A method of meso-modelling and analysis of a concrete structure

Abstract

The present application relates to a kind of mesoscopic modeling and analysis method of concrete structure, comprising the following steps: step 1) according to the size characteristic parameter of concrete component, determine lattice node coordinates, and connect lattice node to obtain the frame of lattice model, wherein the lattice model includes lattice node and the lattice unit connected between lattice node each other;Step 2) according to the grading of each component material used by concrete material, determine the particle size and quantity of coarse aggregate and the mesoscopic two-dimensional parameter of each component of concrete;Step 3) generate coarse bone particle mesoscopic random distribution model based on random distribution algorithm, the particle size and quantity of coarse aggregate, and according to the mesoscopic random distribution model of coarse aggregate, give the corresponding material attribute and element attribute of lattice unit, obtain lattice model;Step 4) import lattice model into ABAQUS secondary development platform, and carry out load calculation.Compared with prior art, the present application comprehensively considers the mesoscopic structure characteristics of concrete material, and provides a refined numerical simulation idea.

Description

Technical Field

[0001] This invention relates to the field of material simulation and numerical analysis, and in particular to a method for microscopic modeling and analysis of concrete structures. Background Technology

[0002] Concrete is a multiphase composite material formed by using cement as the main binder and mixing it with a certain proportion of water, sand, crushed stone, and water-reducing agents. The internal structure of concrete is extremely complex. Based on the range of structural dimensions, concrete structures can be divided into macroscopic and mesoscopic levels. Macroscopic dimensions refer to the dimensions used in everyday engineering projects; these dimensions are generally several times the size of the aggregate. Components of these dimensions are typically analyzed based on physical tests to assess the mechanical properties of the structure and evaluate the reliability and rationality of the design. At the mesoscopic level, concrete material consists of coarse aggregate, cement mortar matrix, and the interfacial transition zone between the two. The material properties, phase distribution, and mix proportions of each phase have a significant impact on the macroscopic mechanics of concrete. This means that even macroscopic specimens of identical dimensions from the same batch of material can exhibit significant differences in mechanical properties, which is related to the increasing number of internal defects in the material. With the continuous increase in building size and concrete usage, the unlimited expansion of research structures and the conduct of large-scale concrete structure tests and research are increasingly limited by time, space, and financial resources. Simulating structures at a microscopic level can effectively avoid the size effects at the macroscopic mechanical level. Therefore, in order to obtain accurate failure mechanisms and mechanical responses, research at the microscopic level is imperative.

[0003] A review of existing technologies revealed that mesoscopic modeling techniques for cement-based materials primarily focus on concrete aggregate modeling to provide effective reference models for numerical calculations. Chinese patent "A Two-Dimensional Random Generation Method for Concrete Aggregate Elements (CN 106874623 A)" proposes a two-dimensional random generation method for concrete aggregate elements. This method continuously generates different polygonal aggregates and calculates key shape parameters, comparing them with the actual required aggregate elements to ultimately obtain polygonal aggregates of a specific shape. In the Chinese patent "A Numerical Model Reconstruction Method for Concrete Aggregates (Authorization No. CN 108280290 A)," Fan Hong et al. proposed a method for reconstructing numerical models of concrete aggregates. This method involves grinding and photographing specimens containing individual aggregates or specimens containing groups of aggregates to obtain continuous cross-sectional images of the aggregates. An aggregate model database is then established based on these continuous cross-sectional images. For specimens containing groups of aggregates, multiple aggregate models can be built simultaneously, overcoming the problem of the high cost and inconvenience of implementing CT scanning equipment. Furthermore, the method uses 3D reconstruction based on real concrete aggregate particles to simulate the actual shape characteristics of aggregates to the greatest extent possible. The established aggregate models are stored in a mesh format, creating conditions for the establishment of a real aggregate model database. However, this method only focuses on the accurate simulation of the microstructure of concrete and lacks systematic modeling and computational analysis. Summary of the Invention

[0004] The purpose of this invention is to provide a method for microscopic modeling and analysis of concrete structures, which comprehensively considers the microstructural characteristics of concrete materials, establishes a lattice model suitable for numerical analysis, and performs refined numerical analysis on it.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for mesoscopic modeling and analysis of concrete structures includes the following steps:

[0007] Step 1) Determine the coordinates of the lattice nodes based on the dimensional characteristic parameters of the concrete components, and connect the lattice nodes to obtain the frame of the lattice model, wherein the lattice model includes lattice nodes and lattice units that are interconnected between the lattice nodes.

