Physical, Rheological, and Morphological Properties of Asphalt Reinforced by Basalt Fiber and Lignin Fiber
Bibliographic record
Abstract
Studies show that each kind of fiber has its own advantages in improving the properties of asphalt binders. However, there are very limited research studies about mixed fiber-reinforced asphalt (MFRA). In this study, two kinds of fibers, basalt fiber (BF) and lignin fiber (LF), were selected to reinforce SBS (styrene-butadiene-styrene triblock copolymer)-modified asphalt, which is now widely used in pavement engineering. MFRA samples with different fiber mix ratios (FMRs) were prepared for the tests of softening point, ductility, and rheological properties, the micromorphology of which was studied by using scanning electron microscope (SEM). The oil (asphalt) absorption rates of mixed fibers with different FMRs were also tested. The results show that the properties of MFRA were affected by the physical and chemical properties of fibers. Basalt fiber can better strengthen the physical properties of MFRA, while lignin fiber is good for improving the rheological properties, and the oil absorption rate of lignin fiber is higher than that of basalt fiber. Furthermore, the best FMR calculated by the efficacy coefficient method (ECM) was recommended as 1:2 (BF:LF). An interface layer between the fiber and asphalt was observed from the micro images, proving that the fibers bond well with the asphalt. Generally, mixing BF and LF together into SBS-modified asphalt could make full use of the advantages of different fibers and reinforce the comprehensive performance of MFRA better.
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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".