Analysis of sand – woven geotextile interface shear behavior using discrete element method (DEM)
Bibliographic record
Abstract
The interaction between soil and geotextile is essential for the performance of reinforced soil. This study reveals the microscopic mechanism of interface shear between sand and geotextile based on the discrete element method (DEM). The surface characteristics of geotextile are simulated by overlapped particles. The micromechanical parameters of sand, geotextile, and interface are calibrated effectively using laboratory test results. Three types of shear tests on the sand–geotextile interface are simulated; namely, interface direct shear test (IDST), double-sided interface shear test (D_IST), and interface direct shear test with periodic boundary (PBST). For IDST, the results show that the thickness of shear band is 2.4∼3.0 times the average particle diameter (D 50 ); the contact force, percentage of sliding contact, and contact normal anisotropy inside the shear band are larger than those outside the shear band, whereas the coordination number is smaller inside the shear band. The mechanical response of D_IST is similar to that of IDST. However, D_IST has a shear band thickness of 3.0D 50 , and greater coordination number, percentage of sliding contact, and contact normal anisotropy. The results of PBST indicate that the peak stress and the shear band no longer appear without boundary constraint and the contact distribution is uniform.
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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".