Utilizing Response Surface Methodology for Design Optimization of Stone Mastic Asphalt Containing Palm Oil Clinker Aggregates
Bibliographic record
Abstract
This study introduces a novel approach to enhance the sustainability of road pavement construction by utilizing palm oil clinker (POC), an industrial waste product, as a replacement for fine aggregates (passing 4.75 mm) in stone mastic asphalt (SMA) mixtures. Departing from conventional practices, this research comprehensively evaluates the feasibility of using POC at varying replacement levels (0% to 100%) across a range of binder contents (5.0% to 7.0%). A significant contribution of this work is the application of Response Surface Methodology (RSM) to optimize the proportions of POC and binder content (BC), achieving target Marshall and volumetric properties for superior pavement performance. The results demonstrate that POC can effectively substitute fine aggregates in SMA mixtures, meeting all requirements for Marshall stability, flow, stiffness, and volumetric properties, even at a 100% replacement rate. Statistical analysis using RSM confirmed the model’s validity, exhibiting a high R-squared value (>0.80), significant p-values, and an adequate precision exceeding 4. Optimization analysis revealed that a 60% POC content with a 6% BC yields the most desirable combination for achieving optimal SMA mixture characteristics. Further validation through experimental testing showed a strong correlation with the theoretical RSM predictions, with an error margin below 5%. This research underscores the potential of POC as a sustainable alternative to traditional aggregates, paving the way for more environmentally friendly and cost-effective road construction practices while simultaneously addressing waste management challenges in the palm oil industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".