Method and device for generating random aggregate model of concrete polyhedron and storage medium

Abstract

The concrete polyhedral random aggregate model generation method, device and storage medium provided by the embodiments of the present disclosure comprise: an aggregate particle size iteration method based on a target grading curve, to obtain the particle size distribution of all aggregates in concrete; calculating initial aggregate grading parameters according to the particle size distribution of the aggregates, and generating convex polyhedral random aggregates in a sample space according to the initial aggregate grading parameters; optimizing the geometry of the convex polyhedral random aggregates to obtain optimized convex polyhedral random aggregates; adjusting the particle size of each optimized convex polyhedral random aggregate to match the target grading curve; and discretizing the convex polyhedral random aggregates matching the target grading curve into finite element grids to obtain a concrete polyhedral random aggregate model. The embodiments of the present disclosure have the characteristics of automation, controllable grading, and large grading span.

Description

Technical Field

[0001] This disclosure belongs to the field of concrete modeling technology, and specifically relates to a method, apparatus and storage medium for generating a random aggregate model of a concrete polyhedron. Background Technology

[0002] In the numerical simulation of concrete microstructure, the accurate representation of aggregates is crucial. Aggregate generation strategies include data-driven aggregate models and random aggregate models. Data-driven aggregate models, based on actual aggregate data or image processing methods, suffer from problems such as difficult data acquisition and high computational costs. In contrast, random aggregate models offer greater flexibility and computational efficiency, and have been widely used. Over the past few decades, researchers have conducted in-depth studies on random aggregate models, which can generate spheres (Stroeven, P. and M. Stroeven, Assessment of packing characteristics by computer simulation. Cement and concrete research, 1999. 29(8): p. 1201-1206.), ellipsoids ( S., et al., Mesoscale modeling of concrete: Geometry and numerics. Computers & Structures, 2006, 84(7): p.450-461.) and polyhedral aggregate morphology (Xu, WX and H.S. Chen, Numerical investigation of effect of particle shape and particle size distribution on fresh cement paste microstructure via random sequential packing of dodecahedral cement particles. Computers & Structures, 2013, 114-115: p.35-45.). These random aggregate models can be broadly classified into two categories based on whether or not intrusion detection between aggregates is required. The first type of method uses a pick-and-place strategy to sequentially place aggregates from the aggregate library into a closed sample space, which can achieve an aggregate volume fraction of 66.2% in the generated model (Ma, D., et al., High fidelity 3D mesoscale modeling of concrete with ultrahigh volume fraction of irregular shaped aggregate. Composite Structures, 2022, 291: p. 115600.). However, the high computational cost of aggregate intrusion detection during each pick-and-place operation limits its widespread adoption. The second type of method divides the closed sample space into bounding boxes and then generates aggregates within each bounding box, thus avoiding the need for intrusion detection. Voronoi tessellation, widely used for characterizing the microstructure of polycrystalline materials, can directly partition the sample space into a set of non-overlapping, seamless irregular convex polyhedra, often used as aggregate encapsulation boxes, offering relatively high computational efficiency and aggregate placement rate. Laguerre tessellation, as a generalized Voronoite ssellation, can control the morphological properties of convex polyhedra, such as particle size distribution, sphericity, and aspect ratio, by optimizing the objective function.However, even with advanced programs like Packing-3D that generate random aggregates through Voronoi tessellation (Mollon, G. and J. Zhao, 3D generation of realistic granular samples based on random fields theory and Fourier shape descriptors. Computer Methods in Applied Mechanics and Engineering, 2014, 279: pp. 46-65), it remains difficult to simultaneously achieve automated adjustment of aggregate gradation and meet the requirements for high aggregate volume fraction. This is especially true for rockfill concrete, a large-volume concrete material formed by pouring high-performance self-compacting concrete into pre-stapled boulders on the surface (Rockfill Concrete Dam Construction Method, Patent No. ZL03102674.5), where even greater challenges exist. The presence of aggregates larger than 300mm in riprap concrete dictates that numerical simulation is the primary research method. A prerequisite for numerical simulation of riprap concrete is generating random aggregates that conform to realistic morphology and modeling the aggregates through gravity packing. However, the current method for generating random aggregates in riprap concrete (application number: 202010478384.3) proposed by Wang Ruijun suffers from computational burdens such as the need for intrusion detection. Specifically, the random aggregates in the mesoscopic numerical model of riprap concrete must simultaneously meet the following conditions: large aggregate size (300mm-1000mm), high packing ratio (40%-70%), and the ability to achieve automated cross-scale particle size distribution. Furthermore, it requires generating angular polyhedral particles to simulate crushed stone. Currently, there is no suitable method for rapidly generating three-dimensional cross-scale polyhedral random aggregates in concrete. Summary of the Invention

[0003] This disclosure aims to address at least one of the technical problems existing in the prior art.

