Defective concrete mesoscopic model construction method and related device

Abstract

The invention belongs to the technical field of concrete models, and discloses a defect-containing concrete mesoscopic model construction method and related device.The method comprises the steps that characteristic parameters of a to-be-simulated concrete test piece are obtained; the characteristic parameters of the to-be-simulated concrete test piece comprise the geometric dimension of the concrete test piece, the characteristic parameters of the aggregate and the characteristic parameters of defects in the aggregate; generating a concrete matrix model based on the geometric dimension of the concrete test piece; based on the aggregate characteristic parameters, generating a random aggregate model by using a random geometric algorithm; based on the defect characteristic parameters in the aggregate, randomly generating an aggregate internal defect model; putting the random aggregate model and the aggregate internal defect model into a concrete matrix model to obtain a concrete mesoscopic model containing defects; according to the method, the concrete random aggregate model containing the defects is simply, conveniently and intuitively generated, the randomness of the aggregate and the defects in the model is improved, and the aggregate and the defects are closer to the form and distribution of real concrete aggregate.

Description

Technical Field

[0001] The present invention belongs to the technical field of concrete models, and in particular relates to a method for constructing a concrete microscopic model containing defects and a related device. Background Art

[0002] At the microscale, concrete is considered a composite material consisting of a mortar matrix, coarse aggregate, and an interface layer, with complex mechanical properties. Aggregate serves as the framework of concrete, and its shape, properties, mix ratio, and volume fraction have a significant impact on the mechanical properties of concrete.

[0003] With the rapid development of computer technology, simulating the mechanical behavior of concrete by establishing a microscopic mechanical model has become an important means to improve the mechanical properties of concrete. The mesoscale usually refers to the scale range within which the internal microstructure of the concrete material (such as aggregate, mortar matrix and interface layer) can be observed. The finite element method, as a commonly used numerical simulation technique, plays an important role in the simulation of concrete mesoscopic damage processes. By obtaining data through mechanical experiments and then using the finite element method to simulate the damage and failure process of concrete during loading, and comparing and analyzing the data with the experimental results, the internal damage mechanism of concrete can be revealed, thereby more accurately explaining its macroscopic mechanical behavior.

[0004] However, existing random aggregate modeling methods have certain limitations when constructing concrete mesoscopic models. Specifically, this method cannot directly simulate the random distribution of initial defects (such as pores or cracks within the aggregate) and is usually only studied on pure aggregate concrete models. Since real concrete often has initial defects inside, and these initial defects have a significant impact on the mechanical properties of concrete, the analysis results of existing concrete mesoscopic models deviate significantly from the actual situation. Summary of the Invention

[0005] In response to the technical problems existing in the prior art, the present invention provides a method for constructing a concrete mesoscopic model containing defects and related devices to solve the technical problem that the existing random aggregate model method cannot directly simulate the random distribution of initial defects when constructing the concrete mesoscopic model, resulting in a large deviation between the analysis results of the existing concrete mesoscopic model and the actual situation.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: The present invention provides a method for constructing a concrete microscopic model containing defects, comprising: Acquiring characteristic parameters of the concrete specimen to be simulated; wherein the characteristic parameters of the concrete specimen to be simulated include geometric dimensions of the concrete specimen, characteristic parameters of aggregates, and characteristic parameters of defects inside the aggregates; generating a concrete matrix model based on the geometric dimensions of the concrete specimen; Based on the aggregate characteristic parameters, a random aggregate model is generated using a random geometric algorithm; Randomly generating an aggregate internal defect model based on the defect characteristic parameters inside the aggregate; The random aggregate model and the aggregate internal defect model are put into a concrete matrix model to obtain a concrete microscopic model containing defects.

[0007] Furthermore, the aggregate characteristic parameters include aggregate gradation particle size distribution, interface transition zone thickness and aggregate area ratio.

[0008] Furthermore, based on the aggregate characteristic parameters, a randomized aggregate model is generated using a randomized geometric algorithm, including: Determine the cross-sectional shape of the simulated aggregate in the random aggregate model; wherein the cross-sectional shape of the simulated aggregate is circular; According to the determined cross-sectional shape of the simulated aggregate and based on the aggregate characteristic parameters, a random geometric algorithm is used to determine the center position and radius of each simulated aggregate; A random aggregate model is generated according to the center position and radius of each simulated aggregate.

[0009] Furthermore, the defect characteristic parameters inside the aggregate include defect type, defect geometric parameters and defect rate; wherein, the defect type includes cracks and pores; the defect geometric parameters include the direction angle range of cracks, the length range of cracks and the radius of pores; the defect rate includes the crack density range and the number range of pores.

