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Application of artificial neural networks to predict the compressive strength of rubberized concrete: a review

Waste rubber tires have been used in building materials to support the environment and green construction. There is a growing demand for rubberized materials as they are cost-effective and useful from a sustainable standpoint. There are several properties of the rubber tire that could be applied u...

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Main Authors: Seyed Hakima, Seyed Jamalaldin, Omar, Abdinasir Mukhtar, A Khalifa, Nasradeen, Shahidan, Shahiron, Jamaluddin, Norwati, Alshalif, Abdullah Faisal
Format: Conference or Workshop Item
Language:English
Published: 2024
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.uthm.edu.my/11750/1/P16815_2fa5341f26325adfef0b4462271bf61c%202.pdf
http://eprints.uthm.edu.my/11750/
https://doi.org/10.1063/5.0198659
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spelling my.uthm.eprints.117502024-12-17T02:29:53Z http://eprints.uthm.edu.my/11750/ Application of artificial neural networks to predict the compressive strength of rubberized concrete: a review Seyed Hakima, Seyed Jamalaldin Omar, Abdinasir Mukhtar A Khalifa, Nasradeen Shahidan, Shahiron Jamaluddin, Norwati Alshalif, Abdullah Faisal TA Engineering (General). Civil engineering (General) Waste rubber tires have been used in building materials to support the environment and green construction. There is a growing demand for rubberized materials as they are cost-effective and useful from a sustainable standpoint. There are several properties of the rubber tire that could be applied usefully such as low density, and waterproofing properties. Rubberized concrete has composed of waste rubber as natural aggregate and is an alternative solution to the use of tire rubber particles in the production of concrete. It has been proven that the addition of waste rubber tires to concrete starts to low strength and this restriction its application in structural elements but benefits to enhance the ductility, impact resistance, thermal conductivity, and acoustic properties. This paper presents a review of the recent studies on the application and development of artificial neural networks (ANNs) to predict the compressive strength of rubberized concrete. From this review, predicting the compressive strength of rubberized concrete by ANNs is generally more accurate, and their development is inexpensive and time-saving. In this review, the advantages, and limitations of A 2024-06-07 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/11750/1/P16815_2fa5341f26325adfef0b4462271bf61c%202.pdf Seyed Hakima, Seyed Jamalaldin and Omar, Abdinasir Mukhtar and A Khalifa, Nasradeen and Shahidan, Shahiron and Jamaluddin, Norwati and Alshalif, Abdullah Faisal (2024) Application of artificial neural networks to predict the compressive strength of rubberized concrete: a review. In: AIP Conference Proceedings. https://doi.org/10.1063/5.0198659
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Seyed Hakima, Seyed Jamalaldin
Omar, Abdinasir Mukhtar
A Khalifa, Nasradeen
Shahidan, Shahiron
Jamaluddin, Norwati
Alshalif, Abdullah Faisal
Application of artificial neural networks to predict the compressive strength of rubberized concrete: a review
description Waste rubber tires have been used in building materials to support the environment and green construction. There is a growing demand for rubberized materials as they are cost-effective and useful from a sustainable standpoint. There are several properties of the rubber tire that could be applied usefully such as low density, and waterproofing properties. Rubberized concrete has composed of waste rubber as natural aggregate and is an alternative solution to the use of tire rubber particles in the production of concrete. It has been proven that the addition of waste rubber tires to concrete starts to low strength and this restriction its application in structural elements but benefits to enhance the ductility, impact resistance, thermal conductivity, and acoustic properties. This paper presents a review of the recent studies on the application and development of artificial neural networks (ANNs) to predict the compressive strength of rubberized concrete. From this review, predicting the compressive strength of rubberized concrete by ANNs is generally more accurate, and their development is inexpensive and time-saving. In this review, the advantages, and limitations of A
format Conference or Workshop Item
author Seyed Hakima, Seyed Jamalaldin
Omar, Abdinasir Mukhtar
A Khalifa, Nasradeen
Shahidan, Shahiron
Jamaluddin, Norwati
Alshalif, Abdullah Faisal
author_facet Seyed Hakima, Seyed Jamalaldin
Omar, Abdinasir Mukhtar
A Khalifa, Nasradeen
Shahidan, Shahiron
Jamaluddin, Norwati
Alshalif, Abdullah Faisal
author_sort Seyed Hakima, Seyed Jamalaldin
title Application of artificial neural networks to predict the compressive strength of rubberized concrete: a review
title_short Application of artificial neural networks to predict the compressive strength of rubberized concrete: a review
title_full Application of artificial neural networks to predict the compressive strength of rubberized concrete: a review
title_fullStr Application of artificial neural networks to predict the compressive strength of rubberized concrete: a review
title_full_unstemmed Application of artificial neural networks to predict the compressive strength of rubberized concrete: a review
title_sort application of artificial neural networks to predict the compressive strength of rubberized concrete: a review
publishDate 2024
url http://eprints.uthm.edu.my/11750/1/P16815_2fa5341f26325adfef0b4462271bf61c%202.pdf
http://eprints.uthm.edu.my/11750/
https://doi.org/10.1063/5.0198659
_version_ 1818836387354378240
score 13.223943

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