Prediction of Compressive Strength of SCC Containing Bottom Ash using Artificial Neural Networks

Authors: Yogesh Aggarwal, Paratibha Aggarwal

Abstract:

The paper presents a comparative performance of the models developed to predict 28 days compressive strengths using neural network techniques for data taken from literature (ANN-I) and data developed experimentally for SCC containing bottom ash as partial replacement of fine aggregates (ANN-II). The data used in the models are arranged in the format of six and eight input parameters that cover the contents of cement, sand, coarse aggregate, fly ash as partial replacement of cement, bottom ash as partial replacement of sand, water and water/powder ratio, superplasticizer dosage and an output parameter that is 28-days compressive strength and compressive strengths at 7 days, 28 days, 90 days and 365 days, respectively for ANN-I and ANN-II. The importance of different input parameters is also given for predicting the strengths at various ages using neural network. The model developed from literature data could be easily extended to the experimental data, with bottom ash as partial replacement of sand with some modifications.

Keywords: Self compacting concrete, bottom ash, strength, prediction, neural network, importance factor.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329661

References:

[1] A. Shigdi, and L.A. Gracia, "Parameter estimation in ground-water hydrology using artificial neural networks", J Comput Civ Eng, 2003, vol.17 (4), pp. 281-289.
[2] J.L. Rogers, "Simulating structural analysis with neural network", J Comput Civ Eng, 1994, vol.8(2), pp. 252-265.
[3] J. Kasperkiewicz, J. Rach, and A. Dubrawski, "HPC strength prediction using Artificial neural network", J Compu Civ Eng, 1995, vol. 9(4), pp. 279-284.
[4] J.W. Oh, J.T. Kim, and G.W. Lee, "Application of neural networks for proportioning of concrete mixes", ACI Mater J, 1999, vol. 96(1), pp. 61- 67.
[5] S. Lai, and M. Serra, "Concrete strength prediction by means of neural network", Const Build Mater, 1997, vol. 11(2), pp. 93-98.
[6] I.C. Yeh, "Modeling Concrete strength Using Augment-Neuron Network", J Mater Civ Eng, Nov. 1998a, vol.10 (4).
[7] I.C. Yeh, "Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks", Cem Concr Res, 1998b, vol. 28(12), pp.1797-1808.
[8] I.C. Yeh, "Design of High-Performance Concrete Mixture Using Neural Networks And Nonlinear Programming", J Comp Civ Eng, Jan. 1999, vol.13(1).
[9] M. Sebastia, I.F. Olmo, and A. Irabien, "Neural network prediction of unconfined compressive strength of coal fly ash-cement mixtures", Cem Concr Res, 2003, vol. 33, pp. 1137-1146.
[10] J.I. Kim, D.K. Kim, M.Q. Feng, and F. Yazdani, "Application of Neural Networks for Estimation of Concrete Strength", J. Mater Civ Eng, 2004, vol.16 (3), pp. 257-264.
[11] W.P.S. Dias, and S.P. Pooliyadda, "Neural networks for predicting properties of concretes with Admixtures", Const Build Mater, 2001, vol.15, pp. 371-379.
[12] N. Hong-Guang, and W. Ji-Zong, "Prediction of compressive strength of concrete by neural networks", Cem Concr Res, 2000, vol. 3(8), pp.1245- 1250.
[13] L.Q. Ren, and Z.Y. Zhao, "An Optimal Neural Network and Concrete Strength modeling", J Adv Eng Software, 2002, vol. 33, pp. 117-130.
[14] S. Lee, "Prediction of concrete strength using artificial neural networks", Engg Struct, 2003, vol.25 (7), pp. 849-857.
[15] M. Nehdi, H.E. Chabib, and M.H.E. Naggar, "Predicting performance of self-compacting concrete mixtures using artificial neural networks", ACI Mater J, 2001, vol. 98(5), pp. 394-401.
[16] M. Sonebi, "Application of Statistical models in proportioning medium strength self-consolidating concrete", ACI Mater J, 2004, vol. 101(5), pp. 339-346.
[17] M. Sonebi, "Medium strength self-compacting concrete containing fly ash: Modelling using factorial experimental plans", Cem Concr Res, 2004, vol. 34(7), pp. 1199-1208.
[18] N. Bouzoubaa, and M. Lachemi, "Self-Compacting concrete incorporating high volumes of class F fly ash Preliminary results", Cem Concr Res, 2001, vol. 31, pp. 413-420.
[19] A. Ghezal, and K.H. Khayat, "Optimizing self-consolidating concrete with limestone filler by using statistical factorial design methods", ACI Mater J, 2002, vol. 99(3), pp. 264-268.
[20] V.K. Bui, Y. Akkaya, and S.P. Shah, "Rheological Model for selfconsolidating concrete", ACI Mater J, 2002, vol. 99(6), pp. 549-559.
[21] R. Patel, K.M.A. Hossain, S. Shehata, N. Bouzoubaa, and M. Lachemi, "Development of statistical models for mixture design of high-volume fly ash self-consolidation concrete", ACI Mater J, 2004, vol. 101(4), pp. 294-302.
[22] IS: 3812-2003, "Specification for Fly ash for Use as Pozzolana and Admixture", New Delhi, India: Bureau of Indian Standard.
[23] IS: 383-1970, "Specification for Coarse and Fine aggregates from natural sources for concrete", New Delhi, India: Bureau of Indian Standard.
[24] G.D.Garson, "Interpreting Neural Network Connection Weights", A.I. Expert 1991, vol. 6(7), pp. 47-51.