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Title: Prediction of fire resistance of concrete encased steel composite columns using artificial neural network
Authors: LI SHAN 
Xiong, Mingxiang 
Keywords: artificial neural network
composite columns
design equations
finite difference method
fire resistance design
high-strength concrete
Issue Date: 15-Oct-2021
Publisher: Elsevier
Citation: LI SHAN, JAT YUEN, RICHARD LIEW, Xiong, Mingxiang (2021-10-15). Prediction of fire resistance of concrete encased steel composite columns using artificial neural network. Engineering structures 245. ScholarBank@NUS Repository.
Abstract: Concrete encased steel (CES) columns, also known as steel reinforced concrete (SRC) composite columns, exhibit superior fire resistance due to concrete acting as a protection layer for the embedded steel section. While modern design codes have provided design guides for the fire resistance of CES columns, they are only applicable to those made of normal strength concrete. For high strength CES columns, advanced analysis is needed to capture the brittleness of high strength concrete at elevated temperature. In this paper, two methods, namely the artificial neural network (ANN) and the analytical equations, are proposed to predict the fire resistance of axially-loaded CES columns made of high strength concrete. To train the ANN, a finite difference model is developed to compute the temperature field in CES columns and it is used to establish a database containing 15,200 specimens. The cross-sectional dimensions and materials grades of the specimens are carefully selected to cover a wide range of values including those commonly adopted in real-life applications. The inputs of the ANN are identified through an extensive parametric analysis. The selected ANN consists of 7 inputs, 3 outputs and 2 hidden layers and achieves a high determination coefficient R2 value of 0.999. For practical implementation, analytical equations are also derived and achieve high R2 values above 0.953. The predictive power of the ANN and the analytical equations are examined against the observations obtained from actual fire tests, showing reasonable accuracy of prediction. Both methods are simple, of high accuracy and have implicitly accounted for temperature-dependent material degradation, and hence do not require input of temperature-dependent material properties and advanced analysis software. © 2021 Elsevier Ltd.
Source Title: Engineering structures
ISSN: 01410296
Appears in Collections:Staff Publications

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