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<p>Hidden cracks within tunnel lining structures pose significant safety hazards, which are difficult to effectively identify using traditional detection methods. This study addresses the challenge of detecting and identifying hidden cracks in lining structures under strong interference environments by proposing a synergistic detection method combining Enhanced Multiple Synchro-squeezing Generalized S-Transform (EMSGST) and Energy-Entropy Coupling Wavelet Packet Transform (EECWPT). The experimental results demonstrate that the proposed method exhibits superior time-frequency concentration, enhanced noise robustness, and improved transient detection capability compared with conventional methods, with Rényi entropy reduced by 3.82%, 3.73%, and 4.0%, and signal-to-noise ratio increased by 2.2%, 3.09%, and 2.36%, respectively. Reflected signals from overlapping crack portions interfere with each other, and reflections from cracks forming smaller angles with the inspection surface are more pronounced. The energy proportions of the three crack types within the 0–2.5 GHz frequency band decrease sequentially, and the time-frequency joint entropy shows a positive correlation with energy concentration. The research validates the robust identification capability of the proposed method for diverse types of hidden cracks as small as 0.5 mm under complex environments. This work provides a theoretical foundation and engineering applicability for the rapid diagnosis of concealed defects in tunnels.</p>

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Characteristic analysis of hidden crack signals in tunnel lining based on FFT-EMSGST-EECWPT synergy

  • 期刊论文

  • 发表日期：

    2026年7月2日

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  • Tong-hua Ling,
  • Yong-zhi Jiang,
  • Fu Huang,
  • Xing Wu,
  • Liang Zhang,
  • Hao Jiang

摘要

Hidden cracks within tunnel lining structures pose significant safety hazards, which are difficult to effectively identify using traditional detection methods. This study addresses the challenge of detecting and identifying hidden cracks in lining structures under strong interference environments by proposing a synergistic detection method combining Enhanced Multiple Synchro-squeezing Generalized S-Transform (EMSGST) and Energy-Entropy Coupling Wavelet Packet Transform (EECWPT). The experimental results demonstrate that the proposed method exhibits superior time-frequency concentration, enhanced noise robustness, and improved transient detection capability compared with conventional methods, with Rényi entropy reduced by 3.82%, 3.73%, and 4.0%, and signal-to-noise ratio increased by 2.2%, 3.09%, and 2.36%, respectively. Reflected signals from overlapping crack portions interfere with each other, and reflections from cracks forming smaller angles with the inspection surface are more pronounced. The energy proportions of the three crack types within the 0–2.5 GHz frequency band decrease sequentially, and the time-frequency joint entropy shows a positive correlation with energy concentration. The research validates the robust identification capability of the proposed method for diverse types of hidden cracks as small as 0.5 mm under complex environments. This work provides a theoretical foundation and engineering applicability for the rapid diagnosis of concealed defects in tunnels.