Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini

Yıl 2009, Cilt: 22 Sayı: 3, 113 - 122, 31.12.2009

İlker Bekir Topçu Cenk Karakurt Mustafa Sarıdemir

Öz

Bu çalışma, çimento üretiminde kullanılan mineral katkılarda en uygun kullanım oranının belirlenmesi için yapılmıştır. Laboratuvarda farklı kaynaklardan sağlanan doğal zeolit, tras, volkanik tüf, uçucu kül ve yüksek fırın cürufu katkıları, çimento üretiminde klinker yerine %10, 20, 30, 35, 40 ve 45 oranlarında kullanılmıştır. Çimentolar üzerinde yapılan 2, 7, 28 ve 180 günlük basınç dayanımı deneyleriyle dayanım gelişimi belirlenmiştir. Daha sonra deneysel çalışmadan elde edilen veriler kullanılarak yapay sinir ağı (YSA) yönteminde model geliştirilmiştir.

Anahtar Kelimeler

Katkılı çimento, Puzolanlar, Basınç dayanımı, Yapay sinir ağları

Predicting The Strength Development Of Different Pozzolan Cements By Artificial Neural Networks

Öz

This study is based on the determination of optimum usage of mineral additives as

supplementary cementing material for blended cement production. For this purpose, blended

cements were produced under laboratory conditions with natural zeolite, trass, volcanic tuff, fly

ash and ground granulated blast furnace slag at 10, 20, 30, 40 and 45% clinker replacement

ratios. Strength development of the cements was determined with compressive strength tests

performed at 2, 7, 28 and 180 days. Experimental results were also obtained by building models

according to artificial neural network (ANN) system.

Anahtar Kelimeler

Blended cement, Pozzolans, Compressive strength, Artificial neural network

Kaynakça

• [1] C. Shi, “An overview on the activation of reactivity of natural pozzolans,” Canadian Journal of Civil Engineering, Vol. 28, pp. 778-786, 2001.
• [2] M. Khandaker, A. Hossain, “Blended cement using volcanic ash and pumice,” Cement and Concrete Research, Vol. 33, pp. 1601-1605, 2003.
• [3] F. Massazza, “Pozzolans and Durability of Concrete”, 1st International Symposium on Mineral Admixtures in Cement, 1997, İstanbul, pp. 1-22.
• [4] C. Gervais, S.K. Ouki, “Performance study of cementitious systems containing zeolite and silica fume: effects of four metal nitrates on the setting time,” Strength and Leaching Characteristics, Journal of Hazardous Materials, Vol. B93, pp. 187-200, 2002.
• [5] M. Canbaz, “Alkalilerle aktive edilmiş yüksek fırın cüruflu harçların özelikleri”, Eskişehir Osmangazi Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, 206 s., 2007.
• [6] B.B. Adhikary, H. Mutsuyoshi, “Prediction of shear strength of steel fiber RC beams using neural networks,” Construction and Building Materials, Vol. 20, pp. 801-811, 2006.
• [7] A. Öztaş, M. Pala, E. Özbay, E. Kanca, N. Çağlar, M. Asghar Bhatti, “Predicting the compressive strength and slump of high strength concrete using neural network,” Construction and Building Materials, Vol. 20, pp. 769-775, 2005.
• [8] İ.B. Topçu, M. Sarıdemir, “Prediction of rubberized concrete properties using artificial neural networks and fuzzy logic,” Construction and Building Materials, Vol. 22, pp. 532- 540, 2008.
• [9] A.M. Kewalramani, R. Gupta, “Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks,” Automation in Construction, Vol. 15, pp. 374-379, 2006.
• [10] J.A. Anderson, “Cognitive and psychological computation with neural models,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 5, pp. 799-814, 1983.
• [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] İ.B. Topçu, M. Sarıdemir, “Prediction of properties of waste AAC aggregate concrete using ANN,” Computational Materials Science, Vol. 41, pp. 117-125, 2007.
• [13] A. Turatsinze S. Bonnet, J.L. Granju, “Potential of rubber aggregates to modify properties of cement based-mortars: improvement in cracking shrinkage resistance,” Construction and Building Materials, Vol. 21, pp. 176-181, 2007.
• [14] İ.B. Topçu, C. Karakurt, M. Sarıdemir, “Predicting the strength development of cements with different pozzolans by neural network and fuzzy logic,” Journal of Materials Design, Vol. 29, pp. 1986-1991, 2008.