Field and Laboratory Evaluation of Recycled Asphalt Shingle Mixes: Canadian Study
Bibliographic record
Abstract
With the growing concern of sustainable development, the Centre for Pavement and Transportation Technology (CPATT) at the University of Waterloo partnered with public and private sectors such as the Ministry of Transportation Ontario (MTO), Ontario Centres of Excellence (OCE) and Miller Paving Limited are committed to develop state-of-the-art technology which will lead to reduce environmental emissions and cost effective solution in transportation sector. Recycled Asphalt Shingles (RAS) is a product that contains approximately 30% asphalt cement by mass weight can be a useful additive to Hot Mix Asphalt (HMA) if engineered properly. Approximately one million tonnes of asphalt roofing shingles waste is generated each year in Canada and 90% of this valuable waste is dumped in the landfill. Reuse of these materials leads to financial savings through avoidance of disposal costs and reduction of the amount of virgin asphalt binder required in HMA. This paper involves an evaluation of the properties of surface course mix HL3 which contains 1.5 % RAS which was placed at the CPATT Test Track in 2009. The laboratory test was carried out for the dynamic modulus and resilient modulus of the mix. For field performance, a deflection test was performed for HL3 RAS surface. In addition, a comprehensive performance comparison of the streets that were paved in the Town of Markham, ON in 2007 is presented. Overall, this paper shares some best practices in Canada on the key aspects of effectively using Recycled Asphalt Shingles
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".