Multistage artificial neural network in structural damage detection

This study addressed two main current issues in the area of vibration-based damage detection. The first issue was the development of a pragmatic method for damage detection through the use of a limited number of measurements. A full set of measurements was required to establish the reliable result,...

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Main Author:	Goh, Lyn Dee
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/54716/1/GohLynDeePFKA2015.pdf
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id	my-utm-ep.54716
record_format	uketd_dc
spelling	my-utm-ep.547162020-11-03T09:07:51Z Multistage artificial neural network in structural damage detection 2015-06 Goh, Lyn Dee TA Engineering (General). Civil engineering (General) This study addressed two main current issues in the area of vibration-based damage detection. The first issue was the development of a pragmatic method for damage detection through the use of a limited number of measurements. A full set of measurements was required to establish the reliable result, especially when mode shape and frequency were used as indicators for damage detection. However, this condition is usually difficult to achieve in real-life applications. Hence, in this study, a multistage artificial neural network (ANN) was employed to predict the unmeasured data at all the unmeasured point locations to obtain full measurement before proceeding to damage detection. The accuracy and efficiency of the proposed method for damage detection was investigated. Furthermore, the sensitivity of the number of measurement points in the proposed method was also investigated through a parametric study. The second issue was the integration of the uncertainties into the proposed multistage ANN. The existence of uncertainties is inevitable in practical applications because of modelling and measurement errors. These uncertainties were incorporated into the multistage ANN through a probabilistic approach. The results were in the means of the probability of damage existence, which were computed using the Rosenblueth’s point-estimate method. The results of this study evidenced that the multistage ANN was capable of predicting the unmeasured data at the unmeasured point locations, and subsequently, was successful in predicting the damage locations and severities. The incorporation of uncertainties into the multistage ANN further improved the proposed method. The results were supported through the demonstration of numerical examples and an experimental example of a prestressed concrete panel. It is concluded that the proposed method has great potential to overcome the issue of using a limited number of sensors in the vibrationbased damaged detection field. 2015-06 Thesis http://eprints.utm.my/id/eprint/54716/ http://eprints.utm.my/id/eprint/54716/1/GohLynDeePFKA2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:94651 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) Goh, Lyn Dee Multistage artificial neural network in structural damage detection
description	This study addressed two main current issues in the area of vibration-based damage detection. The first issue was the development of a pragmatic method for damage detection through the use of a limited number of measurements. A full set of measurements was required to establish the reliable result, especially when mode shape and frequency were used as indicators for damage detection. However, this condition is usually difficult to achieve in real-life applications. Hence, in this study, a multistage artificial neural network (ANN) was employed to predict the unmeasured data at all the unmeasured point locations to obtain full measurement before proceeding to damage detection. The accuracy and efficiency of the proposed method for damage detection was investigated. Furthermore, the sensitivity of the number of measurement points in the proposed method was also investigated through a parametric study. The second issue was the integration of the uncertainties into the proposed multistage ANN. The existence of uncertainties is inevitable in practical applications because of modelling and measurement errors. These uncertainties were incorporated into the multistage ANN through a probabilistic approach. The results were in the means of the probability of damage existence, which were computed using the Rosenblueth’s point-estimate method. The results of this study evidenced that the multistage ANN was capable of predicting the unmeasured data at the unmeasured point locations, and subsequently, was successful in predicting the damage locations and severities. The incorporation of uncertainties into the multistage ANN further improved the proposed method. The results were supported through the demonstration of numerical examples and an experimental example of a prestressed concrete panel. It is concluded that the proposed method has great potential to overcome the issue of using a limited number of sensors in the vibrationbased damaged detection field.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Goh, Lyn Dee
author_facet	Goh, Lyn Dee
author_sort	Goh, Lyn Dee
title	Multistage artificial neural network in structural damage detection
title_short	Multistage artificial neural network in structural damage detection
title_full	Multistage artificial neural network in structural damage detection
title_fullStr	Multistage artificial neural network in structural damage detection
title_full_unstemmed	Multistage artificial neural network in structural damage detection
title_sort	multistage artificial neural network in structural damage detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Civil Engineering
granting_department	Faculty of Civil Engineering
publishDate	2015
url	http://eprints.utm.my/id/eprint/54716/1/GohLynDeePFKA2015.pdf
_version_	1747817709752549376

Multistage artificial neural network in structural damage detection

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