Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS

Advances in Engineering Software

Volumn 67, Issue , 2014, Pages 156-163

  Yuan, Zhe ^a   Wang, Lin Na ^a   Ji, Xu ^a  

^a SICHUAN UNIVERSITY   (China)

Author keywords

ANFIS; ANN; Compressive strength; Concrete; Genetic algorithms; Hybrid method

Indexed keywords

BACKPROPAGATION ALGORITHMS; COMPRESSIVE STRENGTH; CONCRETES; FORECASTING; GENETIC ALGORITHMS; MODELS; NEURAL NETWORKS; REGRESSION ANALYSIS;

ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM; ANFIS; ANN; BACK-PROPAGATION ARTIFICIAL NEURAL NETWORK; COMPRESSIVE STRENGTH OF CONCRETE; CONCRETE COMPRESSIVE STRENGTH; GENETIC-BASED ALGORITHMS; HYBRID METHOD;

MATHEMATICAL MODELS;

EID: 84887072711     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2013.09.004     Document Type: Article

References (25)

1. I-C. Yeh Modeling of strength of high-performance concrete using artificial neural networks Cem Concr Res 28 1998 1797 1808 (Pubitemid 128638428)
2. F. Demir A new way of prediction elastic modulus of normal and high strength concrete - fuzzy logic Cem Concr Res 35 2005 1531 1538 (Pubitemid 41037198)
3. C.-H. Lim, Y.-S. Yoon, and J.-H. Kim Genetic algorithm in mix proportioning of high-performance concrete Cem Concr Res 34 2004 409 420
4. S. Akkurt, G. Tayfur, and S. Can Fuzzy logic model for the prediction of cement compressive strength Cem Concr Res 34 2004 1429 1433
5. S. Tesfamariam, and H. Najjaran Adaptive network-fuzzy inferencing to estimate concrete strength using mix design J Mater Civil Eng 19 2007 550 560 (Pubitemid 46995186)
6. M. Bilgehan A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modelling approaches Nondestruct Test Eva 26 2011 35 55
7. A.U. Khan, T. Bandopadhyaya, and S. Sharma Genetic algorithm based backpropagation neural network performs better than backpropagation neural network in stock rates prediction J Comput Sci Network Secur 8 2008 162 166
8. Yen GG, Lu H. Hierarchical genetic algorithm based neural network design. In: 2000 IEEE symposium on combinations of evolutionary computation and neural networks. IEEE; 2000. p. 168-75.
9. N. Gupta, G.S. Gaba, and H.S. Gill A new approach for function optimization using hybrid GA-ANN algorithm Int J Eng Res Appl 2 2012 386 389
10. Chen D, Zhao W, Sprott JC, Ma X. Application of Takagi-Sugeno fuzzy model to a class of chaotic synchronization and anti-synchronization. Nonlinear Dynam 2013: 1-11.
11. Z. Fu, J. Mo, L. Chen, and W. Chen Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal Mater Des 31 2010 267 277
12. Ahmad F, Mat-Isa NA, Hussain Z, Boudville R, Osman MK. Genetic Algorithm-Artificial Neural Network (GA-ANN) Hybrid Intelligence for Cancer Diagnosis. In: 2010 Second international conference on computational intelligence, communication systems and networks (CICSyN). IEEE; 2010. p. 78-83.
13. H. Lin, J. Zhao, L. Sun, Q. Chen, and F. Zhou Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis Innov Food Sci Emerg 12 2011 182 186
14. Sudarsana RH, Subba RP, Vaishali GG. Development of genetic algorithm based hybrid network model for predicting the ultimate flexural strength of ferrocement elements. Int J Eng Sci 2012.
15. V.S.S. Raja, and S. Rajagopalan A neuro-genetic system for face recognition Int J Comput Sci Issues 9 2012 263 267
16. E.M. Tan, Q.Q. Li, and J.G. Jiang Weighted cellular automata design for test pattern generator based on genetic algorithm Appl Mech Mater 273 2013 840 844
17. Goldberg DE. Genetic algorithms in search, optimization, and, machine learning; 1989.
18. Houck CR, Joines JA, Kay MG. A genetic algorithm for function optimization: a Matlab implementation. NCSU-IE TR; 1995. p. 95.
19. H. Mehmood, and N.K. Tripathi Cascading artificial neural networks optimized by genetic algorithms and integrated with global navigation satellite system to offer accurate ubiquitous positioning in urban environment Comput Environ Urban Syst 37 2013 35 44
20. Z. Stanimirović A genetic algorithm approach for the capacitated single allocation p-hub median problem Comput Inform 29 2012 117 132
21. < http://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+ > strength DATA sets come from UCI.
22. D.E. Goldberg, and J.H. Holland Genetic algorithms and machine learning Mach Learn 3 1988 95 99
23. D.E. Goldberg, and K. Deb A comparative analysis of selection schemes used in genetic algorithms Urbana 51 1991 61801 62996
24. Y.H. Shen, Z.X. Wang, and C.Y. Gao Determine the number of BP neural network hidden layer units J Tianjin Polytechnic Univ 24 2008 13 15
25. M. Neshat, A. Adeli, G. Sepidnam, and M. Sargolzaei Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems Int J Adv Manuf Technol 63 2012 373 390