Hybrid Intelligence Framework for Optimizing Shear Capacity of Lightweight FRP-Reinforced Concrete Beams

This study rigorously assesses the shear capacity of Fiber Reinforced Polymer (FRP) reinforced concrete (RC) beams as a lightweight material alternative, scrutinizing the efficacy of the Eurocode and ACI design codes. Leveraging a dataset of 260 experimental FRP-RC beam cases, two distinct Artificial Neural Network (ANN) models were developed using the Levenberg-Marquardt algorithm. Beams with and without stirrups were considered, with parameters including beam width (), depth (), length (), concrete compressive strength (), FRP modulus of elasticity (, ) and FRP reinforcement ratios (, ). Multi-objective optimization was deployed to integrate Genetic Algorithms (GA) and fmincon to optimize beam parameters for maximizing the shear capacity, . Sensitivity analysis allowed to quantify the influence of each parameter, revealing that and significantly affect , with sensitivity scores of 0.39 and 0.35, respectively. The optimization process, highlighted by a 3D scatter plot, dynamically illustrated trade-offs among key design parameters (, , ), giving insights into the complex interplay in FRP beam design. The hybrid intelligence models reached superior predictive accuracy over traditional codes, achieving values of 0.89. Notably, for beams without stirrups, model predictions closely matched experimental data, with lower average ratio (1.02) compared to Eurocode (1.65) and ACI (1.58). Principal Component Analysis (PCA) unraveled complex variable relationships, enhancing the understanding of the structural behavior of FRP-RC beams. Integrating artificial intelligence, advanced optimization techniques, and rigorous statistical analyses coins a comprehensive framework for the structural analysis of FRP-RC beams, offering enhanced accuracy and insightful perspectives for future design optimization.