Artificial neural network for predicting the flexural bond strength of FRP bars in concrete

Abstract

The bond strength between fibre-reinforced polymer (FRP) rebars and concrete is one of the most significant aspects of composite behaviour for rebars and concrete. In this study, a database of 408 beam type specimens consisting of beam end specimens, beam anchorage specimens and splice beam specimens was compiled from the current literature and used to develop a simple prediction using the artificial neural network (ANN). The data used for modelling were organised in a format of eight input parameters that include FRP type, cover bar surface, confinement, bar diameter (db), concrete compressive strength (fc)$(\sqrt {{f_c}} )$, minimum cover-to-bar-diameter ratio (c/db), bar-development-length-to-bar-diameter ratio (l/db), and the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameters (Atr/sndb). Additionally, a simple prediction formula by regression analysis was developed. The root mean square error and R2 values of the testing data were found in order to compare the results of both ANN and the proposed model with existing regulations. The new ANN model predicts the bond strength of FRP bars in reinforced concrete with 0.8989 R2, thus yielding better results when compared with existing regulations.

Keywords

• ann
• bond strength
• frp bars