Long-term stability analysis and deformation prediction of soft soil foundation pit in Taihu Tunnel

被引:2
作者
Zhou A. [1 ,2 ]
Wang B. [2 ]
Li J.-T. [2 ]
Zhou X. [3 ]
Xia W.-J. [3 ]
机构
[1] School of Civil and Environmental Engineering, Hubei University of Technology, Wuhan
[2] State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan
[3] Jiangsu Provincial Transportation Engineering and Construction Bureau, Nanjing
来源
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2022年 / 56卷 / 04期
关键词
Overload; Soft soil; Stability; Time-dependent deformation;
D O I
10.3785/j.issn.1008-973X.2022.04.008
中图分类号
学科分类号
摘要
An intelligent inversion system based on support vector machine (SVM) was established, and creep tests of soft soil were conducted in laboratory under the background of a large foundation pit project of Taihu tunnel. An innovative analysis method was proposed by combining the intelligent inversion and creep tests. The basic physical and mechanical parameters and creep parameters of relevant soil layers were determined combined with the monitoring data. The feasibility of the method in long-term stability analysis and deformation prediction of soft soil foundation pit under overloading was verified by analyzing the deformation at the site of soft soil foundation pit under overloading. The method was applied to the optimization design under overloading of Taihu tunnel, and good results were obtained. Copyright ©2022 Journal of Zhejiang University (Engineering Science). All rights reserved.
引用
收藏
页码:692 / 701
页数:9
相关论文
共 26 条
[21]  
BENZ T., Small-strain stiffness of soils and its numerical consequences, (2007)
[22]  
ZHANG Shuo, YE Guan-lin, ZHEN Liang, Et al., Constitutive model of soft soil after considering small strain stiffness decay characteristics, Journal of Shanghai Jiao Tong University, 53, 5, pp. 535-539, (2019)
[23]  
XIAO Ming-qing, LIU Hao, PENG Chang-sheng, Et al., Analysis of parameters in deep soft soil layer based on neural network, Chinese Journal of Underground Space and Engineering, 13, 1, pp. 279-286, (2017)
[24]  
WU Bo, ZHAO Fa-suo, HE Zi-guang, Et al., Prediction of the disaster area of loess landslide based on least square support vector machine optimized by bat algorithm, The Chinese Journal of Geological Hazard and Control, 31, 5, pp. 1-6, (2020)
[25]  
LI Xiao-long, Mechanical parameters inversion of rock mass with support vector machine and its engineering application, (2009)
[26]  
SUYKENS J, VANDEWALLE J., Least squares support vector machine classifiers, Neural Processing Letters, 19, 3, pp. 293-300, (1999)