Plastic concrete diaphragm wall actual wall body three-dimensional modeling system and method

Abstract

The invention relates to the technical field of three-dimensional model analysis, in particular to a plastic concrete diaphragm wall actual wall body three-dimensional modeling system and method. The method comprises the following steps: scanning the plastic concrete diaphragm wall through geological radar equipment to obtain scanning data of the plastic concrete diaphragm wall; performing gridding processing on the plastic concrete diaphragm wall scanning data to obtain plastic concrete diaphragm wall gridding data; obtaining wall structure feature data according to the plastic concrete diaphragm wall gridding data; modeling is conducted on the wall body structure feature data, and a plastic concrete diaphragm wall material attribute model is obtained; obtaining a plastic concrete anti-seepage wall fusion model according to the plastic concrete anti-seepage wall gridding data and the plastic concrete anti-seepage wall material attribute model; and analyzing the plastic concrete diaphragm wall fusion model to obtain a plastic concrete diaphragm wall simulation model. The stress concentration area can be identified, and the safety performance of the plastic concrete diaphragm wall can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional model analysis, and in particular to a three-dimensional modeling system and method for an actual wall of a plastic concrete anti-seepage wall. Background Art

[0002] As an important structure in water conservancy and civil engineering, plastic concrete cut-off walls are widely used in buildings such as dams, dikes, and subway tunnels to prevent water leakage and ensure the long-term safety of the structure. However, due to the material properties of plastic concrete cut-off walls and their complex mechanical environment, they are prone to stress concentration, cracks, and structural deformation under long-term loads. In order to ensure the safety of the cut-off wall, the stress distribution and dynamic change characteristics of the plastic concrete cut-off wall under different working conditions must be considered during structural design and analysis. Traditional structural design methods mainly rely on empirical formulas and linear static analysis, which are difficult to accurately reflect the stress concentration areas and potential failure risks under complex stress fields. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a three-dimensional modeling system and method for the actual wall of a plastic concrete anti-seepage wall to solve at least one of the above technical problems.

[0004] The present application provides a method for three-dimensional modeling of an actual wall of a plastic concrete anti-seepage wall, the method comprising:

[0005] S1. Scan the plastic concrete anti-seepage wall by using geological radar equipment to obtain scanning data of the plastic concrete anti-seepage wall;

[0006] S2. Gridding the scanned data of the plastic concrete anti-seepage wall to obtain gridded data of the plastic concrete anti-seepage wall; extracting wall structure features from the gridded data of the plastic concrete anti-seepage wall to obtain wall structure feature data; modeling the wall material properties of the wall structure feature data to obtain a material property model of the plastic concrete anti-seepage wall;

[0007] S3, fusing the grid data of the plastic concrete anti-seepage wall and the material property model of the plastic concrete anti-seepage wall to obtain a fusion model of the plastic concrete anti-seepage wall;

[0008] S4. Perform stress analysis on the plastic concrete anti-seepage wall fusion model to obtain a plastic concrete anti-seepage wall simulation model.

[0009] The scanning data of the plastic concrete anti-seepage wall generated in the present invention greatly improves the accuracy of wall modeling, lays a good foundation for subsequent processing, reduces modeling errors, and improves the accurate reproduction of wall geometry. The grid processing converts the scanning data into an operable three-dimensional grid model, which can accurately describe the surface details of the wall. The material property modeling of the wall structure feature data can be combined with the physical properties of the material (such as density, compressive strength, impermeability, etc.), which increases the physical authenticity of the model, so that the material properties can be more accurately reflected in the subsequent simulation and analysis, especially for multi-material hybrid structures, which can better analyze the performance differences between materials. The generated fusion model not only contains geometric information, but also combines material properties, so that the subsequent stress analysis can be based on the real structure and material interaction, which improves the accuracy and reliability of the simulation results. Stress analysis is performed on the fusion model to effectively simulate the stress state of the wall under actual working conditions and predict potential stress concentration areas or structural weaknesses. The simulation results can provide data support for optimized design, maintenance management and safety assessment.

