Reinforced concrete structures

Building objects are the result of in-depth scientific research and have functions, qualities and properties that are learnt and refined during their operation. Despite the high standards of safety of buildings for various purposes, there is a steady increase in the number and severity of accidents of various origins.There is a qualitative change in threats, in particular military threats, which are comparable in scale to natural disasters. Increasing requirements to their safety are now in contradiction with the material, technical, financial and scientific-methodological resources that ensure these requirements. The statistics of major accidents and disasters shows that despite the achievements of scientific and technological progress, the possibilities of threat parity have either been limited or actually exhausted. These circumstances require a revision of traditional approaches to the problem and have led to the formulation of new concepts and ideas in the field of safety. The main attention is paid to structural aspects that determine the overall level of safety of building systems.The most scientifically developed are the tasks of assessing and ensuring the strength, service life and reliability of structures, while the tasks of ensuring safety, robustness and risk assessment require further scientific development.

The article examines the properties of fiber-reinforced polymer bars (FRP bars) and its use in concrete structures. With its high tensile strength, low density, and corrosion resistance, FRP bars presents a promising alternative to traditional steel reinforcement, particularly in aggressive environments. However, its widespread application is limited by several factors, including the insufficient study of FRP bars behavior and the performance of concrete structures reinforced with it under dynamic loads. The article describes FRP bars as a heterogeneous anisotropic material composed of continuous reinforcing fibers and a polymer matrix. It provides a classification of FRP bars, analyzes its physical and mechanical properties under static, long-term, and dynamic loading conditions, and discusses calculation methods for reinforced concrete structures incorporating FRP bars. A deformation diagram for static tension and compression, obtained from experimental tests, is presented. Under short-term static loading, FRP bars deforms elastically without forming plastic zones. However, under prolonged exposure, polymer creep leads to a reduction in FRP bars’ strength characteristics. The behavior of fiber-reinforced polymer bars under dynamic loads remains underexplored, although some studies indicate the presence of a dynamic hardening effect under short-term dynamic loading. Additional research is needed to determine the influence of strain rate on FRP bars properties. The study proposes recommendations for modeling loading conditions in tests of flexural elements, considering their deformation behavior under cyclic dynamic loads. The analysis of the obtained results highlights the need for further research on FRP bars under cyclic dynamic loading, which would expand the scope of fiber-reinforced polymer bars applications in construction.

This article presents the development and analysis of a Physics-Informed Neural Network (PINN) model for calculating the deflection of a simply supported beam under uniformly distributed load. The training dataset was synthesized based on analytical principles of structural mechanics and included the following parameters: relative length of the measurement section l, number of measurement points N, and noise level R. The training dataset consisted of 1,296 rows describing random points within the beam span. In this study, 480 PINN models were trained to evaluate the impact of the weight of the physics-informed loss function, the number of measurements, and the noise level on prediction accuracy. The results demonstrated that PINN models achieve high accuracy (R2 ≥ 0.88) even with high noise levels (R > 20 %) and exhibit robustness to low and moderate noise levels. The study identified that adjusting the weight of the physics-informed loss function is a key parameter for achieving an optimal balance between the loss functions of physical laws and experimental data. Increasing the number of measurement points positively influences accuracy at low noise levels. However, an increase in the number of measurement points under high noise levels reduces the prediction accuracy of the model. The scientific novelty of the study lies in proposing an approach for structural analysis using PINN, which integrates physical laws into the training process. The findings confirm the potential of using PINN for engineering calculations, particularly under limited data conditions.

In the last decade, as part of the sustainable development strategy, more attention is paid to extending the service life of existing buildings and civil engineering structures. To make an informed decision on the further of existing structures, scientifically based methods for assessing their reliability are required. This article presents a multi-level system for assessing the reliability of an existing concrete structure, included in the new design standards, and shows the stages of practical application using the example of the simplest free supported beam. Methods for calibrating partial safety coefficients of resistance models based on modified reliability indices are proposed, taking into account the projected remaining service life of a construction structure.