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A High-Precision Method for Extracting Lateral Deformation in Operational Shield Tunnels Based on LiDAR Point Cloud

Sijia Tang1, Xiangyang Xu1

  • 1School of Rail Transportation, Soochow University, Suzhou 215006, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

This study introduces a new method for precise lateral deformation monitoring in shield tunnels using LiDAR. The approach enhances accuracy and stability in assessing structural health for urban rail transit.

Keywords:
LiDAR point cloudselliptical fittinglateral deformation monitoringsemantic segmentationshield-driven tunnels

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Area of Science:

  • Civil Engineering
  • Geotechnical Engineering
  • Computer Vision

Background:

  • Structural health assessment of operational shield tunnels is crucial for urban rail transit safety.
  • LiDAR point clouds from tunnels present challenges like occlusions, noise, and uneven density.
  • Existing section-by-section ellipse fitting methods suffer from parameter instability.

Purpose of the Study:

  • To develop a high-precision method for extracting lateral deformation from tunnel LiDAR point clouds.
  • To address the limitations of conventional methods in handling noisy and complex tunnel environments.
  • To improve the stability and accuracy of deformation monitoring in operational shield tunnels.

Main Methods:

  • A point-wise attention Transformer network (PWAT) was developed for accurate lining segmentation.
  • PWAT utilizes k-NN adaptive sampling, geometric position encoding, and geometry-constrained multi-head self-attention.
  • A continuity-constrained RANSAC (CC-RANSAC) algorithm was introduced to enhance ellipse parameter stability.

Main Results:

  • PWAT achieved 99.53% overall accuracy and 99.06% mIoU in six-class segmentation.
  • CC-RANSAC reduced the mean residual to 2.0 mm and the center jump rate to 4.2%.
  • The method demonstrated a mean absolute error of 1.35 mm and RMSE of 1.68 mm compared to total station data.

Conclusions:

  • The proposed PWAT and CC-RANSAC method enables automatic and accurate extraction of lateral deformation from tunnel LiDAR point clouds.
  • This approach significantly improves the stability and precision of deformation monitoring in operational shield tunnels.
  • The findings contribute to enhanced structural health assessment for urban rail infrastructure.