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 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.

中文翻译：

---

带损伤粘塑性模型参数辨识的贝叶斯方法比较

摘要 材料的状态和相应的结构性能在使用期间会发生变化，这可能会影响结构在其生命周期内的可靠性和质量。因此，系统模型参数的识别是结构健康监测内容中备受关注的课题。本构模型的参数通常通过最小化模型响应和实验数据之间的差异来确定。然而，试样中的测量误差和差异导致所确定参数的偏差。在本文中，重点是使用随机模拟技术来识别粘塑性破坏性材料的材料参数，以生成与实验数据具有相同随机行为的人工数据。建议使用贝叶斯逆方法进行参数识别，因此通过应用过渡马尔可夫链蒙特卡罗方法（TMCMC）和高斯-马尔可夫-卡尔曼滤波器（GMKF）方法来识别模型和损伤​​参数。将使用这两种贝叶斯方法识别的参数与模拟中的真实参数进行比较，并讨论识别方法的效率。本研究的目的是观察上述方法中哪一种更适合和更有效地识别材料模型的模型和损伤​​参数，作为高度非线性模型，使用有限的表面位移测量向量，看看有多少确实需要信息来准确估计参数。