[0010] Furthermore, based on the characteristic parameters of the internal defects of the aggregate, a process of randomly generating an internal defect model of the aggregate includes: According to the defect characteristic parameters inside the aggregate, using the Monte Carlo algorithm, obtaining the defect simulation parameters inside the aggregate; An aggregate internal defect model is generated based on the aggregate internal defect simulation parameters.

[0011] Furthermore, in the process of putting the random aggregate model and the aggregate internal defect model into the concrete matrix model to obtain the concrete mesoscopic model containing defects, the random aggregate model and the aggregate internal defect model satisfy a preset interference condition; Among them, the preset interference conditions are as follows:

[0012]

[0013]

[0014] in, For the The horizontal axis of the random aggregate model; For the The horizontal axis of the random aggregate model; For the The vertical coordinate of the random aggregate model; For the The vertical coordinate of the random aggregate model; For the The radius of the random aggregate model; For the The radius of the random aggregate model; For the The horizontal axis of the internal defect model of the aggregate; For the The horizontal axis of the internal defect model of the aggregate; For the The vertical coordinate of the internal defect model of the aggregate; For the The vertical coordinate of the internal defect model of the aggregate; For the The radius of the internal defect model of the aggregate; For the The radius of the internal defect model of the aggregate.

[0015] The present invention also provides a system for constructing a concrete micro-model containing defects, comprising: A parameter acquisition module is used to acquire characteristic parameters of the concrete specimen to be simulated; wherein the characteristic parameters of the concrete specimen to be simulated include geometric dimensions of the concrete specimen, characteristic parameters of aggregates, and characteristic parameters of defects inside the aggregates; A matrix model generating module, configured to generate a concrete matrix model based on the geometric dimensions of the concrete specimen; An aggregate model generation module, configured to generate a random aggregate model based on the aggregate characteristic parameters and using a random geometric algorithm; A defect model generating module, configured to randomly generate an internal defect model of the aggregate based on characteristic parameters of the internal defects of the aggregate; The model integration module is used to put the random aggregate model and the aggregate internal defect model into the concrete matrix model to obtain a concrete microscopic model containing defects.

[0016] The present invention also provides an electronic device, comprising: a processor suitable for executing a computer program; A computer-readable storage medium having a computer program stored therein, wherein when the computer program is executed by the processor, the method for the concrete mesoscopic model containing defects is executed.

[0017] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for the concrete mesoscopic model containing defects is implemented.

[0018] The present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method for the concrete mesoscopic model containing defects is implemented.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The method for constructing a defective concrete mesoscopic model provided by the present invention generates a random aggregate model and an aggregate internal defect model through aggregate characteristic parameters and aggregate internal defect characteristic parameters, and integrates the generated random aggregate model and aggregate internal defect model into a concrete matrix model, thereby achieving a simple, convenient and intuitive generation of a random aggregate model of concrete with defects; wherein, by introducing aggregate characteristic parameters and defect characteristic parameters, the mesoscopic model can truly reflect the heterogeneity of aggregates in concrete and the random distribution of internal defects; by randomly generating an aggregate internal defect model, the influence of initial defects on the mechanical properties of concrete can be quantitatively analyzed; the present invention improves the randomness of aggregates and defects in the model by incorporating the randomness of aggregates and defects into the model, significantly improving the authenticity and predictive ability of concrete mesoscopic simulation, making it closer to the morphology and distribution of real concrete aggregates, and providing a powerful tool for concrete material design, performance optimization and failure analysis.

[0020] The defective concrete mesoscopic model system, electronic device, computer-readable storage medium and computer program product provided by the present invention have all the advantages of the above-mentioned defective concrete mesoscopic model method. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 A flow chart of the method for constructing a mesoscopic model of concrete containing defects provided in Example 1; Figure 2 Schematic diagram of the random aggregate model under the secondary distribution in Example 1; Figure 3 Schematic diagram of the random aggregate model under three gradations in Example 1; Figure 4Schematic diagram of the concrete microscopic model containing defects when the defect rate is 1% in Example 1; Figure 5 Schematic diagram of the concrete microscopic model containing defects when the defect rate is 2% in Example 1; Figure 6 Schematic diagram of the concrete microscopic model containing defects when the defect rate is 3% in Example 1; Figure 7 Schematic diagram of loading of the concrete mesoscopic model with defects in Example 1; Figure 8 is a stress nephogram of the normal stress in the x-direction of the concrete mesoscopic model with defects in Example 1; Figure 9 is a stress cloud diagram of the normal stress in the y direction of the concrete mesoscopic model with defects in Example 1; Figure 10 is a stress cloud diagram of the shear stress of the concrete mesoscopic model with defects in Example 1 on the xy plane; Figure 11 This is the compressive damage cloud diagram when the interface area begins to be damaged in Example 1; Figure 12 This is the compressive damage cloud map when the damage extends from the interface area to the mortar area in Example 1; Figure 13 This is the compressive damage cloud diagram of the specimen in Example 1 when it reaches the ultimate damage failure; Figure 14 This is a structural block diagram of the system for constructing a concrete micro-model containing defects provided in Example 2; Figure 15 This is a structural block diagram of the electronic device provided in Example 3. DETAILED DESCRIPTION

