Method for generating particle accumulation body simulation model of natural three-dimensional shape characteristics of building material

Abstract

The invention discloses a method for generating a particle accumulation body simulation model of natural three-dimensional shape characteristics of a building material. The method comprises the following steps: step 1, manufacturing a natural particle simulation model; the randomly generated three-dimensional coordinate control points are endowed with natural sphericity and roundness to obtain a natural particle simulation model; 2, making a particle accumulation body simulation model; the natural particle simulation model obtained in the first step is filled into a discrete element block mass, gravity deposition is applied to the discrete element block mass through a discrete element method, and a particle accumulation body simulation model is obtained. A solid foundation is provided for exploring the microscopic geometrical characteristics of the contact particle accumulation body and the macromechanical properties of civil engineering materials, and a new way is provided for efficiently generating a particle accumulation body database with accurate contact information.

Description

Technical Field

[0001] The invention relates to the technical field of building models, and in particular to a method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials. Background Art

[0002] Granular materials are key components in civil engineering construction. Randomly stacked particles with different shapes and gradations form widely used granular deposits in engineering practice, such as centimeter-sized concrete blocks and meter-sized dam stones. In the past few decades, a large number of experimental and numerical studies have revealed the significant influence of the microscopic geometric characteristics of granular deposits (including the shape, size, spatial distribution and stacking state of particles) on their macroscopic mechanical properties. For example, the flatness and slenderness ratio of sand particles are key factors determining the mechanical properties of inter-particle contact and further affect the shear strength of granular deposits. Meanwhile, particle roughness and roundness have a negative impact on the small strain properties of cement-based and asphalt mixtures. In natural scenes, various shapes of construction particles are produced by artificial blasting or environmental weathering. Rocks are broken into fragments of different sizes and contours, and their surfaces and edges are carved to different degrees, showing undulating and sharp features. In the past few decades, with the increasing interest in the study of the three-dimensional shape of granular materials, researchers have provided many descriptions aimed at characterizing the three-dimensional data of particles from laser scanners (surface point clouds of particles) and CT (voxel views of particles). At the particle level, the overall shape of the particle is expressed by sphericity, and the similarity of the particle to the sphere can be classified as isosphere, flat sphere, disc, elongated sphere, blade, and rod ( Figure 1 ), these classifications are based on extensibility and flatness. At the angular level, the sharpness of the corners can be expressed by roundness, which is quantified by the radius of curvature of the corners; while at the microscopic surface level, the relief is represented by roughness, which is defined as the ratio of the volume of the particle to its convex hull.

[0003] In order to further explore the influence of microscopic geometric features on the macroscopic mechanical response of granular deposits, it is necessary to apply high-tech technologies such as artificial intelligence, discrete element method or material point method to granular deposit simulation experiments. However, the above applications require the generation of granular deposits that conform to large-scale natural shapes. At present, Zheng Junxing and others have successfully achieved the generation of two-dimensional particles using a diffusion model, but two-dimensional particles cannot meet the spatial distribution required for three-dimensional simulation. Using CT technology, Wang Jianfeng and others scanned the granular deposits and segmented the obtained tomographic images using the contact watershed algorithm to obtain the voxel data of three-dimensional contact granular deposits. However, this method is time-consuming and labor-intensive, and it is impossible to generate granular deposits of the scale required by artificial intelligence or discrete element methods in a short time.

[0004] Therefore, a method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials is provided to solve the above technical problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials.

[0006] The object of the present invention is achieved in that: A method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials comprises the following steps: Step 1: Make a natural particle simulation model; assign the natural sphericity and roundness of the particles to the randomly generated three-dimensional coordinate control points to obtain a natural particle simulation model; Step 2: Make a simulation model of a particle accumulation body; fill the natural particle simulation model obtained in step 1 with discrete element clusters, and apply gravity deposition to the discrete element clusters through the discrete element method to obtain a simulation model of a particle accumulation body.

[0007] In step 1, making a natural particle simulation model includes the following three steps, which define the basic shape / size, surface undulation, and roundness of the particles in a progressive relationship: Step 1.1 Generate different particle contours to control the size and main shape of the particles, such as aspect ratio and flatness, so that the model is close to the shape of natural particles; Step 1.2 generates different surface undulations to simulate natural phenomena such as roughness and weathering on the particle surface and enhance the authenticity of the particles; Step 1.3 generates different particle roundness to simulate the sharpness of particles in nature and avoid overly sharp corners.

