Method and system for generating rough surface of cement-based material based on laguerre tessellation

Abstract

The present application provides a cement-based material rough surface generation method and system based on Laguerre tessellation, comprising the following steps: S1: skeleton particle size distribution characteristic determination step; S2: skeleton particle distribution generation step; S3: Laguerre tessellation structure construction step; S4: rough surface extraction and construction step; S5: rough surface refinement processing step. The present application method considers the particle size distribution and volume proportion of the skeleton particles in the cement-based material, and can generate a rough surface point cloud with statistical significance and physical rationality according to the surface topography requirements. The design method has the advantages of clear concept, high calculation efficiency and strong physical meaning, and facilitates the technical personnel in the field to quickly and accurately generate the cement-based material rough surface point cloud, providing a basic geometric model for test simulation, mesoscopic mechanics analysis and related numerical calculation.

Description

Technical Field

[0001] This invention relates to the technical field of civil engineering, specifically to a method and system for generating rough surfaces of cement-based materials based on Laguerre mosaic. Background Technology

[0002] As research on cement-based materials has evolved from primarily experimental to a combination of experimental and numerical simulation, and even primarily numerical simulation, higher demands have been placed on the realism and controllability of geometric models of material surfaces and interfaces, particularly in areas such as seepage behavior analysis, interfacial bonding research, and multiphysics coupling calculations. Rough surfaces not only need to possess controllable geometric undulations but should also reflect the internal particle composition and spatial structure of cement-based materials to support research on key mechanisms such as seepage channel formation, interfacial contact behavior, and mechanical response.

[0003] Currently, methods for generating rough surfaces can be mainly divided into three categories: methods based on statistical features, methods based on fractal features, and methods based on morphological features. Methods based on statistical and fractal features can better control the surface's geometric statistical properties or multi-scale characteristics, but their modeling process relies on statistical assumptions or empirical parameters and lacks a direct physical correspondence with material particle size distribution and spatial packing structure. In contrast, morphological feature-based rough surface modeling methods, by explicitly introducing rough peaks, peak-valley units, or typical morphological features, enhance the intuitiveness of the surface geometric model and its ability to express local features to some extent, but their versatility is limited, making it difficult to directly extend to material surfaces with complex morphologies or multiple scale features. Therefore, it is necessary to develop a geometrically controllable modeling method with clear physical meaning to provide a reliable geometric foundation for seepage analysis, crack mechanical behavior simulation, and microscopic numerical studies. Summary of the Invention

[0004] This invention is made to solve the above-mentioned problems, and aims to provide a method and system for generating rough surfaces of cement-based materials based on Laguerre mosaic.

[0005] This invention provides a method for generating rough surfaces of cement-based materials based on Laguerre mosaicking, characterized by the following steps: S1: Determining the particle size distribution characteristics of the skeleton particles, based on the compositional characteristics of the target cement-based material; S2: Generating the skeleton particle distribution, within a preset mosaicking computational domain, randomly generating and placing skeleton particles that satisfy the particle size distribution characteristics to obtain a skeleton particle distribution that satisfies the preset volume fraction and does not overlap; S3: Constructing the Laguerre mosaicking structure, based on the skeleton particle distribution, constructing a Laguerre mosaicking structure to form polyhedral units reflecting the spatial distribution and interaction relationships of the skeleton particles; S4: Extracting and constructing the rough surface, based on a preset reference surface, selecting polyhedral units that intersect with the reference surface, and extracting vertices located on opposite sides of the reference surface based on the positional relationship between the centroid of the polyhedral unit and the reference surface, obtaining the original rough surface point cloud after deduplication; S5: Refining the rough surface, constructing a continuous interpolated surface based on the original rough surface point cloud, and performing uniform meshing resampling on the continuous interpolated surface to obtain a refined rough surface point cloud.

[0006] The method for generating rough surfaces of cement-based materials based on Laguerre mosaic provided by the present invention may also have the following features: wherein, in S1, the particle size distribution features include: particle size distribution type, particle size distribution parameters, and particle size distribution range.

[0007] The method for generating a rough surface of cement-based materials based on Laguerre mosaic provided by this invention may also have the following features: In S1, the cement-based material includes concrete, mortar, and cement paste. When the cement-based material is concrete or mortar, the particle size distribution type is Fuller distribution, and the particle size distribution parameters include the maximum particle size. gradation index When the cement-based material is cement paste, the particle size distribution type is RRSB distribution, and the particle size distribution parameters include the characteristic particle size. Uniformity index .

