Micromechanics-based assessment of reliability and applicability of boundary measurements in symmetrical direct shear test
Bibliographic record
Abstract
Development of the discrete element method (DEM) has provided an efficient tool to examine and appraise the performance of a direct shear apparatus (DSA) to overcome ambiguities that arise from the complexity of stress and strain distributions involved. This paper presents DEM analyses of both macro- and micromechanics responses of three-dimensional dense samples of 102 248 glass spheres tested in virtual symmetrical DSA using the computer code SiGran. Particular emphasis is placed on the validation of the DEM model by comparing the results of DEM simulations with their physical counterparts at the macroscale. The performance of the physical direct shear apparatus is optimized by exploring modifications to the symmetrical test configuration. Numerical results provide quantitative data on different forms of energy consumed during shearing, confirming other published physical and numerical results found in the literature. Virtual DSA results are also discussed in terms of the coaxiality between the directions of the principal stresses’ and the principal strains’ increments as well as the deviation of the zero extension direction from the horizontal direction. Microscale results show that peak state parameters obtained from the symmetrical arrangement, adopted in this study, are very close to those of an ideal simple shear test, as this arrangement permits a uniform deformation within the developed shear band, a horizontal orientation of the zero linear extension, and a coaxiality of principal stresses and incremental strains at the peak state. In other words, the microscale results presented in this study provide new evidence that corroborates the further use of the boundary measurements in physical symmetrical direct shear tests.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".