Structural Engineering and Mechanics

  • 제87권6호
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  • Pages.517-527
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  • 2023
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Predicting and analysis of interfacial stress distribution in RC beams strengthened with composite sheet using artificial neural network

  • Bensattalah Aissa (Faculty of Applied Sciences, Department of Civil Engineering, University of Tiaret) ;
  • Benferhat Rabia (Faculty of Applied Sciences, Department of Civil Engineering, University of Tiaret) ;
  • Hassaine Daouadji Tahar (Faculty of Applied Sciences, Department of Civil Engineering, University of Tiaret)
  • 투고 : 2023.01.10
  • 심사 : 2023.08.07
  • 발행 : 2023.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

The severe deterioration of structures has led to extensive research on the development of structural repair techniques using composite materials. Consequently, previous researchers have devised various analytical methods to predict the interface performance of bonded repairs. However, these analytical solutions are highly complex mathematically and necessitate numerous calculations with a large number of iterations to obtain the output parameters. In this paper, an artificial neural network prediction models is used to calculate the interfacial stress distribution in RC beams strengthened with FRP sheet. The R2value for the training data is evaluated as 0.99, and for the testing data, it is 0.92. Closed-form solutions are derived for RC beams strengthened with composite sheets simply supported at both ends and verified through direct comparisons with existing results. A comparative study of peak interfacial shear and normal stresses with the literature gives the usefulness and effectiveness of ANN proposed. A parametrical study is carried out to show the effects of some design variables, e.g., thickness of adhesive layer and FRP sheet.

키워드

과제정보

This research was supported by the Algerian Ministry of Higher Education and Scientific Research (MESRS) as part of the grant for the PRFU research project n° A01L02UN140120200002and by the University of Tiaret, in Algeria.

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