Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
2 results
Search Results
Article Citation - Scopus: 3Soft Computing Implementations for Evaluating Los Angeles Abrasion Value of Rock Aggregates From Kütahya, Turkey(Szechenyi Istvan University, 2024-02-28) Köken, E.The Los Angeles abrasion value (LAAV) of rocks is a critical mechanical aggregate property for designing road infrastructures and concrete quality. However, the determination of this critical aggregate property is labour-intensive and time-consuming and thus, in the literature, there are many predictive models to estimate the LAAV for different rock types. However, most of them are based on classical regression analyses, limiting their broader usage. In this study, several soft computing analyses are performed to develop robust predictive models for the evaluation of LAAV of rocks in the Ilıca region (Kütahya – Turkey). The main motivation for implementing soft computing analyses is that precise predictive models might be useful when exploring suitable rock types that are manufactured in crushing–screening plants. For this purpose, a comprehensive laboratory schedule was established to obtain some inputs for the evaluation of LAAV. As a result of the soft computing analyses, four robust predictive models are developed based on artificial neural networks (ANN), multiple adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) methodologies. The performance of the proposed models is investigated by some statistical indicators such as R2 and RMSE values and scatter plots. As a result, the ANFIS-based predictive model turns out to be the best alternative to estimate the LAAV of the investigated rocks. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 3Estimation of Deformation Modulus of Coals Using Artificial Neural Networks (ANN)(Szechenyi Istvan University, 2022-05-29) Köken, E.In this study, the Young modulus (E) of different coals was investigated using artificial neural networks (ANN). For this purpose, a comprehensive literature survey was carried out to compile such datasets available for the ANN analyses. As a result of the literature survey, a database composed of 81 datasets was formed. In the ANN analyses, uniaxial compressive strength (UCS) and dry density (ρ<inf>d</inf>) of coals were adopted as input parameters. The ANN analysis results demonstrated that the predictive model established in this study could be reliably used to estimate the E for different coals. The correlation of determination value (R2) for the developed model is 0.85, which shows its relative success. In this context, this study can be declared a case study showing the applicability of ANN for the evaluation of E for a wide range of coal types. However, the number of samples and independent variables should be increased to obtain more comprehensive models in future studies. © 2025 Elsevier B.V., All rights reserved.