## 3 The State-of-the-Art Review in Tunnel Blasting Evaluation
### Automation and Advanced Techniques  
In the field of tunnel blasting evaluation, automation, and advanced techniques have emerged as transformative forces, significantly enhancing efficiency, precision, and safety. As technology progresses, the integration of automated systems into tunnel construction and assessment processes allows for more accurate monitoring of blasting effects and the overall health of tunnel structures. This shift not only streamlines operations but also mitigates risks associated with human error. One of the significant advancements in tunnel evaluation is the application of 3D laser scanning technology, as thoroughly reviewed by [PERSON] et al. (2015) [12]. Their comprehensive study emphasized the effectiveness of terrestrial laser scanning (TLS) for detecting structural deformations and changes during tunnel construction. By enabling real-time generation of high-resolution 3D point cloud data, TLS facilitates the precise monitoring of displacement and deformation, allowing engineers to quickly identify potential structural issues. This ensures both the safety and efficiency of construction processes while also significantly enhancing the accuracy of evaluations and minimizing the risks associated with human error. Building on these advancements, [PERSON] et al. (2016) explored the use of 3D laser scanning for tunnel profile extraction, developing algorithms for the automated identification and quantification of over and underexcavation areas [13]. This approach not only improves the precision of excavation assessments but also ensures compliance with design specifications. The automation of data extraction reduces manual labor and accelerates the evaluation process, enabling timely decision-making in response to any detected discrepancies. Further contributing to automation in tunnel monitoring, [PERSON] et al. (2021) introduced a novel OverUnderCut algorithm that analyzes tunnel excavation efficiency using 3D laser scanning technology. Their findings highlight the potential for real-time analysis of excavation performance, enabling proactive adjustments during construction to address inefficiencies [19]. By automating the analysis process, construction teams can quickly adapt to changing conditions, reducing the risk of suboptimal blasting outcomes and enhancing safety measures. Another significant development is the semi-supervised learning-based point cloud network proposed by [PERSON] et al. (2023). This innovative approach enables the seg mentation of 3D tunnel scenes, which enhances the automation of tunnel inspections [25]. The adoption of machine learning techniques facilitates a more comprehensive analysis of tunnel conditions, allowing for quicker identification of potential issues and ensuring adherence to safety standards. By automating the inspection process, construction teams can allocate resources more effectively and reduce the time required for manual assessments. The integration of RANSAC-based methods, as demonstrated by [PERSON] et al. (2022), further exemplifies the advancements in automation and its application to tunnel blasting evaluation [22]. Their research focused on multiprimitive building reconstruction from 3D point clouds, which is crucial for improving the geometric accuracy of tunnel structures. By automating the reconstruction process, engineers can achieve higher precision in assessing tunnel integrity, thereby supporting informed decision-making during blasting operations. Moreover, monitoring technologies such as real-time data analytics have become increasingly vital in the automation landscape. The work by [PERSON] (2017) emphasizes the importance of effective data processing techniques, which enhance the reliability of tunnel assessments by reducing noise in point cloud data [14]. Automated noise reduction processes contribute to the accuracy of monitoring systems, allowing for clearer visualization of tunnel conditions and more reliable evaluations of blasting impacts.