摘要
通过有限元分析对引水渠道进行结构计算时,为了使数值计算的结果更可靠,需要对相关计算参数进行反演。根据工程实际情况,首先选取多种土体结构参数的组合作为参数训练样本,采用有限元法利用不同的土体结构参数组合对渠道的沉降变形进行数值计算;基于水位变化与引水渠道土体沉降的关系,将选取的样本投入RBF(Radical Basis Function,径向基函数)神经网络中训练,建立渠道土体结构参数与因水位变化引起的渠道土体沉降值之间的映射关系;最后根据渠道实际变形监测值,采用RBF神经网络反演得到相关变形参数,以实现对引水渠道结构的精确计算。
In order to make the numerical calculation results more reliable,it is necessary to invert the relevant calculation parameters when calculating the structure of the water diversion channel through finite element analysis. In the study,a variety of structural parameter samples were firstly drawn up and the settlement deformation of the channel was calculated by finite element method.Then the RBF( Radical Basis Function) neural network training sample was used to establish the mapping relationship between channel deformation parameters and channel subsidence deformation.Finally,according to the actual deformation monitoring value of the channel,RBF neural network is used to retrieve the deformation parameters,so as to achieve the accurate calculation of the structure of the diversion channel.
引文
[1]程帅,李守义.基于ANSYS的复杂地基面上高导墙抗滑稳定有限元分析[J].水资源与水工程学报,2016,27(1):217-221.
[2]黄桂林,张兵.鄂北地区水资源配置工程大跨度预应力渡槽三维有限元分析[J].水利水电技术,2016,47(7):27-31.
[3]周娟,黄铭.基于改进BP神经网络的海堤渗压监测模型研究[J].人民长江,2014,45(3):90-93.
[4]肖明清,刘浩,彭长胜,等.基于神经网络的深厚软土地层参数反演分析[J].地下空间与工程学报,2017,13(1):279-286.
[5]马恒臻,刘明明,陈君.基于非稳定过程的岩土体非饱和参数反演分析[J].中国农村水利水电,2017(10):110-114.
[6]陆迎寿,黄铭,蓝祝光.人工神经网络在海堤非稳定渗流参数反演中的应用[J].南水北调与水利科技,2015,13(6):1147-1150.
[7]谢学斌,罗海霞,杨承祥,等.基于遗传单纯形算法与RBF网络的地应力场反演方法[J].铁道科学与工程学报,2015(1):72-78.
[8]迟世春,朱叶.面板堆石坝瞬时变形和流变变形参数的联合反演[J].水利学报,2016,47(1):18-27.