The Dynamic Back Analysis And Deformation Forecast During Construction Process Of Deep Excavation Engineering

Digging the deep excavation in the city can cause earth transfiguration that would influence the works and the surroundings obviously. So it is important to determine the soil physical parameters in digging of the deep excavation to forecast the earth transfiguration levels and direct the works.This study is researched by parameter back analysis method. In order to simulate the digging process of the deep excavation accurately ADINA finite element software is used to establish the simulate model of the digging process of the excavation as forward analysis method, BP Neural Networks is used to established the recognition system of the soil physical parameters as back analysis method and the measure data of displacements of shoring of trench in the real works is used to correct the soil physical parameters with method of inversion which is being input in finite element analysis with method of inversion.Numerical solution that is represented by finite element analysis has the advantages obviously than conventional method in simulate the digging process of the deep excavation. ADINA finite element software, used in this study has kinds of usual earth material models and the element birth/death unit is calculated with unit rigidity fade away method, so it can simulate the digging process of the deep excavation step by step accurately. It is a problem for finite element analysis to choose the constitutive and determines the data of the soil physical parameters. In this study choose the D-P constitutive which is suitable for practical situations of the project and have few parameters and introduce the elastic-plastic constitutive in detail, BP Neural Networks is chose to invert and analyze the earth elastic ratio (E), which is hard to measured, can make a better use of the nonlinearity mapping function of the neural networks. Blocking method is chose in network structure to complete the network convergence smoothly. Conclusion, the model can reflect the displacement of the shoring of trench correctly and can be proved by a real excavation work of a underground metro station in Tianjin. The study indicates that Neural Networks can use in soil physical parameters recognizing, and can used to forecast the displacements in deep excavation.