[0008] The specific operations of step 1.1 are as follows: Step 1.1.1 Generate a point cloud P ini , point cloud P ini The coordinates follow the 3D multivariate standard normal distribution; Step 1.1.2 stretches the x and y coordinates and aligns the z axis so that the point cloud P ini Show the sphericity characteristics of natural building particles; Step 1.1.3 Point cloud P ini Stretching includes: Point cloud P ini The projections on the x, y, and z axes are the shortest axis S, the longest axis L, and the second longest axis I of the particle respectively; Step 1.1.4 respectively converts the point cloud P ini The x-coordinate of the point cloud P is multiplied by the target slenderness ratio. ini The y coordinate of the point cloud P is multiplied by the inverse of the target flatness. ini With targeted sphericity values, different particle profiles are obtained.

[0009] The specific operation of step 1.2 is as follows: step 1.2.1 generates an external grid of a closed point cloud by using the α shape algorithm; step 1.2.2 ensures that the point cloud P ini The external points of are not on the same convex surface to generate a natural surface relief. In step 1.2.3, the α value is adjusted to control the compactness of the generated mesh and the surface relief characteristics.

[0010] The specific operation of step 1.3 is as follows: step 1.3.1 simulates a more realistic and natural geometric shape and surface features by identifying and processing the sharp corners of the particles; step 1.3.2 uses the K-nearest neighbor method to identify sharp corners with high curvature, and smoothes the sharp corners by moving least squares method MLS to generate particles with different roundness. Step 1.1 first defines the basic shape and size of the particles, step 1.2 adds natural surface undulations on this basis, and step 1.3 further adjusts the roundness of the particles to improve their detailed features.

[0011] In step 2, making a particle accumulation simulation model includes the following steps: Step 2.1 generates a particle accumulation body with contact information and marks the contact area; Step 2.2 generates a particle cluster with contact information and uses the discrete element method DEM to simulate the particle accumulation process; Step 2.3 generates an initial block stone sphere discrete element model and uses random three-dimensional coordinate control points in the unit space to generate corresponding convex hull polyhedron triangles, and establishes a block stone sphere model through the rigid ball algorithm; Step 2.4 uses the gravity deposition method in the discrete element software to accumulate a random number of block stone sphere models.

[0012] In step 2.4, during the process of forming the stack, the coordinates of the center points of each ball before and after the stack are recorded to calculate the transformation matrix, and the point cloud P of the corresponding block is moved according to the obtained transformation matrix. ini Coordinates, thus obtaining the particle point cloud P ini data.

[0013] In step 2.1, the contact information of the particle accumulation body is annotated by the normal vector difference method to mark the contact points; specifically, the K-nearest neighbor method is used to select the neighboring points near the target point, and the PCA algorithm is used to fit the neighboring points into a plane, and the plane normal vector is used as the target point normal vector; when the point near the target point changes, its corresponding normal vector also changes accordingly; by calculating the normal vector of the newly generated point cloud, and selecting the point where the normal vector changes in each step as the block stone boundary edge point, the generation of the particle contact particle accumulation body is completed.

[0014] The beneficial effects of the present invention are as follows: the natural particle simulation module production method of the present invention gives the particles natural sphericity and roundness by stretching, smoothing and other methods the randomly generated three-dimensional coordinate control points, and can quickly generate a large number of building material particle models. The particle dynamic accumulation module production method first fills the particle model generated by the natural particle simulation module with a high-reduction discrete element agglomerate, and then applies gravity deposition to the discrete element agglomerate particles through the discrete element method to realize the simulation of the particle accumulation body. According to the characteristic of the discrete element method for real-time tracking of the center of the discrete element agglomerate particles, the particle dynamic accumulation module can track the movement trajectory of the block stone during gravity deposition in real time. The normal vector difference method is used after the particle dynamic accumulation module, and the contact points of the generated particle accumulation body simulation model can also be accurately marked. The present invention realizes the rapid simulation of the particle accumulation body with natural three-dimensional shape characteristics of building materials without pre-scanning information, which provides a solid foundation for exploring the microscopic geometric characteristics of the contact particle accumulation body on the macroscopic mechanical properties of civil engineering materials, and provides a new way to efficiently generate a particle accumulation body database with precise contact information. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the results of generating different overall shapes of particles for the present invention; Figure 2 This is a schematic diagram of the fluctuation of the particle surface generated by different α values ​​of the present invention; Figure 3 It is a schematic diagram of the generation of a contact particle accumulation body under the action of gravity of the present invention; Figure 4 This is a schematic diagram of the contact process of calibrating a particle accumulation body using the normal vector difference method of the present invention; Figure 5 It is a schematic diagram of the particle surface edge recognition based on K proximity of the present invention; Figure 6 A shape parameter characteristic distribution diagram of a virtual building particle database is generated for the present invention. DETAILED DESCRIPTION

