## 3 The State-of-the-Art Review in Tunnel Blasting Evaluation
### 3D Reconstruction and Visualization  
The advent of 3D reconstruction and visualization techniques has transformed the evaluation and assessment of tunnel blasting effects, providing engineers with sophisticated tools to analyze complex geological environments. These methods are pivotal in enhancing our understanding of tunnel conditions, rock integrity, and the overall impact of blasting operations. By leveraging advanced imaging and modeling technologies, researchers have been able to create detailed representations of tunnel structures and assess the effects of blasting with unprecedented accuracy. A significant contribution to this field came from [PERSON] et al. (2012), who introduced Structure-from-Motion (SfM) photogrammetry as a low-cost and effective approach for three-dimensional modeling in geoscience applications [10]. SfM utilizes multiple overlapping images to reconstruct three-dimensional structures, making it particularly useful in tunnel environments where traditional surveying methods may be impractical. This technique allows for efficient documentation of tunnel geometries, which is essential for assessing the impact of blasting on rock formations and ensuring compliance with engineering specifications. The integration of SfM with machine learning algorithms has further enhanced the capabilities of 3D reconstruction. For instance, [PERSON] et al. (2023) developed a semi-supervised learning-based point cloud network specifically for segmenting 3D tunnel scenes, significantly improving the automation and accuracy of tunnel inspections [25]. Automating the analysis of point cloud data not only accelerates the evaluation process but also reduces the potential for human error, leading to more reliable assessments of tunnel conditions post-blasting. Moreover, the utilization of the Scale-Invariant Feature Transform (SIFT) algorithm, introduced by [PERSON] (2004), has been instrumental in the accurate matching and recognition of features across images used in 3D reconstruction [7]. This capability is critical for assessing changes in tunnel structures over time, especially after blasting operations. By reliably identifying distinct features within the tunnel environment, engineers can create detailed models that reflect the true state of the tunnel, allowing for better-informed decisions regarding maintenance and remediation strategies. In recent years, 3D laser scanning has emerged as a powerful tool for capturing detailed spatial data of tunnel environments. [PERSON] et al. (2016) employed this technology to develop algorithms that identify and quantify over and underexcavation areas in tunnels [13]. Additionally, methods utilizing 3D laser scanning have been proposed for detecting the volume of blasted rock in tunnel environments. These methods allow for accurate calculation of soil and rock displacement by extracting and analyzing the global spatial axis from 3D point cloud data obtained from tunnel sections. The high-resolution data generated by 3D laser scanning not only enhances the accuracy of excavation evaluations but also enables real-time monitoring of tunnel conditions, providing immediate feedback on the effectiveness of blasting techniques. Such advancements in laser scanning technologies offer more comprehensive and efficient ways to assess and optimize blasting results in complex tunnel environments. The significance of 3D reconstruction extends beyond mere visualization; it also plays a crucial role in the analysis of half porosity in tunnel blasting. [PERSON] et al. (2018) proposed a digital identification technique for detecting half porosity based on 3D point cloud data extraction [15]. Analyzing post-blast residual holes enhances the precision of assessing blasting effects, contributing to more effective monitoring and optimization of blasting performance. Furthermore, the continuous improvement of 3D visualization technologies allows for the integration of various data sources, enhancing the comprehensiveness of tunnel assessments. For example, [PERSON] et al.