Identification of Microscopic Damage Law of Rocks Through Digital Image Processing of Computed Tomography Images
Bibliographic record
Abstract
This paper introduces digital image processing (DIP) to geotechnical field, aiming to disclose the microscopic damage law of rocks under cyclic water invasion. Firstly, the altered granite specimens under cyclic water invasion were subjected to computed tomography (CT) scanning, producing cross-sectional images. These images then underwent noise removal and threshold segmentation. The pores and cores, rock foundation, and high-density nodules were identified accurately in the processed images, reflecting the microstructure of the original rock mass. Based on the processed images, the 3D rock cores were reconstructed, and a 200200200 representative elementary volume (REV) was extracted from each rock core. The analysis results show that, with the growing number of water invasion cycles, the surface porosity and non-closed surface porosity continued to increase, while the closed surface porosity first increased and then declined. This research lays a theoretical basis for applying the DIP in geotechnical field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".