The influence of viscous behavior and static shear stress on the cyclic strengths of plastic soil: a numerical study
Bibliographic record
Abstract
An accurate understanding of the cyclic behavior of clays and plastic silts is important for system performance predictions during earthquake loading. This paper presents the results of a numerical investigation into the individual and combined influences of static shear stress and viscous strength gain on the cyclic resistance of clays and plastic silts. Using the viscoplastic constitutive model PM4SiltR implemented in the finite difference program FLAC 8.1, the cyclic behaviors of the plastic soils were simulated using single-element cyclic direct simple shear simulations. A parametric analysis was performed with different combinations of viscous strength gains and static shear stresses. The effects of static shear stress and viscous strength gain varied under monotonic and cyclic loading conditions. Numerical findings suggest empirical correlations developed using scant laboratory data may not accurately predict the reduction of cyclic strengths with increasing static shear stress. Furthermore, sizable magnitudes of monotonic viscous strength gains only produced a marginal increase in cyclic strengths. The findings from this study highlight the need for future experimental laboratory testing to validate the numerical findings, to improve the accuracy of performance predictions of geosystems constructed with clays and plastic silts during and following earthquake loading.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".