Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Investigations on Fracture Evolution of Coal Measure Sandstones from Mineralogical and Textural Points of View
    (Springer India, 2020-04-09) Koken, Ekin
    The purpose of the present study is to investigate the variations in fracture evolution of sandstones arising from mineralogical and textural features. For this purpose, eight types of coal measure sandstones located in the Zonguldak Hardcoal Basin (ZHB) were considered. The mineralogical and textural characterizations of the rocks were carried out. Physico-mechanical properties were determined for each rock type. Based on quantitative strain-based methods, the crack initiation (sigma(ci)) and crack damage (sigma(cd)) thresholds of the sandstones were determined. The laboratory test results indicate that the sigma(ci)and sigma(cd)of the sandstones were found to be between 0.27-0.43 and 0.61-0.83 of the UCS, respectively. In general, the sigma(ci)and sigma(cd)correspond to 0.37 and 0.71 of the UCS, respectively. The sigma(ci)and sigma(cd)decrease with increasing the sorting coefficient (S-c), average grain size (d(50), mm), contents of feldspar (F, %), and lithic fragment (LF, %). On the other hand, increasing quartz content (Qtz, %) increases those variables. Remarkable changes were obtained in the sigma(ci)and sigma(cd)when effective porosity (n(e)) and pulse wave velocity (V-p) of the rocks exceed 3% and 3.00 km/s, respectively. As a result of mineralogical analyses and laboratory studies, statistical analyses were carried out. Accordingly, the sigma(ci)and sigma(cd)could be estimated reliably using several empirical relationships established in the present study. In order to represent the importance and utilization of rock mineralogy and texture for underground mining applications in the ZHB, several suggestions and considerations related to aV-cut gallery blasting operation were introduced.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 2
    Estimating Uniaxial Compressive Strength of Pyroclastic Rocks Using Soft Computing Techniques
    (Shahrood Univ Technology, 2024) Koken, Ekin
    In this study, several soft computing analyses are performed to build some predictive models to estimate the uniaxial compressive strength (UCS) of the pyroclastic rocks from central Anatolia, Turkey. For this purpose, a series of laboratory studies are conducted to reveal physico-mechanical rock properties such as dry density (rho d), effective porosity (ne), pulse wave velocity (Vp), and UCS. In soft computing analyses, rho d, ne, and Vp are adopted as the input parameters since they are practical and cost-effective non-destructive rock properties. As a result of the soft computing analyses based on the classification and regression trees (CART), multiple adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN), and gene expression programming (GEP), five robust predictive models are proposed in this study. The performance of the proposed predictive models is evaluated by some statistical indicators, and it is found that the correlation of determination (R2) value for the models varies between 0.82 - 0.88. Based on these statistical indicators, the proposed predictive models can be reliably used to estimate the UCS of the pyroclastic rocks.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 7
    Assessment of Los Angeles Abrasion Value (LAAV) and Magnesium Sulphate Soundness (MWL) of Rock Aggregates Using Gene Expression Programming and Artificial Neural Networks
    (Polska Akad Nauk, Polish Acad Sciences, 2023-07-24) Koken, Ekin
    It has been acknowledged that two important rock aggregate properties are the Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (Mwl). However, the determination of these properties is relatively challenging due to special sampling requirements and tedious testing procedures. In this stu-dy, detailed laboratory studies were carried out to predict the LAAV and Mwl for 25 different rock types located in NW Turkey. For this purpose, mineralogical, physical, mechanical, and aggregate properties were determined for each rock type. Strong predictive models were established based on gene expression programming (GEP) and artificial neural network (ANN) methodologies. The performance of the proposed models was evaluated using several statistical indicators, and the statistical analysis results demonstra-ted that the ANN-based proposed models with the correlation of determination (R2) value greater than 0.98 outperformed the other predictive models established in this study. Hence, the ANN-based predictive models can reliably be used to predict the LAAV and Mwl for the investigated rock types. In addition, the suitability of the investigated rock types for use in bituminous paving mixtures was also evaluated based on the ASTM D692/D692M standard. Accordingly, most of the investigated rock types can be used in bituminous paving mixtures. In conclusion, it can be claimed that the proposed predictive models with their explicit mathematical formulations are believed to save time and provide practical knowledge for evaluating the suitability of the rock aggregates in pavement engineering design studies in NW Turkey.
  • Article
    Citation - WoS: 10
    Citation - Scopus: 9
    Assessment of Deformation Properties of CoAl Measure Sandstones Through Regression Analyses and Artificial Neural Networks
    (Polska Akad Nauk, Polish Acad Sciences, 2023-07-24) Koken, Ekin
    The deformation properties of rocks play a crucial role in handling most geomechanical problems. However, the determination of these properties in laboratory is costly and necessitates special equipment. Therefore, many attempts were made to estimate these properties using different techniques. In this study, various statistical and soft computing methods were employed to predict the tangential Young Modulus (Eti, GPa) and tangential Poisson's Ratio (vti) of coal measure sandstones located in Zonguldak Hardcoal Basin (ZHB), NW Turkey. Predictive models were established based on various regression and artificial neural network (ANN) analyses, including physicomechanical, mineralogical, and textural properties of rocks. The analysis results showed that the mineralogical features such as the contents of quartz (Q, %) and lithic fragment (LF, %) and the textural features (i.e., average grain size, d50, and sorting coefficient, Sc) have remarkable impacts on deformation properties of the investigated sandstones. By comparison with these features, the mineralogical effects seem to be more effective in predicting the Eti and vti. The performance of the established models was assessed using several statistical indicators. The predicted results from the proposed models were compared to one another. It was concluded that the empirical models based on the ANN were found to be the most convenient tools for evaluating the deformational properties of the investigated sandstones.