Concrete mesoscopic model construction method, device and equipment and storage medium

Abstract

The invention relates to a concrete mesoscopic model construction method and device, equipment and a storage medium. The method comprises the steps that mesoscopic model parameters and aggregate parameters are obtained; building model walls based on the model size parameters so as to enable the model walls to form a closed model area; determining a plurality of aggregate particle sizes of each graded aggregate based on the aggregate grading proportion and the grading particle size range, and generating a plurality of aggregates based on the aggregate particle sizes; randomly putting a plurality of aggregates in the model area, simulating the motion state of each aggregate based on the wall performance parameters and the aggregate performance parameters until each aggregate is in a stable state, and obtaining the target position of each aggregate; and constructing a concrete mesoscopic model based on the target position of each aggregate and the aggregate particle size. According to the method, all the aggregates are generated at a time and put into the model area, the target positions of all the aggregates in the stable state are calculated, then the concrete mesoscopic model is constructed, the model construction time can be shortened, and it is ensured that model grading meets the expectation.

Description

Technical Field

[0001] The present disclosure relates to the technical field of concrete model construction, and in particular to a concrete micro-model construction method, device, equipment and storage medium. Background Art

[0002] Concrete is a composite building material composed of mortar and aggregates. A series of factors such as the gradation of aggregates and the proportion of aggregates will have an important impact on the material properties of concrete. Before it is put into use, it is often necessary to study its material properties first, and building a concrete mesoscopic model is an important means to study its material properties. The existing concrete mesoscopic model construction method often adopts the random aggregate method, which is to randomly select positions in the model area to generate aggregates with corresponding particle sizes, then delete the area where the aggregates are located in the model area, and then randomly select positions in the remaining area to generate aggregates with corresponding particle sizes until the model area is filled, and obtain a concrete mesoscopic model with a specified gradation. However, with the increase in the number of aggregates, it becomes increasingly difficult to find a suitable space for generating aggregates in the model area, resulting in a decrease in the later operation efficiency, a longer model construction time, and the gradation of the final generated concrete mesoscopic model may not be consistent with expectations, thereby affecting the accuracy and reliability of the concrete material property analysis results. Summary of the invention

[0003] In order to solve the above technical problems, the present disclosure provides a method, device, equipment and storage medium for constructing a concrete mesoscopic model.

[0004] A first aspect of an embodiment of the present disclosure provides a method for constructing a concrete mesoscopic model, the method comprising:

[0005] Acquire mesoscopic model parameters and aggregate parameters, wherein the mesoscopic model parameters include at least one of model size parameters and wall performance parameters, and the aggregate parameters include at least one of aggregate gradation ratio, gradation particle size range, and aggregate performance parameters;

[0006] constructing a model wall based on the model size parameters so that the model wall forms a closed model area;

[0007] Based on the aggregate grading ratio and the grading particle size range, determining multiple aggregate particle sizes of each graded aggregate, and generating multiple aggregates based on the aggregate particle sizes;

[0008] Randomly placing the plurality of aggregates in the model area, simulating the motion state of each aggregate based on the wall performance parameter and the aggregate performance parameter until each aggregate is in a stable state, and obtaining a target position of each aggregate;

[0009] A concrete mesoscopic model is constructed based on the target position and aggregate size of each aggregate.

[0010] A second aspect of an embodiment of the present disclosure provides a concrete mesoscopic model construction device, the device comprising:

[0011] An acquisition module, used to acquire mesoscopic model parameters and aggregate parameters, wherein the mesoscopic model parameters include at least one of model size parameters and wall performance parameters, and the aggregate parameters include at least one of aggregate gradation ratio, gradation particle size range, and aggregate performance parameters;

[0012] A first construction module is used to construct a model wall based on the model size parameters so that the model wall forms a closed model area;

[0013] A generating module, for determining a plurality of aggregate particle sizes of each aggregate grade based on the aggregate grade ratio and the graded particle size range, and generating a plurality of aggregates based on the aggregate particle sizes;

[0014] A calculation module, used for randomly placing the plurality of aggregates in the model area, simulating the motion state of each aggregate based on the wall performance parameter and the aggregate performance parameter, until each aggregate is in a stable state, and obtaining a target position of each aggregate;

[0015] The second building module is used to build a concrete mesoscopic model based on the target position and aggregate particle size of each aggregate.