[0004] Therefore, the concrete polyhedral random aggregate model generation method provided in the first aspect of this disclosure can automatically generate concrete polyhedral random aggregates with controllable gradation and high aggregate volume fraction with extremely high efficiency.

[0005] To achieve the above objectives, the method for generating a concrete polyhedral random aggregate model provided in the first aspect of this disclosure includes:

[0006] Step S1: Obtain the particle size distribution of all aggregates in concrete using the aggregate particle size iteration method based on the target gradation curve;

[0007] Step S2: Calculate the initial aggregate gradation parameters based on the aggregate particle size distribution, and generate convex polyhedral random aggregates in the sample space based on the initial aggregate gradation parameters;

[0008] Step S3: Optimize the geometry of the convex polyhedral random aggregate to obtain the optimized convex polyhedral random aggregate;

[0009] Step S4: Adjust the particle size of each optimized convex polyhedral random aggregate to match the target gradation curve;

[0010] Step S5: Discretize the convex polyhedral random aggregate that matches the target gradation curve into a finite element mesh to obtain a concrete polyhedral random aggregate model.

[0011] Optionally, the aggregate particle size iteration method based on the target gradation curve obtains the particle size distribution of all aggregates in the concrete, specifically including:

[0012] Step S101: Using the cumulative percentage of aggregate passing through sieves of different aperture sizes, divide the target gradation curve into N segments from largest to smallest, where N is a positive integer greater than or equal to 1. Let the i-th gradation segment be [d]. i,min ,d i,max ], d i,min ,d i,max Let represent the minimum and maximum aggregate particle sizes within the i-th gradation segment; let the gradation segment [d] be... i,min ,d i,max Aggregates are generated sequentially in descending order of particle size, with the aggregate numbers and particle sizes being s and d, respectively. i,s Initialize i = 1;

[0013] Step S102: Initialize s = 1, corresponding to aggregate particle size d i,s =d i,max The gradation segment [d] is calculated according to the following formula. i,min ,d i,max The total volume of remaining aggregate to be generated within V i,s :

[0014]

[0015] Where, d min and d max These are the minimum and maximum particle sizes among all aggregates in concrete, respectively; μ is the volume fraction of aggregates in the sample space; V RVE It is the total volume of the sample space; P(·) is the cumulative percentage of aggregate passing through a sieve with an aperture size of (·); V i-1,a For the portion of the remaining aggregate to be generated within the previous gradation section whose total volume is less than 0, when i = 1, V i-1,a =0;

[0016] Step S103: Let s = s + 1, and calculate the gradation segment [d] according to the following formulas. i.min ,d i,max The particle size d of the s-th aggregate generated in descending order within the [inner] layer. i,s And update the total volume V of the remaining aggregate to be generated. i,s :

[0017]

[0018]

[0019] Where p'(d i,s-1 The target gradation curve is located at a particle size d. i,s-1 The slope at that point;

[0020] Step S104: Repeat step S103 continuously until the gradation segment [d] is reached. i.min ,d i,max The total volume of remaining aggregate to be generated within V i,s If the value is less than 0, proceed to step S105;

[0021] Step S105, let V i,a =V i,s , i = i + 1, return to step S102, until the minimum aggregate of the smallest gradation segment is generated.

[0022] Optionally, the target gradation curve may be a sieving curve or a Fuller curve.

[0023] Optionally, convex polyhedral random aggregates are generated in the sample space based on the Laguerre mosaicking method according to the initial aggregate gradation parameters.

[0024] Optionally, the initial aggregate gradation parameters include: total number of aggregate blocks, sample space dimension, sample space size, aggregate information, sphericity of convex polyhedral random aggregates, and upper limit of the number of iterations for Laguerre mosaicking; the aggregate information includes: the proportion of aggregate quantity in each gradation segment calculated based on the particle size distribution of all aggregates in the concrete obtained in step S1, the equivalent particle size of the convex polyhedral random aggregates in each gradation segment, and the standard deviation of the aggregate particle size. The equivalent particle size of the convex polyhedral random aggregates is defined as the diameter of a sphere with the same volume, and the aggregate particle size is grouped by superimposing multiple sets of normal distributions. To ensure that the total aggregate volume of all gradation segments and the volume of the sample space are consistent, the equivalent particle size of the convex polyhedral random aggregates needs to be multiplied by an amplification factor. μ is the volume fraction of aggregate in the sample space.

