Comprehensive Review of Tunnel Blasting Evaluation Techniques and Innovative Half Porosity Assessment Using 3D Image Reconstruction
Abstract
:1. Introduction
2. Methods
2.1. Literature Review Methodology
2.1.1. Literature Search Strategy
2.1.2. Selection Criteria
2.1.3. Data Extraction and Synthesis
2.1.4. Ensuring Relevance and Quality
2.2. Algorithm Design and Validation for Tunnel Blasting Evaluation
2.2.1. Algorithm Selection and Workflow
- (1)
- Image Overlap: Ensuring at least 30% overlap between images to facilitate effective feature matching and depth calculation.
- (2)
- Pre-Capture Calibration: Correcting lens distortion prior to image capture to improve accuracy.
- (1)
- Key Point Detection: Identifying distinct, reliable points within each image that can be matched consistently.
- (2)
- Orientation Assignment: Assigning an orientation to each key point based on local image gradients, ensuring the descriptors remain rotation invariant.
- (3)
- Descriptor Generation: Creating robust feature descriptors that encode local gradient information, enhancing the reliability of feature matching.
- (1)
- Random Selection: Randomly selecting subsets of points from the 3D point cloud.
- (2)
- Model Fitting: Fitting a model (e.g., plane or cylinder) to these subsets.
- (3)
- Consensus Set Formation: Counting inliers (points that fit the model within a threshold).
- (4)
- Iteration: Repeating the process to maximize the inlier count, ultimately selecting the best fitting model.
2.2.2. Algorithm Validation
3. The State-of-the-Art Review in Tunnel Blasting Evaluation
3.1. Blasting Techniques and Optimization
3.2. 3D Reconstruction and Visualization
3.3. Monitoring and Assessment Technologies
3.4. Automation and Advanced Techniques
3.5. Introduction to Half Porosity in Tunnel Blasting
4. Research on Image Acquisition and Theoretical Methods
4.1. Depth Image Acquisition
4.2. Scene 3D Reconstruction Study
4.3. Analysis and Processing of Point Cloud Data
5. Field Experimental Studies
5.1. Engineering Background
5.2. Image Acquisition and Model Reconstruction
5.3. Cutting of Tunnel Model After Explosion
5.4. Point Cloud Filtering and Denoising Process
5.5. Analysis of HalfHole Point Cloud Data
6. Verification of Blast Hole Digit Recognition Method Applied in Tunnels
6.1. Results of Engineering Experiments
6.2. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. | Year | Author(s) | Research Content | Research Findings or Significance | Literatures |
---|---|---|---|---|---|
1 | 1979 | Holmberg Persson | This study utilized finite element analysis (FEA) to examine the effects of different blast hole patterns on rock damage, aiming to optimize blasting parameters for tunnel construction. | The findings established a foundational understanding of how blast hole arrangement impacts rock integrity, paving the way for more effective blasting designs in tunnel projects. | [3] |
2 | 1982 | Marr | Proposed a theoretical framework for visual systems, advancing the field of computer vision, particularly in understanding visual perception and image processing. | This work laid the groundwork for the evolution from two−dimensional image analysis to three−dimensional spatial reconstruction, significantly influencing various applications in engineering and robotics. | [4] |
3 | 1983 | Hagan | Investigated the influence of controllable blasting parameters on fragmentation and mining costs, focusing on optimizing these parameters for efficiency. | The research provided critical insights that support the economic feasibility of blasting operations by demonstrating how optimized blasting can reduce costs and improve safety in mining activities. | [5] |
4 | 2003 | Hartley, Zisserman | Introduced the theory of multiple view geometry, which serves as a mathematical foundation for three−dimensional reconstruction from two−dimensional images. | This theoretical framework is essential for developing robust algorithms in computer vision, enhancing the precision and accuracy of 3D modeling in various applications, including tunnel inspection. | [6] |
5 | 2004 | Lowe | Developed the Scale−Invariant Feature Transform (SIFT) algorithm, which identifies and matches distinctive image features across different scales. | SIFT’s introduction significantly improved image matching accuracy and robustness, making it a pivotal tool in applications like object recognition and 3D reconstruction in tunnel environments. | [7] |
6 | 2006 | Remondino, El−Hakim | Reviewed various image−based 3D modeling techniques, discussing their methodologies, applications, and limitations in geospatial contexts. | The comprehensive analysis provided valuable insights for researchers and practitioners, guiding them in selecting appropriate techniques for specific projects, especially in tunneling. | [8] |
