Steel and Composite Structures

  • 제40권3호
  • /
  • Pages.461-479
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  • 2021
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Ultimate axial capacity prediction of CCFST columns using hybrid intelligence models - a new approach

  • Luat, Nguyen-Vu (Department of Architectural Engineering, Sejong University) ;
  • Shin, Jiuk (Department of Architectural Engineering, Gyeongsang National University) ;
  • Han, Sang Whan (Department of Architectural Engineering, Hanyang University) ;
  • Nguyen, Ngoc-Vinh (Department of Infrastructure Engineering, Vietnam - Japan University) ;
  • Lee, Kihak (Department of Architectural Engineering, Sejong University)
  • 투고 : 2019.12.12
  • 심사 : 2021.06.16
  • 발행 : 2021.08.10
⟨ 이전 논문 다음 논문 ⟩

초록

This study aims to propose a new intelligence technique of predicting the ultimate capacity of axially loaded circular concrete-filled steel tube (CCFST) columns. A hybrid system based on one of the evolution algorithm - Genetic Algorithm (GA), fused with a well-known data-driven model of multivariate adaptive regression splines (MARS), namely G-MARS, was proposed and applied. To construct the MARS model, a database of 504 experimental cases was collected from the available literature. The GA was utilized to determine an optimal set of MARS' hyperparameters, to improve the prediction accuracy. The compiled database covered five input variables, including the diameter of the circular cross section-section (D), the wall thickness of the steel tube (t), the length of the column (L), the compressive strength of the concrete (${\acute{f}}_c$), and the yield strength of the steel tube (fy). A new explicit formulation was derived from MARS in further analysis, and its estimation accuracy was validated against a benchmark model, G-ANN, an artificial neural network (ANN) optimized using the same metaheuristic algorithm. The simulation results in terms of error range and statistical indices indicated that the derived formula had a superior capability in predicting the ultimate capacity of CCFST columns, relative to the G-ANN model and the other existing empirical methods.

키워드

과제정보

This research was supported by Ministry of Land, Infrastructure and Transport of Korean Government (Grant 20CTAP-C158044-02).

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