Monte carlo characterization method, system and medium for fiber distribution of foamed lightweight soil

Abstract

The present application relates to the technical field of numerical simulation modeling of civil engineering materials, and particularly relates to a Monte Carlo characterization method for fiber distribution of foamed lightweight soil, characterized by comprising the following steps: S101: establishing a numerical model and setting modeling parameters; S102: constructing a random distribution model of fibers and pores; S103: constructing an overall calculation model and dividing a grid; S104: simulating a loading process and determining an optimal fiber content. The present application overcomes the bottleneck that traditional artificial modeling is difficult to restore a micro random structure by introducing a Monte Carlo algorithm, and can quickly construct a three-dimensional random distribution numerical model of fibers and pores with statistical random distribution characteristics. The method can replace a traditional time-consuming and laborious physical trial-and-error method test by using a plastic damage model generated by finite element analysis software, so as to determine the optimal fiber content, and greatly reduce the time and material cost of engineering material research and development.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation modeling technology for civil engineering materials, and in particular to a Monte Carlo characterization method, system, and medium for the distribution of fibers in foamed lightweight soil. Background Technology

[0002] Foamed lightweight soil has broad application prospects in highway engineering subgrade filling and other fields. Unlike ordinary concrete, which is composed of densely packed cement and sand aggregates, foamed lightweight soil contains a large number of closed, independent pores, making it a typical high-porosity, porous, quasi-brittle material. This special microporous structure makes the material highly susceptible to brittle failure under external loads, which to some extent limits its long-term structural safety and load-bearing capacity.

[0003] To overcome the aforementioned shortcomings, fiber-reinforced lightweight foam is typically produced by incorporating a certain proportion of fibers (such as high-strength, low-permeability glass fibers) into the foamed lightweight soil. The fibers within the matrix effectively inhibit further crack propagation, thereby delaying the material's failure process and significantly improving its compressive strength, flexural strength, and overall toughness.

[0004] In the practical application of fiber-reinforced foamed lightweight soil, determining the optimal fiber content is a core prerequisite for ensuring its mechanical properties. Currently, traditional mix design and performance evaluation mainly rely on conventional "trial and error methods," which require numerous physical mix design tests. This approach is not only time-consuming and labor-intensive, but also inefficient in terms of material costs and research and development. With the development of finite element method (FEM) numerical analysis technology, numerical simulation of material mechanical behavior has become a highly efficient research method. It is worth noting that existing conventional concrete numerical simulations typically focus on the microscopic modeling of aggregates and cementitious matrices, while the simulation of foamed lightweight soil must characterize its unique internal pore structure. Since the existence of pores directly determines the stress concentration and damage evolution path of the material, simulating pores in finite element analysis (such as using ABAQUS software) is the biggest difference between foamed lightweight soil simulation and existing ordinary concrete simulation, and this is also the core key to ensuring the reliability of the analysis results.

[0005] However, in real foamed lightweight soil, both fibers and pores exhibit a highly random distribution. Conventional 3D modeling methods typically rely on manual drawing or simple regular arrays, making it difficult to quickly and realistically reproduce this microscopic level of 3D random distribution. If the random distribution characteristics of fibers and pores cannot be effectively characterized in the numerical model, it is impossible to accurately reflect the development law and damage mechanism of the plastic zone inside the material under external loads, resulting in distorted simulation results that are difficult to use to guide the selection of optimal fiber content.

[0006] Therefore, there is an urgent need in this field for a numerical characterization method that can efficiently construct the random distribution characteristics of fibers and pores in order to break through the technical bottleneck of microscopic modeling of porous composite materials. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of conventional three-dimensional modeling methods in the prior art, which are unable to effectively reproduce the three-dimensional random distribution state of the microscopic level of foamed lightweight soil fibers, and to provide a Monte Carlo characterization method, system and medium for the distribution of foamed lightweight soil fibers.

