地下工程并行优化反演分析及算例验证
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摘要
通过重构非线性惯性权重函数和引入"加速因子",对传统粒子群优化算法的收敛性进行改进。同时基于消息传递平台对算法进行主从式并行改进,编程实现基于普通计算机机群系统分布式存储并行模式的大型地下工程并行优化反演分析。算例分析表明,改进的粒子群优化算法其收敛性能得到显著改善,并行改进策略可显著加快反演速度和提高计算效率。探讨围岩松动损伤劣化、监测数据可靠性、并行粒度和负载均衡等并行优化反演分析中所面临的主要问题及其对计算精度和效率的影响,并提出有效解决方案,为大型地下工程的参数反演和动态优化设计提供一种新思路。
The conventional particle swarm optimization is improved by composing a nonlinear inertia weight function and importing in an acceleration factor,which can enhance the convergence and efficiency of computation.At the same time,the improved particle swarm optimization is improved again by message passing interface(MPI)-based master-slave parallel framework.The back analysis process of large-scale underground engineering which based on the ordinary computer fleet system distributed-storage parallel mode is compiled with Fortran language.According to distributed-memory parallel mode,the parallel computation can be conducted and completed using computer cluster networks;thus considerably reduce the cost and enhance the efficiency of computation.The results indicate that the improved particle swarm optimization is efficient.Moreover,the influences of excavation damage of surrounding rock mass,reliability of measured data,parallel granularity and load balance on computational efficiency and accuracy of back analysis in underground engineering are briefly discussed.The proposed improved method and the rational recommendations provide the back analysis of parameters and dynamic optimal design of underground engineering with a new idea.
The conventional particle swarm optimization is improved by composing a nonlinear inertia weight function and importing in an acceleration factor,which can enhance the convergence and efficiency of computation.At the same time,the improved particle swarm optimization is improved again by message passing interface(MPI)-based master-slave parallel framework.The back analysis process of large-scale underground engineering which based on the ordinary computer fleet system distributed-storage parallel mode is compiled with Fortran language.According to distributed-memory parallel mode,the parallel computation can be conducted and completed using computer cluster networks;thus considerably reduce the cost and enhance the efficiency of computation.The results indicate that the improved particle swarm optimization is efficient.Moreover,the influences of excavation damage of surrounding rock mass,reliability of measured data,parallel granularity and load balance on computational efficiency and accuracy of back analysis in underground engineering are briefly discussed.The proposed improved method and the rational recommendations provide the back analysis of parameters and dynamic optimal design of underground engineering with a new idea.
引文
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[2]杨志法,王思敬,冯紫良,等.岩土工程反分析原理及应用[M].北京:地震出版社,2002:1–6.(YANG Zhifa,WANG Sijing,FENGZiliang,et al.Theory and application of back analysis in geotechnicalengineering[M].Beijing:Earthquake Press,2002:1–6.(in Chinese))
[3]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,1996:1–29.(YANG Linde.Theory and engineering practice ofback analysis in geotechnical engineering problems[M].Beijing:Science Press,1996:1–29.(in Chinese))
[4]孙钧,蒋树屏,袁勇,等.岩土力学反演问题的随机理论与方法[M].汕头:汕头大学出版社,1995:1–30.(SUN Jun,JIANGShupin,YUAN Yong,et al.Random theory and methods of geomechanicsback analysis[M].Shantou:Shantou University Press,1995:1–30.(inChinese))
[5]KAVANAFH K T,CLOUGH R W.Finite element applications in thecharacterization of elastic solids[J].International Journal of Solids andStructures,1971,7(1):11–23.
[6]SAKURAI S,TAKEUCHI K.Back analysis of measured displacement oftunnels[J].Rock Mechanics and Rock Engineering,1983,16(3):173–180.
[7]郭怀志,马启超,薛玺成,等.岩体初始地应力场的分析方法[J].岩土工程学报,1983,5(3):64–75.(GUO Huaizhi,MAQichao,XUEXicheng,et al.The analytical method of the initial stress field for rockmasses[J].Chinese Journal of Geotechnical Engineering,1983,5(3):64–75.(in Chinese))
[8]李守巨,刘迎曦,王登刚,等.基于神经网络的岩体渗透系数反演方法及其工程应用[J].岩石力学与工程学报,2002,21(4):479–483.(LI Shouju,LIU Yingxi,WANG Denggang,et al.Inversionalgorithm of permeability coefficients of rock mass and its applicationbased on artificial neural network[J].Chinese Journal of Rock Mechanicsand Engineering,2002,21(4):479–483.(in Chinese))
[9]GIODA G,MEIER G.Direct search solution of an inverse problem inelastic-plasticity,identification of cohesion,friction angle and in-situstress by pressure tunnel tests[J].International Journal of NumericalMethods in Engineering,1980,15:1 823–1 834.
[10]GIODA G,PANDOLFI A,CIVDINI A.A comparative evaluation ofsome back analysis algorithms and their application to in-situ loadtests[C]//Proceedings of the 2nd International Symposium on FieldMeasurement in Geomechanics.[S.l.]:[s.n.],1987:1 131–1 144.