[0010] Optionally, the plastic concrete anti-seepage wall scanning data includes first plastic concrete anti-seepage wall scanning data and second plastic concrete anti-seepage wall scanning data, and S1 includes:

[0011] Scanning the plastic concrete anti-seepage wall at a first angle by using a geological radar device to obtain first plastic concrete anti-seepage wall scanning data;

[0012] The plastic concrete anti-seepage wall is scanned at a second angle by the geological radar device to obtain second plastic concrete anti-seepage wall scanning data, wherein the angle threshold data corresponding to the first angle scan and the angle threshold data corresponding to the second angle scan are in a vertical relationship.

[0013] In the present invention, by scanning at different angles (vertical relationship), it is ensured that each surface of the plastic concrete anti-seepage wall can be captured by the geological radar equipment, thereby reducing the shadow, obstruction or blind area problems caused by a single angle. The scanning data of the first angle and the second angle complement each other and provide complete three-dimensional surface information of the wall. Due to the reflection characteristics of scanning at different angles, scanning at a single angle may cause certain surface features (such as cracks, textures, etc.) to be lost or blurred. By scanning at different angles with a vertical relationship, all the detailed features of the wall surface are captured from different directions, making the surface features clearer and more accurate.

[0014] Optionally, S2 includes:

[0015] Performing point cloud meshing processing on the scanning data of the plastic concrete anti-seepage wall to obtain mesh data of the plastic concrete anti-seepage wall;

[0016] Optimize the mesh data of the plastic concrete anti-seepage wall to obtain simplified mesh data of the plastic concrete anti-seepage wall;

[0017] Performing mesh smoothing processing on the simplified mesh data of the plastic concrete anti-seepage wall to obtain the mesh smoothing data of the plastic concrete anti-seepage wall;

[0018] Calculate the mesh normal of the plastic concrete anti-seepage wall mesh smoothing data to obtain the wall structure characteristic data;

[0019] Initialize the material mapping model according to the wall structure characteristic data to obtain the material mapping model;

[0020] Physical material parameter data is obtained, and finite element modeling is performed on the material mapping model according to the physical material parameter data to obtain a material property model of the plastic concrete anti-seepage wall.

[0021] In the present invention, a large amount of three-dimensional point cloud data is converted into an ordered grid structure by gridding point cloud data, and irregular point cloud data is organized into a structured grid. The grid optimization step reduces redundant information by reducing the number of grid faces and nodes, while maintaining the geometric accuracy of the model. By simplifying the grid, the computational complexity is greatly reduced, and the subsequent simulation and calculation speed are accelerated. The simplified grid is smoothed to eliminate the surface irregularities caused by simplification, making the surface flatter and smoother, which helps to improve the visual effect and physical consistency of the model and ensure the stability of the surface normal. By calculating the normal of the smoothed grid, the orientation of each grid face or vertex is accurately determined, which is crucial for subsequent lighting calculations, material mapping and physical simulation, and can help better capture the structural characteristics of the wall. The material mapping model is initialized according to the wall structure feature data, and the material properties of the wall can correspond to its geometric characteristics by combining the physical material properties with the geometric model. Finite element modeling makes the wall model not only geometrically accurate, but also physically realistic.

[0022] Optionally, S3 includes:

[0023] According to the mesh data of the plastic concrete anti-seepage wall and the material property model of the plastic concrete anti-seepage wall, mesh material parameters are matched to obtain initial fusion model data;

[0024] Perform material parameter interpolation processing on the initial fusion model data to obtain material interpolation model data;

[0025] The material interpolation model data is optimized to obtain the fusion model of plastic concrete anti-seepage wall.

[0026] In the present invention, accurate material parameter matching is used to ensure that each part of the model can reflect its true physical properties, making the simulation analysis results more reliable and helping to accurately identify stress concentration areas, deformation conditions and material failure risks during structural analysis. Material parameter interpolation processing allows material properties to be smoothly distributed between different nodes and regions in the grid. In particular, when material parameters are unevenly distributed, material properties are adjusted according to local stress fields, temperature gradients or other physical conditions to ensure that the material properties in the model transition smoothly and consistently. After the interpolation processing is completed, the material interpolation model is optimized to further improve its accuracy and computational efficiency. The optimization process involves locally correcting the material distribution, removing redundant nodes or adjusting the parameters of the transition area to ensure that the computational complexity is reduced without affecting the simulation results. The present invention ensures that the geometric model and material model of the plastic concrete cut-off wall can be closely combined and generate a physically accurate three-dimensional fusion model. Since both the geometric structure and material properties are accurately modeled, the stress and deformation conditions in the actual project can be better simulated in subsequent structural analysis and simulation.

