Evaluation of Two Constitutive Models to Simulate Behavior during Constant Volume Infiltration on a Swelling Clay Soil
Bibliographic record
Abstract
The Parameter Evaluation Method algorithm is applied to determine parameters of the Barcelona Basic Model and the Blatz and Graham Model for a constant volume infiltration test on an unsaturated swelling clay soil. The limitations of both constitutive models to simulate the stress path for this example are indicated. The recommendation to incorporate different swelling induced pressure for drying and wetting stress paths may be made by introducing parameter κrat = κ/κs, which represents the degree of the swelling induced pressure. The numerical modeling of this test is also accomplished using finite difference analysis incorporating fluid-mechanical interaction. It shows that the hydraulic constitutive models (soil water characteristic curve (SWCC) and permeability laws) play a significant role for this type of analysis. The future application of the PEM algorithm combined with triaxial tests with controlled and measured suction can reduce the necessity of independent permeability testing to establish hydraulic parameters. The PEM can also be used to create a `parameter generator' to characterize the behavior of unsaturated high plastic clay. Consequently, the number of parameters used in the constitutive model is independent of the difficulty associated with calibrating the parameters using physical measurements.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".