[0008] The method for generating a rough surface of cement-based materials based on Laguerre mosaic provided by this invention may also have the following features: In S2, the method for obtaining the distribution of the skeleton particles is as follows: S2-1: Based on the particle size distribution type of the skeleton particles, obtain the corresponding cumulative distribution function (CDF) and inverse cumulative distribution function (ICDF); S2-2: Combining the cumulative distribution function and the particle size distribution range, determine the cumulative probability corresponding to the particle size distribution range, and construct a uniformly distributed cumulative probability random variable accordingly. S2-3: The cumulative probability random variable Substituting into the inverse cumulative distribution function (ICDF), we obtain the random particle size. S2-4: Determine the mosaic computational domain based on the generated rough surface size, determined by the volume fraction of the skeleton particles. With computational domain volume The total volume of the preset skeleton particles was calculated. S2-5: Repeat step S2-3 to continuously generate corresponding random particle sizes. The skeleton particles are processed until the total volume of the generated skeleton particles reaches the preset total volume. That is, satisfying the preset skeleton particle volume fraction; S2-6: Randomly place the generated skeleton particles into the mosaic computing domain in sequence. If there is an overlap between the skeleton particles and the boundary of the computing domain or between the skeleton particles, the placement is repeated until all skeleton particles are placed, and a skeleton particle distribution that satisfies the preset skeleton particle volume fraction and does not overlap is obtained.

[0009] The method for generating rough surfaces of cement-based materials based on Laguerre mosaic provided by this invention may also have the following feature: In S2-1, the particle size distribution type is Fuller distribution, and the cumulative distribution function (CDF) is:

[0010]

[0011] in, For diameter The following is the cumulative volume percentage of the particles; This represents the maximum particle size. The gradation index;

[0012] The particle size distribution is RRSB distribution, and the cumulative distribution function (CDF) is:

[0013]

[0014] in, For diameter The following is the cumulative volume percentage of the particles; Characteristic particle size; It is the uniformity index.

[0015] The method for generating a rough surface of cement-based material based on Laguerre mosaic provided by the present invention may also have the following feature: in S2-6, the order of placement of the skeleton particles is from large to small.

[0016] The method for generating rough surfaces of cement-based materials based on Laguerre mosaic provided by the present invention may also have the following features: In S3, the method for forming polyhedral units is as follows: based on the distribution of skeleton particles, they are converted into weighted seed files, and based on the weighted seed files, the corresponding Laguerre mosaic structure is constructed through the Laguerre mosaic algorithm to form polyhedral units that reflect the spatial distribution and interaction relationship of skeleton particles.

[0017] The method for generating rough surfaces of cement-based materials based on Laguerre mosaic provided by this invention may also have the following feature: in the weighted seed file, the coordinates of the seed are the center coordinates of the skeleton particles, and the weight of the seed is the corresponding particle size.

[0018] The method for generating rough surfaces of cement-based materials based on Laguerre mosaic provided by the present invention may also have the following features: In S5, the method for constructing a continuous interpolation surface is as follows: Select the two coordinate dimensions with the largest scale in the point cloud of the original rough surface, divide them into Delaunay triangular meshes, and construct a continuous interpolation surface based on the centroid coordinate interpolation method of the triangle.

[0019] This invention also provides a system for generating rough surfaces of cement-based materials based on Laguerre mosaicking, characterized by the following features: a skeleton particle size distribution feature determination module, which determines the particle size distribution features of the skeleton particles based on the compositional characteristics of the target cement-based material; a skeleton particle distribution generation module, which randomly generates and deploys skeleton particles that satisfy the particle size distribution features within a preset mosaicking computational domain, thereby obtaining a skeleton particle distribution that satisfies the preset skeleton particle volume fraction and is non-overlapping; a Laguerre mosaicking structure construction module, which constructs a Laguerre mosaicking structure based on the skeleton particle distribution, forming polyhedral units that reflect the spatial distribution and interaction relationships of the skeleton particles; a rough surface extraction and construction module, which selects polyhedral units that intersect with the reference surface based on a preset reference surface, and extracts vertices located on opposite sides of the reference surface based on the positional relationship between the centroid of the polyhedral unit and the reference surface, and obtains the original rough surface point cloud after deduplication; and a rough surface refinement module, which constructs a continuous interpolation surface based on the original rough surface point cloud, and performs uniform meshing resampling on the continuous interpolation surface to obtain a refined rough surface point cloud.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] This invention considers the particle size distribution and spatial packing characteristics of skeleton particles in cement-based materials, and introduces the Laguerre mosaic method. This allows for the generation of statistically significant and physically plausible rough surface models based on surface morphology requirements. By controlling input parameters such as particle size distribution type, particle size distribution range, and skeleton particle volume fraction, quantitative control and physical interpretability of the generated surface roughness characteristics can be achieved, avoiding the problem of unintuitive parameter meanings in existing models. Without relying on measured surface data, only the known or designed proportions of the target material are needed to construct physically interpretable rough surface point clouds, thus significantly reducing modeling costs and improving modeling efficiency. The surface model is constructed based on the compositional characteristics of cement-based materials, making it applicable to various cement-based material systems such as cement paste, mortar, and concrete.