[0016] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0017] A method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials comprises the following steps: Step 1: Make a natural particle simulation model; assign the natural sphericity and roundness of the particles to the randomly generated three-dimensional coordinate control points to obtain a natural particle simulation model.

[0018] In step 1, making a natural particle simulation model includes the following three steps, which define the basic shape / size, surface undulation, and roundness of the particles in a progressive relationship: Step 1.1 generates different particle contours. The technical purpose is to control the size and main shape of the particles, such as aspect ratio and flatness, so that the model is close to the shape of natural particles, such as Figure 1 As shown; the specific operation of step 1.1 is as follows: Step 1.1.1 Generate a point cloud P ini , point cloud P ini The coordinates follow the 3D multivariate standard normal distribution; Step 1.1.2 stretches the x and y coordinates and aligns the z axis so that the point cloud P ini Show the sphericity characteristics of natural building particles; Step 1.1.3 Point cloud P ini Stretching includes: Point cloud P ini The projections on the x, y, and z axes are the shortest axis S, the longest axis L, and the second longest axis I of the particle respectively; Step 1.1.4 respectively converts the point cloud P ini The x-coordinate of the point cloud P is multiplied by the target slenderness ratio. ini The y coordinate of the point cloud P is multiplied by the inverse of the target flatness. ini With targeted sphericity values, different particle profiles are obtained.

[0019] Step 1.2 generates different surface undulations to simulate natural phenomena such as roughness and weathering on the particle surface and enhance the authenticity of the particles; Figure 2 As shown in the figure, different α values ​​will directly control the fluctuation of the particle surface. The specific operation of step 1.2 is as follows: step 1.2.1 generates the external grid of the closed point cloud through the α shape algorithm; step 1.2.2 ensures that the point cloud P ini The external points of are not on the same convex surface to generate a natural surface relief. In step 1.2.3, the α value is adjusted to control the compactness of the generated mesh and the surface relief characteristics.

[0020] Step 1.3 generates different particle roundness to simulate the sharpness of particles in nature and avoid overly sharp corners. The details are as follows: Step 1.3.1 simulates more realistic and natural geometric shapes and surface features by identifying and processing the sharp corners of particles; Step 1.3.2 uses the K-nearest neighbor method to identify sharp corners with high curvature, and smoothes the sharp corners by the moving least squares method MLS to generate particles with different roundness.

[0021] Step 1.1 first defines the basic shape and size of the particles, step 1.2 adds natural surface undulations on this basis, and step 1.3 further adjusts the roundness of the particles and improves their detailed features.

[0022] Step 2: Make a simulation model of a particle accumulation body; fill the natural particle simulation model obtained in step 1 with discrete element clusters, and apply gravity deposition to the discrete element clusters through the discrete element method to obtain a simulation model of a particle accumulation body.

[0023] The preparation of the particle accumulation simulation model includes the following steps: Figure 3 As shown, step 2.1 generates a particle accumulation body with contact information and marks the contact area; Figure 4 As shown in the figure, the contact information of the particle accumulation body is annotated by the normal vector difference method to mark the contact points; specifically, the K-nearest neighbor method is used to select the neighboring points near the target point, and the PCA algorithm is used to fit the neighboring points into a plane, and the plane normal vector is used as the target point normal vector; when the points near the target point change, the corresponding normal vector also changes accordingly; by calculating the normal vector of the newly generated point cloud, and selecting the point where the normal vector changes in each step as the block stone boundary edge point, the generation of the particle contact particle accumulation body is completed.

