Seismic damage identification based on integrated artificial neural networks and wavelet transforms

In recent years, Structural Health Monitoring (SHM) has been proposed and practiced for condition assessment of structures. SHM covers shortcomings of nondestructive tests and is comprised of a sensory system, data acquisition system, and damage identification system. In this study, numerical and ex...

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Main Author:	Vafaei, Mohammadreza
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/36929/5/MohammadrezaVafaePFKA2013.pdf
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id	my-utm-ep.36929
record_format	uketd_dc
spelling	my-utm-ep.369292017-07-18T03:56:02Z Seismic damage identification based on integrated artificial neural networks and wavelet transforms 2013-06 Vafaei, Mohammadreza TA Engineering (General). Civil engineering (General) In recent years, Structural Health Monitoring (SHM) has been proposed and practiced for condition assessment of structures. SHM covers shortcomings of nondestructive tests and is comprised of a sensory system, data acquisition system, and damage identification system. In this study, numerical and experimental investigations are concentrated on the application of Artificial Neural Networks (ANNs) and Wavelet Transforms (WTs) for damage identification of civil engineering structures. As a major outcome of this research, three novel damage identification methods are developed. The first damage identification method enables the SHM systems to identify damage to cantilever structures through decomposition of mode shapes by integrating WTs and ANNs. The second damage identification method enables SHM systems to identify damage to cantilever structures via decomposition of response accelerations by means of WTs and ANNs. The third damage identification method takes advantage of only ANNs and enables the SHM systems to identify seismic-induced damage to concrete shear walls in real-time by measuring inter-storey drifts. In addition, a novel optimal strain gauge placement method for seismic health monitoring of structures is proposed. This method considers the seismicity of construction site and the importance level of structures. Results from the first method showed that when the imposed damage levels were severe, medium, and light, the proposed method could quantify them with less than 5%, 12%, and 16% errors, respectively. In addition, the second method quantified seismic-induced damage to the studied structure with an averaged error of 8%. Moreover, the third method classified damage levels of the studied concrete shear walls with a success rate of 91%. The proposed optimal strain gauge placement method reduced the number of required sensors for the studied structure from 206 sensors to 73 sensors. The obtained results demonstrated the feasibility, robustness, and efficiency of the proposed methods for damage identification of civil engineering structures. 2013-06 Thesis http://eprints.utm.my/id/eprint/36929/ http://eprints.utm.my/id/eprint/36929/5/MohammadrezaVafaePFKA2013.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Civil Engineering Faculty of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Vafaei, Mohammadreza Seismic damage identification based on integrated artificial neural networks and wavelet transforms
description	In recent years, Structural Health Monitoring (SHM) has been proposed and practiced for condition assessment of structures. SHM covers shortcomings of nondestructive tests and is comprised of a sensory system, data acquisition system, and damage identification system. In this study, numerical and experimental investigations are concentrated on the application of Artificial Neural Networks (ANNs) and Wavelet Transforms (WTs) for damage identification of civil engineering structures. As a major outcome of this research, three novel damage identification methods are developed. The first damage identification method enables the SHM systems to identify damage to cantilever structures through decomposition of mode shapes by integrating WTs and ANNs. The second damage identification method enables SHM systems to identify damage to cantilever structures via decomposition of response accelerations by means of WTs and ANNs. The third damage identification method takes advantage of only ANNs and enables the SHM systems to identify seismic-induced damage to concrete shear walls in real-time by measuring inter-storey drifts. In addition, a novel optimal strain gauge placement method for seismic health monitoring of structures is proposed. This method considers the seismicity of construction site and the importance level of structures. Results from the first method showed that when the imposed damage levels were severe, medium, and light, the proposed method could quantify them with less than 5%, 12%, and 16% errors, respectively. In addition, the second method quantified seismic-induced damage to the studied structure with an averaged error of 8%. Moreover, the third method classified damage levels of the studied concrete shear walls with a success rate of 91%. The proposed optimal strain gauge placement method reduced the number of required sensors for the studied structure from 206 sensors to 73 sensors. The obtained results demonstrated the feasibility, robustness, and efficiency of the proposed methods for damage identification of civil engineering structures.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Vafaei, Mohammadreza
author_facet	Vafaei, Mohammadreza
author_sort	Vafaei, Mohammadreza
title	Seismic damage identification based on integrated artificial neural networks and wavelet transforms
title_short	Seismic damage identification based on integrated artificial neural networks and wavelet transforms
title_full	Seismic damage identification based on integrated artificial neural networks and wavelet transforms
title_fullStr	Seismic damage identification based on integrated artificial neural networks and wavelet transforms
title_full_unstemmed	Seismic damage identification based on integrated artificial neural networks and wavelet transforms
title_sort	seismic damage identification based on integrated artificial neural networks and wavelet transforms
granting_institution	Universiti Teknologi Malaysia, Faculty of Civil Engineering
granting_department	Faculty of Civil Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/36929/5/MohammadrezaVafaePFKA2013.pdf
_version_	1747816480018268160

Seismic damage identification based on integrated artificial neural networks and wavelet transforms

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