基于改进PSO算法的岩石蠕变模型参数辨识

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．

Parameter Identification of Creep Constitutive Model of Rock Based on Modified PSO Algorithm

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 creep was regarded as fitness function to evaluate quality of the parameters. Then the parameters are updated continually by using modified PS0 until the optimal parameters are found. Thus parameters of creep constitutive model of rock are identified adaptively during computation. Simulations was done for shale rock creep experiment, the results show that modified particle swarm optimization algorithm is effective in identifying the parameters of creep constitutive model of rock.