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In the last years, a great number of experimental tests have been performed to determine the ultimate strength of reinforced concrete (RC) beams retrofitted in flexure by means of externally bonded carbon fiber-reinforced polymers (CFRP). Most of design proposals for flexural strengthening are based on a regression analysis from experimental data corresponding to specific configurations which makes it very difficult to capture the real interrelation among the involved parameters. To avoid this, an intelligent predicting system such as artificial neural network (ANN) has been developed to predict the flexural capacity of concrete beams reinforced with this method. An artificial neural network model was developed using past experimental data on flexural failure of RC beams strengthened by CFRP laminates. Fourteen input parameters cover the CFRP properties, beam geometrical properties and reinforcement properties; the corresponding output is the ultimate load capacity. The proposed ANN model considers the effect of these parameters which are not generally account together in the current existing design codes with the purpose of reaching more reliable designs. This paper presents a short review of the well-known American building code provisions (ACI 440.2R-08) for the flexural strengthening of RC beams using FRP laminates. The accuracy of the code in predicting the flexural capacity of strengthened beams was also examined with comparable way by using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate flexural strength of the strengthened RC beams. Moreover, the study concludes that the ANN model predicts the flexural strength of FRPstrengthened beams better than the design formulas provided by ACI 440

Intelligent Predicting System for Modeling of Flexurally – Strengthened Reinforced Concrete Beams with CFRP Laminates