[0027] Optionally, S4 includes:

[0028] The stress validity analysis of the plastic concrete anti-seepage wall fusion model is carried out to obtain the first plastic concrete anti-seepage wall simulation model;

[0029] Perform stress multi-scale analysis on the fusion model of plastic concrete anti-seepage wall to obtain the second simulation model of plastic concrete anti-seepage wall;

[0030] The safety factors are integrated and screened according to the first plastic concrete anti-seepage wall simulation model and the second plastic concrete anti-seepage wall simulation model to obtain a concrete anti-seepage wall simulation model.

[0031] In the present invention, the stress validity analysis is performed on the fusion model of the plastic concrete anti-seepage wall. By identifying the stress distribution of the wall under actual working conditions, the stress concentration area and its possible influence are analyzed to ensure that the stress analysis results are true and reliable, and to avoid erroneous conclusions caused by errors or calculation deviations in the preliminary simulation. Through multi-scale analysis, the stress state of the plastic concrete anti-seepage wall is analyzed in detail at different scales (such as global, local, and micro). Global stress analysis can provide overall mechanical behavior, while local and micro stress analysis can capture more subtle stress changes and local stress concentration areas. On the basis of stress validity analysis and multi-scale analysis, the safety factors in each simulation model are fused and screened. The stress analysis results of different scales are combined to perform a unified safety factor evaluation to ensure that the safety factor of each area is fully verified. Through the screening process, the most unsafe area is identified and the design is further optimized.

[0032] Optionally, the stress effectiveness analysis includes:

[0033] Obtaining plastic concrete cut-off wall constraint data;

[0034] According to the constraint data of the plastic concrete cut-off wall, load is applied to the fusion model of the plastic concrete cut-off wall to obtain load application model data;

[0035] Performing static stress processing and dynamic stress processing on the load application model data, and obtaining static stress distribution data and dynamic stress distribution data;

[0036] Extract stress concentration areas according to static stress distribution data and dynamic stress distribution data to obtain stress concentration area data;

[0037] The plastic concrete cut-off wall fusion model is annotated according to the stress concentration area data, the static stress distribution data and the dynamic stress distribution data to obtain the first plastic concrete cut-off wall simulation model.

[0038] The constraint data of the plastic concrete cut-off wall is obtained in the present invention to ensure that the model can truly reflect the constraint conditions (such as support, fixed boundary conditions, foundation conditions, etc.) in the actual project in the simulation analysis. By applying actual loads (such as gravity, wind load, water pressure, etc.) to the fusion model of the plastic concrete cut-off wall, the load application model data is generated to ensure that the stress analysis results can truly reflect the performance of the plastic concrete cut-off wall under different load conditions. Static stress analysis (such as stress distribution under long-term fixed load) and dynamic stress analysis (such as stress changes under transient loads or periodic loads) are performed on the load application model to ensure a comprehensive understanding of the stress distribution of the plastic concrete cut-off wall under static and dynamic conditions. According to the static and dynamic stress distribution data, the stress concentration area is extracted, and the areas with highly concentrated or sudden stress changes in the wall can be identified. These areas are often potential risk points for structural failure or damage. Based on the stress concentration area, static stress distribution data and dynamic stress distribution data, the fusion model of the plastic concrete cut-off wall is annotated. The stress distribution is visualized by annotation, and the stress concentration area is highlighted, so that designers can intuitively understand the stress condition in the structure.

[0039] Optionally, the stress multi-scale analysis includes:

[0040] Initialize the stress field of the plastic concrete cut-off wall fusion model to obtain initial stress field data;

[0041] Perform stress field stratification processing according to the initial stress field data to obtain stress field stratification data;

[0042] Perform multi-scale coupling analysis based on stress field layered data to obtain multi-scale coupling stress data;

[0043] The stress model is fused according to the multi-scale coupled stress data and the plastic concrete anti-seepage wall fusion model to obtain a second plastic concrete anti-seepage wall simulation model;

[0044] The stress models include:

[0045] Extract macro and micro stress data based on multi-scale coupled stress data to obtain macro and micro stress data;

[0046] Extract stress concentration areas based on macro and micro stress data to obtain stress concentration area data;

[0047] The plastic concrete cut-off wall fusion model is annotated using stress concentration area data to obtain a preliminary stress fusion model.

