Rapid assessment of slope deformation in 3D point cloud considering feature-based simplification and deformed area extraction

Abstract Calculating deformation values and determining deformation areas are essential for slope monitoring and safety management. Recently, terrestrial laser scanning has been widely used for deformation monitoring due to to its speed and efficiency. However, handling a large amount of point cloud data to obtain the deformed area is still challenging. To rapidly evaluate slope deformation information, this study proposes a simplification algorithm for point cloud data based on multi-parameter feature preservation. The proposed method retains feature points (i.e. inflection points and edge points) and simplifies data with the octree structure. In addition, a method based on cloud-to-mesh for displacement is used where the winding number is introduced for the signed function. Next, the deformed area over the level of detection is extracted using density-based spatial clustering for applications with a noise clustering algorithm. To verify the reduction method, two types of slope field data are used for experiments. The results reveal that the proposed point cloud approach is superior to the conventional algorithms. Furthermore, the highway slope in Mianyang is selected as a case study to validate the performance of the proposed method. The entire monitoring area is stable with a deformation of approximately 0.43 mm, and only four regions are deformed over the study period. When the mean displacement value is considered in different deformation regions, the minimum deformation is −82.02 mm and the maximum deformation is 85.31 mm. Moreover, comparative experiments on deformation calculation are conducted and reveal the superior performance of the proposed method.