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Damage detection of structures subject to nonlinear effects of changing environmental conditions

Damage detection of structures subject to nonlinear effects of changing environmental conditions

Wah, William Soo Lon, Chen, Yung-Tsang, Roberts, Gethin Wyn and Elamin, Ahmed ORCID: 0000-0003-0783-5185 (2017) Damage detection of structures subject to nonlinear effects of changing environmental conditions. In: Procedia Engineering. Elsevier, pp. 248-255. ISSN 1877-7058 (doi:https://doi.org/10.1016/j.proeng.2017.04.481)

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Abstract

Damage detection of civil structures has been carried out by mainly analysing the vibration properties of the structures which change when damages occur. However, these properties are also affected by the changing environmental conditions the structures are face with, and these conditions usually produce nonlinear effects on the vibration properties. Hence, a method is proposed in this paper to analyse structures subjected to nonlinear effects of environmental conditions. The method first applies Principal Component Analysis (PCA) on a bank of damage sensitivity features, followed by applying Gaussian Mixture Model on the obtained first principal component scores to cluster the data into several linear regions. By creating a baseline for each linear region using two extreme and opposite environmental conditions, and adding new measurements to the baseline one at a time followed by applying PCA, damage detection can be achieved. The method is validated on a numerical truss structure model and on the Z24 Bridge. The results demonstrate the ability of the method to analyse structures under nonlinear environmental effects.

Item Type: Conference Proceedings
Title of Proceedings: Procedia Engineering
Uncontrolled Keywords: principal component analysis, gaussian mixture model, environmental conditions, temperature, damage detection, nonlinear
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 22 Apr 2020 20:28
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/27483

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