Optimizing Crumb Rubber Modifiers (CRM) and Reclaimed Asphalt Pavements (RAP) in Typical Ontario Hot Mix Asphalt
Bibliographic record
Abstract
Genuine sustainability begins with reduction enhanced by a culture of reusing and recycling waste materials. Rather than destroy or discard scrap rubber tires and Reclaimed Asphalt Pavement (RAP) in landfills, both materials have been successfully engineered to improve the mechanical properties of Hot Mix Asphalt (HMA) mixtures. These waste materials are used by select transportation agencies in Canada. However, in comparison to current supply, the percentage used in Ontario is rather conservative. The objective of this paper is to evaluate the use of higher percentages of Crumb Rubber Modifiers (CRM), a by-product of scrap rubber tires, and RAP in typical Ontario HMA. This paper uses the Superpave mix designs system to characterize and compare the low-temperature cracking performance of RAP and CRM in HMA. The effects of the different binder grades, RAP content, and CRM incorporation methods on the fracture parameters of the HMA mixtures are examined. The influence of mix volumetric, aggregate gradation, binder aging, and laboratory compaction method were also observed. Test results demonstrate that the wet-process of incorporating CRM in HMA is effective, and that typical Ontario HMA mixtures incorporating high RAP and CRM percentages can withstand thermal cracks arising from low temperatures. (A) For the covering abstract of this conference see ITRD record number 201402RT334E.
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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) | 0.000 | 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".