Undrained Stability of Unlined Square Tunnels in Clays with Linearly Increasing Anisotropic Shear Strength

Ukritchon, Boonchai; Keawsawasvong, Suraparb

doi:10.1007/s10706-019-01023-8

Cited by 63 publications

(9 citation statements)

References 57 publications

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“…However, these studies are limited to tunnels with circular, square, rectangular, or flat shapes. Note that these previous works [15][16][17][18][19][20][21][22][23][24][25][26] present the FELA solutions of 2D tunnels in undrained and drained soils under surcharge loading, where the pattern of their results are quite similar to the present study. However, stability solutions for elliptical tunnels were not proposed in these past studies, e.g., [15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionsupporting

confidence: 88%

“…More information on the details and the development of UB and LB FELA can be found in Sloan [14]. The problems of unlined tunnels or trapdoors under plane strain conditions have been derived using FELA by some researchers in the past, such as Sloan and Assadi [15], Wilson et al [16][17][18], Yamamoto et al [19,20], Keawsawasvong and Ukritchon [21,22], Keawsawasvong and Likitlersuang [23], Keawsawasvong and Shiau [24], and Ukritchon and Keawsawasvong [25,26]. However, these studies are limited to tunnels with circular, square, rectangular, or flat shapes.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network-Based Prediction Model for the Stability of Unlined Elliptical Tunnels in Cohesive-Frictional Soils

et al. 2022

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The scheme for accurate and reliable predictions of tunnel stability based on an artificial aeural network (ANN) is presented in this study. Plastic solutions of the stability of unlined elliptical tunnels in sands are first derived by using numerical upper-bound (UB) and lower-bound (LB) finite element limit analysis (FELA). These numerical solutions are later used as the training dataset for an ANN model. Note that there are four input dimensionless parameters, including the dimensionless overburden factor γD/c′, the cover–depth ratio C/D, the width–depth ratio B/D, and the soil friction angle ϕ. The impacts of these input dimensionless parameters on the stability factor σs/c′ of the stability of shallow elliptical tunnels in sands are comprehensively examined. Some failure mechanisms are carried out to demonstrate the effects of all input parameters. The solutions will reliably and accurately provide a safety assessment of shallow elliptical tunnels.

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Section: Introductionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Prediction Model for the Stability of Unlined Elliptical Tunnels in Cohesive-Frictional Soils

et al. 2022

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“…The finite element limit analysis (FELA), which is the computational method based on a perfectly plastic material with an associated flow rule, employs the plastic bound theorems, finite element discretization, and mathematical optimization (Sloan, 2013;Keawsawasvong and Ukritchon, 2017;Ukritchon and Keawsawasvong, 2017;Krishnan et al, 2019;Ukritchon et al, 2019;Ukritchon and Keawsawasvong, 2020a;Ukritchon and Keawsawasvong, 2020b;Ukritchon et al, 2020;Keawsawasvong and Ukritchon, 2021). This FELA technique is carried out to derive the bracket of the true limit load from the targeted upper Bound (UB) and lower Bound (LB) solutions.…”

Section: Methodology Finite Element Limit Analysismentioning

confidence: 99%

Application of Artificial Neural Networks for Predicting the Stability of Rectangular Tunnels in Hoek–Brown Rock Masses

Keawsawasvong

Seehavong

Ngamkhanong

2022

Front. Built Environ.

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An artificial neural network (ANN) model for predicting the stability of rectangular tunnels in rock masses based on the Hoek–Brown (HB) failure criterion is presented in this study. Since the safety assessment of the tunnel stability is one critical issue for civil engineers during the construction, it is very important to develop a reliable and accurate stability analysis of such problems. The finite element limit analysis (FELA) with the HB failure criterion is used to develop the numerical upper and lower bound solutions of the problem of rectangular tunnels in rock masses. A novel machine learning-aided prediction of this problem is then developed based on the datasets of the numerical bound solutions obtained from the FELA. The inputs consist of six dimensionless parameters including the cover-depth ratio of tunnels, the width ratio of tunnels, the normalized uniaxial compressive strength, the geological strength index, the mi parameter, and the degree of disturbance of rock masses. The results show that the optimal ANN models provide very great accuracy in predicting the stability of the rectangular tunnels based on the HB failure criterion. The solutions will provide a prompt assessment of tunnel stability in rock masses for geotechnical engineers during the construction of rock tunnels.

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“…To derive the uplift capacity solutions, the LB and UB FELA in conjunction with the AUS failure criterion are employed. Note that several works using the FELA technique and various failure criteria for anisotropic clays have been carried out by Ukritchon and Keawsawasvong [24][25][26][27][28], Keawsawasvong and Ukritchon [29,30], Yodsomjai et al [31], Keawsawasvong and Lawongkerd [32], Nguyen et al [33], Keawsawasvong et al [34,35], and Lai et al [36,37] to compute the stability solutions of several geotechnical problems. In this paper, the numerical results are presented in the form of design charges and tables for practical use.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Uplift Capacity of Cylindrical Caissons in Anisotropic and Inhomogeneous Clays Using Multivariate Adaptive Regression Splines

et al. 2022

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The uplift capacity factor of cylindrical suction caisson in anisotropic and inhomogeneous clays considering the adhesion factor at the interface is investigated in this paper. The finite element limit analysis based on lower bound and upper bound analyses is used for analyzing purposes. The anisotropic undrained shear model is employed to describe the anisotropic and inhomogeneous clay. The impact of these dimensionless parameters on the ratio of inhomogeneity or strength gradient ratio, the adhesion factor, the ratio of depth over diameter, and the ratio of anisotropic undrained shear strengths on the uplift resistance and the collapse mechanisms of suction caisson foundations are determined. The multivariate adaptive regression splines technique is employed to access the sensitivity of all considered dimensionless parameters on the uplift capacity factor and to propose an empirical design equation as an effective tool for predicting the uplift capacity factor. The results presented in this paper can be guidance for the preliminary design of suction caissons in anisotropic and non-homogeneous clays that are useful for engineering practitioners.

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Undrained Stability of Unlined Square Tunnels in Clays with Linearly Increasing Anisotropic Shear Strength

Cited by 63 publications

References 57 publications

Neural Network-Based Prediction Model for the Stability of Unlined Elliptical Tunnels in Cohesive-Frictional Soils

Neural Network-Based Prediction Model for the Stability of Unlined Elliptical Tunnels in Cohesive-Frictional Soils

Application of Artificial Neural Networks for Predicting the Stability of Rectangular Tunnels in Hoek–Brown Rock Masses

Prediction of Uplift Capacity of Cylindrical Caissons in Anisotropic and Inhomogeneous Clays Using Multivariate Adaptive Regression Splines

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