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Uncertainty analysis of system identification results obtained for a seven‐story building slice tested on the UCSD‐NEES shake table
Author(s) -
Moaveni Babak,
Barbosa Andre R.,
Conte Joel P.,
Hemez François M.
Publication year - 2014
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.1577
Subject(s) - earthquake shaking table , parametric statistics , system identification , shake , identification (biology) , modal analysis , structural engineering , modal , noise (video) , nonlinear system , engineering , finite element method , statistics , computer science , mathematics , data mining , artificial intelligence , physics , mechanical engineering , botany , biology , chemistry , quantum mechanics , polymer chemistry , image (mathematics) , measure (data warehouse)
A full‐scale seven‐story reinforced concrete building section/slice was tested on the Network for Earthquake Engineering Simulation (NEES) shake table at the University of California San Diego during the period of October 2005 to January 2006. Three output‐only system identification methods were used to extract the modal parameters (natural frequencies, damping ratios, and mode shapes) of the test structure at different damage states. In this study, the performance of these system identification methods is investigated in two cases: (Case I) when these methods are applied to the measured dynamic response of the structure and (Case II) when these methods are applied to the dynamic response of the structure simulated using a three‐dimensional nonlinear finite element model thereof. In both cases, the uncertainty/variability of the identified modal parameters due to the variability of several input factors is quantified through analysis of variance (ANOVA). In addition to ANOVA, meta‐models are used for effect screening in Case II (based on the simulated data), which also capture the effects of linear interactions of the input factors. The four input factors considered in Case I are amplitude of input excitation, spatial density of measurements, length of response data used for system identification, and model order used in the parametric system identification methods. In the second case of uncertainty analysis, in addition to these four input factors, measurement noise is also considered. The results show that for all three methods considered, the amplitude of excitation is the most significant factor explaining the variability of the identified modal parameters, especially the natural frequencies. Copyright © 2013 John Wiley & Sons, Ltd.