Effect of Recycled Concrete Aggregate on Rutting and Stiffness Characteristics of Asphalt Mixtures
Bibliographic record
Abstract
This study aims to evaluate the influence of the addition of coarse recycled concrete aggregate (CRCA) on the rutting and stiffness of Ontario Superpave mixtures. Mix designs of asphalt mixtures were performed for two types of CRCA at various proportions. To enhance the properties of CRCA, a combination of different treatment methods was investigated. The physical characteristics of treated and untreated CRCA were evaluated. The impact of treated and untreated CRCA on the rutting resistance of asphalt mixtures was determined. The obtained results indicated that the use of a combination technique of different treatment methods is a highly successful method for improving various physical properties. The addition of two types of untreated CRCA in various proportions produces higher rutting resistance and higher stiffness than the control mix. The CRCA type has an effect on the rutting characteristics of asphalt mixtures. The application of treated CRCA with heat treatment and short mechanical treatment leads to an increase in the rutting resistance, a decrease in the total rut depth, a slight increase in the stiffness, and an increase in the rutting parameter of asphalt mixtures depending on the type of CRCA. The application of treated CRCA with a presoaking method and short mechanical treatment results in an increase in the stiffness and rutting factor of mixtures depending on the type of CRCA. The results indicate that the application of different CRCA types in various forms—treated and untreated—is very successful and can contribute significantly toward more RCA applications with asphalt pavements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".