Shear resistance evolution of geogrid–aggregate interfaces under direct shear: insights from 3D DEM simulations
Bibliographic record
Abstract
This paper presents a mesoscopic evaluation of the shear resistance evolution of geogrid–aggregate interfaces subjected to direct shear loading. A three-dimensional discrete element method model was developed based on experimental data. The tensile response of geogrid were simulated through a series of calibration tests. Aggregate with complex particle shapes were simulated to accurately capture the interlocking effect among aggregates based on the real particle surface. The individual shear resistance components were quantified based on particle displacement field and contact distribution characteristics. The influences of aperture–aggregate size ratio and geogrid stiffness on the shear resistance components are discussed. The results indicate that the peak value of shear resistance component follows a descending order from frictional resistance of aggregate, to passive resistance of transverse rib, and to geogrid–aggregate interface frictional resistance. During the shear process, the frictional resistance of aggregate becomes active first, followed by the geogrid–aggregate interface frictional resistance, and then the development of passive resistance of transverse ribs starts with a certain lag. Optimizing the geogrid–aggregate size ratio and utilizing geogrids with higher rib stiffness could enhance the passive resistance of transverse ribs but would not significantly affect the geogrid–aggregate interface frictional resistance and frictional resistance of aggregate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".