Modeling of ultimate load for R.C. beams strengthened with Carbon FRP using artificial neural networks

Abstract

The use of carbon fiber reinforced composite materials is an accepted technology that is being used in practice to strengthen existing reinforced concrete (R/C) elements. An artificial neural network (ANN) model was developed using past experimental data on flexural failure of R/C beams strengthened by carbon FRP. The input parameters cover the carbon sheet properties, beam geometrical properties and reinforcement properties; the corresponding output is the ultimate load capacity. The ANN prediction and the measured experimental values are compared with load prediction of ACI440.2R-02 formulas. A sensitivity study of parameters that affect ultimate load of R/C beams strengthened by carbon FRP is carried out. It is concluded that ANN can predict, to a good degree of accuracy, the ultimate load capacity of R/C beams strengthened by carbon FRP and it is a viable tool to carry out parametric study of flexural behavior of R/C beams strengthened by carbon FRP.