[0008] Step 2) Determine the coarse aggregate size and quantity, as well as the microscopic two-dimensional parameters of each component of the concrete, based on the gradation of the materials used in the concrete.

[0009] Step 3) Generate a micro-random distribution model of coarse aggregate based on the random distribution algorithm, coarse aggregate particle size and quantity, and assign corresponding material properties and element properties to the lattice elements according to the micro-random distribution model of coarse aggregate to obtain the lattice model;

[0010] Step 4) Import the lattice model into the ABAQUS secondary development platform, perform load calculations, and based on the load calculation results, determine the stress state of each lattice element according to the failure criterion. If the determination result is failure, replace the lattice element with a zero element whose elastic modulus and strength are both 0.

[0011] The dimensional characteristic parameters of the concrete component include the overall dimensions of the concrete component, aggregate dimensions, boundary conditions, and load conditions.

[0012] The method for determining the coordinates of the lattice nodes is as follows: based on the concept of the lattice model, the structure is divided into several grids, and each lattice node is randomly distributed in the corresponding grid.

[0013] The microscopic two-dimensional parameters of each component of the concrete include the aggregate accumulation probability corresponding to different particle sizes.

[0014] The coarse aggregate particle size was determined based on the following method:

[0015] Step 2-1-1) Determine the cumulative distribution function of coarse aggregate:

[0016]

[0017] Among them, P 2d Let d be the two-dimensional coarse aggregate cumulative distribution function. m d is the maximum coarse aggregate size, d0 is the minimum coarse aggregate size, and d is the coarse aggregate size.

[0018] Step 2-1-2) Determine the aggregate accumulation probability corresponding to different particle sizes based on the coarse aggregate accumulation distribution function, and obtain the coarse aggregate particle size range and corresponding proportion.

[0019] In the area A of the two-dimensional cross-section of the concrete occupied by coarse aggregate. ca Given a fixed quantity, the method for determining the amount of coarse aggregate includes the following steps:

[0020] Step 2-2-1) Generate a random number z in the interval [0, 1]. i And let z i The aggregate cumulative probability corresponding to the particle size of the i-th aggregate;

[0021] Step 2-2-2) Determine z based on the cumulative distribution function of coarse aggregate. i The corresponding aggregate particle size d i ;

[0022] Step 2-2-3) Accumulate the cross-sectional area of ​​the coarse aggregate that has been generated;

[0023]

[0024] Among them, A iThis refers to the cross-sectional area of ​​the coarse aggregate obtained after adding the i-th coarse aggregate.

[0025] Step 2-2-4) Determine the cross-sectional area A of the coarse aggregate. i Does it exceed the area A of the two-dimensional cross-section of the concrete occupied by the coarse aggregate? ca If yes, output the quantity of coarse aggregate corresponding to different particle sizes; otherwise, accumulate the quantity of coarse aggregate corresponding to the current particle size and repeat steps 2-2-1 to 2-2-3.

[0026] Step 3) includes the following steps:

[0027] Step 3-1) Generate coarse aggregate based on coarse aggregate particle size and quantity;

[0028] Step 3-2) Randomly place coarse aggregate within the size range of the concrete specimen to simulate the random arrangement of coarse aggregate inside the concrete;

[0029] Step 3-3) Generate a coarse aggregate particle size distribution image of the concrete component, and based on the lattice model, assign different material properties and element properties to the corresponding lattice elements using the coarse aggregate particle size distribution image.

