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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 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%.
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 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%.
Prediction of the compressive strength of self‐compacting concrete using artificial neural networks based on rheological parameters
el Asri, Yousef (author) / Benaicha, Mouhcine (author) / Zaher, Mounir (author) / Hafidi Alaoui, Adil (author)
Structural Concrete ; 23 ; 3864-3876
2022-12-01
13 pages
Article (Journal)
Electronic Resource
English
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