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Predicting the Capacity of RC Beams Strengthened in Shear with Side-Bonded FRP Reinforcements Using Artificial Neural Networks

The application of artificial neural network (ANN) to predict the shear capacity of reinforced concrete (RC) beams retrofitted in shear by means of side-bonded fiber-reinforced polymer (FRP) is investigated in this paper. An extensive literature review has been carried out. In addition, ten shear deficient RC beams with different carbon fiber-reinforced polymer (CFRP) configurations were tested and added as data to the collected data. It was aimed to build an efficient and practical ANN model with parameters which can easily be obtained without any calculation and/or experimental investigation. The results are compared with the design guideline equations that emerge as predictions of the FRP contribution using the trained neural networks: these are in good agreement with the experimental results and better than those calculated from the theoretical guideline equations. Based on ANN results, a parametric study has been carried out to study the importance of different influencing parameters on the FRP contribution. Thereafter, a new simple expression is proposed for determining the contribution of externally bonded side-bonded FRP. Accordingly, the suggested design formula is capable of predicting the experimental FRP satisfactorily so that it can be admitted as an alternative to the existing guideline equations within the range of parameters covered in the study.