A well-known property of neural networks is robustness in the presence of noise, and it is hoped to exploit this property for model updating purposes.
Advances in model update frequency.
These methods are both elaborated for the specific case of vibration-based finite element model updating for damage assessment purposes.
For the purposes of model updating, this study uses the recursive-updating process that is based on forecasting the countries' (regions') economies by exogenously shocking the baseline model with projections of macroeconomic variables.
Modal characteristics of the system can be used for a variety of purposes, including model updates, damage assessment, active control and original design re-evaluation.
A Bayesian probabilistic methodology for model updating is first implemented for the purpose of updating the structural model using dynamic data.
Comparisons are fundamental to AR system within the AEC industry to be able to monitor projects and defect detections, therefore, our testing of five comparison purposes (progress monitoring, defect detection, evaluating the model, updating the model, validating the model) need to be further investigated.
The knowledge of the modal parameters can serve various purposes including structural modification (Maia and Silva 1997), assessment of the structural integrity and reliability (Melchers 1999), structural health monitoring (Doebling et al. 1998) and model updating (Friswell and Mottershead 1995).
A set of dedicated experimental and numerical tools are developed for this purpose: first, the design of an experiment, then two-scale displacement field measurements by digital image correlation with dedicated post-processing techniques and finally a model updating technique.
Model updating is, therefore necessary so that updated model should replicate the physical system.
