A critical state constitutive model for gassy clay
Bibliographic record
Abstract
Fine-grained marine sediments often contain gas bubbles that can cause many geotechnical problems. This soil has a composite structure with gas bubbles fitting within the saturated soil matrix. The gas cavity has a detrimental effect on the soil stiffness and strength when they are filled with undissolved gas only. The gas cavity can be filled with gas and pore water due to “bubble flooding”. Bubble flooding has a beneficial effect on the soil stiffness and undrained shear strength because it makes the saturated soil matrix partially drained under a globally undrained condition. A critical state constitutive model for gassy clay is presented that accounts for the composite structure of the soil and bubble flooding. The gas cavity is assumed to have a detrimental effect on the plastic hardening of the saturated soil matrix. Some of the bubbles can be flooded by pore water from the saturated soil matrix, which leads to higher mean effective stress of the saturated soil matrix. Consequently, both soil stiffness and strength increase. Only one new parameter is introduced to model the detrimental effect of gas bubbles on plastic hardening. The model has been validated by the results of three gassy clays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".