Finite element verification of an enhanced limit equilibrium method for slope analysis

Tan, Dongyao; Sarma, S. K.

doi:10.1680/geot.2008.58.6.481

Cited by 26 publications

(7 citation statements)

References 10 publications

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“…The dilation angle ψ is well known to affect the volumetric deformation of the geomaterials during yield . In the aforementioned analyses, a compromise value of ψ = 0, corresponding to a non‐associated flow rule with zero volume change during yield, was adopted.…”

Section: Resultsmentioning

confidence: 99%

“…The dilation angle ψ is well known to affect the volumetric deformation of the geomaterials during yield. 46,47 In the aforementioned analyses, a compromise value of ψ = 0, corresponding to a non-associated flow rule with zero volume change during yield, was adopted. For the 5 types of geomaterials in the STC area, 3 different ratios of the dilation angle ψ to the friction angle φ, ie, 0, 0.5, and 1, are used to evaluate the effects of different dilation angles on the calculated deformation.…”

Section: Effects Of Dilation Anglesmentioning

confidence: 99%

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Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

Jiang

Sun

Yi³

et al. 2018

Num Anal Meth Geomechanics

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Summary An inverse analysis method that combines the back propagation neural network (BPNN) and vector evaluated genetic algorithm (VEGA) was proposed to identify mechanical geomaterial parameters for a more accurate prediction of deformation. The BPNN is used to replace the time‐consuming numerical calculations, thus enhancing the efficiency of the inverse analysis. The VEGA is used to find the Pareto‐optimal solutions to multiobjective functions. Unlike traditional back‐analysis methods which are based on only 1 type of field measurement and a single objective function, this proposed method can consider multiple field observations simultaneously. The proposed method was applied to the Shapingba foundation pit excavation located in Chongqing city, China. Two types of measurements are considered in the method simultaneously: the displacements in the x‐direction (north orientation) and those in the y‐direction (east orientation). Five deformation modulus parameters for artificial backfill soil, silty clay, siltstone, sandstone, and mudstone were selected as the inversion parameters. Compared with the weighted sum approach, the proposed method was demonstrated as an efficient multi‐objective optimization tool for back calculating undetermined parameters. After performing a forward‐calculation using the optimized parameters obtained by the inverse analysis, the predicted results were well consistent with the practical deformation in magnitude and trend.

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Section: Resultsmentioning

confidence: 99%

Section: Effects Of Dilation Anglesmentioning

confidence: 99%

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

Jiang

Sun

Yi³

et al. 2018

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

show abstract

“…6 In slope stability analysis, the evaluation of FS and kc is often performed using either conventional approaches (limit equilibrium and limit analysis) or numeric ones (the strength reduction technique). Therefore, different techniques have been proposed to determine the critical seismic coefficient kc (upper bound formulation [4], variational formulation of limiting equilibrium and a strength reduction technique [5], limit equilibrium method and stress acceptability criteria [6],…etc.). Particularly, there is no exacting formulation for selecting the pseudo-static coefficient for designing earth dams with special conditions as anisotropy seepage.…”

Section: Introductionmentioning

confidence: 99%

Critical seismic coefficients of homogeneous earth dams prediction by a FELA-ANN approach

Zeroual

Fourar

Djeddou

2019

J. Appl. Math. Comput. Mech.

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In this research study, a combination of lower and upper bound finite element limit analysis (FELA) and artificial neural network (ANN) has been adopted in order to forecast critical seismic coefficients (kc) of homogeneous earth dams (HED) subjected to pseudo-static seismic loading. To achieve this, the results of kc obtained by OptumG2 software were used in the development of the ANN and MR models. The ANN models have shown higher prediction performance than the MR models based on the performance indices. The most appropriate architecture was found 8-14-1, as this gave the best kc predict with the minimum statistical measures of error and the high determination coefficient (> 99%). Consequently, the ANN model can be used to easily and accurately predict kc value of the HED as the best substitute for the conventional methods.

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“…Many methods have been developed for slope stability analysis, among which the limit equilibrium methods and numerical modelling are two of the most widely used methods . However, the application of above methods has been limited by several restrictions.…”

Section: Introductionmentioning

confidence: 99%

A hybrid ensemble method for improved prediction of slope stability

Tang

2018

Num Anal Meth Geomechanics

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Accurate prediction of slope stability is a significant issue in geomechanics with many artificial intelligence (AI) techniques being utilised. However, the application of AI has not reached its full potential because of the lack of more robust algorithms. In this paper, we proposed a hybrid ensemble method for the improved prediction of slope stability using classifier ensembles and genetic algorithm. Gaussian process classification, quadratic discriminant analysis, support vector machine, artificial neural networks, adaptive boosted decision trees, and k-nearest neighbours were chosen to be individual AI techniques, and the weighted majority voting was used as the combination method. Validation method was chosen to be the 10-fold cross-validation, and performance measures were selected to be the accuracy, the receiver operating characteristic curve, and the area under the receiver operating characteristic curve (AUC). Grid search and genetic algorithm were used for the hyperparameter tuning and weight tuning respectively. The results show that the proposed hybrid ensemble method has great potential in improving the prediction of slope stability. Compared with individual classifiers, the optimum ensemble classifier achieved the highest AUC value (0.943) and the highest accuracy (0.902) on the testing set, denoting that the predictive performance has been improved. The optimum ensemble classifier with the Youden's cutoff was recommended for slope stability prediction with respect to the AUC value, the accuracy, the true positive rate, and the true negative rate. This research indicates that the use of the classifier ensembles, rather than the search for the ideal individual classifiers, might help for the slope stability prediction.

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Finite element verification of an enhanced limit equilibrium method for slope analysis

Cited by 26 publications

References 10 publications

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

Critical seismic coefficients of homogeneous earth dams prediction by a FELA-ANN approach

A hybrid ensemble method for improved prediction of slope stability

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