基于DEPSO混合智能算法的岩土体应力-渗流-损伤耦合模型多参数反演研究
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摘要
随着复杂岩土工程问题研究的深入,开展关于岩土体应力-渗流-损伤多参数反演问题的研究具有显著意义.引入差异进化算法(Differential Evolution Algorithm,DE)—粒子群算法(Particle Swarm Optimization,PSO)混合智能算法DEPSO到岩土工程应力-渗流-损伤耦合问题参数反演研究之中,该混合智能算法可以保证两个种群的相互协调开发能力和探索能力之间的平衡,维持整个种群的多样性,降低陷入局部最优或早熟停滞的风险.通过两个经典测试函数,针对DEPSO、PSO以及DE这3种算法的收敛性、鲁棒性进行对比分析,并讨论在DEPSO算法中DE控制参数、差异策略的控制作用.将岩体应力-渗流-损伤耦合模型与DEPSO混合智能算法结合起来,建立耦合模型多参数反演方法,并采用C++编制程序,构建基于DEPSO混合算法的耦合多参数反分析平台.依据应力-渗流-损伤耦合正分析计算的结果作为"假想实测值",进行多参数反演分析,以及方法和程序的验证.研究结果表明:所建立的基于DEPSO混合算法的耦合多参数反分析方法,可以同时反演多个力学参数和渗透参数,能够较好地解决岩土工程多参数反演问题,在计算效率方面高于单一智能算法,具有较好的反演精度和鲁棒性,是一种新颖和高效的反分析方法,可以更好地为复杂环境岩土工程动态施工提供帮助和依据.
Research on the stress-seepage-damage parameter inversion problem of the rock is a great significance with the development of the complex geotechnical engineering problems. DEPSO hybrid intelligent algorithm is introduced into the stress-seepage-damage coupling parameters inversion based on differential evolution algorithm and particle swarm optimization. The hybrid intelligent method can make sure the two interact with each other coordinated development of population of balance between ability and the ability to explore,to maintain the diversity of the whole population and reduce the risk of fall into local optimum. Two classical test functions are used in view of the convergence and robustness of DEPSO. PSO,DE algorithm and the control parameters and the importance of the difference strategy of DEPSO algorithm are discussed in details. The coupling multi-parameters method is set up combined the stress-seepage-damage coupling model with DEPSO hybrid intelligent algorithm,and the coupling parameters back analysis program is compiled using C + + programming based on DEPSO. A hypothetical measured values are calculated based on the stress-seepage-damage coupling program to analysis multi-parameter inversion for the method and program validation. Research results show that the multi-mechanics parameters and penetration can be inversion by the coupling parameter back analysis method based on the established DEPSO algorithm at the same time,and the problem of multi-parameters inversion can be well solved in geotechnical engineering. In terms of computation efficiency is higher than the single intelligent algorithm. It is a new and efficient back analysis method which has the good inversion precision and robustness. To provide the help and basis for dynamic construction in complex environmental geotechnical engineering.
Research on the stress-seepage-damage parameter inversion problem of the rock is a great significance with the development of the complex geotechnical engineering problems. DEPSO hybrid intelligent algorithm is introduced into the stress-seepage-damage coupling parameters inversion based on differential evolution algorithm and particle swarm optimization. The hybrid intelligent method can make sure the two interact with each other coordinated development of population of balance between ability and the ability to explore,to maintain the diversity of the whole population and reduce the risk of fall into local optimum. Two classical test functions are used in view of the convergence and robustness of DEPSO. PSO,DE algorithm and the control parameters and the importance of the difference strategy of DEPSO algorithm are discussed in details. The coupling multi-parameters method is set up combined the stress-seepage-damage coupling model with DEPSO hybrid intelligent algorithm,and the coupling parameters back analysis program is compiled using C + + programming based on DEPSO. A hypothetical measured values are calculated based on the stress-seepage-damage coupling program to analysis multi-parameter inversion for the method and program validation. Research results show that the multi-mechanics parameters and penetration can be inversion by the coupling parameter back analysis method based on the established DEPSO algorithm at the same time,and the problem of multi-parameters inversion can be well solved in geotechnical engineering. In terms of computation efficiency is higher than the single intelligent algorithm. It is a new and efficient back analysis method which has the good inversion precision and robustness. To provide the help and basis for dynamic construction in complex environmental geotechnical engineering.
