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

Takayuki Shuku, Akira Murakami, Shin-ichi Nishimura, Kazunori Fujisawa, Kazuyuki Nakamura

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Data assimilation is a versatile methodology, developed in the earth sciences, such as geophysics, meteorology, and oceanography, for estimating the state of a dynamic system of interest by merging sparse observation data into a numerical model for the system. In particular, the data assimilation method referred to as the particle filter (PF) can be applied to nonlinear and non-Gaussian problems, and it holds the greatest potential for application to geotechnical problems. The objective of this study is to demonstrate the theoretical and the practical effectiveness of the PF for a geotechnical problem, i.e., applying the methodology to numerical experiments and actual model tests to identify the parameters of elasto-plastic geomaterials. Since the mechanical behavior of soils depends on both the current stress and the recent stress history of the soil, the sampling method called SIS, which can take into account the stress history experienced by soils, identifies the parameters of elasto-plastic geomaterials remarkably well. The results of the numerical tests have shown that the parameters identified by the PF based on the SIS have converged into their true values, and the approach presented in this study has shown great promise as an accurate parameter identification method for elasto-plastic geomaterials. Moreover, the simulation results using the identified parameters were close to the actual measurement data, and long-term predictions with high accuracy could be achieved, even though short-term measurement data were used. The PF approach produces more information about the parameters of interest than simple estimated values obtained from optimization methods. Namely, the identification comes in the form of probability density functions.

Original languageEnglish
Pages (from-to)279-298
Number of pages20
JournalSoils and Foundations
Volume52
Issue number2
DOIs
Publication statusPublished - Apr 2012

Fingerprint

Cam-clay model
Cams
model test
Identification (control systems)
Clay
Plastics
filter
Soils
Earth sciences
Oceanography
Geophysics
Meteorology
plastic
Merging
data assimilation
Probability density function
Numerical models
Dynamical systems
Sampling
identification method

Keywords

  • Cam-clay model
  • Data assimilation
  • Inverse analysis
  • Parameter identification
  • Particle filter
  • Soil-water coupled finite element analysis (IGC: E2/E13)

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Civil and Structural Engineering

Cite this

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.

In: Soils and Foundations, Vol. 52, No. 2, 04.2012, p. 279-298.

Research output: Contribution to journalArticle

Shuku, Takayuki ; Murakami, Akira ; Nishimura, Shin-ichi ; Fujisawa, Kazunori ; Nakamura, Kazuyuki. / Parameter identification for Cam-clay model in partial loading model tests using the particle filter. In: Soils and Foundations. 2012 ; Vol. 52, No. 2. pp. 279-298.
@article{87f64d69ae784768be96d9caef1aad9b,
title = "Parameter identification for Cam-clay model in partial loading model tests using the particle filter",
abstract = "Data assimilation is a versatile methodology, developed in the earth sciences, such as geophysics, meteorology, and oceanography, for estimating the state of a dynamic system of interest by merging sparse observation data into a numerical model for the system. In particular, the data assimilation method referred to as the particle filter (PF) can be applied to nonlinear and non-Gaussian problems, and it holds the greatest potential for application to geotechnical problems. The objective of this study is to demonstrate the theoretical and the practical effectiveness of the PF for a geotechnical problem, i.e., applying the methodology to numerical experiments and actual model tests to identify the parameters of elasto-plastic geomaterials. Since the mechanical behavior of soils depends on both the current stress and the recent stress history of the soil, the sampling method called SIS, which can take into account the stress history experienced by soils, identifies the parameters of elasto-plastic geomaterials remarkably well. The results of the numerical tests have shown that the parameters identified by the PF based on the SIS have converged into their true values, and the approach presented in this study has shown great promise as an accurate parameter identification method for elasto-plastic geomaterials. Moreover, the simulation results using the identified parameters were close to the actual measurement data, and long-term predictions with high accuracy could be achieved, even though short-term measurement data were used. The PF approach produces more information about the parameters of interest than simple estimated values obtained from optimization methods. Namely, the identification comes in the form of probability density functions.",
keywords = "Cam-clay model, Data assimilation, Inverse analysis, Parameter identification, Particle filter, Soil-water coupled finite element analysis (IGC: E2/E13)",
author = "Takayuki Shuku and Akira Murakami and Shin-ichi Nishimura and Kazunori Fujisawa and Kazuyuki Nakamura",
year = "2012",
month = "4",
doi = "10.1016/j.sandf.2012.02.006",
language = "English",
volume = "52",
pages = "279--298",
journal = "Soils and Foundations",
issn = "0038-0806",
publisher = "Japanese Geotechnical Society",
number = "2",

