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Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks

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Abstract

Design of rectangular concrete-filled steel tubular (CFT) columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network (ANN) models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network model1 (ANN1) and artificial neural network model2 (ANN2) with a 20-neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs: the length (D) and width (B) of cross section, the thickness of steel (t), the yield strength of steel (f y), the cylinder strength of concrete (f′c). ANN2 has ten inputs: D, B, t, f y, f′c, the length to width ratio (D/B), the length to thickness ratio (D/t), the width to thickness ratio (B/t), restraint coefficient (ξ), the steel ratio (α). The axial bearing capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.

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Acknowledgements

This work was sponsored by the National Natural Science Foundation of China (Grant No. 61272264).

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Authors and Affiliations

  1. School of Civil Engineering, Tianjin University, Tianjin, 300072, China

    Yansheng Du & Zhihua Chen

  2. School of Computer Science and Technology, Tianjin University, Tianjin, 300354, China

    Changqing Zhang

  3. SKL of Information Security Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Xiaochun Cao

Authors
  1. Yansheng Du
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  2. Zhihua Chen
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  3. Changqing Zhang
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  4. Xiaochun Cao
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Corresponding author

Correspondence to Zhihua Chen.

Additional information

Yansheng Du received his BE degree in civil engineering from Tianjin University (TJU), China in 2012. He is now a PhD student in the School of Civil Engineering, TJU, and a visiting scholar of the Department of Civil and Environmental Engineering, National University of Singapore, Singapore. His research interests include steelconcrete composite structures and the applications of soft computing methods in civil engineering.

Zhihua Chen received his BE, ME, and PhD degrees from Tianjin University (TJU), China. He is now a professor of the School of Civil Engineering, TJU. His main research interests include the steel structures, the composite structures and the large-span structures. He has authored or co-authored over 150 journal and conference papers. He is the president of the Tianjin Steel Construction Society and the committee member of Chinese National Engineering Research Center for Steel Structures.

Changqing Zhang received the BS and ME degrees in computer science from Sichuan University, China in 2005 and 2008, and the PhD degree from Tianjin University (TJU), China in 2016, respectively. He is currently an assistant professor with TJU. His current research interests include machine learning, data mining, and computer vision.

Xiaochun Cao received the BE and ME degrees in computer science from Beihang University, China, and the PhD degree in computer science from the University of Central Florida, USA, with his dissertation nominated for the university level Outstanding Dissertation Award. From 2008 to 2012, he was a professor at Tianjin University, China. He is currently a Professor with the Institute of Information Engineering, Chinese Academy of Sciences, China. He has authored or co-authored over 100 journal and conference papers. He was the recipients of the Piero Zamperoni Best Student Paper Award at the International Conference on Pattern Recognition in 2004 and 2010. He is a fellow of the IET. He is also an associate editor of the IEEE Transactions on Image Processing.

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Du, Y., Chen, Z., Zhang, C. et al. Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks. Front. Comput. Sci. 11, 863–873 (2017). https://doi.org/10.1007/s11704-016-5113-6

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  • DOI: https://doi.org/10.1007/s11704-016-5113-6

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