[0025] Optionally, in step S3, the geometry of the convex polyhedral random aggregate is optimized. Specifically, the geometry of the convex polyhedral random aggregate is first smoothed to make the aggregate geometry curved. Then, the control lines on the surface of the curved aggregate are reduced to a set number of polyhedral geometric control lines as the optimized convex polyhedral random aggregate.

[0026] Optionally, in step S4, the particle size of each optimized convex polyhedral random aggregate is adjusted by scaling the centroid point to obtain convex polyhedral random aggregate that meets the requirements of gradation and coarse aggregate volume ratio.

[0027] The method for generating a random aggregate model of a concrete polyhedron provided in the first aspect of this disclosure has the following characteristics and beneficial effects:

[0028] Based on the Laguerre mosaick method, a novel strategy for generating three-dimensional random aggregates in concrete with arbitrary gradation and high aggregate volume ratio is proposed. After determining the sample space size, the number of random aggregates and the particle size of each aggregate can be obtained iteratively based on arbitrary gradation curves such as Fuller curves or sieve curves, ensuring accurate matching between the random aggregate particle size distribution and various gradation curves. Laguerre mosaicks can be generated based on the parameters obtained through iteration. The quality of the generated aggregates can be controlled by parameters such as the number of aggregates, sphericity, gradation distribution, and standard deviation. Furthermore, the polyhedral aggregates generated by the Laguerre mosaicks can be automatically scaled to the target size based on the aggregate particle size distribution. This method avoids the tedious process of manually adjusting the random aggregate gradation and features automated generation, adjustable gradation, and a large gradation range.

[0029] Furthermore, the Laguerre mosaic-based random aggregate generation method for riprap concrete avoids aggregate boundary intrusion detection, significantly improving aggregate generation efficiency. The generated angular polyhedral aggregates accurately simulate the fractured rock commonly used in riprap concrete, providing a more realistic representation than spherical or ellipsoidal aggregates. This method can also be applied to generate aggregates for conventional or fully graded concrete samples, achieving aggregate volume fractions of 80% or higher. Smoothing during aggregate generation yields a smooth geometry with high sphericity, and control line optimization improves the quality of the generated mesh, preserving the irregular random geometric characteristics of the polyhedral aggregates. This facilitates the subsequent use of the generated random aggregates in establishing numerical models of riprap concrete.

[0030] A second aspect of this disclosure provides a concrete polyhedral random aggregate model generation device, comprising:

[0031] The first module is configured as an aggregate particle size iteration method based on the target gradation curve to obtain the particle size distribution of all aggregates in the concrete.

[0032] The second module is configured to calculate initial aggregate gradation parameters based on the aggregate particle size distribution, and generate convex polyhedral random aggregates in the sample space based on the initial aggregate gradation parameters.

[0033] The third module is configured to optimize the geometry of the convex polyhedral random aggregate to obtain the optimized convex polyhedral random aggregate.

[0034] The fourth module is configured to adjust the particle size of each optimized convex polyhedral random aggregate to match the target gradation curve;

[0035] The fifth module is configured to discretize the convex polyhedral random aggregate that matches the target gradation curve into a finite element mesh to obtain a concrete polyhedral random aggregate model.

[0036] Optionally, the step of the first module obtaining the particle size distribution of all aggregates in the concrete includes:

[0037] Step S101: Using the cumulative percentage of aggregate passing through sieves of different aperture sizes, divide the target gradation curve into N segments from largest to smallest, where N is a positive integer greater than or equal to 1. Let the i-th gradation segment be [d]. i,min ,d i,max ], d i,min ,d i,max Let represent the minimum and maximum aggregate particle sizes within the i-th gradation segment; let the gradation segment [d] be... i,min ,d i,max Aggregates are generated sequentially in descending order of particle size, with the aggregate numbers and particle sizes being s and d, respectively. i,s Initialize i = 1;

[0038] Step S102: Initialize s = 1, corresponding to aggregate particle size d i,s =d i,max The gradation segment [d] is calculated according to the following formula. i,min ,d i,max The total volume of remaining aggregate to be generated within V i,s :

