Prediction of Compressive Strength of SCC Containing Bottom Ash using Artificial Neural Networks
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.
[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.
[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.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:50240", author = "Yogesh Aggarwal and Paratibha Aggarwal", title = "Prediction of Compressive Strength of SCC Containing Bottom Ash using Artificial Neural Networks", 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.", volume = "5", number = "5", pages = "722-6", }
