Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete

Predicting the properties of concrete before its design and application process allows for refining and optimizing its composition. However, the properties of lightweight concrete are much harder to predict than those of normal weight concrete, especially if the forecast concerns the insulating prop...

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Autores principales:	Marzena Kurpińska, Leszek Kułak, Tadeusz Miruszewski, Marcin Byczuk
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial neural networks thermal conductivity lightweight concrete lightweight aggregate predicting properties Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/84f28979e3b54e19862fff587800159c
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spelling	oai:doaj.org-article:84f28979e3b54e19862fff587800159c2021-11-25T16:31:10ZApplication of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete10.3390/app1122105442076-3417https://doaj.org/article/84f28979e3b54e19862fff587800159c2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10544https://doaj.org/toc/2076-3417Predicting the properties of concrete before its design and application process allows for refining and optimizing its composition. However, the properties of lightweight concrete are much harder to predict than those of normal weight concrete, especially if the forecast concerns the insulating properties of concrete with artificial lightweight aggregate (LWA). It is possible to use porous aggregates and precisely modify the composition of lightweight concrete (LWC) with specific insulating properties. In this case, it is advisable to determine the parameters of the components and perform preliminary laboratory tests, and then use theoretical methods (e.g., artificial neural networks (ANNs) to predict not only the mechanical properties of lightweight concrete, but also its thermal insulation properties. Fifteen types of lightweight concrete, differing in light filler, were tested. Lightweight aggregates with different grain diameters and lightweight aggregate grains with different porosity were used. For the tests, expanded glass was applied as a filler with very good thermal insulation properties and granulated sintered fly ash, characterized by a relatively low density and high crushing strength in the group of LWAs. The aim of the work is to demonstrate the usefulness of an ANN for the determination of the relationship between the selection of the type and quantity of LWA and porosity, density, compressive strength, and thermal conductivity (TC) of the LWC.Marzena KurpińskaLeszek KułakTadeusz MiruszewskiMarcin ByczukMDPI AGarticleartificial neural networksthermal conductivitylightweight concretelightweight aggregatepredicting propertiesTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10544, p 10544 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural networks thermal conductivity lightweight concrete lightweight aggregate predicting properties Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	artificial neural networks thermal conductivity lightweight concrete lightweight aggregate predicting properties Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Marzena Kurpińska Leszek Kułak Tadeusz Miruszewski Marcin Byczuk Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
description	Predicting the properties of concrete before its design and application process allows for refining and optimizing its composition. However, the properties of lightweight concrete are much harder to predict than those of normal weight concrete, especially if the forecast concerns the insulating properties of concrete with artificial lightweight aggregate (LWA). It is possible to use porous aggregates and precisely modify the composition of lightweight concrete (LWC) with specific insulating properties. In this case, it is advisable to determine the parameters of the components and perform preliminary laboratory tests, and then use theoretical methods (e.g., artificial neural networks (ANNs) to predict not only the mechanical properties of lightweight concrete, but also its thermal insulation properties. Fifteen types of lightweight concrete, differing in light filler, were tested. Lightweight aggregates with different grain diameters and lightweight aggregate grains with different porosity were used. For the tests, expanded glass was applied as a filler with very good thermal insulation properties and granulated sintered fly ash, characterized by a relatively low density and high crushing strength in the group of LWAs. The aim of the work is to demonstrate the usefulness of an ANN for the determination of the relationship between the selection of the type and quantity of LWA and porosity, density, compressive strength, and thermal conductivity (TC) of the LWC.
format	article
author	Marzena Kurpińska Leszek Kułak Tadeusz Miruszewski Marcin Byczuk
author_facet	Marzena Kurpińska Leszek Kułak Tadeusz Miruszewski Marcin Byczuk
author_sort	Marzena Kurpińska
title	Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
title_short	Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
title_full	Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
title_fullStr	Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
title_full_unstemmed	Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
title_sort	application of artificial neural networks to predict insulation properties of lightweight concrete
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/84f28979e3b54e19862fff587800159c
work_keys_str_mv	AT marzenakurpinska applicationofartificialneuralnetworkstopredictinsulationpropertiesoflightweightconcrete AT leszekkułak applicationofartificialneuralnetworkstopredictinsulationpropertiesoflightweightconcrete AT tadeuszmiruszewski applicationofartificialneuralnetworkstopredictinsulationpropertiesoflightweightconcrete AT marcinbyczuk applicationofartificialneuralnetworkstopredictinsulationpropertiesoflightweightconcrete
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Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete

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