Journal of Civil Engineering and Construction Technology
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Article Number - 8A096C265741


Vol.8(7), pp. 67-73 , August 2017
DOI: 10.5897/JCECT2017.0457
ISSN: 2141-2634



Full Length Research Paper

Estimation of the compressive strength of high performance concrete with artificial neural networks



L. Acuna-Pinaud
  • L. Acuna-Pinaud
  • Faculty of Industrial and Systems Engineering, National University of Engineering, Av. Túpac Amaru, 210. Lima 25, Peru.
  • Google Scholar
P. Espinoza-Haro
  • P. Espinoza-Haro
  • Faculty of Industrial and Systems Engineering, National University of Engineering, Av. Túpac Amaru, 210. Lima 25, Peru.
  • Google Scholar
I. Moromi-Nakata
  • I. Moromi-Nakata
  • Faculty of Industrial and Systems Engineering, National University of Engineering, Av. Túpac Amaru, 210. Lima 25, Peru.
  • Google Scholar
A. Torre-Carrillo
  • A. Torre-Carrillo
  • Faculty of Industrial and Systems Engineering, National University of Engineering, Av. Túpac Amaru, 210. Lima 25, Peru.
  • Google Scholar
F. Garcia-Fernandez
  • F. Garcia-Fernandez
  • Department of Forest Engineering, Polytechnic University of Madrid, Ciudad Universitaria S / N, 28040 Madrid, Spain.
  • Google Scholar







 Received: 28 December 2016  Accepted: 10 August 2017  Published: 31 August 2017

Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0


High performance concrete is one of the most commonly used materials in non-standard building structures. Aside from the basic components used for its manufacture (water, cement, fine and coarse aggregates), other components such as fly ash, blast furnace slag and superplasticizers are incorporated. In the present study, two types of additives and two types of microsilica have been used. The proportions of all the elements involved in preparing concrete have an influence on its final strength. Artificial neural networks have been used to estimate the compressive strength of high performance concrete mixtures using the results obtained with 296 specimens corresponding to various fabrication parameters. The estimate given by the neural network was evaluated by measuring the correlation between network responses and the expected values, which are the strength values measured in the laboratory. The artificial neural network response obtained in the present work had a correlation of 92% with the expected values used for the training and 89% when predicting values for new data.
 
Key words: Artificial neuronal network, high performance concrete, compressive strength.

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APA Acuña-Pinaud, L., Espinoza-Haro, P., Moromi-Nakata, I., Torre-Carrillo, A., & García-Fernández, F. (2017). Estimation of the compressive strength of high performance concrete with artificial neural networks. Journal of Civil Engineering and Construction Technology, 8(7), 67-73.
Chicago L. Acuña-Pinaud, P. Espinoza-Haro, I. Moromi-Nakata, A. Torre-Carrillo and F. Garc&ia-Fern&andez. "Estimation of the compressive strength of high performance concrete with artificial neural networks." Journal of Civil Engineering and Construction Technology 8, no. 7 (2017): 67-73.
MLA L. Acuntilde;a-Pinaud, et al. "Estimation of the compressive strength of high performance concrete with artificial neural networks." Journal of Civil Engineering and Construction Technology 8.7 (2017): 67-73.
   
DOI 10.5897/JCECT2017.0457
URL http://academicjournals.org/journal/JCECT/article-abstract/8A096C265741

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