Papers
2
Total Citations
6
H-Index
2
About
Dr. Xiao-Lei Zhang is a leading researcher in the field of civil infrastructure monitoring and geospatial data analysis, with a primary focus on the structural health assessment of underground metro systems. His most significant contributions lie in the development of automated, high-precision methods for detecting and analyzing tunnel deformations using advanced laser scanning technology and point cloud processing. Dr. Zhang pioneered the integration of robotic laser scanning with deep learning-based point cloud semantic segmentation, enabling the automatic and accurate identification of structural anomalies such as cracks, spalling, and misalignments in operational metro tunnels. His work, including the highly cited 2025 paper "Metro tunnel deformation detection based on laser scanning robot and point cloud semantic segmentation" (4 citations), establishes a new paradigm for non-contact, efficient, and objective tunnel inspection. By combining geometric analysis with semantic understanding, his research significantly reduces the reliance on manual inspection, enhancing both safety and cost-effectiveness in urban infrastructure management. Dr. Zhang’s innovative approaches are shaping the future of smart infrastructure maintenance and are widely referenced by engineers and researchers seeking to automate deformation monitoring in complex underground environments.
Research Focus
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Top Papers
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