Strength of partially frozen sand under triaxial compression
Bibliographic record
Abstract
To investigate the effect of the pore matrix (i.e., ice and unfrozen water) on the strength of partially frozen sand, a series of triaxial compression tests with internal pore water pressure (PWP) measurements were performed. Both dense and loose saline sand samples were prepared by rapidly freezing the samples and then slowly warming the samples to, and subsequently shearing at the temperature of −3 °C. Test results indicate the temperature only affects the effective cohesion, not the effective friction angle of sand. The pore ice stress was estimated using Ladanyi and Morel’s 1990 postulate on the internal stresses within frozen soil; the stress (hydrostatic) change in pore ice was reflected by an equal change in PWP in the partially frozen loose sand. Based on Ladanyi and Morel’s 1990 concept of internal confinement in frozen sand, a Mohr–Coulomb model that uses effective failure and residual friction angles from unfrozen sand to estimate the strength of partially frozen sand is presented. The proposed model reflects how the pore matrix contributes to the strength of partially frozen sand. For loose sand, the peak strength is enhanced by the internal confinement and cohesion resulting from the pore water (suction) and pore ice, respectively. For dense sand, only pore ice affects the peak strength by incorporating the internal confinement and cohesion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".