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Determination of the Maximum Dynamic Shear Modulus Based on Improved RBF Neural Network
Author(s): 
Pages: 51-56
Year: Issue:  7
Journal: Journal of Yangtze River Scientific Research Institute

Keyword:  径向基神经网络最大动剪切模量Hardin公式模式搜索法;
Abstract: 采用径向基函数(RBF)神经网络的手段,直接建立最大动剪切模量Gmax与孔隙比e、围压σ3、固结比κc这3个影响因素的非线性关系,避开了寻找Gmax与各影响因素之间定量经验公式的繁琐工作。通过模式搜索法计算出径向基函数的扩展速度的最优值,使模型的预测误差最小。以福建标准砂为例,模式搜索法得出的扩展速度SPREAD最优值为2.287,RBF网络预测的Gmax平均相对误差为0.931 6%,误差很小,...
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