A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform. (15th September 2017)

• Record Type: Journal Article
• Authors: Amezquita-Sanchez, Juan P.; Park, Hyo Seon; Adeli, Hojjat
• Is Part Of: Engineering structures. Volume 147(2017:Sep. 15)
• Page Start: 148
• Page End: 159
• Publication Date: 2017-09-15
• Subjects: Modal parameter identification -- Natural frequencies -- Damping ratios -- MUSIC-EWT algorithm -- Super Tall building; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2017.05.054
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A new methodology for modal parameter identification of large civil structures. It uses MUSIC-EWT algorithm and Hilbert transform. It is applied to a 123-story highrise building structure, Lotte World Tower. It is effective for modal parameter identification of superhighrise structures. It can deal with noisy signals. Abstract: 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.