粒子群算法改进及内变量本构模型参数反演
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摘要
为了研究深埋煤矿巷道通常存在长时间、大变形问题,拓展岩土工程反分析的手段,改善岩土工程反分析的效率和精度,首先基于自然选择、自适应变惯性权重、异步变化学习因子的策略改进了粒子群算法并完成了程序实现,通过Sphere和Rastrigrin两函数测试了改进算法的优越性;其次以Matlab软件为平台,联合大型有限元软件ABAQUS,编制了岩土反分析程序Geo PSOInverse.m;最后应用所编程序反演了以不可恢复应变为变量的、不显含时间的泥岩蠕变模型参数。结果证实:改进的粒子群算法在岩土工程参数反演计算中体现出了可靠的反演能力和很快的收敛速度,可进行复杂采矿工程的实践应用。
In order to extend back analysis means and improve the efficiency and accuracy, an improved particle swarm optimization(PSO) with variable inertia weight and synchronous changing learning factor was carried out and the program was completed, the advantage of the enhanced method was tested by Sphere and Rastrigrin functions. Then, a back analysis program Geo PSOInverse.m was developed by the software MATLAB, in which the finite element code of ABAQUS was embedded. At last, the parameters of creep constitutive model of a mudstone with irreversible strain as variable and implicit time in formula were back analysied. The results show that this improved PSO method is a very good inverse analysis method and its efficiency is quite good, and can be proceeded in practical application for complex engineering.
In order to extend back analysis means and improve the efficiency and accuracy, an improved particle swarm optimization(PSO) with variable inertia weight and synchronous changing learning factor was carried out and the program was completed, the advantage of the enhanced method was tested by Sphere and Rastrigrin functions. Then, a back analysis program Geo PSOInverse.m was developed by the software MATLAB, in which the finite element code of ABAQUS was embedded. At last, the parameters of creep constitutive model of a mudstone with irreversible strain as variable and implicit time in formula were back analysied. The results show that this improved PSO method is a very good inverse analysis method and its efficiency is quite good, and can be proceeded in practical application for complex engineering.
引文
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[2]杨林德.岩土工程问题的反演理论与工程实践[M].北京:科学出版社,1999.
[3]李守巨,刘迎曦,孙伟.智能计算与参数反演[M].科学出版社,2008:48–65.
[4]冯夏庭,张治强,杨成祥,等.位移反分析的进化神经网络方法研究[J].岩石力学与工程学报,1999,18(5):529–533.FENG Xiating,ZHANG Zhiqiang,YANG Chengxiang,et al.Study on genetic-neural network method of displacement back analysis[J].Chinese Journal of Rock Mechanics and Engineering,1999,18(5):529–533.
[5]高玮,郑颖人.一种新的岩土工程进化反分析算法[J].岩石力学与工程学报,2003,22(2):192–196.GAO Wei,ZHENG Yingren.New evolutionary back analysis algorithm in geotechnical engineering[J].Chinese Journal of Rock Mechanics and Engineering,2003,22(2):192–196.
[6]李宁.粒子群优化算法的理论分析与应用研究[D].武汉:华中科技大学,2006.
[7]贾善坡,伍国军,陈卫忠.基于粒子群算法与混合罚函数法的有限元优化反演模型及应用[J].岩土力学,2011,32(增刊2):598–603.JIA Shanpo,WU Guojun,CHEN Weizhong.Application of finite element inverse model based on improved particle swarm optimization and mixed penalty function[J].Rock and soil mechanics,2011,32(S2):598–603.
[8]苏国韶,张克实,吕海波.位移反分析的粒子群优化—高斯过程协同优化方法[J].岩土力学,2011,32(增刊2):510–524.SU Guoshao,ZHANG Keshi,LYU Haibo.A cooperative optimization method based on particle swarm optimization and gaussian process for displacement back analysis[J].Rock and Soil Mechanics,2011,32(S2):510–524.
[9]李金凤,杨启贵,徐卫亚.基于改进粒子群算法CHPSO-DS的面板坝堆石体力学参数反演[J].岩石力学与工程学报,2008,27(6):1229–1235.LI Jinfeng,YANG Qigui,XU Weiya.Back analyzing mechanical parameters of rockfill based on modified particle swarm optimization CHPSO-DS[J].Chinese Journal of Rock Mechanics and Engineering,2008,27(6):1229–1235.
[10]KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of the IEEE International Conference on Neural Networks,1995,4:1942–1948.
[11]EBERHART R C,KENNEDY J.A new optimizer using particle swarm theory[C]//Proceedings of the Sixth International Symposium on Micro Machine and Human Science,MHS'95,Nagoya,Japan,1995:39–43.
[12]LANGDON W B,POLI R.Evolving problems to learn about particle swarm and other optimizers[C]//2005 IEEE Congress on Evolutionary Computation,2005,1:81–88.
[13]谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,18(2):129–134.XIE Xiaofeng,ZHANG Wenjun,YANG Zhilian.Overview of particle swarm optimization[J].Control and Decision,2003,18(2):129–134.
[14]SHI Y,EBERHART R C.A modified particle swarm optimizer[C]//Proceedings of the IEEE International Conference on Evolutionary Computation,1998:69–73.
[15]ASANGA R,SAMNA K H.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J].IEEE Transactions on Evolutionary Computation,2004,3(8):240–255.
[16]张顶学,关治洪,刘新芝.一种动态改变惯性权重的自适应粒子群算法[J].控制与决策,2008,23(11):1253–1257.ZHANG Dingxue,GUAN Zhihong,LIU Xinzhi.Adaptive particle swarm optimization algorithm with dynamically changing inertia weight[J].Control and Decision,2008,23(11):1253–1257.
[17]陈卫忠,袁克阔,于洪丹,等.Boom Clay蠕变特性研究[J].岩石力学与工程学报,2013,32(10):1981–1990.CHEN Weizhong,YUAN Kekuo,YU Hongdan,et al.Creep behavior of boom clay[J].Chinese Journal of Rock Mechanics and Engineering,2013,32(10):1981–1990.