[0022] In summary, the method of this invention has a clear concept, controllable parameters and clear physical meaning, is convenient and efficient, and has strong practicality, providing reliable rough surface geometric model support for experimental simulation, micromechanical analysis and related numerical calculations. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for generating a rough surface of a cement-based material based on Laguerre mosaic, as described in an embodiment of the present invention.

[0024] Figure 2 This is a visualization of the distribution of skeletal particles in an embodiment of the present invention; wherein, (a) is a side view and (b) is a front view.

[0025] Figure 3 This is a schematic diagram of the rough surface of the cement paste generated in an embodiment of the present invention, wherein the reference surface of (a) is... (b) The reference plane is (c) The reference plane is The reference plane for (d) is The reference plane for (e) is The reference plane for (f) is The reference plane for (g) is The reference plane for (h) is The reference plane for (i) is .

[0026] Figure 4 This is a schematic diagram of the true rough surface of cement paste obtained by XCT experiment in an embodiment of the present invention, wherein (a) is a schematic diagram of the first true rough surface of cement paste; and (b) is a schematic diagram of the second true rough surface of cement paste. Detailed Implementation

[0027] To make the technical means, creative features, objectives and effects of the present invention easy to understand, the following embodiments, in conjunction with the accompanying drawings, specifically illustrate the method and system for generating rough surfaces of cement-based materials based on Laguerre inlay.

[0028] A laboratory in Shanghai used XCT to perform in-situ scanning of loaded cement paste specimens, obtaining several rough surface data points with cracks. Using the Laguerre mosaic-based rough surface generation method for cement-based materials provided in this embodiment, a corresponding rough surface point cloud with physical meaning and consistent roughness index was generated.

[0029] The method for generating a rough surface of a cement-based material based on Laguerre mosaic provided in this embodiment includes the following steps:

[0030] Figure 1This is a flowchart of a method for generating a rough surface of a cement-based material based on Laguerre mosaic, as described in an embodiment of the present invention.

[0031] like Figure 1 As shown, step S1 is the step of determining the particle size distribution characteristics of the skeleton particles. Based on the compositional characteristics of the target cement-based material, the particle size distribution characteristics of the skeleton particles are determined as the basic parameters for subsequent geometric modeling. Specifically:

[0032] The particle size distribution characteristics of the skeleton particles need to be determined based on the compositional properties of the target cementitious material. Specifically, for concrete and mortar materials, the rough surface morphology is mainly affected by coarse and fine aggregates, and the particle size distribution type can be selected as the fuller distribution. The particle size distribution parameters include the maximum particle size. gradation index For cement paste materials, the rough surface morphology is affected by the particle size distribution of cement particles. The particle size distribution type can be selected as RRSB distribution, and the particle size distribution parameters include the characteristic particle size. Uniformity index At the same time, it is necessary to determine the particle size distribution range ( , This ensures the efficiency and quality of subsequent inlaying. All the above particle size distribution characteristics should be determined based on the raw materials of the research material. You can select empirical values, conduct actual measurements, or consult the supplier according to domestic standards (such as GB / T 14684-2022 "Construction Sand", GB / T 14685-2022 "Construction Pebbles and Crushed Stones", etc.).

[0033] Based on existing research The RRSB distribution can effectively describe the particle size distribution of ungraded cement particles; therefore, this embodiment selects the RRSB distribution as the particle size distribution type for skeleton particles. Based on empirical data, the particle size distribution parameters are determined as follows: characteristic particle size. Uniformity index The particle size distribution range is as follows: maximum particle size Minimum particle size .

