Neuro-swarm fire resistance model of concrete-filled steel tube
Andrei Art Geronimo,Dann Carlo Reformado,Earl Jayson Sarmiento,Crispin Lictaoa,Nolan C. Concha
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
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