WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 5Citation - Scopus: 6Size Reduction Characterization of Underground Mine Tailings: A Case Study on Sandstones(Springer, 2020-06-09) Koken, EkinThe production of construction and building materials starts with reducing the size of natural, industrial, and waste materials. In addition to strength and durability considerations of natural resources recommended by various institutions, size reduction characterization, specific to rock aggregates, has a vital role in their size-related quality. In this study, various sandstones extracted from underground mines located in northwestern Turkey were investigated for size reduction characterizations. Several mineralogical, textural, and physico-mechanical properties were determined for each rock type. Crushability tests were carried out using a laboratory-scale cone crusher for different feeding size fractions, namely + 11.20 - 16.00 mm (size I), + 9.52 - 16.00 mm (size II), and + 6.30 - 16.00 mm (size III). Based on the crushability tests, crushed particles were analyzed, focusing on production yield, size, and shape properties. Each crushability test was also explored for energy consumption arising from varying rock properties of the sandstones. The laboratory test results demonstrated that the degree of rock crushability (DRC) and specific energy consumption (E-cs, kJ/kg) were associated with the Brazilian tensile strength (BTS, MPa) and apparent porosity (n(e), %) of the sandstones. The results also showed that the degree of sorting in mineral constituents, quantified as the sorting coefficient (S-c), affected the DRC. However, mineralogical features of the sandstones have no significant impact on DRC andE(cs). Variations in feeding gradation, irrespective of whether mineralogical, textural, or physico-mechanical properties, have remarkable effects on product flakiness and yields for specific size fractions. In light of the findings obtained, the present study provides knowledge on how the sandstones behave under cone crushing operations.Article Citation - WoS: 24Citation - Scopus: 24Prediction of Mechanical Properties of Coal From Non-Destructive Properties: A Comparative Application of MARS, ANN, and GA(Springer, 2021-10-06) Lawal, Abiodun Ismail; Oniyide, Gafar O.; Kwon, Sangki; Onifade, Moshood; Koken, Ekin; Ogunsola, Nafiu O.Rock properties are useful for safe operation and design of both surface and underground mines including civil engineering projects. However, the cost and time required to perform detailed assessments of rock properties are high. In addition, rock properties are required in numerical modeling. Different models have been proposed for quick and easy assessments of rock properties but majority of these models are regression-based, which are incapable of capturing inherent variabilities in rock properties. Therefore, this study proposed three different soft computing models (i.e., double input-single output ANN, multivariate adaptive regression spline, genetic algorithm) for accurate prediction of several mechanical properties of coal and coal-like rocks. The performances of the proposed models were statistically evaluated using various indices and they were found to predict rock properties suitably with very strong statistical indices. The proposed models were validated further using external datasets aside from those used in the model development to test the generalization potential of the models. The Pearson's correlation coefficients for the validation were close to 1, indicating that the proposed models can be used to assess geo-mechanical properties of coal, shale, and coal-bearing rocks.Article Citation - WoS: 5Citation - Scopus: 5Evaluation of Soft Computing Methods for Estimating Tangential Young Modulus of Intact Rock Based on Statistical Performance Indices(Springer, 2022-04-06) Koken, Ekin; Kadakci Koca, TumayThe tangential Young modulus (E-ti) of intact rock is a critical parameter in engineering geological design calculations and rock mass classification systems. The E-ti of various rock types has been successfully estimated by many studies based on numerous soft computing methods in recent years. However, these studies mainly involve a single analysis method or are valid for a limited number of samples. For this reason, this study aimed to compare artificial neural networks (ANN), adaptive neural fuzzy inference system (ANFIS), and Gene expression programming (GEP) methods to estimate the E-ti of various rock types based on 147 datasets collected from the published literature. As a result of the soft computing analyses, three different predictive models were proposed in this study. In the proposed prediction models, rock properties such as dry density (rho(d)), effective porosity (n(e)), P-wave velocity (V-p), and uniaxial compressive strength (UCS) were used. The estimation performance of the proposed models was examined through several performance indices such as coefficient of determination (R-2), root mean square error (RMSE), the variance accounted for (VAF), and mean absolute percent error (MAPE). As a result of statistical analyses, it was determined that the ANFIS model presents a better prediction performance (R-2 = 0.967) than the other methods in the training datasets. On the other hand, the accuracy of the ANFIS model decreased significantly in the test datasets (R-2 = 0.803). Furthermore, the GEP model presented the lowest predictive performance. Finally, considering the overall estimation accuracy of the proposed models, it was concluded that the proposed ANN model with an R-2 of 0.94 could reliably be used to estimate the E-ti of investigated rocks.Article Citation - WoS: 8Citation - Scopus: 12Assessment of Rock Aggregate Quality Through the Analytic Hierarchy Process (AHP)(Springer, 2020-05-22) Koken, Ekin; Top, Soner; Ozarslan, AhmetThe present study aimed to assess rock aggregate quality through the Analytic Hierarchy Process (AHP). In the context of the AHP analyses, four rock types (i.e., andesite, basalt, granodiorite, and gabbro), five evaluation criteria, and several technical requirements/suggestions for coarse aggregates related to bituminous paving mixtures were considered. In order to set over the evaluation criteria, detailed laboratory studies were conducted. For this purpose, various mineralogical, physical, and mechanical aggregate properties were determined for each rock type concerning their weathering grades. As a result of the laboratory studies, it was determined that the rock weathering processes have substantial negative impacts on the rock aggregate properties considered in this study. The AHP analysis results indicated that that different rock types have several advantages concerning various evaluation criteria. Based on the general evaluation point (EP) of the rocks, the gabbros were found to have the highest rock aggregate quality (EP = 0.393). In contrast, the andesites had the lowest quality (EP = 0.069). Besides, the basalts (EP = 0.271) and granodiorites (EP = 0.267) presented approximately the same quality for their use in bituminous pavement mixtures. It was also demonstrated that the AHP, with its specific methodology, can be utilized to represent different environmental and mechanical conditions by changing the relative weight of the evaluation criteria. In this way, the pros and cons of different rock types could be revealed quantitatively, which enables related engineers to select proper rock types for their use under different environmental and mechanical conditions. From this point of view, the present study could be declared a case study noted for combining theoretical and practical approaches on bituminous paving mixtures as a sign of rock aggregate quality.Article Citation - WoS: 2Citation - Scopus: 3Assessment of Rock Aggregate Quality Through Fuzzy Inference System(Springer, 2022-04-01) Koken, Ekin; Baspinar Tuncay, EbruIn this study, Fuzzy Inference System (FIS) was adopted to evaluate the rock aggregate quality. For this purpose, some technical standards for coarse aggregates were integrated into the FIS analyses as threshold values. As a result, several membership functions were established using rock aggregate properties such as water absorption by weight (w(a)), flakiness index (FI), Los Angeles abrasion value (LAAV), and magnesium sulfate soundness (M-wl). Based on 48 if-then rules, the implementation and verification of the proposed FIS model were carried out using sixteen rock types whose field performances as coarse aggregate were previously evaluated [i.e., low quality (LQ), average quality (NQ), high quality (HQ), etc.] by field engineers. The results obtained from the FIS analyses were declared a Rock Aggregate Quality Assessment Rating (RQAR), where higher RQAR values indicate rock aggregates with higher quality. The results obtained from the FIS analyses are almost in good agreement with those obtained from the field performances of the investigated rocks. However, the number of cases should be increased to improve the proposed FIS model. In this context, the number of if-then rules membership functions can be rearranged according to the need. This study, in this manner, can be declared a case study indicating how to quantity rock aggregate quality based on FIS analyses.