[0034] The water-cement ratio of cement paste specimens The specific gravity of cement particles to water The volume ratio of the skeletal particles can be calculated as follows:

[0035]

[0036] S2: The skeleton particle distribution generation step involves randomly generating and deploying skeleton particles that satisfy the particle size distribution characteristics within a preset mosaic computational domain. This yields a skeleton particle distribution that satisfies the preset skeleton particle volume fraction and does not overlap. Specifically, this includes the following sub-steps:

[0037] S2-1: Based on the particle size distribution type of the skeleton particles, the corresponding cumulative distribution function (CDF) and inverse cumulative distribution function (ICDF) are obtained, specifically:

[0038] When the particle size distribution is a Fuller distribution, the cumulative distribution function (CDF) is:

[0039]

[0040] in, For diameter The following is the cumulative volume percentage of the particles; This represents the maximum particle size. This is the gradation index.

[0041] In this embodiment, the particle size distribution type is RRSB distribution, and the corresponding cumulative distribution function (CDF) is:

[0042]

[0043] And the inverse cumulative distribution function ICDF is:

[0044]

[0045] S2-2: Determine the particle size distribution range by combining the cumulative distribution function and the particle size distribution range. , The cumulative probability corresponding to ) , ), and use this to construct a uniformly distributed cumulative probability random variable. Specifically:

[0046] Substituting the upper and lower limits of the particle size distribution range into the cumulative distribution function (CDF) yields the upper and lower limits of the cumulative probability, where the upper limit is:

[0047]

[0048] The lower limit is:

[0049]

[0050] This leads to the construction of a cumulative probability random variable. satisfy:

[0051]

[0052] S2-3: The cumulative probability random variable Substituting into the inverse cumulative distribution function (ICDF), we obtain the random particle size. That is:

[0053]

[0054] in, Given a defined particle size distribution type and its upper and lower limits, the cumulative probability random variable... satisfy:

[0055]

[0056] S2-4: Determine the mosaic computational domain based on the generated rough surface size, determined by the volume fraction of the skeleton particles. With computational domain volume The total volume of the preset skeleton particles was calculated. Specifically:

[0057] Due to the maximum particle size To ensure the representativeness of the rough surface, the edge length of the tessellation computational domain is set. 10 times the maximum particle size ,Right now:

[0058]

[0059] The tessellation computational domain is a cube, from which the total volume of the preset skeleton particles is obtained. for:

[0060]

[0061] S2-5: Repeat step S2-3 to continuously generate the corresponding random particle size. The process continues until the total volume of the generated skeleton particles reaches the preset total volume. That is, it satisfies the preset skeleton particle volume fraction.

[0062] Figure 2 This is a visualization of the distribution of skeletal particles in an embodiment of the present invention; wherein, (a) is a side view and (b) is a front view.

[0063] S2-6: Randomly place the generated skeleton particles into the tessellation computational domain in descending order. If any skeleton particle overlaps with the domain boundary or with another skeleton particle, the placement process needs to be repeated until all skeleton particles have been placed, resulting in the following: Figure 2 (a) Figure 2 As shown in (b), this represents the distribution of skeletal particles that satisfy the preset volume fraction and do not overlap. The distribution is stored as a txt file with the center coordinates of the skeletal particles and their corresponding radii, as shown in Table 1 (unit: ...). ).

[0064] Table 1. Distribution of skeletal particles

[0065]

[0066] Step S3 is the Laguerre mosaic structure construction step. Based on the distribution of framework particles, a Laguerre mosaic structure is constructed to form a polyhedral unit that reflects the spatial distribution and interaction relationship of framework particles. Specifically, it includes the following sub-steps:

[0067] S3-1: Based on the distribution of the skeleton particles, convert it into a weighted seed file (seed.txt) required for mosaicking. The seed coordinates are the center coordinates of the skeleton particles, and the seed weight is the corresponding particle size.

[0068] S3-2: Based on the weighted seed file seed.txt, perform Laguerre tessellation using Voro++ software. The software's function is to generate the corresponding Laguerre tessellation structure based on the provided weighted seed file and then output the corresponding tessellation data as required. In this embodiment, it is necessary to output the centroid of each tessellation cell (polyhedral unit) and the coordinates of all vertices for subsequent rough surface construction.

