Artificial neural network analysis of concrete carbonation under sustained loads

Two artificial neural networks (ANN), backpropagation neural network (BPNN) and the radial basis function neural network (RBFNN), are proposed to predict the carbonation depth of stressed concrete. In order to generate the training and testing data for the ANNs, an accelerated carbonation experiment was carried out for stressed concrete specimens. Based on the experimental results, the BPNN and RBFNN models which all take the stress level of concrete, water-cement ratio, cement-fine aggregate ratio, cement-coarse aggregate ratio and testing age as input parameters were built and all the training and testing work was performed in MATLAB. It can be found that the two ANN models seem to have a high prediction and generalization capability in evaluation of carbonation depth, and the largest absolute percentage errors of BPNN and RBFNN are 10.88% and 8.46%, respectively. The RBFNN model shows a better prediction precision in comparison to BPNN model.