| 基于改进PSO算法的岩石蠕变模型参数辨识 |
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| 引用本文： | 刘文彬,刘保国,刘中战,崔少东.基于改进PSO算法的岩石蠕变模型参数辨识[J].北京交通大学学报(自然科学版),2009,33(4). |
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| 作者姓名： | 刘文彬 刘保国 刘中战 崔少东 |
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| 作者单位： | 北京交通大学,土木建筑工程学院,北京,100044;山东省菏泽市公路管理局,山东,菏泽,274000 |
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| 摘 要： | 微粒群优化(PSO)算法是一类随机全局优化技术,具有收敛速度快、规则简单、易于实现的优点.针对岩石蠕变本构模型参数的辨识问题,本文利用FLAC软件自带的fish语言实现了改进PSO算法对本构模型参数的辨识.该方法从岩石本构模型参数的随机值出发,以蠕变过程中试件变形的实验值与计算值的误差大小作为适应度函数来评价参数的品质,利用改进PSO算法规则实现模型参数的进化,搜索出全局最优的模型参数值,从而实现了岩石蠕变本构模型参数的自适应辨识.利用该方法对页岩蠕变实验进行了仿真研究,实验结果表明:改进的PSO算法用于岩石蠕变模型的参数辨识是有效的.
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| 关 键 词： | 岩石力学 蠕变模型 参数辨识 微粒群算法 |
Parameter Identification of Creep Constitutive Model of Rock Based on Modified PSO Algorithm |
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| LIU Wenbin,LIU Baoguo,LIU Zhongzhan,CUI Shaodong.Parameter Identification of Creep Constitutive Model of Rock Based on Modified PSO Algorithm[J].JOURNAL OF BEIJING JIAOTONG UNIVERSITY,2009,33(4). |
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| Authors: | LIU Wenbin LIU Baoguo LIU Zhongzhan CUI Shaodong |
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| Institution: | 1.School of Civil Engineering;Beijing Jiaotong University;Beijing 100044;China;2.Highway Administration of Heze City Shandong Province;Shandong Heze 274000;China |
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| Abstract: | Particle swarm optimization(PSO) algorithm is a stochastic global optimization technique with many advantages,such as quick convergence,simple regulation and easy implementation.In order to determine the parameters of creep constitutive model of rock,in this article,a new method is presented by using modified PSO algorithm and fish language,which was contained in FLAC.At first,the stochastic values of parameters are initialized and the difference between the value computed and the datum measured during cree... |
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| Keywords: | rock mechanics creep constitutive model parameter identification particle swarm optimization algorithm |
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