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  4. Data-driven analysis of frictional behavior between CFRP lamella and wave-shaped steel clamp plates under normal compression
 
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2025
Journal Article
Title

Data-driven analysis of frictional behavior between CFRP lamella and wave-shaped steel clamp plates under normal compression

Abstract
Due to supreme mechanical properties and corrosion resistance, carbon fiber reinforced polymer (CFRP) has been utilized in combination with metal to fabricate anchor systems. In these anchor systems, the coefficient of friction of the interface is a crucial design criterion as it determines the mechanical performance of the CFRP-steel element. In this work, a novel anchor system was designed where carbon fiber composites lamella is clamped by a pair of wave-shaped steel plates, with a focus on the frictional behavior between the lamella and wave-shaped steel clamp plate. Specifically, the coefficient of friction of the interface was experimentally measured as a function of the variable thickness of CFRP lamella, the wave peak (height), and the compression force of the steel clamp. It was found that for the wave peak, a height of 2 mm was the optimum value for CFRP with a thickness of 1.2, 2, and 3 mm. Meanwhile, high compression of the clamp yields an early failure of the CFRP at that region and a compression force of 105 kN is measured as the threshold. The failure occurred at lower breaking force because the CFRP lamella was gradually scraped off and its transverse section (thickness) was dropped to a smaller cross-section during friction testing at higher contact forces (70 kN and 105 kN). The developed nonlinear ANN and XGBoost models performed robustly for the training (R<inf>A</inf> = 0.980, R<inf>X</inf> = 0.966), validation (R<inf>A</inf> = 0.963, R<inf>X</inf> = 0.912), and test data (R<inf>A</inf> = 0.911, R<inf>X</inf> = 0.894). Based on the results, it is demonstrated that thickness and wave peak manifested an inverse relationship, and the designed parameters of the anchor system can be optimized for mechanical performance according to practical needs.
Author(s)
Xie, Jiazhan
Southeast University
Liu, Yue
University of Science and Technology Beijing
Wang, Chong
Beihang University
Iqbal, Mudassir
University of Engineering and Technology, Peshawar
Qiang, Xin
Beihang University
Wu, Tao
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Tam, Lik Ho
Beihang University
Journal
Journal of building engineering  
DOI
10.1016/j.jobe.2025.112763
Language
English
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Keyword(s)
  • Artificial neural network

  • CFRP lamella

  • Coefficient of friction

  • Data-driven estimation

  • Wave-shaped steel clamp plates

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