Detection of subsurface cracks in mesostructure models of concrete using Rayleigh wave propagation

Abstract Subsurface cracks may remain undetected until significant harm occurs, which often lead to their neglect in conventional nondestructive evaluations. In addition, the scattering of the elastic waves may affect the detection results. This study investigates the impact of the presence of aggregates on the identification and quantification of subsurface cracks using Rayleigh wave propagation. To achieve this purpose, concrete mesostructure models that integrate absorbing boundaries using the stiffness reduction method and subsurface cracks are constructed. The expected arrival times of the waves are derived and compared with the B-scans to examine the propagation path of the mode-converted body waves. Furthermore, we introduce a novel signal extraction technique using the energy spectrum based on wavelet coefficients and a knowledge-based rule to assess the depth of subsurface cracks. Finally, attempts are made to demonstrate and validate the far-field signal enhancement phenomenon of subsurface cracks using mode conversion theories and the proposed area ratio index. The results show that the theoretical curves and B-scan maps are well aligned, suggesting that the analysis of the waveform propagation path is accurate. The proposed method is quite robust for both homogeneous and heterogeneous material configurations. The crack parameters can be obtained using the fitted curve obtained by determining the peak frequency of the transmitted signal.

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Publication Details

Journal
Measurement Science and Technology
Published
2025-02-20
DOI
https://doi.org/10.1088/1361-6501/adb86e
Citations
1
Primary Topic
Ultrasonics and Acoustic Wave Propagation
Type
article
Field-Weighted Citation Impact
0.86

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article

Detection of subsurface cracks in mesostructure models of concrete using Rayleigh wave propagation

Yingzhou Liu, Xiaohui Sun, Shuli Fan, Chunguang Liu
1 citations
Measurement Science and Technology
Ultrasonics and Acoustic Wave Propagation
0.86
article

Detection of subsurface cracks in mesostructure models of concrete using Rayleigh wave propagation

Yingzhou Liu, Xiaohui Sun, Shuli Fan, Chunguang Liu
article en
1 citations

Abstract

Abstract Subsurface cracks may remain undetected until significant harm occurs, which often lead to their neglect in conventional nondestructive evaluations. In addition, the scattering of the elastic waves may affect the detection results. This study investigates the impact of the presence of aggregates on the identification and quantification of subsurface cracks using Rayleigh wave propagation. To achieve this purpose, concrete mesostructure models that integrate absorbing boundaries using the stiffness reduction method and subsurface cracks are constructed. The expected arrival times of the waves are derived and compared with the B-scans to examine the propagation path of the mode-converted body waves. Furthermore, we introduce a novel signal extraction technique using the energy spectrum based on wavelet coefficients and a knowledge-based rule to assess the depth of subsurface cracks. Finally, attempts are made to demonstrate and validate the far-field signal enhancement phenomenon of subsurface cracks using mode conversion theories and the proposed area ratio index. The results show that the theoretical curves and B-scan maps are well aligned, suggesting that the analysis of the waveform propagation path is accurate. The proposed method is quite robust for both homogeneous and heterogeneous material configurations. The crack parameters can be obtained using the fitted curve obtained by determining the peak frequency of the transmitted signal.

Measurement Science and TechnologyVol. 36(3)
Tianjin University of Technology (CN), Dalian University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 34%
Ultrasonics and Acoustic Wave Propagation
0.86
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Detection of subsurface cracks in mesostructure models of concrete using Rayleigh wave propagation — Yingzhou Liu, Xiaohui Sun, et al. · Measurement Science and Technology (2025) | TGRS Research Map | TGRS