WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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COAR Access Rights: search.filters.coarAccessRights.open accessSubject: search.filters.subject.Soft Computing

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Now showing 1 - 4 of 4
  • Conference Object
    Citation - WoS: 1
    Estimation 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
    Development of Soft Computing-Based Predictive Tools for Estimating the Young Modulus of Weak Rocks
    (Univ Zielona Gora, 2024-09-19) Koken, Ekin; Strzalkowski, Pawel
    The 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: 1
    Citation - Scopus: 1
    Development of Comprehensive Predictive Models for Evaluating Böhme Abrasion Value (BAV) of Dimension Stones Using Non-Destructive Testing Methods
    (MDPI, 2024-12-25) Koken, Ekin
    Due 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: 3
    Citation - Scopus: 3
    A Novel Evaluation Methodology for Dimension Stone Quality
    (Wroclaw Univ Technology, Fac Geoengineering Mining & Geology, 2024) Koken, Ekin; Strzalkowski, Pawel; Strzałkowski, Paweł
    The 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.