北京交通大学学报2017，Vol.41Issue(4)：1-7,7.DOI:10.11860/j.issn.1673-0291.2017.04.001
隧道围岩变形的非线性自回归时间序列预测方法研究
Research on nonlinear auto regressive time series method for predicting deformation of surrounding rock in tunnel
摘要
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.关键词
公路隧道/时间序列模型/非线性自回归神经网络/动态施工影响因子/围岩变形预测Key words
highway tunnel/time series model/nonlinear auto regressive neural network/dynamic construction impact factors/surrounding rock deformation prediction分类
交通工程引用本文复制引用
文明,张顶立,房倩,齐俊,方黄城,陈文博..隧道围岩变形的非线性自回归时间序列预测方法研究[J].北京交通大学学报,2017,41(4):1-7,7.基金项目
中央高校基本科研业务费专项资金(2016YJS116) （2016YJS116）
国家自然科学基金(U1234210) Fundamental Research Funds for the Central Universities(2016YJS116) （U1234210）
National Natural Science Foundation of China (U1234210) （U1234210）
