Computers and Concrete

  • 제30권3호
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  • Pages.209-224
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  • 2022
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Artificial neural network modeling to predict the flexural behavior of RC beams retrofitted with CFRP modified with carbon nanotubes

  • Almashaqbeh, Hashem K. (Department of Civil Engineering, Isra University) ;
  • Irshidat, Mohammad R. (Center for Advanced Materials (CAM), Qatar University) ;
  • Najjar, Yacoub (Department of Civil Engineering, The University of Mississippi) ;
  • Elmahmoud, Weam (Department of Civil Engineering, The University of Mississippi)
  • 투고 : 2021.04.01
  • 심사 : 2022.07.20
  • 발행 : 2022.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, the artificial neural network (ANN) is employed to predict the flexural behavior of reinforced concrete (RC) beams retrofitted with carbon fiber/epoxy composites modified by carbon nanotubes (CNTs). Multiple techniques are used to improve the accuracy of the ANN prediction, as the data represents a multivalued function. These techniques include static ANN modeling, ANN modeling with load history, and ANN modeling with double load history. The developed ANN models are used to predict the load-displacement profiles of beams retrofitted with either CFRP or CNTs modified CFRP, flexural capacity, and maximum displacement of the beams. The results demonstrate that the ANN is able to predict the flexural behavior of the retrofitted RC beams as well as the effect of each parameter including the type of the used epoxy and the presence of the CNTs.

키워드

참고문헌

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