Research and application of Kalman-BP based prediction of surface deformation to high slopes
During construction,the internal forces on the high slopes of pumping stations are complex and the resulting surface deformation may lead to accidents such as landslides and collapses that would endanger the safe operation of the project.Therefore,the trend prediction of the surface deformation is of practical significance.In this paper,four field-monitored characteristic quantities-temperature,anchor stress,pore water pressure and soil pressure-are selected as the model input layer and the white noise of the field-monitored characteristic quantities is removed by Kalman filtering.What followed is the BP neural network for training.The combined model can overcome the discrete phenomenon of Kalman fil-tering and improve the generalization ability and convergence speed of the BP model.The model is ap-plied to the monitoring of high slopes of a pump station in Chongqing,and the results show that com-pared with the traditional BP neural network,the number of training iterations of Kalman-BP neural net-work for the cumulative deformation in the H,Y and X direction is reduced from 1 086 to 1 047,from 1 090 to 1 050 and from 1 080 to 1 044 respectively.The root mean square error of the predicted cumu-lative deformation in the H,Y and X direction is reduced from 0.974 to 0.684,from 1.037 to 0.564 and from 0.982 to 0.526 respectively,which has improved the prediction capability of the model and provid-ed an effective guarantee for safe operation of high slopes.
NETL
NSTL
万方数据
