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Sahinbey, Turkey

The Zirve University is a private university established in 2009, located in Gaziantep, southeastern Anatolia, Turkey. Currently, the university has seven faculties. It is a research-oriented institution with the English language as a primary medium of teaching. This university has total enrollment of more than 5,000 undergraduate and graduate students. Wikipedia.

Nohut S.,Zirve University
Computational Materials Science | Year: 2011

Distinct element method (DEM) is a powerful method to simulate the behavior of the heterogeneous materials. In this article the effect of average grain size on the crack-tip toughness of alumina ceramics is investigated by using a commercial computer program PFC2D with the inclusion of residual stresses caused by thermal expansion anisotropy (TEA) of alumina grains into a DEM model. This is the first time that the residual stresses are included into DEM model and the residual stresses will be included as deviation to the strength of parallel bonds. Since the sintering technology and modeling of sintering is not the main subject of this article, the experimentally measured microlevel residual stress values will be used in order to predict a macrolevel behavior (fracture) of alumina. Comparison of the numerical results with the experimental ones showed that the default parallel-bonded-particle model in program PFC2D gives particle-size dependent results when the fracture of alumina is simulated. With the help of the introduced normalization procedure, the crack-tip toughness of alumina with any average grain size can be predicted if the residual stresses are known and vice versa. © 2010 Elsevier B.V. All rights reserved. Source

Yuen K.K.F.,Zirve University
International Journal of Information Technology and Decision Making | Year: 2011

This research proposes primitive cognitive network process (P-CNP), which comprises five decision processes, as an alternative of analytic hierarchy process (AHP). Two published cases using AHP are revised by P-CNP, and the validity and applicability of P-CNP are demonstrated. The comparison results indicate that AHP produces questionable results due to the ill-defined axioms of the perception of the paired difference, and suggest that the proposed P-CNP performs better than AHP in multicriteria decision-making problems. © 2011 World Scientific Publishing Company. Source

Bilgehan M.,Zirve University
Applied Soft Computing Journal | Year: 2011

The investigation of the effects of cracks or similar weaknesses on the load carrying capacity of structural elements such as columns, beams and shells is an important problem in civil, mechanical, earthquake and aerospace engineering. In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been successfully used for the buckling analysis of slender prismatic columns with a single non-propagating open edge crack subjected to axial loads. The main focus of this work has been to study the feasibility of using ANFIS and neural network (NN) trained with the non-dimensional crack depth and the non-dimensional crack location parameters to predict the critical buckling load of fixed-free, pinned-pinned, fixed-pinned and fixed-fixed supported, axially loaded compression rods. A comparative study is made using the neural nets and neuro-fuzzy techniques. Statistic measures were used to evaluate the performance of the models. Based on comparison of the results it is found that the proposed ANFIS architecture with Gaussian membership function is found to perform better than the multilayer feed forward ANN learning by backpropagation algorithm. The final results show that especially the ANFIS modeling may constitute an efficient tool for elastic buckling analysis of edge cracked columns. Architectures of the ANFIS and NN established in the current study perform sufficiently in the estimation of critical buckling loads, and particularly the ANFIS estimates closely follow the actual values for the whole data sets. These model architectures can be used as a non-destructive procedure for health monitoring of structural elements. © 2011 Elsevier B.V. Source

Akgobek O.,Zirve University
Energy Education Science and Technology Part A: Energy Science and Research | Year: 2012

Classification and rule induction are two important methods/processes to extract knowledge from data. In rule induction, the representation of knowledge is defined as IF-THEN rules which are easily understandable and applicable by problem-domain experts. Classification is to organize a large data set objects into predefined classes, described by a set of attributes, using supervised learning methods. The objective of this study is to present a new classification algorithm, RES (Rule Extraction System), for automatic knowledge acquisition in data mining. It aims at eliminating the pitfalls and the disadvantages of the techniques and algorithms currently in use. The proposed algorithm makes use of the direct rule extraction approach, rather than the decision tree. For this purpose, it uses a set of examples to induce general rules. In this study, the rule base is created through the knowledge discovery by employing RES algorithm, a data mining technique, on the sample sets of the Wisconsin Breast Cancer, Ljubljana Breast Cancer, Dermatology, Hepatitis, Iris, Tic-Tac-Toe, Nursery, Lympograph, CRX and Diabetes, which are real life data and commonly used in the machine learning. In terms of the accuracy rate, the results of this study were compared to the results of the algorithms widely used in this field, such as C4.5, NavieBayes, PART, CN2, CORE, GA-SVM. The proposed algorithm showed promising results. © Sila Science. Source

Nohut S.,Zirve University
Ceramics International | Year: 2014

Strength distribution of advanced ceramics is mostly characterized by Weibull distribution function. The question whether the Weibull distribution always gives the best fit to strength data has been being considered in the last years. The sample size affects the reliable decision of discrimination of different distribution functions (e.g. normal, log-normal, gamma or Weibull). In this paper, 5100 experimental alumina strength data and virtual strength data generated by Monte Carlo simulations are used in order to investigate the effect of sample size on strength distribution of advanced ceramics. It is suggested that, at least 150-200 samples should be used for determination of best fitting distribution function with a statistical fallibility of 10%. Extreme Value Analysis performed with the experimental strength data showed that the Weibull distribution fits the data best and difference between the Weibull and Gumbel distributions appear at the tails. © 2013 Elsevier Ltd and Techna Group S.r.l. Source

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