Determination of the Compressive Strength of Concrete Using Artificial Neural Network

  • Jose Manuel Palomino Ojeda Data Science Institute, National University of Jaen, Jaen, Peru
  • Stefano Rosario Bocanegra Data Science Institute, National University of Jaen, Jaen, Peru
  • Lenin Quiñones Huatangari Data Science Institute, National University of Jaen, Jaen, Peru
Keywords: concrete, ANN, artificial neural network, compressive strength

Abstract

The objective of the work is to estimate the compressive strength of concrete by means of the application of Artificial Neural Networks (ANNs). A database is created with design variables of mixtures of 175, 210, and 280 kgf/cm², which are collected from certified laboratories of soil mechanics and concrete of the city of Jaen. In addition, Weka software is used for the selection of the variables and Matlab software is used for the learning, training, and validation stages of ANNs. Five ANNs are proposed to estimate the compressive strength of concrete at 7th, 14th, and 28th day. The results show that the networks obtain the average error of 4.69% and are composed of an input layer with eleven neurons, two hidden layers with nine neurons each, and the compressive strength of concrete as the output. This method is effective and valid for estimating the compressive strength of concrete as a non-destructive alternative for quality control in the construction industry.

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Published
2021-06-18
How to Cite
[1]
J. M. Palomino Ojeda, S. Rosario Bocanegra, and L. Quiñones Huatangari, “Determination of the Compressive Strength of Concrete Using Artificial Neural Network”, Int. j. eng. technol. innov., vol. 11, no. 3, pp. 204-215, Jun. 2021.
Section
Articles