Browsing by Author "Cetin T."

Now showing 1 - 7 of 7
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    The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces
    (Sharif University of Technology, 2012) Erzin Y.; Cetin T.
    This study deals with the development of Artificial Neural Network (ANN) and Multiple Regression (MR) models for estimating the critical factor of safety (Fs) value of a typical artificial slope subjected to earthquake forces. To achieve this, while the geometry of the slope and the properties of the man-made soil are kept constant, the natural subsoil properties, namely, cohesion, internal angle of friction, the bulk unit weight of the layer beneath the ground surface and the seismic coefficient, varied during slope stability analyses. Then, the Fs values of this slope were calculated using the simplified Bishop method, and the minimum (critical) Fs value for each case was determined and used in the development of the ANN and MR models. The results obtained from the models were compared with those obtained from the calculations. Moreover, several performance indices, such as determination coefficient, variance account for, mean absolute error and root mean square error, were calculated to check the prediction capacity of the models developed. The obtained indices make it clear that the ANN model has shown a higher prediction performance than the MR model. © 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
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    The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions
    (2013) Erzin Y.; Cetin T.
    This study deals with development of artificial neural network (ANN) and multiple regression (MR) models that can be employed for estimating the critical factor of safety (Fs) value of homogeneous finite slopes. To achieve this, the Fs values of 675 homogenous finite slopes having different soil and slope parameters were calculated by using the simplified Bishop method and the minimum (critical) Fs value for each slope was determined and used in the ANN and MR models. The results obtained from ANN and MR models were compared with those obtained from the calculations. The values predicted from ANN models matched the calculated values much better than those obtained from MR models. Additionally, several performance indices such as determination coefficient (R2), variance account for (VAF), mean absolute error (MAE), and root mean square error (RMSE) were calculated; the receiver operating curves (ROC) were drawn, and the areas under the curves (AUC) were calculated to assess the prediction capacity of the ANN and MR models. ANN models have shown higher prediction performance than MR models based on the performance indices and the AUC values. The results demonstrated that the ANN models can be used at the preliminary stage of designing homogeneous finite slope. © 2012 Elsevier Ltd.
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    The prediction of the critical factor of safety of homogeneous finite slopes subjected to earthquake forces using neural networks and multiple regressions
    (2014) Erzin Y.; Cetin T.
    In this study, artificial neural network (ANN) and multiple regression (MR) models were developed to predict the critical factor of safety (Fs) of the homogeneous finite slopes subjected to earthquake forces. To achieve this, the values of Fs in 5184 nos. of homogeneous finite slopes having different slope, soil and earthquake parameters were calculated by using the Simplified Bishop method and the minimum (critical) Fs for each of the case was determined and used in the development of the ANN and MR models. The results obtained from both the models were compared with those obtained from the calculations. It is found that the ANN model exhibits more reliable predictions than the MR model. Moreover, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed. Also, the receiver operating curves were drawn, and the areas under the curves (AUC) were calculated to assess the prediction capacity of the ANN and MR models developed. The performance level attained in the ANN model shows that the ANN model developed can be used for predicting the critical Fs of the homogeneous finite slopes subjected to earthquake forces. © 2014 Techno-Press, Ltd.
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    The use of self-organizing feature map networks for the prediction of the critical factor of safety of an artificial slope
    (Institute of Computer Science, 2016) Erzin Y.; Nikoo M.; Nikoo M.; Cetin T.
    In this study, the performance of three different self organization feature map (SOFM) network models denoted as SOFM1, SOFM2, and SOFM3 having neighborhood shapes, namely, SquareKohonenful, LineKohonenful, and Diamond-Kohenenful, respectively, to predict the critical factor of safety (Fs) of a widely-used artificial slope subjected to earthquake forces was investigated and compared. For this purpose, the reported data sets by Erzin and Cetin (2012) [7], including the minimum (critical) Fs values of the artificial slope calculated by using the simplified Bishop method, were utilized in the development of the SOFM models. The results obtained from the SOFM models were compared with those obtained from the calculations. It is found that the SOFM1 model exhibits more reliable predictions than SOFM2 and SOFM3 models. Moreover, the performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed to evaluate the prediction capacity of the SOFM models developed. The study demonstrates that the SOFM1 model is able to predict the Fs value of the artificial slope, quite efficiently, and is superior to the SOFM2 and SOFM3. © 2017 CTU FTS.
