Using Imaging Techniques to Analyze the Microstructure of Asphalt Concrete Mixtures: Literature Review
Bibliographic record
Abstract
The performance of asphalt concrete (AC) mixtures depends highly on their internal structure and the interaction of the mixture components under different loading conditions. Imaging techniques provide effective tools that can assess the microstructure and failure mechanisms of materials. Imaging techniques have been used in recent research studies to examine and analyze the evolution of the internal structure of AC mixtures resulting from traffic and environmental loading. Increasing knowledge of the microstructural properties and mechanical behaviour of AC mixtures could improve the design process and enable researchers to develop more accurate prediction models for the long-term performance of pavements. This paper reviews three imaging techniques which were used to characterize the microstructure of AC mixtures. These three imaging techniques are digital camera imaging, scanning electron microscope (SEM) imaging, and X-ray computed tomography (CT) scan. Extensive insight has been presented into these imaging techniques, including their principles, methods, sample preparation, and associated instruments. This review provides guidelines for future research on using these imaging techniques to analyze the microstructure of AC mixtures and assess their long-term performance.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".