Bayesian Identification of Elastic Parameters in Composite Laminates Applying Lamb Waves Monitoring

Słoński, Marek

doi:10.7712/120115.3692.2814

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Only elastic moduli are identified without considering the error. Slonski et al [3] also applied a sequential particle filter on an elastic model and Young's modulus was estimated, where the Bayesian setting was compared to the deterministic approach, and the Bayesian setting was preferred. The elastic modulus of a polymeric material is updated by Zhang et al [4], where a Markov Chain Monte Carlo method is used, but very high computation time is reported.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Adeli

Rosić

Matthies

et al. 2020

Metals

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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 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 non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.

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Section: Introductionmentioning

confidence: 99%

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Adeli

Rosić

Matthies

et al. 2020

Metals

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show abstract

“…Only elastic moduli are identified without considering the error. Slonski et al [18] also applied a sequential particle filter on an elastic model and Young's modulus is estimated, where the Bayesian setting is compared to the deterministic approach, and the Bayesian setting is preferred. The elastic modulus of a polymeric material is updated by Zhang et al [24], where a Markov Chain Monte Carlo method is used, but very high computation time is reported.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification in Viscoplasticity using Transitional Markov Chain Monte Carlo Method

Adeli,

Matthies

2019

Preprint

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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 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 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.

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A Bayesian approach for wavenumber identification of metamaterial beams possessing variability

Souza

Beli

Ferguson

et al. 2020

Mechanical Systems and Signal Processing

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Bayesian Identification of Elastic Parameters in Composite Laminates Applying Lamb Waves Monitoring

Cited by 3 publications

References 3 publications

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Parameter Identification in Viscoplasticity using Transitional Markov Chain Monte Carlo Method

A Bayesian approach for wavenumber identification of metamaterial beams possessing variability

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