[0048] According to the macro and micro stress data and the preliminary stress fusion model, hierarchical stress fusion is performed to obtain the second plastic concrete anti-seepage wall simulation model;

[0049] The macro-micro stress data include macro stress data and micro stress data, and the stress concentration area extraction includes:

[0050] Perform graph neural network encoding on macro stress data and micro stress data, and obtain macro stress characteristic graph data and micro stress characteristic graph data;

[0051] Performing distance calculation on the macroscopic stress characteristic map data and the microscopic stress characteristic map data to obtain macroscopic stress map characteristic clustering data and microscopic stress map characteristic clustering data respectively;

[0052] Dynamic stress threshold control is performed according to the macroscopic stress map characteristic clustering data and the microscopic stress map characteristic clustering data, and macroscopic stress threshold matrix data and microscopic stress threshold matrix data are obtained respectively;

[0053] Using the macro stress threshold matrix data to locate the macro stress hotspot of the macro stress map feature clustering data, and obtaining the macro stress hotspot location map data, and using the micro stress threshold matrix data to locate the micro stress hotspot of the micro stress map feature clustering data, and obtaining the micro stress hotspot location map data;

[0054] Perform multi-modal stress layered mapping based on the macroscopic stress hotspot location map data and the microscopic stress hotspot location map data to obtain layered stress mapping matrix data;

[0055] The concentrated area synthesis is performed according to the layered stress mapping matrix data to obtain the stress concentration area data.

[0056] In the present invention, the stress field of the plastic concrete anti-seepage wall fusion model is initialized to generate initial stress field data. Stress field initialization can provide basic data for subsequent stress stratification and multi-scale analysis, ensuring that the initial state of the stress field is based on accurate model geometry and material properties. The initial stress field data is processed in layers, and the entire stress field is divided into different layers according to the stress gradient or stress distribution characteristics, so that the overall stress field can be refined into more manageable layered data, especially for the analysis of complex structures and uneven stress fields. By performing multi-scale coupling analysis on the layered stress field, stress data of various scales (such as macroscopic and microscopic levels) are integrated. Multi-scale coupling analysis can capture stress changes at different levels from overall structural stress to local details. On the basis of multi-scale coupling analysis, the multi-scale coupling stress data is fused with the plastic concrete anti-seepage wall fusion model for stress model fusion to ensure that stress information of different scales is integrated in the overall model to achieve more accurate global stress field simulation. Through stress field stratification, multi-scale coupling analysis and stress model fusion, the stress distribution of plastic concrete cut-off walls can be fully covered, not only focusing on the macroscopic stress state, but also going deep into the local stress concentration area, which comprehensively improves the accuracy and depth of stress analysis.

[0057] In the present invention, macroscopic and microscopic stress data are combined to accurately identify stress concentration areas at different scales, ensuring a significant improvement in the accuracy of the model, being able to more comprehensively display the stress characteristics of the plastic concrete anti-seepage wall, and enhancing the accuracy of stress concentration area identification. The dynamic threshold control mechanism adaptively adjusts the identification criteria according to real-time data, so that the model can flexibly respond to different stress distribution conditions. Through multimodal stress hierarchical mapping, the macroscopic and microscopic stress data are finely hierarchically divided and displayed, and the stress data of each level are mapped into a multimodal matrix, so that the model can display the distribution of stress at different spatial and temporal levels in detail. By using the stress concentration area data to mark the plastic concrete anti-seepage wall fusion model, and then performing hierarchical stress fusion according to the model, the simulation model is closer to the actual situation. Hierarchical stress fusion provides a more realistic simulation effect on stress concentration areas of different scales. The hierarchical mapping data is integrated through concentrated area synthesis to generate a high-precision stress concentration area model. The synthesis step integrates multi-level stress information so that the final generated model can fully display the regional distribution and characteristics of stress concentration.

[0058] Optionally, the stress field layering process includes:

[0059] Material properties are layered according to the initial stress field data to obtain stress field material layering data;

[0060] Perform stress gradient stratification according to the initial stress field data to obtain stress field gradient stratification data;

[0061] The stress field material stratification data and the stress field gradient stratification data are cross-labeled to obtain the stress field stratification data.