[0024] Step 2.2 generates particle clusters with contact information and uses the discrete element method (DEM) to simulate the particle stacking process.

[0025] Step 2.3 generates the initial stone sphere discrete element model by using random three-dimensional coordinate control points in the unit space to generate corresponding convex hull polyhedron triangles, and establishes the stone sphere model through the rigid sphere algorithm.

[0026] Step 2.4: The random number of stone sphere models are deposited using the gravity deposition method in the discrete element software. During the deposition process, the coordinates of the center points of each sphere before and after the deposition are recorded to calculate the transformation matrix, and the point cloud P of the corresponding stone is moved according to the obtained transformation matrix. ini Coordinates, thus obtaining the particle point cloud P ini data.

[0027] Next, we will first generate the closed P ini The mesh represents the particles The surface points can be sampled from the grid as if by laser scanning, and the The voxel data of can be formed by enclosing a binary 3D matrix with a grid. Moderate undulations on the generated grid are necessary to generate particles with natural surface textures. In our method, the random strategy makes P ini The points in P cannot be on the same convex surface, thus ensuring that ini There will inevitably be fluctuations at the external points of P. ini The number of points in is set to 1000, and the alpha shape algorithm (which is an effective method for generating triangular boundary meshes of controllable complexity point clouds) is used to generate closed P iniSpecifically, the α shape algorithm attempts to ini Find the outer grid such that a sphere of radius α cannot penetrate the grid. As α increases from 0 to +∞, the α shape will provide a finite spectrum containing P ini itself (α=0), The convex hull of , and the gradual change between these two extremes. In this spectrum, there is always a critical value , represents the generation of closed all P ini However, when When The tightest closure of the particle, whose fluctuations are more extreme than the surface texture of natural particles. According to our experimental experience, α is set to 1.5 to 2 times The random value of is sufficient for a particle size of 1 P ini A mesh that preserves the undulations is generated to approximate the surface of natural particles. Afterwards, virtual particles with various overall morphologies and surface undulations can be generated by uniformly sampling 10,000 points on the mesh and using this mesh to close a binary 3D matrix with a resolution of 0.3, respectively. and Note that voxel data can be converted to point clouds based on voxel resolution and coordinates. and are all represented as 3D point sets: in, In N is equal to 10000, and in , the number of elements of a voxel is 1.

[0028] Generate particles with different roundness: and Only very sharp corners generated during the sampling and closing process are included. These corners are generated by the process of connecting triangles. In order to generate particles with corners of different sharpness, we first use the K-nearest neighbor method to identify the existing sharp corners. These corners are regarded as clusters of high curvature points, such as Figure 5 These corners are then smoothed at different levels using the widely used moving least squares (MLS) method. Specifically, for each point in the corner cluster , MLS will update To position on a smooth surface , the surface is obtained by the neighborhood The fitted Neighboring vertices within . By solving the optimization problem:

[0029] minimize It means lowering The z coordinate value of and projection value The weighted sum of the differences between . is a Gaussian weight function, indicating that the distance Closer points will have higher weights, giving Provide more local topological features. Therefore, for each , changing the value of r will result in different The discovery of points can further fit different surfaces with different smoothing effects .

[0030] Generation of contact aggregates and contact marking: At present, the dynamic processes of various granular material aggregates, such as landslides and compression tests, can be effectively simulated by discrete element methods (DEM). The present invention uses a highly reduced multi-sphere discrete element cluster model to transmit the forces between particles, thereby achieving a high-precision simulation that takes into account the particle shape characteristics. Therefore, with the method proposed by (Angelidakis) and The grid is converted into discrete element clusters, and the cluster aggregation simulated by DEM is obtained under gravity deposition.

[0031] Since the relative positions of the DEM clusters to the grid and to the grids of the point cloud and voxels remain unchanged, the DEM clusters can be replaced back into the original point cloud and voxels to generate clusters that simulate laser scans and CT scans, such as Figure 3 As shown in . In addition, during this replacement process, the neighboring points of some replaced points will change with the replacement of adjacent particles. By observation, we found that these points correspond to the particle contact areas that need to be marked, such as Figure 4 Therefore, in the present invention, the contact point is marked as the point where the normal vector calculated by the neighboring point changes during the replacement process.

