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均匀设计在岩土工程概率分析中的应用
引用本文:黄 靓,易伟建,汪 优.均匀设计在岩土工程概率分析中的应用[J].岩土力学,2006,27(Z2):137-140.
作者姓名:黄 靓  易伟建  汪 优
作者单位:湖南大学 土木工程学院,长沙 410082
基金项目:国家自然科学基金资助项目(No. 50378034)
摘    要:基于神经网络的响应面蒙特卡罗方法,通过引入均匀设计来安排RBF神经网络的训练样本集,将均匀设计和RBF网络的优势有机地结合起来:一方面尽量减少训练样本点的数量;另一方面,训练样本点均匀地充满参数空间,有利于提高具有较强局部逼近能力的RBF网络的预测精度。应用该方法进行挡土墙结构抗滑移稳定性的概率分析,结果表明该方法与直接蒙特卡罗模拟的结果相当吻合,能够满足实际工程的精度要求。分析还表明,随机因素对挡土墙结构抗滑移稳定性的影响程度由大到小依次为基底摩擦系数、填土摩擦角、填土重度和填土对挡土墙墙背的摩擦角。

关 键 词:均匀设计  蒙特卡罗模拟  RBF神经网络  挡土墙  稳定性  
收稿时间:2006-11-21

Application of uniform design in probability analysis of geotechnical engineering
HUANG Liang,YI Wei-jian,WANG You.Application of uniform design in probability analysis of geotechnical engineering[J].Rock and Soil Mechanics,2006,27(Z2):137-140.
Authors:HUANG Liang  YI Wei-jian  WANG You
Institution:College of Civil Engineering, Hunan University,Changsha 410082 China
Abstract:In the method of response surface Monte-Carlo based on artificial neural network, the uniform design, a kind of design of computer experiments, is introduced to arrange training samplings set of RBF neural network that combines the advantages of uniform design and RBF neural network organically. On the one hand, it may decrease the number of training samples as fully as possible, and on the other, it fills training samples into the parameter space uniformly, and enhances the prediction accuracy of RBF neural network which has strongly local approximate capacity. In the probability analysis of retaining wall stability against sliding, for example, the results of the proposed method are very consistent with those of the direct Monte-Carlo simulations. And in addition this probability analysis shows that friction coefficient of the wall base f, the inner friction angle f ,unit weight γ of filled soils, and the friction angle of the soils against wall back δ have influences on the retaining wall stability against sliding from high to low in turn.
Keywords:uniform design  Monte-Carlo simulation  RBF neural network  retaining wall  stability  
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