Browsing by Author "Ozvan, A."

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Compressive Strength of Scoria Added Portland Cement Concretes
(Gazi Univ, 2012) Ozvan, A.; Tapan, M.; Erik, O.; Efe, T.; Depci, T.
This paper presents the results of preliminary studies investigating the potential effects of using scoria as supplementary and its amount on compressive strength of concrete. Concrete mixtures containing 5, 10, 20, 30, 40 and 50 % scoria type natural pozzolan by mass of the total cementitious material were prepared. In addition, a conventional portland cement concrete mixture with the same w/cm ratio was prepared as a reference. Preliminary results indicated that the compressive strength of the concrete mixtures with scoria added up to 30% exceeded the strength of the conventional mixture at 3, 7, 28 and 91 days, whereas the compressive strength of the 40 and 50% scoria added concrete mixtures decreased at 3, 7, 28 and 91 days. It was found that early strength of scoria added (up to 30%) concrete mixtures showed higher compressive strengths up to 112%.
Modeling of the Angle of Shearing Resistance of Soils Using Soft Computing Systems
(Pergamon-elsevier Science Ltd, 2009) Kayadelen, C.; Gunaydin, O.; Fener, M.; Demir, A.; Ozvan, A.
Precise determination of the effective angle of shearing resistance (phi') value is a major concern and an essential criterion in the design process of the geotechnical structures, such as foundations, embankments, roads, slopes, excavation and liner systems for the solid waste. The experimental determination of phi' is often very difficult, expensive and requires extreme cautions and labor. Therefore many statistical and numerical modeling techniques have been suggested for the phi' value. However they can only consider no more than one parameter, in a simplified manner and do not provide consistent accurate prediction of the phi' value. This study explores the potential of Genetic Expression Programming, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) computing paradigm in the prediction of phi' value of soils. The data from consolidated-drained triaxial tests (CID) conducted in this study and the different project in Turkey and literature were used for training and testing of the models. Four basic physical properties of soils that cover the percentage of fine grained (FG), the percentage of coarse grained (CG), liquid limit (LL) and bulk density (BD) were presented to the models as input parameters. The performance of models was comprehensively evaluated some statistical criteria. The results revealed that GEP model is fairly promising approach for the prediction of angle of shearing resistance of soils. The statistical performance evaluations showed that the GEP model significantly outperforms the ANN and ANFIS models in the sense of training performances and prediction accuracies. (C) 2009 Elsevier Ltd. All rights reserved.