Journal of
Civil Engineering and Construction Technology

  • Abbreviation: J. Civ. Eng. Constr. Technol.
  • Language: English
  • ISSN: 2141-2634
  • DOI: 10.5897/JCECT
  • Start Year: 2010
  • Published Articles: 126

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.
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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.
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F. Garcia-Fernandez
  • F. Garcia-Fernandez
  • Department of Forest Engineering, Polytechnic University of Madrid, Ciudad Universitaria S / N, 28040 Madrid, Spain.
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  •  Received: 28 December 2016
  •  Accepted: 10 August 2017
  •  Published: 31 August 2017

Abstract

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.