Stress–strain pattern–based criterion to assess cyclic shear resistance of soil from laboratory element tests
Bibliographic record
Abstract
The cyclic shear response of soils is commonly examined using undrained (or constant-volume) laboratory element tests conducted using triaxial and direct simple shear (DSS) devices. The cyclic resistance ratio (CRR) from these tests is expressed in terms of the number of cycles of loading to reach unacceptable performance that is defined in terms of the attainment of a certain excess pore-water pressure and (or) strain level. While strain accumulation is generally commensurate with excess pore-water pressure, the definition of unacceptable performance in laboratory tests based purely on cyclic strain criteria is not robust. The shear stiffness is a more fundamental parameter in describing engineering performance than the excess pore-water pressure alone or shear strain alone; so far, no criterion has considered shear stiffness to determine CRR. Data from cyclic DSS tests indicate consistent differences inherent in the patterns between the stress–strain loops at initial and later stages of cyclic loading; instead of relatively “smooth” stress–strain loops in the initial parts of loading, nonsmooth changes in incremental stiffness showing “kinks” are notable in the stress–strain loops at large strains. The point of pattern change in a stress–strain loop provides a meaningful basis to determine the CRR (based on unacceptable performance) in cyclic shear tests.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".