Shape Characterization of Fragmented Sand Grains via X-Ray Computed Tomography Imaging
Bibliographic record
Abstract
The mechanical and hydraulic properties of granular materials are fundamentally affected by the grain size and shape. Three samples of uniformly graded quartz sand with different size ranges were subjected to one-dimensional compression tests up to 40 MPa to fracture the sand into fragments with a variety of sizes and shapes. X-ray computed tomography was used to obtain the morphology of the crushed sand at a resolution of 2.8 μm. A practical divide and stitch method was proposed and implemented to automatically separate and extract individual grains for morphological analysis. This method can reduce the misidentification of grains and voids. Scans of 5,481 grains were used to quantify the three-dimensional morphological properties of grains of different sizes and shapes. The shape descriptors of elongation, flatness, and sphericity were the best way to describe the grain shape. The intermediate Feret diameter was the best parameter for characterizing the grain size. The smaller fragments from the crushed sand were more elongated and had higher flatness and convexity. The distributions of elongation, flatness, sphericity, and convexity for grains in different size ranges followed a normal distribution. The standard deviation in the grain shape descriptors increased for the small grain sizes. The volume and surface area of the grains can be predicted with high confidence using elongation, flatness, and intermediate Feret diameter. Convexity needs to be used along with elongation and flatness to estimate sphericity reliably.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".