Rational Mix-Design Procedure for Cold In-Place Recycling Asphalt Mixtures and Performance Prediction
Bibliographic record
Abstract
A new volumetric mix-design procedure utilizing the Superpave gyratory compactor (SGC) was developed for cold in-place recycling (CIR) asphalt mixtures with assistance from the Federal Highway Administration (FHWA). It was developed for partial-depth CIR using asphalt emulsions as the recycling additive. This procedure was calibrated 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 performance of CIR mixtures prepared in accordance with the new mix-design procedure was evaluated in the laboratory with mechanistic-empirical pavement design guide (MEPDG) models as well as in the field. Creep compliance and strength of the mixtures were determined at 0, −10, and −20°C using the Superpave indirect tensile tester (IDT) with satisfactory results. A field test section also had been established with CIR mixtures in Arizona using this procedure and has been performing well with no significant visible cracking or distresses.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".