[0039]

[0040] Where, d min and d max These are the minimum and maximum particle sizes among all aggregates in concrete, respectively; μ is the volume fraction of aggregates in the sample space; V RVE It is the total volume of the sample space; P(·) is the cumulative percentage of aggregate passing through a sieve with an aperture size of (·); V i-1,a For the portion of the remaining aggregate to be generated within the previous gradation section whose total volume is less than 0, when i = 1, V i-1,a=0;

[0041] Step S103: Let s = s + 1, and calculate the gradation segment [d] according to the following formulas. i.min ,d i,max The particle size d of the s-th aggregate generated in descending order within the [inner] layer. i,s And update the total volume V of the remaining aggregate to be generated. i,s :

[0042]

[0043]

[0044] Where p'(d i,s-1 The target gradation curve is located at a particle size d. i,s-1 The slope at that point;

[0045] Step S104: Repeat step S103 continuously until the gradation segment [d] is reached. i.min ,d i,max The total volume of remaining aggregate to be generated within V i,s If the value is less than 0, proceed to step S105;

[0046] Step S105, let V i,a =V i,s , i = i + 1, return to step S102, until the minimum aggregate of the smallest gradation segment is generated.

[0047] A computer-readable storage medium is provided in the third aspect of this disclosure, the computer-readable storage medium storing computer instructions for causing the computer to execute the method for generating a random aggregate model of a concrete polyhedron according to any embodiment of the first aspect of this disclosure. Attached Figure Description

[0048] Figure 1 This is an overall flowchart of the method for generating a random aggregate model of a concrete polyhedron provided in the first aspect of this disclosure.

[0049] Figure 2 This is a schematic diagram of the distribution of random aggregates in a screening curve in the generation method provided by the first aspect of this disclosure.

[0050] Figure 3 This is a schematic diagram of a Laguerre mosaic obtained by the generation method provided in the first aspect of this disclosure.

[0051] Figure 4 This is a schematic diagram of aggregate geometrically smoothed obtained by the generation method provided in the first aspect of this disclosure.

[0052] Figure 5This is a schematic diagram of the optimized aggregate control line obtained by the generation method provided in the first aspect of this disclosure.

[0053] Figure 6 This is a schematic diagram of the aggregate geometrically scaled mesh obtained by the generation method provided in the first aspect of this disclosure.

[0054] Figure 7 A schematic diagram of the structure of an electronic device provided in a third aspect embodiment of this disclosure. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for explaining this application and are not intended to limit this application.

[0056] Conversely, this application covers any alternatives, modifications, equivalent methods, and schemes made within the spirit and scope of this application as defined by the claims. Furthermore, to provide the public with a better understanding of this application, certain specific details are described in detail below. However, this application can be fully understood by those skilled in the art even without these detailed descriptions.

[0057] See Figure 1 The flowchart of the method for generating a random aggregate model of a concrete polyhedron provided in the first aspect of this disclosure mainly includes five steps:

[0058] Step S1: Based on the target gradation curve, the aggregate particle size iteration method is used to obtain the particle size distribution of each aggregate in the concrete.

[0059] Step S2: Calculate the initial aggregate gradation parameters based on the particle size distribution of each aggregate, and generate convex polyhedral random aggregates in the sample space based on the initial aggregate gradation parameters.

[0060] Step S3: Optimize the geometry of the convex polyhedral random aggregate to obtain the optimized convex polyhedral random aggregate;

[0061] Step S4: Adjust the particle size of each convex polyhedral random aggregate to match the target gradation curve;

[0062] Step S5: Discretize the convex polyhedral random aggregate that matches the target gradation curve into a finite element mesh to obtain a concrete polyhedral random aggregate model.

[0063] Optionally, step S1 specifically includes the following steps:

[0064] Step S101: Using the cumulative percentage of aggregate passing through sieves of different aperture sizes, divide the target gradation curve into N segments from largest to smallest, where N is a positive integer greater than or equal to 1. Let the i-th gradation segment be [d]. i,min ,d i,max ], d i,min ,d i,max Let ] represent the minimum and maximum aggregate particle sizes within the i-th gradation segment; let the gradation segment [d] be... i,min ,d i,max Aggregates are generated sequentially in descending order of particle size, with the aggregate numbers and particle sizes being s and d, respectively. i,s Initialize i = 1;