[0008] In a first aspect, the present invention provides a Monte Carlo characterization method for the fiber distribution of foamed lightweight soil, characterized by comprising the following steps: S101: Establish the matrix model and set the modeling parameters. Establish the matrix model using finite element analysis software and select the plastic damage model to configure the constitutive parameters of the matrix model of the foamed lightweight soil. S102: Construct a fiber random distribution model and a pore random distribution model. Using the Monte Carlo algorithm, based on a uniform random distribution setting and a set fiber content, a fiber random distribution model with controllable fiber position, orientation, and size is generated by randomly generating coordinates and determining boundaries. Furthermore, a corresponding pore random distribution model is constructed. The construction of the pore random distribution model includes the following sub-steps: S211: The pore size is divided into three intervals: greater than or equal to 0.2 mm and less than 0.5 mm, greater than or equal to 0.5 mm and less than 0.7 mm, and greater than or equal to 0.7 mm and less than or equal to 1.0 mm. S212: Preset the target pore generation quantity for each interval; S213: For any of the intervals, execute the following pore generation loop: randomly generate the position coordinates and radius of a pore, and determine whether the pore is within the set boundary range; if it is within the boundary range, retain the pore, and determine whether the total number of pores retained in the current interval meets the target pore generation quantity; if not, continue to generate the next pore; if it is not within the boundary range, discard the pore and regenerate a new pore. S214: Repeat the pore generation cycle until the total number of pores retained in the current interval meets the target number of pores generated; S215: Traverse all the intervals and output the porosity random distribution model; S103: Construct an overall calculation model and mesh it. Based on the constructed fiber random distribution model and pore random distribution model, generate a fiber foam lightweight soil calculation model by adjusting the matrix model in the finite element analysis software. Set the contact surface between the fiber, pore and matrix model in the model as a binding constraint, and perform meshing processing on the calculation model. S104: Simulate the loading process and determine the optimal fiber content. Based on the calculation model after mesh generation, simulate the uniaxial compression and bending loading process of the specimens under different fiber contents, obtain the corresponding stress cloud map, analyze the development law of its plastic zone, and take the widest plastic deformation distribution range and the smallest crack failure area shown in the stress cloud map as the standard for determining the optimal fiber content. Determine the optimal fiber content, and prepare fiber-reinforced foam lightweight soil entities according to the optimal fiber content.

[0009] Preferably, the numerical calculation parameters of the plastic damage model in step S101 include the flow potential offset, shear dilatation angle, ratio of ultimate strength under biaxial compression to that under uniaxial compression, viscosity coefficient, and invariant stress ratio.

[0010] Preferably, the fiber random distribution model described in step S102 is calculated through the following sub-steps: S201: Determine the sample size and fiber parameters, obtain and set the basic parameters for modeling, the basic parameters specifically including: model length, width, height, fiber length, fiber diameter and fiber content; S202: Randomly generate the coordinates of a fiber endpoint, and use a uniform random distribution function to create the three-dimensional coordinates of the fiber's starting endpoint within a three-dimensional space defined by the model's length, width, and height; S203: Generate the coordinates of the other end of the fiber by combining the fiber length, and determine whether the fiber model exceeds the set boundary; S204: If the boundary is exceeded, a new fiber is generated, and the process returns to step S203 to re-determine the boundary. S205: If the boundary is not exceeded, store the fiber endpoint coordinates; S206: After successfully storing the previous fiber, determine whether the total amount of fibers generated in the model meets the fiber content set in step S201. If not, trigger the process of generating the coordinates of the next fiber and repeat steps S202 to S205 until the total amount of fibers generated in the model meets the fiber content set in step S201. S207: Output the fiber random distribution model.

[0011] Preferably, the fiber foam lightweight soil calculation model described in step S103 belongs to the structural static response problem, the analysis type is set to static and general, and the initial and maximum displacement step sizes are set to 0.01 mm and 20 mm, respectively.

[0012] In a second aspect, the present invention provides a computer system comprising: a processor; and a memory storing a computer program, wherein when the computer program is executed by the processor, the processor performs the method as described in any of the first aspects.

