Structural Engineering and Mechanics

  • 제89권3호
  • /
  • Pages.283-300
  • /
  • 2024
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Axial capacity of FRP reinforced concrete columns: Empirical, neural and tree based methods

  • Saha Dauji (Nuclear Recycle Board, Bhabha Atomic Research Center)
  • 투고 : 2021.09.05
  • 심사 : 2023.10.31
  • 발행 : 2024.02.10
⟨ 이전 논문 다음 논문 ⟩

초록

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

키워드

과제정보

The author takes this opportunity to express sincere gratitude to Raza et al. (2021) for publishing the collection of data from literature, which was used in this study. The author is thankful to the Editor/s and anonymous Reviewer/s for their critical review and helpful suggestions that helped to improve upon the manuscript.

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