Volume 41 Issue 7
Jul.  2026
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Xiaoqun Jin, Changjiang Wang, Juan Hu, Hui Jin, Jinbo Li, Yuling Zhang. Characteristics of Magnetic Memory Signals During Real-Time Fatigue Testing of Steel Plates[J]. STEEL CONSTRUCTION(Chinese & English), 2026, 41(7): 36-46. doi: 10.13206/j.gjgS25101301
Citation: Xiaoqun Jin, Changjiang Wang, Juan Hu, Hui Jin, Jinbo Li, Yuling Zhang. Characteristics of Magnetic Memory Signals During Real-Time Fatigue Testing of Steel Plates[J]. STEEL CONSTRUCTION(Chinese & English), 2026, 41(7): 36-46. doi: 10.13206/j.gjgS25101301

Characteristics of Magnetic Memory Signals During Real-Time Fatigue Testing of Steel Plates

doi: 10.13206/j.gjgS25101301
  • Received Date: 2025-10-13
    Available Online: 2026-09-01
  • In order to obtain fatigue damage characteristics as early as possible and to quickly evaluate the fatigue state of in-service bridge steel structures, it is necessary to explore new methods for identifying fatigue damage characteristics. Magnetic memory signals were measured on real-time fatigue samples of steel plates used in steel bridges, and the evolution of the signals during the fatigue process was obtained. The results showed that, under constant ambient conditions, the distribution of the signal curves exhibited similarity and successive evolution during the fatigue process. The identification range of a single probe in the magnetic memory signal measurement reached 40 mm. Additionally, the magnetic memory signal strength and its slope provided strong identification capability when the measurement direction was perpendicular to the crack orientation. When fatigue cracks developed in the steel plate, the magnetic memory strength signals and their slope signals showed distinct curve characteristics. Specifically, the strength signals showed extreme values and a zero-crossing point, while the slope signals presented M-shaped and ∧-shaped patterns. Due to the limited number of specimens in this experimental study, the trend of magnetic memory signals with the increase of fatigue cycles was not yet obvious. Future work should focus on accumulating a larger dataset, expanding to a sufficient number of samples, and exploring more effective methods to obtain the evolving trends.
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