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G_max与孔隙比e、围压σ₃、固结比 k_c 这3个影响因素的非线性关系，避开了寻找G_max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G_max平均相对误差为0.931 6%，误差很小，说明 RBF 神经网络能方便、有效地确定不同条件下的G_max，具有一定的推广利用价值。除了对G_max能够很好地预测外， RBF 网络对G -γ关系曲线也能很好地模拟。

 To avoid the complicated work of searching for quantitative experiential formula, a nonlinear relationship between maximum dynamic shear modulus(G_max) and the influence factors including void ratio(e), cell pressure(σ₃), and consolidation ratio(k_c) was built directly by using Radial Basis Function(RBF) neural network. In addition, the optimal value of spread speed(SPREAD) of RBF was calculated by pattern search method to minimize the prediction error. Taking standard sand in Fujian province as an example, the optimal value of SPREAD calculated by pattern search method equals to 2.287 , and the average relative error of G_max predicted by RBF neural network is 0.931 6 % , which is quite small. It shows that RBF neural network can determine G_max under different conditions conveniently and effectively. Besides, the relationship curve of G-γcan also be simulated by this network. Therefore, the method of using RBF neural network to calculate the maximum dynamic shear modulus is recommended to be used widely.