[0032] Application examples: The contour of the block is controlled by randomly generated three-dimensional coordinate points. In the process of generating different particle contours, the distribution and number of control points need to be determined to ensure that the shape of the particles is as close as possible to the blocks in nature. Furthermore, a preliminary convex hull polyhedron model is generated by stretching the control points. Finally, the convex hull is triangulated using the triangle patch algorithm to form the final block discrete element cluster model.

[0033] In this embodiment, 100-500 three-dimensional coordinate control points are first randomly generated in a unit space. After random stretching, the control points are used to generate corresponding convex hull polyhedron triangle pieces. Subsequently, a rigid sphere algorithm is used to establish a block stone sphere model. At the same time, the PCL computing library is used to convert the convex hull polyhedron triangle pieces into a point cloud format to obtain point cloud data of a single block stone.

[0034] In the initial stage of generating particles with various overall shapes and surface undulations, a point cloud P is generated. ini , whose coordinates follow a three-dimensional multivariate standard normal distribution. Therefore, P ini The external points of P are randomly distributed along the x, y, and z axes, similar to a sphere. ini By multiplying the x- and y-coordinates of the target stretch ratio (S / I) and the inverse of the target flatness (I / L), the x, y, and z-axes can be aligned with the shortest axis (S), the longest axis (L), and the middle axis (I) to make P ini The overall morphology of naturally constructed particles is shown, e.g. Figure 1 As shown in Figure 1. By randomly assigning the slenderness ratio and flatness in the range of 0.3 to 1.0, almost all forms in nature can be generated, such as Figure 6 shown.

[0035] In order to imitate the generation of natural particles, we combined some mature 3D data processing techniques to carve the initial spherical point cloud into particles with surface point cloud and voxel data. The open source code AlphaShapeToolbox (https: / / github.com / bellockk / alphashape.git) and point cloud library (http: / / pointclouds.org) were used in the process. We successfully built a virtual architectural particle database containing 25,000 virtual particles. These particles have many important morphological features, including overall shape, roundness, and roughness. The shape parameter distribution of the particles is as follows Figure 6 As shown, the morphological types of most particles in nature are covered, and the distribution of shape parameters is wide and diverse, fully reproducing the geometric characteristics of particles in the natural environment. These goals were successfully achieved by stretching the overall shape, extracting and modifying external surface points, and grinding edges and corners. In addition, the sizes of all particles are normalized to 1 and dimensionless, so that users can easily obtain particles of the desired shape and size.

[0036] In the process of particle edge recognition and smoothing, by randomly setting The diameter of the particle was successfully smoothed to a range of 0.5 to 0.85 in the range of 0.05 to 0.3. All MLS operations were implemented using KdTree and MovingLeastSquares functions in PCL.

[0037] In this embodiment, 10 discrete element cluster models of blocks of stone are randomly selected from a database of 25,000 virtual particles, and the gravity deposition simulation of the block model is performed using the open source discrete element software YADE. During the simulation, the blocks of stone are piled up against each other under the action of gravity to form a pile. Furthermore, the coordinates of the center points of each ball before and after the accumulation are recorded to calculate the transformation matrix, and the point cloud coordinates of the corresponding blocks of stone are moved according to the obtained transformation matrix to generate accurate point cloud data of the particle aggregate, such as Figure 3 shown.

[0038] In the generated point cloud data, the normal vector difference method is used to mark the contact area between the blocks. The contact points are identified and marked by calculating the normal vector difference of each point. The K-nearest neighbor method and the principal component analysis (PCA) algorithm are combined to refine the marked area to ensure the accuracy of the contact area. The specific method is as follows: Relative contact information It is expressed as: in, yes The number of points in . A value of 1 indicates a contact point, and a value of 0 indicates a non-contact point. are local features, such as normal vectors and curvatures. Therefore, contact points are marked as points where the normal vectors calculated by neighboring points change during the replacement process, such as Figure 4 shown.

[0039] In this example, a contact aggregation particle database of 5000 groups of particle aggregates was generated by discrete element simulation. In order to save computational memory and time costs, each aggregate contains 10 particles of different shapes randomly selected from the contact aggregation particle database and is downsampled to 5000 points. The size of each particle in the aggregate is a random value between 20 and 60, which is achieved by directly multiplying the coordinates of particles with a diameter of 1 by the target diameter.

