Prediction of the compressive strength of self‐compacting concrete using artificial neural networks based on rheological parameters

Self‐compacting concrete (SCC) is a fluid concrete designed to flow freely through reinforcements in order to completely fill the formwork. The appearance of this type of concrete increases the need to precisely characterize its compressive strength as a function of their behavior during flow. This...

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Bibliographic Details
Published in:Structural concrete : journal of the FIB Vol. 23; no. 6; pp. 3864 - 3876
Main Authors: Asri, Yousef, Benaicha, Mouhcine, Zaher, Mounir, Hafidi Alaoui, Adil
Format: Journal Article
Language:English
Published: Weinheim WILEY‐VCH Verlag GmbH & Co. KGaA 01.12.2022
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Subjects:
Artificial neural networks
Compressive strength
Formwork
Mathematical models
Optimization
Parameters
Pearson's correlation
rheological parameters
Rheological properties
Self-compacting concrete
Yield stress
ISSN:1464-4177, 1751-7648
Online Access:Get full text
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Summary:Self‐compacting concrete (SCC) is a fluid concrete designed to flow freely through reinforcements in order to completely fill the formwork. The appearance of this type of concrete increases the need to precisely characterize its compressive strength as a function of their behavior during flow. This article summarizes the use of artificial neural networks for the modelization of compressive strength, at 28 days, of SCC based on rheological parameters found during empirical tests (slump flow diameter, H2/H1 ratio of L‐Box, and V‐Funnel flow time) and the values of plastic viscosity and the yield stress. The objective of this numerical and experimental study is to find an optimal model to modelize the compressive strength. Thus, the results obtained after training of several models are showed that the architecture of the optimum with two hidden layers model is 5‐50‐50‐1 with a Pearson's correlation R = 97.58%.
Bibliography:Discussion on this paper must be submitted within two months of the print publication. The discussion will then be published in print, along with the authors’ closure, if any, approximately nine months after the print publication.
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ISSN:1464-4177
1751-7648
DOI:10.1002/suco.202100796