Spatial variation of void ratio and shear band thickness in sand using X-ray computed tomography
Bibliographic record
Abstract
A detailed study of spatial variation of void ratio and shear band thickness measurements is presented in this paper. A prismatic sand specimen that initially measured 57·4 mm wide × 120·5 mm long × 182·1 mm high was sheared under plane-strain (biaxial) loading conditions. The specimen was prepared at a relative density of 79% using F-75 Ottawa sand. X-ray computed tomography was used to scan the specimen before shearing and after the onset of the shear band. The specimen failed through a single shear band with an inclination angle of 65·6° measured from the direction of the minor principal stress. Computer algorithms were developed to calibrate CT images and quantify void ratio (e) variation within the specimen. CT data analysis revealed cross-sectional spatial variation in void ratio where density is higher in regions close to the edges of the specimen due to membrane effects and confining pressure with no significant variation in void ratio in the axial direction (top to bottom) before shearing. The shear band was easily identified from the CT images, and analysis of void ratio showed a noticeable jump in void ratio profile across the shear band. A detailed statistical summary of the thickness of the shear band and variation of void ratio across the shear band is presented and discussed.
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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 | 0.001 | 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) | 0.000 | 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".