基于量子遗传算法与多输出混合核相关向量机的堆石坝材料参数自适应反演研究
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摘要
为进一步提高堆石坝材料参数反演模型的计算精度与适用性,建立了基于量子遗传算法(QGA)与多输出混合核相关向量机(MMRVM)的自适应反演模型。通过引入混合核函数,使所构建的MMRVM能够高精度地模拟材料参数与大坝沉降间的复杂非线性关系,从而代替耗时较长的有限元(FEM)计算。通过利用参数较固化的QGA优化确定MMRVM核参数,使反演模型具有自适应性。以实测沉降数据为依据,充分发挥QGA的全局搜索能力反演筑坝材料本构模型参数。在分析模型所需测点个数与信噪比对计算结果影响的基础上,通过公伯峡堆石坝应用实例证明:QGA-MMRVM可快速、精确地反演堆石坝筑坝材料本构模型参数,模型凭借其自适应性在实际工程中具有良好的应用前景和推广价值。
In order to improve the accuracy and applicability of inversion analysis model of material parameters for rockfill dam, an adaptive model based on quantum genetic algorithm(QGA) and multi-output mixed kernel relevance vector machine(MMRVM) is established. By introducing mixed kernel function, the MMRVM can accurately simulate the nonlinear relationship between the material parameters and the settlement of rockfill dam. Therefore, the finite element method(FEM) can be replaced by the MMRVM to reduce time consumption. Then, the kernel parameters of the MMRVM is optimized by the QGA, thus the QGA-MMRVM is adaptable to different inversion analysis problems. The parameters of constitutive model of dam materials can be inverted by fully utilizing QGA's global searching ability. Finally, the influences of the signal-noise ratio and the number of measured points on the calculation result are analyzed. The examples of Gongboxia dam show that the parameters of constitutive model of material can quickly and accurately calculated by the QGA-MMRVM. With its adaptability, the QGA-MMRVM has good application prospect and popularization value in practical engineering.
In order to improve the accuracy and applicability of inversion analysis model of material parameters for rockfill dam, an adaptive model based on quantum genetic algorithm(QGA) and multi-output mixed kernel relevance vector machine(MMRVM) is established. By introducing mixed kernel function, the MMRVM can accurately simulate the nonlinear relationship between the material parameters and the settlement of rockfill dam. Therefore, the finite element method(FEM) can be replaced by the MMRVM to reduce time consumption. Then, the kernel parameters of the MMRVM is optimized by the QGA, thus the QGA-MMRVM is adaptable to different inversion analysis problems. The parameters of constitutive model of dam materials can be inverted by fully utilizing QGA's global searching ability. Finally, the influences of the signal-noise ratio and the number of measured points on the calculation result are analyzed. The examples of Gongboxia dam show that the parameters of constitutive model of material can quickly and accurately calculated by the QGA-MMRVM. With its adaptability, the QGA-MMRVM has good application prospect and popularization value in practical engineering.
引文
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[19]余志雄,周创兵,李俊平,等.基于v-SVR算法的边坡稳定性预测[J].岩石力学与工程学报,2005,24(14):2468-2475.YU Zhi-xiong,ZHOU Chuang-bing,LI Jun-ping,et al.Predicting slope stability based on v-SVR algorithm[J].Chinese Journal of Rock Mechanics and Engineering,2005,24(14):2468-2475.
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[21]蒋水华,李典庆,周创兵.基于拉丁超立方抽样的边坡可靠度分析非侵入式随机有限元法[J].岩土工程学报,2013,35(增刊2):70-76.JIANG Shui-hua,LI Dian-qing,ZHOU Chuang-bing.Non-intrusive stochastic finite element method for slope reliability analysis based on Latin hypercube sampling[J].Chinese Journal of Geotechnical Engineering,2013,35(Suppl.2):70-76.
