On the use of Fredlund gas–fluid compressibility relationship to model medium-dense gassy sand behavior
Bibliographic record
Abstract
This paper presents the influence of gas bubbles trapped in a soil mass on the stress–strain–strength response of medium-dense sands. A hypoplastic constitutive sand model enhanced with the intergranular strain concept was coupled with the Fredlund gas–fluid compressibility relationship to capture gassy soil behavior. Boundary value element representations in a finite element platform of oedometer and saturated drained and undrained triaxial compression tests are performed for the calibration of soil parameters. For the numerical simulation of gassy soil behavior, pore fluid compressibility is modified to account for the presence of free gas in the pore fluid. The gassy soil mechanical response is studied by using only one set of parameters determined from the saturated soil response. The testing bed for this evaluation is a laboratory experimental program conducted on sands retrieved from the Oakridge Landfill, a sanitary landfill located in South Carolina, USA. The hypoplasticity sand model specialized with the Fredlund relationship reproduces reasonably well the stress–strain–strength response of these sands for a wide range of loading conditions and at a reasonable level of testing for the calibration of constitutive parameters. It is found that a slight reduction in the degree of saturation significantly decreases the undrained shear strength of the soil and causes changes in volume (i.e., drained-like behavior).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".