Estimation of the mechanical behavior of CFRP-to-steel bonded joints with quantification of uncertainty

• Main Authors: Chatzi, E. (Author), Ghafoori, E. (Author), Li, L. (Author), Pichler, N. (Author)
• Format: Article
• Language: English
• Published: Elsevier Ltd 2022
• Subjects: Bond capacity; Bond length; Bond–slip model; Bond-slip models; Carbon fiber reinforced plastics; Carbon fiber reinforced polymer; Carbon fiber reinforced polymer (CFRP); Carbon fibre reinforced polymer; Chaos expansions; Data-driven analysis; Effective bond length; Energy management; Expansion; Monte Carlo methods; Monte Carlo's simulation; Monte–Carlo (MC) simulation; Polynomial chaos; Polynomial chaos expansion; Polynomial chaos expansion (PCE); Shear stress; Stochastic models; Stochastic systems; Strengthening (metal); Stress analysis; Uncertainty analysis
• Online Access: View Fulltext in Publisher

 The strengthening and repair of existing infrastructures, a large portion of which is comprised of steel structures, is essential for sustainable material use and energy resource management. Bonded strengthening using Carbon Fiber Reinforced Polymers (CFRPs) offers great potential toward a sustainable infrastructure management. In establishing CFRP retrofitting as a reliable solution for steel strengthening, a solid understanding of the mechanical behavior of the CFRP-to-steel bonded joints is essential. Given the variability in the evidence attained by experiments, in this study, we tackle this challenge from an uncertainty quantification perspective by proposing a model based on Polynomial Chaos Expansion (PCE) to predict the load capacity of the bonded joints. A stochastic bond–slip model, featuring a parsimonious representation with one deterministic coefficient and one probabilistic coefficient, is further proposed. A Monte-Carlo (MC) simulation is used to demonstrate the efficacy of the bond–slip model in predicting the mechanical behavior such as load–displacement behavior, shear stress profile, and effective bond length of strengthened specimens. Results are compared with existing deterministic models. © 2022 The Author(s)