[0065] Step S102: Initialize s = 1, corresponding to aggregate particle size d i,s =d i,max The gradation segment [d] is calculated according to the following formula. i,min ,d i,max The total volume of remaining aggregate to be generated within V i,s :

[0066]

[0067] Where, d min and d max These are the minimum and maximum particle sizes among all aggregates in concrete, respectively; μ is the volume fraction of aggregates in the sample space; V RVE V is the total volume of the sample space; P(·) is the cumulative percentage of aggregate passing through a sieve with an aperture size of (·), simply referred to as the aggregate throughput of aperture (·); i-1,a For the portion of the remaining aggregate to be generated within the previous gradation section whose total volume is less than 0, when i = 1, V i-1,a =0;

[0068] Step S103: Let s = s + 1. To fit the aggregate particle size distribution with the target gradation curve for each segment, apply the following formula to the gradation segment [d... i.min ,d i,max The particle size d of the s-th aggregate is generated in descending order. i,s And update the total volume V of the remaining aggregate to be generated. i,s :

[0069]

[0070]

[0071] Where p'(d i,s-1 The target gradation curve is located at a particle size d. i,s-1 The slope at that point;

[0072] Step S104: Repeat step S103 continuously until the gradation segment [d] is reached. i.min ,d i,max The total volume of remaining aggregate to be generated within V i,s If the value is less than 0, proceed to step S105;

[0073] Step S105, let V i,a =V i,s , i = i + 1, return to step S102, until the minimum aggregate of the smallest gradation segment is generated, and the final generated random aggregate parameters can perfectly fit the target gradation curve.

[0074] As an embodiment of this application, the specific iterative method is illustrated using the sieving curve most commonly used in actual engineering as the target gradation curve. See also Figure 2 This shows the fitting results of a typical sieving curve and the aggregate particle size distribution generated based on an iterative method. The specific steps are as follows:

[0075] Step S101: Divide the target gradation curve into N=2 segments, and let the i-th gradation segment be [d]. i,min ,d i,max Assume that the aggregates are generated sequentially in descending order of particle size, and the aggregate numbers and particle sizes are s and d, respectively. i,s Initialize i = 1;

[0076] Step S102: Initialize s = 1, corresponding to aggregate particle size d i,s =d i,max The gradation segment [d] is calculated according to the following formula. i,min ,d i,max The total volume of remaining aggregate to be generated within V i,s :

[0077]

[0078] Where, d min and d max These are the minimum and maximum particle sizes among all aggregates in concrete, respectively; μ is the volume fraction of aggregates in the sample space; V RVE It is the total volume of the sample space; P(·) is the cumulative percentage of aggregate passing through a sieve with an aperture size of (·); V i-1,a For the portion of the remaining aggregate to be generated within the previous gradation section whose total volume is less than 0, when i = 1, V i-1,a =0;

[0079] Step S103: Let s = s + 1. To fit the aggregate particle size distribution with the target gradation curve for each segment, apply the following formula to the gradation segment [d... i.min ,d i,max The particle size d of the s-th aggregate is generated in descending order.i,s And update the total volume V of the remaining aggregate to be generated. i,s :

[0080]

[0081]

[0082] Step S104: Repeat step S103 continuously until the gradation segment [d] is reached. i.min ,d i,max The total volume of remaining aggregate to be generated within V i,s If the value is less than 0, proceed to step S105;

[0083] Step S105, let V i,a =V i,s , i = i + 1, return to step S102, until the minimum aggregate of the smallest gradation segment is generated, and the final generated random aggregate parameters can perfectly fit the target sieve gradation curve.

[0084] Optionally, in S2, convex polyhedral random aggregates are generated in the sample space based on the Laguerre mosaicking method. As an embodiment of this application, the Laguerre mosaicking is generated using the open-source software Neper developed by Quey et al., and the initial aggregate gradation parameters required as input include:

[0085] 1. The total number of aggregate blocks n corresponds to the number of aggregate blocks generated in the iterative input.

[0086] 2. Sample space dimension dim; when using three-dimensional space, the input value of dim is 3.

[0087] 3. The dimensions of the sample space, specifically including its length, width, and height;

[0088] 4. Aggregate information morpho, specifically including: the aggregate particle size distribution obtained based on the iterative method in step S1, the calculated aggregate quantity ratio of each gradation segment, the equivalent particle size of the convex polyhedral random aggregate in each gradation segment, and the standard deviation of the aggregate particle size. The equivalent particle size of the convex polyhedral random aggregate is defined as the diameter of a sphere with the same volume, and the aggregate particle size is grouped by superimposing multiple sets of normal distributions. Since it is necessary to ensure that the total aggregate volume of all gradation segments is consistent with the volume of the sample space, for the case where the aggregate volume fraction in the sample space is μ, the equivalent particle size of the convex polyhedral random aggregate needs to be multiplied by a magnification factor.

