Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN)
The self-compacting concrete (SCC) flows under its weight and does not require external vibration for compaction. However, its formulation requires careful calculation of its constituents. Three methods are considered: the first is an empirical method represented by an approach based on mortar opti...
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| Format: | Article |
| Language: | English |
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Universidade Federal de Viçosa (UFV)
2024-05-01
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| Series: | The Journal of Engineering and Exact Sciences |
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| Online Access: | https://periodicos.ufv.br/jcec/article/view/18818 |
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| author | Amar Mezidi Mourad Serikma Salem Merabti |
| author_facet | Amar Mezidi Mourad Serikma Salem Merabti |
| author_sort | Amar Mezidi |
| collection | DOAJ |
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The self-compacting concrete (SCC) flows under its weight and does not require external vibration for compaction. However, its formulation requires careful calculation of its constituents. Three methods are considered: the first is an empirical method represented by an approach based on mortar optimization, a solution proposed by Japanese researchers who originally introduced the concept of self-compacting concrete; the second is a graphical method by Dreux-Gorisse used for ordinary concrete, which optimizes the composition of the aggregate skeleton by selecting fractions without additives and superplasticizers; and the third is a statistical method that we developed using an approach based on Artificial Neural Networks (ANN) built from a database from previous research projects. The objective is to characterize workability through an ANN model and compare it with experimental methods. Therefore, we focused on the slump flow, L-box, and sieve stability segregation tests.
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| format | Article |
| id | doaj-art-7cdace6cc72b4f49aba9221ae46715ad |
| institution | Kabale University |
| issn | 2527-1075 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Universidade Federal de Viçosa (UFV) |
| record_format | Article |
| series | The Journal of Engineering and Exact Sciences |
| spelling | doaj-art-7cdace6cc72b4f49aba9221ae46715ad2025-02-02T19:53:41ZengUniversidade Federal de Viçosa (UFV)The Journal of Engineering and Exact Sciences2527-10752024-05-0110410.18540/jcecvl10iss4pp18818Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN)Amar Mezidi0Mourad Serikma1Salem Merabti2Acoustic and Civil Engineering Laboratory LAGC, Faculty of Sciences and Technology, University of Khemis Miliana, AlgeriaDepartment Civil Engineering, University of Abderahmane Mira, Béjaia, AlgeriaAcoustics and Civil Engineering Laboratory, Faculty of Science and Technology, University of Khemis-Miliana, Algeria The self-compacting concrete (SCC) flows under its weight and does not require external vibration for compaction. However, its formulation requires careful calculation of its constituents. Three methods are considered: the first is an empirical method represented by an approach based on mortar optimization, a solution proposed by Japanese researchers who originally introduced the concept of self-compacting concrete; the second is a graphical method by Dreux-Gorisse used for ordinary concrete, which optimizes the composition of the aggregate skeleton by selecting fractions without additives and superplasticizers; and the third is a statistical method that we developed using an approach based on Artificial Neural Networks (ANN) built from a database from previous research projects. The objective is to characterize workability through an ANN model and compare it with experimental methods. Therefore, we focused on the slump flow, L-box, and sieve stability segregation tests. https://periodicos.ufv.br/jcec/article/view/18818Mixture design methodFresh state propertiesSCCWorkabilityANN |
| spellingShingle | Amar Mezidi Mourad Serikma Salem Merabti Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN) The Journal of Engineering and Exact Sciences Mixture design method Fresh state properties SCC Workability ANN |
| title | Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN) |
| title_full | Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN) |
| title_fullStr | Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN) |
| title_full_unstemmed | Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN) |
| title_short | Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN) |
| title_sort | study of the workability of self compacting concrete scc using experimental methods and artificial neural networks ann |
| topic | Mixture design method Fresh state properties SCC Workability ANN |
| url | https://periodicos.ufv.br/jcec/article/view/18818 |
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