With the development of high-speed railway in recent years, the previous precision of control surveying and the methods
of data processing will not meet the requirement of high-speed railway any longer. In view of the characteristics of
precision is much higher in large-scale precise construction and the superiority of precision in reform of large-scale
engineering control networks, in this paper, using the algorithm of ellipsoid expansion to deal with overrun coordinate
projection distortion in high-speed railway, then compares with common calculation method of surveying, we get a
conclusion that this method can get minimum projection and it accord with the requirement of high-precision control
surveying.
In the analysis on building deformation, when the deformation is unconscious, the significant effect random noise
contributes, in addition that the prediction model of neural network has slow convergence. A new CPSO-LSSVM
forecasting model is established, based on the combination of wavelet analysis and Kalman filter. The forecasting model
resolves the noise problem and with a high accuracy by improving the PSO algorithm. The results show that it has a
higher accuracy than the BP network and the LSSVM.
The GM (1,1) model uses a discrete form equation to estimate the parameters and employ a continuous form equation to
fit the model and predict the data sequence. The jump between the two form of equation is the fundamental reason to
causing the error of GM (1,1) model. This paper first introduces the theory of the Discrete Grey Model (DGM (1,1)
model), the solving method of model parameter and the solving algorithm of simulation value and the predicted value.
Then, a modified DGM (1,1) model is proposed after analyzing the problems of Discrete Grey Model exited in the
practical application. Finally, some contrast experiments for high speed railway subgrade settlement prediction are
carried on by applying the improved DGM (1,1) model, the GM(1,1) model and the DGM(1,1) model respectively. The
experimental results show that the improved DGM (1,1) model could acquire better model accuracy and forecasting
result in engineering application.
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