[0023] In order to make the technical problems, technical solutions, and beneficial effects solved by this application more clearly understood, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application; it is obvious that the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of this application.

[0024] The present invention provides a method for constructing a concrete microscopic model containing defects, comprising: Step 100: Acquire characteristic parameters of the concrete specimen to be simulated; wherein the characteristic parameters of the concrete specimen to be simulated include geometric dimensions of the concrete specimen, characteristic parameters of aggregates, and characteristic parameters of defects inside the aggregates.

[0025] Step 200: Generate a concrete matrix model based on the geometric dimensions of the concrete specimen.

[0026] Step 300: Generate a random aggregate model based on the aggregate characteristic parameters using a random geometric algorithm.

[0027] Step 400: randomly generate an aggregate internal defect model based on the defect characteristic parameters inside the aggregate.

[0028] Step 500: Put the random aggregate model and the aggregate internal defect model into a concrete matrix model to obtain a concrete mesoscopic model containing defects.

[0029] The method for constructing a defective concrete mesoscopic model described in the present invention directly generates an aggregate internal defect model by introducing characteristic parameters of aggregate internal defects, making the model closer to the actual concrete structure. The aggregate internal defect model is randomly generated, which can simulate the random distribution characteristics of defects in different concrete specimens, avoiding the deviation of simulation results caused by the simplification of defect distribution. By simulating the impact of defect distribution on the mechanical properties of concrete, aggregate selection and grading design can be optimized, significantly improving the authenticity, accuracy and application value of the concrete mesoscopic model.

[0030] The following is a further explanation of the defective concrete micro-model method provided by the present invention using some specific examples: Example 1 As attached Figure 1 As shown, this embodiment 1 provides a method for constructing a concrete micro-model containing defects, comprising the following steps: Step 1: Acquire characteristic parameters of the concrete specimen to be simulated; wherein the characteristic parameters of the concrete specimen to be simulated include geometric dimensions of the concrete specimen, characteristic parameters of aggregates, and characteristic parameters of defects inside the aggregates.

[0031] The geometric dimensions of the concrete specimen include the length and high , in order to utilize the length of the concrete specimen and high A concrete matrix model is constructed for the placement of random aggregate models and aggregate internal defect models. The geometric dimensions of the concrete specimen in a two-dimensional plane meet the following conditions:

[0032]

[0033]

[0034] in, is the range function; is the horizontal coordinate range of the concrete specimen in the two-dimensional plane; is the vertical coordinate range of the concrete specimen in the two-dimensional plane.

[0035] The aggregate characteristic parameters include aggregate gradation particle size distribution, interface transition zone thickness and aggregate area ratio.

[0036] Specifically, aggregate gradation particle size distribution refers to the size distribution of aggregate particles; generally, aggregate particles are divided into four categories according to their size, including small stone (5mm-20mm), medium stone (20mm-40mm), large stone (40mm-80mm), and extra-large stone (80mm-150mm); concrete is divided into one, two, three, and four gradations according to the different aggregate particle size ranges contained; usually, concrete containing only one or two gradations is called small aggregate concrete, and concrete containing three or four gradations is called large aggregate concrete; when concrete contains four gradations, it is called fully graded concrete.

[0037] The interfacial transition zone (ITZ) refers to the thin layer between the hardened cement paste (cement-based phase) and the aggregate in concrete; generally, the thickness of the ITZ is mainly concentrated between 15-50μm.

[0038] The aggregate area ratio is expressed by the Walraven formula. For any concrete specimen, the distribution of aggregates at all levels in its structural cross section can be obtained according to the Walraven formula. Among them, the Walraven formula represents the area ratio of any area on an inner cross section of concrete with a diameter of The probability of , that is, the projected area ratio of aggregate in concrete on the two-dimensional cross section, is as follows:

[0039] in, is the projected area ratio of the aggregate on the two-dimensional cross section; is the particle size of aggregate; is the maximum aggregate particle size.

