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: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| 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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| Summary: | 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 |
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