Prediction of Compressive Strength of Self- Compacting Concrete with Fuzzy Logic

The paper presents the potential of fuzzy logic (FL-I) and neural network techniques (ANN-I) for predicting the compressive strength, for SCC mixtures. Six input parameters that is contents of cement, sand, coarse aggregate, fly ash, superplasticizer percentage and water-to-binder ratio and an output parameter i.e. 28- day compressive strength for ANN-I and FL-I are used for modeling. The fuzzy logic model showed better performance than neural network model.




References:
[1] M. Pala, E. Ozbay, A. Oztas, and M.I. Yuce, "Appraisal of long-term
effects of fly ash and silica fume on compressive strength of concrete by
neural networks", Construction and Building Materials, 2007, vol.
21(2), pp. 384-394.
[2] 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.
[3] J.L. Rogers, "Simulating structural analysis with neural network", J
Comput Civ Eng, 1994, vol. 8(2), pp.252-265.
[4] 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.
[5] 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.
[6] S. Lai, and M. Serra, "Concrete strength prediction by means of neural
network", Const Build Mater, 1997, vol. 11(2), pp. 93-98.
[7] I.C. Yeh, "Modeling Concrete strength Using Augment-Neuron
Network", J Mater Civ Eng, Nov. 1998a, vol.10 (4).
[8] I.C. Yeh, "Modeling of Strength of High-Performance Concrete Using
Artificial Neural Networks", Cem Concr Res, 1998b, vol. 28(12),
pp.1797-1808.
[9] I.C. Yeh, "Design of High-Performance Concrete Mixture Using Neural
Networks And Nonlinear Programming", J Comp Civ Eng, Jan. 1999,
vol.13(1).
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] S. Lee, "Prediction of concrete strength using artificial neural networks",
Engg Struct, 2003, vol.25 (7), pp. 849-857.
[16] 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.
[17] M. Sonebi, "Application of Statistical models in proportioning medium
strength self-consolidating concrete", ACI Mater J, 2004, vol. 101(5),
pp. 339-346.
[18] 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.
[19] L.A. Zadeh, "Fuzzy Sets", Information and Control, 1965, vol. 8, pp.
338-353.
[20] F. Demir, "Prediction of compressive strength of concrete using ANN
and Fuzzy logic", Cement and Concrete Research, 2005, vol. 35, pp.
1531-1538.
[21] Z. S_en, "Combining Back propagations and Genetic Algorithms to train
to train neural networks for Ambient Temperature Modelling", Solar
Energy, 1998 vol. 63 (1), pp. 39-49.
[22] E.H. Mamdani, "Fuzzy Logic control of aggregate production planning",
S. Assilian, International Journal of Man-Machine Studies, 1975, vol. 7,
pp. 1-13.
[23] K.M. Passino, "Stable Fuzzy Logic design of point to point control for
mechanical systems", S. Yurkovich, Fuzzy Control, Addison-Wesley,
1998.
[24] D.W.C. Ho, and P.A. Zhang, "Design of Fuzzy Wavelet Neural
Networks using the GA approach for function approximation and system
identification", J. Xu, IEEE Transactions on Fuzzy Systems, 2001, vol.
9, pp. 200-211.
[25] F.M. McNeill, "Application of Fuzzy Logic in Interior Daylight
Estimation", E. Thro, Fuzzy Logic: A Practical Approach, AP
Professional, Boston, MA, 1994.
[26] G. Inan, and A.B. Goktepe, "Prediction of sulfate expancion of PC
mortar using adaptive neuro-fuzzy methodology", K. Ramyar, A. Sezer,
Building and Environment, 2007 vol. 42 (3), pp. 1264-1269.
[27] M. Sugeno, and G.T. Kang, "Fuzzy Sets Systems Man and Cybernetics",
1993, vol. 23 (3), pp. 665-685.
[28] T. Takagi, and M. Sugeno, "IEEE Transactions on Systems Man and
Cybernetics", 1985, vol. 15, pp. 116-132.
[29] J.S.R. Jang, and C.T. Sun, Proceedings of the IEEE 83 (1995) 378-405.
[30] S. Akbulut, A.S. Hasilog¢lu, and S. Pamukcu, Soil Dynamics and
Earthquake Engineering, 24 (2004) 805-814.
[31] 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.
[32] V.K. Bui, Y. Akkaya, and S.P. Shah, "Rheological Model for selfconsolidating
concrete", ACI Mater J, 2002, vol. 99(6), pp. 549-559.
[33] 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.