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Mapping beam cross-section features to higher-order generalized variables using machine learning
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DOI：10.1016/j.tws.2025.114108.png)
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Abstract
En 中文
• Machine learning is used to map beam features to higher-order structural theories. • A numerical tool to assess the accuracy of generalized variables is developed. • Convolutional and deep neural networks correlate structural theories, geometries, and accuracy. • Dynamic problems are considered, and the accuracy is evaluated via natural frequencies. • The trained network estimates the accuracy of a structural theory.
Keywords:
CUF
Structural theories
Beams
Neural networks
FEM
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