Undrained Stability of Unsupported Conical Slopes in Anisotropic Clays Based on Anisotropic Undrained Shear Failure Criterion

Yodsomjai, Wittawat; Keawsawasvong, Suraparb; Senjuntichai, Teerapong

doi:10.1007/s40515-021-00153-y

Cited by 52 publications

(8 citation statements)

References 30 publications

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“…The results from FELA will be used as a training data in the ANN model in the next section. FELA is a widely used numerical method for successfully computing safety factors or limit loads of several problems in the geotechnical engineering field [42][43][44][45][46][47][48][49][50][51]. Commercial FELA software, namely OptumG2 [52], was carried out to derive the stability solutions for tunnel problems in this study.…”

Section: Numerical Analysismentioning

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 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: Numerical Analysismentioning

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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“…Besides, a three-noded triangular element with three undetermined stresses is employed in the lower bound (LB) analysis. The lower bound assessment aims to optimize the limit force of foundations by Yodsomjai et al, 2021a;Yodsomjai et al, 2021b;Chauhan, 2021;Keawsawasvong and Lai, 2021;Keawsawasvong et al, 2022b;Chauhan et al, 2022;Keawsawasvong et al, 2022c;Eskandarinejad, 2022).…”

Section: Methodsmentioning

confidence: 99%

A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load

Lai

Sangjinda

Keawsawasvong

et al. 2022

Front. Built Environ.

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In this study, the Multivariate Adaptive Regression Splines (MARS) model is employed to create a data-driven prediction for the bearing capacity of a strip footing on rock mass subjected to an inclined and eccentric load. The strengths of rock masses are based on the Hoek-Brown failure criterion. To develop the set of training data in MARS, the lower and upper bound finite element limit analysis (FELA) is carried out to obtain the numerical results of the bearing capacity of a strip footing with the width of B. There are six considered dimensionless variables, including the geological strength index (GSI), the rock constant/yield parameter (mi), the dimensionless strength (γB/σci), the adhesion factor (α), load inclined angle from the vertical axis (β), and the eccentricity of load (e/B). A total of 5,120 FELA solutions of the bearing capacity factor (P/σciB) are obtained and used as a training data set. The influences of all dimensionless variables on the bearing capacity factors and the failure mechanisms are investigated and discussed in detail. The sensitivity analysis of these dimensionless variables is also examined.

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“…Using this technique, the number of elements is automatically increased in the zone with high shear power dissipation that requires sensitivity analyses. The five iterations of the adaptive meshing are used in this study, where the number of elements is set to be automatically increased from 5000 to 10,000 elements [56][57][58][59][60][61][62][63][64][65][66][67].…”

Section: Problem Statement and Modelling Techniquementioning

confidence: 99%

Determining Seismic Bearing Capacity of Footings Embedded in Cohesive Soil Slopes Using Multivariate Adaptive Regression Splines

Lai

Yang

et al. 2022

Int. J. of Geosynth. and Ground Eng.

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Seismic bearing capacity of strip footings in cohesive soil slopes considering various embedded depths is investigated in this study. Novel solutions using pseudo-static method and finite element limit analysis (FELA) with upper bound (LB) and lower bound (LB) theorems are presented. The influences of footing depth, slope angle, slope height, undrained shear strength and pseudo-static acceleration on bearing capacity and failure mechanisms are examined using dimensionless parameters. With the comprehensive numerical results, the multivariate adaptive regression splines (MARS) model is then utilized to simulate the sensitivity of all dimensionless input parameters (i.e., the normalized depth of footing D/B, the normalized slope height H/B, the normalized distance from top slope to edge of the footing L/B, slope angle β, the strength ratio cu/γB, and the pseudo-static acceleration factor, kh). The degree of influence of each design parameter is produced, and an empirical equation for the dimensionless output parameter (i.e., bearing capacity factor Nc) is proposed. The study results are accessible in the design charts, tables, empirical equation for design practitioners.

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Undrained Stability of Unsupported Conical Slopes in Anisotropic Clays Based on Anisotropic Undrained Shear Failure Criterion

Cited by 52 publications

References 30 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

A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load

Determining Seismic Bearing Capacity of Footings Embedded in Cohesive Soil Slopes Using Multivariate Adaptive Regression Splines

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