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<p>When employing the New Austrian Tunneling Method (NATM) in constructing mountain tunnels, the flatness of shotcrete lining surface is a critical aspect of quality acceptance. However, due to the harsh construction environment and the challenges posed by large-span structures, current measurement of surface flatness heavily relies on manual labor, which is prone to errors and possible omissions. Furthermore, with the advance of data acquisition technology, 3D point cloud data would generate more rich features of surface flatness, which enable the evaluation of flatness from more aspects, updating the evaluation index system which is a one-dimensional assessment of flatness and is mainly elicited with traditional dataset is of high urgency. To address this issue, this research employs terrestrial laser scanning (TLS) to obtain point cloud data and conducts frequency domain analysis of surface deviations using two-dimensional discrete wavelet transform. This approach reconstructs rich undulation feature signals, thereby establishing a comprehensive method for assessing the flatness of shotcrete. The experimental results demonstrate that this method outperforms traditional geometric indicators in detecting defective areas, achieving a higher detection rate. In addition, the distribution of these areas is more continuous, facilitating more accurate identification of defects. Furthermore, more detailed information is provided, accommodating the detection requirements across various analysis scales.</p>

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Flatness assessment for NATM tunnel shotcrete lining quality monitoring based on point cloud and 2D discrete wavelet transform

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Shi Qiu,
  • Chunzhong Xiao,
  • Xiao Wei,
  • Qasim Zaheer,
  • Weidong Wang,
  • Jiacen Guan,
  • Runan Cheng,
  • Yangming Luo,
  • Jiakai Kuang,
  • Jin Wang

摘要

When employing the New Austrian Tunneling Method (NATM) in constructing mountain tunnels, the flatness of shotcrete lining surface is a critical aspect of quality acceptance. However, due to the harsh construction environment and the challenges posed by large-span structures, current measurement of surface flatness heavily relies on manual labor, which is prone to errors and possible omissions. Furthermore, with the advance of data acquisition technology, 3D point cloud data would generate more rich features of surface flatness, which enable the evaluation of flatness from more aspects, updating the evaluation index system which is a one-dimensional assessment of flatness and is mainly elicited with traditional dataset is of high urgency. To address this issue, this research employs terrestrial laser scanning (TLS) to obtain point cloud data and conducts frequency domain analysis of surface deviations using two-dimensional discrete wavelet transform. This approach reconstructs rich undulation feature signals, thereby establishing a comprehensive method for assessing the flatness of shotcrete. The experimental results demonstrate that this method outperforms traditional geometric indicators in detecting defective areas, achieving a higher detection rate. In addition, the distribution of these areas is more continuous, facilitating more accurate identification of defects. Furthermore, more detailed information is provided, accommodating the detection requirements across various analysis scales.