[0069] Voro++ can be controlled via command line. The command line for generating Laguerre mosaics based on weighted seed files is:

[0070] voro++ -c "%i %C %P" -p -r 0 800 0 800 0 800 'seed.txt'

[0071] Where, [-c "%i %C %P"] indicates that the output of the tessellation is, in order, the tessellation cell id (%i), the centroid coordinates of the tessellation cell (%C), and the vertex coordinates contained in the tessellation cell (%P); [-p] indicates that a periodic tessellation result is generated; [-r] indicates that the generated tessellation is a Laguerre tessellation (weighted Voronoi tessellation); [0 800 0 800 0 800] indicates that the tessellation computational domain size is ; ['seed.txt'] indicates that the weighted seed file name is specified. The output of the mosaic is a vol format file provided by voro++, as shown in Table 2.

[0072] Table 2 Output results of tessellation

[0073]

[0074] Step S4 is the rough surface extraction and construction step. Based on a preset reference surface, polyhedral elements intersecting with the reference surface are selected, and vertices located on the opposite side of the reference surface are extracted according to the positional relationship between the centroid of the polyhedral elements and the reference surface. After deduplication, the original rough surface point cloud is obtained. Specifically, it includes the following sub-steps:

[0075] S4-1: Select a reference surface undulation morphology based on research needs and construct a reference surface equation, specifically as follows:

[0076] Based on research requirements, a plane is chosen as the reference surface. For a single tessellation output, multiple reference surfaces can be selected to generate multiple rough surfaces; therefore, [the following is chosen]. , , (unit: There are a total of 9 planes, and subsequent steps will follow... The following explanation uses a reference plane as an example.

[0077] S4-2: Based on the vertex coordinates of each damascene cell, determine the positional relationship between the damascene cell and the reference surface, and filter out the damascene cells that intersect with the reference surface, specifically:

[0078] Based on the coordinates of all vertices of each damascene cell, the z-coordinate coverage range of each damascene cell can be obtained. When this range includes z=200, it indicates that the damascene cell intersects with the reference plane and needs to be included in the screening.

[0079] S4-3: Based on the above screening results, vertices are selected by the positional relationship between the centroid of the tessellation cell and the reference surface, specifically as follows:

[0080] When the center of gravity If the coordinates are greater than 200, then filter within this mosaic unit cell. Vertices with coordinates less than 200 are included in the rough surface point cloud set, and vice versa, thus obtaining the filtered rough surface point cloud set.

[0081] S4-4: Since there are common vertices between the tessellated cells, there are also duplicate points in the above rough surface point cloud set. Therefore, deduplication is required to obtain the rough surface in point cloud form, which is the original rough surface point cloud.

[0082] Step S5 is the rough surface refinement step. A continuous interpolated surface is constructed based on the original rough surface point cloud, and uniform mesh resampling is performed on the interpolated surface to obtain a refined rough surface point cloud. Specifically, it includes the following sub-steps:

[0083] S5-1: Select the two largest coordinate dimensions in the original rough surface point cloud, divide it into Delaunay triangular meshes, and construct a continuous interpolated surface based on the centroid coordinate interpolation method of the triangles, specifically:

[0084] Point clouds on rough surfaces , The coordinates are divided into Delaunay triangulations, and the internal coordinates of each triangle are interpolated using the barycentric coordinates of the triangles. Coordinates are interpolated to construct a continuous interpolation surface. The interpolation method is as follows:

[0085] Given the coordinates of the three vertices of the triangle , , Then the interpolation point in the triangle of The coordinates are:

[0086]

[0087] in:

[0088]

[0089]

[0090]

[0091] in, Indicates the interpolation point In triangle The three coordinates in the centroid coordinate system.

[0092] Figure 3 This is a schematic diagram of the rough surface of the cement paste generated in an embodiment of the present invention, wherein the reference surface of (a) is... (b) The reference plane is (c) The reference plane is The reference plane for (d) is The reference plane for (e) is The reference plane for (f) is The reference plane for (g) is The reference plane for (h) is The reference plane for (i) is .

[0093] S5-2: Perform uniform gridding sampling based on a continuous interpolated surface, with a sampling interval of [missing value]. This yields a more refined rough surface point cloud with a more uniform point distribution, higher quality, and easier use for subsequent analysis and numerical calculations, such as... Figure 3 (a) Figure 3 As shown in (i).

