Numerical Determination of RVE for Heterogeneous Geomaterials Based on Digital Image Processing Technology
Bibliographic record
Abstract
Representative volume element (RVE) is an important parameter in numerical tests of mechanical properties of heterogeneous geomaterials. For this study, a digital image processing (DIP) technology was proposed for estimating the RVE of heterogeneous geomaterials. A color image of soil and rock mixture (SRM) with size of 400 × 400 mm2 taken from a large landslide was used to illustrate the determination procedure of the SRM. Six sample sizes ranging from 40 × 40 mm2 to 240 × 240 mm2 were investigated, and twelve random samples were taken from the binarized image for each sample size. A connected-component labeling algorithm was introduced to identify the microstructure. After establishing the numerical finite difference models of the samples, a set of numerical triaxial tests under different confining pressures were carried out. Results show that the size of SRM sample affects the estimation of the mechanical properties, including compressive strength, cohesion, and internal friction angle. The larger the size of the samples, the less variability of the estimated mechanical properties. The coefficient of variation (CV) was applied to measure the variability of mechanical properties, and the RVE of the SRM was determined easily with a predefined acceptance threshold of the CV. The results show that a DIP-based modeling method is an effective method got the RVE determination of heterogeneous geomaterials.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".