Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - Scopus: 3Soft Computing Implementations for Evaluating Los Angeles Abrasion Value of Rock Aggregates From Kütahya, Turkey(Szechenyi Istvan University, 2024-02-28) Köken, E.The Los Angeles abrasion value (LAAV) of rocks is a critical mechanical aggregate property for designing road infrastructures and concrete quality. However, the determination of this critical aggregate property is labour-intensive and time-consuming and thus, in the literature, there are many predictive models to estimate the LAAV for different rock types. However, most of them are based on classical regression analyses, limiting their broader usage. In this study, several soft computing analyses are performed to develop robust predictive models for the evaluation of LAAV of rocks in the Ilıca region (Kütahya – Turkey). The main motivation for implementing soft computing analyses is that precise predictive models might be useful when exploring suitable rock types that are manufactured in crushing–screening plants. For this purpose, a comprehensive laboratory schedule was established to obtain some inputs for the evaluation of LAAV. As a result of the soft computing analyses, four robust predictive models are developed based on artificial neural networks (ANN), multiple adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) methodologies. The performance of the proposed models is investigated by some statistical indicators such as R2 and RMSE values and scatter plots. As a result, the ANFIS-based predictive model turns out to be the best alternative to estimate the LAAV of the investigated rocks. © 2025 Elsevier B.V., All rights reserved.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.Conference Object Modelling of Rock Comminution Using Statistical and Soft Computing Analyses – A Case Study on a Laboratory-Scale Jaw Crusher(Baski, 2022) Köken, E.The present study encompasses a quantitative investigation on rock comminution using statistical and soft computing analyses. For this purpose, physical and mechanical rock aggregate properties were determined for nine different rock types (R1-R9) in Turkey. Then, crushability tests were performed to determine the size reduction ratio (SRR) using a laboratory-scale jaw crusher. Based on statistical and soft computing analyses, five different predictive models (M1 to M5) were established to estimate the SRR in this study. Consequently, the SRR values are associated with water absorption by weight (w<inf>a</inf>), dry unit weight (γ<inf>d</inf>), and aggregate impact value (AIV) of the investigated rocks. However, the individual use of these independent variables results in undulating SRR estimations. Therefore, among the established predictive models, the empirical formulation based on artificial neural networks (ANN) (M5) was found to be the most reliable model with a correlation of determination value (R2) of 0.88. However, the predictive models stated in this study should be implemented to several portable jaw crushers to observe the similarities or difficulties in quantifying SRR as a function of rock properties in future studies. © 2022 Elsevier B.V., All rights reserved.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.Conference Object Citation - WoS: 1Estimation of Cohesion for Intact Rock Materials Using Regression and Soft Computing Analyses(IOP Publishing Ltd, 2024-01-01) Koken, E.; Strzalkowski, P.; Kazmierczak, U.; Strzałkowski, P.Shear strength parameters such as cohesion (c) and internal friction angle (phi) are among the most critical rock properties used in the geotechnical design of most engineering projects. However, the determination of these properties is laboring and requires special equipment. Therefore, this study introduces several predictive models based on regression and artificial intelligence methods to estimate the c of different rock types. For this purpose, a comprehensive literature survey is carried out to collect quantitative data on the shear strength properties of different rock types. Then, regression and soft computing analyses are performed to establish several predictive models based on the collected data. As a result of these analyses, five different predictive models (M1-M5) were established. Based on the performance of the established predictive models, the artificial neural network-based predictive model (model 5, M5) was the most suitable choice for evaluating the c for different rock types. In addition, mathematical expressions behind the M5 model are also presented in this study to allow users to implement it more efficiently. In this regard, the present study can be declared a case study showing the applicability of regression and soft computing analyses to evaluate the c of different rock types. However, the number of datasets used in this study should be increased to get more comprehensive predictive models in future studies.Article Citation - WoS: 1Citation - Scopus: 2Estimating Uniaxial Compressive Strength of Pyroclastic Rocks Using Soft Computing Techniques(Shahrood Univ Technology, 2024) Koken, EkinIn 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: 1Citation - Scopus: 1Development of Comprehensive Predictive Models for Evaluating Böhme Abrasion Value (BAV) of Dimension Stones Using Non-Destructive Testing Methods(MDPI, 2024-12-25) Koken, EkinDue to the global demand for dimension stones, fast and reliable evaluation tools are essential for assessing the quality of dimension stones. For this reason, this study aims to develop comprehensive tools for estimating the abrasion resistance of various dimension stones from Turkey. Non-destructive rock properties, including dry density (rho d), water absorption by weight (wa), and pulse wave velocity (Vp), were determined to build a comprehensive database for soft computing analyses. Three predictive models were established using multivariate adaptive regression spline (MARS), M5P, and artificial neural networks (ANN) methodologies. The performance of the models was assessed through scatter plots and statistical indicators, showing that the ANN-based model outperforms those based on M5P and MARS. The applicability of the models was further validated with independent data from the existing literature, confirming that all models are suitable for estimating varying B & ouml;hme abrasion values (BAVs). A MATLAB-based software tool, called B & ouml;hme abrasion calculator (v1.00), was also developed, allowing users to estimate BAV values by inputting adopted non-destructive rock properties. This tool is available upon request, supporting the dimension stone industry and fostering future research in this field.Article Citation - WoS: 3Citation - Scopus: 3A Novel Evaluation Methodology for Dimension Stone Quality(Wroclaw Univ Technology, Fac Geoengineering Mining & Geology, 2024) Koken, Ekin; Strzalkowski, Pawel; Strzałkowski, PawełThe physical and mechanical properties of natural stones are crucial factors in determining their quality, predicting their durability, and assessing their potential uses. In this study, a novel method is introduced to assess the quality of dimension stone using the Fuzzy logic inference system (FIS). The FIS analysis results are described as dimension stone field performance coefficient (DSFPC), which indicates the quality of dimension stones. The analysis results are also compared with different approaches, and it is concluded that the proposed FIS model can reliably be used to quantify the quality of dimension stones. The present study, in this manner, contributes to the natural stone industry by proposing a comprehensive predictive model used to quantify the dimension stone quality based on critical physicomechanical rock properties.