[0030] Step 3-2) includes the following steps:

[0031] Step 3-2-1) Arrange the generated coarse aggregates in descending order of particle size;

[0032] Step 3-2-2) Determine the placement range of the center of each coarse aggregate particle size, wherein, for the i-th coarse aggregate, the horizontal placement range is determined to be [d]. i / 2,bd i / 2], the vertical deployment range is [d i / 2,ad i / 2], (a,b) represents the overall dimensions of the concrete component;

[0033] Step 3-2-3) Generate two random numbers X according to the distribution range. i and Y i , representing the coordinates of the center point of the coarse aggregate;

[0034] Step 3-2-4) Determine X i and Y i If the coarse aggregate to be placed at the corresponding center coordinates coincides with the coarse aggregate that has already been placed, delete the center coordinates of the current coarse aggregate and repeat step 3-2-3; if they do not coincide, place the current coarse aggregate.

[0035] Step 3-2-5) Repeat steps 3-2-2) to 3-2-4) until all generated coarse aggregates have been randomly added.

[0036] Step 3-3) includes the following steps:

[0037] Step 3-3-1) Based on the coordinates of the center point of the coarse aggregate (X... i ,Y i ) and particle size d i Generate images of coarse aggregate size distribution in concrete components;

[0038] Step 3-3-2) Perform grayscale processing on the coarse aggregate particle size distribution image, and use the size of the lattice unit as the smallest pixel point to segment the coarse aggregate particle size distribution image;

[0039] Step 3-3-3) Digitize the segmented grayscale image to digitize the concrete microstructure, and use different numbers to represent coarse aggregate, matrix and aggregate-matrix interface area respectively.

[0040] Step 3-3-4) Assign material properties and element properties to the lattice elements based on the digital processing results to obtain the lattice model.

[0041] Step 4) includes the following steps:

[0042] Step 4-1) Import the lattice model into the ABAQUS computing platform;

[0043] Step 4-2) Use Python to perform secondary development on the lattice model, assigning pre-configured boundary and load constraints;

[0044] Step 4-3) Use the Standard module to perform load calculations on the lattice model;

[0045] Step 4-4) Based on the load calculation results, the stress state of each lattice element is judged according to the failure criterion. If the judgment result is failure, the lattice element is replaced with a zero element with an elastic modulus and strength of 0.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] (1) This invention comprehensively considers the microstructural characteristics of concrete materials and fully utilizes the features of microstructure models. Based on lattice models and random distribution models of concrete aggregates, it proposes a microstructure analysis model for concrete. This invention can simulate the microstructure of concrete with a higher degree of precision, thereby studying its precise failure process and failure mechanism, which is unmatched by traditional macroscopic models. At the same time, compared with the traditional finite element microstructure modeling method, this invention uses the failure of lattice members to simulate the cracking process of concrete, which makes the failure process more consistent with reality, has higher computational efficiency, and is simpler in modeling method.

[0048] (2) Based on the ABAQUS platform, this invention uses Python language to perform secondary development of the model calculation method, uses the failure criterion to replace the failure element, and uses the sequential failure of the lattice element to simulate the sequential failure process of concrete material, providing a good algorithm idea for the refined numerical simulation of concrete material. Attached Figure Description

[0049] Figure 1 This is a flowchart of the method of the present invention;

[0050] Figure 2 A schematic diagram of the basic elements of a lattice model;

[0051] Figure 3 A schematic diagram showing the correspondence between the lattice model and the material's microstructure.

[0052] Figure 4 This is an axial compression lattice model in one embodiment;

[0053] Figure 5 This is a micro-random distribution model of coarse aggregate in one embodiment;

[0054] Figure 6 This is a two-dimensional schematic diagram of the distribution of coarse aggregate in concrete in one embodiment;

[0055] Figure 7 This is a schematic diagram of the constraints and loads of a lattice model in one embodiment;

[0056] Figure 8 This is a schematic diagram of the crack change process in an axial compression model in one embodiment.