[0016] A third aspect of an embodiment of the present disclosure provides a computer device, including a memory and a processor, and a computer program, wherein the computer program is stored in the memory, and when the computer program is executed by the processor, the method for constructing a concrete mesoscopic model as described in the first aspect above is implemented.

[0017] A fourth aspect of the embodiments of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method for constructing a concrete mesoscopic model as described in the first aspect above is implemented.

[0018] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0019] In the concrete mesoscopic model construction method, device, equipment and storage medium provided by the embodiments of the present disclosure, by obtaining mesoscopic model parameters and aggregate parameters, the mesoscopic model parameters include at least one of model size parameters and wall performance parameters, and the aggregate parameters include at least one of aggregate gradation ratio, gradation particle size range and aggregate performance parameters, a model wall is constructed based on the model size parameters so that the model wall forms a closed model area, multiple aggregate particle sizes of each graded aggregate are determined based on the aggregate gradation ratio and the gradation particle size range, and multiple aggregates are generated based on the aggregate particle size, multiple aggregates are randomly placed in the model area, and the movement state of each aggregate is simulated based on the wall performance parameters and the aggregate performance parameters until each aggregate is in a stable state. After the concrete is in a stable state, the target position of each aggregate is obtained, and the concrete mesoscopic model is constructed based on the target position and aggregate particle size of each aggregate. All aggregates can be generated at one time in strict accordance with the concrete gradation and placed in the model area, and the target position of each aggregate when it is in a stable state is calculated, and then the concrete mesoscopic model is constructed based on the target position, thereby avoiding the situation in which multiple calculations in the later stage of modeling take a long time, or even the situation in which aggregates of the corresponding particle size cannot be generated. At the same time, since the model area only needs to be set once, there is no need to change the model area boundary following the generated aggregates, which shortens the model construction time and ensures that the model gradation meets expectations, thereby ensuring that the material property analysis based on the concrete mesoscopic model in the later stage can obtain accurate analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0022] Figure 1 is a flow chart of a method for constructing a concrete mesoscopic model provided by an embodiment of the present disclosure;

[0023] Figure 2 is a schematic diagram of a concrete mesoscopic model provided by an embodiment of the present disclosure;

[0024] Figure 3 is a flow chart of a method for generating aggregate provided by an embodiment of the present disclosure;

[0025] Figure 4 is a flow chart of a method for dividing a finite element mesh provided by an embodiment of the present disclosure;

[0026] Figure 5 is a schematic diagram of a finite element mesh provided by an embodiment of the present disclosure;

[0027] Figure 6 is a structural schematic diagram of a concrete mesoscopic model building device provided by an embodiment of the present disclosure;

[0028] Figure 7 It is a structural diagram of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0031] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0032] Figure 1 The flowchart of a concrete mesoscopic model construction method provided by the embodiment of the present disclosure can be performed by a concrete mesoscopic model construction device, which can be implemented in software and / or hardware, and can be configured in an electronic device, such as a server or a terminal, wherein the terminal specifically includes a mobile phone, a computer or a tablet computer, etc. Figure 1 As shown, the concrete mesoscopic model construction method provided in this embodiment includes the following steps:

[0033] S101. Acquire mesoscopic model parameters and aggregate parameters. The mesoscopic model parameters include at least one of model size parameters and wall performance parameters. The aggregate parameters include at least one of aggregate gradation ratio, gradation particle size range, and aggregate performance parameters.

[0034] The mesoscopic model in the embodiment of the present disclosure may be a two-dimensional or three-dimensional mesoscopic model. The mesoscopic model parameters may include model size parameters, wall performance parameters, etc. The wall performance parameters may specifically include wall normal stiffness, wall tangential stiffness, friction coefficient between wall and aggregate particles, etc. For example, the model size parameters may be set to 10m×10m, the wall normal stiffness may be set to 1.0×10 8, the wall is not subjected to force in the tangential direction, and the friction between the wall and the aggregate particles is zero.