[0040] The defect characteristic parameters inside the aggregate include defect type, defect geometric parameters and defect rate; among them, the defect type includes cracks and pores; the defect geometric parameters include the direction angle range of cracks, the length range of cracks and the radius of pores; the defect rate includes the crack density range and the number range of pores; the value range of the defect rate varies depending on the type and use of concrete; generally, the porosity of ordinary concrete is generally between 10% and 25%; the specific value depends on the concrete mix ratio and the use environment; the porosity of high-strength concrete should usually be controlled below 5% to ensure its high strength and durability.

[0041] Step 2: Generate a concrete matrix model based on the geometric dimensions of the concrete specimen. Specifically, based on the geometric dimensions of the concrete specimen, MATLAB software is used to generate a two-dimensional placement area in a two-dimensional plane as the concrete matrix model.

[0042] Step 3: Based on the aggregate characteristic parameters, a random aggregate model is generated using a random geometric algorithm. Specifically, the process of generating the random aggregate model includes: Step 31: Determine the cross-sectional shape of the simulated aggregate in the random aggregate model; wherein the cross-sectional shape of the simulated aggregate is circular.

[0043] Step 32: According to the determined cross-sectional shape of the simulated aggregate and based on the aggregate characteristic parameters, a random geometric algorithm is used to determine the center position and radius of each simulated aggregate.

[0044] Specifically, the aggregate particle size range is determined based on the determined cross-sectional shape of the simulated aggregate and in combination with the aggregate gradation particle size distribution; a random geometric algorithm is used to randomly generate a radius size within the aggregate particle size range to obtain the radius of each simulated aggregate; wherein the aggregate particle size range is set according to the aggregate characteristic parameters; similarly, the center position of each simulated aggregate is also determined using the random geometric algorithm; wherein, when determining the center position of each simulated aggregate, the thickness of the interface transition zone and the aggregate area ratio are used as a basis.

[0045] Step 33: Generate a random aggregate model based on the center position and radius of each simulated aggregate. Figure 2-3 As shown, attached Figure 2 A schematic diagram of the random aggregate model under the secondary distribution in Example 1 is given in FIG. Figure 3 A schematic diagram of the random aggregate model under three gradations in Example 1 is given in FIG.

[0046] Take the process of generating a random aggregate model using MATLAB software as an example: In MATLAB software, use the rand function to generate uniformly distributed random numbers and map the uniformly distributed random numbers to the aggregate particle size range to obtain the radius of each simulated aggregate. For example, if the aggregate particle size range is set to 5-20 mm, first use any Generate random numbers uniformly in the interval, and then map the generated random numbers to The radius of each simulated aggregate is obtained within the interval of ; the linear transformation process is as follows:

[0047] in, For the radius of each simulated aggregate, is a uniformly generated random number.

[0048] In MATLAB software, the horizontal coordinate and vertical coordinate of the center of the aggregate circle are randomly generated; wherein, the horizontal coordinate and vertical coordinate of the center of the aggregate circle meet the following conditions: ,

[0049] ,

[0050] in, For the The horizontal axis of the random aggregate model; For the The vertical coordinate of the random aggregate model; For the The radius of the random aggregate model.

[0051] Step 4: Randomly generate an aggregate internal defect model based on the internal defect characteristic parameters of the aggregate. The internal aggregate defect model includes a crack defect model and a pore defect model. Specifically, based on the internal aggregate defect characteristic parameters, a Monte Carlo algorithm is used to obtain internal aggregate defect simulation parameters. The internal aggregate defect model is generated based on the internal aggregate defect simulation parameters.

[0052] In this embodiment 1, the shape, size, direction and density of the defects inside the aggregate are simulated by the Monte Carlo algorithm based on the defect characteristic parameters inside the aggregate to obtain the defect simulation parameters inside the aggregate. When the Monte Carlo algorithm is used to simulate the generation of discrete particles that meet the preset distribution, random numbers that meet the preset distribution are first generated; wherein, the process of generating random numbers that meet the preset distribution is random sampling; preferably, the random numbers that meet the preset distribution are A random variable that follows a uniform distribution on the interval [0,1] will be generated. , after linear transformation, we can get A uniformly distributed random variable on ; Among them, we get A uniformly distributed random variable on The process is as follows:

[0053] When the defects inside the aggregate are cracks, the crack length is randomly generated between [0.1mm, 1mm]; the crack width is also randomly generated between [0.1mm, 1mm]; when the defects inside the aggregate are pores, circular pores are randomly generated to simulate them; the shape of the pores is simplified to a circle; for example, the pore radius can be between [1mm, 2mm].

