RILEM interlaboratory study on the mechanical properties of asphalt mixtures modified with polyethylene waste
Bibliographic record
Abstract
This research aims to determine if the observed improvements using polyethylene (PE) waste in asphalt binder translate into better performance at the asphalt mixture scale in the laboratory environment while overcoming the stability and homogeneity issues experienced at the binder level. This is accomplished through a round-robin multinational experimental program covering four continents, with the active participation of eleven laboratories within the RILEM TC 279-WMR. PE modified AC16 mixtures were prepared employing the dry process using local materials with the PE waste provided by one source. Various mechanical tests were performed to investigate the compactability, strength, moisture sensitivity, stiffness and permanent deformation. Compared to the control mixtures, the following observations were made for PE modified mixtures: easier to compact, lower time dependence of stiffness, higher elastic behavior, lower creep rate, and higher creep modulus. Furthermore, cyclic compression test results showed that the resistance to permanent deformation is improved when using PE in asphalt mixtures, whereas the wheel tracking tests showed relatively similar or better results when 1.5% PE was added to the control mixture. The wheel tracking test results in water showed an increase in deformation with increasing PE content. The interlaboratory investigation showed that the use of PE as a performance-enhancing additive in asphalt pavements is a viable, environmentally friendly option for recycling waste plastic and could potentially reduce the use of polymer additives in asphalt.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".