7 | 2008 | Hirschmuller | Proposed the Semi−Global Matching (SGM) algorithm for stereo matching, significantly improving depth estimation accuracy in three−dimensional reconstructions. | The SGM method enhanced the precision of 3D reconstruction techniques, which are critical for accurately assessing tunnel conditions and planning interventions based on blast effects. | [9] |
8 | 2012 | Westoby et al. | Introduced Structure−from−Motion (SfM) photogrammetry as a low−cost and effective method for three−dimensional modeling in geoscience applications, including tunnel assessments. | The application of SfM in tunnel environments allows for efficient and accurate documentation of tunnel geometry and surface conditions, facilitating better blasting design and safety evaluations. | [10] |
9 | 2014 | Nuttens et al. | Developed a methodology for monitoring newly constructed circular train tunnels using laser scanning technology to track changes in tunnel shape and stability. | This innovative approach provided real−time data on tunnel deformation, improving safety measures and maintenance strategies for tunnel infrastructure. | [11] |
10 | 2014 | Zhang et al. | Investigated the integration of remote sensing data with distinct element analysis to assess quarry slope stability, applicable to tunnel environments. | This case study illustrated the effectiveness of combining different analytical methods to enhance safety assessments in challenging geological conditions, particularly for tunneling operations. | [12] |
11 | 2015 | W.Mukup et al. | Developed a method for using terrestrial laser scanning (TLS) to monitor deformation in large structures, enabling precise detection of small−scale changes. | This review emphasized the precision of terrestrial laser scanning (TLS) in detecting small deformations, enhancing structural safety and maintenance strategies. | [13] |
12 | 2016 | Cheng et al. | Utilized 3D laser scanning technology to extract tunnel profile data and developed algorithms for automated identification and quantification of over− and under−excavation areas. | The research significantly enhanced the efficiency and accuracy of excavation evaluations, ensuring that blasting operations meet design specifications and safety standards. | [14] |
13 | 2017 | Lin | Investigated the key technologies for processing and visualizing tunnel point cloud data, focusing on noise reduction and data accuracy enhancement. | The research demonstrated that effective noise reduction techniques can significantly improve the reliability of tunnel assessments, contributing to safer and more efficient construction practices. | [15] |
14 | 2018 | Li et al. | Developed a digital identification technique for detecting half porosity in tunnel blasting, utilizing 3D point cloud data extraction. | This approach enhanced the precision of assessing tunnel blasting effects, providing a reliable method for monitoring and optimizing blasting performance. | [16] |
15 | 2018 | Qiu et al. | Explored advanced techniques for reconstructing tunnel deformation based on 3D laser scanning and dynamic monitoring of instabilities in real−time. | The findings offered vital insights into tunnel stability management, providing tools for real−time assessment and intervention in response to geological changes. | [17] |
16 | 2019 | Xie et al. | Proposed a method combining machine vision and digital images to analyze the rock surface characteristics of tunnel excavation faces. | The integration of machine vision with 3D reconstruction provided a more accurate representation of tunnel surfaces, facilitating improved decision−making in tunnel design and blasting strategies. | [18] |
17 | 2020 | Yang et al. | Developed a method for detecting over− and under−digging of tunnels using 3D reconstruction from images, applying SfM and semi−global matching techniques. | The study provided a robust methodology for real−time monitoring of excavation deviations, ensuring compliance with engineering specifications and enhancing safety. | [19] |
18 | 2021 | Yao et al. | Explored the application of a novel Over−Under−Cut algorithm for analyzing tunnel excavation efficiency, utilizing 3D laser scanning technology. | This research highlighted the potential for real−time analysis of excavation performance, allowing for proactive adjustments during construction to minimize inefficiencies. | [20] |