[0057] Figure 9 This is a comparison diagram of axial compressive stress-strain curves in one embodiment. Detailed Implementation

[0058] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0059] In one embodiment, axial compression of concrete is modeled and analyzed, and a method for mesoscopic modeling and analysis of concrete structures is proposed, such as... Figure 1 As shown, it includes the following steps:

[0060] Step 1) Determine the coordinates of the lattice nodes based on the dimensional characteristic parameters of the concrete component, and connect the lattice nodes to obtain the frame of the lattice model. The lattice model includes lattice nodes and lattice units that are interconnected between the lattice nodes. The dimensional characteristic parameters of the concrete component include the overall dimensions of the concrete component, aggregate dimensions, boundary conditions, and load conditions.

[0061] The method for determining the coordinates of the lattice nodes is as follows: based on the concept of the lattice model, such as... Figure 2 As shown, the structure is divided into several grids, and each grid node is randomly distributed in the corresponding grid.

[0062] In this embodiment, C50 concrete was used for the concrete strength test. The axial compression specimens were 150mm×150mm×150mm cubes, and a total of 5 specimens were used. The test results are shown in Table 1, with an average axial compression strength of 58.4MPa.

[0063] Table 1 Results of Axial Compression Tests on C50 Concrete

[0064]

[0065] The dimensions of the two-dimensional lattice model are determined based on the dimensions of the test specimen. The standard dimensions of the two-dimensional lattice model are selected as 150mm × 150mm. In this embodiment, the central node model is generated with n = 0.5 degrees of freedom, and the edge nodes are set to n = 0 degrees of freedom. The final axial compression lattice model is as follows. Figure 4 As shown.

[0066] Step 2) Determine the coarse aggregate size and quantity, as well as the aggregate accumulation probability corresponding to different aggregate sizes, based on the gradation of each component material used in the concrete.

[0067] Since the concrete used in this experiment is C50 grade concrete, and the common gradation range for C50 coarse aggregate is 5–20 mm, based on the mix proportion of C50 material, the cross-sectional proportion of coarse aggregate in the C50 specimen under the simulated two-dimensional plane can be set to 46%, which is A. s =10350mm 2 Based on the boundary range of the specimen, the gradation range of C50 coarse aggregate, and the proportion of the cross section occupied by the coarse aggregate, the number and corresponding size of coarse aggregate spheres can be generated. It is necessary to determine the range of coarse aggregate particle size and the proportion of each particle size.

[0068] Step 2-1) Determine the aggregate accumulation probability and coarse aggregate particle size;

[0069] Step 2-1-1) Determine the cumulative distribution function of coarse aggregate:

[0070]

[0071] Among them, P 2d Let d be the two-dimensional coarse aggregate cumulative distribution function. m d is the maximum coarse aggregate size, d0 is the minimum coarse aggregate size, and d is the coarse aggregate size.

[0072] Step 2-1-2) Given the maximum and minimum aggregate sizes, the cumulative probability of aggregates within this range can be calculated based on the cumulative distribution function of coarse aggregates, thus obtaining the range of coarse aggregate sizes and the corresponding proportions.

[0073] Step 2-2) The area A of the two-dimensional cross-section of the concrete occupied by the coarse aggregate. ca Given a fixed quantity, determine the amount of coarse aggregate.

[0074] Step 2-2-1) Generate a random number z in the interval [0, 1]. i And let z i The aggregate cumulative probability corresponding to the particle size of the i-th aggregate;

[0075] Step 2-2-2) Determine z based on the cumulative distribution function of coarse aggregate. i The corresponding aggregate particle size d i ;

[0076] Step 2-2-3) Accumulate the cross-sectional area of ​​the coarse aggregate that has been generated;

[0077]

[0078] Among them, A i This refers to the cross-sectional area of ​​the coarse aggregate obtained after adding the i-th coarse aggregate.

