| 基于HPSO算法的岩石非定常蠕变本构模型辨识 |
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| 引用本文： | 刘文彬.基于HPSO算法的岩石非定常蠕变本构模型辨识[J].岩土工程技术,2011,25(4):168-172. |
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| 作者姓名： | 刘文彬 |
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| 作者单位： | 北京航天勘察设计研究院,北京,100071 |
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| 摘 要： | 复合微粒群优化(HPSO)是一类随机全局优化技术,具有搜索能力强、收敛速度快、搜索精度高的优点.针对岩石蠕变本构模型非定常参数的辨识问题,利用FLAC软件自带的fish语言实现了HPSO算法对非定常参数的辨识.该方法从非定常参数的随机值出发,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,...
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| 关 键 词： | 岩石力学 蠕变本构模型 参数辨识 复合微粒群算法 |
Time-varying Creep Constitutive Model Identification of Rock Based on HPSO Algorithm |
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| Liu Wenbin.Time-varying Creep Constitutive Model Identification of Rock Based on HPSO Algorithm[J].Geotechnical Engineering Technique,2011,25(4):168-172. |
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| Authors: | Liu Wenbin |
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| Institution: | Liu Wenbin (Geotechnical Institute of Beijing Aerospace, Beijing 100071, China) |
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| Abstract: | Hybrid Particle swarm optimization （HPSO） algorithm is a stochastic global optimization technique with many advantages, such as quick convergence, simple regulation and easy implementation. In order to determine the time-varying parameters of creep constitutive model of rock, in this article, a new method is presented using HPSO 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 creep was regarded as fitness function to evaluate quality of the parameters. Then the parameters are updated continually using HPSO until the optimal parameters are found. Thus time-varying parameters of creep constitutive model of rock are identified adaptively during computation. Simulations was done for shale creep experiment, the results show that hybrid particle swarm optimization algorithm is effective in identifying the time-varying parameters of creep constitutive model of rock and viscoelastic characteristics of shale can be described better by using inconstant creep constitutive model. |
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| Keywords: | rock mechanics creep constitutive model parameter identification hybrid particle swarm optimization algorithm |
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