Mechanical evaluation of asphalt–aggregate mixtures prepared with fly ash as a filler replacement
Bibliographic record
Abstract
The objective of this study is to investigate the effect of fly ash as a filler replacement on the mechanical properties of asphalt–aggregate mixtures. Utilization of fly ash, which is the by-product of coal-fired power generation, is of great importance from an environmental and economical point of view. In this study, a dense bituminous mixture composed of calcareous aggregate was selected as the reference mixture. It was observed that there was a definite increase in Marshall stability and decrease in flow values, especially when calcareous filler was replaced by Soma-type fly ash, which was one of the three types of fly ashes used. The mechanical properties, namely elastic strain, elastic modulus, and permanent strain, of the asphalt mixtures were determined by carrying out fatigue tests with a UMATTA tester for three types of fly ashes, portland cement, lime, and control specimens. The changes in mechanical properties are important in the sense that they affect the behavior of asphalt concrete pavement under applied loads. This mechanism can be explained basically by bitumen extension. The fatigue life of fly ash specimens, especially Soma fly ash, was found to be considerably higher than that of calcareous filler specimens. Based on this study, it is demonstrated that fly ash can be used effectively in a dense-graded wearing course as a filler replacement.
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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) | 0.001 | 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".