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Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi

Year 2014, Volume: 35 Issue: 1, 1 - 11, 19.03.2014

Cengiz Özel Alper Topsakal

Abstract

 Özet. Beton basınç dayanımı betonun sahip olduğu diğer özelliklerle yakından ilişkili olduğundan en önemli özelliklerden biridir. Bu nedenle beton basınç dayanımının önceden belirlenmesine yönelik birçok çalışma son zamanlarla yoğun olarak yapılmaktadır. Bu çalışmada beton basınç dayanımın belirlenmesi için alternatif bir metot olarak veri madenciliği kullanılarak modeller geliştirilmiştir. Çalışma sonucunda beton basınç dayanımının modellenmesinde veri madenciliğinin başarılı bir şekilde kullanılabileceği sonucuna varılmıştır.

 

Abstract. Compressive strength is one of the most important mechanical properties of hardened concrete because it is related to other properties or performance of concrete. Therefore, many research of the early prediction of concrete properties has been intensively achieved in recent times. In this study, models for the determination of concrete compressive strength have been developed using data mining as an alternative method. These results suggested that the data mining algorithms can be used as an alternative approach to predict the concrete compressive strength.

Keywords

Beton Basınç Dayanımı Veri Madenciliği

References

  • Özel, C., Soykan, O., Zengin, B., 2012. Filler Olarak Mermer Tozu İçeren Beton
  • Özelliklerinin Bulanık Mantık Kullanılarak Belirlenmesi, e-Journal of New World Sciences Academy Engineering Sciences, 2A0075, 7, (2), 28-46.
  • Han, S.H., Kim, J.K., Park, Y.D., 2003. Prediction of compressive strength of fly ash concrete by new apparent activation energy function, Cem. Concr. Res., 33 (7), 96597
  • Chen, H.S., Sun, W., Stroeven, P., 2003. Prediction of compressive strength and optimization of mixture proportioning in ternary cementitious systems, Mater. Struct., 36 (260), 396-401.
  • Gupta, R., Kewalramani, M.A., Goel, A., 2006. Prediction of concrete strength using neural-expert system, J. Mat. Civ. Engrg., 18 (3), 462-466.
  • Peng, C.H., Yeh, I.C., Lien, L.C., 2009. Modeling strength of high-performance concrete using genetic operation trees with pruning techniques, Comput. Concr., 6 (3), 203-2
  • Sobhani, J., Najimi, M., Pourkhorshidi, A.R., Parhizkar, T., 2010. Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models, Const. Build. Mat., 24, 709–718.
  • Ozbay, E., Oztas, A., Baykasoglu, A., 2010. Cost optimization of high strength concretes by soft computing techniques, Comput. Concr., 7 (3), 221-237.
  • Bilgehan, M., Turgut, P., 2010. The use of neural networks in concrete compressive strength estimation, Comput. Concr., 7 (3), 271–283.
  • Atici, U., 2011. Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network, Expert Syst. Appl., 38 (8), 9609-9618.
  • Duan, Z.H., Kou, S.C., Poon, C.S., 2013. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks, Construction and Building Materials, 40, 2013, 1200-1206.
  • Dantas, A.T.A., Leite, M.B., Nagahama, K. J., 2013. Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks. Construction and Building Materials, 38, 2013, 717-722.
  • Erdal, H. İ., 2013. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction, Engineering Applications of
  • Artificial Intelligence, 26 (7), 2013, 1689-1697.
  • Chou, J.S., Pham, A.D., 2013. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials, 49, 554-563.
  • Yuan, Z., Wang, L. N., Ji, X., 2014. Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS, Advances in
  • Engineering Software 67, 156–163. Metwally, A.A.E., 2014. Compressive strength prediction of Portland cement concrete with age using a new model Housing and Building National Research Center
  • (HBRC Journal) http://dx.doi.org/10.1016/j.hbrcj.2013.09.005, (In Press).
  • Özel, C., 2007. Katkılı Betonların Reolojik Özeliklerinin Taze Beton Deney
  • Yöntemlerine Göre Belirlenmesi, S.D.Ü. Fen Bilimleri Enstitüsü İnşaat Mühendisliği A.B.D, Isparta. Yücel, K.T., Özel C, 2012. Modeling of mechanical properties and bond relationship using data mining process, Advances in Engineering Software 45, 54–60.
  • Terzi, Ö., Küçüksille, E.U., Keskin, M.E., 2005. Modeling of Daily Pan
  • Evaporation Using Data Mining. International Symposium on Innovations in Intelligent Systems and Applications, 182-185, İstanbul. Uyan, M., Çay, T. 2008. Mekânsal Uygulamalar İçin Veri Madenciliği Yaklaşımı, Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu, 13-15 Ekim 2008, 531538, Kayseri.
  • Terzi, Ö., Küçüksille, E.U., Ergin, G., İlker, A., 2011. Veri Madenciliği Süreci
  • Kullanılarak Güneş Işınımı Tahmini. SDU International Technologic Science, 3 (2), 29Terzi, S., 2006. Modelling the pavement present serviceability index of flexible highway pavements using data mining. J. Appl. Sci., 6 (1), 193–197.
  • Zhang, J., Shi, Y., Zhang, P., 2009. Several multi-criteria programming methods for classification. Comput. Operat. Res., 36, 823–836.
  • Keskin, M.E, Terzi, Ö., Küçüksille, E.U., 2009. Data mining process for integrated evaporation model. J. Irrig. Drain. Eng., 135(1), 39–43.
  • Küçüksille, E.U., Selbas, R., Şencan, A., 2009. Data mining techniques for thermophysical properties of refrigerants. Energy Convers. Manage, 50, 399–412.
  • Han, J., Kamber, M., 2006. Data Mining: Concepts and Techniques, Second Edition, Elsevier, 743 p.

