Nonlinear constitutive model of soil–bentonite based on triaxial tests along different stress paths
Bibliographic record
Abstract
To investigate the constitutive behavior of soil–bentonite, which is commonly used as backfill for cutoff walls, a series of triaxial tests was conducted along different stress paths. The tested soil–bentonite comprises 5% Wyoming bentonite and 95% silty clay excavated from a landfill site located in Jiangsu Province, China. Some mechanical properties of the soil–bentonite, including the compression characteristic, shearing characteristic, and coupled deformation of mean and deviatoric stress, were examined based on the test data. Then, a nonlinear constitutive model was developed under the axisymmetric condition based on a modified hypoelastic model framework. All six independent parameters included in the model were calibrated according to the test data. The mechanical behaviors that the triaxial tests revealed can be fully reflected by the nonlinear model; therefore, it can reasonably describe the stress–strain behaviors of the soil–bentonite in triaxial tests of this study and other literature. Compared with the Modified Cam-Clay model, the prediction effect for the shear strain of the nonlinear model is better. According to a large-scale in situ test employing a soil–bentonite cutoff wall, the actual stress paths of soil–bentonite are basically included in the application scope of the nonlinear model, preliminarily indicating its applicability for practical engineering projects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".