Predicting strengths of concrete-type specimens using hybrid multilayer perceptrons with center-unified particle swarm optimization

Expert Systems with Applications

Volumn 37, Issue 2, 2010, Pages 1104-1112

  Tsai, Hsing Chih ^a  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

High order; Hybrid connectors; Multilayer perceptrons; Neural networks; Particle swarm optimization; Specimen strengths

Indexed keywords

A-CENTER; CONCRETE CYLINDERS; CONCRETE DEEP BEAMS; DATA SETS; HIGH-ORDER; HYBRID CONNECTORS; HYBRID MULTILAYERS; MULTI-LAYER PERCEPTRONS; OPTIMIZATION TASK; UNIFIED PARTICLE SWARM OPTIMIZATION;

CYBERNETICS; ELECTRIC LOADS; MULTILAYERS; PARTICLE SWARM OPTIMIZATION (PSO); PATTERN RECOGNITION SYSTEMS; SEMICONDUCTING MANGANESE COMPOUNDS;

MULTILAYER NEURAL NETWORKS;

EID: 71749110340     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.06.093     Document Type: Article

References (28)

1. Abdelbar A., and Tagliarini G. HONEST: A new high-order feedforward neural network. IEEE International Conference on Neural Networks - Conference Proceedings 2 (1996) 1257-1262
2. Adhikary B.B., and Mutsuyoshi H. Prediction of shear strength of steel fiber RC beams using neural networks. Construction and Building Materials 20 9 (2006) 801-811
3. American Concrete Institute (ACI) (1995). Building code requirements for reinforced concrete, ACI 318-95, Detroit.
4. Artyomov E., and Yadid-Pecht O. Modified high-order neural network for invariant pattern recognition. Pattern Recognition Letters 26 6 (2005) 843-851
5. Cladera A., and Mari A.R. Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Engineering Structures 26 7 (2004) 917-926
6. Eberhart, R. C., & Kennedy, J. (1995a). A new optimizer using particles swarm theory. In The sixth international symposium on micro machine and human science (p. 39-43).
7. Eberhart R.C., and Kennedy J. Particle swarm optimization. The IEEE International Conference on Neural Networks 4 (1995) 1942-1948
8. Eberhart, R. C., & Shi, Y. (2000), Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings 2000 IEEE CEC, IEEE service center, Piscataway, NJ (pp. 84-88).
9. Foresti G.L., and Dolso T. An adaptive high-order neural tree for pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34 2 (2004) 988-996
10. Hwang S.J., Fang W.H., Lee H.J., and Yu H.W. Analytical model for predicting shear strength of squat walls. Journal of Structural Engineering 127 1 (2001) 43-50
11. Kennedy, J. (2003). Bare bones particle swarms. In Proceedings of the IEEE swarm intelligence symposium (pp. 80-87). Indianapolis, USA: IEEE Press.
12. Liu Y., Qin Z., Shi Z., and Lu J. Center particle swarm optimization. Neurocomputing 70 4-6 (2007) 672-679
13. Lee S.J., Lee S.R., and Kim Y.S. An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation. Computers and Geotechnics 30 6 (2003) 489-503
14. MacKay, D. J. C. (1995). Bayesian non-linear modelling with neural networks. Cambridge University Programme for Industry.
15. Mansour M.Y., Dicleli M., Lee J.Y., and Zhang J. Predicting the shear strength of reinforced concrete beams using artificial neural networks. Engineering Structures 26 6 (2004) 781-799
16. Mau S.T., and Hsu T.C. Formula for the shear strength of deep beams. ACI Structural Journal 86 5 (1989) 516-523
17. Mendes R., Kennedy J., and Neves J. The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation 8 3 (2004) 204-210
18. Oh J.W., Lee I.W., Kim J.T., and Lee G.W. Application of neural networks for proportioning of concrete mixes. ACI Materials Journal 96 1 (1999) 61-67
19. Oreta A.W.C. Simulating size effect on shear strength of RC beams without stirrups using neural networks. Engineering Structures 26 5 (2004) 681-691
20. Parsopoulos, K. E., & Vrahatis, M. N. (2004). UPSO: A unified particle swarm optimization scheme. In Proceedings of the international conference of computational methods in sciences and engineering, ICCMSE 2004. In Lecture series on computer and computational sciences (pp. 868-873) Vol. 1. Zeist, The Netherlands: SP International Science Publishers.
21. Parsopoulos K.E., and Vrahatis M.N. Parameter selection and adaptation in unified particle swarm optimization. Mathematical and Computer Modelling 46 1-2 (2007) 198-213
22. Rovithakis G.A., Chalkiadakis I., and Zervakis M.E. High-order neural network structure selection for function approximation applications using genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34 1 (2004) 150-158
23. Sanad A., and Saka M.P. Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks. Journal of Structural Engineering 127 7 (2001) 818-828
24. Tsai H.C. Hybrid high order neural networks. Applied Soft Computing 9 (2009) 874-881
25. Wang J.H., and Lin J.H. Qualitative analysis of the BP composed of product units and summing units. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 1 (1995) 35-39
26. Yeh I.C. Analysis of strength of concrete using design of experiments and neural networks. Journal of Materials in Civil Engineering 18 4 (2006) 597-604
27. Zhang J.R., Zhang J., Lok T.M., and Lyu M.R. A hybrid particle swarm optimization algorithm for feedforward neural network training. Applied Mathematics and Computation 185 (2007) 1026-1037
28. Zurada, J. M. (1992). Introduction to artificial neural systems. ST. Paul: West Publishing Company.