Undrained face stability of tunnels in Bangkok subsoils

Ukritchon, Boonchai; Keawsawasvong, Suraparb; Yingchaloenkitkhajorn, Kongkit

doi:10.1080/19386362.2016.1214773

Cited by 46 publications

(8 citation statements)

References 20 publications

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“…Following the recommendation in the FE Plaxis code, the values of s C,TX u ∕s A u = 0.99, 0 ∕s A u = 0.7, u = 0.495 are adopted for all analyses in the paper. The selected E u ∕s A u ratio follows a previous stability analysis using FEA (Ukritchon et al 2017a, b), such that there is very small to none effect of this parameter on the limit load of this stability problem. It should be noted that 0 is initial in situ maximum shear stress and E u is undrained young modulus.…”

Section: Problem Statement and Numerical Simulationmentioning

confidence: 99%

“…Although the recent finite element limit analysis is considered as a powerful technique to solve various stability problems in the geotechnical engineering field (e.g., Shiau and Al-Asadi 2020a, b, c, d;Keawsawasvong et al, 2022;Keawsawasvong and Ukritchon, 2017a, 2021Lai et al 2021a;Yodsomjai et al 2021b, c;Beygi et al 2020;Ukritchon and Keawsawasvong, 2017, 2019Tho et al 2014;Bhattacharya and Sahoo 2017;Bhattacharya 2016Bhattacharya , 2018, the displacement-based finite element analysis is also useful in solving the complex boundary conditions (e.g. Shiau and Yu 2000;Shiau and Smith 2006;Shiau et al 2006a;b, 2018;Shiau and Watson 2008;Halder and Manna 2020;Pirastehfar et al 2020;Chakraborty and Goswami, 2021;Chatterjee and Murali Krishna 2021;Keawsawasvong and Ukritchon, 2017b;Hamouma et al 2021;Huynh et al 2022a, b;Ukritchon et al 2017a, b).…”

Section: Introductionmentioning

confidence: 99%

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Bearing Capacity of Ring Foundations on Anisotropic and Heterogenous Clays: FEA, NGI-ADP, and MARS

et al. 2022

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Axisymmetric solutions for the bearing capacity of ring foundation resting on anisotropic and heterogenous clays are presented in this paper using finite element analysis (FEA). The NGI-ADP model in PLAXIS FEA, a widely used anisotropic soil model, is adopted to study the stability responses of ring foundations, with special consideration given to the effects of increasing undrained shear strength with the depth. Numerical results are formulated in terms of a dimensionless stability number (bearing capacity ratio) that is a function of three dimensionless input parameters: namely, the ratio of inner and outer radius, the increasing strength gradient ratio, and the anisotropic shear strength ratio. The influence of each dimensionless input parameter on the bearing capacity ratio is investigated using design charts and failure mechanisms, and they are scored by relative importance indexes in multivariate adaptive regression splines (MARS) model—a machine learning approach. A highly accurate equation generated from the MARS model is proposed as an effective tool for engineering practitioners.

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Section: Problem Statement and Numerical Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bearing Capacity of Ring Foundations on Anisotropic and Heterogenous Clays: FEA, NGI-ADP, and MARS

et al. 2022

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“…Finally, the problem of tunnel face stability can also be solved by numerical analysis. Mostly, the tunnel support pressure is obtained with the FEM [3,18,55,57,58,68], but also the FDM is largely adopted [12,34,61]. Thanks to the ever increasing computational speed of modern computing, the DEM is recently gaining momentum [13,37,59,60,69].…”

Section: Tunnel Face Stabilitymentioning

confidence: 99%

“…The same procedure is followed to obtain the dry density from the data of Lüe et al [37] and Zhang et al [69]. One reference [57] is neglected in further calculations, given the impossibility of obtaining the soil dry unit weight. The support pressure is sometimes given as a ratio.…”

Section: Dataset Preparationmentioning

confidence: 99%

A soft computing approach to tunnel face stability in a probabilistic framework

Soranzo

Guardiani

2021

Acta Geotech.

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Tunnel face is important for shallow tunnels to avoid collapses. In this study, tunnel face stability is studied with soft computing techniques. A database is created based on the literature which is used to train some broadly adopted soft computing techniques, ranging from linear regression to the artificial neural network. The soil dry density, cohesion, friction angle, cover depth and the tunnel diameter are used as the input parameters. The soft computing techniques state whether the face support is stable and predict the face support pressure. It is found that the artificial neural network outperforms the other techniques. The face support pressure is predicted with the artificial neural network for statistically distributed samples, and the failure probability is obtained with Monte Carlo simulations. In this way, the stability of the tunnel face can be reliably assessed and the support pressure can be estimated fairly accurately.

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“…Thus, the results can be applied only to relatively long-wall tunnels. However, based on a study by Ukritchon et al (2017a), the results for plane strain headings provide a conservative estimate for the face stability of tunnels under fully 3D geometry owing to the 3D effect between the plane strain and actual problems.…”

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confidence: 99%