[0054] Specifically, the following steps are included: Step 41: Generate the shape and size of the cracks according to the crack direction angle range, crack length range, and crack density range, and obtain a crack defect model inside the random aggregate. It should be noted that the crack density range refers to the number of cracks per unit volume. The distribution of the cracks is simulated by randomly generating the starting point and the ending point of the cracks. For example, multiple random points are generated inside the aggregate, and the multiple random points are used as the starting point and the ending point of the cracks. The direction angle range of the cracks is randomly generated. Specifically, a random angle is generated in the interval [0, 2π] to indicate the direction of the crack. The shape of the crack is simplified to a line segment, and its length can be determined by randomly generating a random value within a preset range. For example, the crack length can be randomly generated between [0.1mm, 1mm]. Similarly, the crack width can also be randomly generated, usually between [0.01mm, 0.1mm].

[0055] Step 42: Based on the pore radius and the pore quantity range, a pore defect model inside the random aggregate is obtained; as shown in the attached figure: Figure 4-6 As shown, attached Figure 4 The schematic diagram of the concrete microscopic model with defects when the defect rate is 1% in Example 1 is given in FIG. Figure 5 The schematic diagram of the concrete microscopic model with defects when the defect rate is 2% in Example 1 is given in the appendix. Figure 6 A schematic diagram of the microscopic model of concrete containing defects when the defect rate is 3% in Example 1 is given.

[0056] Among them, the position coordinates of the pore defect model inside the random aggregate meet the following conditions: ,

[0057] ,

[0058] in, For the The horizontal axis of the internal defect model of the aggregate; For the The vertical coordinate of the internal defect model of the aggregate.

[0059] The number of pore defect models inside the random aggregate is determined by randomly generating a random value within a preset range; for example, multiple pore center positions can be randomly generated inside the aggregate; the pore radius is determined by randomly generating a value within a preset range; the pore shape is simplified to a circle; for example: the pore radius can be between [1mm, 2mm].

[0060] Step 5: Place the random aggregate model and the aggregate internal defect model into the concrete matrix model to obtain a concrete microscopic model containing defects. The random aggregate model and the aggregate internal defect model satisfy a preset interference condition; the preset interference condition is as follows:

[0061]

[0062]

[0063] in, For the The horizontal axis of the random aggregate model; For the The horizontal axis of the random aggregate model; For the The vertical coordinate of the random aggregate model; For the The vertical coordinate of the random aggregate model; For the The radius of the random aggregate model; For the The radius of the random aggregate model; For the The horizontal axis of the internal defect model of the aggregate; For the The horizontal axis of the internal defect model of the aggregate; For the The vertical coordinate of the internal defect model of the aggregate; For the The vertical coordinate of the internal defect model of the aggregate; For the The radius of the internal defect model of the aggregate; For the The radius of the internal defect model of the aggregate.

[0064] It should be noted that a complete concrete model is formed by constructing a random aggregate model, an aggregate internal defect model, and a concrete matrix model respectively, and then integrating the random aggregate model and the aggregate internal defect model into the concrete matrix model. The generated model is visualized using the drawing function of MATLAB software to check the rationality and completeness of the model to ensure that there is no overlap or exceeding the boundary, thus obtaining a concrete microscopic model with defects.

[0065] Step 6: Use Python to create ABAQUS post-processing steps and save the model. The process of using Python to create ABAQUS post-processing steps includes: Step 61: Create a rigid indenter and assign material properties. Specifically, based on the geometric dimensions of the concrete specimen, create an indenter with both ends 5 mm longer than the specimen. Based on the actual material properties, load it into a Python script that assigns material properties. The Concrete Damaged Plasticity (CDP) model in finite element software is used. The CDP model simulates the complex behavior of concrete under different loads, including elastic, plastic, and damage stages, to predict and analyze the performance of concrete structures in practical applications.

[0066] Step 62: Create reference points to facilitate coupling constraints and process output. Specifically, establish the midpoint of the rigid indenter as a reference point and create a new set. This facilitates subsequent process output and the setting of loading and fixed constraints. The new set includes the upper indenter midpoint TOP and the lower indenter reference point BOTTOW.