[0035] The aggregate parameters in the embodiments of the present disclosure can be understood as the parameters related to the aggregates in the concrete, which can specifically include the aggregate grading ratio, the grading particle size range, the aggregate performance parameters, etc., wherein the aggregate grading ratio can be understood as the ratio between aggregate particles of different particle sizes in the concrete, which can be the number ratio, volume ratio, mass ratio, etc., which is not limited here. The grading particle size range can be understood as the particle size distribution range of each level of aggregate. The aggregate performance parameters can be understood as a series of parameters describing the physical and mechanical properties of the aggregate, which can specifically include the aggregate density, the normal contact stiffness between the aggregates, the tangential contact stiffness, the friction coefficient, and the energy dissipation rate during the movement, etc. For example, the aggregate grading ratio can be set to a ratio of 30:30:20:20 for super large stone, large stone, medium stone and small stone, and the grading particle size ranges are 80-150 mm, 40-80 mm, 20-40 mm and 5-20 mm, respectively, and the aggregate density is 2300 kg / m 3 , the contact normal stiffness between aggregates is 1.0×10 6 , the tangential stiffness is 1.0×10 6 , the friction coefficient is 0.3, and the energy dissipation rate is 0.1.

[0036] In the disclosed embodiment, the concrete meso-model construction device can obtain the meso-model parameters and aggregate parameters of the concrete custom model to be constructed. For example, the meso-model parameters and aggregate parameters uploaded by the user can be obtained through a preset parameter upload interface, or the meso-model parameters and aggregate parameters can be obtained from a model configuration file, which is not limited here.

[0037] S102: constructing a model wall based on the model size parameters so that the model wall forms a closed model area.

[0038] In the disclosed embodiment, the concrete mesoscopic model construction device can construct a model wall corresponding to the model size parameters after acquiring the model size parameters, so that the model wall constitutes a closed model area, and after the model wall is constructed, define the properties of the model wall based on the wall performance parameters.

[0039] Optionally, after constructing the model wall based on the model size parameters, the concrete mesoscopic model constructing device may construct at least one layer of protective wall on the outer side of the model wall based on a preset wall spacing.

[0040] Specifically, the concrete mesoscopic model construction device can construct at least one layer of protective wall on the outside of the magic wall after the model wall is constructed. The number of layers of the protective wall can be pre-set according to user needs or model construction experience. There is a preset wall spacing between the model wall and the inner protective wall and the two adjacent protective walls, thereby preventing some aggregates with smaller particle sizes from passing through the model wall and causing changes in grading.

[0041] S103: Determine multiple aggregate particle sizes of each graded aggregate based on the aggregate grading ratio and the graded particle size range, and generate multiple aggregates based on the aggregate particle sizes.

[0042] In the disclosed embodiment, the concrete mesoscopic model building device can, after obtaining the aggregate grading ratio and the grading particle size range, determine multiple aggregate particle sizes within the grading particle size range corresponding to each gradation until the aggregate grading ratio corresponding to the gradation is reached. After the multiple aggregate particle sizes of the aggregates at each gradation are determined, corresponding aggregates are generated based on each aggregate particle size.

[0043] S104, randomly placing multiple aggregates in the model area, simulating the movement state of each aggregate based on the wall performance parameters and the aggregate performance parameters, until each aggregate is in a stable state, and then obtaining the target position of each aggregate.

[0044] In the disclosed embodiment, the concrete mesoscopic model building device can randomly place multiple generated aggregates in the model area after the aggregates are generated. Contact forces will be generated between the overlapping aggregates, and contact forces will also be generated between the aggregates and the model walls. The generated contact forces satisfy Newton's second law. The concrete mesoscopic model building device can simulate the motion state of each aggregate based on the wall performance parameters and the aggregate performance parameters, and obtain the target position of each aggregate after determining through calculation that each aggregate is in a stable state.

[0045] In an exemplary implementation of the disclosed embodiment, the concrete mesoscopic model building device can input the information of the generated multiple aggregates into a preset motion analysis model, and the motion analysis model randomly determines the initial position of each aggregate, and then uses a physical engine to simulate the interaction between the aggregates and the collision between the aggregates and the wall, and define a time step. In each time step, the positions and velocities of all aggregates are updated, and collisions between aggregates and between aggregates and walls are detected. When a collision is detected, the speed and direction after the collision are calculated based on the wall performance parameters and the aggregate performance parameters. After each time step, check whether all aggregates have reached a stable state, such as whether the speed of the aggregate is lower than a certain threshold, or the position of the aggregate changes very little in several consecutive time steps. If so, their positions are recorded as target positions.

