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

Amar Mezidi; Mourad Serikma; Salem Merabti

doi:10.18540/jcecvl10iss4pp18818

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.