Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    Predicting the Area Moment of Inertia of Beam and Column Using Machine Learning and Hypernetexplorer
    (Springer Science and Business Media Deutschland GmbH, 2025) Aydın, Y.; Nigdeli, S.M.; Roozbahan, M.; Bekdaş, G.; Işıkdağ, Ü.
    Beams and columns are the most important elements of steel frame structures. Damage to the beam or column can lead the structure to serious hazards and cause collapse. In the structural engineering literature, it has been observed that there is not much work for area moment of inertia estimation of beam and column. The aim of this study was to predict the area moment of inertia of beam and column using HyperNetExplorer developed by the authors. This method aims to bring innovation by optimizing artificial neural networks (ANNs). In this study, a prediction study is performed using 306 collected data on beam and column area moment of inertia. Classical ML models (linear regression (LR), decision tree regression (DTR), K neighbors regression (KNN), polynomial regression (PR), random forest regression (RFR), gradient boosting regression (GBR), histogram gradient boosting regression (HGBR)) and NAS and HyperNetExplorer were applied to predict beam and column area moment of inertia. The prediction performances were compared using different performance metrics (coefficient of determination (R2) and mean squared error (MSE)) and HyperNetExplorer developed by the authors showed the highest performance (R2 = 0.98, MSE = 246.88). Furthermore, SHapley additive explanations (SHAP) were used to explain the effects of features in the prediction models and it was observed that the most effective features for model predictions were loading on beam and length. The results show that the proposed NAS base approach and the developed tool, HyperNetExplorer, provides better performance when compared with classical ML methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.
  • Article
    Regression Analysis of Material Properties and Hardness of Dense Boron Carbide
    (Wiley, 2025) Toksoy, M. Fatih; Haber, Richard A.
    Material properties directly affect the final performance of the produced articles. This study aims to establish a correlation between materials properties and hardness of boron carbide samples. Ten different boron carbide powders were sintered to high densities by spark plasma sintering, and material properties (grain size, density, stoichiometry, and free carbon) were analyzed. Hardness tests were conducted on these samples. All experimental procedures were completed by a single operator, and the same instruments were used for all the samples. Multiple linear regressions using the ordinary least squares method in SPSS were carried out to identify the relationship between hardness and material properties. Analyses showed density is the most dominant property, surpassing any other parameter. Grain size became more dominant at higher densities (>99%) and affected hardness results. Both grain size and density are the result of the starting powder and the densification procedure. This study showed that 80% of the hardness variation can be explained by this model.