19 | 2021 | Quan et al. | Presented a reconstitution method for assessing tunnel spatiotemporal deformation using 3D laser scanning, focusing on stability warning mechanisms. | The findings contributed to the understanding of tunnel behavior over time, offering a framework for preventive maintenance and timely interventions based on deformation monitoring. | [21] |
20 | 2022 | Li et al. | Proposed a RANSAC−based method for multi−primitive building reconstruction from 3D point clouds, targeting tunnel structures for better geometric accuracy. | The research demonstrated improved reconstruction accuracy for tunnel geometries, facilitating better assessments of tunnel integrity and supporting more informed engineering decisions. | [22] |
21 | 2022 | Yang et al. | Developed an efficient plane extraction technique using RANSAC and normal estimation for processing 3D point clouds, aimed at tunnel applications. | This study enhanced the efficiency of processing large−scale 3D point cloud data, allowing for quicker and more reliable evaluations of tunnel conditions post−blasting. | [23] |
22 | 2023 | AI et al. | Introduced a central axis elevation extraction method for metro shield tunnels based on 3D laser scanning technology, enhancing structural monitoring. | The method improved the accuracy of structural assessments and provided a basis for ongoing maintenance strategies, ensuring safer operations within tunnel environments. | [24] |
23 | 2023 | Ankang et al. | Developed a semi−supervised learning−based point cloud network for segmenting 3D tunnel scenes, aimed at enhancing automation in tunnel inspections. | This innovative approach significantly improved the speed and accuracy of tunnel inspections, facilitating better maintenance practices and ensuring compliance with safety standards. | [25] |
24 | 2023 | Xu et al. | Proposed a novel SfM−DLT method for metro tunnel 3D reconstruction, integrating different data sources for comprehensive visualization. | The research contributed to more detailed and accurate visualizations of tunnel environments, aiding in both operational planning and risk assessment during construction phases. | [26] |
25 | 2024 | Bao et al. | Developed a framework for integrating 3D point cloud data with traditional survey methods to enhance tunnel blasting evaluation processes. | This integration enhanced the overall accuracy of blasting assessments, leading to improved operational efficiency and safety in tunnel construction projects. | [27] |
26 | 2024 | Dotto, M.S. et al. | Investigated the impact of rock mass characteristics on the effectiveness of blasting in tunnels, focusing on data−driven approaches to optimize blasting outcomes. | The study emphasized the importance of understanding geological factors in blasting design, leading to more efficient and safer blasting practices in tunnel construction. | [28] |
2D Image | 3D Point Cloud | |
---|---|---|
form of expression | 2D projection of the 3D world without z-values | 3D data with z-value |
data structure | Orderly, sequential links | Disorganization of data points |
Environmental sensitivity | Sensitive to ambient light conditions | Insensitive to ambient light conditions |
Treatment | Direct use of CNN, etc. | Cannot use CNN directly |
HalfHole Numbering | Recognized Length (m) | Design Value Length (m) | SemiPorosity |
---|---|---|---|
1 | 0.98 | 2 | 49.0% |
2 | 1.87 | 2 | 93.5% |
3 | 1.03 | 2 | 51.5% |
4 | 1.12 | 2 | 56.0% |
5 | 1.05 | 2 | 52.5% |
6 | 0.99 | 2 | 49.5% |
add up the total | 7.04 | 12 | 58.7% |
Lithology | Half Porosity (%) | |||
---|---|---|---|---|
Favorable | Moderate | Ordinary | Differ | |
hard rock | >85 | 70~85 | 50~70 | <50 |
medium | >70 | 50~70 | 30~50 | <30 |
limestone | >50 | 30~50 | 20~30 | <20 |
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Shi, J.; Wang, Y.; Yang, Z.; Shan, W.; An, H. Comprehensive Review of Tunnel Blasting Evaluation Techniques and Innovative Half Porosity Assessment Using 3D Image Reconstruction. Appl. Sci. 2024, 14, 9791. https://doi.org/10.3390/app14219791
Shi J, Wang Y, Yang Z, Shan W, An H. Comprehensive Review of Tunnel Blasting Evaluation Techniques and Innovative Half Porosity Assessment Using 3D Image Reconstruction. Applied Sciences. 2024; 14(21):9791. https://doi.org/10.3390/app14219791
Chicago/Turabian StyleShi, Jianjun, Yang Wang, Zhengyu Yang, Wenxin Shan, and Huaming An. 2024. "Comprehensive Review of Tunnel Blasting Evaluation Techniques and Innovative Half Porosity Assessment Using 3D Image Reconstruction" Applied Sciences 14, no. 21: 9791. https://doi.org/10.3390/app14219791
APA StyleShi, J., Wang, Y., Yang, Z., Shan, W., & An, H. (2024). Comprehensive Review of Tunnel Blasting Evaluation Techniques and Innovative Half Porosity Assessment Using 3D Image Reconstruction. Applied Sciences, 14(21), 9791. https://doi.org/10.3390/app14219791