[0046] Optionally, the concrete mesoscopic model building device can randomly place multiple aggregates in the model area, and simulate the movement state of each aggregate based on the wall performance parameters and the aggregate performance parameters. If each aggregate stops moving at the target time, it is determined that each aggregate is in a stable state at the target time, and the target position of each aggregate at the target time is obtained; otherwise, it is determined that each aggregate is in a stable state at a preset time, and the target position of each aggregate at the preset time is obtained. The target time is any time before the preset time.

[0047] Specifically, after randomly placing multiple aggregates in the model area, the concrete micro-model construction device can determine the moment after a preset time as the preset moment starting from the moment of placing the aggregates, and simulate the motion state of each aggregate based on the wall performance parameters and the aggregate performance parameters. If each aggregate stops moving at any target moment before the preset moment, it is determined that each aggregate is in a stable state at the target moment, and there is no need to continue simulating the motion state of the aggregate, and the position of each aggregate at the target moment is determined as the target position, and then the target position is obtained. If there are aggregates that are still in motion when the preset moment is reached, in order to avoid the subsequent calculation taking a long time, it can be considered that each aggregate has reached a stable state at the preset moment, and the target position of each aggregate at the preset moment is obtained. Among them, the preset time length can be set by the user according to the user's demand for modeling efficiency. For example, the preset time length can be 10s.

[0048] S105. Construct a concrete mesoscopic model based on the target position and aggregate particle size of each aggregate.

[0049] In the disclosed embodiment, the concrete mesoscopic model building device can build a concrete mesoscopic model according to the target position and aggregate particle size of each aggregate after determining the target position of each aggregate. Specifically, the model size parameters, the target position and aggregate particle size of each aggregate can be read and drawn into a Drawing Exchange Format (DXF) file.

[0050] Figure 2 is a schematic diagram of a concrete mesoscopic model provided by an embodiment of the present disclosure, such as Figure 2 As shown in the figure, circles of different diameters are randomly distributed in the two-dimensional concrete mesoscopic model, representing aggregates of different particle sizes. The distribution of aggregates in different particle size ranges conforms to the preset aggregate gradation ratio.

[0051] The disclosed embodiment acquires mesoscopic model parameters and aggregate parameters, wherein the mesoscopic model parameters include at least one of model size parameters and wall performance parameters, and the aggregate parameters include at least one of aggregate gradation ratio, gradation particle size range, and aggregate performance parameters, and constructs a model wall based on the model size parameters so that the model wall forms a closed model area, determines multiple aggregate particle sizes of each graded aggregate based on the aggregate gradation ratio and the gradation particle size range, and generates multiple aggregates based on the aggregate particle sizes, randomly places multiple aggregates in the model area, and simulates the motion state of each aggregate based on the wall performance parameters and the aggregate performance parameters until each aggregate is After being in a stable state, the target position of each aggregate is obtained, and a concrete mesoscopic model is constructed based on the target position and aggregate particle size of each aggregate. All aggregates can be generated at one time in strict accordance with the concrete grading and placed in the model area, and the target position of each aggregate when it is in a stable state is calculated, and then the concrete mesoscopic model is constructed based on the target position, thereby avoiding multiple calculations in the later stage of modeling that take a long time, or even the situation where aggregates of the corresponding particle size cannot be generated, shortening the model construction time, ensuring that the model gradation meets expectations, and then ensuring that the material property analysis based on the concrete mesoscopic model in the later stage can obtain accurate analysis results.

[0052] Figure 3 is a flow chart of a method for generating aggregate provided by an embodiment of the present disclosure, such as Figure 3 As shown, based on the above embodiment, aggregate can be generated by the following method, wherein the microscopic model parameters also include the glue ratio and porosity.

[0053] S301. The total proportion of aggregate in the concrete mesoscopic model is calculated based on the proportion of adhesive and porosity.

[0054] In the disclosed embodiment, it can be roughly considered that the concrete micro-model is composed of various adhesives (such as cement, fly ash, slag powder, silica fume, etc.), aggregates and pores, and the adhesive ratio and porosity are the proportions of various adhesives and pores in the concrete micro-model.