[0094] In this embodiment, the roughness index of the refined rough surface point cloud (hereinafter referred to as the generated surface) is compared and verified with that of the real surface.

[0095] Figure 4This is a schematic diagram of the true rough surface of cement paste obtained by XCT experiment in an embodiment of the present invention, wherein (a) is a schematic diagram of the first true rough surface of cement paste; and (b) is a schematic diagram of the second true rough surface of cement paste.

[0096] The actual surface data was obtained by in-situ scanning of the loaded cement paste specimen using XCT. For ease of roughness calibration, as shown... Figure 4 (a) Figure 4 As shown in (b), the surface data of cracks and roughness on the real surface are rotated and corrected to approximate the actual surface. flat.

[0097] Roughness calibration was performed on the generated surface and the real surface, using two roughness indices, one of which was the fractal dimension. Its definition is:

[0098]

[0099] in, For scale (measurement unit size); for The measurement value of the object at the specified scale.

[0100] Specifically, the box-counting method can be used. For the size of the box, Box dimensions The number of boxes required to cover a point cloud on a rough surface determines the possible selection of different box sizes. ,right The slope obtained by performing linear regression is the fractal dimension. .

[0101] The second is the root mean square roughness. Its definition is:

[0102]

[0103] in, This represents the number of points contained in the point cloud. The height of each point ( coordinate); The height of the quadratic fitted plane is used to characterize the surface undulations, and its specific form is as follows:

[0104]

[0105] The roughness of the generated surface and the real surface were calibrated, and the results are shown in Tables 3-5.

[0106] Table 3. Fractal Dimension of Generated Surfaces Calibration results

[0107]

[0108] Table 4. Generated Root Mean Square Roughness of Surface Calibration results

[0109]

[0110] Table 5 Comparison of surface roughness indices between generated and real surfaces

[0111]

[0112] As shown in Tables 3-5, the errors of the above two roughness indicators are both less than 10%, indicating that the generated surface has roughness characteristics similar to the real surface.

[0113] In summary, the method provided in this embodiment generates a rough surface point cloud with physical meaning and consistent roughness index.

[0114] This embodiment also provides a system for generating rough surfaces of cement-based materials based on Laguerre mosaics, including:

[0115] The skeleton particle size distribution characteristic determination module is used to implement S1, that is: to determine the particle size distribution characteristics of the skeleton particles based on the composition characteristics of the target cement-based material.

[0116] The skeleton particle distribution generation module is used to implement S2, that is, within the preset mosaic computing domain, by randomly generating skeleton particles that meet the particle size distribution characteristics and placing them, a skeleton particle distribution that meets the preset skeleton particle volume fraction and does not overlap is obtained.

[0117] The Laguerre mosaic structure building module is used to implement S3, namely: based on the distribution of skeleton particles, construct a Laguerre mosaic structure to form a polyhedral unit that reflects the spatial distribution and interaction relationship of skeleton particles.

[0118] The rough surface extraction and construction module is used to implement S4, namely: based on a preset reference surface, it filters polyhedral elements that intersect with the reference surface, and extracts vertices located on the opposite side of the reference surface according to the positional relationship between the centroid of the polyhedral element and the reference surface. After deduplication, the original rough surface point cloud is obtained.

[0119] The rough surface refinement module is used to implement S5, namely: constructing a continuous interpolated surface based on the original rough surface point cloud, and performing uniform meshing resampling on the continuous interpolated surface to obtain a refined rough surface point cloud.

[0120] The role and effect of the embodiments

[0121] The method and system for generating rough surfaces of cement-based materials based on Laguerre mosaic, as described in this invention, have the following beneficial effects:

[0122] (1) The physical meaning of the materials is clear and the model is highly realistic.

[0123] The method of this invention considers the particle size distribution and spatial packing characteristics of the skeleton particles in cement-based materials. By using Laguerre mosaic to characterize the geometric relationship and spatial segmentation mechanism between particles, the rough surface morphology is directly related to the material microstructure. It can generate a rough surface model with statistical significance and physical rationality according to the surface morphology requirements, fundamentally overcoming the shortcomings of traditional fractal or stochastic process models that only have mathematical statistical significance and lack material physical connotation.

[0124] (2) Achieve a natural transition from an experiment-driven to a numerical simulation-driven research model.