[22]舒苏荀,龚文惠.边坡稳定分析的神经网络改进模糊点估计法[J].岩土力学,2015,36(7):2111-2116.SHU Su-xun,GONG Wen-hui.An improved fuzzy point estimate method for slope stability analysis based on neural network[J].Rock and Soil Mechanics,2015,36(7):2111-2116.
[23]WEN L,CHAI J,WANG X,et al.Behaviour of concrete-face rockfill dam on sand and gravel foundation[J].Geotechnical Engineering,2015,168(5):439-456.
[24]DUNCAN J M,CHANG C Y.Nonlinear analysis of stress and strain in soils[J].Journal of Soil Mechanics&Foundation Division,1970,96(5):1629-1653.
[25]殷宗泽.土工原理[M].北京:中国水利水电出版社,2007.YIN Zong-ze.Soil theory[M].Beijing:China Water&Power Press,2007.
[2]沈珠江,赵魁芝.堆石坝流变变形的反馈分析[J].水利学报,1998,29(6):1-6.SHEN Zhu-jiang,ZHAO Kui-zhi.Back analysis of creep deformation of rockfill dams[J].Journal of Hydraulic Engineering,1998,29(6):1-6.
[3]YU Y,ZHANG B,YUAN H.An intelligent displacement back-analysis method for earth-rockfill dams[J].Computers and Geotechnics,2007,34(6):423-434.
[4]ZHOU W,HUA J,CHANG X,et al.Settlement analysis of the Shuibuya concrete-face rockfill dam[J].Computers and Geotechnics,2011,38(2):269-280.
[5]康飞,李俊杰,许青.堆石坝参数反演的蚁群聚类RBF网络模型[J].岩石力学与工程学报,2009,28(增刊2):3639-3644.KANG Fei,LI Jun-jie,XU Qing.Ant colony clustering radial basis function network model for inverse analysis of rockfill dam[J].Chinese Journal of Rock Mechanics and Engineering,2009,28(Suppl.2):3639-3644.
[6]马刚,常晓林,周伟,等.高堆石坝瞬变-流变参数三维全过程联合反演方法及变形预测[J].岩土力学,2012,33(6):1889-1895.MA Gang,CHANG Xiao-lin,ZHOU Wei,et al.Integrated inversion of instantaneous and rheological parameters and deformation prediction of high rockfill dam[J].Rock and Soil Mechanics,2012,33(6):1889-1895.
[7]李守巨,张军,梁金泉,等.基于堆石坝竣工期沉降观测数据的材料非线性本构模型参数反演[J].岩土力学,2014,35(增刊2):61-67,74.LI Shou-ju,ZHANG Jun,LIANG Jin-quan,et al.Parameter inversion of nonlinear constitutive model of rockfill materials using observed deformations after dam construction[J].Rock and Soil Mechanics,2014,35(Suppl.2):61-67,74.
[8]杨荷,周伟,马刚,等.基于响应面法的高堆石坝瞬变-流变参数反演方法[J].岩土力学,2016,37(6):1697-1705.YANG He,ZHOU Wei,MA Gang,et al.Inversion of instantaneous and rheological parameters of high rockfill dams based on response surface method[J].Rock and Soil Mechanics,2016,37(6):1697-1705.
[9]ZHAO H B,YIN S.Geomechanical parameters identification by particle swarm optimization and support vector machine[J].Applied Mathematical Modelling,2009,33(10):3997-4012.
[10]ZHENG D J,CHENG L,BAO T F,et al.Integrated parameter inversion analysis method of a CFRD based on multi-output support vector machines and the clonal selection algorithm[J].Computers and Geotechnics,2013,47:68-77.
[11]倪沙沙,迟世春.基于粒子群支持向量机的高心墙堆石坝渗透系数反演[J].岩土工程学报,2017,39(4):727-734.NI Sha-sha,CHI Shi-chun.Back analysis of permeability coefficient of high core rockfill dam based on particle swarm optimization and support vector machine[J].Chinese Journal of Geotechnical Engineering,2017,39(4):727-734.
