Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Calibration of Soil Model Parameters Using Particle Swarm Optimization
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 () 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
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 () 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. (Autor:in) / Kalantary, F. (Autor:in) / Sadoghi Yazdi, H. (Autor:in)
International Journal of Geomechanics ; 12 ; 229-238
23.05.2011
102012-01-01 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Calibration of Soil Model Parameters Using Particle Swarm Optimization
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Calibration of Soil Model Parameters Using Particle Swarm Optimization
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