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Automatic Extraction of Rock Discontinuities from the Point Cloud Using Dynamic DBSCAN Algorithm
Detection and mapping of rock discontinuities are important during excavation. The terrestrial laser scanning (TSL) technology is widely used to acquire accurate quantitative. However, there is rarely study about the influence of discontinuities parameters on the detection. Through the 3D printing technology, we have built discontinuity models with different roughness and connectors with different angles. Therefore, we can control the variables in the scanning. Several open-source packages were applied to derive the information from the point cloud acquired by TSL. The result shows that the recognition effect decreases with the angle between discontinuities. Moreover, the presence of roughness of discontinuity makes it prone to lead to lousy classification in the detection process. The proposed method has successfully extracted discontinuity dip, dip direction, and roughness automatically from the point cloud. The application on the two datasets showed great adaptability and accuracy. Consequently, the method could meet realistic engineering needs.
Automatic Extraction of Rock Discontinuities from the Point Cloud Using Dynamic DBSCAN Algorithm
Detection and mapping of rock discontinuities are important during excavation. The terrestrial laser scanning (TSL) technology is widely used to acquire accurate quantitative. However, there is rarely study about the influence of discontinuities parameters on the detection. Through the 3D printing technology, we have built discontinuity models with different roughness and connectors with different angles. Therefore, we can control the variables in the scanning. Several open-source packages were applied to derive the information from the point cloud acquired by TSL. The result shows that the recognition effect decreases with the angle between discontinuities. Moreover, the presence of roughness of discontinuity makes it prone to lead to lousy classification in the detection process. The proposed method has successfully extracted discontinuity dip, dip direction, and roughness automatically from the point cloud. The application on the two datasets showed great adaptability and accuracy. Consequently, the method could meet realistic engineering needs.
Automatic Extraction of Rock Discontinuities from the Point Cloud Using Dynamic DBSCAN Algorithm
Ming Tang (author) / Song Yang (author) / Guohua Huang (author) / Xiongyao Xie (author) / Jiafu Guo (author) / Junli Zhai (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Rock Discontinuities Identification from 3D Point Clouds Using Artificial Neural Network
| Online Contents | 2022
| Elsevier | 2024