[0079] Step 2-2-4) Determine the cross-sectional area A of the coarse aggregate. i Does it exceed the area A of the two-dimensional cross-section of the concrete occupied by the coarse aggregate? ca If yes, output the quantity of coarse aggregate corresponding to different particle sizes; otherwise, accumulate the quantity of coarse aggregate corresponding to the current particle size and repeat steps 2-2-1 to 2-2-3.

[0080] Regarding the mechanical properties of the three components—coarse aggregate, matrix, and ITZ—based on literature review, the mechanical properties adopted in this implementation are shown in Table 2.

[0081] Table 2 Material parameters of various components of concrete

[0082]

[0083] Step 3) Generate a micro-random distribution model of coarse aggregate based on the random distribution algorithm, coarse aggregate particle size and quantity, and assign corresponding material properties and element properties to the lattice elements according to the micro-random distribution model of coarse aggregate to obtain the lattice model;

[0084] Step 3-1) Generate coarse aggregate based on coarse aggregate particle size and quantity;

[0085] Step 3-2) Randomly place coarse aggregate within the size range of the concrete specimen to simulate the random arrangement of coarse aggregate inside the concrete;

[0086] Step 3-2-1) Arrange the generated coarse aggregates in descending order of particle size;

[0087] Step 3-2-2) Determine the placement range of the center of each coarse aggregate particle size, wherein, for the i-th coarse aggregate, the horizontal placement range is determined to be [d]. i / 2,bd i / 2], the vertical deployment range is [d i / 2,ad i / 2], (a,b) represents the overall dimensions of the concrete component;

[0088] Step 3-2-3) Generate two random numbers X according to the distribution range. i and Y i , representing the coordinates of the center point of the coarse aggregate;

[0089] Step 3-2-4) Determine X i and Y i If the coarse aggregate to be placed at the corresponding center coordinates coincides with the coarse aggregate that has already been placed, delete the center coordinates of the current coarse aggregate and repeat step 3-2-3; if they do not coincide, place the current coarse aggregate.

[0090] Step 3-2-5) Repeat steps 3-2-2) to 3-2-4) until all generated coarse aggregates have been randomly added. The resulting coarse aggregate mesoscopic random distribution model is as follows: Figure 5 As shown;

[0091] Step 3-3) Generate a coarse aggregate particle size distribution image of the concrete component, and based on the lattice model, assign different material properties and element properties to the corresponding lattice elements using the coarse aggregate particle size distribution image;

[0092] The most crucial step in generating the lattice model is to assign different material and element properties to the corresponding lattice elements based on the generated coarse aggregate size distribution image. The correspondence between the lattice model and the microstructure of concrete is as follows: Figure 3 As shown.

[0093] Step 3-3-1) Based on the coordinates of the center point of the coarse aggregate (X... i ,Y i ) and particle size d i Generate images of coarse aggregate size distribution in concrete components;

[0094] Step 3-3-2) Perform grayscale processing on the coarse aggregate particle size distribution image, using the size of the lattice unit as the smallest pixel, and segment the coarse aggregate particle size distribution image, such as... Figure 6 As shown;

[0095] Step 3-3-3) Digitize the segmented grayscale image to digitize the concrete microstructure, and use different numbers to represent coarse aggregate, matrix and aggregate-matrix interface area respectively.

[0096] Step 3-3-4) Assign material properties and element properties to the lattice elements based on the digital processing results to obtain the lattice model;

[0097] The specific steps for assigning material properties and unit properties to lattice units are as follows: determine the value of the two-dimensional image corresponding to one node of the lattice unit, query the value of the two-dimensional image corresponding to another node of the lattice unit, and if the two are the same, assign the corresponding material properties to the coarse aggregate; otherwise, assign the material properties to the boundary area.

[0098] Step 4) Import the lattice model into the ABAQUS secondary development platform, perform load calculations, and based on the load calculation results, determine the stress state of each lattice element according to the failure criterion. If the determination result is failure, replace the lattice element with a zero element whose elastic modulus and strength are both 0.