[0013] In a third aspect, the present invention provides a computer-readable storage medium, characterized in that it stores a computer program thereon, wherein when the computer program is executed by a processor, the processor performs the method as described in any of the first aspects.

[0014] This invention overcomes the bottleneck of traditional manual modeling's inability to reproduce microscopic random structures by introducing the Monte Carlo algorithm, enabling the rapid construction of a three-dimensional random distribution numerical model of fibers and pores with statistical random distribution characteristics. This method, combined with a plastic damage model generated using finite element analysis software, can not only simulate the true mechanical response of porous quasi-brittle materials under uniaxial compression and bending loads, revealing the microscopic reinforcement mechanism of fibers inhibiting crack propagation through bridging, but also replace the traditional time-consuming and laborious physical trial-and-error method to determine the optimal fiber content, significantly reducing the time and material costs of engineering material development. Attached Figure Description

[0015] Figure 1 A flowchart of the Monte Carlo characterization method for fiber distribution in foamed lightweight soil.

[0016] Figure 2 This is a flowchart illustrating the construction method of the fiber random distribution model.

[0017] Figure 3 This is a schematic diagram of a random distribution model of glass fibers generated according to an embodiment of the present invention.

[0018] Figure 4 This is a schematic diagram of a random pore distribution model generated according to an embodiment of the present invention.

[0019] Figure 5 This is a schematic diagram illustrating the setting of binding constraints and mesh generation for a fiber foam lightweight soil calculation model according to an embodiment of the present invention.

[0020] Figure 6 The stress cloud diagram is a numerical simulation of the compressive strength under different fiber content conditions obtained according to an embodiment of the present invention.

[0021] Figure 7The fiber flexural stress cloud diagram obtained according to an embodiment of the present invention is shown when the fiber content is equal to 0.75%.

[0022] Figure 8 This is a schematic block diagram of a computer system 800 according to an embodiment of the present invention.

[0023] Marked in the image: 100 represents the Monte Carlo characterization method for fiber distribution in foamed lightweight soil, and S101~S104 represent the method steps. 200 shows the method for constructing the fiber random distribution model, S201~S207 are the method steps, and 300 is a schematic diagram of the generated glass fiber random distribution model. 310 represents the random distribution model of glass fibers generated when the volumetric doping content is 0.25%; 320 represents the random distribution model of glass fibers generated when the volumetric doping content is 0.50%; 330 represents the random distribution model of glass fibers generated when the volumetric doping content is 0.75%; and 340 represents the random distribution model of glass fibers generated when the volumetric doping content is 1.00%. 400 is a schematic diagram of the generated random pore distribution model. 510 is a schematic diagram of the binding constraint setting of the fiber foam lightweight soil calculation model according to an embodiment of the present invention, and 520 is a schematic diagram of the mesh generation setting of the fiber foam lightweight soil calculation model according to an embodiment of the present invention. Figure 600 shows the numerical simulation stress cloud diagrams of compressive strength obtained under different fiber content conditions according to embodiments of the present invention; Figure 610 shows the numerical simulation stress cloud diagrams of compressive strength with a fiber content of 0.00%; Figure 620 shows the numerical simulation stress cloud diagrams of compressive strength with a fiber content of 0.25%; Figure 630 shows the numerical simulation stress cloud diagrams of compressive strength with a fiber content of 0.50%; Figure 640 shows the numerical simulation stress cloud diagrams of compressive strength with a fiber content of 0.75%; Figure 650 shows the numerical simulation stress cloud diagrams of compressive strength with a fiber content of 1.00%; and Figure 660 shows the numerical simulation stress cloud diagrams of compressive strength with a fiber content of 1.25%. 700 represents the simulated fiber flexural stress cloud diagram obtained according to an embodiment of the present invention when the fiber content is equal to 0.75%. 800 is a computer system according to an embodiment of the present invention, 810 is a processor, and 820 is a memory. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0025] Unless otherwise specified, the use of terms such as "first," "second," and "third" in terminology is merely for distinguishing descriptions of identical or similar components and should not be interpreted as emphasizing or implying the relative importance of a particular component.

