隧洞开挖下的边坡变形灰色预测分析

Gray Prediction Analysis of Slope Deformation under Tunnel Excavation

隧洞是常见的水利工程结构形式。在开挖过程中受施工条件和受力形式变化的影响,隧洞进出口边坡会产生一定的变形,一旦变形过大,会对工程安全造成不利影响。及时监测和预测边坡变形具有重要的工程意义。针对隧洞开挖过程中的边坡变形预测问题,引入灰色预测模型,通过构建经典灰色预测的GM(1,1)模型,基于实测点位的变形数据,对未来的变形趋势进行预测。选取某输水隧洞实例进行分析,在该隧洞开挖的进出口边坡上设置若干监测点,记录监测点的水平、垂直位移数据,采用文章提出的灰色预测模型对监测点位的变形进行预测,并将预测值和实测值进行对比。结果表明预测的位移量与真实的位移量相差较小,误差均在5%以内,可以较好地实现隧洞开挖过程中边坡位移的预测,具有较高的预测精度。提出的边坡变形灰色预测分析模型可以较好实现隧洞开挖下的变形预测,方法简单实用,可为隧洞开挖过程中的变形控制提供技术支持,可在类似工程中推广应用。

Tunnel is a common structural form of hydraulic engineering.In the excavation process,the slope at the entrance and exit of the tunnel will be deformed to a certain extent due to the changes of construction conditions and stress forms.Once the deformation is too large,it will adversely affect the engineering safety.It is of great engineering significance to monitor and predict the slope deformation in time.engineering significance.Aiming at the problem of slope deformation prediction in the process of tunnel excavation,a gray prediction model is introduced in this paper.The future deformation trend is predicted based on the deformation data of the measured points by constructing the GM(1,I)Model of classic gray prediction.An example of a water conveyance tunnel is selected for analysis.Several monitoring points are set on the entrance and exit slopes of the tunnel excavation,and the horizontal and vertical displacement data of the monitoring points are recorded.The gray prediction model proposed in this paper is used to predict the deformation of the monitoring points and to compare the predicted value with the measured value.The results show that the difference between the predicted displacement and the real displacement is small,and the errors are all within 5%.The gray prediction analysis model of slope deformation proposed in this paper can better realize the deformation prediction under tunnel excavation.The method is simple and practical,and it can provide technical support for deformation control in the process of tunnel excavation.