Optimisation of Recoverable Horizontal Deformation and Size Ratio of Compacted Rubberized Stone Mastic Asphalt Based on Rubber and Binder Contents Using Response Surface Methodology
Bibliographic record
Abstract
Abstract The use of waste materials such as rubber powder in stone mastic asphalt (SMA) has improved the structural performance to some extent, and the research on the improvement is still ongoing. Rubberised stone mastic asphalt has shown good performance in terms of resilient modulus. In this research, response surface methodology was utilised to increase the efficiency in determining the recoverable horizontal deformation and samples compaction level using optimum rubber and binder content. Thirty-one tests were performed on different binders and rubber contents, and the recoverable horizontal deformation and ratio of height to the diameter of Marshall compacted samples were recorded. The ANOVA analysis showed a low P-value with a high correlation coefficient, and the optimisation showed that adding almost 3% rubber powder to SMA could improve the compaction level and desirable recoverable deformation.
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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 | 0.000 | 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) | 0.001 | 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".