Comparison of liquefaction behavior of granular material under SH- and Love-wave strain conditions by 3D DEM

Ming-jin, Jiang; Kamura, Akiyoshi; Kazama, Motoki

doi:10.1016/j.sandf.2021.06.013

Cited by 10 publications

(1 citation statement)

References 30 publications

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“…Lastly, the present DEM analysis is limited to triaxial testing conditions. Recently, Jiang et al (2021) compared the dynamic response of spherical particles using DEM under horizontally polarised shear-and Love-wave strain conditions. While acknowledging the above limitations, this study nonetheless provides the essential mechanical responses of granular materials subjected to both monotonic and cyclic loading influenced by inherent anisotropy.…”

Section: Limitations Of This Contributionmentioning

confidence: 99%

Linking inherent anisotropy with liquefaction phenomena of granular materials by means of DEM analysis

Otsubo

Chitravel

Kuwano

et al. 2022

Soils and Foundations

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Section: Limitations Of This Contributionmentioning

confidence: 99%

Linking inherent anisotropy with liquefaction phenomena of granular materials by means of DEM analysis

Otsubo

Chitravel

Kuwano

et al. 2022

Soils and Foundations

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Application of factorization machine with quantum annealing to hyperparameter optimization and metamodel‐based optimization in granular flow simulations

Xiao,

Endo,

Muramatsu

et al. 2024

Num Anal Meth Geomechanics

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This study examined the applicability of factorization machines with quantum annealing (FMQA) to the field of landslide risk assessment for two specific black‐box optimization problems, hyperparameter optimization (HPO) for metamodeling and metamodel‐based simulation optimization (MBSO) targeting granular flow simulation using discrete element method (DEM). These two optimization problems are solved successively: HPO is first performed to determine the hyperparameters of the Gaussian process regression (GPR) metamodel, which is then used as a low‐cost, fast approximate solver of granular flow simulations for MBSO. After conducting a series of granular flow simulations using DEM, a metamodel is created that outputs a risk index of interest, the run‐out distance, from its input parameters by employing GPR with two hyperparameters, length‐scale and signal variance. Subsequently, HPO is performed to obtain the optimal set of hyperparameters by applying FMQA and other optimization methods using another set of hyperparameters determined using the gradient‐ascent method as the reference solution. Finally, using the metamodel created by each optimization method as an approximate solver for DEM simulations, MBSO is performed to find the optimal target output, the maximum run‐out distance, in the space of physical input parameters for risk assessment. A comparison of the performance of FMQA with that of other methods shows that FMQA is competitive in terms of efficiency and stability with state‐of‐the‐art algorithms such as Bayesian optimization.

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Volumetric Strains After Undrained Cyclic Shear Governed by Residual Mean Effective Stress: Numerical Studies Based on 3D DEM

Ming-jin

Kamura²,

Kazama³

2022

Proceedings of the 4th International Conference on Performance Based Design in Earthquake Geotechnical Engineering (Beijing 202

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Comparison of liquefaction behavior of granular material under SH- and Love-wave strain conditions by 3D DEM

Cited by 10 publications

References 30 publications

Linking inherent anisotropy with liquefaction phenomena of granular materials by means of DEM analysis

Linking inherent anisotropy with liquefaction phenomena of granular materials by means of DEM analysis

Application of factorization machine with quantum annealing to hyperparameter optimization and metamodel‐based optimization in granular flow simulations

Volumetric Strains After Undrained Cyclic Shear Governed by Residual Mean Effective Stress: Numerical Studies Based on 3D DEM

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