Implication of end restraint in triaxial tests on the derivation of stress–dilatancy rule for soils having high compressibility
Bibliographic record
Abstract
Constitutive models for soils are developed and validated against laboratory tests assuming these give representative information on the true material behaviour. However, data from standard laboratory tests reflect the sample response rather than the true material behaviour, due to nonuniformities in stresses and strains generated over the experimental test. The work examines the implications of end restraint on the definition of the stress–dilatancy rule of highly compressible soils with a finite element numerical approach. The numerical model replicates a reconstituted peat, typically characterized by a combination of high compressibility and high friction angle, which increases the severity of end restraint effects. Simulated results show that the global measurements from standard triaxial tests with rough end platens would not give the proper stress–dilatancy rule, if they were interpreted as the response of a single soil element at the constitutive level. Both overestimation and underestimation of the true dilatancy compared to the material response can be observed, depending on the deformation mode. To support the validity of the numerical results, experimental findings from drained triaxial tests on reconstituted peat are presented. Practical indications are given on how the standard interpretation of drained triaxial tests data on peats can be improved.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".