WMA Overlay Optimization Based on the LTPP Database: Using the RSM Method
Bibliographic record
Abstract
This study investigates the field performance of Warm Mix Asphalt (WMA) road overlays containing various amounts of RAP and binder. Rutting, the International Roughness Index (IRI), and transverse, longitudinal and alligator cracking are the key parameters considered here. Our research is based on a Specific Pavement Study-10 experiment (SPS-10) conducted in nine states of North America (eight in the USA and one in Canada) that included 31 road sections in dry and wet regions. Road overlays were evaluated 1 and 4 years after their placement in terms of anti-cracking behavior and were compared with the pre-treatment status of the road. The best rutting resistance occurred at 15% and 12% RAP in dry and wet regions, respectively. For IRI, 30% and 0.0% RAP were the best for dry and wet regions as well. The maximum longitudinal crack recovery rates were found at site 3 (BA01, Arizona; dry region) and site 26 (AA65, Missouri; wet region), with RAP contents of 20% and 36%, respectively. In addition, alligator cracking did not occur post-overlay, so optimal RAP and binder contents cannot be suggested. The greatest improvements were found at site 15 (AA01, Washington state; dry) and site 30 (AA63, Oklahoma; wet). The response surface method (RSM) was also developed to explore the optimal models for RAP and selection of binder contents to minimize the rutting, IRI, and transverse and longitudinal crack lengths.
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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 | 0.001 | 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) | 0.003 | 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".