[12]马春辉,杨杰,程琳,等.基于混合核函数HS-RVM的边坡稳定性分析[J].岩石力学与工程学报,2017,36(增刊1):3409-3415.MA Chun-hui,YANG Jie,CHENG Lin,et al.Slope stability analysis based on HS-RVM with mixed kernel[J].Chinese Journal of Rock Mechanics and Engineering,2017,36(Suppl.1):3409-3415.
[13]NARAYANAN A,MOORE M.Quantum-inspired genetic algorithms[C]//IEEE International Conference on Evolutionary Computation.Washington,DC:IEEE,1996.
[14]HAN K H,KIM J H.Genetic quantum algorithm and its application to combinatorial optimization problem[C]//Proceedings of the 2000 Congress on Evolutionary Computation.Washington,DC:IEEE,2002.
[15]邱道宏,李术才,薛翊国,等.基于数字钻进技术和量子遗传-径向基函数神经网络的围岩类别超前识别技术研究[J].岩土力学,2014,35(7):2013-2018.QIU Dao-hong,LI Shu-cai,XUE Yi-guo,et al.Advanced prediction of surrounding rock classification based on digital drilling technology and QGA-RBF neural network[J].Rock and Soil Mechanics,2014,35(7):2013-2018.
[16]TIPPING M E.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,1:211-244.
[17]THAYANANTHANA A,NAVARATNAMA R,STENGERB R.Pose estimation and tracking using multivariate regression[J].Pattern Recognition Letters,2008,29(9):1302-1310.
[18]郑志成,徐卫亚,徐飞,等.基于混合核函数PSO-LSSVM的边坡变形预测[J].岩土力学,2012,33(5):1421-1426.ZHENG Zhi-cheng,XU Wei-ya,XU Fei,et al.Forecasting of slope displacement based on PSO-LSSVMwith mixed kernel[J].Rock and Soil Mechanics,2012,33(5):1421-1426.
[19]余志雄,周创兵,李俊平,等.基于v-SVR算法的边坡稳定性预测[J].岩石力学与工程学报,2005,24(14):2468-2475.YU Zhi-xiong,ZHOU Chuang-bing,LI Jun-ping,et al.Predicting slope stability based on v-SVR algorithm[J].Chinese Journal of Rock Mechanics and Engineering,2005,24(14):2468-2475.
[20]ZHAO H B,YIN S D,RU Z L.Relevance vector machine applied to slop stability analysis[J]International Journal for Numerical and Analysis Methods in Geomechanics,2012,36:643-652.
[21]蒋水华,李典庆,周创兵.基于拉丁超立方抽样的边坡可靠度分析非侵入式随机有限元法[J].岩土工程学报,2013,35(增刊2):70-76.JIANG Shui-hua,LI Dian-qing,ZHOU Chuang-bing.Non-intrusive stochastic finite element method for slope reliability analysis based on Latin hypercube sampling[J].Chinese Journal of Geotechnical Engineering,2013,35(Suppl.2):70-76.
[22]舒苏荀,龚文惠.边坡稳定分析的神经网络改进模糊点估计法[J].岩土力学,2015,36(7):2111-2116.SHU Su-xun,GONG Wen-hui.An improved fuzzy point estimate method for slope stability analysis based on neural network[J].Rock and Soil Mechanics,2015,36(7):2111-2116.
[23]WEN L,CHAI J,WANG X,et al.Behaviour of concrete-face rockfill dam on sand and gravel foundation[J].Geotechnical Engineering,2015,168(5):439-456.
[24]DUNCAN J M,CHANG C Y.Nonlinear analysis of stress and strain in soils[J].Journal of Soil Mechanics&Foundation Division,1970,96(5):1629-1653.
[25]殷宗泽.土工原理[M].北京:中国水利水电出版社,2007.YIN Zong-ze.Soil theory[M].Beijing:China Water&Power Press,2007.