[0099] Step 4-1) Import the lattice model into the ABAQUS computing platform;

[0100] Step 4-2) Use Python to perform secondary development on the lattice model, assigning pre-configured boundary and load constraints;

[0101] Step 4-3) Use the Standard module to perform load calculations on the lattice model;

[0102] Step 4-4) Based on the load calculation results, the stress state of each lattice element is judged according to the failure criterion. If the judgment result is failure, the lattice element is replaced with a zero element with an elastic modulus and strength of 0.

[0103] To ensure the stability of the lattice simulation loading process, a displacement loading mode was used in the simulation calculation at a loading rate of 0.1 mm / step. The Standard module was used to calculate the model. After each loading step, the stress state of each lattice element was determined according to the failure criterion. If a lattice element (member) failed, it was replaced with a zero element with both elastic modulus and strength of 0, and then the next loading step was performed. The locations of the loading points and constraint points are shown below. Figure 7 As shown, the loading point and constraint point of the axial compression model are located at the upper and lower edges of the lattice model, respectively.

[0104] The failure process is as follows: Under axial pressure, the transverse concrete elements fail first. Then, as the load continues to increase, the number of failed transverse elements gradually increases, manifesting as vertical cracks, which are relatively evenly distributed. Finally, approaching the ultimate bearing capacity, the overall crack pattern is outward expansion cracking, forming vertical cracks that penetrate the entire model. The crack evolution process of the axially compressed model is as follows: Figure 8 As shown in (a)-(c).

[0105] The axial compressive stress-strain curves corresponding to the axial compressive lattice model are as follows: Figure 9 As shown. This embodiment compares it with the axial compression curve in the CEB-FIP Model Code (1990). The strength curves of the lattice model and the standard are relatively mild in the elastic segment, while the descent rate of the lattice model is faster than the calculation method proposed in the standard in the descent segment. This is because the element failure of the lattice model is brittle fracture failure, and its final failure mode will also exhibit a certain degree of brittle fracture.

[0106] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method of meso-scale modeling and analysis of a concrete structure, characterized by, The method comprises the following steps: Step 1) determining the lattice node coordinates according to the size characteristic parameters of the concrete member, and connecting the lattice nodes to obtain the framework of the lattice model, wherein the lattice model comprises lattice nodes and lattice units connected between the lattice nodes; Step 2) determining the size and quantity of the coarse aggregate and the mesoscopic two-dimensional parameters of each component of the concrete according to the gradation of each component material used by the concrete material; Step 3) generating a coarse aggregate mesoscopic random distribution model based on a random distribution algorithm, the size and quantity of the coarse aggregate, and assigning corresponding material properties and unit properties to the lattice units according to the coarse aggregate mesoscopic random distribution model to obtain the lattice model; Step 4) importing the lattice model into an ABAQUS secondary development platform for load calculation, and based on the load calculation result, judging the stress condition of each lattice unit according to a failure criterion, if the judgment result is failure, replacing the lattice unit with a zero unit with an elastic modulus and strength of 0; The step 3) comprises the following steps: Step 3-1) generating coarse aggregate based on the size and quantity of the coarse aggregate; Step 3-2) randomly placing the coarse aggregate in the size range of the concrete specimen to simulate the random arrangement of the coarse aggregate in the concrete; Step 3-2-1) arranging the generated coarse aggregate in descending order from large to small according to the particle size; Step 3-2-2) determining the placement range of each coarse aggregate particle size center, wherein, for the first coarse aggregate, the horizontal placement range is determined as i , and the vertical placement range is determined as , , , which represents the overall size of the concrete member; Step 3-2-3) Two random numbers are respectively generated according to the delivery range and representing the coarse aggregate center point coordinates; Step 3-2-4) judging whether the center coordinates of the corresponding coarse aggregate to be pre-placed coincide with the coarse aggregate that has been placed, if they coincide, deleting the center coordinates of the current coarse aggregate and re-performing step 3-2-3), if they do not coincide, placing the current coarse aggregate; Step 3-2-5) repeating steps 3-2-2) to 3-2-4) until all the generated coarse aggregate is randomly placed; Step 3-3) generating a coarse aggregate size distribution image of the concrete member, and based on the lattice model, assigning different material properties and unit properties to the corresponding lattice units by using the coarse aggregate size distribution image; Step 3-3-1) generating a concrete member coarse aggregate particle size distribution image according to coarse aggregate center point coordinates (X, Y) and particle sizes (D) of the coarse aggregate , ) and particle sizes (D) of the coarse aggregate Step 3-3-2) performing grayscale processing on the coarse aggregate size distribution image, taking the size of the lattice unit as the minimum pixel point, and segmenting the coarse aggregate size distribution image; Step 3-3-3) performing digital processing on the segmented grayscale image to digitize the concrete mesoscopic structure, and using different numbers to represent coarse aggregate, matrix and aggregate-matrix interface, respectively; Step 3-3-4) assigning corresponding material properties and unit properties to the lattice units according to the digital processing result to obtain the lattice model.

