Fine Structural Characterization of Construction Asphalt Mixtures Based on Image Processing Techniques
Bibliographic record
Abstract
The development of economy is inseparable from the construction of traffic buildings.Especially in the current road construction, asphalt mixture is mainly used for pouring.Over time, the asphalt mixture is disturbed by other external factors, resulting in a decrease in the performance of the asphalt mixture.Under this background, this paper mainly studied the mesoscopic angle of the construction asphalt mixture through image processing technology, and analyzed the mesoscopic structural characteristics of the construction asphalt mixture.This paper took the void structure as the research index, and performs image enhancement, image denoising, image sharpening, image segmentation and image edge detection on the collected images of building asphalt mixture in turn.In terms of image enhancement, the image after histogram equalization is clearer in texture, distinct in layers and more prominent than the original image.In terms of image denoising, the median filter method is used, and the noise reduction effect is obviously better than other methods.In terms of image sharpening, the contour of the image sharpened by the Laplacian operator is clearer.In the aspect of image segmentation, the threshold segmentation method has obvious image void boundary and detail information, which is conducive to extracting void information.In terms of edge detection, the image lines under the Canny operator are complete, which greatly reduces the loss of edge information.On this basis, the void structure model was constructed and tested experimentally.The results showed that the average equivalent diameter, average perimeter, and average contour area of the voids in each layer have roughly the same trends as the layers increase.Not only that, the detected void ratio was about 8.14%, which was only 1.17% different from the actual void ratio.This showed that the void structure model constructed under the image processing technology has a significant effect on the porosity detection, and this result brought certain guiding suggestions for the follow-up study of mesostructure characteristics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".