Exploring the role of fabric anisotropy in cyclic liquefaction resistance under non-hydrostatic consolidation: Insights from DEM analysis
Bibliographic record
Abstract
This study investigates the cyclic liquefaction resistance of granular materials under non-hydrostatic consolidation using 3D discrete element method (DEM) simulations. The study specifically examines how various sample preparation techniques affect the cyclic liquefaction resistance of polydisperse spherical particle samples with a Kc value of 0.5, where Kc represents the ratio of initial horizontal to vertical normal stresses. The results reveal that the choice of sample preparation technique significantly affects the cyclic liquefaction resistance of the samples. Furthermore, this study explores the inherent fabric of the samples using coordination number and contact-normal fabric anisotropy, and demonstrates that it plays a critical role in controlling the cyclic liquefaction resistance of granular materials under nonhydrostatic consolidation. The study emphasizes the significance of considering the inherent fabric in understanding the behavior of granular materials under non-hydrostatic consolidation. This can inform the design of experiments and the development of constitutive models to explore the interplay between sample preparation techniques, fabric anisotropy, and cyclic liquefaction resistance.
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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 | 0.001 |
| 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".