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Steel and Composite Structures Volume 58, Number 5, March 10 2026 , pages 647-676 DOI: https://doi.org/10.12989/scs.2026.58.5.647 |
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Neuro-swarm fire resistance model of concrete-filled steel tube |
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Andrei Art Geronimo, Dann Carlo Reformado, Earl Jayson Sarmiento, Crispin Lictaoa, Nolan C. Concha
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| Abstract | ||
| This study investigated the behavior of concrete-filled steel tubes (CFST) under extreme fire conditions, focusing on two key fire performance indicators: the fire resistance rating (FRR) and residual strength index (RSI). Advanced prediction models were developed using neural networks optimized with a particle swarm optimization algorithm. A comprehensive experimental database and a diverse range of neural network architectures were utilized. The models demonstrated superior predictive accuracy, as validated through multiple performance metrics and comparisons with existing prediction equations. Furthermore, causal inference techniques were applied to identify the influence and relative importance of each variable. Visualization tools were instrumental in uncovering patterns and correlations that would be difficult to detect through numerical data alone. The proposed FRR and RSI models offer a cost-effective, non-destructive method for assessing and designing CFST elements in concrete structures. | ||
| Key Words | ||
| concrete-filled steel tubes; fire resistance rating; machine learning; neuro-swarm; residual strength index | ||
| Address | ||
| Andrei Art Geronimo:Department of Civil Engineering, Adamson University, Ermita, Manila, Philippines Dann Carlo Reformado:Department of Civil Engineering, Adamson University, Ermita, Manila, Philippines Earl Jayson Sarmiento:Department of Civil Engineering, Adamson University, Ermita, Manila, Philippines Crispin Lictaoa:Department of Civil Engineering, Adamson University, Ermita, Manila, Philippines Nolan C. Concha:Department of Civil Engineering, National University, Sampaloc, Manila, Philippines | ||