基于RBF神经网络的隧洞围岩变形预测方法  

A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks

作　　者：张俊艳[1] 冯守中[2] 刘东海[2] 

机构地区：[1]天津大学管理学院,天津300072 [2]天津大学建筑工程学院,天津300072

出　　处：《中国工程科学》2005年第10期87-90,共4页Strategic Study of CAE

基　　金：国家自然科学基金资助项目(50479048)

摘　　要：传统回归方法对于围岩变形时程曲线存在反弯点,这种情况的模拟具有难度。提出的基于RBF神经网络的隧道围岩变形预测方法,不仅能很好地描述复杂的围岩变形时程曲线,而且比BP神经网络具有更快的收敛速度和更好的全局搜索能力。实例研究验证了该方法的有效性与可行性。Owing to the difficulty of traditional multi-variable regression methods to represent the surrounding rock deformation curve with inflexion points, a method for forecasting tunnel surrounding rock deformation using radial basis function neural networks is presented. This method not only can be utilized to approximate the complex deformation curves, but also has higher convergence speed and better globally-searching ability than those using BP neural networks. An example is given to show the effectiveness and practicability of this method.

关 键 词：RBF神经网络 隧洞施工 围岩变形 预测 

分 类 号：U456.31[建筑科学—桥梁与隧道工程] TP183[交通运输工程—道路与铁道工程]