Deformation Prediction of Excavated Slopes with a Neural Network Model Based on Nonlinear Numerical Analyses

The control and predication of deformations of excavated slopes is one of the most important problems in foundation engineering, but the evaluation of the deformation of excavated slopes has been lack of adequate methods, to which special attentions need to pay for the sack of protection of engineering environment. In order to make prediction of deformations of excavated slopes, an artificial neural network model was set up based on nonlinear finite element analyses of excavated slopes. Firstly, the pattern of the deformation of excavated slopes is generalized through a large amount of finite element analyses. Secondly, a practical and fast algorithm for predicting the deformations of excavated slopes-a neural network predication model based on numerical analysis was set up. The neural network for predicting deformations of excavated soil slopes contains four layers of neural elements, i.e. the input layer, the first and second hidden layers and the output layer. Totally 70 sets of data from finite element analyses were used for training the network, while deformation prediction were conducted with other 20 sets of dada, which given a good accuracy with errors within 10% for practical applications. The result of predictions with the neural network model demonstrates that the combination of the numerical methods and neural networks is a feasible way of deformation predication.