Quantitative Analysis of Macrotexture of Asphalt Concrete Pavement Surface Based on 3D Data
Bibliographic record
Abstract
This research conducts a quantitative analysis on the macrotexture of asphalt concrete pavement based on three-dimensional (3D) point cloud data. A binocular stereovision-based 3D point cloud data collection system is developed. The system is composed of a packaged component that includes a lighting source and two cameras, a dark shading cloth, and the computer control side with the configuration of the operation interface. Meanwhile, specimens of both asphalt concrete and open graded friction course (OGFC) are prepared as the test specimens. Next, 3D point cloud data of the specimens are collected using the proposed system. The macrotexture information is then extracted using the robust Gaussian method. The macrotextures of the pavement surface are characterized by 10 indicators; profile arithmetic average deviation, profile root mean square difference, mean texture depth, profile skewness value, profile steepness, profile unevenness distance, profile peak distance, profile root mean square slope, profile root mean square wavelength, and surface roughness area ratio. At the same time, the friction coefficients of these specimens are measured by British Pendulum Number. Finally, the correlations between each indicator and the friction conditions of different specimens are assessed. Results demonstrate that the proposed macrotexture indicators are reliable for evaluating the friction conditions because significant correlations have been observed. Meanwhile, the correlations for the OGFC gradations are always higher than the asphalt concrete gradations. All the findings prove that the proposed quantitative indicators are effective for the characterization of the macrotexture of asphalt concrete pavement.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".