The Effect of a Chemical Warm Mix Additive on the Self-Healing Capability of Bitumen
Bibliographic record
Abstract
ABSTRACT Warm mix asphalt (WMA) technologies reduce the production temperature of hot mix asphalt allowing for mixing and paving at lower temperatures. As a result, the use of WMAs reduces emissions and allows for longer transport times. Because of the recent increase of chemical warm mix additives in industry, the effect of a chemical warm mix additive (cWMA) on the intrinsic self-healing ability of the bitumen was investigated. Bitumen specimens containing three concentrations of cWMA were evaluated at four aging levels (unaged, rolling thin film oven [RTFO]-163°C, RTFO-130°C, and RTFO+ pressure aging vessel [PAV] aged) using the simplified–linear amplitude sweep healing (SLASH) (linear amplitude sweep with a single rest period fatigue-healing) test. Results indicate that oxidative aging of bitumen is reduced with increasing cWMA concentration but may be more heavily influenced by the aging temperature. It was also observed that RTFO+PAV-aged bitumen samples demonstrate greater fatigue restoration ability compared to RTFO and unaged binders. Supplementary work using video-based analysis of dynamic shear rheometer samples revealed that issues may arise from the calibration of the cohesive failure damage level as described in the original LASH procedure because of significant changes in sample geometry observed during the amplitude sweeps for unaged and RTFO-aged material. These results demonstrate that LAS-based healing tests warrant further research to optimize loading and rest period parameters for a wider range of bituminous materials.
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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) | 0.000 | 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) | 0.001 | 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".