The study examines the problem of preserving and improving the architectural heritage in Ukraine. Many buildings and structures have a long service life or are already deteriorating due to their age and other factors. This is particularly true for reinforced concrete structures, which often have various defects and damage. Unfortunately, there are no clear methods for assessing the residual load-bearing capacity of such structures. However, the research indicates that the residual potential of damaged elements may be significantly underestimated. Therefore, it is crucial to explore and apply effective innovative solutions for strengthening these constructions. One such solution involves using composite materials (fiber-reinforced polymers, FRP) for external reinforcement of structures. Composite materials offer numerous advantages, including high strength, low weight, resistance to aggressive environments, and durability. The article presents the results of a numerical experiment aimed at investigating the influence of damage and reinforcement with carbon fiber-reinforced polymer (CFRP) on the stress-strain state and residual load-bearing capacity of concrete beams with basalt-plastic reinforcement (BFRP). For the experiment, 15 rectangular beams with dimensions of 2000×200×100 mm were prepared and nonlinearly analyzed using the "LIRA-SAPR" software, which employs the finite element method. The data obtained for each beam were compared with the results of laboratory tests, revealing that CFRP reinforcement increases the residual load-bearing capacity of the beams without significantly affecting their working deformation. Additionally, a comparative analysis was conducted on the residual load-bearing capacity and stress-strain state of the beam components: CFRP fabric, concrete, and reinforcement. The authors assert that modeling the complex stress-strain state of experimental basalt-concrete beams using nonlinear finite element calculations through the "LIRA-SAPR" software accurately reproduces experimental results, provides insight into the most likely failure mode, and reliably predicts their load-bearing capacity.
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