Abstract

AbstractThis chapter made a comprehensive analysis of the influence factors on subway settlement and proposed a prediction model on the basis of gray system theory: 1. In soft soil area, ground settlement caused by tunnel excavation is controlled by many factors such as grouting, volume of excavated earth, and the soil pressure and advancing speed. For controlling the influence of tunnel excavation on the surrounding environment, it is necessary to take all the key factors into consideration to make appropriate construction projects. 2. Grouting volume and slurry consistency are crucial factors to control ground settlement. Considering the heave effect on the tunnel axis, the volume of grouting should be between 2.5 and 2.8 m3 when the slurry consistency is between 9 and 11. 3. The non-isochronous nonhomogeneous exponential gray prediction model NNGM (1, 1) was improved based on a traditional model GM (1, 1). We reconstructed the whitenization differential equation of GM (1, 1) to expand its application and predict nonhomogeneous exponential data series. To meet the requirements of gray model of the raw data, non-isochronous data sequence were transformed into the isochronous by use of cubic spline interpolation and then transformed back by the same method. In addition, we analyzed the long-term settlement of the subway tunnel in the aspect of effective stress in the last part of this chapter. 4. Gray prediction model is a reasonable and potential approach to simulate and predict cumulative plastic deformation of soils under cyclic loads and long-term settlement of subway tunnel. 5. Compared with gray prediction theory, the effective stress method is more like an empirical predicted model for predicting the settlement of subway. It is more suitable to make a simple qualitative analysis and evaluation for deformation and settlement. The gray model could be used to predict the long-term settlement. KeywordsGround SettlementGray ModelTunnel ExcavationTunnel AxisGray System TheoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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