Innovative Design of Paving Cold Mix and Cohesive Overlays for Sustainable Pavement Maintenance
Bibliographic record
Abstract
A cold mix overlay is a typical preventive maintenance treatment that is applied to an existing pavement surface. However, the service life of cold mix overlay is limited because of its poor skid resistance and high tendency to crack, especially in cold regions. This study presents a new technology of high-performance cold mix overlay materials that slows skid resistance reduction, increases the resistance to thermal cracking, and shows long-lasting anti−icing performance. The sustainable performance of paving cold mix overlays can be assured by adding high-performance anti−icing agents, fiber, and emulsified asphalt to the cold mix. A series of laboratory tests were conducted to evaluate the performance and anti−icing effect of the cold mix. The results showed that the freezing temperature of the cold mix dropped to more than −10 °C. The open-to-traffic time can be shortened to 3.5 h after construction. The anti-wearing ability and cracking resistance were evidently increased in comparison with traditional micro-surfacing techniques by conducting indoor wet-wheel wearing tests and low-temperature bending beam tests. Based on the study, the new-tech cold mix overlay has shown promising applications in North America.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".