Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete
Prominence of concrete is characterized by its high mechanical properties and durability, combined with multifunctionality and aesthetic appeal. Development of alternative eco-friendly or multipurpose materials has conditioned improvements in concrete mix design to optimize concrete production speed...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/14/19/5637 |
| _version_ | 1797516157884825600 |
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| author | Sofija Kekez Jan Kubica |
| author_facet | Sofija Kekez Jan Kubica |
| author_sort | Sofija Kekez |
| collection | DOAJ |
| description | Prominence of concrete is characterized by its high mechanical properties and durability, combined with multifunctionality and aesthetic appeal. Development of alternative eco-friendly or multipurpose materials has conditioned improvements in concrete mix design to optimize concrete production speed and price, as well as carbon footprint. Artificial neural networks represent a new and efficient tool in achieving optimal concrete mixtures according to its intended function. This paper addresses concrete mix design and the application of artificial neural networks (ANNs) for self-sensing concrete. The authors review concrete mix design methods and the development of ANNs for prediction of properties for various types of concrete. Furthermore, the authors present developments and applications of ANNs for prediction of compressive strength and flexural strength of carbon nanotubes/carbon nanofibers (CNT/CNF) reinforced concrete using experimental results for the learning process. The goal is to bring the ANN approach closer to a variety of concrete researchers and possibly propose the implementation of ANNs in the civil engineering practice. |
| first_indexed | 2024-03-10T06:57:21Z |
| format | Article |
| id | doaj.art-d000f6919f2e48fb9e7a0500b3d0e8ac |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T06:57:21Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-d000f6919f2e48fb9e7a0500b3d0e8ac2023-11-22T16:24:47ZengMDPI AGMaterials1996-19442021-09-011419563710.3390/ma14195637Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced ConcreteSofija Kekez0Jan Kubica1Department of Structural Engineering, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 5, 44-100 Gliwice, PolandDepartment of Structural Engineering, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 5, 44-100 Gliwice, PolandProminence of concrete is characterized by its high mechanical properties and durability, combined with multifunctionality and aesthetic appeal. Development of alternative eco-friendly or multipurpose materials has conditioned improvements in concrete mix design to optimize concrete production speed and price, as well as carbon footprint. Artificial neural networks represent a new and efficient tool in achieving optimal concrete mixtures according to its intended function. This paper addresses concrete mix design and the application of artificial neural networks (ANNs) for self-sensing concrete. The authors review concrete mix design methods and the development of ANNs for prediction of properties for various types of concrete. Furthermore, the authors present developments and applications of ANNs for prediction of compressive strength and flexural strength of carbon nanotubes/carbon nanofibers (CNT/CNF) reinforced concrete using experimental results for the learning process. The goal is to bring the ANN approach closer to a variety of concrete researchers and possibly propose the implementation of ANNs in the civil engineering practice.https://www.mdpi.com/1996-1944/14/19/5637concrete mix design methodsartificial neural networksself-sensing concreteCNT/CNF reinforced concrete |
| spellingShingle | Sofija Kekez Jan Kubica Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete Materials concrete mix design methods artificial neural networks self-sensing concrete CNT/CNF reinforced concrete |
| title | Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete |
| title_full | Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete |
| title_fullStr | Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete |
| title_full_unstemmed | Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete |
| title_short | Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete |
| title_sort | application of artificial neural networks for prediction of mechanical properties of cnt cnf reinforced concrete |
| topic | concrete mix design methods artificial neural networks self-sensing concrete CNT/CNF reinforced concrete |
| url | https://www.mdpi.com/1996-1944/14/19/5637 |
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