-

Year 2014, Volume: 35 Issue: 1, 1 - 11, 19.03.2014

Cengiz Özel Alper Topsakal

Abstract

Compressive strength is one of the most important mechanical properties of hardened concrete because it is related to other properties or performance of concrete. Therefore, many research of the early prediction of concrete properties has been intensively achieved in recent times. In this study, models for the determination of concrete compressive strength have been developed using data mining as an alternative method. These results suggested that the data mining algorithms can be used as an alternative approach to predict the concrete compressive strength.

Keywords

Concrete Compressive Strength Data Mining

References

  • Özel, C., Soykan, O., Zengin, B., 2012. Filler Olarak Mermer Tozu İçeren Beton
  • Özelliklerinin Bulanık Mantık Kullanılarak Belirlenmesi, e-Journal of New World Sciences Academy Engineering Sciences, 2A0075, 7, (2), 28-46.
  • Han, S.H., Kim, J.K., Park, Y.D., 2003. Prediction of compressive strength of fly ash concrete by new apparent activation energy function, Cem. Concr. Res., 33 (7), 96597
  • Chen, H.S., Sun, W., Stroeven, P., 2003. Prediction of compressive strength and optimization of mixture proportioning in ternary cementitious systems, Mater. Struct., 36 (260), 396-401.
  • Gupta, R., Kewalramani, M.A., Goel, A., 2006. Prediction of concrete strength using neural-expert system, J. Mat. Civ. Engrg., 18 (3), 462-466.
  • Peng, C.H., Yeh, I.C., Lien, L.C., 2009. Modeling strength of high-performance concrete using genetic operation trees with pruning techniques, Comput. Concr., 6 (3), 203-2
  • Sobhani, J., Najimi, M., Pourkhorshidi, A.R., Parhizkar, T., 2010. Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models, Const. Build. Mat., 24, 709–718.
  • Ozbay, E., Oztas, A., Baykasoglu, A., 2010. Cost optimization of high strength concretes by soft computing techniques, Comput. Concr., 7 (3), 221-237.
  • Bilgehan, M., Turgut, P., 2010. The use of neural networks in concrete compressive strength estimation, Comput. Concr., 7 (3), 271–283.
  • Atici, U., 2011. Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network, Expert Syst. Appl., 38 (8), 9609-9618.
  • Duan, Z.H., Kou, S.C., Poon, C.S., 2013. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks, Construction and Building Materials, 40, 2013, 1200-1206.
  • Dantas, A.T.A., Leite, M.B., Nagahama, K. J., 2013. Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks. Construction and Building Materials, 38, 2013, 717-722.
  • Erdal, H. İ., 2013. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction, Engineering Applications of
  • Artificial Intelligence, 26 (7), 2013, 1689-1697.
  • Chou, J.S., Pham, A.D., 2013. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials, 49, 554-563.
  • Yuan, Z., Wang, L. N., Ji, X., 2014. Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS, Advances in
