Reliability analysis of the complex mode indicator function and Hilbert Transform techniques for operational modal analysis

This study presents application of the CMIF and the Hilbert Transform techniques onto simulated response data obtained using a numerical model of a typical school building from Turkey. White noise is added to the data in order to achieve a noise to signal ratio of 5%. 100 Monte Carlo analysis sequences are carried out and the modal parameters (the frequencies, the mode shapes and the damping ratios) are identified at each Monte Carlo run for both techniques. The results are compared with the identifications obtained from the simulated data using stochastic subspace based system identification technique. The overall results of the study show that the mode shapes are clearly identified the best by using the CMIF technique. The damping ratios are estimated better by using the stochastic subspace based system identification technique whereas the frequencies are best determined by the CMIF. The results also show that both the CMIF and the Hilbert Transform techniques are sensitive to the type of window used as well as the averaging and the decimation process. It is apparent that the CMIF technique is as robust as the frequently used stochastic subspace based system identification technique and can be confidently used for modal parameter estimation of stiff low to mid rise reinforced concrete structures.

► CMIF, Hilbert Transform and stochastic subspace based system identification (SSID) techniques are compared.
► Response data obtained from a numerical model of a school building is processed using these methods.
► Results demonstrate that the Hilbert Transform does not bring any substantial advantage over the CMIF.
► The mode shapes and frequencies are identified best by using the CMIF technique.
► CMIF can be confidently used for modal parameter estimation of low to mid rise concrete structures.