## 7 Conclusions  
This study presents a thorough review of the current state of tunnel blasting evaluation, organized into five critical areas: Blasting Techniques and Optimization, 3D Reconstruction and Visualization, Monitoring and Assessment Technologies, Automation and Advanced Techniques, and Half Porosity in Tunnel Blasting. Each section provides an in-depth analysis of recent advancements and ongoing research, showcasing the rapid evolution in tunnel blasting methodologies and highlighting key areas for continued innovation. The growing application of digital technologies in this field reflects a concerted effort to improve precision, operational efficiency, and safety in tunnel engineering. Building upon this foundation, we introduce a novel technique for half porosity recognition that utilizes computer vision to enhance post-blast analysis in tunnel construction. This approach leverages advanced 3D reconstruction principles to provide a non-intrusive, rapid, and accurate assessment of tunnel blast quality. By capturing high-resolution depth images with a handheld digital camera and applying the Structure-from-Motion (SfM) algorithm, our method enables precise visual and quantitative evaluation of the palisade surface and tunnel contour. Additionally, specialized software processes the 3D point cloud model using the Random Sample Consensus (RANSAC) algorithm to calculate half porosity based on the arc length of fitted curves. Compared to traditional methods, this technique offers several advantages: it reduces costs, expands the detection range, and achieves higher accuracy without disrupting ongoing construction activities. The practical value of this method was validated through field experiments on the Huangtai Tunnel segment of China's National Highway 109, where the calculated half porosity of 58.7% closely aligned with conventional measurements of 60%. The minimal deviation observed demonstrates not only the method's robustness but also its potential to surpass the accuracy of traditional approaches. Furthermore, these findings underscore the broader applicability of our method to other geotechnical and mining contexts, such as open-pit blasting, where precise post-blast assessment is equally critical. In addition to improving tunnel blasting practices, this study paves the way for further technological integration in subsurface construction. The combination of computer vision, 3D reconstruction, and algorithmic analysis represents a significant step toward more sophisticated and scalable blast evaluation techniques. Future research will benefit from exploring enhancements such as machine learning algorithms for predictive modeling, enabling proactive adjustments to blasting parameters based on site-specific geological data. Additionally, real-time data processing capabilities will further expand the practical utility of this method, facilitating immediate feedback that can inform decision-making throughout the blasting process. As the demands of modern tunnel engineering continue to grow, the integration of these advanced digital methods promises not only to improve operational safety and efficiency but also to contribute to more sustainable and cost-effective practices. By setting a new standard for accuracy and practicality in blasting assessments, this approach aligns with the ongoing shift toward digitalization in construction, underscoring the importance of adopting innovative solutions in challenging engineering environments. Conceptualization, J.S., H.A., Y.W., Z.Y. and W.S. ; methodology, H.A., J.S., Y.W., and Z.Y. ; software, J.S., Y.W. and Z.Y. ; validation, Y.W. and Z.Y. ; formal analysis, J.S. and H.A. ; investigation, J.S., Z.Y. and W.S. ; resources, J.S. ; data curation, Y.W. and Z.Y. ; writing--original draft preparation, Y.W. and Z.Y. ; writing--review and editing, H.A., J.S. and Y.W. ; visualization, Z.Y. and W.S. ; supervision, J.S. and H.A. ; project administration, H.A. and J.S. ; funding acquisition, H.A. and J.S.