摘要
针对隧洞软弱围岩变形动态性、在时间和空间表现出的复杂性等特征,建立了基于非线性自回归神经网络(NARNN)的隧道围岩变形分析预测模型,并应用于某软岩公路隧道的围岩变形进行预测。结果表明NARNN方法能够较好的反映隧道围岩变形的非线性特征,很好地解决软岩隧道开挖后的变形预测问题,为类似的工程提供一种新的围岩变形预测途径。
The weak surrounding rock in tunnels features complex deformation dynamics in time and space,the pre⁃diction model of which is established via nonlinear autoregressive neural network(NARNN)and applied to the sur⁃rounding rock deformation of a soft rock highway tunnel.The results show that NARNN method reflects the nonlin⁃ear characteristics of tunnel surrounding rock deformation and solves the deformation prediction problem of soft rock tunnel excavation,providing a new surrounding rock deformation prediction path for similar engineerings.
作者
杜润泽
姜志伟
DU Runze;JIANG Zhiwei(College of Resources,Shandong University of Science and Technology,Shandong Taian 271019,China;School of Civil Engineering,Qingdao University of Technology,Shandong Qingdao 266033,China)
出处
《低温建筑技术》
2022年第5期115-118,128,共5页
Low Temperature Architecture Technology