[0089] 5. The sphericity of random aggregates in convex polyhedra can generally be above 0.90;

[0090] 6. The upper limit of the number of iterations for Laguerre mosaicking, itermax, is generally set to 50,000 for good convergence.

[0091] See Figure 3 This shows a typical Laguerre mosaic generated in a cubic sample space.

[0092] Optionally, in step S3, the geometry of the convex polyhedral random aggregate is optimized. Specifically, the geometry of the convex polyhedral random aggregate is first smoothed to make the aggregate geometry curved. Then, the control lines on the surface of the curved aggregate are reduced to a set number (such as triangular mesh, quadrilateral mesh, etc.) of polyhedral geometric control lines as the optimized convex polyhedral random aggregate.

[0093] As an embodiment of this application, the geometric information of the Laguerre tessellation generated by Neper is exported, and the constructed convex polyhedral random aggregates fill the sample space. To obtain a more realistic random aggregate morphology, the boundary geometry of all convex polyhedral random aggregates in the sample space in step S2 is smoothed to optimize the aggregate geometry. Specifically, the geometry of all convex polyhedral random aggregates is smoothed using the "Smooth Objects" command in AutoCAD. During this process, the smoothness of each aggregate can be controlled by adjusting the smoothness level. This process avoids the parallel topological positional relationship of adjacent aggregate boundaries, resulting in tiny gaps between adjacent convex polyhedral random aggregates generated by the Laguerre tessellation, thereby avoiding complex aggregate boundary intrusion judgments. See also... Figure 4 This is a geometric schematic diagram of the aggregate after smoothing using Auto-CAD.

[0094] During geometric smoothing, the smoothness of the aggregate varies significantly under different smoothness levels, but this also increases the number of nodes, lines, and surfaces. To ensure the uniformity of the mesh during finite element analysis, the control lines for each aggregate surface need further optimization. Using preprocessing software such as Rhino, the curved surfaces of the aggregate can be converted into meshes. By reducing the number of mesh faces and triangulating the mesh, the control lines for the aggregate geometry can be formed. The reduction ratio can vary according to the accuracy requirements of the concrete numerical simulation calculation; a higher reduction ratio represents fewer aggregate geometric control lines and simpler polyhedral shapes. Finally, the mesh is exported as a surface to determine the aggregate geometry. See also... Figure 5 This is a schematic diagram showing the optimized geometric control lines of random aggregates after optimization using the software Rhino.

[0095] Optionally, in step S4, the optimized convex polyhedral random aggregate is scaled by scaling the centroid point to obtain convex polyhedral random aggregate that conforms to the target gradation and aggregate volume ratio. Specifically, this includes:

[0096] After geometric optimization, the average particle size of the normally distributed convex polyhedral random aggregates at each stage is larger than the actual value. To achieve the target volume fraction of the aggregates, the volume and topological information of all aggregates are statistically analyzed, and the aggregates are renumbered in descending order of volume. Based on the aggregate volume distribution parameters generated in the initial iteration, the corresponding target volume V can be found for each aggregate. target The aggregate is scaled using centroid scaling, with a volume of V. current The aggregate particle size scaling factor is The final result is a multi-convex random aggregate that meets the gradation and coarse aggregate volume ratio.

[0097] Optionally, in step S5, after scaling, finite element mesh generation can be performed based on the aggregate geometry using preprocessing software such as Hypermesh, thereby establishing a polyhedral random aggregate mesh in the mesoscopic numerical model of the concrete material, ultimately obtaining a concrete polyhedral random aggregate model. See also... Figure 6 This is a schematic diagram of the mesh generated by Hypermesh after scaling random aggregates through the centroid point.

[0098] It should be noted that the polyhedral random aggregate model generated by the concrete polyhedral random aggregate model generation method provided in this application is applicable to conventional concrete, fully graded concrete samples, and riprap concrete. Given any target gradation curve and aggregate volume fraction, mathematically equivalent convex polyhedral random aggregates can be generated in the sample space, laying the foundation for finite element numerical simulation of various types of concrete materials. In particular, the polyhedral random aggregate model generated by the method in this application can simultaneously meet the following conditions required for random aggregates in the mesoscopic numerical model of riprap concrete: sufficiently large aggregate size (300mm-1000mm), high riprap ratio (40%-70%), and the ability to achieve automated cross-scale particle size distribution, generating angular polyhedral particles to simulate crushed stone in riprap concrete.