引文
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[16]漆祖芳,姜清辉,周创兵,等.基于v-SVR和MVPOS算法的边坡位移反分析方法及其应用[J].岩石力学与工程学报,2013,32(6):1185-1196Qi Zufang,Jiang Qinghui,Zhou Chuangbing,et al.A new slope displacement back analysis method based on v-SVR and MVPSO algorithm and its application[J].Chinese Journal of Rock Mechanics and Engineering,2013,32(6):1185-1196
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[22]王军祥.岩石弹塑性损伤MHC耦合模型及数值算法研究[D].大连:大连海事大学,2014Wang Junxiang.Study on elastoplastic damage and MHC coupling model of rock and numerical algorithm[D].Dalian:Dalian Maritime University,2014
[23]叶源新,刘光廷.岩石渗流应力耦合特性研究[J].岩石力学与工程学报,2005,24(14):2518-2525Ye Yuanxin,Liu Guangtingl.Research on coupling characteristics of fluid flow and stress within rock[J].Rock and Soil Mechanics,2005,24(14):2518-2525
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[2]刘武,陈益峰,胡冉,等.基于非稳定渗流过程的岩体渗透特性反演分析[J].岩石力学与工程学报,2015,34(2):362-373Liu Wu,Chen Yifeng,Hu Ran,et al.Back analysis of rock permeability with consideration of transinet flow process[J].Chinese Journal of Rock Mechanics and Engineering,2015,34(2):362-373
[3]Shunsuke Sakurai,Shinichi Akutagawa,Kunifumi Takeuchi,et al.Back analysis for tunnel engineering as a modern observational method[J].Tunnelling and Underground Space Technology,2003,18:185-196
[4]王媛,刘杰.基于敏感性分析的裂隙岩体渗流与应力静态全耦合参数反演[J].岩土力学,2009,30(2):311-317Wang Yuan,Liu Jie.Parameter inversion for fully coupled problem of steady fluid flow ans stress in fractured rock masses based on sensitivity analysis[J].Rock and Soil Mechanics,2009,30(2):311-317
[5]刘成学,杨林德,李鹏.渗流-应力耦合问题的多参数优化反演研究[C].第三届全国水工岩石力学学术会议论文集.上海:中国岩石力学与工程学会,2010Liu Chengxue,Yang Linde,Li Peng.Parameters optimization inversion of seepage and stress coupling problem[C].The Third National Conference on Hydraulic Rock Mechanics.Shanghai:Cinese Society for Rock Mechanics&Engineering,2010
[6]吴创周,杨林德,刘成学,等.各向异性岩应力-渗流耦合问题的反分析[J].岩土力学,2013,34(4):1156-1162Wu Chuangzhou,Yang Linde,Liu Chengxue,et al.Back analysis of coupled seepage-stress fields in anisotropic rocks[J].Rock and Soil Mechanics,2013,34(4):1156-1162
[7]贾善坡,龚俊,高敏,等.考虑自愈合效应的泥岩巷道开挖扰动区渗透性反演分析[J].岩土力学,2015,36(5):1444-1454Jia Shanpo,Gong Jun,Gao Min,et al.Inversion analysis of permeability coefficients of excavation-disturbed zone around a mudstone roadway with considering self-heading effect[J].Rock and Soil Mechanics,2015,36(5):1444-1454
[8]王登刚,刘迎曦,李守巨.岩土工程位移反分析的遗传算法[J].岩石力学与工程学报,2000,19(增1):979-982Wang Denggang,Liu Yingxi,Li Shouju.Genetic algorithms for inverse analysis of displacements in geotechnical engineering[J].Chinese Journal of rock mechanics and engineering,2000,19(S1):979-982
[9]高玮.基于粒子群优化的岩土工程反分析研究[J].岩土力学,2006,27(5):795-798Gao Wei.Back analysis algorithm in geotechnical engineering based on particle swarm optimization[J].Rock and Soil Mechanics,2006,27(5):795-798
[10]曹文贵,卢山,胡坚丽.基于自适应退火算法的非线性位移反分析方法研究[J].岩土力学,2008,29(7):1753-1758Cao Wengui,Lu Shan,Hu Jianli.Research on method of nonlinear displacement back analysis besed on adaptive annealing algorithm[J].Rock and Soil Mechanics,2008,29(7):1753-1758
