Deformation monitoring artificial neural network model of deep foundation pit considering the excavation effect

Horizontal deformation of foundation pit during excavation is impacted by the excavation process. In order to express the regulation and characteristic information of displacement during excavation period of foundation pit, a method based on genetic creep theory and project practice was presented. The equal effect excavation depth was defined. The input factors were formed considering the instantaneous deformation and history deformation. Radial basis function (RBF) was used to establish the monitoring model which describes the cause-effect relationship between deformation and excavation. After procuring the input factors, the model structure was obtained. The displacement in the late excavation stages can be forecast by them in this way with the consideration of creep and excavation. Examples were given with survey and table data. They show that this kind of artificial neural network monitoring model is very good to be used to imitate and predict the deformation of deep foundation pit during excavation work period.