Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSM
S. Azhagarsamy, N. Pannirselvam
Abstract
• Investigated graphene oxide (GO) and steel fibers in recycled aggregate concrete (RAC) for enhanced performance. • GO improves hydration, microstructure, and porosity; steel fibers boost toughness and ductility. • Optimal mix (25 % RA, 0.3 % GO) achieved strength gains at 28 days. • applied Two Stages Mixing Approach (TSMA) and used RSM & ANN for optimization. • Promotes sustainability through reduced cement use and lower carbon emissions. The rapid increase in construction and demolition waste (CDW) has prompted considerable ecological apprehensions, steering research towards sustainable concrete alternatives. This study investigates the synergistic effects of graphene oxide (GO) and steel fibers (SF) on enhancing the mechanical properties of recycled aggregate concrete (RAC). Due to its extensive surface area and crack-bridging properties, GO improves hydration and microstructure and diminishes porosity, augmenting strength and durability. SF improve post-cracking performance, energy absorption, and stress redistribution. Concrete mixtures were developed by replacing natural coarse aggregate (NA) with recycled aggregate (RA) at proportions of 25, 50, and 100 % while using GO at 0.03, 0.05, and 0.1 wt percent of cement. The Two-Stage Mixing Approach (TSMA) was employed with untreated RA. Evaluations of workability, compressive strength, tensile strength, and flexural strength were performed and contrasted with control concrete. The ideal combination (25 % RA and 0.03 % GO) showed enhancements at 28 days: 15.62 % in compressive strength, 68.47 % in splitting tensile strength, and 38.21 % in flexural strength. The amalgamation of GO and SF presents a promising method for generating resilient, high-performance RAC. Predictive modeling employing Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) enhances mix design optimization for sustainable construction.