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Published: Građevinar 72 (2020) 7
Paper type: Scientific research paper
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Prediction of concrete compressive strength through artificial neural networks

Pablo Neira, Leonardo Bennun, Mauricio Pradena, Jaime Gómez

Abstract

Concrete properties, including its compressive strength, are in general highly nonlinear functions of its components. Concrete mix design methods are basically simulations that require costly and time consuming adjustments in laboratory. A useful support tool based on artificial neural networks, using a multilayer perceptron network, is proposed in this paper as a means to predict compressive strength of concrete mixes. The developed models are useful for reducing the quantity of laboratory tests required for concrete mix design adjustments.

Keywords
concrete mix design, compressive strength, laboratory tests, artificial neural networks

HOW TO CITE THIS ARTICLE:

Neira, P., Bennun, L., Pradena, M., Gómez, J.: Prediction of concrete compressive strength through artificial neural networks, GRAĐEVINAR, 72 (2020) 7, pp. 585-592, doi: https://doi.org/10.14256/JCE.2438.2018

OR:

Neira, P., Bennun, L., Pradena, M., Gómez, J. (2020). Prediction of concrete compressive strength through artificial neural networks, GRAĐEVINAR, 72 (7), 585-592, doi: https://doi.org/10.14256/JCE.2438.2018

LICENCE:

Creative Commons License
This paper is licensed under a Creative Commons Attribution 4.0 International License.
Authors:
Pablo Neira WEB
Pablo Neira
Universidad de Concepción, Chile
Faculty of Physical and Mathematical Sciences
Physics Department
Leonardo Bennun WEB
Leonardo Bennun
Universidad de Concepción, Chile
Faculty of Physical and Mathematical Sciences
Laboratory of Applied Physics, Physics Department
M Pradena WEB
Mauricio Pradena
University of Concepción, Chile
Department of Civil Engineering
Delft University of Technology, the Netherlands
Section Pavement Engineering
Jaime Gomez WEB
Jaime Gómez
University Simón Bolívar, Venezuela