Soil Particle Movement and Shear Band Development during Plane Strain Compression
Bibliographic record
Abstract
Most geotechnical structures failed by formation and development of shear bands in soils. Thus, shear deformation and shear bands development evaluation are necessary to understand shear failure mechanism. During shearing, deformation behaviour analysis for soil particles within entire soil specimen are evaluated to understand the soil behaviour and shear strength characteristics. In this paper, a series of plane strain compression tests using Nevada sand and Ottawa sand were conducted to identify the shear strain and shear failure mechanism. With the results of plane strain compression tests, image analyses using Particle Image Velocimetry (PIV) were carried out in order to measure the change in position of soil particles and shear bands development. Deformation vectors and contours were constructed to see the entire deformation mechanism in the soil specimen. During shearing, shear band was identified after peak stress and most visually distinctive at residual state. However, shear band started to develop invisibly immediately after starting loading and this invisible development was able to be observed by horizontal and vertical movement analyses of PIV. Soil particles moved actively in horizontal and vertical direction to generate shear band in the beginning of shearing. After development of shear band, soil particles moved along the shear band.
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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".