Research on nonlinear auto regressive time series method for predicting deformation of surrounding rock in tunnel
WEN Ming
ZHANG Dingli
FANG Qian
QI Jun
FANG Huangcheng
CHEN Wenbo
Abstract：Because traditional time series prediction models have the characteristics of single linear and static limitation due to the ignorance of the impacts of construction process,nonlinear auto regressive (including NARNN and NARXNN) time series prediction models are proposed in this paper.The models have their own feedback architectural and delay units,whose structural and dynamic properties are more coincide with the actual tunnel projects.Meanwhile,in order to non-linearly and dynamically represent the tunneling process,dynamic construction impact factors,as a part of additional external inputs,are applied in this prediction model.Based on the nonlinear auto regressive time series prediction models,the transversal convergence and ground surface deformation of Shijiashan 2nd tunnel are calculated.The comparison between the prediction results and the actual values shows that:1) Compared with the traditional ARMA time series prediction model,nonlinear auto regressive time series prediction models have a better adaptability and a higher precision.2) The prediction precision and the robustness of the nonlinear auto regressive prediction models can be improved by multiple calculations and taking the average.3) The predic tion precision of NARXNN time series prediction models can be improved by the optimizing the value of dynamic construction impact factors.
Keywords：highway tunneltime series modelnonlinear auto regressive neural networkdynamic construction impact factorssurrounding rock deformation prediction
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 1-7 )