欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
隧道洞口处多为软弱岩或浮土，稳定性差，地表位移监测成为判断洞口稳定性的重要手段，因此仰坡沉降变形预测显得格外重要。鉴于仰坡沉降变形具有很强的非线性特征，选取BP神经网络对仰坡的沉降变形进行预测，并验证其可行性，进而利用BP神经网络扩大沉降变形监测的样本。在此基础上，再利用R／S分析对新的监测样本进行重标极差分析，分别得到隧道仰坡沉降一时间序列和变形速率一时间序列的Hurst指数，并结合两项指数确定了隧道仰坡沉降变形的趋势，为判断仰坡的稳定性及治理提供了有力依据。
英文摘要：
There are much weak weathered rockmass and topsoil in tunnel slope, its＇ poor stability makes settlement monitoring much more important to decide the stability of tunnel entrance. Therefore the settlement deformation prediction in tunnel slope is necessary. In view of strong non-linear characteristics of the slope settlement deformation, this paper selects BP neural network to predict deformation of the slope and verifies the feasibility, and then uses the BP neural network to expand the sample of settlement monitoring. Based on the above, the new monitoring samples are analyzed by R/S analysis, and Hurst index of settlement-time sequence and deformation rate-time series of the overlaying slope is achieved. Then the two Hurst indexes are combined to determine settlement deformation trend of the front slope in tunnel, which provides a strong basis for judging the slope stability and governance.
同期刊论文项目
同项目期刊论文
Coupled mechanical and hydraulic modeling of a geosynthetic-reinforced and pile-supported embankment
期刊信息
位置：