[0026] Furthermore, in the description of the embodiments of the present invention, "several", "more than", and "a number of" represent at least two. The number can be any number, such as two, three, four, five, six, seven, eight, or nine, and can even exceed nine.

[0027] Furthermore, in the description of the technical solution of this invention, unless otherwise explicitly specified / limited / restricted, the terms "set up," "install," "connect," "link," "provided with," "laid out," and "arranged" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections, as commonly used connection methods in the art. Such connections can be mechanical, electrical, or communication connections; they can be direct connections or indirect connections through an intermediate medium; and they can be internal connections between two components.

[0028] To make the technical solution and advantages of the present invention clearer, the following describes in more detail the implementation of the Monte Carlo characterization method for fiber distribution in foamed lightweight soil.

[0029] Figure 1 This is a basic flowchart of the implementation of the method of the present invention.

[0030] In step S101, a matrix model is established and modeling parameters are set. The matrix model is established using finite element analysis software, and the constitutive parameters of the matrix model of the foamed lightweight soil are configured using a plastic damage model.

[0031] Currently, several finite element analysis software programs (such as ANSYS and Midas) are available for fiber simulation. Among them, ABAQUS has significant advantages in handling highly nonlinear and complex material constitutive models. Therefore, the embodiments of this invention use ABAQUS software to construct the numerical model.

[0032] In the ABAQUS finite element analysis software, typical constitutive models suitable for foamed lightweight soil mainly include three types: dispersed fracture model, brittle fracture model, and plastic damage model. Since fiber-reinforced foamed lightweight soil is a fiber-reinforced porous quasi-brittle material, the dispersed fracture model and brittle fracture model have poor applicability, while the plastic damage model is more advantageous. This model has a wide range of applications and can accurately simulate the mechanical response of concrete-like materials under cyclic and dynamic loads. Therefore, considering both model applicability and research requirements, this invention ultimately selects the plastic damage model for numerical simulation. The numerical calculation parameters of the plastic damage model include flow potential offset, dilatation angle, ratio of ultimate strength under biaxial compression to uniaxial compression, viscosity coefficient, and invariant stress ratio. In one embodiment, the above calculation parameters are analyzed using the following values: Table 1 Numerical Calculation Parameters for Foamed Lightweight Soil

[0033] in, This represents the flow potential offset. For shear expansion angle; The ratio of ultimate strength under biaxial compression to ultimate strength under uniaxial compression; The viscosity coefficient; The invariant stress ratio.

[0034] In step S102, a random distribution model of fibers and pores is constructed. For the fiber model, a Monte Carlo algorithm is used. Based on a uniform random distribution setting and a predetermined fiber content, a random distribution model with controllable fiber position, orientation, and size is generated through random coordinate generation and boundary judgment. A corresponding random distribution model of pores is also constructed. Optionally, the generation of the random distribution model with controllable fiber position, orientation, and size, and the construction of the corresponding random distribution model of pores, can be achieved using a computer programming language combined with a scientific computing library. For example, the Python scientific computing library can be used for secondary development.

[0035] The Monte Carlo method is a numerical computation method based on probability and statistics theory, using random numbers to solve complex problems. Fibers in foamed lightweight soil exhibit random distribution characteristics, and their random distribution model can be quickly constructed using the Monte Carlo algorithm. This model is based on a uniform random distribution setting, allowing for the controllable generation of fiber position, orientation, and size. The construction process of the fiber random distribution model is as follows: Figure 2 As shown, the generated random distribution model of glass fibers is as follows: Figure 3 As shown. In the embodiments described above, the fiber random distribution model uses 6mm glass fibers as the object, and the fiber doping amounts are set to 0.00%, 0.25%, 0.50%, 0.75%, 1.00%, and 1.25%.

[0036] The following describes in conjunction with Figure 2 the specific steps of the Monte Carlo algorithm for generating a random distribution model with controllable fiber positions, orientations and sizes.

[0037] In step S201, the specimen size and fiber parameters are determined. In this step, basic parameters for modeling are acquired and set, and the basic parameters specifically include: the length, width and height of the model, as well as the fiber length, fiber diameter and fiber content.

