Staff View: Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]

Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]

Reinforced concrete is the most widely used material for Malaysian building construction. However, the significant disadvantage of this material is it is prone to the material damage, which causes a decrease in the durability of the concrete and causes structural damage. To determine the suitable re...

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Main Authors:	Mat, Nur Haziqah, Ahmad Zahida, Athifa Aisha, Abdul Malik, Siti Nurhaliza, Azmadi, Nur Athirah Syuhada, Senin, Syahrul Fithry
Format:	Conference or Workshop Item
Language:	English
Published:	2021
Subjects:	TH Building construction Building inspection
Online Access:	https://ir.uitm.edu.my/id/eprint/56403/1/56403.pdf https://ir.uitm.edu.my/id/eprint/56403/ https://ispike2021.uitm.edu.my/
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id	my.uitm.ir.56403
record_format	eprints
spelling	my.uitm.ir.564032022-11-29T07:24:44Z https://ir.uitm.edu.my/id/eprint/56403/ Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.] Mat, Nur Haziqah Ahmad Zahida, Athifa Aisha Abdul Malik, Siti Nurhaliza Azmadi, Nur Athirah Syuhada Senin, Syahrul Fithry TH Building construction Building inspection Reinforced concrete is the most widely used material for Malaysian building construction. However, the significant disadvantage of this material is it is prone to the material damage, which causes a decrease in the durability of the concrete and causes structural damage. To determine the suitable repair technique on this material, proper identification procedure on damage classification must be executed. Currently, manual inspection performed by a qualified inspector is the primary inspection method to determine the concrete damage. The manual inspection is a process that is subjective and scarcely effective since it depends heavily on the personal experience and expertise of the inspector to interpret the damage classification. Besides its subjective nature, manual inspection is also to be a time consuming approach, dangerous, inconsistent, costly, and a laborious task. The demand of experienced inspectors also presents a challenge for the pressing lack of highly skilled and experienced construction inspectors. To overcome the issues, datasets of reinforced concrete damage images are intelligently trained and classified by selected Machine Learning algorithms such as Naïve- Bayesian, Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine. This invention can recognize a certain damage while the classification of defects is classified according to the features extracted from the images by using GLCM algorithm. The performance of these algorithms is evaluated by dividing the dataset into two sections: testing and training. Cost and time usage can be minimized by using this invention which can help the engineers or construction inspectors. This invention is a significant tool that can predict types of reinforced concrete damage accurately. 2021 Conference or Workshop Item PeerReviewed text en https://ir.uitm.edu.my/id/eprint/56403/1/56403.pdf Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]. (2021) In: International Exhibition & Symposium on Productivity, Innovation, Knowledge, Education & Design (i-SPiKe 2021). (Submitted) https://ispike2021.uitm.edu.my/
institution	Universiti Teknologi Mara
building	Tun Abdul Razak Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Mara
content_source	UiTM Institutional Repository
url_provider	http://ir.uitm.edu.my/
language	English
topic	TH Building construction Building inspection
spellingShingle	TH Building construction Building inspection Mat, Nur Haziqah Ahmad Zahida, Athifa Aisha Abdul Malik, Siti Nurhaliza Azmadi, Nur Athirah Syuhada Senin, Syahrul Fithry Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]
description	Reinforced concrete is the most widely used material for Malaysian building construction. However, the significant disadvantage of this material is it is prone to the material damage, which causes a decrease in the durability of the concrete and causes structural damage. To determine the suitable repair technique on this material, proper identification procedure on damage classification must be executed. Currently, manual inspection performed by a qualified inspector is the primary inspection method to determine the concrete damage. The manual inspection is a process that is subjective and scarcely effective since it depends heavily on the personal experience and expertise of the inspector to interpret the damage classification. Besides its subjective nature, manual inspection is also to be a time consuming approach, dangerous, inconsistent, costly, and a laborious task. The demand of experienced inspectors also presents a challenge for the pressing lack of highly skilled and experienced construction inspectors. To overcome the issues, datasets of reinforced concrete damage images are intelligently trained and classified by selected Machine Learning algorithms such as Naïve- Bayesian, Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine. This invention can recognize a certain damage while the classification of defects is classified according to the features extracted from the images by using GLCM algorithm. The performance of these algorithms is evaluated by dividing the dataset into two sections: testing and training. Cost and time usage can be minimized by using this invention which can help the engineers or construction inspectors. This invention is a significant tool that can predict types of reinforced concrete damage accurately.
format	Conference or Workshop Item
author	Mat, Nur Haziqah Ahmad Zahida, Athifa Aisha Abdul Malik, Siti Nurhaliza Azmadi, Nur Athirah Syuhada Senin, Syahrul Fithry
author_facet	Mat, Nur Haziqah Ahmad Zahida, Athifa Aisha Abdul Malik, Siti Nurhaliza Azmadi, Nur Athirah Syuhada Senin, Syahrul Fithry
author_sort	Mat, Nur Haziqah
title	Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]
title_short	Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]
title_full	Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]
title_fullStr	Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]
title_full_unstemmed	Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]
title_sort	automated system for concrete damage classification identification using various classification techniques in machine learning / nur haziqah mat ... [et al.]
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/56403/1/56403.pdf https://ir.uitm.edu.my/id/eprint/56403/ https://ispike2021.uitm.edu.my/
_version_	1751539855077146624
score	13.211869

Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]

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