Modal Identification for High-Rise Building Structures Using Orthogonality of Filtered Response Vectors

Doyoung Kim, Byung Kwan Oh, Hyo Seon Park, Hak Bo Shim, Jiyoung Kim

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

The modal parameters of civil structures (natural frequency, mode shape, and mode damping ratio) are used for structural health monitoring (SHM), damage detection, and updating the finite element model. Long-term measurement has been necessary to conduct operational modal analysis (OMA) under various loading conditions, requiring hundreds of thousands of discrete data points for estimating the modal parameters. This article proposes an efficient output-only OMA technique in the form of filtered response vector (frv)-based modal identification, which does not need complex signal processing and matrix operations such as singular value decomposition (SVD) and lower upper (LU) factorization, thus overcoming the main drawback of the existing OMA technique. The developed OMA technique also simplifies parameters such as window or averaging, which should be designed for signal processing by the OMA operator, under well-separated frequencies and loading conditions excited by white noise. Using a simulation model and a 4-story steel frame specimen, the accuracy and applicability were verified by comparing the dynamic properties obtained by the proposed technique and traditional frequency-domain decomposition (FDD). In addition, the applicability and efficiency of the method were verified by applying the developed OMA to measured data, obtained through a field test on a 55-story, 214-m-tall high-rise building.

Original languageEnglish
Pages (from-to)1064-1084
Number of pages21
JournalComputer-Aided Civil and Infrastructure Engineering
Volume32
Issue number12
DOIs
Publication statusPublished - 2017 Dec 1

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All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics

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