Novel particle reconstruction and tracking algorithms to reveal 3D micromechanical behaviors of coral sands
Bibliographic record
Abstract
The micromechanical behaviors of coral sands remain poorly understood, primarily due to the inherent complexity of their highly irregular particle shapes, which pose significant difficulties for accurate three-dimensional (3D) particle reconstruction and tracking using X-ray tomography (µCT). To address these challenges, this study proposes a novel framework that integrates large vision models and discrete label optimization for efficient and accurate 3D particle reconstruction with optimal transport for robust particle tracking. This framework effectively resolves tracking both particle breakage and internal voids in coral sands, as validated by in situ mini-triaxial µCT tests. Compared with the state-of-the-art method, the proposed approach achieves comparable reconstruction accuracy (90%) while reducing computational time by 50%. For particle tracking, accuracy between adjacent µCT scans (corresponding to axial strain increments of 2.5%, 5%, and 10%) reached 95%, 86%, and 71%, respectively. Micromechanical analysis of coral sands further reveals that heterogeneous local shear deformation develops as axial strain increases, forming an X-shaped shear band. Significant fabric anisotropy emerges after peak stress, with preferred orientations aligning with the shear band. Moreover, particle breakage was observed to occur primarily during the strain-softening stage. Splitting induced by stress concentration was the predominant failure mode.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".