Using Construction and Demolition Waste Materials to Develop Chip Seals for Pavements
Bibliographic record
Abstract
Construction and demolition waste (CDW) materials account for a considerable part of waste materials throughout the world. As these materials are not usually recycled, reusing them in construction projects is of major significance. In this study, recycled concrete, bricks, and glass were used as 100% aggregates of chip seal, which is a corrective or preventive pavement maintenance method. A cationic rapid setting (CRS-2) bitumen emulsion was also used to prepare the chip seal. Different tests, including the sand patch test, sweep test, British pendulum tester (BPT), interface bond, and Vialit test, were conducted. The results of these tests revealed that all these materials had sufficient aggregate embedment for vehicle speeds of more than 70 km/h, and the number of chips was less than 10%, indicating their good performance. All developed chip seals ranked as high skid resistance pavement at ambient temperature. The chip seals developed with concrete and glass showed the best adhesion with an asphalt pavement surface and an aggregate–bitumen adhesion at very cold and ambient temperatures due to the fact of their chemical compositions. Overall, using concrete aggregates to develop chip seals under different traffic loads is recommended. Finally, these findings can provide a novel approach for recycling CDW materials with low costs.
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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) | 0.001 | 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) | 0.000 | 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 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".