A Performance-Related Approach to the Utilization of Reclaimed Asphalt Shingles (RAS) in Asphalt Mixtures
Bibliographic record
Abstract
Over the past decade processed asphalt shingles from manufacturer's waste has been added to the suite of recycled materials usedi asphalt mixtures. More recently the use of post-consumer Reclaimed (or Recycled) Asphalt Shingles (RAS) has been introduced to the industry. In 2012, a program was initiated to assess the utilization of RAS in Hot Mix Asphalt (HMA). This program assessed the processes for post-consumer RAS production, RAS consistency, rheological properties, and RAS dosage evaluation. Based on this work, RS at a relatively modest proportion was accepted by the City of Calgary for use in a standard City mix type. In 2013, a second stage of evaluation focussed on assessment of asphalt mixtures incorporating a range of RAS addition. A control mix without RAS was included in the assessment program. The mixture testing program was developed to consider volumetric mix properties, moisture susceptibility, low temperature cracking resistance, fatigue resistance and instability rutting resistance. Generally, the results supported the concept of partial contribution of RAS binder. The indicated acceptable rate of RAS addition was about double what would be appropriate when total (or 100 percent) contribution is assumed. This could justify higher rates of RAS addition, without compromising pavement performance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".