Laboratory evaluation on the high temperature rheological properties of rubber asphalt: a preliminary study
Bibliographic record
Abstract
Crumb rubber is the recycled rubber particle obtained from mechanical shearing or grinding scrap tires into small particle sizes less than 6.3 mm (or approximately 1/4”). The rheological properties of asphalt binder have an important effect on the field performance of asphalt mixtures and the long-term serviceability of asphalt pavement. The objective of this research is to evaluate the high temperature rheological performance of rubber asphalt binder based on the complex shear modulus (|G*|) and the phase angle (δ) values using the dynamic shear rheometer. Five rubber asphalt dosages at 0, 10, 15, 20, and 25% by weight of asphalt (Superpave PG 64-22), respectively, were used to modify asphalt binder; and three rubber particle meshes, 20#, 30#, and 40#, were utilized in this research. The |G*| at various temperatures and frequencies were tested on each sample, including original and short-term aging binder using the rolling thin-film over. The master curves of |G*|/sin(δ) for each type of rubber asphalt was generated to investigate its rheological properties over a broad range of temperatures and frequencies. Based on the testing results, it was found that the addition of crumb rubber significantly increases the |G*| of asphalt binder, which is desirable to potentially improve the anti-rutting performance of asphalt mixtures. It was also found that the addition of 10% mesh crumb rubbers bumps up the high temperature grade of asphalt, from PG64 to PG76 in this case. The master curve using the |G*|/sin(δ) of rubber asphalt shows a substantial improvement in rutting resistant at each testing temperature and loading frequency. However, it was noteworthy that the rutting resistance enhancement of crumb rubber was affected by the percentage of rubber used, the rubber particle size and its aging condition.
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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 | 0.002 | 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) | 0.000 | 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".