Modal identification is a technique that can assess modal properties of structures based on vibration data. This technique can be categorized into known and unknown input modal identification. Known input modal identification, e.g. forced vibration tests, is more economically demanding because of the need of special devices to generate artificial loading but the data obtained has higher signal-to-noise ratio. Unknown input modal identification, e.g. ambient vibration, could be performed economically with structures under working conditions. This study employs a fast Bayesian FFT method to not only identify the modal parameters, such as natural frequencies and damping ratios, but also quantify the uncertainties associated with the modal identification results. This provides a tool to investigate the uncertainties in the modal identification. In this study two numerical examples are used to generate synthetic data for investigating and comparing the uncertainties in the known and unknown input modal identification.
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