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Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites

A material-tailored special concrete composite that uses a synthetic fiber to make the concrete ductile and imposes strain-hardening characteristics with eco-friendly ingredients is known as an “engineered geopolymer composite (EGC)”. Mix design of special concrete is always tedious, particularly wi...

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Autores principales: Kuppusamy, Yaswanth, Jayaseelan, Revathy, Pandulu, Gajalakshmi, Sathish Kumar, Veerappan, Murali, Gunasekaran, Dixit, Saurav, Vatin, Nikolai Ivanovich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146445/
https://www.ncbi.nlm.nih.gov/pubmed/35629470
http://dx.doi.org/10.3390/ma15103443
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author Kuppusamy, Yaswanth
Jayaseelan, Revathy
Pandulu, Gajalakshmi
Sathish Kumar, Veerappan
Murali, Gunasekaran
Dixit, Saurav
Vatin, Nikolai Ivanovich
author_facet Kuppusamy, Yaswanth
Jayaseelan, Revathy
Pandulu, Gajalakshmi
Sathish Kumar, Veerappan
Murali, Gunasekaran
Dixit, Saurav
Vatin, Nikolai Ivanovich
author_sort Kuppusamy, Yaswanth
collection PubMed
description A material-tailored special concrete composite that uses a synthetic fiber to make the concrete ductile and imposes strain-hardening characteristics with eco-friendly ingredients is known as an “engineered geopolymer composite (EGC)”. Mix design of special concrete is always tedious, particularly without standards. Researchers used several artificial intelligence tools to analyze and design the special concrete. This paper attempts to design the material EGC through an artificial neural network with a cross-validation technique to achieve the desired compressive and tensile strength. A database was formulated with seven mix-design influencing factors collected from the literature. The five best artificial neural network (ANN) models were trained and analyzed. A gradient descent momentum and adaptive learning rate backpropagation (GDX)–based ANN was developed to cross-validate those five best models. Upon regression analysis, ANN [2:16:16:7] model performed best, with 74% accuracy, whereas ANN [2:16:25:7] performed best in cross-validation, with 80% accuracy. The best individual outputs were “tacked-together” from the best five ANN models and were also analyzed, achieving accuracy up to 88%. It is suggested that when these seven mix-design influencing factors are involved, then ANN [2:16:25:7] can be used to predict the mix which can be cross-verified with GDX-ANN [7:14:2] to ensure accuracy and, due to the few mix trials required, help design the SHGC with lower costs, less time, and fewer materials.
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spelling pubmed-91464452022-05-29 Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites Kuppusamy, Yaswanth Jayaseelan, Revathy Pandulu, Gajalakshmi Sathish Kumar, Veerappan Murali, Gunasekaran Dixit, Saurav Vatin, Nikolai Ivanovich Materials (Basel) Article A material-tailored special concrete composite that uses a synthetic fiber to make the concrete ductile and imposes strain-hardening characteristics with eco-friendly ingredients is known as an “engineered geopolymer composite (EGC)”. Mix design of special concrete is always tedious, particularly without standards. Researchers used several artificial intelligence tools to analyze and design the special concrete. This paper attempts to design the material EGC through an artificial neural network with a cross-validation technique to achieve the desired compressive and tensile strength. A database was formulated with seven mix-design influencing factors collected from the literature. The five best artificial neural network (ANN) models were trained and analyzed. A gradient descent momentum and adaptive learning rate backpropagation (GDX)–based ANN was developed to cross-validate those five best models. Upon regression analysis, ANN [2:16:16:7] model performed best, with 74% accuracy, whereas ANN [2:16:25:7] performed best in cross-validation, with 80% accuracy. The best individual outputs were “tacked-together” from the best five ANN models and were also analyzed, achieving accuracy up to 88%. It is suggested that when these seven mix-design influencing factors are involved, then ANN [2:16:25:7] can be used to predict the mix which can be cross-verified with GDX-ANN [7:14:2] to ensure accuracy and, due to the few mix trials required, help design the SHGC with lower costs, less time, and fewer materials. MDPI 2022-05-10 /pmc/articles/PMC9146445/ /pubmed/35629470 http://dx.doi.org/10.3390/ma15103443 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
Kuppusamy, Yaswanth
Jayaseelan, Revathy
Pandulu, Gajalakshmi
Sathish Kumar, Veerappan
Murali, Gunasekaran
Dixit, Saurav
Vatin, Nikolai Ivanovich
Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites
title Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites
title_full Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites
title_fullStr Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites
title_full_unstemmed Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites
title_short Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites
title_sort artificial neural network with a cross-validation technique to predict the material design of eco-friendly engineered geopolymer composites
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146445/
https://www.ncbi.nlm.nih.gov/pubmed/35629470
http://dx.doi.org/10.3390/ma15103443
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