Steel and Composite Structures

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Analyzing behavior of circular concrete-filled steel tube column using improved fuzzy models

  • Zheng, Yuxin (School of Civil Engineering and Architecture, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Jin, Hongwei (School of Civil Engineering and Architecture, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Jiang, Congying (School of Civil Engineering and Architecture, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Moradi, Zohre (Faculty of Engineering and Technology, Department of Electrical Engineering, Imam Khomeini International University) ;
  • Khadimallah, Mohamed Amine (Prince Sattam Bin Abdulaziz University, College of Engineering, Civil Engineering Department) ;
  • Safa, Maryam (Faculty of Civil Engineering, Duy Tan University)
  • Received : 2021.06.30
  • Accepted : 2022.04.22
  • Published : 2022.06.10
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Abstract

Axial compression capacity (Pu) is a significant yet complex parameter of concrete-filled steel tube (CFST) columns. This study offers a novel ensemble tool, adaptive neuro-fuzzy inference system (ANFIS) supervised by equilibrium optimization (EO), for accurately predicting this parameter. Moreover, grey wolf optimization (GWO) and Harris hawk optimizer (HHO) are considered as comparative supervisors. The used data is taken from earlier literature provided by finite element analysis. ANFIS is trained by several population sizes of the EO, GWO, and HHO to detect the best configurations. At a glance, the results showed the competency of such ensembles for learning and reproducing the Pu behavior. In details, respective mean absolute errors along with correlation values of 4.1809% and 0.99564, 10.5947% and 0.98006, and 4.8947% and 0.99462 obtained for the EO-ANFIS, GWO-ANFIS, and HHO-ANFIS, respectively, indicated that the proposed EO-ANFIS can analyze and predict the behavior of CFST columns with the highest accuracy. Considering both time and accuracy, the EO provides the most efficient optimization of ANFIS and can be a nice substitute for experimental approaches.

Keywords

References

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