Use of artificial neural networks to predict concrete compression strength / Uso de redes neurais artificiais na predição da resistência à compressão do concreto

Authors

  • Dennis Santos Tavares Brazilian Journals Publicações de Periódicos, São José dos Pinhais, Paraná
  • David Augusto Ribeiro
  • Tadayuki Yanagi Junior
  • Wilian Soares Lacerda
  • Eduardo Tadeu Tiradentes
  • Robson Guilherme Teixeira
  • Hudson Venâncio Silva Garcia

DOI:

https://doi.org/10.34117/bjdv6n7-050

Keywords:

Concrete, Compressive strength, Artificial neural networks.

Abstract

Concrete is one of the most widely used building materials, being composed of different components with different properties, which makes the task of dosing and strength determination complex. Artificial Neural Networks is a tool that has the ability to generalize and learn from previous experiences that are provided by a previously built database. This work aims the implementation of RNA in determining the compressive strength of concrete of various ages. The input data is the material quantities and the output is the compressive strength. The results obtained are satisfactory and promising from the point of view of civil engineering.

 

 

References

ACUÑA, L.; TORRE, A. V.; MOROMI, I.; GARCÍA, F. Uso de las redes neuronales artificiales en el modelado del ensayo de resistencia a compresión de concreto de construcción según la norma ASTM C39/C 39M. Informacion Tecnologica, v. 25, n. 4, p. 3–12, 2014.

AKPINAR, P.; KHASHMAN, A. Intelligent classification system for concrete compressive strength. Procedia Computer Science, v. 120, p. 712–718, 2017. Disponível em: <https://doi.org/10.1016/j.procs.2017.11.300>.

ARORA, S.; SINGH, B.; BHARDWAJ, B. Strength performance of recycled aggregate concretes containing mineral admixtures and their performance prediction through various modeling techniques. Journal of Building Engineering, v. 24, p. 100741, 2019. Disponível em:<https://doi.org/10.1016/j.jobe.2019.100741>.

BEHNOOD, A.; GOLAFSHANI, E. M. Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production, v. 202, p. 54–64, 2018. Disponível em: <https://doi.org/10.1016/j.jclepro.2018.08.065>.

COLOMBO, I. G. et al. Textile reinforced concrete: experimental investigation on design parameters. Materials and Structures, Dordrecht, v. 46, p. 1933-1951, 2013.

GETAHUN, M. A.; SHITOTE, S. M.; ABIERO GARIY, Z. C. Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes. Construction and Building Materials, v. 190, p. 517–525, 2018. Disponível em: <https://doi.org/10.1016/j.conbuildmat.2018.09.097>.

I-CHENG YEH, "Modeling of strength of high performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998).

MORETTI, J. F. Sistema Inteligente Baseado nas Redes Neurais Artificiais para Dosagem do Concreto, 2010. 95p. Tese (Doutorado em engenharia elétrica) – Universidade Estadual Paulista Júlio de Mesquita Filho, Ilha Solteira, 2010.

NIKBIN, I. M.; RAHIMI R., S.; ALLAHYARI, H. A new empirical formula for prediction of fracture energy of concrete based on the artificial neural network. Engineering Fracture Mechanics, v. 186, p. 466–482, 2017. Disponível em:<https://doi.org/10.1016/j.engfracmech.2017.11.010>.

OLIVEIRA, H. M. de; BIONDI NETO, L.; TAVARES, M. E. de N. Redes Neurais Artificiais na Obtenção do Traço e na Definição da Resistência à Compressão de Concretos de Alta Resistência. Simpósio de Pesquisa Operacional e Logística da Marinha, 2007.

RUMELHART, D. E.; HINTON, G. E.; WILLIAMS, R. J. Learning internal representations by error propagation. La Jolla: Institute for Cognitive Science- University of California, 1985.49 p. (ICS Report, 8506).

SIDDIQUE, R.; AGGARWAL, P.; AGGARWAL, Y. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering Software, v. 42, n. 10, p. 780–786, 2011. Disponível em: <http://dx.doi.org/10.1016/j.advengsoft.2011.05.016>.

WIDROW, B.; LEHR, M. A. 30 Years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE, New York, v.78, n.9, p. 1415-1442, 1999.

YAMAN, M. A.; ELATY, M. A; TAMAN, M. Predicting the ingredients of self compacting concrete using artificial neural network. Alexandria Engineering Journal, v. 56, n. 4, p. 523–532, 2017. Disponível em: <http://dx.doi.org/10.1016/j.aej.2017.04.007>.

YOUNIS, K. H.; PILAKOUTAS, K. Strength prediction model and methods for improving recycled aggregate concrete. Constrution and Building Materials, Am

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Published

2020-07-02

How to Cite

Tavares, D. S., Ribeiro, D. A., Junior, T. Y., Lacerda, W. S., Tiradentes, E. T., Teixeira, R. G., & Garcia, H. V. S. (2020). Use of artificial neural networks to predict concrete compression strength / Uso de redes neurais artificiais na predição da resistência à compressão do concreto. Brazilian Journal of Development, 6(7), 42815–42826. https://doi.org/10.34117/bjdv6n7-050

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Original Papers