Fuzzy polynomial neural networks for approximation of the compressive strength of concrete

Applied Soft Computing Journal

Volumn 8, Issue 1, 2008, Pages 488-498

  Fazel Zarandi, M H ^a   Turksen I B ^b   Sobhani, J ^a   Ramezanianpour, A A ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

BP; Compressive strength; Concrete; Fuzzy polynomial neural network; LSE; Mix design

Indexed keywords

COMPRESSIVE STRENGTH; CONCRETES; FUZZY CONTROL; MATHEMATICAL MODELS; MEAN SQUARE ERROR; POLYNOMIALS;

CONCRETE MIX-DESIGN; FUZZY POLYNOMIAL NEURAL NETWORK; MIX-DESIGN;

NEURAL NETWORKS;

EID: 34548516370     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2007.02.010     Document Type: Article

References (21)

1. Bai Y., and Amirkhanian S.N. Knowledge-based expert system for concrete mix-design. J. Constr. Eng. Manage. 120 2 (1994) 223-229
2. Chan L.W. The interpolation property of fuzzy polynomial approximation. International Symposium on Speech Image Processing & NNs (1994) 13-16
3. Dias W.P.S., and Pooliyadda S.P. Neural networks for predicting properties of concretes with admixtures. Constr. Build. Mater. 15 (2001) 371-379
4. Ghaboussi J., Garrett J.H., and Wu X. Knowledge-based modeling of material behavior with neural networks. J. Eng. Mech. 117 1. (1991)
5. Ivahenko G. The group method of data handling: a rival of method of stochastic approximation. Soviet Auto. Contr. 13 3 (1968) 43-55
6. Jang J.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cyber. 23 3 (1993) 665-685
7. Jang J.R., and Sun C.T. Neuro-fuzzy modeling and control. IEEE Proc. 83 3 (1995) 378-381
8. Kasperkiewicz J., and Dubrawski A. HPC strength prediction using artificial neural network. J. Comput. Civil Eng. 9 4 (1995) 279-284
9. Mostofi D., and Samaee A. HPC Strength prediction using artificial neural network. Proceedings of the First International conference of C&D (2001) 44-49
10. Oh J., Lee I., Kim J., and Lee G. Applications of neural networks for proportioning of concrete mixes. ACI Mater. J. 96 1 (1999) 51-59
11. Oha S., Pedrycz W., and Park B.J. Polynomial neural networks architecture: analysis and design. Comput. Electr. Eng. 29 6 (2003) 703-725
12. Oztas A., Pala M., Ozbay E., Kanca K., Caglar N., and Bhatti M.A. Predicting the compressive strength and slump of high strength concrete using neural network. Constr. Build. Mater. 20 (2006) 769-775
13. Park B.J., Lee D.Y., and Oh S.K. Rule-based fuzzy polynomial neural networks in modeling software process data. Int. J. Contr. Auto. Syst. 1 3 (2003) 321-332
14. Park B.J., Pedrycz W., and Oh S.K. Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling. IEEE Trans. Fuzzy Syst. 10 5 (2002) 607-621
15. Park H.S., Oh S.K., Chon Ahn O.T., and Pedrycz A. Study on multi-layer fuzzy polynomial inference system base on an extended GMDH algorithm. Proceedings of the IEEE International Fuzzy System Conference, vol. 1 (1999) 354-359
16. Ramezanianpour A.A., Sobhani M., and Sobhani J. Application of network based neuro-fuzzy system for prediction of the strength of high strength concrete. AKU J. Sci. Technol. 15 59-C (2004) 78-93
17. Sergio L., and Mauro S. Concrete strength prediction by means of neural network. Constr. Build. Mater. 11 2 (1997) 93-98
18. Shekarchi M., Nemati C.M., and Dormohammadi H. Prediction of compressive strength of concrete using adaptive networked based inference system (ANFIS). Proceedings of the CONMAT Conference (2005)
19. Uomoto T., Ohya T., and Tsutsumi T. Development of new concrete mixing system using neural network. Proceedings of the CONSEC Conference (1998) 282-290
20. Yeh C. Design of High-performance concrete mixture using neural networks and nonlinear programming. J. Comput. Civil Eng. 13 1 (1999) 36-42
21. Yeh I.-C. Modeling concrete strength with augment-neuron networks. J. Mater. Civil Eng. 10 4 (1998) 263-268