TY  - JOUR
TT  - Predicting The Strength Development Of Different Pozzolan Cements By Artificial Neural Networks
AU  - Topçu, İlker Bekir
AU  - Karakurt, Cenk
AU  - Sarıdemir, Mustafa
PY  - 2009
DA  - December
Y2  - 2009
JF  - Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi
JO  - ESOGÜ Müh Mim Fak Derg
PB  - Eskişehir Osmangazi Üniversitesi
WT  - DergiPark
SN  - 2630-5712
SP  - 113
EP  - 122
VL  - 22
IS  - 3
KW  - Katkılı çimento
KW  - Puzolanlar
KW  - Basınç dayanımı
KW  - Yapay sinir ağları
N2  - This study is based on the determination of optimum usage of mineral additives assupplementary cementing material for blended cement production. For this purpose, blendedcements were produced under laboratory conditions with natural zeolite, trass, volcanic tuff, flyash and ground granulated blast furnace slag at 10, 20, 30, 40 and 45% clinker replacementratios. Strength development of the cements was determined with compressive strength testsperformed at 2, 7, 28 and 180 days. Experimental results were also obtained by building modelsaccording to artificial neural network (ANN) system.
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neural networks and fuzzy logic,” Construction and Building Materials, Vol. 22, pp. 532-
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CR  - [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.
CR  - [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
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CR  - [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.
UR  - https://dergipark.org.tr/tr/pub/ogummf/issue//325477
L1  - https://dergipark.org.tr/tr/download/article-file/320470
ER  -