PREDICTION OF ULTIMATE LOAD OF CONCRETE BEAMS REINFORCED WITH FRP BARS USING ARTIFICIAL NEURAL NETWORKS

Abstract

Artificial neural networks (ANN) were used in this study to predict ultimate load of simply supported concrete beams reinforced with FRP bars under four point loading. A proposed neural model was used to predict the ultimate load of these beams. A total number of (199) beams (samples) were collected as data set and it was decided to use eight input variables, representing the dimensions of beams and properties of concrete and FRP bars, while the output variable was only the ultimate load of these beams. It was found that the use of 11 and 10 nodes in the two hidden layers was very efficient for predicting the ultimate load. The obtained results were compared with available experimental results and with the ACI 440.1R specifications. The proposed neural model gave very good predictions and more accurate results than the ACI 440.1R approach. The overall average error, in the value of the predicted ultimate load, was 3.6% and 21.7% for the proposed neural model and the ACI 440.1R approach, respectively.