Research on Deformation Prediction of Surrounding Rock in Special Geotechnical Tunnels Based on Deep Learning

被引：0
|
作者：
Lü Q. [1 ]
Li Y. [1 ]
Niu R. [2 ]
Xu X. [1 ]
Mao N. [1 ]
Kang Q. [1 ]
机构：
[1] College of Civil Engineering and Mechanics, Lanzhou University, Lanzhou
[2] Bridge Highway Investment Co.,Ltd., Gansu Road, Lanzhou
来源：
Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering | 2023年 / 31卷 / 06期
关键词：
deep learning; deformation prediction; long short-term memory network; monitoring measurement; special geotechnical tunnels; WMIC;
D O I：
10.16058/j.issn.1005-0930.2023.06.015
中图分类号：
学科分类号：
摘要：
Special geotechnical tunnels, such as mudstone, silty clay, and crushed stone soil, have large deformation amounts, fast deformation rates, and complex influencing factors. Based on construction monitoring measurement data,a WMIC-LSTM model is established using weighted maximum mutual information coefficient (WMIC) and long short-term memory network (LSTM) to comprehensively consider the combined effects of different influencing factors for deep learning and predict the trend changes of tunnel rock deformation. The results show that the deformation of surrounding rock in special rock and soil tunnels has the greatest correlation with groundwater conditions (WMIC = 0.21),and the selection of initial input step has a significant impact on the model prediction results. Compared with traditional LSTM models,WMIC-LSTM has smaller prediction errors for tunnel deformation curves with different deformation trends in long-term series models,with the optimal mean absolute error (MAE) reaching 0.079.The deep learning simulation prediction based on recurrent neural network algorithm meets the expected results and can provide technical means and early warning basis for intelligent monitoring and safe construction of tunnels. © 2023 Editorial Board of Journal of Basic Science and. All rights reserved.
引用
收藏
页码：1590 / 1600
页数：10
相关论文
共 23 条
  • [1] Hou Weiya, Wen Yanfeng, Deng Gang, Et al., Deformation prediction of high embankment dams by combining time series decomposition and deep learning, Journal of Hydroelectric Engineering, 41, 3, pp. 123-132, (2022)
  • [2] Wei Shiyu, Research and application of new austrian tunneling method tunnel deformation monitoring system [ D ], (2013)
  • [3] Chen Xiangsheng, Zeng Shiqi, Han Wenlong, Et al., Review and prospect of machine learning method in shield tunnel construction, Journal of Civil and Environmental Engineering, pp. 1-14
  • [4] Wang Shengtao, Liang Xiaoyong, Zhou Yitao, Regression analysis on monitoring data of tunnel works [ J], Tunnel Construction, 29, 6, pp. 629-632, (2009)
  • [5] Wang Yuanzhu, Liang Cheng, Regression analysis of monitoring data during the tunnel excavation, Soil Engineering and Foundation, 27, 5, pp. 70-72, (2013)
  • [6] Liang Xiao, Di Fangdian, Regression analysis and application of monitoring measurement data of new Austrian tunneling method, Architecture Technology, 48, 11, pp. 1188-1190, (2017)
  • [7] Liu Nengzhu, Prediction and analysis of rock stability of tunnel based on the theory gray, (2007)
  • [8] Wei Weiqiong, Li Ying, Ye Mingliang, Deformation monitoring and grey prediction for surrounding rock of highway tunnel, Journal of Water Resources and Architectural Engineering, 12, 2, pp. 84-87, (2014)
  • [9] Chen Huashen, Research on displacement deformation prediction of tunnel surrounding rock based on support vector machine, (2015)
  • [10] Qiang Yue, Li Shaohong, Liu Chaoqiong, Deformation prediction for a tunnel rock mass based on the multi-scale combination kernel extreme learning machine model, Modern Tunneling Technology, 54, 6, pp. 70-76, (2017)
123