[0055] In the disclosed embodiment, the concrete meso-model construction device can calculate the total proportion of aggregates in the concrete meso-model according to the proportion of adhesive and porosity. Specifically, the total proportion of aggregates in the concrete meso-model can be calculated in the following way: total proportion of aggregates = 1-proportion of adhesive-porosity.

[0056] S302: Based on the total proportion of aggregate and the aggregate gradation ratio, determine the target proportions corresponding to each grade of aggregate in the concrete mesoscopic model.

[0057] The target in the embodiments of the present disclosure can be understood as the proportion of each level of aggregate in the concrete mesoscopic model.

[0058] In the disclosed embodiment, the concrete meso-model construction device can determine the target proportions corresponding to each level of aggregate in the concrete meso-model according to the total proportion of aggregates and the aggregate grading ratio in the concrete meso-model. Specifically, the product of the total proportion and the aggregate grading ratio can be calculated to obtain the target proportions corresponding to each level of aggregate.

[0059] S303, within the graded particle size range corresponding to each level of aggregate, a normal distribution is used for random sampling to determine a plurality of aggregate particle sizes for each level of aggregate, and a plurality of aggregates are generated based on the aggregate particle sizes until the generated aggregates reach the target proportion corresponding to each level of aggregate.

[0060] In the disclosed embodiment, the concrete mesoscopic model building device can determine the graded particle size range corresponding to each graded aggregate, and then, for each gradation, perform random sampling using a normal distribution within the graded particle size range, and generate aggregates of corresponding particle sizes based on the sampling results until the generated aggregates reach the target proportion corresponding to the graded aggregates.

[0061] The disclosed embodiment obtains the total proportion of aggregates in the concrete meso-model by calculating based on the proportion of adhesive and the porosity, determines the target proportions corresponding to each level of aggregate in the concrete meso-model based on the total proportion of aggregates and the aggregate gradation ratio, and randomly samples using normal distribution within the graded particle size range corresponding to each level of aggregate to determine multiple aggregate particle sizes of each level of aggregate, and generates multiple aggregates based on the aggregate particle sizes until the generated aggregates reach the target proportions corresponding to each level of aggregate. The embodiment can simulate the randomness and diversity of aggregate particle sizes in nature to quickly generate a large number of aggregates while meeting the gradation requirements, so that the constructed concrete meso-model is closer to the microstructure of real concrete, and further improves the accuracy of material property analysis based on the concrete meso-model in the later stage.

[0062] In some embodiments, the aggregate parameters also include the width of the interface transition zone. After obtaining the target position of each aggregate, the concrete mesoscopic model building device can perform aggregate overlapping area detection based on the target position of each aggregate and the aggregate particle size. For each aggregate group with an overlapping area, the aggregate particle size of each aggregate in the aggregate group is reduced by a target reduction amount, and the target reduction amount is the maximum value between the overlapping area width and twice the interface transition zone width.

[0063] Specifically, after obtaining the target position of each aggregate, the concrete mesoscopic model building device can perform aggregate overlapping area detection based on the target position and aggregate particle size of each aggregate, and determine two aggregates with overlapping areas as a group of aggregate groups. For each group of aggregate groups, the overlapping area width of each aggregate contained in the aggregate group is obtained, the overlapping area width is compared with twice the interface transition zone width, and the maximum value of the two is determined as the target reduction amount. The target reduction amount is used to reduce the aggregate particle size of each aggregate in the aggregate group so that the radius of each aggregate after reduction is shortened by the target reduction amount.

[0064] Optionally, when the same aggregate is included in multiple aggregate groups, that is, when there are overlapping areas between the same aggregate and multiple aggregates, after determining multiple target reduction amounts for each aggregate group to which the aggregate belongs, the maximum value among the multiple target reduction amounts is determined, and the maximum value is determined as the target reduction amount corresponding to the aggregate, and then the aggregate particle size of the aggregate is reduced, so as to avoid the existence of overlapping aggregates in the final established model, and further improve the accuracy of the later material property analysis of the concrete mesoscopic model.

[0065] In other embodiments, the concrete meso-model construction device can adjust the aggregate particle size of each aggregate based on the width of the interface transition zone when constructing the concrete meso-model based on the target position and aggregate particle size of each aggregate, and construct the concrete meso-model based on the target position of each aggregate, the adjusted aggregate particle size and the width of the interface transition zone.

