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Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing...

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Autores principales: Amin, Muhammad Nasir, Khan, Kaffayatullah, Ahmad, Waqas, Javed, Muhammad Faisal, Qureshi, Hisham Jahangir, Saleem, Muhammad Umair, Qadir, Muhammad Ghulam, Faraz, Muhammad Iftikhar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147713/
https://www.ncbi.nlm.nih.gov/pubmed/35632011
http://dx.doi.org/10.3390/polym14102128
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author Amin, Muhammad Nasir
Khan, Kaffayatullah
Ahmad, Waqas
Javed, Muhammad Faisal
Qureshi, Hisham Jahangir
Saleem, Muhammad Umair
Qadir, Muhammad Ghulam
Faraz, Muhammad Iftikhar
author_facet Amin, Muhammad Nasir
Khan, Kaffayatullah
Ahmad, Waqas
Javed, Muhammad Faisal
Qureshi, Hisham Jahangir
Saleem, Muhammad Umair
Qadir, Muhammad Ghulam
Faraz, Muhammad Iftikhar
author_sort Amin, Muhammad Nasir
collection PubMed
description The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na(2)SiO(3)/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study’s aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient (R(2)). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts.
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spelling pubmed-91477132022-05-29 Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches Amin, Muhammad Nasir Khan, Kaffayatullah Ahmad, Waqas Javed, Muhammad Faisal Qureshi, Hisham Jahangir Saleem, Muhammad Umair Qadir, Muhammad Ghulam Faraz, Muhammad Iftikhar Polymers (Basel) Article The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na(2)SiO(3)/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study’s aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient (R(2)). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts. MDPI 2022-05-23 /pmc/articles/PMC9147713/ /pubmed/35632011 http://dx.doi.org/10.3390/polym14102128 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
Amin, Muhammad Nasir
Khan, Kaffayatullah
Ahmad, Waqas
Javed, Muhammad Faisal
Qureshi, Hisham Jahangir
Saleem, Muhammad Umair
Qadir, Muhammad Ghulam
Faraz, Muhammad Iftikhar
Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
title Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
title_full Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
title_fullStr Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
title_full_unstemmed Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
title_short Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
title_sort compressive strength estimation of geopolymer composites through novel computational approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147713/
https://www.ncbi.nlm.nih.gov/pubmed/35632011
http://dx.doi.org/10.3390/polym14102128
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