## 4 Discussion  
This study proposes an advanced methodology for monitoring tunnel cross-section deformation using MLS point cloud displacement analysis. Traditional tunnel monitoring methods, such as manual total station surveys, optical leveling, and inclinometer measurements, are widely recognized for their reliability and simplicity. However, these methods often lack the spatial resolution necessary to detect subtle deformation patterns across entire tunnel structures, as they rely on discrete measurement points. In contrast, the proposed MLS-based approach leverages dense point cloud data, providing a comprehensive spatial representation and enabling the detection of fine-grained deformations with precision down to 0.02 mm. The comparative analysis of average point cloud displacement between the SILSS and the Leica Pegasus Two Ultimate scanner showed differences predominantly below 0.1 mm, while ovality variations remained largely under 1 mm across 102 shield rings. These findings validate the reliability and accuracy of the SILSS for tunnel deformation monitoring, confirming its applicability as an alternative to conventional methods with comparable precision. Compared to recent studies, such as [PERSON] et al. 's [2], which utilized multiple systems to validate data accuracy, our approach employs a single SILSS system for multi-epoch 3D deformation analysis. This simplification improves workflow efficiency while maintaining high accuracy. While [PERSON] et al. focused on system performance validation and 2D analysis, our study emphasizes advanced data processing techniques, including point density denoising based on statistical outlier removal, to enhance tunnel lining extraction and improve overall data quality for accurate 3D deformation analysis. Similarly, [PERSON] et al. [45] reported that minor displacements often do not pose structural risks, aligning with our findings that observed ovality changes (ranging from \(-\)0.02 mm to 0.12 mm) are well within acceptable thresholds, confirming tunnel stability. The MLS-based methodology outperforms conventional techniques like total station surveys or ground-based radar in terms of spatial resolution, efficiency, and sensitivity. For example, while traditional methods often struggle to capture millimeter-level changes, the MLS approach reliably detected displacements as small as 0.02 mm, with a high correlation coefficient (R = 0.98) between measurements [46]. Additionally, the MLS method offers significant time savings in data acquisition and analysis, making it an attractive option for continuous structural health monitoring. Baseline comparisons with historical data further validate the robustness of the proposed methodology. For instance, earlier studies by [PERSON] et al. [47] and [PERSON] et al. [48] indicate  
Figure 12: Shield ring ovality difference. Difference between ovality measured by the proposed method and the Leica Pegasus Two Ultimate scanner system. that displacement thresholds above 10 mm typically raise structural concerns. Our results are consistent with these benchmarks, showing displacements well within safe limits. Notably, shield ring 53 exhibited the highest displacement of 1 mm. While this is within acceptable thresholds, localized variations such as these highlight the importance of periodic monitoring to detect the potential progression of deformation over time. These findings suggest that the MLS approach can provide early warnings for localized structural issues, thereby enhancing safety measures. Despite its advantages, the MLS-based methodology does have certain limitations. For instance, obstructions and geometric constraints in the tunnel environment can hinder data acquisition. Additionally, the method is not specifically designed to identify small-scale surface anomalies, such as cracks or rust, which could complement deformation monitoring. Addressing these limitations through the integration of alternative techniques, such as ground-penetrating radar or accelerometer data, could further enhance the approach's utility.