Modeling Cyclic Volumetric Strain Accumulation in Sand under Drained Simple Shear Conditions
Bibliographic record
Abstract
This study presents a critical state two-surface plasticity model formulated such that it accurately simulates the volumetric behavior of sands under drained cyclic simple shear (CSS) loading. The model is based on critical state theory and accounts for the effects of inherent fabric anisotropy and evolving fabric upon cyclic loading. To represent the effects of fabric evolution under cyclic loading, a multiplier is introduced to the plastic modulus equation to express accumulated fabric changes occurring during the dilation phases that manifest as enhanced contraction in subsequent load reversals. Moreover, a conical surface is incorporated into the general stress space, in addition to the yield surface, to capture the experimentally observed cyclic threshold shear strain associated with the onset of volume changes. The model is calibrated using data from low- to high-amplitude CSS tests on Toyoura sand and Ottawa sand. The model manages to reproduce the net contraction that sand experiences during each loading cycle up to a very high number of cycles (of the order of 103 to 105). The magnitude of contraction diminishes progressively over successive cycles, with the compaction process approaching asymptotically a limiting minimum void ratio state at a very large number of cycles. The model accounts for the effects of relative density, anisotropic consolidation, and shear strain amplitude on the rate of accumulation of contractive volumetric strains.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".