[0038] In step S202, the coordinates of one endpoint of a fiber are randomly generated. In this step, a uniform random distribution function is used to generate the three-dimensional coordinates of the starting endpoint of the fiber within the specimen size range. First, within the spatial range of the model, the uniform random distribution function is used to generate the coordinate of one endpoint of the fiber. Let the coordinates of one end of the fiber be x_1, y_1, z_1, and the coordinates of the other end be x_2, y_2, z_2. The specific generation rules for x_1, y_1 and z_1 are: x_1=random_uniform(0, length), y_1=random_uniform(0, width), z_1=random_uniform(0, height).

[0039] In step S203, it is determined whether the boundary is exceeded. The coordinates (x_2, y_2, z_2) of the other endpoint of the fiber are generated in combination with the fiber length, and it is determined whether the fiber model exceeds the set specimen boundary. After obtaining the coordinates of one end of the fiber, the other end of the fiber is calculated jointly based on the random direction vector and the fixed fiber length. For example, the length of the fiber can be fixed at 6 mm, and a direction in the three-dimensional space can be randomly generated. In the process of randomly generating the direction in three-dimensional space, random values can be respectively assigned to the included angles between the fiber and the X, Y, Z axes (for example, denoted as θx, θy, θz) within the range of 0 to 2π. Finally, using this random direction vector and the fixed fiber length, the coordinates of the other end are calculated starting from the starting point. After obtaining the coordinates of the other end of the fiber, the judgment condition for determining whether it exceeds the boundary is: 0<x_2<model length, 0<y_2<model width, and 0<z_2<model height.

[0040] In step S204, if the boundary is exceeded, the current coordinates are discarded, a new fiber is regenerated, and the process returns to step S203 to perform the boundary judgment again.

[0041] In step S205, if the judgment result of step S203 is no (that is, the boundary is not exceeded), the coordinates of the two endpoints of the fiber are recorded and stored, and the fiber segment is generated and written into the model.

[0042] In step S206, after successfully storing the previous fiber, it is determined whether the total amount of fibers generated in the model meets the fiber content set in step S201. If not, the process of generating the coordinates of the next fiber is triggered, and steps S202 to S205 are repeated until the total amount of fibers generated in the model meets the fiber content set in step S201.

[0043] In step S207, the generated fiber random distribution model is output.

[0044] Figure 3 The diagram illustrates the generated random distribution models of glass fibers, showing the models generated when the fiber length is fixed at 6 mm and the volumetric doping concentration is 0.25%, 0.50%, 0.75%, and 1.00%. Specifically, 310 represents the random distribution model generated with a volumetric doping concentration of 0.25%, 320 with 0.50%, 330 with 0.75%, and 340 with 1.00%.

[0045] Furthermore, in step S102, the pore random distribution model is constructed. The specific logic for constructing the pore random distribution model is as follows: First, in step S211, based on the microscopic characteristics of the foamed lightweight soil, the pore size is divided into three intervals: greater than or equal to 0.2 mm and less than 0.5 mm, greater than or equal to 0.5 mm and less than 0.7 mm, and greater than or equal to 0.7 mm and less than or equal to 1.0 mm.

[0046] Next, in step S212, based on the overall porosity setting value of the sample and the gradation ratio of pores in different intervals, the corresponding target pore generation quantity is pre-allocated and set for each interval.

[0047] In step S213, for each pore size range, pores are generated sequentially based on the Monte Carlo algorithm. A pore's spatial coordinates and radius are randomly generated within the sample size range using a uniform random distribution function, and it is determined whether the pore model is completely within the set sample boundary range. If the pore is within the set boundary range, it is recorded and retained. Then, it is determined whether the total number of successfully generated pores in the current range has reached the preset target number of generated pores. If not, the three-dimensional coordinates and radius of the next pore are randomly generated. If the pore exceeds the set boundary range, the currently generated pore coordinates are discarded and removed, a new pore is generated, and the boundary check is performed again.

