Uncertainty Quantification of Complex Nonlinear Systems Using Structural Health Monitoring Techniques
Author | : Dana Nasr |
Publisher | : |
Total Pages | : |
Release | : 2018 |
ISBN-10 | : OCLC:1061208496 |
ISBN-13 | : |
Rating | : 4/5 (96 Downloads) |
Book excerpt: Structural Health Monitoring (SHM) is a multidisciplinary field used to monitor the health conditions of structures and predict damage in its early stages using periodically spaced realtime observed measurements. The presence of different sources of significant uncertainties, mainly due to model errors, parametric variability and measurement data inadequacy, is inevitable when modeling nonlinear dynamical systems with physical complexities. Therefore these uncertainties must be accurately quantified and represented within the mathematical framework of Structural Health Monitoring methods, to reduce failure risks through early detections of damage and to improve identification of the unknown system responses and parameters. In this study, two different variants of the Kalman Filter (KF) method, the ordinary Ensemble Kalman Filter (EnKF) method and the Polynomial Chaos based Ensemble Kalman Filter (PCKF) method, are implemented for uncertainty quantification and system identification pur ...