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• normalization of mode shape, load estimation, and analysis in the presence of harmonic forces
• were evaluated. After, we discussed about studies on OMA methods based on wavelet
• transform, using mode indexes in OMA, order and accuracy estimation, methods based on
• cepstrum, and so forth. Furthermore, we tested one building piece of seven RC shear wall with
• full index over vibration tale NEES. Three Sys ID methods of output-only were utilized for
• extraction of modal parameters, i.e. natural frequency, damping ratio, and modes shape taken
• from the building sample. These methods include: a) natural excitation technique integrated
• with eigensystem realization algorithm (NExT-ERA), b) data-driven stochastic subspace
• identification (SSI-Data), and c) enhanced frequency domain decomposition (EFDD). In the
• current research, an analysis of variability or uncertainty of these is identification methods of
• system were done in two stages.
• The first stage is when these methods are employed on measured response of the structure and
• the second stage is when these methods are applied on response of the structure simulated using
• a 3D non-linear finite element model which is calibrated and confirmed. the input factors we
• considered in the first stage are 1) amplitude of input excitation, A) a level of non-linearity of
• response, 2) spatial density, S)number of sensors, 3) the length of the structural response data
• used in the process of data identification (L) and 4) model order utilized in identification
• methods of parametric system (O).
• In the second stage of uncertainty analysis, in addition to four input factors in the first stage
• such as measurement noise, (N) is also included. Using ANOVA test for system identification
• results based on experimental measured data I the first stage, we observed that input factor A
• has the greatest impact on changeability of modal parameters which were identified through
• using these three methods (especially the natural frequency of the first mode). In the second
• stage of uncertainty analysis, ANOVA test was used on standard deviation and mean scores (for
• a set of 100 identification runs for random description of measurement noise)of modal
• parameters which were identified through finite element simulated data . We understood that
• variability and mean scores of identified modal parameters (especially natural frequencies)
• show the greatest sensivitiy towards input factor A for all methods which shows a good
• agreement with the results of the first stage. Input factors of S and N showed the least possible
• impact on mean scores of modal parameters which are identified through Next-ERA and EFDD.
• Although the level of measurement noise (N) greatly contribute (compared with input factors) to
• variability of standard deviations in identified modal parameters, but it does not work for mean
• scores of identified modal parameters. Also, meta-models are in good agreement with identified
• modal parameters in the second stage. According to relative amplitude of coefficients ß
• (regression) of meta-models, we found out that identified natural frequencies show the highest
• sensivitiy towards input factor A (as the variance analysis results indicated). Next, input factor
• L and linear interactions AL revealed the highest sensivitiy. Moreover, we observed that
• generally modal damping ratios and MAC values showed greater sensivitiy towards input
• factors S, N, and O against natural frequencies.
• Relative amplitudes of coefficients ß relative amplitude of coefficients demonstrate that
• specified input factors have more significant impact on variability of identified modal
• parameters of the first mode compared with the parameters of higher order modes. Thus, we
• conclude that level of accuracy/certainty in system identification results not only depends upon
• estimation error of used identification methods as well as measurement noise, but it also is a
• variable of test plans (e.g. excitation domain, sensors spatial density, response length of
• measurement data, and model order). Consequently, dynamic tests must be designed so that the
• most influential input factors place in optimum or appropriate levels in order to have more
• precise and meaningful system identification results.
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