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State-of-the-art review on advancements of data mining in structural health monitoring

To date, data mining (DM) techniques, i.e. artificial intelligence, machine learning, and statistical methods have been utilized in a remarkable number of structural health monitoring (SHM) applications. Nevertheless, there is no classification of these approaches to know the most used techniques in...

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Main Authors:	Gordan, Meisam, Sabbagh-Yazdi, Saeed-Reza, Ismail, Zubaidah, Ghaedi, Khaled, Carroll, Paraic, McCrum, Daniel, Samali, Bijan
Format:	Article
Published:	Elsevier 2022
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/43000/
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spelling	my.um.eprints.430002023-10-05T03:22:03Z http://eprints.um.edu.my/43000/ State-of-the-art review on advancements of data mining in structural health monitoring Gordan, Meisam Sabbagh-Yazdi, Saeed-Reza Ismail, Zubaidah Ghaedi, Khaled Carroll, Paraic McCrum, Daniel Samali, Bijan TA Engineering (General). Civil engineering (General) To date, data mining (DM) techniques, i.e. artificial intelligence, machine learning, and statistical methods have been utilized in a remarkable number of structural health monitoring (SHM) applications. Nevertheless, there is no classification of these approaches to know the most used techniques in SHM. For this purpose, an intensive review is carried out to classify the aforementioned techniques. In doing so, a brief background, models, functions, and classification of DM techniques are presented. To this end, wide range of researches are collected in order to demonstrate the development of DM techniques, detect the most popular DM techniques, and compare the applicability of existing DM techniques in SHM. Eventually, it is concluded that the application of artificial intelligence has the highest demand rate in SHM while the most popular algorithms including artificial neural network, genetic algorithm, fuzzy logic, and principal component analysis are utilized for damage detection of civil structures. Elsevier 2022-04 Article PeerReviewed Gordan, Meisam and Sabbagh-Yazdi, Saeed-Reza and Ismail, Zubaidah and Ghaedi, Khaled and Carroll, Paraic and McCrum, Daniel and Samali, Bijan (2022) State-of-the-art review on advancements of data mining in structural health monitoring. Measurement, 193. ISSN 0263-2241, DOI https://doi.org/10.1016/j.measurement.2022.110939 <https://doi.org/10.1016/j.measurement.2022.110939>. 10.1016/j.measurement.2022.110939
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Gordan, Meisam Sabbagh-Yazdi, Saeed-Reza Ismail, Zubaidah Ghaedi, Khaled Carroll, Paraic McCrum, Daniel Samali, Bijan State-of-the-art review on advancements of data mining in structural health monitoring
description	To date, data mining (DM) techniques, i.e. artificial intelligence, machine learning, and statistical methods have been utilized in a remarkable number of structural health monitoring (SHM) applications. Nevertheless, there is no classification of these approaches to know the most used techniques in SHM. For this purpose, an intensive review is carried out to classify the aforementioned techniques. In doing so, a brief background, models, functions, and classification of DM techniques are presented. To this end, wide range of researches are collected in order to demonstrate the development of DM techniques, detect the most popular DM techniques, and compare the applicability of existing DM techniques in SHM. Eventually, it is concluded that the application of artificial intelligence has the highest demand rate in SHM while the most popular algorithms including artificial neural network, genetic algorithm, fuzzy logic, and principal component analysis are utilized for damage detection of civil structures.
format	Article
author	Gordan, Meisam Sabbagh-Yazdi, Saeed-Reza Ismail, Zubaidah Ghaedi, Khaled Carroll, Paraic McCrum, Daniel Samali, Bijan
author_facet	Gordan, Meisam Sabbagh-Yazdi, Saeed-Reza Ismail, Zubaidah Ghaedi, Khaled Carroll, Paraic McCrum, Daniel Samali, Bijan
author_sort	Gordan, Meisam
title	State-of-the-art review on advancements of data mining in structural health monitoring
title_short	State-of-the-art review on advancements of data mining in structural health monitoring
title_full	State-of-the-art review on advancements of data mining in structural health monitoring
title_fullStr	State-of-the-art review on advancements of data mining in structural health monitoring
title_full_unstemmed	State-of-the-art review on advancements of data mining in structural health monitoring
title_sort	state-of-the-art review on advancements of data mining in structural health monitoring
publisher	Elsevier
publishDate	2022
url	http://eprints.um.edu.my/43000/
_version_	1781704663880433664
score	13.211869

State-of-the-art review on advancements of data mining in structural health monitoring

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