Prediction of the Bond Strength of Externally Bonded FRP Sheets Applied to Concrete via Grooves Technique Using Artificial Neural Networks

Abstract

The present study aims to fill a gap in the literature on the estimation of the bond strength of fiber reinforced polymer sheets bonded to concrete, via the externally bonded reinforcement on grooves (EBROG) technique, employing the curve-fitting on existing datasets in the literature and the methodology of Artificial Neural Networks (ANNs). Therefore, a dataset of 39 experimental results derived from EBROG technique is collected from the literature. A mathematical equation for the bond strength of FRP sheets applied on concrete via the EBROG technique was suggested using curve-fitting and general regression. The proposed mathematical equation is compared and validated with experimental results. The developed ANN model was constructed after testing diverse hidden layers and neurons to find the optimal predictions. The validation of the model is carried out using the experimental results and a statistical analysis is applied to assess the proposed mathematical equation and the proposed ANN model. Furthermore, a parametric study using the ANN model was also performed to investigate the influence of various factors on the bond strength of FRP sheets bonded to concrete. The parametric study proves that the bond strength increases with increasing the tensile stiffness per width, the FRP sheet width, and the concrete compressive strength; however, the effect of the Groove’s width and depth is found to be not monotonous.