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<p>The failure mechanisms of reinforced concrete (RC) structures are primarily characterized by tensile failure and shear failure. The ability to distinguish between various types of acoustic emission (AE) signals provides a scientific foundation for real-time structural health monitoring. In this study, four-point bending experiments were conducted on RC beams with varying reinforcement configurations. The AE data were analyzed using K-means clustering and RA (Rise Time to Amplitude) -AF (Average Frequency) analysis. The results demonstrate that K-means clustering effectively categorizes the AE data into three distinct groups, which exhibit a strong correlation with the damage states of the RC beams. RA-AF analysis further reveals that the predominant crack type in cluster 3 corresponds to the ultimate failure modes of the RC beams. Specifically, when the dominant crack in cluster 3 is identified as a tensile crack, the beam undergoes bending failure, characterized by a vertical primary crack at the mid-span. Conversely, when the dominant crack is a shear crack, the beam experiences shear failure, with the primary crack propagating from the support to the loading point. These findings highlight the potential of AE signal analysis in elucidating the failure mechanisms and damage progression in RC structures, offering valuable insights for structural health monitoring and failure prediction.</p>

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Damage Evolution in Different Reinforcement Configurations during Four-Point Bending: Acoustic Emission Analysis Using K-means Clustering

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Alipujiang Jierula,
  • Shilong Ding,
  • Huan Liu,
  • Bin Yang

摘要

The failure mechanisms of reinforced concrete (RC) structures are primarily characterized by tensile failure and shear failure. The ability to distinguish between various types of acoustic emission (AE) signals provides a scientific foundation for real-time structural health monitoring. In this study, four-point bending experiments were conducted on RC beams with varying reinforcement configurations. The AE data were analyzed using K-means clustering and RA (Rise Time to Amplitude) -AF (Average Frequency) analysis. The results demonstrate that K-means clustering effectively categorizes the AE data into three distinct groups, which exhibit a strong correlation with the damage states of the RC beams. RA-AF analysis further reveals that the predominant crack type in cluster 3 corresponds to the ultimate failure modes of the RC beams. Specifically, when the dominant crack in cluster 3 is identified as a tensile crack, the beam undergoes bending failure, characterized by a vertical primary crack at the mid-span. Conversely, when the dominant crack is a shear crack, the beam experiences shear failure, with the primary crack propagating from the support to the loading point. These findings highlight the potential of AE signal analysis in elucidating the failure mechanisms and damage progression in RC structures, offering valuable insights for structural health monitoring and failure prediction.