Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic

Advances in Engineering Software

Volumn 40, Issue 9, 2009, Pages 920-927

  Saridemir, Mustafa ^a  

^a NIGDE UNIVERSITY   (Turkey)

Author keywords

Compressive strength; Fuzzy logic; Metakaolin; Mortar; Neural network

Indexed keywords

BACKPROPAGATION; BINDERS; CIVIL ENGINEERING; COMPRESSIVE STRENGTH; FEEDFORWARD NEURAL NETWORKS; FUZZY INFERENCE; FUZZY SETS; MORTAR; MULTILAYER NEURAL NETWORKS; MULTILAYERS; PLASTER;

ARTIFICIAL NEURAL NETWORKS; BINDER RATIOS; CIVIL ENGINEERING APPLICATIONS; EXPERIMENTAL STUDIES; FUZZY INFERENCE MODELS; FUZZY LOGIC APPROACHES; FUZZY LOGIC SYSTEMS; HUMAN ACTIVITIES; INPUT PARAMETERS; METAKAOLIN; MIXTURE PROPORTIONS; MULTI-LAYER FEED-FORWARD NEURAL NETWORKS; REPLACEMENT RATIOS; SUPERPLASTICIZER; TECHNICAL LITERATURES; TRAINING AND TESTING;

FUZZY NEURAL NETWORKS;

EID: 67349283336     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2008.12.008     Document Type: Article

References (39)

1. Gleize P.J.P., Cyr M., and Escadeillas G. Effects of metakaolin on autogenous shrinkage of cement pastes. Cement Concrete Compos 29 2 (2007) 80-87
2. Sabir B.B., Wild S., and Bai J. Metakaolin and calcined clays as pozzolans for concrete: a review. Cement Concrete Compos 23 6 (2001) 441-454
3. Vu D.D., Stroeven P., and Bui V.B. Strength and durability aspects of calcined kaolin-blended Portland cement mortar and concrete. Cement Concrete Compos 23 6 (2001) 471-478
4. Poon C.S., Kou S.C., and Lam L. Compressive strength, chloride diffusivity and pore structure of high performance metakaolin and silica fume concrete. Constr Build Mater 20 10 (2006) 858-865
5. Parande A.K., Babu B.R., Karthik M.A., Deepak Kumaar K.K., and Palaniswamy N. Study on strength and corrosion performance for steel embedded in metakaolin blended concrete/mortar. Constr Build Mater 22 3 (2008) 127-134
6. Zhang M.H., and Malhotra V.M. Characteristics of a thermally activated alumino-silicate pozzolanic material and its use in concrete. Cement Concrete Res 25 8 (1995) 1713-1725
7. Brooks J.J., and Megat Johari M.A. Effect of metakaolin on creep and shrinkage of concrete. Cement Concrete Compos 23 6 (2001) 495-502
8. Courard L., Darimont A., Schouterden M., Ferauche F., Willem X., and Degeimbre R. Durability of mortars modified with metakaolin. Cement Concrete Res 33 9 (2003) 1473-1479
9. Caldarone M.A., Gruber K.A., and Burg R.G. High reactivity metakaolin: a new generation mineral admixture. Concrete Int November (1994) 37-40
10. Wild S., Kahib J.M., and Jones A. Relative strength, pozzolanic activity and cement hydration in superplasticised metakaolin concrete. Cement Concrete Res 26 10 (1996) 1537-1544
11. 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 Softw 34 11-12 (2003) 663-669
12. Ince R. Prediction of fracture parameters of concrete by artificial neural networks. Eng Fract Mech 71 15 (2004) 2143-2159
13. Adhikary B.B., and Mutsuyoshi H. Prediction of shear strength of steel fiber RC beams using neural networks. Constr Build Mater 20 9 (2006) 801-811
14. Kewalramani A.M., and Gupta R. Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Auto Constr 15 15 (2006) 374-379
15. Pala M., Özbay E., Öztaş A., and Yüce M.I. Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Constr Build Mater 21 2 (2007) 384-394
16. Topçu I.B., and Sari{dotless}demir M. Prediction of properties of waste AAC aggregate concrete using artificial neural networks. Comput Mater Sci 41 1 (2007) 117-125
17. Demir F. Prediction of elastic modulus of normal and high strength concrete by artificial neural network. Constr Build Mater 22 7 (2008) 1428-1435
18. Altun F., Kişi O., and Aydin K. Predicting the compressive strength of steel fiber added lightweight concrete using neural network. Comput Mater Sci 42 2 (2008) 259-265
19. Şen Z. Fuzzy algorithm for estimation of solar irradiation from sunshine duration. Sol Energy 63 1 (1998) 39-49
20. Muthukumaran P., Demirli K., Stiharu I., and Bhat R.B. Boundary conditioning for structural tuning using fuzzy logic approach. Comput Struct 74 5 (2000) 547-557
21. Demir F. A new way of prediction elastic modulus of normal and high strength concrete-fuzzy logic. Cement Concrete Res 35 8 (2005) 1531-1538
22. İnan G., Göktepe A.B., Ramyar K., and Sezer A. Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology. Build Environ 42 7 (2007) 1264-1269
23. Topçu I.B., and Sari{dotless}demir M. Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Constr Build Mater 22 4 (2008) 532-540
24. Topçu I.B., and Sari{dotless}demir M. Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic. Comput Mater Sci 41 3 (2008) 305-311
25. 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 41 1 (2008) 74-82
26. Topçu I.B., and Sari{dotless}demir M. Prediction of rubberized mortar properties using artificial neural network and fuzzy logic. J Mater Process Technol 199 1-3 (2008) 108-118
27. Lippman R. An introduction to computing with neural nets. IEEE ASSP Mag 4 (1987) 4-22
28. Mansour M.Y., Dicleli M., Lee J.Y., and Zhang J. Predicting the shear strength of reinforced concrete beams using artificial neural network. Eng Struct 26 6 (2004) 781-799
29. Mukherjee A., and Biswas S.N. Artificial neural networks prediction of mechanical behavior of concrete at high temperature. Nucl Eng Des 178 1 (1997) 1-11
30. Tosun N., and Özler L. A study of tool life in hot machining using artificial neural networks and regression analysis method. J Mater Process Technol 124 1-2 (2002) 99-104
31. Rumelhart D.E., Hinton G.E., and William R.J. Learning internal representations by error propagations. In: Rumelhart D.E., and McClelland J.L. (Eds). Proceedings parallel distributed processing foundations vol. 1 (1986), MIT Press, Cambridge
32. Demir F., and Korkmaz K.A. Prediction of lower and upper bounds of elastic modulus of high strength concrete. Constr Build Mater 22 7 (2008) 1385-1393
33. Firat M., and Güngör M. River flow estimation using adaptive neuro fuzzy inference system. Math Comput Simulat 75 3-4 (2007) 87-96
34. Mamdani E.H., and Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Machine Stud 7 (1975) 1-13
35. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cyb 15 (1985) 116-132
36. Sugeno M., and Kang G.T. Structure identification of fuzzy model. Fuzzy Sets Syst Man Cyb 23 (1993) 665-685
37. Akbulut S., Hasiloǧlu A.S., and Pamukcu S. Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system. Soil Dyn Earthquake Eng 24 11 (2004) 805-814
38. Fazel Zarandi M.H., Türksen I.B., Sobhani J., and Ramezanianpour A.A. Fuzzy polynomial neural networks for approximation of the compressive strength of concrete. Appl Soft Comput 8 11 (2008) 488-498
39. Jang J.S.R., and Sun C.T. Neuro-fuzzy modeling and control. Proc IEEE 83 (1995) 378-405