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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidade Federal de Viçosa (UFV)
2024-05-01
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| Series: | The Journal of Engineering and Exact Sciences |
| Subjects: | |
| Online Access: | https://periodicos.ufv.br/jcec/article/view/18818 |
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| Summary: | 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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| ISSN: | 2527-1075 |