In this study, an innovative ternary bio-enhanced asphalt mixture system was proposed and developed utilizing the RAP, bio-oil, and recycled polymer fibers from agricultural waste, to effectively improve the performance and sustainability of the pavement system. The mixture system was thoroughly characterized in the laboratory through a series of tests including rheology, Marshall stability and flow tests, moisture damage assessment, dynamic modulus testing, and durability evaluation. The findings revealed that the optimized ternary mixture with 40% RAP, 5% bio-oil, and 0.3% recycled PET fibers exhibited a significant increase in Marshall stability (22% higher) of 14.8 kN, tensile strength ratio (TSR) above the specified limit (92% higher), and flow number (180% higher) of 2,850 cycles compared to the conventional high-RAP mixtures. Moreover, dynamic modulus results showed that the ternary mixture has 25–35% higher stiffness at intermediate-high temperatures while maintaining good low-temperature flexibility. Additionally, the ternary mixture system exhibited excellent freeze–thaw durability with only 1.8% mass loss and 88% strength retention after 16 severe freeze–thaw cycles. A set of strong predictive models with R2 > 0.87 were developed to optimize performance and facilitate practical application. The life cycle assessment showed that the proposed mixture system achieved a 28% reduction in carbon emissions and an 18% decrease in total costs considering the extended service life of the pavement system. This study successfully demonstrated a sustainable and high-performance ternary bio-enhanced asphalt mixture system, offering a promising solution for the pavement industry. The ternary bio-enhanced system achieved 22% higher Marshall stability with 40% RAP content. Agricultural bio-oil and recycled PET fibers improved rutting resistance by 180%. The system demonstrated 28% carbon emission reduction and 18% lower lifecycle costs. Exceptional moisture resistance (92% TSR) and freeze-thaw durability performance were observed. Predictive models were developed (R²>0.87) for optimized mixture design applications.
Avinash H. Talkeri (Sat,) studied this question.