Dynamic Modulus of Recycled Pavement Mixtures
Bibliographic record
Abstract
Pavement recycling techniques have been shown to be effective for rehabilitating pavements by reducing environmental impacts, construction costs, and time. For various reasons, many highway agencies have not widely embraced these processes despite the demonstrated advantages. One such reason is that the mechanical properties of these materials have not been widely studied, resulting in a lack of consensus on proper design values, which causes concern for highway agencies. This study sought to determine the dynamic modulus of field-produced and field-cured recycled pavement materials from 24 projects constructed in the United States and Canada. The dynamic modulus is one of the primary material parameters for mechanistic–empirical pavement design and performance prediction. On the basis of a statistical test and observation of the constructed master curves, this study found that the three pavement recycling processes studied (cold central-plant recycling, cold in-place recycling, and full-depth reclamation) had a similar range of dynamic modulus values. In addition, cold central-plant recycling and cold in-place recycling showed greater stiffness temperature dependency than that of full-depth reclamation, suggesting that the binder from the existing reclaimed asphalt pavement may play a role in their stiffness properties. The master curves also showed that the use of chemical additives generally increased the stiffness and reduced the temperature dependency of the recycled materials. The master curves showed that dynamic modulus values were similar when emulsified asphalt and foamed asphalt were used as the stabilizing and recycling agents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".