Synergistic enhancement of bituminous concrete mixture performance with polypropylene granule modified binder and rice husk ash as mineral filler substitute
Bibliographic record
Abstract
Flexible pavements are typically constructed as layered systems, wherein high-quality materials are utilized in the surface layers while low quality materials are employed in the base layers. The use of conventional materials like straight-run bitumen and natural aggregates has led to resource depletion and significant environmental concerns. To address these issues, innovative waste and recyclable materials, including modified binders, reclaimed asphalt pavement (RAP), and artificial aggregates, are being adopted. This research aimed to investigate the performance of bituminous concrete grade I by substituting the mineral fillers with waste materials and straight run binder with modified binder. Plastics derived from polyethylene and polypropylene offer environmental benefits, as they lack chlorine and do not emit harmful gases. The optimal dosage of polypropylene granules (PPG) to enhance VG 40 bitumen properties was determined as 3%. Fourier transform infrared spectroscopy (FTIR) showed improved aging resistance in the PPG-modified binder, while X-ray diffraction (XRD) revealed a semi-crystalline structure. Bituminous concrete Grade I mixes were prepared by replacing conventional mineral filler with rice husk ash (RHA) at varying levels (25%, 50%, 75%, and 100%) as per MoRT&H guidelines. The performance was evaluated for Marshall stability, indirect tensile strength, moisture resistance, resilient modulus, fatigue, rutting behavior, and fracture properties. The mix containing 3% PPG and 50% RHA (MBRHA50) showed improved moisture susceptibility by 7%, resilient modulus by 20.5%, rutting resistance by 29%, and fatigue life by 48.61% compared to the control mix. Fracture investigations revealed superior performance of the MBRHA50 mix over the control mix. Grey relational analysis was conducted to rank the mixes. This study has demonstrated the clear enhancement of bituminous concrete mix performance through the blending of waste and recycled materials, contributing to sustainable asphalt pavement technologies. • Change in structure from sol to sol-gel for 5% and 7% PPG modified binders. • The carbonyl Index of 3% PPG binder was observed to be the least. • MBRHA50 mixture exhibited improved fatigue performance with 60,560 load cycles. • The RRI for MBRHA50 mixture was found to be 12 which is 29% higher than BM mixture. • Grey relational analysis has helped to find the relative ranking of mixes.
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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 | 0.000 | 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 | 0.001 |
| 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".