Correlation analysis of shield driving parameters and structural deformation prediction based on MK-LSTM algorithm

被引：0
|
作者：
Chen, Cheng [1 ]
Shi, Pei-Xin [1 ]
Jia, Peng-Jiao [1 ,2 ]
Dong, Man-Man [3 ]
机构：
[1] School of Rail Transportation, Suzhou University, Suzhou
[2] School of Resources & Civil Engineering, Northeastern University, Shenyang
[3] Changshu Institute of Technology, Changshu
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2024年 / 54卷 / 06期
关键词：
deformation; machine learning; parameter correlation; parameter dimension; shield tunnelling;
D O I：
10.13229/j.cnki.jdxbgxb.20220975
中图分类号：
学科分类号：
摘要：
MIC-K-median-LSTM（MK-LSTM）algorithm was proposed to analyze the correlation of parameters and predict the structural deformation. Firstly，the improved MIC algorithm is developed to analyze the correlation between the different input parameters and structural deformation， then to preprocess the input parameters based on their correlation coefficients. The prediction accuracy and efficiency using different dimensions of input parameters are analyzed through the LSTM model and the optimal input parameter dimensions are selected. The results show that：The influence of the shield parameters on the existing structural deformation is larger than soil parameters；The MK algorithm can effectively reduce the computational complexity and the impact of noise in raw data and the data preprocess is beneficial to improve prediction accuracy；MK-LSTM algorithm can effectively predict the deformation law of the structure over time，considering the effect of the data dimension on the improvement of the prediction accuracy and the influence of the calculation efficiency，dimension pruning can be adopted in the actual engineering based on the parameter correlation. © 2024 Editorial Board of Jilin University. All rights reserved.
引用
收藏
页码：1624 / 1633
页数：9
相关论文
共 24 条
  • [1] Wang L T, Yang X, Gong G F, Et al., Pose and tra⁃ jectory control of shield tunneling machine in compli⁃ cated stratum, Automation in Construction, 93, pp. 192-199, (2018)
  • [2] Huo J, Xu Z, Meng Z, Et al., Coupled modeling and dynamic characteristic of TBM cutterhead system un⁃ der uncertain factors, Mechanical Systems and Sig⁃ nal Processing, 140, (2020)
  • [3] Cui Guo-hua, Wang Guo-qiang, Wang Ji-xin, Et al., Cutting dynamic model for cutters of shield machine un⁃ der soft soil, Journal of Jilin University (Engineering and Technology Edition), 38, 2, pp. 139-143, (2008)
  • [4] Deng K, Tang X, Wang L, Et al., On the analysis of force transmission performance for the thrust systems of shield tunneling machines, International Confer⁃ ence on Intelligent Robotics and Applications, (2009)
  • [5] Zhu He-hua, Ding Wen-qi, Qiao Ya-fei, Et al., Mi⁃ cro-disturbed construction control technology system for shield driven tunnels and its application, Chi⁃ nese Journal of Geotechnical Engineering, 36, 11, pp. 1983-1993, (2014)
  • [6] Zhang Ding-li, Cao Li-qiang, Fang Qian, Environ⁃ mental mechanical response prediction and in-process control of urban tunnel construction, Journal of Bei⁃ jing Jiaotong University, 45, 4, pp. 1-8, (2021)
  • [7] Gao Li-hong, Prediction of structural deformation caused by a new subway crossing existing subway, Journal of Municipal Technology, 38, 4, pp. 146-148, (2020)
  • [8] Zhang Cheng-ping, Zhang Ding-li, Wang Mengshu, Structural deformation of overlying subway tun⁃ nels induced by tunnelling, Chinese Journal of Geo⁃ technical Engineering, 31, 5, pp. 805-810, (2009)
  • [9] Liu Dong, Research on prediction method of founda⁃ tion pit deformation based on spatio-temporal correla⁃ tion under deep learning framework, (2022)
  • [10] Li Luo-bin, Gong Xiao-nan, Gan Xiao-lu, Et al., Prediction of maximum ground settlement induced by shield tunneling based on recurrent neural network, China Civil Engineering Journal, 53, pp. 13-19, (2020)
123