High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, hydrogen embrittlement, and progressive preload loss, which pose significant challenges for reliable condition monitoring and early fault diagnosis. This review provides a structured synthesis of recent advances in bolt health monitoring and intelligent fault diagnosis. A unified framework is established to link multi-physics failure mechanisms with multi-modal sensing technologies and data-driven diagnostic methods. Key sensing approaches—such as piezoelectric impedance techniques, ultrasonic phased array inspection, and computer vision-based monitoring—are critically reviewed in terms of their physical principles, diagnostic capabilities, and limitations. Furthermore, the transition from traditional model-based and signal-processing-driven methods to machine learning- and deep learning-based approaches is examined, with emphasis on multi-modal data fusion, real-time monitoring, and lifecycle-oriented health management enabled by IoT and digital twin technologies. Finally, key challenges and future research directions toward robust and scalable intelligent bolt health management systems are outlined. This review’s primary contribution lies in establishing a novel, integrated framework that links failure physics to sensing and diagnosis, thereby providing a structured roadmap for transitioning from isolated component monitoring to lifecycle-oriented, intelligent health management systems for critical bolted connections.

Under impact loading, the performance of ordinary waste glass powder concrete is lower than that of ordinary concrete. To enhance the impact resistance of waste glass powder concrete, renewable polyvinyl alcohol (PVA) fibers are introduced. Considering the effects of PVA fiber volume content (0.4%, 0.8%, 1.2%) and fiber length (6 mm, 12 mm, 18 mm), the basic mechanical properties and impact resistance of PVA-fiber-reinforced waste glass powder concrete are investigated through compression tests, flexural tests, and drop hammer impact tests. The results show that the incorporation of PVA fibers can effectively improve the impact resistance of waste glass powder concrete, and both the compressive and flexural strength are enhanced to varying degrees. The experimental results show that when the fiber volume content is 0.8% and the length is 12 mm, the flexural strength of the sample is the best. In addition, the impact of fiber length on performance varies by index; shorter fibers (6 mm) perform more prominently in enhancing impact toughness, while longer fibers (12 mm) are more conducive to the growth of flexural strength. There is a positive correlation between the impact resistance of the specimens and the fiber content; as the content of PVA fibers increases, both the initial crack impact number and the failure impact number show an increasing trend, and the energy absorbed by the concrete during initial cracking and failure gradually increases. The highest impact energy dissipation is achieved with the incorporation of 1.2% 6 mm fibers. Based on the experimental results, a functional relationship between fiber length, fiber content, and impact energy absorption is proposed for PVA-fiber-reinforced waste glass powder concrete.