Glass fibre-reinforced polymer (GFRP) offers a durable, high-tensile strength alternative to steel rebar in reinforced concrete (RC). However, the inherent lack of ductility in GFRP limits its structural applications, which has led to the development of hybrid GFRP–steel RC systems. The composite nature of these systems requires an accurate understanding of the bond interaction between GFRP rebar and concrete. Existing bond models often fall short of accurately representing the distinct mechanical properties and surface characteristics of GFRP bars, particularly within finite element (FE) analysis environments. To address this gap, the present study proposes a computational method that employs a feedforward neural network (FFNN) trained on experimental data encompassing a specific range of parameters (bar diameters 8–16 mm, concrete strengths 18–50 MPa), including bar diameter, bond length, concrete strength, and cover thickness. Unlike conventional models that typically focus on peak bond strength, the developed FFNN accurately predicts the complete bond–slip relationship. The developed bond model is then integrated into the FE analysis. The simulation results demonstrate strong agreement with experimental data (average R2 = 0.93) and effectively capture key behavioral aspects such as crack initiation and propagation.
Devaraj et al. (Tue,) studied this question.