关键词: 反问题, 回归分析, RBF神经网络, 力学参数估计, 土层分类

Abstract: Mechanical parameters estimation and classification of soils are very important in geologic examination. On the basis of inverse problem theory, a new intelligent method combining RBF neural network and regression analysis is proposed. Then an intellectualized simulation system of soil is established, consisting of two neural networks for mechanical parameter estimation and model recognition. In the system, considering variability of physical parameters is much smaller than mechanical parameters of soils, an artificial neural network model is established to approach the function relationship of the two kinds of parameters. It is effective to reflect mechanical parameters according physical parameters. The mechanical parameters will be input vectors applied to the other network. Then, the new neural network is established; it can offer a good approach to soil classification. This intellectualized simulation system is applied to analyzing geologic examination data in Huangshi; and the results show that the method is simple and effective.

Key words: inverse problem, regression analysis, RBF neural network, mechanical parameters estimation, soil classification