Effect of Anisotropic Consolidation on Cyclic Liquefaction Resistance of Granular Materials via 3D-DEM Modeling
Bibliographic record
Abstract
The influence of anisotropic consolidation on the cyclic liquefaction resistance of granular materials is explored using 3D discrete element method simulations. In this study, the term anisotropic consolidation was defined as the ratio of initial horizontal and vertical normal stresses, and the hypothesis was that the conflicting results from previous laboratory experiments could be attributed to differences in inherent fabric. To test this hypothesis, three unique sample preparation protocols were employed to construct polydisperse spherical particle samples with varying inherent fabrics, as quantified by coordination number and contact-normal fabric anisotropy, under consistent initial mean stress and density conditions. The results were intriguing, as they revealed that anisotropic consolidation had a consistent impact on the cyclic liquefaction resistance of loose and medium-dense samples, regardless of preparation protocol. However, this relationship was not as straightforward in dense samples. In addition, the study assessed the correlations between various parameters, including initial shear wave velocity, state parameters associated with both void ratio and coordination number, fabric anisotropy, and their impact on the cyclic liquefaction resistance of the samples. The findings enhance the understanding of the intricate interplay between anisotropic consolidation and the resistance of granular materials to cyclic liquefaction, providing valuable insights that can inform the development of accurate models for predicting and mitigating cyclic liquefaction in various applications.
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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 | 0.000 |
| 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".