Analysis of the recyclability of thermosetting pavement materials: A case study of reclaimed epoxy asphalt pavement (REAP)
Bibliographic record
Abstract
The sustainable recycling of thermosetting pavement materials is a critical challenge in modern infrastructure. This study addresses this issue by investigating the potential for recycling reclaimed epoxy asphalt pavement (REAP) derived from engineering. Through a series of comparative crushing and abrasion tests, the compressive strength and wear resistance of REAP were evaluated against limestone and basalt as control groups. Surface energy and water immersion tests were conducted to analyze the adhesion behavior of REAP with matrix asphalt and asphalt mastic at three different powder-to-asphalt ratios. Additionally, mixture tests assessed the performance trends associated with incorporating REAP. The findings reveal that REAP exhibits compressive strength between that of limestone and basalt, while its wear resistance is slightly inferior to limestone. Notably, REAP exhibits no significant adhesion deficiencies with the matrix asphalt, achieving the highest adhesion work of 72.48 mJ/m² with asphalt mastic at a filler-to-asphalt ratio of 1.0. However, the water immersion tests indicate that the adhesion between REAP and asphalt mastic is significantly better than that with matrix asphalt, suggesting that the REAP possesses rich textural characteristics. These results highlight the unique surface textural characteristics of REAP produced through mechanical crushing. A mass conversion method was proposed for designing mixture gradation, effectively minimizing fluctuations caused by density differences between REAP and natural aggregate. Mixture performance tests show that the incorporation of REAP has a significant impact on crack resistance and water damage performance, particularly when combined with natural aggregates.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".