Predicting the fire-induced structural performance of steel tube columns filled with SFRC-enhanced concrete: using artificial neural networks approach
Auteur(s): |
Christo George
Edwin Zumba Maria Alexandra Procel Silva S. Senthil Selvan Mary Subaja Christo Rakesh Kumar Atul Kumar Singh S. Sathvik Kennedy Onyelowe |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Frontiers in Built Environment, février 2024, v. 10 |
DOI: | 10.3389/fbuil.2024.1403460 |
Abstrait: |
Predicting the axial Shortening strength of concrete-filled steel tubular (CFST) columns is an important problem that this study attempts to solve for civil engineering projects. We suggest using a deep learning-based artificial neural network (ANN) model to address this issue, taking into account the intricate relationship between steel tube and core concrete. The model, called ANN-SFRC (Steel Fibre Reinforced Concrete), surpasses an R2 threshold of 0.90 and achieves impressive R2 values across different types of CFST columns. Compared to traditional linear regression methods, the ANN-SFRC model significantly improves accuracy, with an observed inaccuracy of less than 3% compared to actual values. With its reliable approach to forecasting the behavior of CFST columns under axial compression, this high-performance instrument enhances safety and accuracy during the design and planning stages of civil engineering. |
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sur cette fiche - Reference-ID
10789946 - Publié(e) le:
20.06.2024 - Modifié(e) le:
20.06.2024