  • Engineering Software 67, 156–163. Metwally, A.A.E., 2014. Compressive strength prediction of Portland cement concrete with age using a new model Housing and Building National Research Center
  • (HBRC Journal) http://dx.doi.org/10.1016/j.hbrcj.2013.09.005, (In Press).
  • Özel, C., 2007. Katkılı Betonların Reolojik Özeliklerinin Taze Beton Deney
  • Yöntemlerine Göre Belirlenmesi, S.D.Ü. Fen Bilimleri Enstitüsü İnşaat Mühendisliği A.B.D, Isparta. Yücel, K.T., Özel C, 2012. Modeling of mechanical properties and bond relationship using data mining process, Advances in Engineering Software 45, 54–60.
  • Terzi, Ö., Küçüksille, E.U., Keskin, M.E., 2005. Modeling of Daily Pan
  • Evaporation Using Data Mining. International Symposium on Innovations in Intelligent Systems and Applications, 182-185, İstanbul. Uyan, M., Çay, T. 2008. Mekânsal Uygulamalar İçin Veri Madenciliği Yaklaşımı, Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu, 13-15 Ekim 2008, 531538, Kayseri.
  • Terzi, Ö., Küçüksille, E.U., Ergin, G., İlker, A., 2011. Veri Madenciliği Süreci
  • Kullanılarak Güneş Işınımı Tahmini. SDU International Technologic Science, 3 (2), 29Terzi, S., 2006. Modelling the pavement present serviceability index of flexible highway pavements using data mining. J. Appl. Sci., 6 (1), 193–197.
  • Zhang, J., Shi, Y., Zhang, P., 2009. Several multi-criteria programming methods for classification. Comput. Operat. Res., 36, 823–836.
  • Keskin, M.E, Terzi, Ö., Küçüksille, E.U., 2009. Data mining process for integrated evaporation model. J. Irrig. Drain. Eng., 135(1), 39–43.
  • Küçüksille, E.U., Selbas, R., Şencan, A., 2009. Data mining techniques for thermophysical properties of refrigerants. Energy Convers. Manage, 50, 399–412.
  • Han, J., Kamber, M., 2006. Data Mining: Concepts and Techniques, Second Edition, Elsevier, 743 p.
There are 28 citations in total.

Details

Primary Language Turkish
Journal Section Editorial
Authors

Cengiz Özel

Alper Topsakal

Publication Date March 19, 2014
Published in Issue Year 2014 Volume: 35 Issue: 1

Cite

APA Özel, C., & Topsakal, A. (2014). Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 35(1), 1-11.
AMA Özel C, Topsakal A. Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. March 2014;35(1):1-11.
Chicago Özel, Cengiz, and Alper Topsakal. “Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 35, no. 1 (March 2014): 1-11.
EndNote Özel C, Topsakal A (March 1, 2014) Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 35 1 1–11.
IEEE C. Özel and A. Topsakal, “Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi”, Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, vol. 35, no. 1, pp. 1–11, 2014.
ISNAD Özel, Cengiz - Topsakal, Alper. “Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 35/1 (March 2014), 1-11.
JAMA Özel C, Topsakal A. Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2014;35:1–11.
MLA Özel, Cengiz and Alper Topsakal. “Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, vol. 35, no. 1, 2014, pp. 1-11.
Vancouver Özel C, Topsakal A. Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2014;35(1):1-11.