Computers and Concrete

  • 제34권4호
  • /
  • Pages.427-446
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  • 2024
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Study on bond strength between recycled aggregate concrete and I-shaped steel

  • Biao Liu (College of Water Conservancy and Civil Engineering, Northwest A&F University) ;
  • Feng Xue (College of Water Conservancy and Civil Engineering, Northwest A&F University) ;
  • Yu-Ting Wu (College of Water Conservancy and Civil Engineering, Northwest A&F University) ;
  • Guo-Liang Bai (School of Civil Engineering, Xi'an University of Architecture & Technology) ;
  • Zheng-Zhong Wang (College of Water Conservancy and Civil Engineering, Northwest A&F University)
  • 투고 : 2023.08.19
  • 심사 : 2024.03.04
  • 발행 : 2024.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The I-shaped steel reinforced recycled aggregate concrete (SRRC) composite structure has the advantages of high bearing capacity and environmental protection, and the interfacial bond strength is an important theory. To this end, the I-shaped SRRC bond strength and its calculation based on artificial neural network (ANN) will be studied. Firstly, 39 push out tests of I-shaped SRRC were conducted, the load-slip curve has obvious regularity, which is divided into 4 segments by 3 regular points. Three bond strengths were defined based on these three rule points, and the approximate ranges of their values and the laws of influence of each factor on them were found. Secondly, the Elman ANN model used for the prediction of bond strength was established, and the parameters of Elman ANN predicting I-shaped SRRC bond strength were studied, and the effects of detailed parameters on the prediction results were revealed. Finally, the bond strength of SRRC was predicted using Elman and BP (back propagation) neural network models, both of which showed good prediction results. This study is a theoretical basis for the design and fine simulation of I-shaped SRRC composite structures.

키워드

과제정보

This study was supported by Natural Science Basic Research Plan in Shaanxi Province (2023-JC-QN-0519), Chinese Universities Scientific Fund (2452021082).

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