Cold In-place Recycling for Sustainable Streets and Highways
Bibliographic record
Abstract
The high cost and environmental impact of traditional asphalt pavement maintenance and rehabilitation (M&R) has led to an increase in the use of Cold In-Place Recycling (CIR) as an effective alternative. An attempt was made to develop a rational mix design method of CIR asphalt mixture with the assistance from the Federal Highway Administration (FHWA). The Superpave mix design procedure for the hot mix asphalt (HMA) was utilized through laboratory evaluation and field verification. A new volumetric mix-design with the Superpave gyratory compactor (SGC) was developed for CIR materials. It was primarily developed for partial-depth CIR, using emulsion as the recycling additive. It was evaluated using materials from five geographically varied locations in North America: Connecticut, Kansas, Ontario, Arizona and New Mexico. It required that specimens be prepared at densities similar to those found in the field. The resistance characteristics against thermal cracking were also investigated as the first step to examine performance of CIR mixtures. Creep compliance and strength of the mixtures have been determined at 0°C (32°F), -10°C (14°F), and -20°C (-4°F) using the Superpave Indirect Tensile Tester (IDT) to evaluate the resistance against thermal cracking. A test section also had been established in Arizona using the CIR mixtures prepared with the new procedure in October 2000, and is performing well with no visible cracking or distresses. It has been disseminated through the pavement recycling community and further research is currently on going to verify and/or improve the mix design, e.g., resistance characteristics against rutting and fatigue cracking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".