Investigation on Moisture Damage Resistance of Plastic-Modified Asphalt Binder using Surface Free Energy Technique
Bibliographic record
Abstract
Moisture damage of asphalt pavement has always been one of the major concerns for researchers in the pavement engineering field. Mitigating this moisture-induced damage is essential for improving pavement performance, extending service life, and reducing lifecycle costs. Several studies have reported that waste plastic can potentially increase the cohesion between asphalt and plastic molecules and enhance the adhesion between asphalt and aggregate, improving the moisture damage resistance of asphalt pavements. The present study aims to understand the effect of incorporating different waste plastics as modifiers on a binder’s fundamental properties, such as cohesive bond energies. To achieve this goal, three different waste plastics—high-density polyethylene (HDPE), polypropylene (PP), and polyethylene terephthalate (PET) in 2%, 4%, 6%, and 8% by weight of the total binder—were used to modify the conventional asphalt binder (PG 58-28). The surface free energy (SFE) was determined by depositing one polar and one non-polar liquid on the solid samples by using the liquid needle drop deposition technique while adopting three different theories. Finally, the cohesive bond energies of the modified asphalt binders were calculated. The results showed that waste plastics significantly increased the total SFE and cohesive bond energy of the asphalt binder up to 4% plastic addition and then dropped. Besides, the comparative analysis revealed that PP modification was most effective for improving moisture damage resistance among the three plastics. Therefore, the use of plastic waste for asphalt binder modification was found to be a promising approach for enhancing moisture damage resistance.
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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".