Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

Abstract

In this study, the efficiency of artificial neural networks (ANN) in predicting the shear strength of reinforced concrete (RC) beams, strengthened by means of externally bonded fiber-reinforced polymers (FRP), is explored. Experimental data of 96 rectangular RC beams from an existing database in the literature were used to develop the ANN model. Eight different input parameters affecting the shear strength were selected for creating the ANN structure. Each parameter was arranged in an input vector and a corresponding output vector that includes the shear strength of the RC beam. For all outputs, the ANN model was trained and tested using a three-layered back-propagation method. The initial performance of back-propagation was evaluated and discussed. In addition, the accuracy of well-known building codes in predicting the shear strength of FRP-strengthened RC beams was also examined, in a comparable way, using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate shear strength of RC beams (R2≈0.93). Moreover, the study concludes that the ANN model predicts the shear strength of FRP-strengthened RC beams better than existing building code approaches.

Keywords

• artificial neural network
• beam
• externally bonded frp
• shear strength
• strengthening