[0066] Specifically, the concrete meso-model construction device can adjust the aggregate particle size of each aggregate contained in the concrete meso-model based on the width of the interface transition zone when constructing the concrete meso-model, so that the radius of each adjusted aggregate is shortened by the width of the interface transition zone, and construct a concrete meso-model including the interface transition zone based on the target position of each aggregate, the adjusted aggregate particle size and the width of the interface transition zone. Through the above-mentioned aggregate shrinkage method, the interface transition zone between aggregate and paste can be efficiently established, further improving the model accuracy and modeling speed.

[0067] Figure 4 1 is a flow chart of a method for dividing a finite element mesh provided by an embodiment of the present disclosure. Figure 4 As shown, based on the above embodiment, the finite element mesh can be divided by the following method.

[0068] S401. Randomly select a preset number of analysis domains in the concrete mesoscopic model, and convert the analysis domains into surface domains.

[0069] In the disclosed embodiment, the concrete meso-model construction device can import the concrete meso-model into the corresponding model drawing software after the concrete meso-model is constructed, and randomly select a preset number of analysis domains in the concrete meso-model imported into the model drawing software according to the size information of the analysis domain obtained in advance. For example, 10 squares with a side length of 0.3m can be selected as analysis domains, and then the corresponding conversion instructions are used to convert each analysis domain into a face domain.

[0070] S402 , setting a mesh seed point for each face region, and performing mesh division based on the mesh seed point to obtain a finite element mesh.

[0071] In the disclosed embodiment, the concrete micro-model construction device can set mesh seed points on the edge of each face domain after converting the face domain, and perform mesh division based on the mesh seed points to obtain a finite element mesh. For example, the finite element mesh can be divided using triangular units.

[0072] Figure 5 is a schematic diagram of a finite element mesh provided by an embodiment of the present disclosure, such as Figure 5 As shown in the figure, an analysis domain is randomly selected from the concrete mesoscopic model, and after converting it to a surface domain, the surface domain is meshed using triangular units to obtain a finite element mesh.

[0073] The disclosed embodiment randomly selects a preset number of analysis domains in the concrete mesoscopic model, converts the analysis domains into surface domains, sets mesh seed points for each surface domain, and performs mesh division based on the mesh seed points to obtain a finite element mesh, thereby facilitating subsequent material property analysis of the concrete based on the finite element mesh.

[0074] Figure 6 Schematic diagram of a structure of a concrete micro-model construction device provided by an embodiment of the present disclosure. Figure 6As shown, the concrete mesoscopic model construction device 600 includes: an acquisition module 610, a first construction module 620, a generation module 630, a calculation module 640, and a second construction module 650, wherein the acquisition module 610 is used to obtain mesoscopic model parameters and aggregate parameters, wherein the mesoscopic model parameters include at least one of model size parameters and wall performance parameters, and the aggregate parameters include at least one of aggregate gradation ratio, gradation particle size range, and aggregate performance parameters; the first construction module 620 is used to construct a model wall based on the model size parameters so that the model wall A closed model area is formed; a generation module 630 is used to determine multiple aggregate particle sizes of each graded aggregate based on the aggregate grading ratio and the graded particle size range, and generate multiple aggregates based on the aggregate particle size; an operation module 640 is used to randomly place the multiple aggregates in the model area, simulate the movement state of each aggregate based on the wall performance parameters and the aggregate performance parameters, until each aggregate is in a stable state, and obtain the target position of each aggregate; a second construction module 650 is used to construct a concrete mesoscopic model based on the target position and aggregate particle size of each aggregate.

[0075] Optionally, the meso-model parameters also include a binder ratio and a porosity, and the generation module 630 includes: a calculation unit, used to calculate the total ratio of aggregates in the concrete meso-model based on the binder ratio and the porosity; a determination unit, used to determine the target ratios corresponding to each level of aggregate in the concrete meso-model based on the total ratio of aggregates and the aggregate grading ratio; a generation unit, used to randomly sample using a normal distribution within the grading particle size range corresponding to each level of aggregate, determine multiple aggregate particle sizes of each level of aggregate, and generate multiple aggregates based on the aggregate particle sizes until the generated aggregates reach the target ratios corresponding to each level of aggregate.