}

TY - JOUR

T1 - Parameter identification for Cam-clay model in partial loading model tests using the particle filter

AU - Shuku, Takayuki

AU - Murakami, Akira

AU - Nishimura, Shin-ichi

AU - Fujisawa, Kazunori

AU - Nakamura, Kazuyuki

PY - 2012/4

Y1 - 2012/4

N2 - Data assimilation is a versatile methodology, developed in the earth sciences, such as geophysics, meteorology, and oceanography, for estimating the state of a dynamic system of interest by merging sparse observation data into a numerical model for the system. In particular, the data assimilation method referred to as the particle filter (PF) can be applied to nonlinear and non-Gaussian problems, and it holds the greatest potential for application to geotechnical problems. The objective of this study is to demonstrate the theoretical and the practical effectiveness of the PF for a geotechnical problem, i.e., applying the methodology to numerical experiments and actual model tests to identify the parameters of elasto-plastic geomaterials. Since the mechanical behavior of soils depends on both the current stress and the recent stress history of the soil, the sampling method called SIS, which can take into account the stress history experienced by soils, identifies the parameters of elasto-plastic geomaterials remarkably well. The results of the numerical tests have shown that the parameters identified by the PF based on the SIS have converged into their true values, and the approach presented in this study has shown great promise as an accurate parameter identification method for elasto-plastic geomaterials. Moreover, the simulation results using the identified parameters were close to the actual measurement data, and long-term predictions with high accuracy could be achieved, even though short-term measurement data were used. The PF approach produces more information about the parameters of interest than simple estimated values obtained from optimization methods. Namely, the identification comes in the form of probability density functions.

AB - Data assimilation is a versatile methodology, developed in the earth sciences, such as geophysics, meteorology, and oceanography, for estimating the state of a dynamic system of interest by merging sparse observation data into a numerical model for the system. In particular, the data assimilation method referred to as the particle filter (PF) can be applied to nonlinear and non-Gaussian problems, and it holds the greatest potential for application to geotechnical problems. The objective of this study is to demonstrate the theoretical and the practical effectiveness of the PF for a geotechnical problem, i.e., applying the methodology to numerical experiments and actual model tests to identify the parameters of elasto-plastic geomaterials. Since the mechanical behavior of soils depends on both the current stress and the recent stress history of the soil, the sampling method called SIS, which can take into account the stress history experienced by soils, identifies the parameters of elasto-plastic geomaterials remarkably well. The results of the numerical tests have shown that the parameters identified by the PF based on the SIS have converged into their true values, and the approach presented in this study has shown great promise as an accurate parameter identification method for elasto-plastic geomaterials. Moreover, the simulation results using the identified parameters were close to the actual measurement data, and long-term predictions with high accuracy could be achieved, even though short-term measurement data were used. The PF approach produces more information about the parameters of interest than simple estimated values obtained from optimization methods. Namely, the identification comes in the form of probability density functions.

KW - Cam-clay model

KW - Data assimilation

KW - Inverse analysis

KW - Parameter identification

KW - Particle filter

KW - Soil-water coupled finite element analysis (IGC: E2/E13)

UR - http://www.scopus.com/inward/record.url?scp=84871018714&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84871018714&partnerID=8YFLogxK

U2 - 10.1016/j.sandf.2012.02.006

DO - 10.1016/j.sandf.2012.02.006

M3 - Article

AN - SCOPUS:84871018714

VL - 52

SP - 279

EP - 298

JO - Soils and Foundations

JF - Soils and Foundations

SN - 0038-0806

IS - 2

ER -