[0062] In the present invention, material properties are layered according to the initial stress field data to ensure that the stress analysis of each layer can reflect the difference in material properties, taking into account the different mechanical properties of the material (such as elastic modulus, density, strength, etc.), so that the stress field distribution can more truly reflect the different reactions of the material. According to the initial stress field data, the stress gradient is layered, and the region where the stress gradient is large or changes rapidly is identified, and the stress concentration region and the region where the stress changes slowly are distinguished, so that the subsequent stress field analysis can focus on the key position. According to the material property layered data and the stress gradient layered data, cross-marking is performed to generate more refined stress field layered data, and the material properties are combined with the stress gradient to mark the regions where the stress changes significantly and the material property differences are obvious. The combination of material property layering and stress gradient layering enables the method to perform a deeper stress field analysis on complex structures. Different materials may show different stress behaviors in the structure, and the rapid change of stress gradient may also indicate a potential risk of structural failure. Through cross-marking, these complex stress distribution characteristics can be captured.

[0063] Optionally, the stress gradient layering comprises:

[0064] Perform gradient calculation based on initial stress field data to obtain stress field gradient data;

[0065] Cluster calculation is performed based on stress field gradient data to obtain stress field gradient cluster characteristic data;

[0066] The initial stress field data are partitioned into adjacent areas according to the stress field gradient clustering feature data to obtain the initial stress field gradient stratified data;

[0067] Calculate the local gradient change rate according to the initial stress field gradient stratification data to obtain the gradient stratification local change rate data;

[0068] According to the local change rate data of gradient stratification, the gradient sensitive areas are classified according to the initial stress field gradient stratification data to obtain the stress field gradient stratification data.

[0069] The stress gradient calculation in the present invention ensures that the complex changes in the stress field can be captured, so that the local stress behavior of the structure can be accurately reflected. Cluster calculation helps to more clearly show the characteristics of the wall stress distribution by grouping areas with similar stress gradients. The initial stress field data is partitioned into adjacent areas according to the stress field gradient clustering feature data, and the structure is divided into multiple adjacent areas, the local stress differences in the structure are identified, and they are summarized into specific areas. By calculating the local gradient change rate of the initial stress field gradient stratification data, the dynamic changes of the stress gradient in each area can be captured, revealing the local change characteristics in the stress distribution, especially identifying areas where the stress gradient suddenly changes or transitions slowly. The initial stress field gradient stratification data is classified into gradient sensitive areas according to the gradient stratification local change rate data to ensure that sensitive areas can be labeled and identified.

[0070] Optionally, the present invention further provides a plastic concrete anti-seepage wall actual wall 3D modeling system, which is used to execute the plastic concrete anti-seepage wall actual wall 3D modeling method as described above, and the plastic concrete anti-seepage wall actual wall 3D modeling system comprises:

[0071] The plastic concrete anti-seepage wall scanning module is used to scan the plastic concrete anti-seepage wall through geological radar equipment to obtain the plastic concrete anti-seepage wall scanning data;

[0072] The plastic concrete anti-seepage wall material property model building module is used to perform grid processing on the plastic concrete anti-seepage wall scanning data to obtain the plastic concrete anti-seepage wall grid data; perform wall structure feature extraction on the plastic concrete anti-seepage wall grid data to obtain the wall structure feature data; perform wall material property modeling on the wall structure feature data to obtain the plastic concrete anti-seepage wall material property model;

[0073] The plastic concrete anti-seepage wall model fusion module is used to fuse the plastic concrete anti-seepage wall grid data and the plastic concrete anti-seepage wall material property model to obtain the plastic concrete anti-seepage wall fusion model;

[0074] The plastic concrete anti-seepage wall stress analysis module is used to perform stress analysis on the plastic concrete anti-seepage wall fusion model to obtain a plastic concrete anti-seepage wall simulation model.

[0075] The objects of the present invention are:

[0076] 1. The present invention uses geological radar equipment to perform high-precision scanning on plastic concrete anti-seepage walls to capture the full range of geometric shapes and surface details of the wall. Geological radar scanning can not only quickly obtain a large amount of accurate point cloud data, but also reduce human errors in traditional measurement methods. Especially for plastic concrete anti-seepage walls with complex geometric structures, geological radar scanning can effectively detect the internal structure of plastic concrete anti-seepage walls (such as cracks, cavities, material density changes, etc.), ensuring the comprehensiveness and high accuracy of the data.

