Hot Mix Asphalt Behavior with Recycled PET and Crumb Rubber as Aggregate Substitutions
Bibliographic record
Abstract
This study analyzes the performance of asphalt mixtures incorporating waste particles, specifically polyethylene terephthalate (PET) and crumb rubber from disused tires, as partial substitutes for natural aggregates.The primary objective is to determine their applicability as a wearing course in medium-traffic roads (MAC-2 type), according to the Ecuadorian Road Standard (NEVI 12).A quantitative analysis was conducted through an experimental design that included the characterization of natural aggregates, the determination of the optimum asphalt cement content (AC-20 type), and the design and preparation of asphalt mixtures with the waste materials.Marshall evaluation was applied to determine characteristics such as stability, flow, density, and air voids content in a base mixture and mixtures prepared with 1%, 1.5%, and 2% of PET and crumb rubber as fine aggregate substitutes.The Marshall evaluation results of the mixtures prepared with various percentages of waste in aggregate substitution, when compared to the base asphalt mixture, demonstrate compliance with NEVI 12.It was determined that the low density of PET and crumb rubber is the primary factor causing a significant variation in the volumetric properties of the mixture, with the air voids percentage being the most relevant.The best performance, according to the parameters specified in the standard, was achieved by substituting 1% of fine aggregate with PET and by substituting 1% and 1.5% of fine aggregate with crumb rubber, concluding their suitability as a wearing course.Recycled materials, such as PET and crumb rubber, can be utilized as modifiers in asphalt mixtures, allowing for the preservation of properties.Their use contributes to environmental protection by promoting the development of recycling focused on solid waste management for pavement construction applications.
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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".