WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Editorial Advances in Natural Building and Construction Materials(MDPI, 2025-12-16) Strzalkowski, Pawel; Sousa, Luis; Koken, Ekin; Strzałkowski, PawełArticle Citation - WoS: 2Citation - Scopus: 4Influence of Basalt Aggregate Crushing Technology on Its Geometrical Properties-Preliminary Studies(MDPI, 2023-01-08) Duchnowska, Magdalena; Strzalkowski, Pawel; Bakalarz, Alicja; Kazmierczak, Urszula; Koken, Ekin; Karwowski, Piotr; Stepien, Tomasz; Strzałkowski, PawełThe use of mineral aggregates is related to the increasing demand in construction, railway and road infrastructures. However, mineral aggregates can appear to be of variable quality, directly affecting their suitability for respective earthwork applications. Since the production of mineral aggregates should ensure the standardized, high-quality requirements of the final product, rock-crushing mechanisms should be investigated in a detailed manner. In this context, the aim of the present study is to evaluate and analyze the geometric parameters of basalt aggregates as a result of several rock comminution processes. Basalt aggregates from two deposits in Poland were used in the study. The samples are differentiated regarding both lithological variances, mineral composition as well as the host rock's tuff content. The rock comminution processes were conducted using two types of crushers, namely the laboratory-scale jaw and cone crushers. The feed for crushing was designed based on the original geometric grain composition and the separated feed in the form of flaky and non-flaky particles. The crushability test results demonstrated that the interparticle compression in the jaw crusher resulted in finer products compared to the one in the cone crusher. It was also observed that the flakiness and shape indexes decreased after crushing, both in the feed with the original geometric composition of the grains and those with flaky and non-flaky particles. Nevertheless, a higher flakiness index was obtained after the crushing of non-flaky particles and a lower one after the crushing of flaky particles. The flakiness index for grains below 16 mm after the crushing process was less than 10%, which indicates a more favorable result compared to the original feed. In addition, it was shown that flaky and non-cubical particles were accumulated in the finest (below 8 mm) and coarsest (above 20 mm) fractions in jaw and cone crushing processes, receiving flakiness and shape indexes ranging up to 80-100%. Finally, it was also observed that the lithological variances of the feed material have a significant impact on the particle size distribution of the product. More profoundly, basalt aggregates with a higher tuff content and weathering degree have a higher degree of crushing. The present study, in this context, provides accurate and satisfying information on understanding the crushing mechanisms of two important crushing equipment as well as their rock-crusher interactions.Article Citation - WoS: 11Citation - Scopus: 11Guidelines for Natural Stone Products in Connection With European Standards(MDPI, 2023-10-26) Strzalkowski, Pawel; Koken, Ekin; Sousa, Luis; Strzałkowski, PawełThe selection of ornamental stones for specific applications requires technical guidance since it should be based on the durability, service life, and aesthetic value of the stones. In most cases, these fundamentals provide quantitative data on the usability and performance of ornamental stones. The present study attempts to put forward a quantitative classification system for natural stone products concerning critical rock properties. For this purpose, fundamental physical and mechanical rock properties are listed based on European standards. Then, minimum limit values are proposed for different applications of natural stone products based on retrospective analyses of numerous ornamental stone applications. The suggested limit values based on several physical and mechanical rock properties can guide relevant engineers to initially consider possible rock types for use as natural stones in a wide range of applications. In this context, it is believed that the present study contributes to the natural stone industry by discussing the minimum limit values for the consideration of a wide range of rock types possibly usable in the dimension stone industry.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: 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: 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.Article Citation - WoS: 8Citation - Scopus: 9Assessment of Bohme Abrasion Value of Natural Stones Through Artificial Neural Networks (ANN)(MDPI, 2022-03-30) Strzalkowski, Pawel; Koken, Ekin; Strzałkowski, PawełThis present study explored the Bohme abrasion value (BAV) of natural stones through artificial neural networks (ANNs). For this purpose, a detailed literature survey was conducted to collect quantitative data on the BAV of different natural stones from Turkey. As a result of the ANN analyses, several predictive models (M1-M13) were established by using the rock properties, such as the dry density (rho(d)), water absorption by weight (w(a)), Shore hardness value (SHV), pulse wave velocity (V-p), and uniaxial compressive strength (UCS) of rocks. The performance of the established predictive models was evaluated by using several statistical indicators, and the performance analyses indicated that four of the established models (M1, M5, M10, and M11) could be reliably used to estimate the BAV of natural stones. In addition, explicit mathematical formulations of the proposed ANN models were also introduced in this study to let users implement them more efficiently. In this context, the present study is believed to provide practical and straightforward information on the BAV of natural stones and can be declared a case study on how to model the BAV as a function of different rock properties.Article Citation - WoS: 10Citation - Scopus: 9A Comparative Study on Power Calculation Methods for Conveyor Belts in Mining Industry(Taylor & Francis Ltd, 2021-07-29) Koken, Ekin; Lawal, Abiodun Ismail; Onifade, Moshood; Ozarslan, AhmetThis paper covers different methods to evaluate the power consumption of several conveyor belt systems (CBSs) used in the Turkish Mining Industry (TMI). Based on each CBS's operational features, the power consumption (P-c, kW) was measured directly on motorised head-pulleys. The P-c was investigated through several conventional, statistical, and machine learning methods. This study shows that the DIN 22,101 could be the most convenient conventional method for the investigated CBSs. On the other hand, based on the nonlinear regression (NLR) and genetic expression programming (GEP) models, two new approaches were suggested for the design and optimisation of the P-c.Article Citation - WoS: 7Citation - Scopus: 7A Combined Application of Two Soft Computing Algorithms for Weathering Degree Quantification of Andesitic Rocks(Elsevier, 2022-12) Koca, Tumay Kadakci; Koken, Ekin; Kadakci Koca, TümayUnderstanding the variations in physical and mechanical behavior of rock materials due to progressive weathering is vital to carry on time and cost-effective engineering projects. Up to date, soft computing algorithms have been established to quantify the weathering degree (WD) of various rocks due to better prediction performance and problem-solving capability. However, the complexity of the weathering process does not allow the use of a single weathering quantification model for a wide range of rock types. Therefore, this study aims to provide a practical, quantitative, and effective framework for predicting the WD of andesitic rocks. To fulfill the aims of this study, a wide range of cases were collected from the previous studies to establish a predictive model based on dry unit weight (gamma d), effective porosity (ne), and uniaxial compressive strength (UCS). Consequently, a combined application of fuzzy inference system (FIS) and artificial neural network (ANN) was introduced to assess the WD of the investigated andesitic rocks. The WD ratings were presented as four different weathering classes (from fresh (W0) to highly weathered (W3)). Since most soft computing algorithms are black-box models that cannot be efficiently utilized in any other study, an explicit neural network formulation was firstly developed for WD prediction in this study. As a result, the proposed formulation will provide a practical and straightforward assessment of WD for andesitic rocks. However, to improve the reliability and consistency of the proposed model, different datasets should be used in the explicit neural network formulation proposed.