临近基坑开挖引起的隧道变形预测分析

随着城市地下空间开发利用的快速发展,基坑开挖对邻近隧道的影响成为工程中不可忽视的问题。文中分析了基于反向传播神经网络法(BPNN)预测临近基坑开挖引起的隧道变形。首先,基于土体的小应变特性,采用有限元软件(Plaxis 3D)数值模拟计算得到关于隧道变形的完整数据库,基于此数据库训练检测BPNN模型的准确性,并利用此BPNN模型定量分析各输入变量(隧道与支护结构相对水平距离、隧道相对埋深、支护结构的最大位移)对隧道变形的影响。最后,将BPNN预测结果与17个工程案例实测结果进行对比分析,可以看出两者具有很好的一致性,说明BPNN模型能够准确预测基坑开挖引起的隧道变形,为实际工程提供一种简便的预测方法。

Prediction of the tunnel displacement induced by adjacent excavations

With the rapid development and utilization of urban underground space, the phenomenon of excavation adjacent to tunnels is becoming more and more common. Therefore, the influence of excavation on adjacent tunnels has become a serious problem that cannot be ignored in engineering. This study proposed a back propagation neural network (BPNN) to predict the tunnel deformation caused by adjacent excavation. Firstly, the finite element software Plaxis (3D) based on a small-strain stiffness constitutive model was used to generate a complete database of tunnel deformation. The accuracy of the BPNN model was trained and tested by this database. Furthermore, the relative importance of each input variable (i.e., the relative horizontal distance of the tunnel as opposed to a supporting structure, the tunnel buried depth, the maximal displacement of retaining structure) was estimated by this BPNN model. Finally, the prediction of BPNN model showed great agreement with the measured deformations of 17 case histories. The analysis results show that the BPNN model can accurately predict the tunnel deformation caused by adjacent excavation, and can provide a more convenient prediction method for practical engineering.