Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN)

Autores

  • Amar Mezidi Acoustic and Civil Engineering Laboratory LAGC, Faculty of Sciences and Technology, University of Khemis Miliana, Algeria
  • Mourad Serikma Department Civil Engineering, University of Abderahmane Mira, Béjaia, Algeria
  • Salem Merabti Acoustics and Civil Engineering Laboratory, Faculty of Science and Technology, University of Khemis-Miliana, Algeria

DOI:

https://doi.org/10.18540/jcecvl10iss4pp18818

Palavras-chave:

Mixture design method, Fresh state properties, SCC, Workability, ANN

Resumo

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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Publicado

2024-05-24

Como Citar

Mezidi, A., Serikma, M., & Merabti, S. (2024). Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN). The Journal of Engineering and Exact Sciences, 10(4), 18818. https://doi.org/10.18540/jcecvl10iss4pp18818

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