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<p>Reinforced concrete (RC) slabs are the core horizontal load-bearing members in civil and protective engineering. Under extreme loads such as explosive impacts and falling object impacts, they are prone to sudden structural collapse due to the continuous accumulation of internal damage. However, traditional detection methods can only obtain macroscopic mechanical responses and cannot realize real-time dynamic monitoring of the entire process of damage initiation and propagation. As a passive dynamic non-destructive testing method, acoustic emission (AE) technology can achieve real-time characterization of internal damage by capturing elastic wave signals released by material damage. Nevertheless, its application under impact load conditions still suffers from the lack of a systematic correlation mechanism between damage and parameters. This paper proposes a multi-parameter fusion analysis method based on AE information entropy CV. By conducting increasing-amplitude drop weight impact tests on RC slabs with three thicknesses (80, 100&#xa0;and 120&#xa0;mm), the mechanical responses, stiffness degradation and AE characteristics were systematically investigated. The results show that the cumulative displacement-stiffness degradation model proposed in this paper can effectively quantify the degradation rate through the attenuation index α after component failure, and this parameter decreases with increasing slab thickness. This corresponds to different failure modes: the 80&#xa0;mm slab exhibits rapid stiffness degradation and damage-accumulative punching shear failure, the 120&#xa0;mm thick slab undergoes a more ductile punching shear failure, and the 100&#xa0;mm thick slab presents a transitional response. In addition, AE information entropy CV can serve as an early warning indicator for component damage and failure. The evaluation system constructed by combining multi-dimensional signal characteristics including AE activity analysis, AE information entropy CV, RA-AF distribution and b-value with macroscopic data such as displacement can accurately characterize damage stages, crack modes and failure precursors, providing a quantitative basis for damage identification and health monitoring of RC structures under impact loads. Unlike single-parameter methods, which may fail when damage becomes complex, the proposed multi-parameter fusion strategy overcomes this limitation, and simultaneous anomalies across multiple parameters provide stronger evidence of impending failure.</p>

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Assessment of the Impact Resistance of RC Slabs using Acoustic Emission Information Entropy: A Multi-Parameter Fusion Approach

  • 期刊论文

  • 发表日期：

    2026年7月8日

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  • Alipujiang Jierula,
  • Weicheng Wang,
  • Cungen Wang,
  • Shuhong Wang,
  • Tae-Min Oh

摘要

Reinforced concrete (RC) slabs are the core horizontal load-bearing members in civil and protective engineering. Under extreme loads such as explosive impacts and falling object impacts, they are prone to sudden structural collapse due to the continuous accumulation of internal damage. However, traditional detection methods can only obtain macroscopic mechanical responses and cannot realize real-time dynamic monitoring of the entire process of damage initiation and propagation. As a passive dynamic non-destructive testing method, acoustic emission (AE) technology can achieve real-time characterization of internal damage by capturing elastic wave signals released by material damage. Nevertheless, its application under impact load conditions still suffers from the lack of a systematic correlation mechanism between damage and parameters. This paper proposes a multi-parameter fusion analysis method based on AE information entropy CV. By conducting increasing-amplitude drop weight impact tests on RC slabs with three thicknesses (80, 100 and 120 mm), the mechanical responses, stiffness degradation and AE characteristics were systematically investigated. The results show that the cumulative displacement-stiffness degradation model proposed in this paper can effectively quantify the degradation rate through the attenuation index α after component failure, and this parameter decreases with increasing slab thickness. This corresponds to different failure modes: the 80 mm slab exhibits rapid stiffness degradation and damage-accumulative punching shear failure, the 120 mm thick slab undergoes a more ductile punching shear failure, and the 100 mm thick slab presents a transitional response. In addition, AE information entropy CV can serve as an early warning indicator for component damage and failure. The evaluation system constructed by combining multi-dimensional signal characteristics including AE activity analysis, AE information entropy CV, RA-AF distribution and b-value with macroscopic data such as displacement can accurately characterize damage stages, crack modes and failure precursors, providing a quantitative basis for damage identification and health monitoring of RC structures under impact loads. Unlike single-parameter methods, which may fail when damage becomes complex, the proposed multi-parameter fusion strategy overcomes this limitation, and simultaneous anomalies across multiple parameters provide stronger evidence of impending failure.