2. The method of meso-scale modeling and analysis of concrete structures according to claim 1, characterized in that, The size characteristic parameters of the concrete member include the overall size of the concrete member, the size of the aggregate, the boundary condition and the load condition.

3. The method of meso-scale modeling and analysis of concrete structures according to claim 1, characterized in that, The determination method of the lattice node coordinates is that the structure is divided into a plurality of grids according to the concept of the lattice model, and each lattice node is randomly distributed in the corresponding grid.

4. The method of meso-scale modeling and analysis of concrete structures according to claim 1, characterized in that, The mesoscopic two-dimensional parameters of each component of the concrete include the aggregate cumulative probability corresponding to different particle sizes.

5. The method of meso-scale modeling and analysis of concrete structures according to claim 4, characterized in that, The size of the coarse aggregate is determined based on the following method: Step 2-1-1) determining the coarse aggregate cumulative distribution function: wherein, is a two-dimensional coarse aggregate cumulative distribution function, is a maximum coarse aggregate particle size, is a minimum coarse aggregate particle size, is a coarse aggregate particle size; Step 2-1-2) determining the aggregate cumulative probability corresponding to different particle sizes according to the coarse aggregate cumulative distribution function to obtain the coarse aggregate size range and the corresponding proportion.

6. The method of meso-scale modeling and analysis of a concrete structure according to claim 5, wherein, In the case where coarse aggregates occupy the area of a two-dimensional section of concrete The method for determining the number of coarse aggregates in the determined case includes the following steps: Step 2-2-1) generating a random number in the interval [0, 1] and let be the cumulative probability of the particle size of the i th aggregate. Step 2-2-2) determining from the coarse aggregate cumulative distribution function corresponding to the aggregate size ; Step 2-2-3) accumulating the cross-sectional area of the generated coarse aggregate; wherein, is the cross-sectional area of the coarse aggregate after the cumulative addition of the i coarse aggregate. Step 2-2-4) judging the cross-sectional area of the coarse aggregate whether the area of the coarse aggregate occupying the two-dimensional section of the concrete exceeds If yes, the number of coarse aggregates corresponding to different particle sizes is output, otherwise, the number of coarse aggregates corresponding to the current particle size is accumulated, and steps 2-2-1) to 2-2-3) are repeated.

7. The method of meso-scale modeling and analysis of concrete structures as claimed in claim 1 wherein, The step 4) comprises the following steps: Step 4-1) importing the lattice model into the ABAQUS calculation platform; Step 4-2) The lattice model is secondarily developed by using Python language to give pre-configured boundary and load constraints; Step 4-3) The lattice model is subjected to load calculation by using the Standard module; Step 4-4) Based on the load calculation result, the stress status of each lattice unit is judged according to the failure criterion. If the judgment result is failure, the lattice unit is replaced by a zero unit with elastic modulus and strength of 0.

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