Effect of Load Path on Parameter Identification for Plasticity Models using Bayesian Methods
To evaluate the cyclic behavior under different loading conditions
using the kinematic and isotrop... more To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model parameters are identified by applying an estimation using Bayes’s theorem. The Gauss-Markov-Kalman filter using functional approximation is introduced and employed to estimate the model parameters in the Bayesian setting. Identified parameters are compared with the true parameters in the simulation, and the efficiency of the identification method is discussed. At the end, the effect of the load path on the parameter identification is investigated.
Parameter Identification in Viscoplasticity using Transitional Markov Chain Monte Carlo Method
To evaluate the cyclic behavior under different loading conditions using the kinematic and isotro... more To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplas-tic material model is employed. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model parameters are identified by applying an estimation using Bayes's theorem. The Transitional Markov Chain Monte Carlo method (TMCMC) is introduced and employed to estimate the model parameters in the Bayesian setting. The uniform distributions of the parameters representing their priors are considered which literally means no knowledge of the parameters is available. Identified parameters are compared with the true parameters in the simulation, and the efficiency of the identification method is discussed. In fact, the main purpose of this study is to observe the possibility of identifying the model and hardening parameters of a viscoplastic model as a very high non-linear model with only a surface 1 displacement measurement vector in the Bayesian setting using TMCMC and evaluate the number of measurements needed for a very acceptable estimation of the uncertain parameters of the model.
Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage
The state of materials and accordingly the properties of structures are changing over the period ... more The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its lifetime. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly 1 non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.
Bayesian Parameter Determination of a CT-Test described by a Viscoplastic-Damage Model considering the Model Error
The state of materials and accordingly the properties of structures are changing over the period ... more The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its lifetime. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach. Identified parameters by using this Bayesian approach are compared with the true parameters in the simulation and with each other, and the efficiency of the identification method is discussed. The aim of this study is to observe whether the mentioned method is suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, for a real structural specimen using a limited surface displacement measurement vector gained by Digital Image Correlation (DIC) and see how much information is indeed 1 needed to estimate the parameters accurately even by considering the model error and whether this approach can also practically be used for health monitoring purposes before the occurrence of severe damage and collapse.
To evaluate the cyclic behavior under different loading conditions using the kinematic and isotro... more To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model parameters are identified by applying an estimation using Bayes's theorem. The Transitional Markov Chain Monte Carlo method (TMCMC) is introduced and employed to estimate the model parameters in the Bayesian setting. The uniform distributions of the parameters representing their priors are considered which literally means no knowledge of the parameters is av...
The state of materials and accordingly the properties of structures are changing over the period ... more The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach as this method is determined as the most efficient method for time consumi...
Effect of Load Path on Parameter Identification for Plasticity Models using Bayesian Methods
To evaluate the cyclic behavior under different loading conditions
using the kinematic and isotrop... more To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model parameters are identified by applying an estimation using Bayes’s theorem. The Gauss-Markov-Kalman filter using functional approximation is introduced and employed to estimate the model parameters in the Bayesian setting. Identified parameters are compared with the true parameters in the simulation, and the efficiency of the identification method is discussed. At the end, the effect of the load path on the parameter identification is investigated.
Parameter Identification in Viscoplasticity using Transitional Markov Chain Monte Carlo Method
To evaluate the cyclic behavior under different loading conditions using the kinematic and isotro... more To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplas-tic material model is employed. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model parameters are identified by applying an estimation using Bayes's theorem. The Transitional Markov Chain Monte Carlo method (TMCMC) is introduced and employed to estimate the model parameters in the Bayesian setting. The uniform distributions of the parameters representing their priors are considered which literally means no knowledge of the parameters is available. Identified parameters are compared with the true parameters in the simulation, and the efficiency of the identification method is discussed. In fact, the main purpose of this study is to observe the possibility of identifying the model and hardening parameters of a viscoplastic model as a very high non-linear model with only a surface 1 displacement measurement vector in the Bayesian setting using TMCMC and evaluate the number of measurements needed for a very acceptable estimation of the uncertain parameters of the model.
Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage
The state of materials and accordingly the properties of structures are changing over the period ... more The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its lifetime. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly 1 non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.
Bayesian Parameter Determination of a CT-Test described by a Viscoplastic-Damage Model considering the Model Error
The state of materials and accordingly the properties of structures are changing over the period ... more The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its lifetime. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach. Identified parameters by using this Bayesian approach are compared with the true parameters in the simulation and with each other, and the efficiency of the identification method is discussed. The aim of this study is to observe whether the mentioned method is suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, for a real structural specimen using a limited surface displacement measurement vector gained by Digital Image Correlation (DIC) and see how much information is indeed 1 needed to estimate the parameters accurately even by considering the model error and whether this approach can also practically be used for health monitoring purposes before the occurrence of severe damage and collapse.
To evaluate the cyclic behavior under different loading conditions using the kinematic and isotro... more To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model parameters are identified by applying an estimation using Bayes's theorem. The Transitional Markov Chain Monte Carlo method (TMCMC) is introduced and employed to estimate the model parameters in the Bayesian setting. The uniform distributions of the parameters representing their priors are considered which literally means no knowledge of the parameters is av...
The state of materials and accordingly the properties of structures are changing over the period ... more The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach as this method is determined as the most efficient method for time consumi...
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Drafts by Ehsan Adeli
using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic
material model is employed. The parameters of a constitutive model
are usually identified by minimization of the distance between model response
and experimental data. However, measurement errors and differences in the
specimens lead to deviations in the determined parameters. In this article
the Choboche model is used and a stochastic simulation technique is applied
to generate artificial data which exhibit the same stochastic behavior
as experimental data. Then the model parameters are identified by applying
an estimation using Bayes’s theorem. The Gauss-Markov-Kalman filter using
functional approximation is introduced and employed to estimate the model
parameters in the Bayesian setting. Identified parameters are compared with
the true parameters in the simulation, and the efficiency of the identification
method is discussed. At the end, the effect of the load path on the parameter
identification is investigated.
Papers by Ehsan Adeli
using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic
material model is employed. The parameters of a constitutive model
are usually identified by minimization of the distance between model response
and experimental data. However, measurement errors and differences in the
specimens lead to deviations in the determined parameters. In this article
the Choboche model is used and a stochastic simulation technique is applied
to generate artificial data which exhibit the same stochastic behavior
as experimental data. Then the model parameters are identified by applying
an estimation using Bayes’s theorem. The Gauss-Markov-Kalman filter using
functional approximation is introduced and employed to estimate the model
parameters in the Bayesian setting. Identified parameters are compared with
the true parameters in the simulation, and the efficiency of the identification
method is discussed. At the end, the effect of the load path on the parameter
identification is investigated.