[0067] Step 63, create an analysis step, modify the output; load constraints; fixed constraints. Specifically, in the analysis step setting stage, create a new analysis step, modify the field output of the entire model to output compressive and tensile damage (DAMAGEC, DAMAGET) and the new set of loading point history outputs, the history output includes vertical displacement and support reaction force U2, RF2; create mutual constraints between the indenter and the specimen, set to face-to-face contact, and the tangential friction of the contact surface is set according to the classic Coulomb friction model, and the friction coefficient is set to 0.3; the upper loading constraint is set to vertical displacement loading, and the bottom is fixed constraint, such as Figure 7 shown.

[0068] Step 64: Define the mesh and divide it, create a task, and save the model. It should be noted that in order to obtain more accurate calculation results, the mesh must be carefully divided to reduce the sensitivity of the simulation results to the mesh size, including reducing the dependence of the stress-strain curve and failure mode on the initial finite element mesh. This ensures the accuracy and reliability of the analysis results, thus providing a solid foundation for further structural design and optimization. Specifically, using Abaqus software, regular triangular plane elements are used to directly mesh the aggregate, cement mortar, and interface layer, with a basic element size of 1.5 mm. Create a new task, name it, perform model analysis and calculations, and save the model after the run is complete.

[0069] Example description: Taking the micro-model construction process of a two-stage concrete specimen as an example, the details are as follows: Obtain the characteristic parameters of the concrete specimen to be simulated; the secondary concrete aggregate ratio is: m (medium stone): m (small stone) = 4.5:5.5, the aggregate diameters are medium stone (20mm~40mm) and small stone (5mm~20mm), respectively; the specimen size is 150mm×150mm, that is, W =150mm, H =150mm; Set the aggregate area ratio to P =40%.

[0070]

[0071] The defect type is set to circular pores, and the circular pore radius is D The thickness is 4mm and the defect rate is 2%.

[0072] The radius of the simulated aggregate depends on the random number generation function. In MATLAB software, the rand function is used to generate uniformly distributed random numbers. Taking small stones as an example, the aggregate particle size range is set to 5-20mm. First, generate A uniform random number in the interval is then mapped to interval, that is, a random aggregate radius value is obtained.

[0073] The horizontal coordinate of the center of the simulated aggregate is: x=rand(1)*(Range(1,2)-Range(1,1))+Range(1,1) ; The horizontal coordinate of the center of the simulated aggregate is: y=rand(1)*(Range(2,2)-Range(2,1))+Range(2,1) ; The radius of the simulated circular aggregate is: r=rand(1)*(R1max-R1min)+R1min ; The simulated aggregate coordinate position meets the following conditions: (xr>Range(1,1)+1&&x+r<Range(1,2)-1)&&(y-r> Range(2,1)+1&&y+r <Range(2, 2)-1) .

[0074] The generation cycle continues until the aggregate area ratio condition is met.

[0075] The circular pore defects are also randomly generated in the same way as above until the set defect rate is reached; the generation process of the pore defects at the center of the circle will not be repeated here.

[0076]

[0077] Integrate the placed aggregate and defects into the test piece, that is, the set rectangular area, to ensure that there is no overlap or beyond the boundary; then, create a rigid indenter in the ABAQUS finite element software; assign material properties; create analysis steps and modify the output; load constraints; fixed constraints; among them, the upper loading constraint is set to vertical displacement loading, and the bottom is a fixed constraint; use regular triangular plane units to directly mesh the aggregate, cement mortar and interface layer separately, with the basic size of the unit being 1.5mm; create a new task and name it, perform model analysis and calculation, and save the model after the run is completed.

[0078] As attached Figure 8-13 As shown, attached Figure 8 The stress cloud diagram of the normal stress in the x-direction of the concrete micro-model with defects in Example 1 is given in the appendix. Figure 9 The stress cloud diagram of the normal stress in the y direction of the concrete micro-model with defects in Example 1 is given in the appendix. Figure 10 The stress cloud diagram of the shear stress of the concrete micro-model with defects in Example 1 on the xy plane is given in the appendix. Figure 11 The compressive damage cloud diagram when the interface area begins to be damaged in Example 1 is given in the attached figure. Figure 12 The compressive damage cloud diagram of Example 1 when the damage extends from the interface area to the mortar area is given in the appendix. Figure 13 The compressive damage cloud diagram of the specimen in Example 1 when it reaches the ultimate damage failure is given in the attached figure. Figure 8-13 It can be seen from the figure that in the initial stage of loading, the stress is mainly concentrated on both sides of the aggregate; the initial damage first appears relatively evenly in the aggregate and the interface transition zone on both sides of the defect; as time goes by, the damage spreads from the interface transition zone to the surrounding mortar area; with the increase of displacement load, more and more units begin to be damaged, forming an area with relatively concentrated damage; the above-mentioned damage zone bypasses the aggregate, continues to expand, bridges and eventually penetrates the defect, causing the specimen to be damaged.

