摘要
以瓦屋山水电站砂岩地层引水隧洞作为研究对象,针对砂岩的流变特性,采用Burgers模型反映其流变变形规律,基于现场位移监测资料,通过遗传算法优化的BP神经网络对模型中的力学参数进行反演,利用参数化程序设计语言编写Burgers流变方程并接入通用有限元软件ANSYS模拟隧洞岩体充水运行条件下的流变力学行为。通过对模拟结果的分析,该模型能够适应隧洞蓄水时围岩应力状态的改变并对围岩流变变形做出准确预测,研究成果可为引水隧洞运行期的稳定性分析提供参考。
The diversion tunnel in sandstone formation of a hydropower station is taken as the research object.The sandstone exhibits remarkable rheological characteristics.The Burgers model is adopted to reflect its rheological deformation law.Based on the field displacement monitoring data,the mechanical parameters in the model are inversed through the BP neural network optimized by genetic algorithm.In order to simulate the rheological behavior of the tunnel rock mass under water-filling conditions,the Burgers rheological equation is written with a parametric programming language and connected to the general finite element software ANSYS.Through an analysis of the simulation results,the model can adapt to the change of stress state of surrounding rock during tunnel impoundment and make accurate prediction of rheological deformation of surrounding rock.The research results can provide reference for the stability analysis of the diversion tunnel during its operation.
作者
王兆强
陈新
张磊
朱文凯
WANG Zhao-qiang;CHEN Xin;ZHANG Lei;ZHU Wen-kai(College of Water Resources and Hydropower,Sichuan University,Chengdu 610065,China)
出处
《中国农村水利水电》
北大核心
2019年第12期160-164,共5页
China Rural Water and Hydropower
基金
水资源高效开发利用重点专项“新型胶结颗粒料坝建设关键技术”(2018YFC0406803)
关键词
砂岩地层
引水隧洞
流变变形
反演分析
数值模拟
运行期稳定分析
sandstone
diversion tunnel
rheological deformation
parametric inversion
numerical analysis
runtime stability analysis
