J. Today’s Ideas - Tomorrow’s Technol.

Concrete Mix Design Using Artificial Neural Network

Sakshi Gupta

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  • DOI Number
    https://doi.org/10.15415/jotitt.2013.11003
KEYWORDS

concrete mix, artificial neural network (ANN), 7-days strength, 14-days strength, 28-days compressive strength, fineness modulus, activation function, modeling.

PUBLISHED DATE June 2013
PUBLISHER The Author(s) 2013. This article is published with open access at www.chitkara.edu.in/publications
ABSTRACT

Concrete mix design is a process based on sound technical principles for proportioning of ingredients in right quantities. This paper demonstrates the applicability of Artificial Neural Networks (ANN) Model for approximate proportioning of concrete mixes. For ANN a trained back propagation neural network is integrated in the model to learn experimental data pertaining to predict 7, 14 and 28-day compressive strength which have been loaded into a model, containing 55 concrete mixtures. The ANN model proposed is based on 5 input parameters such as cement, sand, coarse aggregate, and water and fineness modulus. The proposed concrete mix proportion design is expected to reduce the number of trials in laboratory as well as field, saves cost of material as well as labor and also saves time as it provides higher accuracy. The concrete designed is expected to have higher durability and hence is economical.

Page(s) 29–43
URL http://dspace.chitkara.edu.in/jspui/bitstream/1/35/1/11003_JOTTITT_Sakshi%20Gupta.pdf
ISSN Print : 2321-3906, Online : 2321-7146
DOI https://doi.org/10.15415/jotitt.2013.11003
REFERENCES
  • Shigdi A, Gracia LA. Parameter estimation in ground-water hydrology using artificial neural networks, Journal of Computer and Civil Engineering , 17(4) (2003) 281–289.
  • Rogers JL. Simulating structural analysis with neural network.Journal of Computer and Civil Engineering, 8(2) (1994) 252–265.
  • Kasperkiewicz J, Rach J, Dubrawski A. HPC strength prediction using artificial neural network, Journal of Computer and Civil Engineering 9(4) (1995) 279–284.
  • Oh JW, Kim JT, Lee GW. Application of neural networks for proportioning of concrete mixes. ACI Material Journal , 96(1) (1999) 61–67.
  • Lai S, Serra M. Concrete strength prediction by means of neural network. Construction Building Material , 11(2) (1997) 93–98.
  • Yeh I-Cheng. Modeling concrete strength using augment-neuron network. Journal of Material in Civil Engineering , 10(4) (1998).
  • Yeh I-Cheng. Modeling of strength of high-performance concrete using artificial neural networks. Cem Concrete Research , 28(12) (1998) 1797–1808.
  • Yeh I-Cheng. Design of high-performance concrete mixture using neural networks and nonlinear programming. Journal of Computer and Civil Engineering, 13(1) (1999).
  • Sebastia M, Olmo IF, Irabien A. Neural network prediction of unconfined compressive strength of coal fly ash–cement mixtures. Cem Concrete Research, 33 (2003) 1137–1146.
  • Kim JI, Kim DK, Feng MQ, Yazdani F. Application of neural networks for estimation of concrete strength. Journal of Material in Civil Engineering, 16(3) (2004) 257–264.
  • Dias WPS, Pooliyadda SP. Neural networks for predicting properties of concretes with admixtures. Construction Building Material , 15 (2001) 371–379.
  • Hong-Guang N, Ji-Zong W. Prediction of compressive strength of concrete by neural networks. Cem Concrete Research, 3(8) (2000) 1245–1250.
  • Ren LQ, Zhao ZY. An optimal neural network and concrete strength modeling. Journal of Advance Engineering Software , 33 (2002) 117–130.
  • Lee S. Prediction of concrete strength using artificial neural networks. Engineering Structure, 25(7) (2003) 849–857.