A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform

A key issue in health monitoring of smart structures is the estimation of modal parameters such as natural frequencies and damping ratios from acquired dynamic signals. In this article, a new methodology is presented for calculating the natural frequencies (NF) and damping ratios (DR) of large civil infrastructure from acquired dynamic signals using a multiple signal classification (MUSIC) algorithm, the empirical wavelet transform (EWT), and the Hilbert transform. The effectiveness of the proposed method is validated by means of three examples: a benchmark 3D 4-story steel frame structure, a benchmark problem, subjected to dynamic loading, an 8-story steel frame subjected to white noise input on a shaking table, and a 123-story highrise building structure, Lotte World Tower (LWT), under construction in Seoul, South Korea. The results demonstrate that the new methodology is accurate for estimating the NF and DR of a superhighrise building structure using low-amplitude ambient vibrations data, a complex and challenging task since the measured vibrations signals are noisy and present non-stationary characteristics. The new methodology can deal with noisy signals without degrading its ability to estimate the NF and DR of different one-of-a kind civil structures thus is particularly suitable for health monitoring of large smart structures under dynamic loading.