Buradasınız

Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi

Prediction of Concrete Compressive Strength Using Data Mining

Publication Year:

Keywords (Original Language):

Abstract (2. Language): 
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.
Abstract (Original Language): 
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.
1
11

REFERENCES

References: 

[1] 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.
[2] 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), 965-
971.
[3] 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.
[4] Gupta, R., Kewalramani, M.A., Goel, A., 2006. Prediction of concrete strength using
neural-expert system, J. Mat. Civ. Engrg., 18 (3), 462-466.
[5] 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),
203-223.
Cengiz ÖZEL, Alper TOPSAKAL
56
[6] 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.
[7] Ozbay, E., Oztas, A., Baykasoglu, A., 2010. Cost optimization of high strength
concretes by soft computing techniques, Comput. Concr., 7 (3), 221-237.
[8] Bilgehan, M., Turgut, P., 2010. The use of neural networks in concrete compressive
strength estimation, Comput. Concr., 7 (3), 271–283.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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).
[16] 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.
[17] Yücel, K.T., Özel C, 2012. Modeling of mechanical properties and bond
relationship using data mining process, Advances in Engineering Software 45, 54–60.
Veri Madenciliği Kullanarak Beton Basınç Dayanımının Belirlenmesi
57
[18] 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.
[19] Uyan, M., Çay, T. 2008. Mekânsal Uygulamalar İçin Veri Madenciliği Yaklaşımı,
2. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu, 13-15 Ekim 2008, 531-
538, Kayseri.
[20] Terzi, Ö., Küçüksille, E.U., Ergin, G., İlker, A., 2011. Veri Madenciliği Süreci
Kullanılarak Güneş Işınımı Tahmini. SDU International Technologic Science, 3 (2),
29-37.
[21] Terzi, S., 2006. Modelling the pavement present serviceability index of flexible
highway pavements using data mining. J. Appl. Sci., 6 (1), 193–197.
[22] Zhang, J., Shi, Y., Zhang, P., 2009. Several multi-criteria programming methods
for classification. Comput. Operat. Res., 36, 823–836.
[23] Keskin, M.E, Terzi, Ö., Küçüksille, E.U., 2009. Data mining process for integrated
evaporation model. J. Irrig. Drain. Eng., 135(1), 39–43.
[24] Küçüksille, E.U., Selbas, R., Şencan, A., 2009. Data mining techniques for
thermophysical properties of refrigerants. Energy Convers. Manage, 50, 399–412.
[25] Han, J., Kamber, M., 2006. Data Mining: Concepts and Techniques, Second
Edition, Elsevier, 743 p.

Thank you for copying data from http://www.arastirmax.com