[11]邓建辉,李焯芬,葛修润.BP网络和遗传算法在岩石边坡位移反分析中的应用[J].岩石力学与工程学报,2001,20(1):1–5.(DENGJianhui,LEE C F,GE Xiurun.Application of BP network and geneticalgorithm to displacement back analysis of rock slopes[J].ChineseJournal of Rock Mechanics and Engineering,2001,20(1):1–5.(inChinese))
[12]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.(FENGXiating,ZHANG Zhiqiang,YANG Chengxiang,et al.Study on genetic-neuralnetwork method of displacement back analysis[J].Chinese Journal ofRock Mechanics and Engineering,1999,18(5):529–533.(in Chinese))
[13]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):979–982.(WANG Denggang,LIU Yingxi,LI Shouju.Genetic algorithm for inverse analysis ofdisplacements in geotechnical engineering[J].Chinese Journal of RockMechanics and Engineering,2000,19(Supp.1):979–982.(in Chinese))
[14]高玮,冯夏庭,郑颖人.地下工程围岩参数反演的仿生算法及其工程应用研究[J].岩石力学与工程学报,2002,21(增2):2521–2 526.(GAO Wei,FENG Xiating,ZHENG Yingren.Bionics algorithmfor parameter inversion in underground engineering and engineeringapplication[J].Chinese Journal of Rock Mechanics and Engineering,2002,21(Supp.2):2 521–2 526.(in Chinese))
[15]丁德馨.弹塑性位移反分析的智能化方法及其在地下工程中的应用[博士学位论文][D].上海:同济大学,2000.(DING Dexin.Studiesof intelligence-based methods for elasto-plastic displacement backanalysis problem and their application to underground excavation[Ph.D.Thesis][D].Shanghai:Tongji University,2000.(in Chinese))
[16]刘福深,刘耀儒,杨强,等.基于改进遗传算法的拱坝位移反分析[J].岩石力学与工程学报,2005,24(23):4341–4345.(LIUFushen,LIU Yaoru,YANG Qiang,et al.Displacement back analysisof arch dams based on improved genetic algorithm[J].Chinese Journalof Rock Mechanics and Engineering,2005,24(23):4 341–4 345.(inChinese))
[17]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(Supp.2):598–603.(JIA Shanpo,WU Guojun,CHEN Weizhong.Application of finiteelement inverse model based on improved particle swarm optimizationand mixed penalty function[J].Rock and Soil Mechanics,2011,32(Supp.2):598–603.(in Chinese))
[18]倪绍虎,肖明,王继伟.改进粒子群算法在地下工程反分析中的运用[J].武汉大学学报:工学版,2009,42(3):326–330.(NI Shaohu,XIAO Ming,WANG Jiwei.Application of modified particle swarmoptimization to back analysis of underground engineering[J].EngineeringJournal of Wuhan University,2009,42(3):326–330.(in Chinese))
[19]常晓林,喻胜春,马刚,等.基于粒子迁徙的粒群优化算法及其在岩土工程中的应用[J].岩土力学,2011,32(4):1077–1082.(CHANG Xiaolin,YU Shengchun,MA Gang,et al.Particle swarmoptimization based on particle migration and its application togeotechnical engineering[J].Rock and Soil Mechanics,2011,32(4):1 077–1 082.(in Chinese))
[20]KENNEDY J,EBERHART R C.Particle swarm optimization[C]//International Conference on Neural Networks IV.Perth,Australia:[s.n.],1995:1 942–1 948.
[21]张可村,李换琴.工程优化方法及其应用[M].西安:西安交通大学出版社,2007:150–168.(ZHANGKecun,LI Huanqin.Engineeringoptimization method and its application[M].Xi an:Xi an JiaotongUniversity Press,2007:150–168.(in Chinese))
[22]张俊,程春田,廖胜利,等.改进粒子群优化算法在水电站群优化调度中的应用[J].水利学报,2009,40(4):435–441.(ZHANGJun,CHENG Chuntian,LIAO Shengli,et al.Application of improvedparticle swarm optimization algorithm to operation of hydropowerstation group[J].Journal of Hydraulic Engineering,2009,40(4):435–441.(in Chinese))
[23]王伟.改进粒子群优化算法在边坡工程力学参数反演中的应用[硕士学位论文][D].南京:河海大学,2007.(WANG Wei.Applicationof an improved particle swarm optimization in inversion of mechanicalparameter of slope engineering[M.S.Thesis][D].Nanjing:HohaiUniversity,2007.(in Chinese))
[24]张雯雰,王刚,朱朝晖,等.粒子群优化算法种群规模的选择[J].计算机系统应用,2010,19(5):125–128.(ZHANG Wenfen,WANGGang,ZHU Zhaohui,et al.Population size selection of particle swarmoptimizer algorithm[J].Computer Systems and Applications,2010,19(5):125–128.(in Chinese))
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