Application of neural network to back analysis of mechanical parameters and initial stress field of rock masses

At present, BP neural network has been widely used in back analysis of material parameters and initial stress field of rock masses in geomechanics. However, BP neural network is prone to over-being-trained, slow in convergence, not global minimum but local ones obtained and number of neurons in hidden layer hard to be determined. Authors using RBF neural network and BP neural network respectively identified mechanical parameters and initial stresses according to measured normal stresses of some specific points. Direct computations based on fast Lagrangian analysis of continuum (FLAC) were performed to get enough training samples for RBF neural network and BP neural network. An example shows that combination of RBF neural network with FLAC is more effective and rapid than application of BP neural network.