边坡岩体稳定性的RBF人工神经网络预测模型  

PREDICTING MODELS TO ESTIMATE STABILITY OF ROCK SLOPE BASED ON RBF-ARTIFICIAL NEURAL NETWORK

作　　者：邱秀梅[1] 姜德贵[1] 

机构地区：[1]山东农业大学水利土木工程学院,泰安271018

出　　处：《山东农业大学学报（自然科学版）》2008年第4期601-604,共4页Journal of Shandong Agricultural University：Natural Science Edition

基　　金：山东省教育厅资助项目(J07YE04-32426)

摘　　要：利用RBF人工神经网络理论,采用影响边坡稳定性的复合指标,以大量边坡工程的稳定状况为学习训练和预测样本,建立了RBF预报模型。讨论了基于RBF人工神经网络技术的边坡岩体稳定性分析方法及有效性。研究表明,用RBF人工神经网络方法预测边坡岩体的稳定状况是可行的。Using the theory of RBF - artificial neural network and the compound indices for affecting the stability of rock slopes, based on a large amound of rock slope engineering cases related to project, a RBF - model for prediction was set up. The practical effectiveness of the theory of RBF - artificial neural network to predict the safety of rock slopes was discussed. The study suggested that the method was feasible for prediction of rock slope stability.

关 键 词：(RBF)人工神经网络技术 复合指标 边坡岩体稳定性分析 

分 类 号：TU457[建筑科学—岩土工程]