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Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members

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Abstract

A Machine Learning (ML) model based on Gaussian regression, using different kernel functions, is introduced in this paper to assess the load-carrying capacity of square concrete-filled steel tubular (CFST) columns. The input data used to develop the prediction model, which consists of 314 datasets including the structural geometrical parameters and the mechanical properties of the materials, was collected from available resources in the literature. The performance of the prediction model has also been validated by comparing with: (i) other ML models such as Artificial neural network, Support vector machine, etc.; and (ii) existing formulations in the literature for predicting load-carrying capacity of square CFST columns (including several codes such as EC4, AISC and ACI). The obtained results showed that the proposed model has outperformed them. The drawbacks of the model have been investigated by studying the influence of the input variables, together with uncertainty analysis providing 68, 95, and 99% prediction confidence intervals. Finally, a user-friendly interface has been developed to facilitate the application of the proposed model, providing the prediction value as well as confidence levels.

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Data availability

The raw/processed data is appended to this paper as a supplementary material.

Code availability

The user-friendly interface is appended to this paper as a supplementary material.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering and Mechatronics, PHENIKAA University, Yen Nghia, Ha Dong, Hanoi, 12116, Viet Nam

    Tien-Thinh Le

  2. PHENIKAA Research and Technology Institute (PRATI), A&A Green Phoenix Group JSC, No.167 Hoang Ngan, Trung Hoa, Cau Giay, Hanoi, 11313, Viet Nam

    Tien-Thinh Le

  3. Laboratoire Modélisation et Simulation Multi Echelle, Université Paris-Est, MSME UMR 8208 CNRS, 5 bd Descartes, 77454, Marne-la-Vallée, France

    Minh Vuong Le

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  1. Tien-Thinh Le
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Conceptualization: Tien-Thinh Le; Methodology: Tien-Thinh Le and Minh Vuong Le; Formal analysis and investigation: Tien-Thinh Le and Minh Vuong Le; Writing—original draft preparation: Tien-Thinh Le; Writing—review and editing: Tien-Thinh Le and Minh Vuong Le.

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Correspondence to Tien-Thinh Le.

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Supplementary Information

Supplementary Materials: The dataset and user-friendly interface are appended to this paper as supplementary materials.

Appendix A. Supporting information file is attached to this paper [114,115,116,117,118,119,120,121].

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Le, TT., Le, M.V. Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members. Mater Struct 54, 59 (2021). https://doi.org/10.1617/s11527-021-01646-5

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  • DOI: https://doi.org/10.1617/s11527-021-01646-5

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