[0076] Optionally, the operation module 640 is specifically used to randomly place the multiple aggregates in the model area, simulate the movement state of each aggregate based on the wall performance parameters and the aggregate performance parameters, and if each aggregate stops moving at the target time, determine that each aggregate is in a stable state at the target time, and obtain the target position of each aggregate at the target time; otherwise, determine that each aggregate is in a stable state at a preset time, and obtain the target position of each aggregate at the preset time, and the target time is any time before the preset time.

[0077] Optionally, the aggregate parameters also include the width of the interface transition zone, and the concrete mesoscopic model building device 600 also includes: a reduction module, which is used to detect the aggregate overlapping area based on the target position and aggregate particle size of each aggregate, and for each aggregate group with an overlapping area, the aggregate particle size of each aggregate in the aggregate group is reduced by a target reduction amount, and the target reduction amount is the maximum value between the overlapping area width and twice the interface transition zone width.

[0078] Optionally, the second construction module 650 includes: an adjustment unit for adjusting the aggregate particle size of each aggregate based on the width of the interface transition zone; and a construction unit for constructing the concrete mesoscopic model based on the target position of each aggregate, the adjusted aggregate particle size and the width of the interface transition zone.

[0079] Optionally, the concrete mesoscopic model building device 600 further includes: a third building module, configured to build at least one layer of protective wall outside the model wall based on a preset wall spacing.

[0080] Optionally, the concrete mesoscopic model construction device 600 also includes: a conversion module, used to randomly select a preset number of analysis domains in the concrete mesoscopic model and convert the analysis domains into surface domains; a partitioning module, used to set a grid seed point for each surface domain, and perform grid partitioning based on the grid seed point to obtain a finite element grid.

[0081] The concrete micro-model construction device provided in this embodiment can execute the method described in any of the above embodiments, and its execution method and beneficial effects are similar, which will not be repeated here.

[0082] Figure 7 It is a structural diagram of a computer device provided in an embodiment of the present disclosure.

[0083] like Figure 7 As shown, the computer device may include a processor 710 and a memory 720 storing computer program instructions.

[0084] Specifically, the processor 710 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0085] The memory 720 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 720 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 720 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 720 may be inside or outside the integrated gateway device. In a particular embodiment, the memory 720 is a non-volatile solid-state memory. In a particular embodiment, the memory 720 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), an electrically rewritable ROM (EAROM) or a flash memory, or a combination of two or more of these.

[0086] The processor 710 reads and executes the computer program instructions stored in the memory 720 to perform the steps of the method for constructing a concrete mesoscopic model provided in the embodiment of the present disclosure.

[0087] In one example, the computer device may further include a transceiver 730 and a bus 740. Figure 7 As shown, the processor 710, the memory 720 and the transceiver 730 are connected via a bus 740 and communicate with each other.

[0088] The bus 740 includes hardware, software, or both. For example, but not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a Memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 740 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0089] The embodiment of the present disclosure further provides a computer-readable storage medium, which may store a computer program. When the computer program is executed by a processor, the processor implements the concrete mesoscopic model construction method provided by the embodiment of the present disclosure.

[0090] The above-mentioned storage medium may, for example, include a memory 720 of computer program instructions, and the above-mentioned instructions may be executed by the processor 710 of the concrete mesoscopic model construction device to complete the concrete mesoscopic model construction method provided by the embodiment of the present disclosure. Optionally, the storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (Random Access Memory, RAM), a compact disc read-only memory (Compact Disc ROM, CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc. The above-mentioned computer program may be written in any combination of one or more programming languages ​​to perform the program code of the operation of the embodiment of the present disclosure, and the programming language includes an object-oriented programming language, such as Java, C++, etc., and also includes a conventional procedural programming language, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on the remote computing device, or completely on the remote computing device or server.

[0091] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0092] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a concrete mesoscopic model, characterized in that: The method comprises: Acquire mesoscopic model parameters and aggregate parameters, wherein the mesoscopic model parameters include at least one of model size parameters and wall performance parameters, and the aggregate parameters include at least one of aggregate gradation ratio, gradation particle size range, and aggregate performance parameters; constructing a model wall based on the model size parameters so that the model wall forms a closed model area; Based on the aggregate grading ratio and the grading particle size range, determining multiple aggregate particle sizes of each graded aggregate, and generating multiple aggregates based on the aggregate particle sizes; Randomly placing the plurality of aggregates in the model area, simulating the motion state of each aggregate based on the wall performance parameter and the aggregate performance parameter until each aggregate is in a stable state, and obtaining the target position of each aggregate; A concrete mesoscopic model is constructed based on the target position and aggregate size of each aggregate.

