An artificial neural network for predicting the ultimate bending moments in reinforced concrete beams with fiber-reinforced polymer strengthening

A practical artificial neural network tool is proposed for predicting the ultimate bending moments of reinforced concrete beams strengthened by the techniques of externally bonded fiber-reinforced polymer and near-surface mounted fiber-reinforced polymer. Accordingly, the testing database of 131 specimens was gathered for use in developing the artificial neural network model. In this regard, the breadth and height of the beam section, the compression strength of the concrete, the ratio of material reinforcement, and the elastic modulus of fiber reinforced polymer were regarded as input variables, whereas the ultimate bending moment was regarded as an output variable. The performance of the proposed artificial neural network model was compared to the current design model of the American Concrete Institute guide. The comparative analysis demonstrated that the proposed model made more accurate predictions than the current model. Based on the proposed model, a graphical user interface was created to facilitate the prediction of the ultimate bending moments of reinforced concrete beams with fiber-reinforced polymer strengthening.