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Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach
Several advantages of supplementary cementitious materials (SCMs) have led to widespread use in the concrete industry. Many various SCMs with different characteristics are used to produce sustainable concrete. Each of these materials has its specific properties and therefore plays a different role i...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369809/ https://www.ncbi.nlm.nih.gov/pubmed/35955269 http://dx.doi.org/10.3390/ma15155336 |
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author | Moradi, Nozar Tavana, Mohammad Hadi Habibi, Mohammad Reza Amiri, Moslem Moradi, Mohammad Javad Farhangi, Visar |
author_facet | Moradi, Nozar Tavana, Mohammad Hadi Habibi, Mohammad Reza Amiri, Moslem Moradi, Mohammad Javad Farhangi, Visar |
author_sort | Moradi, Nozar |
collection | PubMed |
description | Several advantages of supplementary cementitious materials (SCMs) have led to widespread use in the concrete industry. Many various SCMs with different characteristics are used to produce sustainable concrete. Each of these materials has its specific properties and therefore plays a different role in enhancing the mechanical properties of concrete. Multiple and often conflicting demands of concrete properties can be addressed by using combinations of two or more SCMs. Thus, understanding the effect of each SCM, as well as their combination in concrete, may pave the way for further utilization. This study aims to develop a robust and time-saving method based on Machine Learning (ML) to predict the compressive strength of concrete containing binary SCMs at various ages. To do so, a database containing a mixture of design, physical, and chemical properties of pozzolan and age of specimens have been collected from literature. A total of 21 mix design containing binary mixes of fly ash, metakaolin, and zeolite were prepared and experimentally tests to fill the possible gap in the literature and to increase the efficiency and accuracy of the ML-based model. The accuracy of the proposed model was shown to be accurate and ML-based model is able to predict the compressive strength of concrete containing any arbitrary SCMs at ay ages precisely. By using the model, the optimum replacement level of any combination of SCMs, as well as the behavior of binary cementitious systems containing two different SCMs, can be determined. |
format | Online Article Text |
id | pubmed-9369809 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93698092022-08-12 Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach Moradi, Nozar Tavana, Mohammad Hadi Habibi, Mohammad Reza Amiri, Moslem Moradi, Mohammad Javad Farhangi, Visar Materials (Basel) Article Several advantages of supplementary cementitious materials (SCMs) have led to widespread use in the concrete industry. Many various SCMs with different characteristics are used to produce sustainable concrete. Each of these materials has its specific properties and therefore plays a different role in enhancing the mechanical properties of concrete. Multiple and often conflicting demands of concrete properties can be addressed by using combinations of two or more SCMs. Thus, understanding the effect of each SCM, as well as their combination in concrete, may pave the way for further utilization. This study aims to develop a robust and time-saving method based on Machine Learning (ML) to predict the compressive strength of concrete containing binary SCMs at various ages. To do so, a database containing a mixture of design, physical, and chemical properties of pozzolan and age of specimens have been collected from literature. A total of 21 mix design containing binary mixes of fly ash, metakaolin, and zeolite were prepared and experimentally tests to fill the possible gap in the literature and to increase the efficiency and accuracy of the ML-based model. The accuracy of the proposed model was shown to be accurate and ML-based model is able to predict the compressive strength of concrete containing any arbitrary SCMs at ay ages precisely. By using the model, the optimum replacement level of any combination of SCMs, as well as the behavior of binary cementitious systems containing two different SCMs, can be determined. MDPI 2022-08-03 /pmc/articles/PMC9369809/ /pubmed/35955269 http://dx.doi.org/10.3390/ma15155336 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Moradi, Nozar Tavana, Mohammad Hadi Habibi, Mohammad Reza Amiri, Moslem Moradi, Mohammad Javad Farhangi, Visar Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach |
title | Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach |
title_full | Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach |
title_fullStr | Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach |
title_full_unstemmed | Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach |
title_short | Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach |
title_sort | predicting the compressive strength of concrete containing binary supplementary cementitious material using machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369809/ https://www.ncbi.nlm.nih.gov/pubmed/35955269 http://dx.doi.org/10.3390/ma15155336 |
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