Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

Advances in Engineering Software

Volumn 40, Issue 9, 2009, Pages 856-863

  Özcan, Fatih ^a   Atiş, Cengiz D ^b   Karahan, Okan ^b   Uncuoǧlu, Erdal ^c   Tanyildizi, Harun ^d  

^a NIGDE UNIVERSITY   (Turkey)

^b ERCIYES UNIVERSITY   (Turkey)

^c CUKUROVA UNIVERSITY   (Turkey)

^d FIRAT UNIVERSITY   (Turkey)

Author keywords

Compressive strength; Concrete; Fuzzy logic; Neural networks; Silica fume

Indexed keywords

BACKPROPAGATION; CEMENTS; CONCRETES; FUZZY LOGIC; FUZZY NEURAL NETWORKS; FUZZY SETS; OXIDES; SILICA; SILICA FUME;

ALTERNATIVE APPROACHES; ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC; ARTIFICIAL NEURAL NETWORKS; CONCRETE MIXTURES; DATA SETS; EXPERIMENTAL METHODS; LABORATORY WORKS; REPLACEMENT RATIOS; SILICA FUME CONCRETES; WATER-CEMENT RATIOS;

COMPRESSIVE STRENGTH;

EID: 67349263822     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2009.01.005     Document Type: Article

References (41)

