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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10544 |
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| author | Marzena Kurpińska Leszek Kułak Tadeusz Miruszewski Marcin Byczuk |
| author_facet | Marzena Kurpińska Leszek Kułak Tadeusz Miruszewski Marcin Byczuk |
| author_sort | Marzena Kurpińska |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:44:17Z |
| format | Article |
| id | doaj.art-84f28979e3b54e19862fff587800159c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:44:17Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-84f28979e3b54e19862fff587800159c2023-11-22T22:15:19ZengMDPI AGApplied Sciences2076-34172021-11-0111221054410.3390/app112210544Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight ConcreteMarzena Kurpińska0Leszek Kułak1Tadeusz Miruszewski2Marcin Byczuk3Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233 Gdańsk, PolandFaculty of Applied Physics and Mathematics, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233 Gdańsk, PolandFaculty of Applied Physics and Mathematics, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233 Gdańsk, PolandFaculty of Applied Physics and Mathematics, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233 Gdańsk, PolandPredicting 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.https://www.mdpi.com/2076-3417/11/22/10544artificial neural networksthermal conductivitylightweight concretelightweight aggregatepredicting properties |
| spellingShingle | Marzena Kurpińska Leszek Kułak Tadeusz Miruszewski Marcin Byczuk Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete Applied Sciences artificial neural networks thermal conductivity lightweight concrete lightweight aggregate predicting properties |
| title | 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_short | 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 |
| topic | artificial neural networks thermal conductivity lightweight concrete lightweight aggregate predicting properties |
| url | https://www.mdpi.com/2076-3417/11/22/10544 |
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