Discrete element analysis of geogrid–aggregate interface shear behavior under cyclic normal loading
Bibliographic record
Abstract
The stability of a geogrid-stabilized structure affected by cyclic normal loading (CNL) is significant but has not been fully revealed. Using the discrete element method (DEM), the effect of CNL on the microscale mechanical responses (i.e., stress states, contact evolution, fabric deformation) of the geogrid–aggregate interface direct shear test is first investigated. The complex shear behaviors at the interface with normal cyclic excitation at different frequencies and amplitudes are simulated. The DEM model is able to capture the macroscopic dynamic shear laws at the geogrid–aggregate interface in a similar way to those tested experimentally. The detailed behavior of the aggregate interacting with the geogrid under CNL is investigated. Compared with the simulation under static normal loading (SNL), CNL makes the stabilized layer more prone to failure, which could be quantitively evaluated by analyzing the local shear strain and the interparticle interlocking level. Microscale studies on the load wave propagation process and the confinement zone indicate that the present method can provide an applicable tool for dynamic service assessment and reliable forecasting of the undesirable effect of CNL on a mechanically stabilized layer.
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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 | 0.000 |
| 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 | 0.000 |
| 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".