岩土力学2011，Vol.32Issue(4)：1018-1024,7.
软土物理力学性质指标与微结构参数的灰色关联-神经网络模型
Grey-relation analysis and neural networks model for relationship between physico-mechanical indices and microstructure parameters of soft soils
摘要
Abstract
Through a large number of physico-mechanical tests and microstructure analysis of soft soils in Nansha area Guangzhou China, 40 groups of physico-mechanical indices and microstructure parameters are obtained.Using data analysis abitity of grey-relation analysis method and nonlinear mapping ability of artificial neural networks, a model for the relationship between physico-mechanical indices and microstructure parameters of soft soil is established based on grey-relation analysis and radial basis function (RBF) neural networks.In this model, grey-relation analysis is applied to preprocess data and extract key components as the input of the neuraJ networks.RBF algorithm can fully utilize the information contained in the training data, adaptively choose the centers of radial basis functions, the widths and the weights of networks; therefore, the problems of determining node number of hidden layer and centers, slow leaming speed and weaken generalization ability of traditional RBF neural networks when the input data are generous and complex are solved.Model A and model B show that this method can reduce the structure of neural networks,and raise efficiency of training and accuracy of prediaion, and provide an efficient way to quantitatively study about relationship between physico-mechanical properties and microstructure parameters of soft soils.关键词
软土/物理力学性质指标/微观结构参数/灰色关联分析/径向基神经网络Key words
soft soil/ physico-mechanical index/ microstructure parameter/ grey-relation analysis/ radial basis function neural networks分类
建筑与水利引用本文复制引用
刘勇健,李彰明..软土物理力学性质指标与微结构参数的灰色关联-神经网络模型[J].岩土力学,2011,32(4):1018-1024,7.基金项目
广东省自然科学基金项目 (No.6021462) （No.6021462）
广东省重点扶持学科基金和博士基金项目(No.09033). （No.09033）
