DOI QR코드

DOI QR Code

Predicting strength of SCC using artificial neural network and multivariable regression analysis

  • Saha, Prasenjit (Department of Civil Engineering, NIT) ;
  • Prasad, M.L.V. (Department of Civil Engineering, NIT) ;
  • Kumar, P. Rathish (Department of Civil Engineering, National Institute of Technology)
  • 투고 : 2016.09.09
  • 심사 : 2017.03.15
  • 발행 : 2017.07.25

초록

In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN's are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.

키워드

참고문헌

  1. Acikgenc, M. and Ulas, M. (2015), "Using an artificial neural network to predict mix compositions of steel fiber-reinforced concrete", Arab J. Sci. Eng., 40(2), 407-419. https://doi.org/10.1007/s13369-014-1549-x
  2. Alqadi, A.N., Mustapha, K.N., Naganathan, S. and Al-Kadi, Q.N. (2012), "Uses of central composite design and surface response to evaluate the fluency of constituent materials on fresh and hardened properties of self-compacting concrete", KSCE J. Civil Eng., 16(3), 407-416. https://doi.org/10.1007/s12205-012-1308-z
  3. Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Exp. Syst. Appl., 38(8), 9609-9618. https://doi.org/10.1016/j.eswa.2011.01.156
  4. Bondar, D. (2014) "Use of a neural network to predict strength and optimum compositions of natural alumina-silica-based geopolymers", J. Mater. Civil Eng., 26(3), 499-503. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000829
  5. Cahit, B., Cengiz, D. Atis, H.T. and Okan, K. (2009), "Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network", Adv. Eng. Soft., 40(5), 334-340. https://doi.org/10.1016/j.advengsoft.2008.05.005
  6. Duan, Z.H., Kou, S.C. and Poon, C.S. (2013), "Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete", Constr. Build. Mater., 44, 524-532. https://doi.org/10.1016/j.conbuildmat.2013.02.064
  7. EFNARC (2005), Specifications and Guidelines for Self-Compacting Concrete, European Federation of Producers nd Applicators of Specialist Products for Structures, Association House, Farnham, U.K.
  8. Ghafari, E., Bandarabadi, M., Costa, H. and Julio, E. (2015), "Prediction of fresh and hardened state properties of UHPC: Comparative study of statistical mixture design and an artificial neural network model", J. Mater. Civil Eng., 27(11), 04015017. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001270
  9. Hossain, K.M.A. and Lachemi, M. (2006), "Time dependent equations for the compressive strength of self-consolidating concrete through statistical optimization", Comput. Concrete, 3(4), 249-260. https://doi.org/10.12989/cac.2006.3.4.249
  10. Indian Standard Code IS: 12269 (2013), Specifications for 53 Grade Ordinary Portland Cement, Bureau of Indian Standards, New Delhi, India.
  11. Indian Standard Code IS: 2386-1997 (2002), Methods of Test for Aggregates for Concrete, Bureau of Indian Standards, New Delhi, India.
  12. Indian Standard Code IS: 3812-Part 1 (2003), Specification for Pulverized Fuel Ash, Bureau of Indian Standards, New Delhi, India.
  13. Indian Standard Code IS: 383-1970 (2002), Specification for Coarse and Fine Aggregates from Natural Sources for Concrete, Bureau of Indian Standards, New Delhi, India.
  14. Indian Standard Code IS: 516-1956 (2004), Indian Standard Methods of Tests for Strength of Concrete, Bureau of Indian Standards, New Delhi, India.
  15. Karatas, M., Balun, B. and Benli, A. (2017), High temperature resistance of self-compacting concrete lightweight mortar incorporating expanded perlite and pumice", Comput. Concrete, 19(2), 121-126. https://doi.org/10.12989/cac.2017.19.2.121
  16. Khan, A., Do, J. and Kim, D. (2016), "Cost effective optimal mix proportioning of high strength selfcompacting concrete using response surface methodology", Comput. Concrete, 17(5), 629-638. https://doi.org/10.12989/cac.2016.17.5.629
  17. Kostic, S. and Vasovic, D. (2015), "Prediction model for compressive strength of basic concrete mixture using artificial neural networks", Neur. Comput. Appl., 26, 1005-1024. https://doi.org/10.1007/s00521-014-1763-1
  18. Li, M.C., Chen, Y.S., Chan, Y.W. and Hoang, V.L. (2012), "A study of statistical models application for mixture of highflowing concrete", J. Mar. Sci. Technol., 20(3), 325-335.
  19. Li, S. and An, X. (2014), "Method for estimating workability of self-compacting concrete using mixing process images", Comput. Concrete, 13(6), 181-198.
  20. McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", Bullet. Mothemnt. Biol., 52, 99-115.
  21. Murali, T.M. and Kandasamy, S. (2009), "Mix proportioning of high performance self-compacting concrete using response surface methodology", Open Civil Eng. J., 3, 93-97. https://doi.org/10.2174/1874149500903010093
  22. Najigivi, A., Khaloo, A., Irajizad, A. and Rashid, S.A. (2013), "An artificial neural networks model for predicting permeability properties of nano silica-rice husk ash ternary blended concrete", J. Concrete Struct. Mater., 7(3), 225-238. https://doi.org/10.1007/s40069-013-0038-z
  23. Okamura, H. and Ouchi, M. (2003), "Self-compacting concrete", J. Adv. Concrete Technol., 1(1), 5-15. https://doi.org/10.3151/jact.1.5
  24. Ouchi, M., Hibino, M. and Okamura, H. (1996), "Effect of superplasticizer on self compactability of fresh concrete", TRR 1574, 37-40.
  25. Prasad, M.L.V., Saha, P. and Kumar, R. (2016), "Self compacting reinforced concrete beam strengthened with natural fibre under cyclic loading", Comput. Concrete, 17(5), 597-611. https://doi.org/10.12989/cac.2016.17.5.597
  26. Prasad, M.L.V., Saha, P. and Laskar, A.I. (2016), "Behaviour of self-compacting reinforced concrete beams strengthened with hybrid fiber under static and cyclic loading", J. Civil Eng., 1-10.
  27. Shanker, R. and Sachan, A.K. (2014), "Concrete mix design using neural network", J. Civil Arch. Struct. Constr. Eng., 8(8), 883-886.
  28. Siddique, R., Aggarwal, P. and Aggarwal, Y. (2011), "Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks", Adv. Eng. Soft., 42(10), 780-786. https://doi.org/10.1016/j.advengsoft.2011.05.016
  29. Su, N., Hsu, K.C. and Chai, H.W. (2001), "A simple mix design method for self-compacting concrete", Cement Concrete Res., 31(12), 1799-1807. https://doi.org/10.1016/S0008-8846(01)00566-X
  30. Uysal, M. and Yilmaz, K. (2011), "Effect of mineral admixtures on properties of self-compacting concrete", Cement Concrete Compos., 33(7), 771-776. https://doi.org/10.1016/j.cemconcomp.2011.04.005
  31. Yaprak, H., Karaci, A. and Demir, H. (2013), "Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks", Neur. Comput. Appl., 22, 133-141.

피인용 문헌

  1. Prediction of UCS and STS of Kaolin clay stabilized with supplementary cementitious material using ANN and MLR vol.5, pp.2, 2017, https://doi.org/10.12989/acd.2020.5.2.195
  2. Statistical Approach for the Design of Structural Self-Compacting Concrete with Fine Recycled Concrete Aggregate vol.8, pp.12, 2017, https://doi.org/10.3390/math8122190