[0048] In step S214, the system will continue to cycle through the above coordinate generation and boundary judgment process until the total number of pores successfully retained in the current interval fully meets the preset target number of pores generated in that interval.

[0049] In step S215, the pore generation operation for all three intervals is completed in the same way, and finally a complete pore model that conforms to the set random distribution characteristics is output.

[0050] The generated pore random distribution model is as follows Figure 4 As shown.

[0051] The present invention employs a random pore distribution model, dividing the material into three intervals and performing a random pore generation process for each interval. This random pore distribution model is adopted because the pore size of real foamed lightweight soil is not uniformly distributed; existing research clearly indicates that its pore size distribution almost follows a log-normal distribution. The underlying logic of dividing the pore size into three intervals and controlling the generation quantity for each interval is precisely to fit this asymmetric statistical distribution characteristic of "many small pores and few large pores." When setting the pore size, the model can allocate different weights based on their varying influence on compressive strength, thus establishing a quantitative correlation between the generated pore model and the material's macroscopic mechanical properties.

[0052] The generation method of the pore random distribution model adopted in this invention has the following beneficial effects: First, the interval-based random generation process adopted in this invention can accurately fit the real distribution. By setting three different intervals and controlling the probability of their occurrence, the statistical distribution characteristics of real materials, such as fewer large pores and more small pores, are accurately reproduced in the model, rather than pursuing absolute uniformity in numerical terms. Second, the interval-based random generation process adopted in this invention can avoid unreasonable random clustering. Completely random generation of the entire interval, although seemingly random, can easily lead to the accidental clustering of large-pore pores in the same local area in finite element simulation, forming stress concentration areas or weak surfaces that do not conform to reality. Interval generation can effectively control the spatial distribution of large-pore pores, making it more consistent with the more uniform stress distribution in real materials on a macroscopic scale.

[0053] In step S103, an overall computational model is constructed and meshed. Based on the constructed fiber random distribution model and pore random distribution model, a fiber-reinforced foam lightweight soil computational model is generated in the finite element analysis software by adjusting the matrix model. The contact surfaces between the fibers, pores, and the matrix model are set as binding constraints, and the computational model is meshed. The binding constraints are set as follows: Figure 5 As shown in 510, the settings for meshing the overall model are as follows: Figure 5 As shown in 520.

[0054] In the embodiment described above, the calculation model of fiber foam lightweight soil belongs to the static response problem of structure. The analysis type is set to static and general, the geometric nonlinearity is set to on, and the initial and maximum displacement steps are set to 0.01 mm and 20 mm, respectively.

[0055] In step S104, the loading process is simulated and the optimal fiber content is determined. Based on the computational model after mesh generation, the uniaxial compression and bending loading processes of the specimens under different fiber contents are simulated to obtain the corresponding stress cloud diagrams. The development law of the plastic zone is analyzed to determine the optimal fiber content, and fiber-reinforced foam lightweight soil entities are prepared according to the optimal fiber content. For example, the size of the failure area can be determined by extracting quantitative data such as the volume ratio of plastic strain elements or damage dissipation energy from the finite element analysis results.

[0056] In the embodiments described above, the corresponding stress cloud diagrams were obtained by simulating the uniaxial compressive strength of specimens with different fiber content. Figure 6 Numerical simulation stress cloud diagrams of compressive strength obtained according to this embodiment are shown. Specifically, 610 is the numerical simulation stress cloud diagram of compressive strength with a fiber content of 0.00%, 620 is the numerical simulation stress cloud diagram of compressive strength with a fiber content of 0.25%, 630 is the numerical simulation stress cloud diagram of compressive strength with a fiber content of 0.50%, 640 is the numerical simulation stress cloud diagram of compressive strength with a fiber content of 0.75%, 650 is the numerical simulation stress cloud diagram of compressive strength with a fiber content of 1.00%, and 660 is the numerical simulation stress cloud diagram of compressive strength with a fiber content of 1.25%. Figure 7 The simulated stress cloud diagram of fiber flexural strength obtained according to this embodiment is shown when the fiber content is equal to 0.75%.