1. Atis C.D. Strength properties of high-volume fly ash roller compacted and workable concrete and influence of curing condition. Cement Concr Res 35 6 (2005) 1112-1121
2. Erdogan T.Y. Concrete (2003), Metu Press, Ankara [in Turkish]
3. Khayat K.H., and Aitcin P.C. Silica fume in concrete - an overview. ACI SP-132 2 (1992) 835-872
4. Jahren P. Use of silica fume in concrete. ACI SP-79 1 (1993) 625-642
5. Hooton R.D. Influence of silica fume replacement of cement on physical properties and resistance to sulfate attack, freezing and thawing and alkali-silica reactivity. ACI Mater J 90 2 (1993) 143-151
6. Ozturan T. A re-evaluation of IV international conference on the use of fly ash, silica fume, slag and natural pozzolans in concrete. In: Proceedings of symposium on the use of industrial solid waste in construction sector. Turkey; November 1993. p. 57-78 [in Turkish].
7. Mehta PK. Role of fly ash in sustainable development. In: Forum proceedings, HDD Architecture and Pacific Energy Center; December 1998. .
8. Toutanji A.H., and Bayasi Z. Effect of curing procedures on properties of silica fume concrete. Cement Concr Res 29 4 (1999) 497-501
9. Massazza F. Evaluation of cements and cementitious systems: history and prospects. In: Proceedings of the second international symposium on cement and concrete technology in the 2000s. Turkey, Istanbul; 2000. p. 3-28.
10. Akoz F., Koral S., Yuzer N., and Turker F. Effects of sodium sulfate concentration on the sulfate resistance of mortars with and without silica fume. Cement Concr Res 25 6 (1995) 1360-1368
11. Turker F., Akoz F., Koral S., and Yuzer N. Effects of magnesium sulfate concentration on the sulfate resistance of mortars with and without silica fume. Cement Concr Res 27 2 (1997) 205-214
12. Akoz F., Turker F., Koral S., and Yuzer N. Effects of raised temperature of sulfate solutions on the sulfate resistance of mortars with and without silica fume. Cement Concr Res 29 4 (1999) 537-544
13. Mehta PK. Chemical attack of low water cement ratio concretes containing latex or silica fume as admixtures. In: ACI/RILEM, symposium on technology of concrete when pozzolans, slags and chemicals are used. Monterrey; 1985. p. 325-40.
14. Hong-Guang N., and Ji-Zong W. Prediction of compressive strength of concrete by neural networks. Cement Concr Res 30 8 (2000) 1245-1250
15. Lee S.-C. Prediction of concrete strength using artificial neural networks. Eng Struct 25 7 (2003) 849-857
16. Yeh I.-C. Modeling of strength of high-performance concrete using artificial neural network. Cement Concr Res 28 12 (1998) 1797-1808
17. Pala M., Ozbay O., Oztas A., and Yuce M.I. Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Construct Build Mater 21 2 (2005) 384-394
18. Akkurt S., Tayfur G., and Can S. Fuzzy logic model for prediction of cement compressive strength. Cement Concr Res 34 8 (2004) 1429-1433
19. Baykasoǧlu A., Dereli T., and Tani{dotless}ş S. Prediction of cement strength using soft computing techniques. Cement Concr Res 34 11 (2004) 2083-2090
20. Akkurt S., Ozdemir S., Tayfur G., and Akyol B. The use of GA-ANNs in the modelling of compressive strength of cement mortar. Cement Concr Res 33 7 (2003) 973-979
21. Oztas A., Pala M., Ozbay E., Kanca E., Çaglar N., and Bhatti M.A. Predicting the compressive strength and slump of high strength concrete using neural network. Construct Build Mater 20 9 (2006) 769-775
22. Sebastia M., Olmo I.F., and Irabien A. Neural network prediction of unconfined compressive strength of coal fly ash-cement mixtures. Cement Concr Res 33 8 (2003) 1137-1146
23. Lai S., and Serra M. Concrete strength prediction by means of neural network. Construct Build Mater 11 2 (1997) 93-98
24. Bai J., Wild S., Ware J.A., and Sabir B.B. Using neural networks to predict workability of concrete incorporating metakaolin and fly ash. Adv Eng Software 34 11-12 (2003) 663-669
25. Dias W.P.S., and Pooliyadda S.P. Neural networks for predicting properties of concretes with admixtures. Construct Build Mater 15 7 (2001) 371-379
26. Demir F. A new way of prediction elastic modulus of normal and high strength concrete-fuzzy logic. Cement Concr Res 35 8 (2005) 1531-1538
27. Ünal O., Demir F., and Uygunoǧlu T. Fuzzy logic approach to predict stress-strain curves of steel fiber-reinforced concretes in compression. Build Environ 42 10 (2007) 3589-3595
28. Topçu I.B., and Sari{dotless}demir M. Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic. Comput Mater Sci 42 1 (2008) 74-82
29. TS EN 197-1. Cement-composition, specifications and conformity criteria - Part 1: Common cements. Ankara; 2000. [in Turkish].
30. TSI. TS 706 - aggregate for concretes. Ankara; 1980. [in Turkish].
31. TSI. TS 802 - design of concrete mixture. Ankara; 1985. [in Turkish].
32. Nielsen RH. Theory of the backpropagation neural network. In: IJCNN international joint conference on neural networks. USA, Washington DC; 1989. p. 593-605.
33. Haykin S. Neural networks: a comprehensive foundation (1994), Macmillan College Publishing Company, New York
34. Ozcan F. Properties of silica fume mortar and concrete and prediction of compressive strength by accelerated curing, PhD thesis, Cukurova University; 2005.
35. Masters T. Practical neural network recipes C++ (1993), Academic Press, San Diego, California
36. Kisi O., and Uncuoglu E. Comparison of the three backpropagation training algorithms for two case studies. Indian J Eng Mater Sci 12 5 (2005) 434-442
37. Hornik K. Multilayer feedforward networks are universal approximators. Neural Networks 2 (1989) 359-366
38. Hornik K., Stihchcombo M., and White H. Universal approximation of an unknown mapping and its derivatives using multiplayer feedforward networks. Neural Networks 3 (1990) 551-560
39. Sari{dotless}demir M. Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks. Adv Eng Software 40 5 (2009) 350-355
40. Zadeh L.A. Fuzzy set. Inform Control 8 (1965) 338-353
41. Mamdani E.H. Application of fuzzy algorithms for control of simple dynamic plants. Proc IEEE 121 12 (1976) 1585-1588