2. The method according to claim 1, characterized in that The microscopic model parameters also include the ratio of adhesive and porosity. Based on the aggregate grading ratio and the grading particle size range, multiple aggregate particle sizes of each graded aggregate are determined, and multiple aggregates are generated based on the aggregate particle sizes, including: The total proportion of aggregate in the concrete mesoscopic model is calculated based on the proportion of the adhesive and the porosity; Based on the total proportion of the aggregate and the aggregate grading ratio, determining the target proportions corresponding to each grade of aggregate in the concrete mesoscopic model; In the graded particle size range corresponding to each level of aggregate, normal distribution is used for random sampling to determine multiple aggregate particle sizes for each level of aggregate, and multiple aggregates are generated based on the aggregate particle sizes until the generated aggregate reaches the target proportion corresponding to each level of aggregate.

3. The method according to claim 1, characterized in that The step of randomly placing the plurality of aggregates in the model area, simulating the motion state of each aggregate based on the wall performance parameter and the aggregate performance parameter until each aggregate is in a stable state, and obtaining the target position of each aggregate includes: The plurality of aggregates are randomly placed in the model area, and the movement state of each aggregate is simulated based on the wall performance parameters and the aggregate performance parameters. If each aggregate stops moving at a target time, it is determined that each aggregate is in a stable state at the target time, and the target position of each aggregate at the target time is obtained; otherwise, it is determined that each aggregate is in a stable state at a preset time, and the target position of each aggregate at the preset time is obtained, and the target time is any time before the preset time.

4. The method according to claim 1, characterized in that: The aggregate parameters also include the width of the interface transition zone. After obtaining the target position of each aggregate, the method further includes: Aggregate overlapping area detection is performed based on the target position and aggregate particle size of each aggregate. For each aggregate group with overlapping area, the aggregate particle size of each aggregate in the aggregate group is reduced by a target reduction amount, and the target reduction amount is the maximum value between the overlapping area width and twice the interface transition zone width.

5. The method according to claim 4, characterized in that The concrete mesoscopic model is constructed based on the target position and aggregate particle size of each aggregate, including: adjusting the aggregate particle size of each aggregate based on the width of the interface transition zone; The concrete mesoscopic model is constructed based on the target position of each aggregate, the adjusted aggregate particle size and the width of the interface transition zone.

6. The method according to claim 1, characterized in that After constructing the model wall based on the model size parameters, the method further includes: At least one layer of protective wall is constructed outside the model wall based on a preset wall spacing.

7. The method according to claim 1, characterized in that After constructing the concrete mesoscopic model based on the target position and aggregate particle size of each aggregate, the method further includes: randomly selecting a preset number of analysis domains in the concrete mesoscopic model, and converting the analysis domains into surface domains; A mesh seed point is set for each face region, and mesh division is performed based on the mesh seed point to obtain a finite element mesh.

8. A concrete micro-model construction device, characterized in that: The device comprises: An acquisition module, used to acquire mesoscopic model parameters and aggregate parameters, wherein the mesoscopic model parameters include at least one of model size parameters and wall performance parameters, and the aggregate parameters include at least one of aggregate gradation ratio, gradation particle size range, and aggregate performance parameters; A first construction module is used to construct a model wall based on the model size parameters so that the model wall forms a closed model area; A generating module, for determining a plurality of aggregate particle sizes of each aggregate grade based on the aggregate grade ratio and the graded particle size range, and generating a plurality of aggregates based on the aggregate particle sizes; A calculation module, used for randomly placing the plurality of aggregates in the model area, simulating the motion state of each aggregate based on the wall performance parameter and the aggregate performance parameter, until each aggregate is in a stable state, and obtaining a target position of each aggregate; The second building module is used to build a concrete mesoscopic model based on the target position and aggregate particle size of each aggregate.

9. A computer device, characterized in that: include: Memory; processor; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the concrete mesoscopic model construction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for constructing a concrete mesoscopic model according to any one of claims 1 to 7 is implemented.

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