Parameter identification for Cam-clay model in partial loading model tests using the particle filter

Shuku, Takayuki; Murakami, Akira; Nishimura, Shin Ichi; Fujisawa, Kazunori; Nakamura, Kazuyuki

doi:10.1016/j.sandf.2012.02.006

Cited by 34 publications

(21 citation statements)

References 25 publications

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“…The SQMC filter approximates posterior PDFs via a set of samples (referred to as an ensemble) and the associated weights [25,43]. From the ensemble {x…”

Section: Sqmc Filtermentioning

confidence: 99%

“…For the current inverse problem, the sequential quasi-Monte Carlo (SQMC) filter 1 and sequential importance sampling (SIS) are implemented, which can jointly account for the effects of loading history on the elastoplastic behavior of granular materials [43]. The SQMC filter applies the recursive formula of sequential Bayesian estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental data obtained step by step during a loading process are assimilated into DEM models to approximate the evolution of posterior PDFs in the corresponding parameter space. This "inversemodeling calibration" approach is expedient, because the synergy of the SQMC and SIS algorithms is well-justified for nonlinear and non-Gaussian geomechanical problems, as demonstrated in the applications of these methods to inverse finite element analyses [33,43]. A few innovative calibration methods for DEM or molecular dynamics simulations were recently proposed using Bayesian approaches [3,40,53].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

et al. 2018

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The calibration of discrete element method (DEM) simulations is typically accomplished in a trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal with these issues, the sequential quasi-Monte Carlo (SQMC) filter is employed as a novel approach to calibrating the DEM models of granular materials. Within the sequential Bayesian framework, the posterior probability density functions (PDFs) of micromechanical parameters, conditioned to the experimentally obtained stress-strain behavior of granular soils, are approximated by independent model trajectories. In this work, two different contact laws are employed in DEM simulations and a granular soil specimen is modeled as polydisperse packing using various numbers of spherical grains. Knowing the evolution of physical states of the material, the proposed probabilistic calibration method can recursively update the posterior PDFs in a five-dimensional parameter space based on the Bayes' rule. Both the identified parameters and posterior PDFs are analyzed to understand the effect of grain configuration and loading conditions. Numerical predictions using parameter sets with the highest posterior probabilities agree well with the experimental results. The advantage of the SQMC filter lies in the estimation of posterior PDFs, from which the robustness of the selected contact laws, the uncertainties of the micromechanical parameters and their interactions are all analyzed. The micro-macro correlations, which are byproducts of the probabilistic calibration, are extracted to provide insights into the multiscale mechanics of dense granular materials.

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“…The SQMC filter approximates posterior PDFs via a set of samples (referred to as an ensemble) and the associated weights [25,43]. From the ensemble {x…”

Section: Sqmc Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

et al. 2018

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“…An inverse analysis is a powerful tool for reevaluating the parameters based on measurements to overcome the many sources of uncertainty. The particle filter (henceforth, PF) (Gordon, et al, 1993), one of the methods of inverse analysis, is advantageous for nonlinear problem such as the simulation of geotechnical problems (Shuku et al, 2012;Murakami et al, 2013;Shibata et al, 2014). We have reported the identification of elasto-plastic parameters in terms of secondary consolidation, based on the results of model tests by means of the PF, and the prediction of the long-term behavior by using the identified parameters ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of long-term settlement and accurate analysis of horizontal displacement based on model test results

Shibata

Nishimura

Shuku

2016

JGS Special Publication

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This paper presents long-term predictions of the ground behavior, including secondary consolidation via the particle filter, using model test results. There have been some difficulties in evaluating lateral displacements, because an unsuitable value for Poisson's ratio and the constitutive model are generally employed for consolidation simulations. In this paper, the Poisson's ratio is identified in addition to the compression index, the permeability, the initial volumetric strain, and the secondary compression index in the modified Cam-clay model considering anisotropy, to achieve more accurate predictions of lateral displacements. The prediction is performed using the identified parameters, and numerical examples of accurate predictions of the ground behavior are shown.

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“…Sequential data assimilation techniques, such as the particle filter, PF (Gordon et al, 1993;Kitagawa, 1996;Higuchi, 2005), are prospective approaches for this type of inverse problem, because the time evolution of state variables, i.e., displacement and pore pressure for geotechnics, under controlled input, like external loading, is incorporated into the system equation in a rational manner without any limitations. The PF can easily deal with nonlinear state equations and is robust when employing the Monte Carlo method in conjunction with a numerical simulation, e.g., the FEM for soil-water coupled problems with an elasto-plastic model (Shuku et al, 2012;Murakami et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Prediction of embankment behavior of regulating reservoir with foundation improved by vacuum consolidation method

Shibata

Murakami

Fujii³

2014

Soils and Foundations

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The present paper addresses the numerical prediction of the behavior of a ground and a reservoir dyke with a retaining wall at the site of a regulating reservoir whose soft soil foundation is improved by using both the usual embankment preloading and vacuum consolidation. To evaluate the settlement, the lateral deformation, and the dissipation of pore pressure during vacuum preloading, a numerical analysis using an elasto-plastic FEM for soil-water coupled problems, incorporating the SYS Cam-clay model, is carried out in two dimensions. However, a change in the soil parameters during the vacuum preloading leads to a less accurate computation. To account for the uncertainties in the input parameters of the constitutive model for the improved ground, an inverse analysis approach is adopted. The particle filter is used to identify the compression index of the clay layers and the coefficient of permeability of the organic soil layer based on the measured settlement at the bottom of the preloading embankment during the vacuum consolidation. The reservoir dyke with a retaining wall is constructed on an improved foundation after removing the preloading embankment, and an attempt is made to predict its performance after construction by an elasto-plastic FEM for soil-water coupled problems using the identified parameters.

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Parameter identification for Cam-clay model in partial loading model tests using the particle filter

Cited by 34 publications

References 25 publications

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

Prediction of long-term settlement and accurate analysis of horizontal displacement based on model test results

Prediction of embankment behavior of regulating reservoir with foundation improved by vacuum consolidation method

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