Crack width prediction of reinforced concrete structures by artificial neural networks

This paper proposes the use of artificial neural networks (ANN) for the prediction of the maximum surface crack width of precast reinforced concrete beams joined by steel coupler connectors and anchor bars (jointed beams). Two different training algorithms are used in this study and their performance is compared. The first approach used backpropagation and the second one includes genetic algorithms during the training process. Input and output vectors are designed on the basis of empirical equations available in the literature to estimate crack widths in common reinforced concrete (RC) structures and parameters that characterize the mechanical behavior of RC beams with overlapped reinforcement. Two well-defined points of loading are considered in this study to demonstrate the suitability of this approach in both a linear and a highly nonlinear stage of the mechanical response of this type of structure. Remarkable results were obtained, however, in all cases using the combined genetic artificial neural network (GANN) approach which resulted in improved prediction performance over networks trained by error backpropagation.