Carbonation depth prediction of pre-stressed concrete based on artificial neural network

In order to calculate the carbonation depth of pre-stressed concrete under certain conditions, the stress level of concrete was regarded as an influencing factor on concrete carbonation. Based on the present test data, a practical model for calculating the carbonation depth of pre-stressed concrete was built. And three artificial neural networks (ANN): the BP network, the radial basis function (RBF) network and the generalized regression neural network (GRNN), were established to predict the carbonation depths. The predicted values of the three network models were compared with experimental values and calculated values. The results show that the carbonation depth calculation model with concrete stress level is practicable and its relative error is within 9%; and the three networks have high precision and good generalization ability, whose simulation and prediction errors are within 5% and 4%, lower than the error of calculation. Thus the results of the networks are good, which proves that ANN is an effective method in analyzing and predicting the carbonation depth.