X-ray CT quantification of in situ fabric evolution and shearing behaviour of granular soils of different particle shapes
Bibliographic record
Abstract
The role of particle shape on soil mechanical response has been studied extensively especially through numerical means. The underlying micromechanics of how particle shape may affect the soil mechanical responses at element scale remains unclear. Systematic micromechanical experiments that consider in situ tracking of the evolution of fabric during the shearing process is missing. Aided by a miniaturised triaxial apparatus and X-ray computed tomography (CT), this study presents a series of triaxial compression on four granular soils with different particle shapes yet the same mineralogy, grading, and initial density. Evolution of three-dimensional soil fabric quantifiers during shearing was captured based on 192 full-field CT images. The results revealed that the initial shearing reduced the packing density without changing the particle packing pattern, followed by particle sliding and particle rotation, which redistributed the force chains and formed a new packing pattern to resist shearing, causing strain localisation, and reductions in both the contact number and concentration of contacts direction. Fabric anisotropy increased before reaching the peak and attained the maximum value as the soil approached the critical state. Particle shape, especially when quantified by overall regularity or other combinations of descriptors, displayed more significant linear correlations with critical-state parameters than by local descriptor.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".