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.
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页码：1624 / 1633
页数：9
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