Mechanical and Durability Performance of Dry-Process Hybrid LDPE-PET Modified Hot-Mix Asphalt
Bibliographic record
Abstract
Plastic waste valorization has increased interest in using low-density polyethylene (LDPE) and polyethylene terephthalate (PET) in hot mix asphalt (HMA), yet their combined behavior under dry processing remains largely undocumented, especially for hot-climate pavements.Twelve mixtures containing 4-10% LDPE-PET blends (< 2 mm) were prepared to examine hybrid dry process modification of HMA.Marshall stability and flow, volumetric properties, indirect tensile strength (ITS), and tensile strength ratio (TSR) were used to assess performance.The control mix recorded a Marshall stability of 10.78 kN, an ITS850 kPa, and a TSR of 83%.LDPE enhanced cohesion and compaction, whereas PET contributed stiffness at moderate contents; when combined, they produced a balanced effect with LDPE providing uniform coating and PET serving as dispersed reinforcement.Statistical analysis confirmed significant differences among mixtures (p < 0.001) and identified the LDPE5-PET3 blend at 8% total polymer as the most effective, achieving approximately 17% higher stability, 14% higher ITS, and a TSR of about 93%, while maintaining acceptable flow values and a favorable void structure.Systems dominated by PET or containing 10% polymer exhibited reduced compressibility and lower moisture resistance.The paper gives a practical range of dosages for hybrid modification of HMA.Although laboratory testing has inherent limitations, the results offer valuable insights for future research on performance improvement and the utilization of recycled plastic waste.
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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 | 0.000 | 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) | 0.001 | 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".