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Steel and Composite Structures
  Volume 39, Number 4, May25 2021, pages 471-491

Soft computing-based estimation of ultimate axial load of rectangular concrete-filled steel tubes
Panagiotis G. Asteris, Minas E. Lemonis, Thuy-Anh Nguyen, Hiep Van Le and Binh Thai Pham

    In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.
Key Words
    CFST column; artificial neural network; ultimate axial load; balancing composite motion optimization
Panagiotis G. Asteris and Minas E. Lemonis: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Thuy-Anh Nguyen and Binh Thai Pham: University of Transport Technology, Hanoi 100000, Vietnam
Hiep Van Le: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

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