Investigating the Polymer Structure Effects on SBS-Modified Asphalt Binders under Short-Term Aging through the Artificial Neural Networks, Genetic Algorithms, and Fuzzy Logic

SBS-modified asphalt (SBSMA) exhibits complicated aging behavior in the weathering environment due to oxidation of the matrix asphalt, degradation of the SBS, and disruption of the SBS-asphalt relationship. The weathering process of SBS-modified asphalt has been studied extensively. However, most of this research has taken a broad, environmental approach. This research delved into the nuanced effects of aging on low polymer-modified asphalt (LPMA) compositions integrated with a rejuvenating agent (RA), leveraging advanced computational intelligence methodologies. The study uniquely employed fuzzy logic, Artificial Neural Networks (ANN), and genetic algorithms to forecast and analyze the performance dynamics of distinct asphalt mixtures. Five asphalt mixtures were examined: a PG 58-28 binder served as the foundational benchmark, while the subsequent four used modified PG 58-28 with different ratios of Ethylene Vinyl Acetate (EVA) and sesame oil (RA). Assessment methodologies comprised the Resilient Modulus Test, Wheel Track Test, Flow Time, Direct Tension, and Quarter-Circle Bend tests. The ANN framework was instrumental in predicting rutting and crack trajectories, while fuzzy logic facilitated understanding the intricate interplays between component ratios and resultant performance metrics. Genetic algorithms optimized the EVA and RA ratios for enhanced efficacy. Initial results demonstrated that the amalgamation of EVA and RA strengthened the overall lifespan and rutting resilience of the asphalt composites. Enhanced blends, optimized using the genetic algorithm, exhibited improved resistance to initial crack formations relative to the baseline. As the aging process advanced, the modified blend's resilience to recurrent stress-induced cracks experienced minor reductions but paralleled the foundational benchmark. A harmonized combination, pinpointed via the genetic algorithm containing 6.5% EVA and 6% RA, emerged as the most proficient in amplifying durability, resistance against deformations, and immediate crack resistance.