Evaluating Self-Healing Behaviour of Asphalt Binders Modified with Phase-Change Materials, Polymers and Recycled Glass Powder
Bibliographic record
Abstract
The objective of this paper is to evaluate the fatigue resistance and self-healing properties of asphalt binders modified with different types of additives (Styrene-Butadiene-Styrene (SBS), Glass Powder (GP) and Phase-Change Materials blended with Glass Powder (GPCM)). Two base binders were used in this study: a PG 58-28 straight-run asphalt binder and a PG 70-28 Polymer modified with 3%SBS. Moreover, the GP was added to the two base binders at two different percentages of 3.5% and 5% by binder weight. However, the GPCM was added with two different percentages of 5% and 7% by binder weight. In this paper, the fatigue resistance and self-healing properties were evaluated using Linear Amplitude Sweep (LAS) test. Two different procedures were adopted. In the first procedure, the load was applied continuously until failure (without a rest period), whereas, in the second procedure, rest periods of 5 and 30 min were introduced. The obtained results of the experimental campaign were ranked based on three different categories: Linear Amplitude Sweep (LAS), Pure Linear Amplitude Sweep (PLAS) and modified Pure Linear Amplitude Sweep (PLASH). The addition of GPCM appears to positively impact the fatigue performance of both straight-run and polymer-modified asphalt binders. Furthermore, when a short rest period of 5 min was introduced, the use of GPCM did not appear to improve the healing potential. However, a better healing capacity was observed when the 30 min rest period was applied. Moreover, the addition of GP alone to the base binder was not beneficial in improving fatigue performance based on LAS and PLAS methods. However, there was a slight reduction in the fatigue performance based on the PLAS method. Finally, unlike the PG 58-28, the healing capacity of the GP 70-28 was negatively affected by the addition of the GP.
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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".