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Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini

Year 2009, Volume: 22 Issue: 3, 113 - 122, 31.12.2009

Abstract

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.


References

  • [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.

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

Year 2009, Volume: 22 Issue: 3, 113 - 122, 31.12.2009

Abstract

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.


References

  • [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.
There are 14 citations in total.

Details

Subjects Civil Engineering
Journal Section Research Articles
Authors

İlker Bekir Topçu

Cenk Karakurt

Mustafa Sarıdemir This is me

Publication Date December 31, 2009
Acceptance Date July 1, 2009
Published in Issue Year 2009 Volume: 22 Issue: 3

Cite

APA Topçu, İ. B., Karakurt, C., & Sarıdemir, M. (2009). Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 22(3), 113-122.
AMA Topçu İB, Karakurt C, Sarıdemir M. Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini. ESOGÜ Müh Mim Fak Derg. December 2009;22(3):113-122.
Chicago Topçu, İlker Bekir, Cenk Karakurt, and Mustafa Sarıdemir. “Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 22, no. 3 (December 2009): 113-22.
EndNote Topçu İB, Karakurt C, Sarıdemir M (December 1, 2009) Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 22 3 113–122.
IEEE İ. B. Topçu, C. Karakurt, and M. Sarıdemir, “Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini”, ESOGÜ Müh Mim Fak Derg, vol. 22, no. 3, pp. 113–122, 2009.
ISNAD Topçu, İlker Bekir et al. “Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 22/3 (December 2009), 113-122.
JAMA Topçu İB, Karakurt C, Sarıdemir M. Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini. ESOGÜ Müh Mim Fak Derg. 2009;22:113–122.
MLA Topçu, İlker Bekir et al. “Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, vol. 22, no. 3, 2009, pp. 113-22.
Vancouver Topçu İB, Karakurt C, Sarıdemir M. Farklı Puzolanlarla Üretilmiş Çimentoların Dayanım Gelişiminin Yapay Sinir Ağlarıyla Tahmini. ESOGÜ Müh Mim Fak Derg. 2009;22(3):113-22.

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