[0125] In situations where high-precision experimental point cloud data is lacking or experimental costs are high, this invention does not rely on actual surface test results. It can construct physically interpretable rough surface point clouds using only known or designed proportions of the target material (such as water-cement ratio). This facilitates parametric analysis, mechanism research, and predictive numerical simulation, significantly reducing experimental costs and improving research efficiency and flexibility.

[0126] (3) The generated surface roughness features are adjustable and have clear interpretability.

[0127] The method of this invention can directly and quantitatively control the morphological characteristics such as roughness of the generated surface by adjusting input parameters (such as particle size distribution type, particle size distribution range, and volume fraction of skeleton particles). Furthermore, changes in the roughness characteristics of the generated surface can be directly traced back to changes in the input parameters, achieving physical interpretability of the control process and avoiding the problem of unintuitive parameter meanings in existing random or fractal methods.

[0128] (4) Wide range of applicable material types and research scenarios

[0129] The method of this invention constructs a surface model based on the compositional characteristics of cement-based materials. It is applicable to various cement-based material systems such as cement paste, mortar, and concrete. It can be used for interface contact and mechanical behavior analysis, as well as for various numerical simulation scenarios such as seepage, freeze-thaw damage, and wear evolution. It has good versatility and scalability.

[0130] (5) Facilitates coupling with multiphysics and multiscale numerical methods

[0131] The rough surface model generated by this invention has good geometric continuity and numerical stability. It can be directly used as input in the finite element method, discrete element method and multi-scale coupled calculation framework, which facilitates the coupled analysis of multiple physical fields such as mechanical field, seepage field and thermal and humid field, and has high engineering applicability value.

[0132] In summary, the method of this invention has a clear concept, controllable parameters and clear physical meaning, is convenient and efficient, and has strong practicality, providing reliable rough surface geometric model support for experimental simulation, micromechanical analysis and related numerical calculations.

[0133] Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

[0134] References

[0135] [1] GAO P, ZHANG TS, WEI JX, et al. Evaluation of RRSB distribution and lognormal distribution for describing the particle size distribution of graded cementitious materials [J]. Powder Technology, 2018, 331: 137–45.

[0136] [2] Wang Kechun, Zhang Zongjian, Xuan Hongzhong, et al. Effect of RRSB particle size distribution on cement performance in finished cement products [J]. Building Materials Development Guide, 2019, 17(20): 31–3.

[0137] [3] Lu Difen, Hu Haipeng. Characterization and control of cement particle size[J]. Cement, 2004, (08): 1–4.

Claims

1. A method for generating rough surfaces of cement-based materials based on Laguerre mosaics, characterized in that, Includes the following steps: S1: Step for determining the particle size distribution characteristics of the skeleton particles: Based on the compositional characteristics of the target cement-based material, determine the particle size distribution characteristics of the skeleton particles. S2: Skeleton particle distribution generation step: Within a preset mosaic computing domain, skeleton particles that satisfy the particle size distribution characteristics are randomly generated and deployed to obtain a skeleton particle distribution that satisfies the preset skeleton particle volume fraction and does not overlap. S3: Laguerre mosaic structure construction step: Based on the distribution of the skeleton particles, construct the Laguerre mosaic structure to form a polyhedral unit that reflects the spatial distribution and interaction relationship of the skeleton particles; S4: Rough surface extraction and construction steps: Based on a preset reference surface, the polyhedral units that intersect with the reference surface are selected, and the vertices located on the opposite side of the reference surface are extracted according to the positional relationship between the centroid of the polyhedral unit and the reference surface. After deduplication, the original rough surface point cloud is obtained. S5: Rough surface refinement step: Construct a continuous interpolation surface based on the original rough surface point cloud, and perform uniform meshing resampling on the continuous interpolation surface to obtain a refined rough surface point cloud.

2. The method for generating a rough surface of a cement-based material based on Laguerre mosaic as described in claim 1, characterized in that: in, In S1, the particle size distribution characteristics include: particle size distribution type, particle size distribution parameters, and particle size distribution range.

3. The method for generating a rough surface of a cement-based material based on Laguerre mosaic as described in claim 2, characterized in that: in, In S1, cement-based materials include: concrete materials, mortar materials, and cement paste materials. When the cement-based material is concrete or mortar: the particle size distribution type is Fuller distribution, and the particle size distribution parameters include the maximum particle size. gradation index ; When the cement-based material is cement paste, the particle size distribution type is RRSB distribution, and the particle size distribution parameters include the characteristic particle size. Uniformity index .

