Performance Optimization of Fiber-Modified High-Performance Asphalt Concrete Based on Balanced Mix Design Principle
Bibliographic record
Abstract
ABSTRACT This study aims to evaluate the performance of high-performance asphalt concrete modified with 12 % of asphaltenes and 0.15 % of polyethylene terephthalate (PET) fibers using a balanced mix design (BMD) approach. The addition of asphaltenes changes the performance grade (PG) of the base binder with a continuous PG 70.2–25.9 to a continuous PG 82.9–21.8. The research focuses on assessing the cracking resistance of the modified mixes using the indirect tensile asphalt cracking test (IDEAL-CT) while ensuring sufficient rutting resistance examined with the Hamburg wheel-track (HWT) test. The HWT test results demonstrate considerable improvements in the modified fiber mixes tested at 60°C, with a rut depth of 5.5 mm at 20,000 passes. This indicates excellent rutting resistance with a rutting resistance index that is almost 5 times compared with that of the control mix, which shows signs of moisture damage. The IDEAL-CT test results show a strong impact of fiber addition in that the CTIndex at 37°C of the fiber-modified mixes (a value of 87) is higher than the threshold for a Super Mix (a value of 70), and it is also very similar to that of the softer unmodified mixes tested at a standard temperature of 25°C (a value of 91). The higher test temperature for IDEAL-CT is based on the modified binder PG and is selected because at 25°C the tensile strength of the modified mixtures is very high, which masks the influence of fibers. Based on the BMD analysis and using a performance space diagram, the incorporation of asphaltenes and waste PET fibers yields a Super Mix that warrants field verification. The BMD approach proves valuable in understanding the effects of different modifications on asphalt mixes, providing a thorough understanding of their performance characteristics.
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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 | 0.003 | 0.001 |
| 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".