Anti-pulling force and displacement deformation analysis of the anchor pulling system of the new debris flow grille dam

Abstract

To avoid waste from a large section space structure layout and deep burial, improve the structural strength and stability. Anchor technology is introduced, and combined with the advantages of the supporting wall, a new debris-flow grille dam is proposed. Starting from the force process and damage mechanism of the new debris-flow grille dam, the computation formula for the anti-pulling force and the total displacement is given. The anti-pulling force includes the sidewall frictional resistance of the anchor pier and the positive pressure of the front end face of the anchor pier. The total displacement includes three parts: the elastic deformation of the cable, the relative shear displacement between the anchor pier and the surrounding soil, and the compression deformation of the soil at the front of the anchor pier. Finally, the influence of soil parameters and anchor pier size on the anti-pulling force and displacement deformation of the anchor-pulling system is analyzed by examples, and the results are compared with the numerical results. The results show that the displacement deformation decreases gradually with increasing elastic modulus of the soil around the anchor pier and increases with increasing Poisson's ratio. The change in elastic modulus mainly affects the relative shear displacement of the anchor pier and soil and the compressive deformation of the soil at the front end of the anchor pier. Poisson's ratio has the greatest influence on the relative shear displacement of the anchor pier and soil. A larger anchor pier is not better; thus, it is wise to choose the economic design dimension. Theoretical and numerical simulation results are consistent, showing a linear growth trend. The results of this paper can further improve the theoretical calculation method of the new debris-flow grille dam, thus making it widely used in more debris flow control projects.

Arch dam point cloud segmentation based on deep feature learning and normal vector data optimization

“…The relative position relationship and geometric shape have different degrees of influence on point cloud segmentation (the relative position relationship represents the spatial position relationship between different types of objects; the geometric shape represents the inherent physical features of different types of objects), and the relative position relationship has a greater impact on the segmentation results than the geometric shape, which is mainly reflected in the following two points: (1) The geometric shape of the point cloud on the right bank of the dam area where an omission error (plant commission error) exists is similar to the point cloud shape of the dam crest road, but no confusion occurs in classification between the two, and this error occurs between plant and rock mass. (2) The visualization results show that most of the commission and omission errors occurred between interconnected categories (plant-rock mass, dam body-spillway, spillway-dam top-sentry box), while almost no omission or commission error was found between disconnected categories in space.…”

“…During the service period of a dam, a certain degree of deformation may occur along and perpendicular to the dam axis because of the influence of water level, environmental temperature, and other geological factors in the reservoir area [1][2][3]. Once deformation reaches a certain extent, the hydraulic structure will face safety hazards.…”