Enhancing Marshall Properties Through the Integration of Waste Plastic Water Bottles in Dry Process Asphalt Production
Bibliographic record
Abstract
Climatic conditions, escalating traffic loads, and inadequate maintenance have been identified as significant contributors to the degradation of road quality, resulting in suboptimal performance of paved roads.One of the critical forms of deterioration is the premature hardening of asphalt, which leads to early onset of cracks and, consequently, the premature failure of pavement surfaces.In response to these challenges, this study explores an innovative approach to improve the properties of asphalt mixes by integrating plastic waste, thus prolonging pavement service life, reducing construction and maintenance costs, and mitigating environmental pollution.Specifically, waste Polyethylene Terephthalate (PET), derived from plastic water bottles, was incorporated as a polymer additive in asphalt mixtures.Asphalt specimens, both modified and unmodified, were produced by integrating plastic waste in proportions of 0, 3, 6, 9, and 12 percent by the weight of asphalt.The specimens were evaluated for their Marshall Stability, flow, and volumetric properties using digital Marshall testing equipment on a selected size range (2.36-1.18mm) employing a dry process.All test results conformed to Iraqi standard specifications, highlighting an enhancement in the mix properties compared to the conventional mixture.The augmentation of plastic waste was consistent with previous findings and demonstrated improved engineering features of the mixtures.The ideal proportion of waste plastic water bottle integration was found to be 7.77%, resulting in an increase in stiffness and stability by 70.45% and 42.10%, respectively, compared to a conventional mix.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".