Evaluation of select warm mix additives with polymer and rubber modified asphalt mixtures
Bibliographic record
Abstract
Warm mix technologies are being increasingly investigated in an effort to conserve energy, reduce emissions, and extend paving season. Most of the laboratory research conducted to date on warm mix technologies has been concentrated on studying the impact of warm mix technologies on unmodified asphalt binders. This research effort evaluates the use of select warm mix additives with unmodified, polymer-modified, and terminal blend tire rubber asphalt mixtures from Nevada and California. The study evaluated two different warm mix asphalt (WMA) technologies: Sasobit and Advera. The experimental program evaluated the resistance to moisture damage and rutting of different mixtures with and without liquid anti-strip and lime. Additionally, fatigue resistance of modified and unmodified asphalt mixtures without anti-strip was conducted. In this research effort, the unmodified WMA mixtures were able to achieve mixing temperature reductions of 26.7 to 29.4 °C while the modified WMA mixtures were able to achieve mixing temperature reductions of 16.7 to 25.0 °C compared to their corresponding HMA control mixtures. The results showed that the use of modified binders with WMA technologies and anti-strip additives can result in mixtures with a better resistance to moisture damage compared to unmodified mixtures. Additionally, the combination of modified asphalt binders with lime will eventually generate better resisting mixtures to permanent deformation. Moreover, the resistance to fatigue cracking of HMA and WMA mixtures was also enhanced using modified asphalt binders. Furthermore, a mechanistic fatigue analysis using 3D-Move software confirmed the outcomes of fatigue testing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".