Simplified procedure for finite element analysis of the longitudinal performance of shield tunnels considering spatial soil variability in longitudinal direction

Huang, Hongwei; Gong, Wenping; Khoshnevisan, Sara; Juang, C. Hsein; Zhang, Dong Ming; Wang, Lei

doi:10.1016/j.compgeo.2014.11.010

Cited by 105 publications

(25 citation statements)

References 28 publications

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“…Since the first shield tunnel was completed in London 170 years ago, shield tunnelling is greatly popular with its flexibility, cost-effectiveness, and little impact on ground traffic and surface structures [ 47 ]. One of the key factors in shield tunnelling is the prediction of ground surface settlement since very often construction is undertaken in densely populated urban areas with heavy traffic and public utilities, such as storm drain, sewer, steam, water, gas pipes, and electrical and telephone ducts [ 48 ].…”

Section: Tunnel Deformation Prediction Systemmentioning

confidence: 99%

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

Lai

Qiu

Feng

et al. 2016

Computational Intelligence and Neuroscience

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In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability.

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Section: Tunnel Deformation Prediction Systemmentioning

confidence: 99%

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

Lai

Qiu

Feng

et al. 2016

Computational Intelligence and Neuroscience

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“…Shen et al (2014) pointed out that the tunnel deforms primarily in the dislocation mode, supplemented by the bending mode in the longitudinal direction based on the field observations. In many previous studies, the shield tunnel was often treated as a bending tube, and the longitudinal behaviour was analysed based on the Euler-Bernoulli beam theory (Murakanu and Koizumi, 1978;Bogaards and Bakker, 1999;Huang et al, 2012;Talmon and Bezuijen, 2013;Huang et al, 2015). Fig.…”

Section: Longitudinal Deformation Modementioning

confidence: 99%

Longitudinal structural modelling of shield tunnels considering shearing dislocation between segmental rings

Shen

Liao

et al. 2015

Tunnelling and Underground Space Technology

292

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“…The longitudinal settlement mechanism of the tunnel can also be analyzed by the two-dimensional and three-dimensional numerical simulation methods [20][21][22]. Huang et al [23] developed a finite element method for analyzing the longitudinal performance of shield tunnels considering the longitudinal variation of geotechnical parameters. Yuan et al [9] analyzed the deformation of the shield tunnel and the surrounding soil by building a three-dimensional model of shield tunnel.…”

Section: Introductionmentioning

confidence: 99%

Study on the Horizontal Axis Deviation of a Small Radius TBM Tunnel Based on Winkler Foundation Model

Qiao

Liu

et al. 2020

Applied Sciences

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During the construction stage of the small radius TBM (tunnel boring machine) interval, the improper control of the boring parameters and the boring posture can cause the horizontal axis deviation of the shield tunnel. In order to address this issue, the TBM segments lining structure of the small radius interval is simplified as the continuous circular curved beam based on the longitudinal equivalent continuous model and Winkler elastic foundation beam theory. The theoretical model is solved through the transfer matrix method, and its applicability is verified by comparing it with the field monitoring data. It is found that the horizontal axis deviation of the completed tunnel increases with the total jack thrust, and the lateral displacement tends to be stable when the distance between the ring and the tail is far. The horizontal axis deviation has a negative relationship with the thrust difference or path difference when the jack thrust in the outside of the shield curve is larger than that of inside the shield curve. The horizontal axis deviation has a positive relationship with the thrust difference or path difference when the jack thrust in the outside of the shield curve is smaller than that of inside the shield curve.

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Simplified procedure for finite element analysis of the longitudinal performance of shield tunnels considering spatial soil variability in longitudinal direction

Cited by 105 publications

References 28 publications

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

Longitudinal structural modelling of shield tunnels considering shearing dislocation between segmental rings

Study on the Horizontal Axis Deviation of a Small Radius TBM Tunnel Based on Winkler Foundation Model

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