Analytical solution for stiffness prediction of bonded CFRP-to-steel double strap joints. (15th December 2018)

• Record Type: Journal Article
• Authors: Li, Weijie; Ghafoori, Elyas; Lu, Yiyan; Li, Shan; Motavalli, Masoud
• Is Part Of: Engineering structures. Volume 177(2018)
• Page Start: 190
• Page End: 197
• Publication Date: 2018-12-15
• Subjects: Carbon fiber-reinforced polymer (CFRP) -- Bonded strengthening -- Steel -- Double strap joints -- Stiffness prediction; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2018.09.024
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: An analytical solution is presented for stiffness prediction of bonded CFRP-to-steel double strap joints. More than 20 experimental data were collected from the existing studies to validate the proposed model. A parametric study with respect to the bond length and the adherend stiffness ratio was conducted. Abstract: Carbon fiber-reinforced polymer (CFRP) composites have been increasingly used in the strengthening and rehabilitation of steel structures. This paper presents an analytical model for stiffness prediction of CFRP-to-steel double strap joints. Mechanical analysis was performed to determine the shear stress and strain distributions in the bond region, which resulted in deriving a model for stiffness prediction of joints. More than 20 test results from the existing works in the literature were used to validate the proposed model. Results show that the predictions are mostly between 0.8 and 1.2 times the experimental values, with an average error of less than 10%, which demonstrates the effectiveness of the proposed model. Finally, a parametric study with respect to the bond length and the adherend stiffness ratio was performed to give a better understanding of the effect of different parameters.