A recent study published in Engineering presents a groundbreaking approach to assess the performance of reinforced concrete (RC) structures using self-sensing steel fiber-reinforced polymer composite bars (SFCBs). This innovative research, led by Yingwu Zhou, has the potential to transform the way we monitor and maintain the structural integrity of buildings and infrastructure.
Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of structures. Traditional point sensors have limitations in monitoring complex components, while distributed fiber optic sensing (DFOS) technology offers a more comprehensive solution. By integrating DFOS with SFCBs, the researchers developed a composite bar that combines damage control, self-sensing, and structural reinforcement functions.
The study proposes a multilevel damage assessment method that evaluates RC structures from the perspectives of safety, durability, and suitability. Stiffness is used as a key metric to define damage variables, and the relationship between SFCB strain and various performance characteristics, such as moment, curvature, load, deflection, and crack width, is established. Threshold values for damage variables at different levels are determined based on loading peak, mid-span deflection limits, and crack width limits.
To improve the accuracy of damage identification, the researchers developed a modified fiber damage model. This model takes into account the stiffness degradation throughout the service life of the structure and uses DFOS strain data for correction. The reliability of the proposed theoretical and numerical models was verified through three-point flexural tests of SFCB-RC beams.
The experimental results showed that increasing the reinforcement ratio can lower the damage thresholds at all levels and improve the ability of flexural beams to control damage. The proposed crack width prediction method was also found to be effective in estimating the crack width of RC beams before yielding. Additionally, the simplified theoretical model accurately predicted the performance parameters and damage variables at the characteristic points of RC beams, and the modified fiber damage model effectively identified and reflected the progression of damage.
This research makes significant contributions to the advancement of structural intelligence. The multilevel damage assessment approach enables a rapid assessment of the safety, serviceability, and durability of RC structures using the monitored SFCB strain and relevant material parameters. This not only provides valuable insights for the design of intelligent RC structures but also has the potential to reduce maintenance costs and prevent catastrophic failures.
The development of self-sensing SFCBs and the proposed multilevel damage assessment method represent a major step forward in the field of structural health monitoring. As this technology continues to evolve, it is expected to play an increasingly important role in ensuring the safety and reliability of our built environment.
The paper "Performance Assessment of Reinforced Concrete Structures Using Self-Sensing Steel Fiber-Reinforced Polymer Composite Bars: Theory and Test Validation," authored by Zenghui Ye, Zhongfeng Zhu, Feng Xing, Yingwu Zhou. Full text of the open access paper: https://doi.org/10.1016/j.eng.2024.11.022
