## 3 The State-of-the-Art Review in Tunnel Blasting Evaluation
### Blasting Techniques and Optimization  
Blasting techniques are fundamental to successful tunnel excavation, significantly impacting both the efficiency of operations and the integrity of surrounding rock formations. The optimization of blasting parameters is critical not only for minimizing rock damage but also for enhancing overall safety and cost-effectiveness. Numerous studies have focused on understanding how various blasting methods and configurations influence the outcome of tunnel construction. [PERSON] and [PERSON] (1979) laid the groundwork for this field by utilizing finite element analysis (FEA) to explore the effects of different blast hole patterns on rock damage [3]. Their work established a crucial understanding of how the arrangement and depth of blast holes can impact the integrity of the surrounding rock, effectively paving the way for more sophisticated blasting designs tailored to specific geological conditions. The significance of optimizing these parameters cannot be overstated, as improper blasting can lead to excessive fragmentation, which complicates the excavation process and increases costs. [PERSON] (1984) expanded on this foundation by investigating controllable blasting parameters such as charge weight, timing, and hole spacing [5]. This research highlighted the direct correlation between optimized blasting and reduced mining costs, thereby demonstrating the economic feasibility of employing precise blasting techniques. By focusing on fragmentation and its relationship with various parameters, [PERSON]'s work emphasized that tailored blasting not only enhances operational efficiency but also minimizes environmental impact by reducing the volume of overexcavated material. The advent of computational modeling techniques has further revolutionized the optimization of blasting operations. [PERSON] and [PERSON] (2003) introduced multiple-view geometry as a mathematical framework for three-dimensional reconstruction from two-dimensional images, which laid the foundation for various applications in computer vision [6]. These methodologies have since been employed in tunnel blasting assessments to visualize and analyze the effects of different blasting techniques, enabling engineers to make data-driven decisions regarding blasting configurations. In recent years, the introduction of advanced algorithms, such as the Scale-Invariant Feature Transform (SIFT) developed by [PERSON] (2004), has improved the accuracy of image matching in rock assessments, which is crucial for evaluating blasting effects in three-dimensional space [7]. The ability to match and track distinct features across images facilitates a more comprehensive understanding of rock behavior during and after blasting, leading to enhanced optimization strategies. Furthermore, modern approaches have begun to incorporate machine learning and artificial intelligence into blasting optimization processes. These technologies enable the analysis of vast amounts of data collected from past blasting operations to identify patterns and predict outcomes under varying conditions. For instance, machine learning algorithmscan analyze historical data to recommend optimal blasting parameters based on specific geological profiles and project requirements. The integration of real-time monitoring technologies has also been instrumental in optimizing blasting techniques. As highlighted by [PERSON] et al. (2014), methodologies that utilize laser scanning for monitoring tunnel deformation provide immediate feedback on the effectiveness of blasting operations [11]. This real-time data can inform adjustments to blasting plans, allowing for on-the-fly optimizations that enhance both safety and efficiency. Overall, the field of blasting techniques and optimization has witnessed significant advancements driven by computational modeling, data analytics, and real-time monitoring technologies.