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The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection

The effectiveness of artificial neural networks (ANNs) when applied to pattern recognition in vibration-based damage detection has been demonstrated in many studies because they are capable of providing accurate results and the reliable identification of structural damage based on modal data. Howeve...

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Main Authors:	Padil, Khairul Hazman, Bakhary, Norhisham, Hao, Hong
Format:	Article
Published:	Academic Press Ltd- Elsevier Science Ltd 2017
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/66178/ http://dx.doi.org/10.1016/j.ymssp.2016.06.007
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spelling	my.utm.661782017-07-17T01:52:18Z http://eprints.utm.my/id/eprint/66178/ The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection Padil, Khairul Hazman Bakhary, Norhisham Hao, Hong TA Engineering (General). Civil engineering (General) The effectiveness of artificial neural networks (ANNs) when applied to pattern recognition in vibration-based damage detection has been demonstrated in many studies because they are capable of providing accurate results and the reliable identification of structural damage based on modal data. However, the use of ANNs has been questioned in terms of its reliability in the face of uncertainties in measurement and modeling data. Attempts to incorporate a probabilistic method into an ANN by treating the uncertainties as normally distributed random variables has delivered promising solutions to this problem, but the probabilistic method is less straightforward in practice because it is often not possible to obtain unbiased probabilistic distributions of the uncertainties. Moreover, the probabilistic ANN method is computationally complex, especially when generating output data. In this study, a non-probabilistic ANN is proposed to address the problem of uncertainty in vibration damage detection using ANNs. The input data for the network consist of natural frequencies and mode shapes, and the output is the Young's modulus (E values), which acts as an elemental stiffness parameter (ESP). Through the interval analysis method, the noise in measured frequencies and mode shapes are considered to be coupled rather than statistically distributed. This method calculates the interval bound (lower and upper bounds) of the ESP changes based on an interval analysis method. The ANN is used to predict the output of this interval bound by considering the uncertainties in the input parameters. To establish the relationship between the input parameters and output parameters, a possibility of damage existence (PoDE) parameter is defined for the undamaged and damaged states. A stiffness reduction factor (SRF) is also used to represent changes in the stiffness parameter. A numerical model and a laboratory-tested steel portal frame demonstrate the efficacy of the method in improving the accuracy of the ANN in the presence of uncertainties. The effect of different severity levels and the influence of different noise levels on the identification results are discussed. Academic Press Ltd- Elsevier Science Ltd 2017-01-01 Article PeerReviewed Padil, Khairul Hazman and Bakhary, Norhisham and Hao, Hong (2017) The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection. Mechanical Systems and Signal Processing, 83 . pp. 194-209. ISSN 0888-3270 http://dx.doi.org/10.1016/j.ymssp.2016.06.007 DOI:10.1016/j.ymssp.2016.06.007
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Padil, Khairul Hazman Bakhary, Norhisham Hao, Hong The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection
description	The effectiveness of artificial neural networks (ANNs) when applied to pattern recognition in vibration-based damage detection has been demonstrated in many studies because they are capable of providing accurate results and the reliable identification of structural damage based on modal data. However, the use of ANNs has been questioned in terms of its reliability in the face of uncertainties in measurement and modeling data. Attempts to incorporate a probabilistic method into an ANN by treating the uncertainties as normally distributed random variables has delivered promising solutions to this problem, but the probabilistic method is less straightforward in practice because it is often not possible to obtain unbiased probabilistic distributions of the uncertainties. Moreover, the probabilistic ANN method is computationally complex, especially when generating output data. In this study, a non-probabilistic ANN is proposed to address the problem of uncertainty in vibration damage detection using ANNs. The input data for the network consist of natural frequencies and mode shapes, and the output is the Young's modulus (E values), which acts as an elemental stiffness parameter (ESP). Through the interval analysis method, the noise in measured frequencies and mode shapes are considered to be coupled rather than statistically distributed. This method calculates the interval bound (lower and upper bounds) of the ESP changes based on an interval analysis method. The ANN is used to predict the output of this interval bound by considering the uncertainties in the input parameters. To establish the relationship between the input parameters and output parameters, a possibility of damage existence (PoDE) parameter is defined for the undamaged and damaged states. A stiffness reduction factor (SRF) is also used to represent changes in the stiffness parameter. A numerical model and a laboratory-tested steel portal frame demonstrate the efficacy of the method in improving the accuracy of the ANN in the presence of uncertainties. The effect of different severity levels and the influence of different noise levels on the identification results are discussed.
format	Article
author	Padil, Khairul Hazman Bakhary, Norhisham Hao, Hong
author_facet	Padil, Khairul Hazman Bakhary, Norhisham Hao, Hong
author_sort	Padil, Khairul Hazman
title	The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection
title_short	The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection
title_full	The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection
title_fullStr	The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection
title_full_unstemmed	The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection
title_sort	use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection
publisher	Academic Press Ltd- Elsevier Science Ltd
publishDate	2017
url	http://eprints.utm.my/id/eprint/66178/ http://dx.doi.org/10.1016/j.ymssp.2016.06.007
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score	13.211869

The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection

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