In-Situ Block Characterization of Jointed Rock Exposures Based on a 3D Point Cloud Model
Abstract
:1. Introduction
2. Study Sites
2.1. Site 1: A Road Cut Slope in Catalonia, Spain
2.2. Site 2: Yinshan Open-pit Copper Mine in Dexing, China
2.3. A Synthetic Test Model: The Dataset of Cardboard Boxes
3. Methodology
3.1. Treatment of Data Source
3.2. Identification of Discontinuities from PCM
3.3. PCM-DDN: Global Searching of Block Candidates
3.4. PCM-DDN: Polyhedral Modeling
3.5. PCM-DDN: Block Characterization
3.6. PCM-SDS: Deriving Fundamental Discontinuity Parameters
3.7. PCM-SDS: 3D Stochastic DFN Simulation
3.8. PCM-SDS: Block Extraction and Characterization
4. Results
4.1. Results of the Synthetic Box Model
4.2. Comparison of the Results of Site 1 Using Two Procedures
5. Discussion
5.1. Practical Application of the Proposed Method
- (1)
- (2)
- Blasting design, e.g., explosive consumption and fragmentation energy optimization for mining or tunneling [73]. Taking a quarry project as an example, the IBSD curve and the block shape distribution are important for blasting design and quarry yield prediction. The blasting can be considered as a transformation from IBSD to fragmented size distribution (or blasted block size distribution, BBSD) and the main framework of the energy-block- transition (E-B-T) model can be established [74] (Figure 15c).
- (3)
- (4)
- Stability evaluation and support design of slopes or excavations in jointed rock masses [8].
- (5)
5.1.1. Practical Applicability of PCM-DDN
5.1.2. Practical Applicability of PCM-SDS
5.2. Comparison with an Representative Analytical Approach
5.2.1. The Persistence Factor Estimation
5.2.2. Comparison with Calculation
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Set | Orientation (°) | Trace Length (m) | Normal Set Spacing (m) | ||||
---|---|---|---|---|---|---|---|
Fisher Distribution | Log Normal Distribution | Log Normal Distribution | |||||
Dip Dir | Dip | Mean | Standard Deviation | Mean | Standard Deviation | ||
Set 1 | 127 | 56 | 15.17 | 0.27 | 0.135 | 0.34 | 0.016 |
Set 2 | 291 | 45 | 35.09 | 0.55 | 0.304 | 0.15 | 0.067 |
Set 3 | 227 | 83 | 34.89 | 0.45 | 0.386 | 0.25 | 0.061 |
Set 4 | 279 | 81 | 37.96 | 0.44 | 0.107 | 0.18 | 0.119 |
Block ID | Manually Measured Size (m3) | Extracted Size (m3) | Percent Deviation |
---|---|---|---|
1 | 0.2160 | 0.2111 | 2.27% |
2 | 0.4320 | 0.4307 | 0.30% |
3 | 0.0689 | 0.0701 | 1.74% |
4 | 0.3645 | 0.3780 | 3.70% |
5 | 0.0506 | 0.0531 | 4.94% |
6 | 0.0540 | 0.0561 | 3.89% |
7 | 0.0630 | 0.0642 | 1.90% |
8 | 0.0180 | 0.0187 | 3.89% |
9 | 0.0270 | 0.0284 | 5.19% |
Method | Block Size (m3) | Block Orientation (Dip Dir/Dip, °) | |||
---|---|---|---|---|---|
D25 | D50 | D75 | Mean | ||
PCM-DDN | 0.005 | 0.014 | 0.038 | 0.041 | 115/26 |
PCM-SDS: Model ID 75 | 0.006 | 0.035 | 0.120 | 0.101 | 124/17 |
PCM-SDS: All models 1 | M: 0.005 | M: 0.027 | M: 0.092 | M: 0.084 | -- |
SD: 0.003 | SD: 0.014 | SD: 0.044 | SD: 0.060 |
Set | Orientation (°) | Trace Length (m) | Normal Set Spacing (m) | Persistence Factor | ||||
---|---|---|---|---|---|---|---|---|
Fisher Distribution | Log Normal Distribution | Log Normal Distribution | -- | |||||
Dip Dir | Dip | Mean | Standard Deviation | Mean | Standard Deviation | Value Range | ||
Set 1 | 305 | 81 | 93.76 | 1.14 | 0.601 | 0.25 | 0.069 | 85–100% |
Set 2 | 6 | 76 | 99.60 | 1.52 | 0.862 | 0.61 | 0.240 | 85–100% |
Set 3 | 108 | 42 | 273.68 | 1.52 | 1.183 | 0.745 | 0.387 | 90–100% |
Method | Block Size (m3) | Block Orientation (Dip dir/Dip, °) | |||
---|---|---|---|---|---|
D25 | D50 | D75 | Mean | ||
PCM-SDS: Model ID 61 | 0.009 | 0.053 | 0.212 | 0.192 | 306/33 |
PCM-SDS: All models 1 | M: 0.008 | M: 0.051 | M: 0.196 | M: 0.179 | -- |
SD: 0.004 | SD: 0.025 | SD: 0.092 | SD: 0.079 |
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Kong, D.; Wu, F.; Saroglou, C.; Sha, P.; Li, B. In-Situ Block Characterization of Jointed Rock Exposures Based on a 3D Point Cloud Model. Remote Sens. 2021, 13, 2540. https://doi.org/10.3390/rs13132540
Kong D, Wu F, Saroglou C, Sha P, Li B. In-Situ Block Characterization of Jointed Rock Exposures Based on a 3D Point Cloud Model. Remote Sensing. 2021; 13(13):2540. https://doi.org/10.3390/rs13132540
Chicago/Turabian StyleKong, Deheng, Faquan Wu, Charalampos Saroglou, Peng Sha, and Bo Li. 2021. "In-Situ Block Characterization of Jointed Rock Exposures Based on a 3D Point Cloud Model" Remote Sensing 13, no. 13: 2540. https://doi.org/10.3390/rs13132540