Rheological Evaluation of Field-Aged Asphalt Cements
Bibliographic record
Abstract
The appearance of excessive and premature cracking on asphalt pavement after the winter seasons has been a major challenge and concern to transportation agencies in Canada and the northern U.S. states. At low temperatures, thermal stresses develop in the pavement causing detachment of the binder from the aggregates and a subsequent deterioration in stiffness which eventually leads to gross thermal cracking. Further distresses are induced on these roads in the form of wheel path fatigue by heavy vehicles leading to alligatortype cracks. Governments spend large portions of their yearly budgets on the maintenance and reconstruction of damaged roads. Through the collaborative efforts of government agencies and research institutions various laws and specifications have been implemented to ensure durability of asphalt pavement across various provinces. In this study, approved American Association of State and Highway Transportation Officials (AASHTO) and Ministry Transportation of Ontario (MTO) methods namely dynamic shear rheometer (DSR), extended bending beam rheometer (EBBR), and double edge-notched tension (DENT) tests were used to evaluate the rheological properties and long-term performance of recovered binders with 3 to 7 years of service. The recovered samples showed similar properties as their corresponding tank samples even though there exists a difference in their performance due to extent of aging which is only poorly simulated by the rolling thin film oven (RTFO) aging and pressure aging vessel (PAV) treatment of the tank samples. Easier, more automated test methods were attempted in this study to replace or complement the EBBR and DENT. Gel point temperature and Glover- Rowe analysis were conducted on all recovered samples. An r-squared value 0.72 was obtained for the correlation between EBBR and gel point temperature while that between iii critical tip opening displacement (CTOD) from DENT test and the Glover-Rowe parameter was 0.96. These results look promising and would require further studies to come to more precise decisions. Chemical analysis using X-ray fluorescence (XRF) and Fourier transform infrared (FTIR) were compared with rheological and physical tests, and able to distinguish good performing binders from poor ones.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".