Various methods to correlate the state of practice asphalt mixture laboratory aging conditioning methods with field aging durations
Bibliographic record
Abstract
The aging of asphalt pavements leads to less flexible asphalt mixtures that are prone to cracking and spalling. In this study, the relationship between lab and field aging was evaluated based on both theoretical asphalt aging models and practical asphalt and asphalt mixture performance tests. The results show that the corresponding field aging duration calculated using the mixture testing, especially the cracking test, is more conservative than the traditional aging models or binder rheological measurements. 5 and 12 days appear to simulate 16 and 38 years of field aging (in New Hampshire) for the top 12.5 mm pavement, respectively, based on the asphalt binder test results. In contrast, the theoretical aging model considers climatic conditions and suggests that 5 and 12 days simulate in-field aging of 6.2 and 15.0 years, respectively. The asphalt mixture test results indicate that the laboratory aging conditions simulate minimal field aging durations. This is because the damage to the asphalt pavement structure caused by climatic conditions and traffic loads is fully considered. This could be very useful for designing a more reliable and durable pavement incorporating intricate field conditions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".