The tunnel deformation tends to induce corresponding engineering problems,and the prevention and prediction of tunnel deformation has become a hot issue in the field of underground engineering.To improve prediction accuracy of tunnel deformation and effectively control tunnel deformation,the original series of tunnel deformation are separated into trend and error sequences with reference to a tunnel engineering based on monitoring data denoising.The prediction of the two sequences is conducted by using GA-BP neural network and time series model,and the prediction error of the former is corrected with the modified support vector machine model so as to ensure the accuracy.The results show that the denoising effect of optimal semi parameter optimization of Calman filter is the best,followed by Sym8 wavelet denoising and singular spectrum analysis;as far as prediction is concerned,the separation prediction can improve the prediction accuracy to a certain content,but the effect is not obvious;while,the error correction model can greatly improve prediction accuracy;and the average relative error of the prediction is 1.08％.The prediction model has the advantage of high precision and can provide some reference for deformation prediction of deep buried tunnel.
国家科技期刊平台
NSTL
万方数据
维普
