Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Department: search.filters.department.Abdullah Gül UniversitySubject: search.filters.subject.Fuzzy Inference System

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  • Article
    Citation - WoS: 2
    Citation - Scopus: 3
    Assessment of Rock Aggregate Quality Through Fuzzy Inference System
    (Springer, 2022-04-01) Koken, Ekin; Baspinar Tuncay, Ebru
    In 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: 7
    Citation - Scopus: 7
    A Combined Application of Two Soft Computing Algorithms for Weathering Degree Quantification of Andesitic Rocks
    (Elsevier, 2022-12) Koca, Tumay Kadakci; Koken, Ekin; Kadakci Koca, Tümay
    Understanding 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.