AUTHOR=George Christo , Zumba Edwin , Procel Silva Maria Alexandra , Selvan S. Senthil , Christo Mary Subaja , Kumar Rakesh , Kumar Singh Atul , S. Sathvik , Onyelowe Kennedy TITLE=Predicting the fire-induced structural performance of steel tube columns filled with SFRC-enhanced concrete: using artificial neural networks approach JOURNAL=Frontiers in Built Environment VOLUME=10 YEAR=2024 URL=https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2024.1403460 DOI=10.3389/fbuil.2024.1403460 ISSN=2297-3362 ABSTRACT=

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.