A stress-dilatancy relationship considering the variation of contact number based on granular micromechanics
Bibliographic record
Abstract
As a critical factor governing the strength and deformation behaviours of granular materials, dilatancy characteristics inherently exhibit intricate complexity arising from stochastic particle arrangements and dynamic motion patterns. This article introduces a stress-dilatancy relationship considering the variation of contact number within granular assemblies from the perspective of micromechanics. At microscopic scale, average contact force and displacement expressions are derived using the true stress tensor. And then the variation of contact number is introduced to further optimise the description of the energy dissipation behaviours of granular materials. Following that, the stress dilatancy relationship for granular materials considering of the evolution of fabric anisotropy are established through macro-micro energy conservation. The stress-dilatancy relationship can well describe the phenomenon that the stress ratio for dense sand at the phase transition point is inconsistent with the critical stress ratio. Compared with the reults of experiments obtained from Ottawa sand, the established stress-dilatancy relationship can better describe macroscopic deformation response than the classical flow law such as the Cam-Clay model and Rowe dilatancy model. Moreover, under true triaxial stress paths and different initial anisotropies, the mechanical responses of the proposed stress-dilatancy relationship are consistent with the results of discrete element method simulation experiments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".