[0079] The method for constructing a microscopic model of defective concrete described in Example 1 can generate a random aggregate model of defective concrete in a simple, convenient, and intuitive manner; it can generate a random aggregate model of defective concrete with a specified aggregate area ratio and defect rate, which facilitates the simulation test of random aggregate models of concrete with different aggregate areas and different defect rates, and is more consistent with real concrete; running the script in ABAQUS finite element analysis software automatically generates a rigid indenter, analysis steps, and material properties, so that simulation tests of random aggregate models with defects can be performed.

[0080] In this embodiment 1, by randomly generating a random aggregate model and an aggregate internal defect model, using random numbers for statistical analysis, and randomly generating the position and size of defects and aggregates, the randomness of the aggregates and defects in the model is improved, making it closer to the morphology and distribution of real concrete aggregates; secondly, by specifying the aggregate particle size range, the particle size is randomly generated in the Fuller gradation, which can meet various grading and aggregate volume fraction requirements, thereby solving the problems of insufficient randomness of aggregate distribution and insufficient versatility of the model; by generating a random aggregate model of concrete with defects with specified aggregate area ratio and defect rate, the aggregate area ratio and defect rate are specified in the parameter definition step, which facilitates the simulation test of random aggregate models of concrete with different aggregate areas and different defect rates, which is more consistent with real concrete.

[0081] Example 2 As attached Figure 14 As shown, this embodiment 2 provides a system for constructing a concrete micro-model containing defects, including a parameter acquisition module, a matrix model generation module, an aggregate model generation module, a defect model generation module and a model integration module.

[0082] A parameter acquisition module is used to obtain characteristic parameters of the concrete specimen to be simulated; wherein the characteristic parameters of the concrete specimen to be simulated include the geometric dimensions of the concrete specimen, the characteristic parameters of the aggregate, and the characteristic parameters of the defects inside the aggregate; a matrix model generation module is used to generate a concrete matrix model based on the geometric dimensions of the concrete specimen; an aggregate model generation module is used to generate a random aggregate model based on the characteristic parameters of the aggregate using a random geometric algorithm; a defect model generation module is used to randomly generate an aggregate internal defect model based on the characteristic parameters of the defects inside the aggregate; and a model integration module is used to put the random aggregate model and the aggregate internal defect model into the concrete matrix model to obtain a concrete microscopic model containing defects.

[0083] Optionally, this embodiment 2 further includes a post-processing module; the post-processing module is used to create ABAQUS post-processing steps using Python language and save the model.

[0084] Example 3 As attached Figure 15 As shown, this embodiment 3 provides an electronic device, including: a memory for storing a computer program; a processor for implementing the steps of the method for a mesoscopic model of concrete containing defects when executing the computer program; or, when the processor executes the computer program, implementing the functions of each module in the above-mentioned mesoscopic model system of concrete containing defects.

[0085] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing preset functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0086] The electronic device may be a computing device such as a desktop computer, laptop, PDA, or cloud server. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the above are examples of electronic devices and do not constitute a limitation on electronic devices. The electronic device may include more components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0087] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0088] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0089] The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0090] Example 4 This embodiment 4 also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for a concrete mesoscopic model containing defects are implemented.

[0091] If the integrated modules / units of the defective concrete mesoscopic model system are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0092] Based on this understanding, the present invention implements all or part of the process of the above-mentioned method for a mesoscopic model of concrete containing defects, and can also be accomplished by using a computer program to instruct related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, the computer program can implement the steps of the above-mentioned method for a mesoscopic model of concrete containing defects. The computer program includes computer program code, which can be in source code form, object code form, executable file, or a preset intermediate form.

[0093] The computer-readable storage medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0094] Example 5 This embodiment 5 provides a computer product, which includes a computer program product, and the computer program is stored in a computer-readable storage medium; the processor of the electronic device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the electronic device can execute the defective concrete mesoscopic model described in embodiment 1, which will not be repeated here.

[0095] It should be noted that a person skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0096] The method for constructing a defective concrete mesoscopic model described in the present invention provides a more accurate theoretical basis for analyzing the mechanical properties of concrete materials by simulating the random distribution and defect characteristics of aggregates in actual concrete. The defective concrete mesoscopic model can effectively reflect the complexity and heterogeneity of concrete in its microstructure, providing a powerful tool for durability assessment of concrete structures, research on failure mechanisms, and development of new materials. The constructed model has high accuracy and reliability and has broad application prospects.

