Yazar "Gunaydin, O." seçeneğine göre listele

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    A brittleness index to estimate the sawability of carbonate rocks
    (A A BALKEMA PUBLISHERS, 2005) Kahraman, S.; Fener, M.; Gunaydin, O.; Konecny, P
    Performance measurements of large-diameter circular diamond saws were conducted on eight different carbonate rocks in marble factories located in some areas of Turkey and hourly slab productions were calculated. Rock samples were collected from these factories for laboratory tests. Triaxial compressive strength test were carried out on each rock type and a brittleness index determined from Mohr's envelope. Slab productions of circular diamond saws and the brittleness index evaluated using both linear and exponential regression analysis. The both regression analysis produced strong correlations between the slab production and the brittleness index. Concluding remark is that the sawability of carbonate rocks can be predicted from the brittleness index determined from Mohr's envelope. However, the validity of the derived equations must be checked for the other rock types.
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    Adaptive neuro-fuzzy modeling for the swelling potential of compacted soils
    (SPRINGER, 2009) Kayadelen, C.; Taskiran, T.; Gunaydin, O.; Fener, M.
    This paper aims to present the usability of an adaptive neuro fuzzy inference system (ANFIS) for the prediction swelling potential of the compacted soils that are important materials for geotechnical purposes such as engineered barriers for municipal solid waste, earth dams, embankment and roads. In this study the swelling potential that is also one of significant parameters for compacted soils was modeled by ANFIS. For the training and testing of ANFIS model, data sets were collected from the tests performed on compacted soils for different geotechnical application in Nigde. Four parameters such as coarse-grained fraction ratio (CG), fine-grained fraction ratio (FG), plasticity index (PI) and maximum dry density (MDD) were presented to ANFIS model as inputs. The results obtained from the ANFIS models were validated with the data sets which are not used for the training stage. The analyses revealed that the predictions from ANFIS model are in sufficient agreement with test results.
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    Estimation of California bearing ratio by using soft computing systems
    (PERGAMON-ELSEVIER SCIENCE LTD, 2011) Yildirim, B.; Gunaydin, O.
    This study presents the application of different methods (simple-multiple analysis and artificial neural networks) for the estimation of the California bearing ratio (CBR) from sieve analysis, Atterberg limits, maximum dry unit weight and optimum moisture content of the soils. The resistance of granular soils, which are in the superstructure foundation and subgrade layers are usually tested by CBR (California bearing ratio), which is an old and still extensively used experiment. The data were collected from the public highways of Turkey's different regions. Regression analysis and artificial neural network estimation indicated strong correlations (R-2 = 0.80-0.95) between the sieve analysis, Atterberg limits, maximum dry unit weight (MOD) and optimum moisture content (OMC). It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time. (c) 2010 Elsevier Ltd. All rights reserved.
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    Evaluating the geomechanical properties of Misis fault breccia (Turkey)
    (PERGAMON-ELSEVIER SCIENCE LTD, 2008) Kahraman, S.; Alber, M.; Fener, M.; Gunaydin, O.
    This study investigated the correlations between the uniaxial compressive strength (UCS), elastic modulus (E) or the differential stress (Delta sigma) and density, P- and S-wave velocity, volumetric block proportion (VBP), average block diameter (($) over bar (block)), average block diameter factor (ABD(F)), aspect ratio and roundness. The results were evaluated by both simple and multiple regression analysis. Simple regression analysis results show no correlation between UCS, E or Delta sigma and the other examined parameters, except S-wave velocity. A correlation was, however, found between UCS and S-wave velocity for 25% and 75% VBP. Other significant models including density, P- and S-wave velocity and textural properties for the prediction of UCS and Ds were found by multiple regression analysis. From these results it is concluded that the strength of the Misis fault breccia cannot be defined by a single parameter such as VBP as is the case for some bimrocks. It is also concluded that the models developed by multiple regression analysis can be used to predict the UCS and Ds of the Misis fault breccia. The multiple regression models including two or three independent variables are the most practical equations. Using these equations to predict the UCS and Ds of the fault breccia is easier, faster and cheaper than conducting triaxial or uniaxial compressive strength tests. (C) 2008 Elsevier Ltd. All rights reserved.
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    Evaluating the strength and deformability properties of Misis fault breccia using artificial neural networks
    (PERGAMON-ELSEVIER SCIENCE LTD, 2009) Kahraman, S.; Gunaydin, O.; Alber, M.; Fener, M.
    Since the preparation of smooth specimens from the fault breccias are usually difficult and expensive, the development of some predictive models for the geomechanical properties of fault breccias will be useful. In this study, artificial neural networks (ANNs) analysis was applied on the data pertaining to Misis fault breccia to develop some predictive models for the uniaxial compressive strength (UCS) and elastic modulus (E) from the indirect methods. The developed ANNs models were also compared with the regression models. As a result of ANNs analysis, very good models were derived for both UCS and E estimation. It was shown that ANNs models were more reliable than the regression models. Concluding remark is that UCS and E values of Misis fault breccia can reliably be estimated from the indirect methods using ANNs analysis. (c) 2008 Elsevier Ltd. All rights reserved.
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    Indentation hardness test to estimate the sawability of carbonate rocks
    (SPRINGER HEIDELBERG, 2008) Kahraman, S.; Gunaydin, O.
    The performance of large-diameter circular saws on eight carbonate rocks was recorded and indentation hardness, density and porosity tests were undertaken on the five travertines, two limestone and one dolomitic limestone samples returned to the laboratory. A strong linear correlation between indentation hardness index values and the hourly production of the circular saws was found. The slab production was slowest for the dolomitic limestone rocks with the highest indentation hardness, lowest porosity and highest density values.
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    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.
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    The effect of porosity on the relation between uniaxial compressive strength and point load index
    (PERGAMON-ELSEVIER SCIENCE LTD, 2005) Kahraman, S.; Gunaydin, O.; Fener, M.
    [Abstract Not Available]
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    The effect of rock classes on the relation between uniaxial compressive strength and point load index
    (SPRINGER HEIDELBERG, 2009) Kahraman, S.; Gunaydin, O.
    Uniaxial compressive strength and point load tests were carried out on 17 igneous, 16 metamorphic and 19 sedimentary rocks and the values correlated with their I (s) values. The influence of the different rock type was investigated using regression analysis and the derived equations were statistically tested. Although the derived equation for all data is significant, the data points are scattered and the coefficient of correlation is not strong. However, when the regression analysis was repeated for igneous, metamorphic and sedimentary rocks respectively, the data were less scattered and stronger correlation coefficients were obtained.
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    The usability of Cerchar abrasivity index for the prediction of UCS and E of Misis Fault Breccia: Regression and artificial neural networks analysis
    (PERGAMON-ELSEVIER SCIENCE LTD, 2010) Kahraman, S.; Alber, M.; Fener, M.; Gunaydin, O.
    The derivation of some predictive models for the geomechanical properties of fault breccias will be useful due to the fact that the preparation of smooth specimens from the fault breccias is usually difficult and expensive. To develop some predictive models for the uniaxial compressive strength (UCS) and elastic modulus (E) from the indirect methods including the Cerchar abrasivity index (CAI), regression and artificial neural networks (ANNs) analysis were applied on the data pertaining to Misis Fault Breccia. The CAI was included to the best regression model for the prediction of UCS. However, the CAI was not included to the best regression model for the prediction of E. The developed ANNs model was also compared with the regression model. It was concluded that the CAI is a useful property for the prediction of UCS of Misis Fault Breccia. Another conclusion is that ANNs model is more reliable than the regression models. (C) 2010 Elsevier Ltd. All rights reserved.