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From conventional to sustainable SHM: implementation of artificial intelligence in the Department of Civil Engineering, University of Malaya

Computer-based technologies and their applications pervade everywhere in real life, especially in different fields of civil engineering. For example, conventional structural health monitoring (SHM) has been rapidly upgraded to sustainable SHM using artificial intelligence. It is because conventional...

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Main Authors: Gordan, M., Ghaedi, K., Ismail, Zubaidah, Benisi, H., Hashim, Huzaifa, Ghayeb, H.H.
Format: Conference or Workshop Item
Published: 2021
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/35643/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119094297&doi=10.1109%2fIICAIET51634.2021.9573713&partnerID=40&md5=b4a0cedd0c793ced37b88c4cd399a4bd
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spelling my.um.eprints.356432025-02-13T03:50:06Z http://eprints.um.edu.my/35643/ From conventional to sustainable SHM: implementation of artificial intelligence in the Department of Civil Engineering, University of Malaya Gordan, M. Ghaedi, K. Ismail, Zubaidah Benisi, H. Hashim, Huzaifa Ghayeb, H.H. TA Engineering (General). Civil engineering (General) Computer-based technologies and their applications pervade everywhere in real life, especially in different fields of civil engineering. For example, conventional structural health monitoring (SHM) has been rapidly upgraded to sustainable SHM using artificial intelligence. It is because conventional approaches are challenged by real-time, low-cost, and quality-guaranteed SHM. In this direction, a number of innovative researches have been carried out in the Department of Civil Engineering, University of Malaya. This paper attempts to present the latest developments of SHM-based artificial intelligence in Structural Health Monitoring Research Group (StrucHMRSGroup) and Advance Shock and Vibration Research Group (ASVR). To this end, the applications of artificial neural networks, fuzzy logic, genetic algorithm, data mining, and regression analysis in SHM are presented with the aim of showing the efficiency of these methods. © 2021 IEEE. 2021 Conference or Workshop Item PeerReviewed Gordan, M. and Ghaedi, K. and Ismail, Zubaidah and Benisi, H. and Hashim, Huzaifa and Ghayeb, H.H. (2021) From conventional to sustainable SHM: implementation of artificial intelligence in the Department of Civil Engineering, University of Malaya. In: 3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021, 13-15 September 2021, Kota Kinabalu, Sabah. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119094297&doi=10.1109%2fIICAIET51634.2021.9573713&partnerID=40&md5=b4a0cedd0c793ced37b88c4cd399a4bd
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, M.
Ghaedi, K.
Ismail, Zubaidah
Benisi, H.
Hashim, Huzaifa
Ghayeb, H.H.
From conventional to sustainable SHM: implementation of artificial intelligence in the Department of Civil Engineering, University of Malaya
description Computer-based technologies and their applications pervade everywhere in real life, especially in different fields of civil engineering. For example, conventional structural health monitoring (SHM) has been rapidly upgraded to sustainable SHM using artificial intelligence. It is because conventional approaches are challenged by real-time, low-cost, and quality-guaranteed SHM. In this direction, a number of innovative researches have been carried out in the Department of Civil Engineering, University of Malaya. This paper attempts to present the latest developments of SHM-based artificial intelligence in Structural Health Monitoring Research Group (StrucHMRSGroup) and Advance Shock and Vibration Research Group (ASVR). To this end, the applications of artificial neural networks, fuzzy logic, genetic algorithm, data mining, and regression analysis in SHM are presented with the aim of showing the efficiency of these methods. © 2021 IEEE.
format Conference or Workshop Item
author Gordan, M.
Ghaedi, K.
Ismail, Zubaidah
Benisi, H.
Hashim, Huzaifa
Ghayeb, H.H.
author_facet Gordan, M.
Ghaedi, K.
Ismail, Zubaidah
Benisi, H.
Hashim, Huzaifa
Ghayeb, H.H.
author_sort Gordan, M.
title From conventional to sustainable SHM: implementation of artificial intelligence in the Department of Civil Engineering, University of Malaya
title_short From conventional to sustainable SHM: implementation of artificial intelligence in the Department of Civil Engineering, University of Malaya
title_full From conventional to sustainable SHM: implementation of artificial intelligence in the Department of Civil Engineering, University of Malaya
title_fullStr From conventional to sustainable SHM: implementation of artificial intelligence in the Department of Civil Engineering, University of Malaya
title_full_unstemmed From conventional to sustainable SHM: implementation of artificial intelligence in the Department of Civil Engineering, University of Malaya
title_sort from conventional to sustainable shm: implementation of artificial intelligence in the department of civil engineering, university of malaya
publishDate 2021
url http://eprints.um.edu.my/35643/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119094297&doi=10.1109%2fIICAIET51634.2021.9573713&partnerID=40&md5=b4a0cedd0c793ced37b88c4cd399a4bd
_version_ 1825160574033461248
score 13.244413

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