Deterioration and damage identification in building structures using a novel feature selection method

Gharehbaghi, Vahid Rea and Farsangi, Ehsan Noroozinejad and Yang, T.Y. and Hajirasouliha, Iman (2021) Deterioration and damage identification in building structures using a novel feature selection method. Structures, 29. pp. 458-470.


Abstract

Identifying structural defects in complex structures is one of the main objectives in real-world structural health
monitoring (SHM) applications. In this article, a signal-based supervised methodology is proposed for detecting
deterioration and damage in building structures. This method benefits from a novel feature selection method called signal simulation-based feature selection (SSFS) algorithm, which only relies on baseline signals to extract the most sensitive features from any type of structure. The results showed that the offered methodology is capable of identifying damage and deterioration precisely, and therefore, can be a viable alternative to conventional techniques that require additional information


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Date Deposited: 13 Jan 2022 04:26
Last Modified: 07 Feb 2022 05:15
Uncontrolled Keywords: Damage identification; Deterioration; SSFS algorithm; Structural Health Monitoring (SHM)
Fields of Research (2008): 09 Engineering > 0905 Civil Engineering > 090506 Structural Engineering
Fields of Research (2020): 40 ENGINEERING > 4005 Civil engineering > 400506 Earthquake engineering
Identification Number or DOI: https://doi.org/10.1016/j.istruc.2020.11.040
URI: http://eprints.usq.edu.au/id/eprint/45457

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