[11]吕颖慧,王水林,葛修润,等.一种新的全局优化算法在岩土工程反分析中的应用[J].岩土力学,2008,29(6):1451-1454Lu Yinghui,Wang Shuilin,Ge Xiurun,et al.Application of a new global optimization to displacement back analysis for geotechnical engineering[J].Rock and Soil Mechanics,2008,29(6):1451-1454
[12]王军祥,姜谙男.孔隙水压力作用的弹塑性CPPM算法及隧道围岩力学参数反演[J].应用基础与工程科学学报,2014,22(3):525-538Wang Junxiang,Jiang An'nan.Elastoplastic CPPM algorithm and mechanical parameters inversion of tunnel surrounding rock under the action of pore water pressure[J].Journal of Bnsic Science and Engineering,2014,22(3):525-538
[13]冯夏庭,周辉,李韶军,等.岩石力学与工程综合集成智能反馈分析方法及应用[J].岩石力学与工程,2007,26(9):1737-1744Feng Xiating,Zhou Hui,Li Shaojun,et al.Integrated intelligent feedback analysis of rock mechanics and engineering problems and its applications[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(9):1737-1744
[14]Bidyadhar Subudhi,Debashisha Jena.Differential evolution and levenberg marquardt trained neural network scheme for nonlinear system identification[J].Neural Processing Letters,2008,27:285-296
[15]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(2):510-515,524Su Guoshao,Zhang Keshi,Lu Haibo.A cooperative optimization method based on particle swarm optimization and Gaussian process for displacement back analysis[J].Rock and Soil Mechanics,2011,32(2):510-515,524
[16]漆祖芳,姜清辉,周创兵,等.基于v-SVR和MVPOS算法的边坡位移反分析方法及其应用[J].岩石力学与工程学报,2013,32(6):1185-1196Qi Zufang,Jiang Qinghui,Zhou Chuangbing,et al.A new slope displacement back analysis method based on v-SVR and MVPSO algorithm and its application[J].Chinese Journal of Rock Mechanics and Engineering,2013,32(6):1185-1196
[17]Ahuja R K,Orlin J B.Developing fitter genetic algorithms[J].Journal of Computing,1997,9(3):251-253
[18]Stom R.On the usage of differential evolution for function optimization[C].Proceedings of the Biennial Conference of the North American Fuzzy Information Processing Society.Piscataway,NJ USA:IEEE,1996,519-523
[19]Kennedy J,Eberhart R C.Particle swarm optimization[C].Proceedings of IEEE International Conference on Neutral Networks.Piscataway,NJ:IEEE Service Center,1995:1942-1948
[20]Shi Y,Eberhart R.A modified particle swarm optimizer[C].Proceedings of the 1998 IEEE International Conference on Evolutionary Compution.Piscataway,NJ USA:IEEE,1998:69-73
[21]栾丽君,谭立静,牛奔.一种基于粒子群优化算法和差分进化算法的新型混合全局优化算法[J].信息与控制,2007,36(6):708-714Luan Lijun,Tan Lijing,Niu Ben.A novel hybrid global optimization algorithm based on particle swarm optimization and different evolution[J].Information and Control,2007,36(6):708-714
[22]王军祥.岩石弹塑性损伤MHC耦合模型及数值算法研究[D].大连:大连海事大学,2014Wang Junxiang.Study on elastoplastic damage and MHC coupling model of rock and numerical algorithm[D].Dalian:Dalian Maritime University,2014
[23]叶源新,刘光廷.岩石渗流应力耦合特性研究[J].岩石力学与工程学报,2005,24(14):2518-2525Ye Yuanxin,Liu Guangtingl.Research on coupling characteristics of fluid flow and stress within rock[J].Rock and Soil Mechanics,2005,24(14):2518-2525
[24]Rui Mendes,Arvind S.Mohais.Dyn DE:A different evolution for dynamic optimization problems[C].IEEE Congress on Evolutionary Computation.Edinburgh:IEEE,2005:2808-2815