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    Feasible Sanitary Sewer Network Generation Using Graph Theory
    (Hindawi Limited, 2019) Turan M.E.; Bacak-Turan G.; Cetin T.; Aslan E.
    A graph theory-based methodology is proposed for the sewer system optimization problem in this study. Sewer system optimization includes two subproblems: layout optimization and hydraulic design optimization, which can be solved independently or solved simultaneously. No matter which method is chosen for the solution of the optimization problem, a feasible layout that satisfies the restrictions of the sewer system must be obtained in any step of the solution. There are two different layout options encountered: the layouts containing all sewer links and the layouts not containing all sewer links. The method proposed in this study generates a feasible sewer layout that contains all sewer links and satisfies all restrictions of a sanitary sewer system by using graph theory without any additional strategies unlike other studies. The method is applied to two different case studies. The results of the case studies have shown that graph theory is well applicable to sewer system optimization and the methodology proposed based on it is capable of generating a feasible layout. This study is expected to stimulate the use of graph theory on similar studies. © 2019 Mustafa Erkan Turan et al.
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    Analyzing the Effect of Sewer Network Size on Optimization Algorithms’ Performance in Sewer System Optimization
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Turan M.E.; Cetin T.
    Sewer systems are a component of city infrastructure that requires large investment in construction and operation. Metaheuristic optimization methods have been used to solve sewer optimization problems. The aim of this study is to investigate the effects of network size on metaheuristic optimization algorithms. Cuckoo Search (CS) and four versions of Grey Wolf Optimization (GWO) were utilized for the hydraulic optimization of sewer networks. The purpose of using different algorithms is to investigate whether the results obtained differ depending on the algorithm. In addition, to eliminate the parameter effect, the relevant algorithms were run with different parameters, such as population size. These algorithms were performed on three different-sized networks, namely small-sized, medium-sized, and large-sized networks. Friedman and Wilcoxon tests were utilized to statistically analyze the results. The results were also evaluated in terms of the optimality gap criterion. According to the results based on the optimality gap, the performance of each algorithm decreases as the network size increases. © 2024 by the authors.
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    Oncological Outcomes of Chromophobe Versus Clear Cell Renal Cell Carcinoma: Results from A Contemporary Turkish Patient Cohort
    (Urology and Nephrology Research Centre, 2024) Cetin T.; Celik S.; Sozen S.; Ozen H.; Akdogan B.; Aslan G.; Baltaci S.; Suer E.; Bayazit Y.; Izol V.; Muezzinoglu T.; Gokalp F.; Tinay I.
    Purpose: To compare the oncological outcomes of clear cell RCC (ccRCC), which is common in renal cell carcinomas (RCC), and chromophobic RCC (chRCC), which is less common, and to define the factors affecting survival in the Turkish patient population for both RCC subclassifications. Materials and Methods: Patients with a pathologically confirmed RCC diagnosis after radical or partial nephrectomy in the Turkish Urooncology Association (TUOA), Urological Cancers Database-Kidney (UroCaD-K), were retrospectively reviewed. Patients with ccRCC and chRCC were included in the study. Primary outcomes of this study are recurrence-free survival (RFS), overall survival (OS) and cancer-specific survival (CSS) for each histological subtype. Results: Data from 5300 patients in the TUOA UroCaD-K are reviewed and a total of 2560 patients (2225 in the ccRCC group and 335 in the chRCC group) are included in the final analysis. In the comparison of the groups, tumor size was greater both radiologically and pathologically in chRCC (p=0.019 vs 0.002 respectively). Recurrence-free survival (RFS), overall survival (OS) and cancer-specific survival (CSS) rates are worse in ccRCC subgroup. In the evaluation of risk factors; pathological stage, local invasion and Fuhrmann grade were found to be significant for recurrence in ccRCC. Age, body mass index and pathological stage were the risk factors affecting overall mortality (OM). Pathological tumor size was an independent risk factor for recurrence in chRCC, while age was analyzed as the only parameter affecting OM. Conclusion: chRCC oncological data and OS, CSS and RFS rates were found to be better than ccRCC in the Turkish patient population. © (2024), (Urology and Nephrology Research Centre). All Rights Reserved.