LSTM-based deformation forecasting for additional stress estimation of existing tunnel structure induced by adjacent shield tunneling

被引:7
|
作者
Ye, Xiao-Wei [1 ,2 ]
Ma, Si-Yuan [2 ]
Liu, Zhi-Xiong [2 ]
Chen, Yan-Bo [1 ,2 ,3 ]
Lu, Ci-Rong [4 ]
Song, Yue-Jun [4 ]
Li, Xiao-Jun [4 ]
Zhao, Li-An [4 ]
机构
[1] Zhejiang Univ, MOE Key Lab Soft Soils & Geoenvironm Engn, Hangzhou 310058, Peoples R China
[2] Zhejiang Univ, Dept Civil Engn, Hangzhou 310058, Peoples R China
[3] Zhejiang Univ, Ctr Hypergrav Expt & Interdisciplinary Res, Hangzhou 310058, Peoples R China
[4] Shaoxing Rail Transit Grp Co Ltd, Shaoxing 312000, Peoples R China
基金
美国国家科学基金会;
关键词
Structural health monitoring; Shield tunnel; Circumferential deformation; Load pattern; Machine learning;
D O I
10.1016/j.tust.2024.105664
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A redistribution of stress is caused by the excavation of a new tunnel within the soil surrounding the existing shield tunnel structure. This stress redistribution results in alterations to the soil and water loads around the existing tunnel structure, ultimately impacting its stress and deformation. The deformation response of existing tunnels is primarily focused on in most existing studies, with limited research conducted on the coupling system of new tunnel excavation, soil stress state, and deformation and stress of existing tunnels. This paper proposed an innovative methodology to predict deformation in existing tunnel structures and estimate additional stress during the excavation of a new shield tunnel. The reliability of the suggested calculation method was verified using measured internal force and convergence data of the structures. The results demonstrated that the Long Short-Term Memory (LSTM) model could more effectively establish the temporal correlation between the excavation parameters of new tunnels and the deformation of existing tunnels. Compared to the Recurrent Neural Network (RNN) model, the R2 of the LSTM model increased by 11.81% in the prediction of the tunnel waist and 9.38% in the prediction of the tunnel crown. The Light gradient boosting machine (LightGBM) algorithm was used to analyze the crucial factors of deformation of the existing tunnel structures during shield tunneling, and the results showed that the relative position and the average earth pressure were the essential factors. Furthermore, a calculation method for the additional stress of existing tunnel structures was proposed based on the load structure model. The method assumed that the additional load was distributed rectangularly at the top and bottom of the existing tunnel, and triangularly at the waist of the tunnel. The comparison between the calculated results and the measured results indicated that the phased characteristics of the internal force changes in the existing tunnel structure during the construction of a new tunnel could be accurately reflected by this method.
引用
收藏
页数:14
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