The output data from a structure is the building block for output-only modal analysis. The structure response in the output data, however, is usually contaminated with noise. Naturally, the success of output-only methods in determining the modal parameters of a structure depends on noise level. In this paper, the possibility and accuracy of identifying the modal parameters of a simply supported beam in the presence of noise has been discussed. The output-only modal analysis method with frequency domain decomposition was used and output data with various noise levels were considered. Initially, finite element modal analysis was used to determine the modal parameters for the beam which were afterwards enforced as the reference modal parameters. Then, appropriate input was applied to the beam and the acceleration signals of different nodes were produced through finite element transient analysis. In order to simulate noisy data, noises with different power levels were generated and added to the signals. Finally, the modal parameters were obtained by frequency domain decomposition method. The results showed that the modal parameters corresponding to the first vibration mode could only be identified with acceptable validity at low to moderate noise levels, whereas for higher modes, the modal parameters can be correctly obtained even at high noise levels.

Modal parameters of large civil engineering structures such as modal damping ratios (MDRs) are determined mainly by output-only modal identification. In this paper, MDRs of a double layer grid were obtained using output-only modal identification. For this purpose, a double layer grid constructed from ball-joint system was tested. Doing some random tapping on the structure, the acceleration response in multiple locations was measured. The acquired data was processed using output-only modal identification to arrive at MDRs. The MDRs corresponding to the first eight modes of the grid were extracted by five output-only modal identification techniques; namely enhanced frequency domain decomposition (EFDD), curve-fit frequency domain decomposition (CFDD) and three different methods of data-driven stochastic subspace identification. To determine the appropriate model order used in SSI methods, a sensitivity analysis was carried out and the resulting number of order was 200. The proper frequency resolution of 1600 was also determined to estimate the MDRs of the grid by EFDD and CFDD. The results showed that the MDRs of the grid, obtained from different methods, are in good agreement with each other. The grid has very low MDRs, as the MDRs of the modes measured from different methods varied from 0.06% to 0.11%.