CONSTITUTIVE MODEL OF ARTIFICIAL FROZEN SOIL BASED ON CASCADE-CORRELATION NEURAL NETWORK

In order to avoid the drawback of BP (back propagation) neural network of too slow velocity convergence, the network structure has to be defined in advance. A cascade-correlation artificial neural network model is adopted to create the relationship between stress and strain of the artificial frozen soil, the consistent stiffness matrix is derived based on this model for the frozen soil, the neural network model is trained by the triaxial test data to replace the traditional finite element constitutive model, and the calculated results of the properties and the moisture content of the frozen soil are compared with the experimental results. It is found that this neural network constitutive model can represent the nonlinear response of the material very well, can improve the numerical analysis results, with very good agreement with the measured results, and the results are closer to the measured results than the BP model with same number of neurons in the hidden layer.