Artificial Intelligence based model of pozzolanic and non-pozzolanic micro fillers in the binary and ternary blend to produce high-performance hybrid fiber-reinforced concrete
摘要
The development of sustainable, high-performance construction materials is imperative for modern infrastructure. This research presents a novel High-Performance Hybrid Fiber-Reinforced Concrete (HPHFRC) engineered through the synergistic integration of pozzolanic (silica fume, fly ash) and non-pozzolanic (micro quartz) micro-fillers in binary, ternary, and quaternary cement blends, reinforced with a hybrid system of steel and polyvinyl alcohol (PVA) fibers. The primary objective was to mitigate the inherent brittleness of high-strength concrete while enhancing its durability and sustainability. Experimental results demonstrate that micro-fillers can effectively replace up to 30% of cement, yielding a composite with superior mechanical properties and durability. The optimal mixture, incorporating 12% silica fume and hybrid fibers, exhibited a remarkable 234% increase in flexural strength and superior post-peak ductility compared to the control. Durability indicators, including chloride penetrability and porosity, were significantly improved. Furthermore, an Artificial Neural Network (ANN) model was developed to predict compressive strength with high accuracy (R2 = 0.9593), and practical predictive equations were derived to facilitate industrial adoption. This study provides a comprehensive framework for designing an innovative, predictable, and durable HPHFRC, making it a promising candidate for demanding applications such as seismic-resistant structures, industrial flooring, and tunnel linings.