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The rock discontinuity orientation has a significant impact on the engineering stability. This paper proposes a semi-automatic method for identifying the orientation of rock discontinuities based on 3D point clouds. First, the normal of each point is calculated by k-nearest neighbors (KNN) and least squares (LS) algorithms. Then the point normal is regarded as the input parameter and the number of joint sets can be determined by density-based spatial clustering of applications with noise (DBSCAN). For each joint set, the point coordinate is considered as the input parameter, and DBSCAN is employed again to obtain the individual joint plane. Finally, the orientation of each joint plane is computed by LS algorithm, and the weighted mean of the orientation based on size is taken as the orientation of each joint set. The correctness of the method is verified by two standard geometric solid objects, and it is applied in real slope engineering to identify the orientation of rock discontinuities. The results are consistent with those observed in the field, and the differences with other methods are discussed. In comparison with the open source procedure DSE and the commercial software ShapeMetrix, the proposed method shows remarkable advantages in terms of precision and accuracy, and can preserve more point cloud information. This study extends the method of semi-automatic identification of rock discontinuities and can provide a reference for similar engineering. [ABSTRACT FROM AUTHOR]

The geological strength index (GSI) plays an important role in the quality evaluation and stability analysis of rock mass. Traditional methods for quantitatively estimating GSI are often subjective, time-consuming, and dangerous. This paper proposed a method for rapid and quantitative GSI estimate using 3D point clouds, which can be generated through non-contact measurement methods such as photogrammetry and Light Detection and Ranging (LiDAR). The overall methodology is as follows: (1) point clouds were acquired using a terrestrial laser scanner; (2) discontinuities were identified through artificial neural networks (ANN) and density-based spatial clustering of applications with noise (DBSCAN); (3) geometric information was extracted for the detected discontinuities; (4) GSI was estimated according to the detection and characterization of discontinuities. The proposed method was used for the Yujiashan road cut to calculate the GSI and the GSI partitioning was performed simultaneously. Three sets of discontinuity were detected in the Yujiashan road cut, and Structure Rating (SR) and Surface Conditions Point Clouds Rating (SCPC) were calculated to be 13.2 and 25, respectively. Correspondingly, the GSI was estimated to be 32, which was consistent with the results of the in-situ evaluation (rating 25–40). Furthermore, the Yujiashan road cut was divided into 17 segments, and the effect of sampling size on the GSI calculation was discussed. The application results show that the GSI of the rock mass can be obtained objectively and efficiently through 3D point clouds, which can be used as a potential alternative to the traditional method for GSI estimation. Article Highlights: A semi-automatic method was developed to determine the rockmass GSI using laser scanning. Discontinuities were detected from the 3D point cloud using a machine-learning algorithm. Four geometric parameters of detected discontinuities were extracted for GSI calculation. The scale effect of GSI calculation was investigated using the proposed partitioning algorithm. GSI estimation obtained from the proposed method matched the traditional manual surveys. [ABSTRACT FROM AUTHOR]

In rockfall hazard management, the investigation and detection of potential rockfall source areas on rock cliffs by remote-sensing-based susceptibility analysis are of primary importance. However, when the rockfall analysis results are used as feedback to the fieldwork, the irregular slope surface morphology makes it difficult to objectively locate the risk zones of hazard maps on the real slopes, and the problem of straightforward on-site visualization of rockfall susceptibility remains a research gap. This paper presents some of the pioneering studies on the augmented reality (AR) mapping of geospatial information from cyberspace within 2D screens to the physical world for on-site visualization, which directly recognizes the rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes. A novel method of edge-based tracking of the rock mass target for mobile AR is proposed, where the model edges extracted from unmanned aerial vehicle (UAV) structure-from-motion (SfM) 3D reconstructions are aligned with the corresponding actual rock mass to estimate the camera pose accurately. Specifically, the visually prominent edges of dominant structural planes were first explored and discovered to be a robust visual feature of rock mass for AR tracking. The novel approaches of visual-geometric synthetic image (VGSI) and prominent structural plane (Pro-SP) were developed to extract structural planes with identified prominent edges as 3D template models which could provide a pose estimation reference. An experiment verified that the proposed Pro-SP template model could effectively improve the edge tracking performance and quality, and this approach was relatively robust to the changes of sunlight conditions. A case study was carried out on a typical roadcut cliff in the Mentougou District of Beijing, China. The results validate the scalability of the proposed mobile AR strategy, which is applicable and suitable for cliff-scale fieldwork. The results also demonstrate the feasibility, efficiency, and significance of the geoinformation AR mapping methodology for on-site zoning and locating of potential rockfalls, and providing relevant guidance for subsequent detailed site investigation.

In rockfall hazard management, the investigation and detection of potential rockfall source areas on rock cliffs by remote-sensing-based susceptibility analysis are of primary importance. However, when the rockfall analysis results are used as feedback to the fieldwork, the irregular slope surface morphology makes it difficult to objectively locate the risk zones of hazard maps on the real slopes, and the problem of straightforward on-site visualization of rockfall susceptibility remains a research gap. This paper presents some of the pioneering studies on the augmented reality (AR) mapping of geospatial information from cyberspace within 2D screens to the physical world for on-site visualization, which directly recognizes the rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes. A novel method of edge-based tracking of the rock mass target for mobile AR is proposed, where the model edges extracted from unmanned aerial vehicle (UAV) structure-from-motion (SfM) 3D reconstructions are aligned with the corresponding actual rock mass to estimate the camera pose accurately. Specifically, the visually prominent edges of dominant structural planes were first explored and discovered to be a robust visual feature of rock mass for AR tracking. The novel approaches of visual-geometric synthetic image (VGSI) and prominent structural plane (Pro-SP) were developed to extract structural planes with identified prominent edges as 3D template models which could provide a pose estimation reference. An experiment verified that the proposed Pro-SP template model could effectively improve the edge tracking performance and quality, and this approach was relatively robust to the changes of sunlight conditions. A case study was carried out on a typical roadcut cliff in the Mentougou District of Beijing, China. The results validate the scalability of the proposed mobile AR strategy, which is applicable and suitable for cliff-scale fieldwork. The results also demonstrate the feasibility, efficiency, and significance of the geoinformation AR mapping methodology for on-site zoning and locating of potential rockfalls, and providing relevant guidance for subsequent detailed site investigation. [ABSTRACT FROM AUTHOR]