A critical-state constitutive model for liquefiable sand
Bibliographic record
Abstract
This paper presents a critical-state constitutive model for sands over a wide range of void ratios and consolidation pressures in a triaxial plane. A single set of parameters, including a unique critical-state line reached at large strain, is also used in the model, and differences in behavior in triaxial compression and extension are modeled by accounting for anisotropy at small and medium ranges of strain. The model uses a capped yield surface (YS), which is characterized by its size and shape. Following evidence in past literature, the stress ratio at the peak point of the capped YS of loose sands is approximated by the stress ratio measured at the peak point of their undrained effective stress path. Yielding parameters obtained using this stress ratio are also applied in modeling dense sand behavior and drained loading. These parameters account for the effects of inherent anisotropy, void ratio, and confining pressure on yielding stresses and are readily determined from laboratory tests, but further research is required on their determination from field data. The model accounts for stress-induced and inherent anisotropies, using different parameters, which develop and evolve independently. Emphasis is placed on proper modeling of aspects of loose sand behavior that affect their susceptibility to flow liquefaction.Key words: constitutive modeling, liquefaction, loose sand, critical state, dilatancy, hardening.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".