[0099] The concrete polyhedral random aggregate model generation apparatus provided in the second aspect of this disclosure includes:

[0100] The first module is configured as an aggregate particle size iteration method based on the target gradation curve to obtain the particle size distribution of all aggregates in the concrete.

[0101] The second module is configured to calculate initial aggregate gradation parameters based on the aggregate particle size distribution, and generate convex polyhedral random aggregates in the sample space based on the initial aggregate gradation parameters.

[0102] The third module is configured to optimize the geometry of the convex polyhedral random aggregate to obtain the optimized convex polyhedral random aggregate.

[0103] The fourth module is configured to adjust the particle size of each optimized convex polyhedral random aggregate to match the target gradation curve;

[0104] The fifth module is configured to discretize the convex polyhedral random aggregate that matches the target gradation curve into a finite element mesh to obtain a concrete polyhedral random aggregate model.

[0105] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing a computer program that is executed by a processor to perform the concrete polyhedral random aggregate model generation method of the above embodiments.

[0106] The following is for reference. Figure 7 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of the present disclosure. It should be noted that the electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs, desktop computers, and servers. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0107] like Figure 7 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage device 108 into a random access memory (RAM) 103. The RAM 103 also stores various programs and data required for the operation of the electronic device. The processing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0108] Typically, the following devices can be connected to I / O interface 105: input devices 106 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, etc.; output devices 107 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 108 including, for example, magnetic tapes, hard disks, etc.; and communication devices 109. Communication device 109 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0109] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, this embodiment includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via communication device 109, or installed from storage device 108, or installed from ROM 102. When the computer program is executed by processing device 101, it performs the functions defined above in the methods of embodiments of this disclosure.

[0110] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0111] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0112] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the aforementioned method for generating a random aggregate model of a concrete polyhedron.

[0113] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and Python, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0115] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0116] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0118] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0119] Those skilled in the art will understand that implementing all or part of the steps of the methods in the above embodiments can be accomplished by instructing related hardware through a program. The developed program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0120] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0121] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for generating a random aggregate model of a concrete polyhedron, characterized in that, include: Step S1: Based on the target gradation curve, an iterative method for aggregate particle size distribution is used to obtain the particle size distribution of all aggregates in the concrete, including: Step S101: Using the cumulative percentage of aggregate passing through sieves with different aperture sizes, divide the target gradation curve from largest to smallest. part, Let be a positive integer greater than or equal to 1, and let the th... Each grade segment is , The first The minimum and maximum aggregate particle sizes within each gradation section; Let the gradation section be... The aggregates are generated sequentially in descending order of particle size. The generated aggregates are numbered and their particle sizes are as follows: and ;initialization ; Step S102, Initialization Corresponding aggregate particle size Calculate the gradation section according to the following formula Total volume of remaining aggregate to be generated : in, and These are the minimum and maximum particle sizes among all aggregates in concrete; It is the volume fraction of aggregate within the sample space; It is the total volume of the sample space; The aggregate passes through pores with a diameter of... The cumulative percentage of the screen; This refers to the portion of the remaining aggregate to be generated within the previous gradation section whose total volume is less than 0. hour, ; Step S103, let Calculate the gradation section according to the following formulas respectively. The first one generated in descending order The particle size of the aggregate And update the total volume of the remaining aggregate to be generated. : in, For the target gradation curve at particle size The slope at that point; Step S104: Repeat step S103 continuously until the gradation segment is reached. Total volume of remaining aggregate to be generated If the value is less than 0, proceed to step S105; Step S105, let , Return to step S102 until the minimum aggregate of the smallest gradation segment is generated; Step S2: Calculate the initial aggregate gradation parameters based on the aggregate particle size distribution, and generate convex polyhedral random aggregates in the sample space based on the initial aggregate gradation parameters; Step S3: Optimize the geometry of the convex polyhedral random aggregate to obtain the optimized convex polyhedral random aggregate, including: first, smoothing the geometry of the convex polyhedral random aggregate so that the geometry of the aggregate is curved; then, reducing the control lines on the surface of the curved aggregate to a set number of polyhedral geometric control lines as the optimized convex polyhedral random aggregate. Step S4: Adjust the particle size of each optimized convex polyhedral random aggregate to match the target gradation curve; Step S5: Discretize the convex polyhedral random aggregate that matches the target gradation curve into a finite element mesh to obtain a concrete polyhedral random aggregate model.

