The use of glass fiber-reinforced polymer (GFRP) has gained increasing attention over the past decades, aiming at replacing traditional steel rebar in concrete structures, especially in corrosion or magnetic conditions. Understanding the working mechanism between the reinforcements and concrete is crucial in many practical applications, in which the corresponding bond strength is considered as a critical element. In this study, a database including 159 experimental beam results gathered from the available literature was used for the development of an artificial neural network (ANN) model in an effort to predict the bond strength between GFRP bars and concrete. Two ANN models using BFGS quasi-Newton backpropagation and conjugate gradient backpropagation with Polak-Ribiére algorithms were constructed and evaluated in terms of bond strength prediction accuracy. The considered database consisted of five input parameters, including the bar diameter, concrete compressive strength, minimum cover to bar diameter ratio, bar development length to bar diameter ratio, the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameter. The evaluation of the models was conducted and compared using well-known statistical measurements, namely the correlation coefficient (R), root mean square error (RMSE), and absolute mean error (MAE). The results demonstrated that both ANN models could accurately predict the bond strength between GFRP bars and concrete, paving the way for engineers to possess a useful alternative design solution for reinforced concrete structures
Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams
