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Calibration of Soil Model Parameters Using Particle Swarm Optimization

Sadoghi Yazdi, J. / Kalantary, F. / Sadoghi Yazdi, H.

Abstract

In this paper, a neuro-fuzzy model in conjunction with particle swarm optimization (PSO) are used for calibration of soil parameters used within a linear elastic-hardening plastic constitutive model with the Drucker-Prager yield criterion. The neuro-fuzzy system is used to provide a nonlinear regression between the deviatoric stress and axial strain ( $σ_{d} - ε$ ) obtained from a consolidated drained triaxial test on samples of poorly graded sand. The soil model parameters are determined in an iterative optimization loop with PSO and an adaptive network based on a fuzzy inference system such that the equations of the linear elastic model and (where appropriate) the hardening Drucker-Prager yield criterion are simultaneously satisfied. It is shown that the model parameters can be determined with relatively high accuracy in spite of the limited insight gained by a single set of data. To verify the robustness of the technique, a second set of data obtained under different confining pressures is then used in a separate run. The outcome shows a close match with the same order of accuracy.

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Calibration of Soil Model Parameters Using Particle Swarm Optimization

Sadoghi Yazdi, J. / Kalantary, F. / Sadoghi Yazdi, H.

Abstract

In this paper, a neuro-fuzzy model in conjunction with particle swarm optimization (PSO) are used for calibration of soil parameters used within a linear elastic-hardening plastic constitutive model with the Drucker-Prager yield criterion. The neuro-fuzzy system is used to provide a nonlinear regression between the deviatoric stress and axial strain ( $σ_{d} - ε$ ) obtained from a consolidated drained triaxial test on samples of poorly graded sand. The soil model parameters are determined in an iterative optimization loop with PSO and an adaptive network based on a fuzzy inference system such that the equations of the linear elastic model and (where appropriate) the hardening Drucker-Prager yield criterion are simultaneously satisfied. It is shown that the model parameters can be determined with relatively high accuracy in spite of the limited insight gained by a single set of data. To verify the robustness of the technique, a second set of data obtained under different confining pressures is then used in a separate run. The outcome shows a close match with the same order of accuracy.

Calibration of Soil Model Parameters Using Particle Swarm Optimization

Sadoghi Yazdi, J. / Kalantary, F. / Sadoghi Yazdi, H.

Abstract

In this paper, a neuro-fuzzy model in conjunction with particle swarm optimization (PSO) are used for calibration of soil parameters used within a linear elastic-hardening plastic constitutive model with the Drucker-Prager yield criterion. The neuro-fuzzy system is used to provide a nonlinear regression between the deviatoric stress and axial strain ( $σ_{d} - ε$ ) obtained from a consolidated drained triaxial test on samples of poorly graded sand. The soil model parameters are determined in an iterative optimization loop with PSO and an adaptive network based on a fuzzy inference system such that the equations of the linear elastic model and (where appropriate) the hardening Drucker-Prager yield criterion are simultaneously satisfied. It is shown that the model parameters can be determined with relatively high accuracy in spite of the limited insight gained by a single set of data. To verify the robustness of the technique, a second set of data obtained under different confining pressures is then used in a separate run. The outcome shows a close match with the same order of accuracy.

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Title:

Calibration of Soil Model Parameters Using Particle Swarm Optimization

Contributors:

Sadoghi Yazdi, J. (author) / Kalantary, F. (author) / Sadoghi Yazdi, H. (author)

Published in:

International Journal of Geomechanics ; 12 ; 229-238

Publication date:

2011-05-23

Size:

102012-01-01 pages

ISSN:

1532-3641 , 1943-5622

DOI:

https://doi.org/10.1061/(ASCE)GM.1943-5622.0000142

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

English

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