Steel and Composite Structures

  • 제49권2호
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  • Pages.161-180
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  • 2023
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

A GMDH-based estimation model for axial load capacity of GFRP-RC circular columns

  • Mohammed Berradia (Department of Civil Engineering, Laboratory of Structures, Geotechnics and Risks (LSGR), Hassiba Benbouali University of Chlef) ;
  • El Hadj Meziane (Department of Civil Engineering, Geomaterials Laboratory (LaG), Hassiba Benbouali University of Chlef) ;
  • Ali Raza (Department of Civil Engineering, University of Engineering and Technology Taxila) ;
  • Mohamed Hechmi El Ouni (Department of Civil Engineering, College of Engineering, King Khalid University) ;
  • Faisal Shabbir (Department of Civil Engineering, University of Engineering and Technology Taxila)
  • 투고 : 2021.12.07
  • 심사 : 2023.10.16
  • 발행 : 2023.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

In the previous research, the axial compressive capacity models for the glass fiber-reinforced polymer (GFRP)-reinforced circular concrete compression elements restrained with GFRP helix were put forward based on small and noisy datasets by considering a limited number of parameters portraying less accuracy. Consequently, it is important to recommend an accurate model based on a refined and large testing dataset that considers various parameters of such components. The core objective and novelty of the current research is to suggest a deep learning model for the axial compressive capacity of GFRP-reinforced circular concrete columns restrained with a GFRP helix utilizing various parameters of a large experimental dataset to give the maximum precision of the estimates. To achieve this aim, a test dataset of 61 GFRP-reinforced circular concrete columns restrained with a GFRP helix has been created from prior studies. An assessment of 15 diverse theoretical models is carried out utilizing different statistical coefficients over the created dataset. A novel model utilizing the group method of data handling (GMDH) has been put forward. The recommended model depicted good effectiveness over the created dataset by assuming the axial involvement of GFRP main bars and the confining effectiveness of transverse GFRP helix and depicted the maximum precision with MAE = 195.67, RMSE = 255.41, and R2 = 0.94 as associated with the previously recommended equations. The GMDH model also depicted good effectiveness for the normal distribution of estimates with only a 2.5% discrepancy from unity. The recommended model can accurately calculate the axial compressive capacity of FRP-reinforced concrete compression elements that can be considered for further analysis and design of such components in the field of structural engineering.

키워드

과제정보

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through a large group Research Project under grant number RGP 2/6/44.

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