Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy inter...

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Autores principales:	Muhammad Nasir Amin, Muhammad Faisal Javed, Kaffayatullah Khan, Faisal I. Shalabi, Muhammad Ghulam Qadir
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	compressive strength volcanic ash mortar artificial neural network adaptive neuro fuzzy interface system Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/0dceb255397843ab8a7315de8e8b07ec
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spelling	oai:doaj.org-article:0dceb255397843ab8a7315de8e8b07ec2021-11-25T19:06:00ZModeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking10.3390/sym131120092073-8994https://doaj.org/article/0dceb255397843ab8a7315de8e8b07ec2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2009https://doaj.org/toc/2073-8994Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. The modeling was correlated with four parameters. To train and test the projected models, data for more than 150 samples were collected from the literature. Furthermore, mortar samples with varying proportions of volcanic ash were prepared in the laboratory and tested, and the results were used to validate the models. The performance of the developed models was assessed using numerous statistical measures. The results show that both the ANN and ANFIS models accurately predict the compressive strength of VAM with R-square above 0.9 and lower error statistics. The permutation feature analysis confirmed that the age of specimens affects the strength of VAM the most, followed by the water-to-cement ratio, curing temperature, and percentage of volcanic ash.Muhammad Nasir AminMuhammad Faisal JavedKaffayatullah KhanFaisal I. ShalabiMuhammad Ghulam QadirMDPI AGarticlecompressive strengthvolcanic ashmortarartificial neural networkadaptive neuro fuzzy interface systemMathematicsQA1-939ENSymmetry, Vol 13, Iss 2009, p 2009 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	compressive strength volcanic ash mortar artificial neural network adaptive neuro fuzzy interface system Mathematics QA1-939
spellingShingle	compressive strength volcanic ash mortar artificial neural network adaptive neuro fuzzy interface system Mathematics QA1-939 Muhammad Nasir Amin Muhammad Faisal Javed Kaffayatullah Khan Faisal I. Shalabi Muhammad Ghulam Qadir Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking
description	Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. The modeling was correlated with four parameters. To train and test the projected models, data for more than 150 samples were collected from the literature. Furthermore, mortar samples with varying proportions of volcanic ash were prepared in the laboratory and tested, and the results were used to validate the models. The performance of the developed models was assessed using numerous statistical measures. The results show that both the ANN and ANFIS models accurately predict the compressive strength of VAM with R-square above 0.9 and lower error statistics. The permutation feature analysis confirmed that the age of specimens affects the strength of VAM the most, followed by the water-to-cement ratio, curing temperature, and percentage of volcanic ash.
format	article
author	Muhammad Nasir Amin Muhammad Faisal Javed Kaffayatullah Khan Faisal I. Shalabi Muhammad Ghulam Qadir
author_facet	Muhammad Nasir Amin Muhammad Faisal Javed Kaffayatullah Khan Faisal I. Shalabi Muhammad Ghulam Qadir
author_sort	Muhammad Nasir Amin
title	Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking
title_short	Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking
title_full	Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking
title_fullStr	Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking
title_full_unstemmed	Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking
title_sort	modeling compressive strength of eco-friendly volcanic ash mortar using artificial neural networking
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0dceb255397843ab8a7315de8e8b07ec
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Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

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