[0097] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention.

Claims

1. A method for constructing a microscopic model of concrete containing defects, characterized in that: include: Acquiring characteristic parameters of the concrete specimen to be simulated; wherein the characteristic parameters of the concrete specimen to be simulated include geometric dimensions of the concrete specimen, characteristic parameters of aggregates, and characteristic parameters of defects inside the aggregates; generating a concrete matrix model based on the geometric dimensions of the concrete specimen; Based on the aggregate characteristic parameters, a random aggregate model is generated using a random geometric algorithm; Randomly generating an aggregate internal defect model based on the defect characteristic parameters inside the aggregate; The random aggregate model and the aggregate internal defect model are put into a concrete matrix model to obtain a concrete microscopic model containing defects.

2. The method for constructing a concrete microscopic model containing defects according to claim 1, characterized in that: The aggregate characteristic parameters include aggregate gradation particle size distribution, interface transition zone thickness and aggregate area ratio.

3. The method for constructing a concrete micro-model containing defects according to claim 2, wherein: The process of generating a random aggregate model based on the aggregate characteristic parameters using a random geometric algorithm includes: Determine the cross-sectional shape of the simulated aggregate in the random aggregate model; wherein the cross-sectional shape of the simulated aggregate is circular; According to the determined cross-sectional shape of the simulated aggregate and based on the aggregate characteristic parameters, a random geometric algorithm is used to determine the center position and radius of each simulated aggregate; A random aggregate model is generated according to the center position and radius of each simulated aggregate.

4. The method for constructing a concrete micro-model containing defects according to claim 1, wherein: The defect characteristic parameters inside the aggregate include defect type, defect geometric parameters and defect rate; wherein, the defect type includes cracks and pores; the defect geometric parameters include the direction angle range of cracks, the length range of cracks and the radius of pores; the defect rate includes the crack density range and the number range of pores.

5. The method for constructing a concrete micro-model containing defects according to claim 4, characterized in that: The process of randomly generating an aggregate internal defect model based on the defect characteristic parameters inside the aggregate includes: According to the defect characteristic parameters inside the aggregate, using the Monte Carlo algorithm, obtaining the defect simulation parameters inside the aggregate; An aggregate internal defect model is generated based on the aggregate internal defect simulation parameters.

6. The method for constructing a concrete micro-model containing defects according to claim 1, characterized in that: In the process of putting the random aggregate model and the aggregate internal defect model into a concrete matrix model to obtain a concrete mesoscopic model containing defects, the random aggregate model and the aggregate internal defect model satisfy a preset interference condition; Among them, the preset interference conditions are as follows: in, For the The horizontal axis of the random aggregate model; For the The horizontal axis of the random aggregate model; For the The vertical coordinate of the random aggregate model; For the The vertical coordinate of the random aggregate model; For the The radius of the random aggregate model; For the The radius of the random aggregate model; For the The horizontal axis of the internal defect model of the aggregate; For the The horizontal axis of the internal defect model of the aggregate; For the The vertical coordinate of the internal defect model of the aggregate; For the The vertical coordinate of the internal defect model of the aggregate; For the The radius of the internal defect model of the aggregate; For the The radius of the internal defect model of the aggregate.

7. A system for constructing a microscopic model of concrete containing defects, characterized in that: include: A parameter acquisition module is used to acquire characteristic parameters of the concrete specimen to be simulated; wherein the characteristic parameters of the concrete specimen to be simulated include geometric dimensions of the concrete specimen, characteristic parameters of aggregates, and characteristic parameters of defects inside the aggregates; A matrix model generating module, configured to generate a concrete matrix model based on the geometric dimensions of the concrete specimen; An aggregate model generation module, configured to generate a random aggregate model based on the aggregate characteristic parameters and using a random geometric algorithm; A defect model generating module, configured to randomly generate an internal defect model of the aggregate based on characteristic parameters of the internal defects of the aggregate; The model integration module is used to put the random aggregate model and the aggregate internal defect model into the concrete matrix model to obtain a concrete microscopic model containing defects.

8. An electronic device, characterized in that: include: a processor suitable for executing a computer program; A computer-readable storage medium having a computer program stored therein, wherein when the computer program is executed by the processor, the method for determining a concrete mesoscopic model containing defects according to any one of claims 1 to 6 is executed.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the defective concrete mesoscopic model method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the method for the defective concrete mesoscopic model is implemented according to any one of claims 1 to 6.

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