The mechanical behaviour of gap-graded soils under triaxial compression and extension loading paths
Bibliographic record
Abstract
The influence of mechanical loading paths on the characteristics of gap-graded granular assemblies was investigated using the discrete element method (DEM). Dense and loose gap-graded assemblies with finer fraction content, $${f}_{c}$$ , ranging from 0–100% were prepared and subjected to drained triaxial compression and extension loading paths. After examining key macroscale quantities, micromechanical analyses were conducted to elicit the particle-scale characteristics including the evolution of the fabric of the assemblies under the different loading paths. The results of the DEM analysis confirm the validity of the Mohr–Coulomb failure criteria at the critical state. While the mobilised friction angle at the peak is higher under extension than in compression, no significant difference was obtained in the critical state friction angle for both loading paths. Despite the higher mean stress transmitted by the gap-graded assemblies under compression in comparison with extension, the contribution of the finer particles to the total mean stress is not significantly influenced by the loading paths. Our data show that the variation in the fabric of granular assemblies under different loading paths does not always stem from an initial inherent anisotropy. Fabric anisotropy is marginally higher under extension than in compression despite having an initial isotropic fabric.
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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) | 0.000 | 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".