Prediction of the axial compression capacity of stub CFST columns using machine learning techniques
Abstract Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing unce...
Main Authors: | Khaled Megahed, Nabil Said Mahmoud, Saad Elden Mostafa Abd-Rabou |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-02-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-024-53352-1 |
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