Goller B, Schuëller G I
Institute of Engineering Mechanics, University of Innsbruck, Technikerstr. 13, 6020 Innsbruck, Austria.
J Sound Vib. 2011 Dec;330(25-15):6122-6136. doi: 10.1016/j.jsv.2011.07.036.
Model updating procedures are applied in order to improve the matching between experimental data and corresponding model output. The updated, i.e. improved, finite element (FE) model can be used for more reliable predictions of the structural performance in the target mechanical environment. The discrepancies between the output of the FE-model and the results of tests are due to the uncertainties that are involved in the modeling process. These uncertainties concern the structural parameters, measurement errors, the incompleteness of the test data and also the FE-model itself. The latter type of uncertainties is often referred to as model uncertainties and is caused by simplifications of the real structure that are made in order to reduce the complexity of reality. Several approaches have been proposed for taking model uncertainties into consideration, where the focus of this manuscript will be set on the updating procedure within the Bayesian statistical framework. A numerical example involving different degrees of nonlinearity will be used for demonstrating how this type of uncertainty is considered within the Bayesian updating procedure.
应用模型更新程序以改善实验数据与相应模型输出之间的匹配度。更新后的，即改进后的有限元（FE）模型可用于更可靠地预测目标力学环境中的结构性能。有限元模型的输出与测试结果之间的差异是由于建模过程中涉及的不确定性。这些不确定性涉及结构参数、测量误差、测试数据的不完整性以及有限元模型本身。后一种类型的不确定性通常称为模型不确定性，是由为降低现实复杂性而对实际结构进行的简化所导致的。已经提出了几种考虑模型不确定性的方法，本文将重点关注贝叶斯统计框架内的更新程序。一个涉及不同非线性程度的数值示例将用于演示在贝叶斯更新程序中如何考虑这类不确定性。
