A multi‐reference‐based mode selection approach for the implementation of NExT–ERA in modal‐based damage detection. Issue 8 (14th January 2014)

• Record Type: Journal Article
• Authors: Mahmood, S M Faisal; Haritos, Nicholas; Gad, Emad; Zhang, Lihai
• Is Part Of: Structural control and health monitoring. Volume 21:Issue 8(2014)
• Page Start: 1137
• Page End: 1153
• Publication Date: 2014-01-14
• Subjects: Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.1638
• Languages: English
• ISSNs: 1545-2255

Abstract

<abstract abstract-type="main"> <title>SUMMARY</title> <p>A modal‐based global method for identification, localization and quantification of damage of a shear frame structure is presented in this paper. The method involves identification of modal parameters from the vibration responses of the structure only (output only) using a combination of natural excitation technique (NExT) and eigensystem realization algorithm (ERA), and determination of stiffness parameters using very few identified modes by employing a least squares solution procedure. Finally, damage is indicated by comparing the stiffnesses of the (possibly) damaged structure with that of the undamaged structure. The implementation of NExT–ERA requires a reference channel for modal identification, but several limitations of using a single reference channel have been identified from the investigations of this study. To overcome the limitations, a new approach called <italic>multi‐reference‐based mode selection</italic> is proposed in this paper. The approach makes use of all the available DOFs, but one at a time, as reference channels and identifies the best representative modes of the structure by a mode screening process and a mode separation process. The approach is also automated for expediency and possible real‐time application. The presented damage detection methodology is implemented on the analytical phase I and experimental phase II of the IASC‐ASCE SHM benchmark problem and shows an accuracy of around 90% in damage detection. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>