Microstructural analysis of asphalt mixtures using digital image processing techniques
Bibliographic record
Abstract
In this paper, the internal microstructure of asphalt mixture is analyzed through digital image processing (DIP) of two-dimensional asphalt mixture images. A set of 12 mixtures prepared with two binders, two air voids percentages, and different recycled asphalt pavement (RAP) contents is used. First, small asphalt mixture beams of the same size of bending beam rheometer specimens are prepared for the images acquisition. Then, based on mixture volumetric properties, a three-phase material model is obtained. Finally, 2- and 3-point correlation functions of the material phases are numerically evaluated. No significant differences were observed in the microstructure and spatial distributions of aggregates, asphalt mastic, and air voids for asphalt mixtures containing up to 40% of RAP. However, an increase in auto correlation length (ACL) was found for RAP mixtures in comparison with the conventional mixtures.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".