[0057] from Figure 6 and Figure 7 As can be seen, in this embodiment, the stress distribution simulated for flexural strength is similar to that for compressive strength, and the failure area of ​​the specimen is also concentrated in the middle of the specimen. Compared with other fiber dosages, when the fiber dosage is 0.75%, the stress cloud diagram shows the widest range of plastic deformation distribution in the specimen, and the failure area with high stress concentration is relatively small, indicating that the specimen with this dosage can more evenly disperse internal stress after being subjected to force. This is mainly attributed to the fiber effectively inhibiting crack propagation through bridging, thereby delaying the failure process and reflecting the improvement of material toughness.

[0058] The Monte Carlo characterization method for fiber distribution in foamed lightweight soil disclosed in this invention overcomes the bottleneck of traditional manual modeling, which struggles to recreate microscopic random structures, by introducing the Monte Carlo algorithm. This method enables the rapid construction of a three-dimensional random distribution numerical model of fibers and pores with statistically random distribution characteristics. Combined with a plastic damage model generated using finite element analysis software, this method can not only simulate the mechanical response of porous quasi-brittle materials under uniaxial compression and bending loads, revealing the microscopic reinforcement mechanism by which fibers inhibit crack propagation through bridging, but also replace traditional time-consuming and labor-intensive physical trial-and-error experiments to determine the optimal fiber content, significantly reducing the time and material costs of engineering material development.

[0059] According to another aspect of the invention, a computer system is also provided, which may include a processor; and a memory storing a computer program, which, when executed by the processor, executes a Monte Carlo characterization method for the distribution of foamed lightweight soil fibers according to an embodiment of the invention.

[0060] Figure 8 This is a schematic block diagram illustrating a computer device 800 according to an embodiment of the present invention.

[0061] like Figure 8 As shown, the computer device 800 according to this embodiment may include a processor 810 and a memory 820. The memory 820 stores a computer program.

[0062] Processor 810 is any processing device, such as a microprocessor, that operates according to a computer program installed / stored in memory 820. Memory 820 is any volatile or non-volatile storage element, such as a hard disk, solid-state drive, ROM, or RAM. The program executed by processor 810, etc., is stored in memory 820. Figure 8 The computer device 800 shown can be used to implement the Monte Carlo characterization method for the fiber distribution of foamed lightweight soil according to an embodiment of the present invention.

[0063] According to another aspect of the invention, a computer-readable storage medium is also provided, comprising a computer program that, when executed by a processor, enables the processor to implement a Monte Carlo characterization method for the fiber distribution of foamed lightweight soil according to an embodiment of the invention.

[0064] The computer program product described in this invention may be a computer software package, computer code instructions, or a computer-readable tangible medium having computer instructions tangibly stored (and / or encoded) thereon, which can be executed by a processor to perform the operations described herein.

[0065] The hardware computing device or its components described in this invention can be implemented by various suitable hardware means, including but not limited to FPGA, ASIC, SoC, discrete gate or transistor logic, discrete hardware components, or any combination thereof. The devices, apparatuses, methods, and systems involved in this invention are not limited to any specific hardware architecture or configuration. Components in the disclosed devices, apparatuses, and systems may be discrete or integrated, and may be combined in different ways and / or replaced or supplemented by other components. It should be understood that the teachings of this invention can be implemented in various forms, including hardware, software, firmware, dedicated processors, or combinations thereof.

[0066] The block diagrams of the devices, equipment, methods, systems, etc., involved in this invention are merely exemplary and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these circuits, devices, equipment, and systems can be connected, arranged, and configured in any manner that achieves the desired purpose.

[0067] In the foregoing description, the present invention has been described based on embodiments. The various embodiments described above are merely illustrative, and those skilled in the art should understand that the combination of constituent elements and processes of these embodiments can be modified in various ways, and such modifications are also within the scope of the present invention.

