In a recent study, researchers unveiled a breakthrough self-sensing steel fiber-reinforced polymer composite bar (SFCB) that enhances damage control, self-monitoring, and structural reinforcement using distributed fiber optic sensing (DFOS). This innovative material integrates damage assessment directly into reinforcement, providing real-time monitoring and improved structural performance.
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The team proposed a multilevel damage assessment method based on stiffness and damage variables to evaluate safety, serviceability, and durability. To improve damage identification and tracking, they developed a modified fiber damage model validated through three-point flexural tests on reinforced concrete beams. The findings indicated that increasing the reinforcement ratio lowers damage thresholds while enhancing overall structural integrity.
Damage Assessment Framework
This study combined theoretical and nonlinear numerical methods to assess flexural beam performance and damage conditions. Stiffness and moment were identified as key safety indicators, while deflection and crack width were used to evaluate serviceability and durability. A high-precision fiber damage model was developed, incorporating DFOS strain data for enhanced accuracy. Stiffness degradation was used as the primary damage assessment criterion.
To further refine this approach, researchers established a multilevel damage and performance assessment method for hybrid SFCB-reinforced concrete (RC) flexural beams. Damage phases were categorized according to the 50011–2010 standards, ranging from minor damage to structural failure.
A simplified load-deflection curve was created to identify key points such as crack initiation, yield, and peak stress. The yield point determined the stress distribution in the compression area, which was modeled as triangular. The study also provided equations for calculating yield moment, stiffness, and deflection.
At peak strain, the maximum stress was reached on external compression fibers. Moment capacity was determined using force equilibrium and an equivalent rectangular stress block model, with the compression reinforcement's yielding condition defining the height of the stress block. The SFCB strain at peak load was derived, and peak curvature was expressed in terms of key parameters. The researchers then calculated peak stiffness and the corresponding damage variable using theoretical equations.
This framework provides an efficient method for assessing damage in flexural beams. By integrating strain data into the fiber damage model, the study enables precise detection and correction of structural weaknesses. The results confirmed the reliability of this approach in predicting structural performance degradation, making it a valuable tool for structural health monitoring and safety evaluations.
Strain Monitoring CFRP
The study also examined the strain monitoring capabilities of SFCB in RC beams. Three beams with reinforcement ratios of 0.27 %, 0.82 %, and 1.67 % were tested under three-point flexural loading. The tests showed that the beams exhibited ductile failure, transitioning from FRP rupture to concrete crushing as the reinforcement ratio increased.
Load-deflection analysis revealed that carbon FRP (CFRP) layers improved post-yield stiffness. While higher reinforcement ratios enhanced deformation capacity, they also increased the likelihood of concrete crushing. These findings validated SFCB’s effectiveness in monitoring strain and assessing structural performance.
To further support these conclusions, the researchers validated their theoretical and numerical models by predicting the flexural behavior of hybrid steel-FRP-reinforced concrete beams using experimental data. Load-deflection and moment-curvature curves showed strong alignment between predictions and test results, even with the simplified model approximating key characteristic points.
A dataset of 20 prior experiments covering various beam dimensions and reinforcement ratios further reinforced the model's accuracy.
A comparison of experimental and predicted values across different load stages confirmed that both numerical and theoretical models provided reliable estimates. The numerical model demonstrated greater consistency due to its ability to account for material nonlinearity, yielding a lower coefficient of variation than the theoretical model. Despite minor deviations, both models effectively captured the load-deflection behavior of hybrid beams across different parameters.
The predicted-to-test value ratios for yield load, peak load, and deflection parameters remained within acceptable ranges, reinforcing the reliability of both models. While the numerical model exhibited higher precision, particularly in moment capacity predictions, the simplified theoretical model proved sufficiently accurate for structural evaluations. These findings suggest that both methods serve as valuable tools for assessing hybrid steel-FRP-reinforced concrete beams.
Conclusion
This study highlights the benefits of increasing steel reinforcement in SFCB-RC beams, particularly in crack prevention and strain monitoring. The proposed models demonstrated strong accuracy in predicting performance parameters and damage progression, reinforcing their value in structural assessments. By integrating DFOS strain data, the approach enhances crack tracking and improves damage identification.
Journal Reference
Ye, Z., et al. (2024). Performance Assessment of Reinforced Concrete Structures Using Self-Sensing Steel Fiber-Reinforced Polymer Composite Bars: Theory and Test Validation. Engineering. DOI: 10.1016/j.eng.2024.11.022, https://linkinghub.elsevier.com/retrieve/pii/S2095809924006751
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