Rubberized Asphalt Mixtures with RAP: A Case for Use in Ontario
Bibliographic record
Abstract
In 2011, the Centre for Pavement and Transportation Technology (CPATT) at the University of Waterloo, the Ontario Tire Stewardship (OTS), and the Ministry of Transportation of Ontario (MTO) partnered to conduct several demonstration studies on the use of Rubberized Asphalt with the intent to better understand and resolve the technical challenges associated with such mixtures as well as to advance the pavement industry to a more sustainable and economically viable direction. To evaluate field performance, placement of rubberized roads in Ontario, Canada was conducted. This paper reviews past experiences and reports on the laboratory performance of characterized hot mix asphalt (HMA) mixtures incorporating 0.5 to 1% Crumb Rubber Modifier (CRM) and 15 to 20% Reclaimed Asphalt Pavement (RAP) by total weight of the mixture. Overall observations suggest that combining RAP with CRM in typical Ontario HMA compensates for RAP shortfalls such as its effects on binder aging and mix stiffness thus improving the mixture’s durability, susceptibility to the combined effects of rutting, stripping and moisture damage, including its overall resistance to fatigue and thermal cracking if properly designed, mixed and compacted. Findings further indicate the potential to incorporate higher RAP contents (i.e. > 25%) into Ontario rubberized pavements.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".