2. The method for generating a random aggregate model of a concrete polyhedron according to claim 1, characterized in that, The target gradation curve is selected from either the sieving curve or the Fuller curve.

3. The method for generating a random aggregate model of a concrete polyhedron according to claim 1, characterized in that, Based on the Laguerre mosaic method, convex polyhedral random aggregates are generated in the sample space according to the initial aggregate gradation parameters.

4. The method for generating a random aggregate model of a concrete polyhedron according to claim 3, characterized in that, The initial aggregate gradation parameters include: total number of aggregate blocks, sample space dimension, sample space size, aggregate information, sphericity of convex polyhedral random aggregates, and upper limit of Laguerre mosaic iteration count. The aggregate information includes: the proportion of aggregate quantity in each gradation segment calculated based on the particle size distribution of all aggregates in the concrete obtained in step S1, the equivalent particle size of the convex polyhedral random aggregates in each gradation segment, and the standard deviation of the aggregate particle size. The equivalent particle size of the convex polyhedral random aggregates is defined as the diameter of a sphere with the same volume. Aggregate particle sizes are grouped by superimposing multiple sets of normal distributions. To ensure that the total aggregate volume of all gradation segments and the volume of the sample space remain consistent, the equivalent particle size of the convex polyhedral random aggregates needs to be multiplied by an amplification factor. , It is the volume fraction of aggregate within the sample space.

5. The method for generating a random aggregate model of a concrete polyhedron according to claim 1, characterized in that, In step S4, the particle size of each optimized convex polyhedral random aggregate is adjusted by scaling the centroid point to obtain convex polyhedral random aggregate that meets the requirements of gradation and coarse aggregate volume ratio.

6. A device for generating random aggregate models of concrete polyhedra, characterized in that, include: The first module is configured as an aggregate size iteration method based on the target gradation curve to obtain the particle size distribution of all aggregates in the concrete, including: Step S101: Using the cumulative percentage of aggregate passing through sieves with different aperture sizes, divide the target gradation curve from largest to smallest. part, Let be a positive integer greater than or equal to 1, and let the th... Each grade segment is , The first The minimum and maximum aggregate particle sizes within each gradation section; Let the gradation section be... The aggregates are generated sequentially in descending order of particle size. The generated aggregates are numbered and their particle sizes are as follows: and ;initialization ; Step S102, Initialization Corresponding aggregate particle size Calculate the gradation section according to the following formula Total volume of remaining aggregate to be generated : in, and These are the minimum and maximum particle sizes among all aggregates in concrete; It is the volume fraction of aggregate within the sample space; It is the total volume of the sample space; The aggregate passes through pores with a diameter of... The cumulative percentage of the screen; This refers to the portion of the remaining aggregate to be generated within the previous gradation section whose total volume is less than 0. hour, ; Step S103, let Calculate the gradation section according to the following formulas respectively. The first one generated in descending order The particle size of the aggregate And update the total volume of the remaining aggregate to be generated. : in, For the target gradation curve at particle size The slope at that point; Step S104: Repeat step S103 continuously until the gradation segment is reached. Total volume of remaining aggregate to be generated If the value is less than 0, proceed to step S105; Step S105, let , Return to step S102 until the minimum aggregate of the smallest gradation segment is generated; The second module is configured to calculate initial aggregate gradation parameters based on the aggregate particle size distribution, and generate convex polyhedral random aggregates in the sample space based on the initial aggregate gradation parameters. The third module is configured to optimize the geometry of the convex polyhedral random aggregate to obtain optimized convex polyhedral random aggregate, including: firstly, smoothing the geometry of the convex polyhedral random aggregate so that the geometry of the aggregate is curved; then reducing the control lines on the surface of the curved aggregate to a set number of polyhedral geometric control lines as optimized convex polyhedral random aggregate. The fourth module is configured to adjust the particle size of each optimized convex polyhedral random aggregate to match the target gradation curve; The fifth module is configured to discretize the convex polyhedral random aggregate that matches the target gradation curve into a finite element mesh to obtain a concrete polyhedral random aggregate model.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for generating a random aggregate model of a concrete polyhedron as described in any one of claims 1 to 5.

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