Claims

1. A Monte Carlo characterization method for fiber distribution in foamed lightweight soil, characterized in that, Includes the following steps: S101: Establish the matrix model and set the modeling parameters. Establish the matrix model using finite element analysis software and select the plastic damage model to configure the constitutive parameters of the matrix model of the foamed lightweight soil. S102: Construct a fiber random distribution model and a pore random distribution model. Using the Monte Carlo algorithm, based on a uniform random distribution setting and a set fiber content, a fiber random distribution model with controllable fiber position, orientation, and size is generated by randomly generating coordinates and determining boundaries. Furthermore, a corresponding pore random distribution model is constructed. The construction of the pore random distribution model includes the following sub-steps: S211: The pore size is divided into three intervals: greater than or equal to 0.2 mm and less than 0.5 mm, greater than or equal to 0.5 mm and less than 0.7 mm, and greater than or equal to 0.7 mm and less than or equal to 1.0 mm. S212: Preset the target pore generation quantity for each interval; S213: For any of the intervals, execute the following pore generation loop: randomly generate the position coordinates and radius of a pore, and determine whether the pore is within the set boundary range; if it is within the boundary range, retain the pore, and determine whether the total number of pores retained in the current interval meets the target pore generation quantity; if not, continue to generate the next pore; if it is not within the boundary range, discard the pore and regenerate a new pore. S214: Repeat the pore generation cycle until the total number of pores retained in the current interval meets the target number of pores generated; S215: Traverse all the intervals and output the porosity random distribution model; S103: Construct an overall calculation model and mesh it. Based on the constructed fiber random distribution model and pore random distribution model, generate a fiber foam lightweight soil calculation model by adjusting the matrix model in the finite element analysis software. Set the contact surface between the fiber, pore and matrix model in the model as a binding constraint, and perform meshing processing on the calculation model. S104: Simulate the loading process and determine the optimal fiber content. Based on the calculation model after mesh generation, simulate the uniaxial compression and bending loading process of the specimens under different fiber contents, obtain the corresponding stress cloud map, analyze the development law of its plastic zone, and take the widest plastic deformation distribution range and the smallest crack failure area shown in the stress cloud map as the standard for determining the optimal fiber content. Determine the optimal fiber content, and prepare fiber-reinforced foam lightweight soil entities according to the optimal fiber content.

2. The method according to claim 1, characterized in that, The numerical calculation parameters of the plastic damage model in step S101 include the flow potential offset, shear dilatation angle, ratio of ultimate strength under biaxial compression to that under uniaxial compression, viscosity coefficient, and invariant stress ratio.

3. The method according to claim 1, characterized in that, The fiber random distribution model described in step S102 is calculated through the following sub-steps: S201: Determine the sample size and fiber parameters, obtain and set the basic parameters for modeling, the basic parameters specifically including: model length, width, height, fiber length, fiber diameter and fiber content; S202: Randomly generate the coordinates of a fiber endpoint, and use a uniform random distribution function to create the three-dimensional coordinates of the fiber's starting endpoint within a three-dimensional space defined by the model's length, width, and height; S203: Combine the fiber length to generate the coordinates of the other end of the fiber, and determine whether the fiber model exceeds the set boundary; S204: If the boundary is exceeded, a new fiber is generated, and the process returns to step S203 to re-determine the boundary. S205: If the boundary is not exceeded, store the fiber endpoint coordinates; S206: After successfully storing the previous fiber, determine whether the total amount of fibers generated in the model meets the fiber content set in step S201. If not, trigger the process of generating the coordinates of the next fiber and repeat steps S202 to S205 until the total amount of fibers generated in the model meets the fiber content set in step S201. S207: Output the fiber random distribution model.

4. The method according to claim 1, characterized in that, The fiber foam lightweight soil calculation model described in step S103 belongs to the structural static response problem. The analysis type is set to static and general, and the initial and maximum displacement step sizes are set to 0.01 mm and 20 mm, respectively.

5. A computer system, characterized in that, include: processor; as well as Memory, which stores computer programs When the computer program is executed by the processor, the processor performs the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, causes the processor to perform the method as described in any one of claims 1 to 4.

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