Failure Criterion of Concrete under Triaxial Stresses Using Neural Networks

A neural network approach to model the strength of concrete under triaxial stresses is presented in this paper. A radial basis function neural network (RBFNN) and a backpropagation neural network (BPNN) are used for training and testing the experimental data in order to acquire the failure criterion of concrete strength. Unlike the traditional regression analyses where the explicit forms of the equation must be defined first, the neural network approach provides a general form of strength envelope. The study shows that the RBFNN model provides better prediction than the BPNN model. Parametric studies on both models are carried out to find the best neural network structure. Finally, a comparison study between the neural network model and two regression models is made.