Study on microstructure influence mechanism to mechanical behavior of OGFC asphalt mixture
Bibliographic record
Abstract
Microstructure characteristics of OGFC (open graded friction course) asphalt mixture are closely related to the mechanical behavior. To study the microstructure influence mechanism to mechanical behavior of OGFC asphalt mixture, the digital image processing technology is used to process the specimen’s fault images and obtain the microstructure parameters of the OGFC asphalt mixture with different gradation. At the same time, the microstructure influence on mechanical behavior of OGFC asphalt mixture is explored by using the Grey correlation theory based on the experimental results of mechanical performance and the microstructure parameters. The influence of four key sieve passing percentage (1.18, 2.36, 4.75, and 9.5 mm) on the microstructure was discussed by using the Grey correlation theory, and the influence mechanism to mechanical behavior of OGFC asphalt mixture was further revealed. The results show that with the increase of air void content, the total void area, the equivalent pore diameter, and the number of big voids (here, “big” means voids with an area greater than 10 mm 2 ) increase, the length of void and corresponding void area increase synchronously, the drainage performance is enhanced, and the mechanical performance is decreased. With the increase of nominal maximum particle size, the average area of single void, the percentage of number of big voids, and the equivalent diameter increase, and the drainage performance, anti-skidding performance, and mechanical performance of pavement are enhanced. It is considered that the key sieve passing percentage affects the mechanical behavior by influencing the microstructure of asphalt mixtures. The influence degree of microstructure parameters on mechanical behavior and the influence degree of the key sieve passing percentage on microstructure parameters were obtained. The research results have a certain reference value for the optimization of mechanical behavior of OGFC asphalt mixture.
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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".