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Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models

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Abstract

The main objective of the present work is to estimate the load-carrying capacity of concrete-filled steel tubes (CFST) under axial compression using hybrid artificial intelligence (AI) algorithms. In particular, the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic optimization methods, such as the biogeography-based optimization (ANFIS-BBO), the particle swarm optimization (ANFIS-PSO), and the genetic algorithm (ANFIS-GA), have been employed taking into account the variability of input parameters. Commonly used statistical criteria, such as the coefficient of determination (R2), the a20-index, and the root mean squared error (RMSE), were utilized to evaluate and compare the effectiveness of the proposed AI models. The Monte Carlo approach was used to propagate the variability in the input space to the predicted output. The results showed that the ANFIS system, optimized by PSO, was the most effective and robust model with respect to three considered criteria (a20-index = 0.881, R2 = 0.942 and RMSE = 185.631). Sensitivity analysis was performed, indicating that the minor axis length and thickness of the steel tube exhibited the highest contribution to the axial compression load-carrying capacity of the CFST.

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Authors and Affiliations

  1. University of Transport Technology, Hanoi, 100000, Vietnam

    Hai-Bang Ly & Binh Thai Pham

  2. Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan

    Binh Thai Pham

  3. Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi, 100000, Vietnam

    Lu Minh Le

  4. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Tien-Thinh Le

  5. Université Paris-Est, 5 bd Descartes, 77454, Marne-la-Vallée, France

    Vuong Minh Le

  6. Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121, Heraklion, Athens, Greece

    Panagiotis G. Asteris

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Ly, HB., Pham, B.T., Le, L.M. et al. Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Comput & Applic 33, 3437–3458 (2021). https://doi.org/10.1007/s00521-020-05214-w

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