Claims

1. A method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials, characterized by: The following steps are involved: Step 1: Make a natural particle simulation model; assign the natural sphericity and roundness of the particles to the randomly generated three-dimensional coordinate control points to obtain a natural particle simulation model; Step 2: Make a simulation model of a particle accumulation body; fill the natural particle simulation model obtained in step 1 with discrete element clusters, and apply gravity deposition to the discrete element clusters through the discrete element method to obtain a simulation model of a particle accumulation body.

2. The method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials according to claim 1, characterized in that: In step 1, making a natural particle simulation model comprises the following steps: Step 1.1 generates different particle contours to control the size and main shape of the particles so that the natural particle simulation model approximates the shape of natural particles; Step 1.2 generates different surface reliefs to enhance the realism of the particles; Step 1.3 generates different particle roundness to simulate the sharpness of particles in nature and avoid overly sharp corners.

3. The method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials according to claim 2, characterized in that: The specific operations of step 1.1 are as follows: Step 1.1.1 Generate a point cloud P ini , point cloud P ini The coordinates of follow a 3D multivariate standard normal distribution; Step 1.1.2 By stretching the x and y coordinates and aligning the z axis, the point cloud P ini Shows the sphericity characteristics of natural building particles; Step 1.1.3 Point cloud P ini Stretching includes: Point cloud P ini The projections on the x, y, and z axes are the shortest axis S, longest axis L, and second longest axis I of the particle, respectively; Step 1.1.4: Point cloud P ini The x-coordinate of the point cloud P is multiplied by the target slenderness ratio. ini The y coordinate of the point cloud P is multiplied by the inverse of the target flatness. ini With targeted sphericity values, different particle profiles are obtained.

4. The method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials according to claim 2, characterized in that: The specific operation of step 1.2 is as follows: Step 1.2.1 Generate a surface mesh of a closed point cloud using an alpha shape algorithm; Step 1.2.2 Ensure that the point cloud P is ini The outer points of are not on the same convex surface to generate a natural surface relief; Step 1.2.3 adjusts the α value to control the compactness and surface relief characteristics of the generated mesh.

5. The method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials according to claim 2, characterized in that: The specific operation of step 1.3 is as follows: Step 1.3.1: Simulate real, natural geometry and surface features by identifying and processing sharp corners on the particle surface; Step 1.3.2 uses the K-nearest neighbor method to identify sharp corners with high curvature, and smoothes the sharp corners by moving least squares method MLS to generate particles with different roundness.

6. The method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials according to claim 1, characterized in that: In step 2, making a particle accumulation simulation model includes the following steps: Step 2.1 generates a particle accumulation body with contact information and marks the contact area; Step 2.2 generates particle clusters with contact information and uses the discrete element method DEM to simulate the particle accumulation process; Step 2.3 generates the initial block stone sphere discrete element model by using random three-dimensional coordinate control points in the unit space to generate corresponding convex hull polyhedron triangles, and establishes the block stone discrete element cluster model through the rigid sphere algorithm; Step 2.4: The random number of block discrete element cluster models are deposited using the gravity deposition method in the discrete element software.

7. The method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials according to claim 6, characterized in that: In step 2.4, during the process of forming the stack, the coordinates of the center points of each ball before and after the stack are recorded to calculate the transformation matrix, and the point cloud P of the corresponding block is moved according to the obtained transformation matrix. ini Coordinates, thus obtaining the particle point cloud P ini data.

8. The method for generating a simulation model of a particle accumulation body with natural three-dimensional shape characteristics of building materials according to claim 6, characterized in that: In step 2.1, the contact information of the particle accumulation body is annotated by the normal vector difference method to mark the contact points; specifically, the K-nearest neighbor method is used to select the neighboring points near the target point, and the PCA algorithm is used to fit the neighboring points into a plane, and the plane normal vector is used as the target point normal vector; when the point near the target point changes, its corresponding normal vector also changes accordingly; by calculating the normal vector of the newly generated point cloud, and selecting the point where the normal vector changes in each step as the block stone boundary edge point, the generation of the particle contact particle accumulation body is completed.

Images