WoS İndeksli Yayınlar Koleksiyonu

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

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Subject: search.filters.subject.Soft ComputingWoS Q: search.filters.wosQuartile.Q4

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  • 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: 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.