### A Comparative Review on Operational Modal Analysis Methods

#### Abstract

**Abstract.** In the present study we investigate development of operational modal analysis (OMA), different OMA techniques as well as relevant issues and experimental studies. Furthermore, we classify previously performed studies have been conducted so far and consider the most important one. So, first we review the fundamental concepts. Then, the history and application of the OMA method will be examined comprehensively to observe their role in the development of the technique. Three basic methods of FDD, SSI, and Next will be discussed and the research works associated to them will be analyzed.

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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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