4. The method for generating a rough surface of cement-based materials based on Laguerre mosaic as described in claim 3, characterized in that: in, In S2, the method for obtaining the distribution of skeletal particles is as follows: S2-1: Based on the particle size distribution type of the skeleton particles, the corresponding cumulative distribution function CDF and inverse cumulative distribution function ICDF are obtained; S2-2: Combining the cumulative distribution function and the particle size distribution range, determine the cumulative probability corresponding to the particle size distribution range, and construct a uniformly distributed cumulative probability random variable based on this. ; S2-3: The cumulative probability random variable Substituting into the inverse cumulative distribution function (ICDF), we obtain the random particle size. ; S2-4: Determine the mosaic computational domain based on the generated rough surface size, determined by the volume fraction of the skeleton particles. With computational domain volume The total volume of the preset skeleton particles was calculated. ; S2-5: Repeat step S2-3 to continuously generate the corresponding random particle size. The skeleton particles are processed until the total volume of the generated skeleton particles reaches the preset total volume. That is, it satisfies the preset skeleton particle volume fraction; S2-6: Randomly place the generated skeleton particles into the mosaic computing domain in sequence. If there is an overlap between the skeleton particles and the boundary of the computing domain or between the skeleton particles themselves, the particles are re-placed until all skeleton particles are placed, resulting in a skeleton particle distribution that satisfies the preset skeleton particle volume fraction and does not overlap with each other.

5. The method for generating a rough surface of a cement-based material based on Laguerre mosaic as described in claim 4, characterized in that: in, In S2-1, the particle size distribution is a Fuller distribution, and the cumulative distribution function (CDF) is: ， in, For diameter The following is the cumulative volume percentage of the particles; This represents the maximum particle size. The gradation index; The particle size distribution is RRSB distribution, and the cumulative distribution function (CDF) is: ， in, For diameter The following is the cumulative volume percentage of the particles; Characteristic particle size; It is the uniformity index.

6. The method for generating a rough surface of a cement-based material based on Laguerre mosaic as described in claim 4, characterized in that: in, In steps S2-6, the order in which the skeleton particles are placed is from largest to smallest.

7. The method for generating a rough surface of a cement-based material based on Laguerre mosaic as described in claim 1, characterized in that: in, In S3, the method for forming polyhedral units is as follows: based on the distribution of skeleton particles, they are converted into weighted seed files, and based on the weighted seed files, the corresponding Laguerre mosaic structure is constructed using the Laguerre mosaic algorithm to form polyhedral units that reflect the spatial distribution and interaction relationship of skeleton particles.

8. The method for generating a rough surface of a cement-based material based on Laguerre mosaic as described in claim 7, characterized in that: in, In the weighted seed file, the coordinates of the seed are the center coordinates of the skeleton particle, and the weight of the seed is the corresponding particle size.

9. The method for generating a rough surface of a cement-based material based on Laguerre mosaic as described in claim 1, characterized in that: in, In S5, the method for constructing a continuous interpolation surface is as follows: select the two coordinate dimensions with the largest scale in the original rough surface point cloud, divide them into Delaunay triangular meshes, and construct a continuous interpolation surface based on the centroid coordinate interpolation method of the triangle.

10. A system for generating rough surfaces of cement-based materials based on Laguerre mosaics, characterized in that, include: The module for determining the particle size distribution characteristics of the skeleton particles determines the particle size distribution characteristics of the skeleton particles based on the compositional characteristics of the target cement-based material. The skeleton particle distribution generation module, within a preset mosaic computing domain, randomly generates and deploys skeleton particles that satisfy the particle size distribution characteristics, thereby obtaining a skeleton particle distribution that satisfies the preset skeleton particle volume fraction and does not overlap. The Laguerre mosaic structure construction module constructs a Laguerre mosaic structure based on the distribution of the skeleton particles, forming a polyhedral unit that reflects the spatial distribution and interaction relationship of the skeleton particles; The rough surface extraction and construction module, based on a preset reference surface, filters the polyhedral units that intersect with the reference surface, and extracts the vertices located on the opposite side of the reference surface according to the positional relationship between the centroid of the polyhedral unit and the reference surface. After deduplication, the original rough surface point cloud is obtained. The rough surface refinement module constructs a continuous interpolation surface based on the original rough surface point cloud, and performs uniform mesh resampling on the continuous interpolation surface to obtain a refined rough surface point cloud.

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