Сritical analysis of analytical and numerical models of bond between reinforcement and concrete
DOI:
https://doi.org/10.32347/2410-2547.2025.115.244-261Keywords:
reinforced concrete, bond-slip model, finite-element simulation, 3D cohesive zone model, Pull-out test, Beam-end test, deep learning methodAbstract
This paper presents a comprehensive critical analysis of existing approaches to modeling the bond between reinforcement and concrete, which is a fundamental factor in ensuring the reliability and durability of reinforced concrete structures. The relevance of this research is amplified in the context of the current challenges posed by the full-scale war in Ukraine, which demands accurate prediction of the behavior of protective structures under dynamic, impact, and blast loadings.
The key research areas have been systematized and analyzed: experimental methods, analytical models, and numerical simulations. The limitations of classical experimental methods, such as pull-out tests and beam-end tests, are reviewed, and the advantages of modern monitoring technologies are highlighted. These include distributed fiber optic sensing (DFOS) for quasi-continuous measurement of reinforcement strains and digital image correlation (DIC) for analyzing crack kinematics, both of which provide detailed data on local bond behavior.
A critical review of analytical bond-slip models is conducted, ranging from semi-empirical relationships like the BPE model to more theoretically grounded approaches based on the thick-walled cylinder theory and the fictitious crack model. It is demonstrated that due to their dependence on specific experimental conditions and significant data scatter, these models often lack universal applicability.
Particular attention is given to the classification and analysis of numerical models based on their level of detail. Macroscopic models, from simplified (SDOF, perfect bond model) to advanced approaches (layered section model with equivalent stiffness, models based on systems of differential equations), are evaluated in terms of computational efficiency and accuracy in accounting for the slip effect. Mesoscopic approaches that model reinforcement and concrete as separate bodies are discussed in detail, including models with spring and cohesive zone elements (CZM), frictional-cohesive zone models (FCZM), contact algorithms (1D Slide Line), and lattice models. The advantages and disadvantages of each method, from physical justification to computational complexity, are highlighted. Furthermore, the prospects of applying machine learning methods (e.g., NARX, SSA-ELM) for the rapid and accurate prediction of failure modes and bond-slip relationships are considered.
The paper concludes that accounting for the bond-slip effect is critically important for the adequate modeling of the behavior of reinforced concrete structures, especially after the reinforcement reaches its yield point. The choice of a model should be based on a balance between the required accuracy and available resources. Finally, promising directions for future research are formulated, aimed at creating universal and computationally efficient numerical models.
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