## 1 Introduction  
The structural integrity of tunnel infrastructure is vital for ensuring safe and efficient urban transit systems, particularly as underground construction expands in densely populated areas. Monitoring deformation in tunnel cross-sections is crucial for assessing the stability of these structures and safeguarding their long-term performance, while effective, traditional monitoring methods often struggle with precision and spatial coverage limitations, especially within complex, extensive tunnel environments. Mobile Laser Scanning (MLS) technology offers a promising alternative, enabling rapid, high-resolution 3D point cloud data acquisition for detailed tunnel analysis. MLS technology has emerged as a transformative tool for the non-intrusive monitoring of tunnel structures, enabling rapid and precise collection of high-resolution 3D point cloud data [1]. This advancement facilitates effective deformation monitoring over time, enhancing structural health assessments [2]. The technology enables high-frequency data acquisition, significantly improving the speed of deformation monitoring compared to traditional methods [3]. Recent advancements in MLS have enabled more accurate monitoring of complex tunnel structures, allowing for high-frequency data acquisition that provides an unprecedented level of detail. Studies such as [PERSON] et al. 's [4] demonstrate that MLS point clouds can effectively identify and quantify deformations in tunnel structures, with accuracy levels reaching less than 5 mm. However, they often include noise or outliers that can complicate processing for accurate deformation analysis. The need for advanced, intelligent data processing methods in tunnel analysis has spurred noise removal, segmentation, and data interpretation developments. Traditional approaches to these tasks lack automation, affecting the accuracy and efficiency of point cloud data analysis. Recent research highlights the potential of machine learning and deep learning to overcome these challenges, improving data processing accuracy and speed in large-scale applications. [PERSON] et al. [5] developed an approach for the automatic detection and classification of surface defects based on shape and texture feature analysis using machine learning. Several studies have refined MLS point cloud processing through techniques like denoising and feature abstraction, thus enhancing data reliability and resolution in complex environments [6; 7]. Optimized deep learning algorithms have been shown to significantly enhance the speed and accuracy of real-time data processing across various applications. [PERSON] et al. 's [8] study indicated that optimized deep learning algorithms can significantly enhance the speed and accuracy of real-time data processing. According to [PERSON] et al. 's [9] study, techniques such as model pruning, quantization, and knowledge distillation have been identified as effective methods to reduce latency while maintaining accuracy. [PERSON] et al. [10] demonstrated that deep learning models can rapidly process complex data inputs, enabling autonomous vehicle applications and manufacturing monitoring. [PERSON] et al. [11] proposed a segmentation approach that slices point clouds into 2D planes, facilitating the extraction of boundary points essential for deformation analysis. [PERSON] et al. [12] proposed the CA-PointNet++ model and achieved a mean Intersection over Union (mIoU) of 67.4% for semantic segmentation of transmission corridor 3D point clouds. [PERSON] et al. [4] demonstrated a method combining visual SLAM and point cloud slicing, achieving high precision in deformation detection under varying lighting conditions. Point cloud alignment is also a crucial step in MLS-based deformation analysis, particularly for aligning consecutive scans of tunnel sections over time. Techniques like the Iterative Closest Point (ICP) algorithm have become widely used for point cloud alignment, providing robust alignment between scans and supporting accurate deformation assessment [13]. [PERSON] et al.