Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error

Abstract

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 consuming finite element model updating problems among filtering and random walk approaches. The parameters identified using this Bayesian approach are compared with the true parameters in the simulation, and further, 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 to see how much information is indeed 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.

Detection and localization of multiple small damages in beam

“…Some researchers have used model updating methods based on iterative process or drawn a comparison with a numerical model and acquired better identification by considering more number of nodes. [10][11][12] However, it entails very accurate model to generate meaningfully precise results. To overcome this problem, researchers investigated ways of making traditional methods baseline-free.…”

An ensemble classifier for vibration-based quality monitoring

“…Quality monitoring is a crucial step in any manufacturing process. In this regard, several methods have been developed based on X-ray CT and µCT [1][2][3], ultrasonic [4], thermography [5,6], and also vibrationbased methods [7,8]. Extensive reviews of different methods can be found in [9,10].…”

Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA MAT_187_SAMP-1 Considering Failure with GISSMO

“…To additionally examine the accuracy of the identified MP and their correlation, Unger and Könke [27] used a Bayesian neural network [28] for the direct inverse MPI. Furthermore, Adeli et al [29] used a Bayesian approach to determine the parameters of a viscoplastic damage model. Meißner et al [15] showed the applicability of the NN-based method for the prediction of a yield curve for the simulation of a thermoplastic polymer as well as examined influences on the prediction accuracy.…”