WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Assessment of the Quality of Tuffs in Central Anatolia, Turkey: A Quantitative Classification Approach(Acad Sci Czech Republic Inst Rock Structure & Mechanics, 2025-12-03) Koken, Ekin; Ince, IsmailThe growing global demand for dimension stones necessitates efficient and accurate evaluation methods to ensure their optimal use in various industries. To assess their suitability for various dimension stone applications, this study investigates tuffs from Central Anatolia, Turkey. For this purpose, the fundamental physical and mechanical properties of the tuffs were determined in laboratory studies, and a detailed durability assessment was conducted for each rock type. The analysis results indicate that most of the examined rocks are of low quality and more suitable for non-load-bearing applications. Based on the collected data, fuzzy clustering techniques were applied to develop a new classification system, categorising the tuffs into four classes (Class A-D) according to their potential applications. Additionally, a user-friendly MATLAB-based software tool was also developed to facilitate the implementation of the proposed classification system.Article Citation - WoS: 3Citation - Scopus: 3Roles of Curing Conditions on Properties of Soil Reinforced With Palm Fiber and Lime(Ice Publishing, 2021-03) Qu, Jili; Wang, Junfeng; Batugin, Andrian; Zhu, Hao; Koken, Ekin; Mihaela, Cristea Lavinia; Zhang, YawenDue to the environment-friendly properties of palm fiber, its use was attempted to improve the quality of soil together with lime. Unconfined compressive tests were carried out on soils mixed with palm fiber and lime under the three curing conditions of immersion in water, cyclic wetting-drying and air-curing for a series of contents of additives. The static stiffness of five types of samples (the number 1 type is the control sample) was also analyzed against curing conditions, curing time and sample type. Results from the tests show that the immersion in water condition is the best for the formation of unconfined compressive strength (UCS) and static stiffness, while the air-curing condition is the worst. The highest UCS can be acquired with 1% palm fiber and 20.7% lime, and the highest static stiffness was acquired with purely 20.7% lime content. The fastest increase rate is presented by the curing condition of immersion in water. The logarithmic function is more suitable for expressing the relationship between static stiffness and curing time. It is important for site engineers to understand the curing conditions and stabilizing mechanism of palm fiber and lime for the design and construction of civil engineering projects.Article Citation - WoS: 1Estimating the Power Draw of Grizzly Feeders Used in Crushing-Screening Plants Through Soft Computing Algorithms(Konya Teknik Univ, 2024-01-02) Koken, EkinIn this study, the power draw (P) of several grizzly feeders used in the Turkish Mining Industry (TMI) is investigated by considering the classification and regression tree (CART), random forest (RF) and adaptive neuro-fuzzy inference system (ANFIS) algorithms. For this purpose, a comprehensive field survey is performed to collect quantitative data, including power draw (P) of some grizzly feeders and their working conditions such as feeder width (W), feeder length (L), feeder capacity (Q), and characteristic feed size (F80). 80 ). Before applying the soft computing methodologies, correlation analyses are performed between the input parameters and the output (P). According to these analyses, it is found that W and L are highly associated with P. On the other hand, Q is moderately correlated with P. Consequently, numerous soft computing models were run to estimate the P of the grizzly feeders. Soft computing analysis results demonstrate no superiority between the performances of RF and CART models. The RF analysis results indicate that the W is necessary for evaluating P for grizzly feeders. On the other hand, the ANFIS-based predictive model is found to be the best tool to estimate varying P values, and it satisfies promising results with a correlation of determination value (R2) of 0.97. It is believed that the findings obtained from the present study can guide relevant engineers in selecting the proper motors propelling grizzly feeders.Article Development of Soft Computing-Based Predictive Tools for Estimating the Young Modulus of Weak Rocks(Univ Zielona Gora, 2024-09-19) Koken, Ekin; Strzalkowski, PawelThe deformation characteristics of rocks are of vital importance in addressing most geomechanical issues as they are one of the most critical input parameters in rock engineering analyses. For this reason, robust forecasting models are required when analysing the stability of tunnels, slopes, mine galleries, and other underground excavations. In this research, novel predictive models are proposed to estimate the tangential Young modulus (E-ti) of weak rocks. To achieve this, an extensive literature review is performed to obtain a comprehensive database including critical physico-mechanical properties of various weak rocks. Thanks to the advantages of soft neural networks (ANN) and multivariate adaptive regression splines (MARS), novel predictive models are established. The effectiveness of the developed predictive models is investigated using various statistical measures and it is concluded that empirical models utilizing ANN and ANFIS methodologies are the most effective tools for estimating the E-ti of weak rocks. In addition, a practical design chart is also developed for assessing the E-ti of weak rocks.Article Citation - WoS: 1Assessment of Installed Power for Inclined Belt Conveyors Using Genetic Algorithm and Artificial Neural Networks(Konya Teknik Univ, 2022-06-01) Koken, EkinIn this study, the installed power (P inst , kW) of several inclined belt conveyors operating in the mining industry of Turkey was investigated through two soft computing algorithms (i.e., genetic expression programming (GEP) and artificial neural networks (ANN)). For this purpose, the most crucial belt (i.e., belt length (L), belt width (W), belt inclination (alpha)), operational (i.e., belt speed (Vb) b ) and throughput (Q)) and infrastructural (belt weight (Wb) b ) and idler weight (Wid)) id )) features of 42 belt conveyors were collected for each investigated belt conveyor. The collected data was transformed into a comprehensive dataset for soft computing analyses. Based on the GEP and ANN analyses, two robust predictive models were proposed to estimate the P inst . The performance of the proposed models was evaluated using several statistical indicators, and the statistical evaluations demonstrated that the models yielded a correlation of determination (R2) 2 ) greater than 0.95. Nevertheless, the ANN-based model has slightly overperformed in predicting the P inst values. In conclusion, the proposed models can be reliably used to estimate the P inst for the investigated conveyor belts. In addition, the mathematical expressions of the proposed models were given in the present study to let users implement them more efficiently.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.