine content under freezing conditions, facilitating the optimization of AGF applications.
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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 | 0.001 |
| 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) | 0.001 | 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".