Yüksek-Düşük Kireçli Uçucu Kül İçeren Betonların Basınç Dayanımının YSA Ve BM Kullanarak Tahmini

Year 2008, Volume: 21 Issue: 1, 39 - 56, 30.06.2008

İlker Bekir Topçu , Mustafa Sarıdemir

Abstract

Bu calısmada, yuksek ve dusuk kirecli ucucu kuller iceren betonların 7, 28 ve 90 gunluk basınc dayanımını tahmin etmek icin yapay sinir ağları ve bulanık mantıkta modeller olusturulmustur. Bu modelleri olusturmak amacıyla 52 farklı karısımda 180 numune literaturden elde edilmistir. Yapay sinir ağları ve bulanık mantık modellerinde kullanılan veriler; gun, Portland cimento, su, kum, kırmatas-I, kırmatas-II, yuksek oranda su azaltıcı katkı yer değisim oranı, ucucu kul yer değişim oranı ve CaO icerecek bir formatta 9 girdi parametreli; cıktı parametresi betonun basınc dayanımı olarak duzenlenmistir. Modellerdeki eğitim ve test sonucları, yapay sinir ağları ve bulanık mantık sistemlerinin, ucucu kul iceren betonların 7, 28 ve 90 gunluk basınc dayanımını tahmin etmek icin guclu potansiyele sahip olduğunu gostermistir.

Keywords

Dayanım, uçucu kül, yapay sinir ağları, bulanık mantık.

Prediction Of Compressive Strength Of Concrete Containing High-Low Fly Ash Using ANN And FL

Abstract

In this study, artificial neural networks and fuzzy logic models for

predicting the 7, 28 and 90-day compressive strength of concretes containing highlime

and low-lime fly ashes have been developed. For purpose of constructing these

models, 52 different mixes with 180 specimens were gathered from the literature.

The data used in the artificial neural networks and fuzzy logic models are arranged

in a format of nine input parameters that cover the day, Portland cement, water,

sand, crushed stone-I, crushed stone-II, high range water reducing agent

replacement ratio, fly ash replacement ratio and CaO, and an output parameter

which is compressive strength of concrete. In the models of the training and testing

results have shown that artificial neural networks and fuzzy logic systems have

strong potential for predicting 7, 28 and 90-day compressive strength of concretes

containing fly ash.

Keywords

Strength, fly ash, artificial neural networks, fuzzy logic

References

• [1] S.-H. Han, J.-K. Kim, Y.-D. Park, “Prediction of compressive strength of fly ash concrete by new apparent activation energy function”, Cement and Concrete Research, Vol.33, pp. 965-971, 2003.
• [2] L. Lam, Y.L. Wong, C.S. Poon, “Effect of FA and SF on compressive and fracture behaviors of concrete”, Cement and Concrete Research, Vol.28, pp.271-83, 1998.
• [3] R. Siddique, “Performance characteristics of high-volume Class F fly ash concrete”, Cement and Concrete Research, Vol.34, pp. 487-493, 2004.
• [4] K.G. Babu, G.S.N. Rao, “Early strength of FA concrete”, Cement and Concrete Research, Vol.24, pp.277-84, 1994.
• [5] M. Pala, E. Özbay, A. Öztas, M.I. Yüce, “Appraisal of long-term effects of fly ash and silika fume on compressive strength of concrete by neural networks”, Construction and Building Materials, Vol.12, No.2, pp. 384-394, 2007.
• [6] M. Tokyay, “Strength prediction of fly ash concretes by accelerated testing”, Cement and Concrete Research, Vol. 29, pp.1737-1741, 1999.
• [7] R. Đnce, “Prediction of fracture parameters of concrete by artificial neural networks”, Engineering Fracture Mechanics, Vol.71, pp. 2143-2159, 2004.
• [8] F. Rosenblatt, “Principles of neuro dynamics: Perceptrons and the theory of brain mechanisms”, Washington, DC: Spartan Books, 1962.
• [9] W.S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in neural nets”, Bulletin of Mathematical Biophysics, Vol.5, pp.115-137, 1943.
• [10] D.E. Rumelhart, G.E. Hinton, R.J. William, “Learning internal representation by error propagation”, In: Rumelhart DE, McClelland JL, editors. Proceeding Parallel Distributed Processing. Foundation, Vol. 1. Cambridge: MIT Press; 1986.
• [11] S.W. Liu, J.H. Huang, J.C. Sung, C.C. Lee, “Detection of cracks using neural networks and computational mechanics”, Computer Methods in Applied Mechanics Engineering, Vol.191, pp. 2831-2845, 2002.
• [12] Đ.B. Topçu, M. Sarıdemir, “Prediction of rubberized concrete properties using artificial neural network and fuzzy logic”, Construction and Building Materials, 2007 (in press).
• [13] H.M. Günaydın, S.Z. Doğan, “A neural network approach for early cost estimation of structural systems of building”, International Journal of Project Management, Vol.22, No.7, pp. 595-602, 2004.
• [14] Đ.B. Topçu, M. Sarıdemir, “Prediction of properties of waste AAC aggregate concrete using ANN”, Computational Materials Science, Vol. 41, No.1, pp.117-125, 2007.
• [15] L.A. Zadeh, “Fuzzy sets”, Information and Control, Vol.8, pp.338-353, 1967.
• [16] F. Demir, “A new way of prediction elastic modulus of normal and high strength concrete-fuzzy logic”, Cement and Concrete Res., Vol.35, pp.1531- 1538, 2005.
• [17] Z. Sen, “Fuzzy algorithm for estimation of solar irradiation from sunshine duration”, Solar Energy, Vol.63, No.1, pp. 39-49, 1998.
• [18] E.H. Mamdani, S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller”, Int. Journal of Man-Machine Studies, Vol.7, pp.1-13, 1975.
• [19] K.M. Passino and S. Yurkovich, “Fuzzy Control”, Addison-Wesley, 1998.
• [20] F.M. McNeill, E. Thro, “Fuzzy Logic: A practical approach”, AP Professional, Boston, MA, 1994.
• [21] S. Akkurt, G. Tayfur, S. Can, “Fuzzy logic model for the prediction of cement compressive strength”, Cement and Concrete Research, Vol.34, No.8, pp. 1429-1433, 2004.
• [22] G. Đnan, A.B. Göktepe, K. Ramyar, A. Sezer, “Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology”, Building and Environment, 2005 (in press).
• [23] M. Sugeno, G.T. “Kang Structure identification of fuzzy model”, Fuzzy Sets Syst Man Cybern, Vol.23, No.3, pp. 665-685, 1993.
• [24] T. Takagi, M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control”, IEEE Transactions on Systems Man and Cybernetics, Vol.15, pp. 116-132, 1985.
• [25] J.S.R. Jang, C.T. Sun, “Neuro-fuzzy modeling and control”, In: Proceeding of the IEEE, Vol.83, pp. 378-405, 1995.
• [26] S. Akbulut, AS, Hasiloğlu, S. Pamukcu, “Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system”, Soil Dynamics and Earthquake Engineering, Vol.24, pp. 805-814, 2004.