Browsing by Publisher "Higher Education Press Limited Company"
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Item Constant Angle Ruled Surfaces Associated with Slant Ruled Surfaces(Higher Education Press Limited Company, 2024) Kaya O.In this study, we investigate constant angle ruled surfaces whose surface normals are parallel to the rulings, central normal vectors, central tangent vectors or unit Darboux vectors of slant ruled surfaces in the Euclidean 3-space. We show that such ruled surfaces are developable and unit director curve of such surfaces coincide with the rulings, Darboux vectors, central tangent vectors or central normal vectors of slant ruled surfaces. Furthermore, we calculate the position vectors of those ruled surfaces along with some examples. © Peking University 2024.Item Fire resistance evaluation through synthetic fire tests and generative adversarial networks(Higher Education Press Limited Company, 2024) Çiftçioğlu A.Ö.; Naser M.Z.This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns. Our approach begins by creating deep learning models, namely generative adversarial networks and variational autoencoders, to learn the spatial distribution of real fire tests. We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns. The generated data are employed to train state-of-the-art machine learning techniques, including Extreme Gradient Boost, Light Gradient Boosting Machine, Categorical Boosting Algorithm, Support Vector Regression, Random Forest, Decision Tree, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, and K-Nearest Neighbors, which can predict the fire resistance of the columns through regression and classification. Machine learning analyses achieved highly accurate predictions of fire resistance values, outperforming traditional models that relied solely on limited experimental data. Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments. © The Author(s) 2024.