[0077] 2. The scanned point cloud data is meshed and converted into a structured geometric model. The meshing step converts irregular point cloud data into a regular mesh model, extracts structural features from the meshed data, and captures key geometric features of the wall, such as cracks, holes, corners, etc.

[0078] 3. By modeling the material properties of the wall structure characteristic data, the present invention can accurately describe the material properties (such as density, elastic modulus, compressive strength, etc.) of the plastic concrete anti-seepage wall. Material property modeling is combined with the physical property database, so that the model can not only reflect the geometric form, but also accurately predict the mechanical performance of the wall under different working conditions through the precise description of physical parameters.

[0079] 4. Stress analysis provides a multi-level structural mechanics assessment, allowing designers to fully understand the stress state of the wall. By analyzing the performance of the wall under different loads, potential failure areas can be effectively identified, helping engineers to optimize during the design phase, thereby improving the safety and stability of the structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

[0081] Figure 1 A flowchart showing a method for three-dimensional modeling of an actual wall of a plastic concrete anti-seepage wall according to an embodiment of the present invention is provided;

[0082] Figure 2 A flowchart showing a method for constructing a material property model of a plastic concrete anti-seepage wall according to an embodiment of the present invention is provided;

[0083] Figure 3 A flowchart of a method for merging a plastic concrete anti-seepage wall model according to an embodiment is shown;

[0084] Figure 4 A flowchart showing a method for analyzing stress of a plastic concrete cut-off wall according to an embodiment of the present invention is provided;

[0085] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0086] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0087] In addition, the drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0088] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0089] Detect a section of plastic concrete anti-seepage wall located in a water conservancy project. The wall is 10 meters long and 0.5 meters thick. There is no steel bar in the wall and cracks and pores are expected. Use geological radar (GPR) to scan, with the goal of identifying internal structural features and reconstructing a 3D model.

[0090] The detection path is planned and designed with a length of 10 meters, a spacing of 0.5 meters, and a total of 20 scanning points. The path diagram shows that the scanning path is divided longitudinally along the wall, and each point is scanned once.

[0091] Scanning parameter settings, geological radar equipment uses high-frequency GPR (such as 1GHz frequency), which is suitable for detecting smaller cracks. The scanning speed is 0.5 m / s. The sampling frequency is 100 samples per scanning point. The reflection signal is recorded at each scanning point. As shown in the following table:

[0092] Scanning depth calculation table 2

[0093]

[0094]

[0095] The depth is calculated based on the reflection time. Through data query, it is found that the propagation speed of electromagnetic waves in plastic concrete is 0.1m / ns. Depth (m) = time (ns) × 0.1 / 2, the time of the reflected signal multiplied by the propagation speed (m / ns), and then divided by 2 (upward and downward reflection). As shown in the following table:

[0096] Reflection signal data table 1

[0097]

[0098]

[0099] By calculating the gradient (rate of change) of the intensity of the reflected signal, the area with significant intensity changes is identified. Areas with larger gradients often indicate stress concentration or internal defects. The difference in intensity between adjacent scanning points is calculated to obtain the gradient value. A gradient threshold is set. When the gradient exceeds the threshold, it is considered to be a stress concentration area. Alternatively, an intensity threshold (such as 0.5) is set to extract reflected signals with intensities greater than this value. The stress concentration area extraction results show that the depths corresponding to the stress concentration areas (such as cracks, pores, etc.) are: scanning point 3 (depth 0.70m, intensity 0.60), scanning point 4 (depth 0.80m, intensity 0.55), scanning point 7 (depth 1.10m, intensity 0.70), scanning point 8 (depth 1.20m, intensity 0.80).

[0100] Generate 3D point cloud data based on the above extraction results, point cloud coordinates (x, y, z): point 1: (0, 0, 0.50), point 2: (0, 0.5, 0.60), point 3: (0, 1.0, 0.70), point 4: (0, 1.5, 0.80), point 5: (0, 2.0, 1.10), point 6: (0, 2.5, 1.20). Import the generated point cloud data using 3D visualization software (such as AutoCAD, 3D Studio Max, Revit, etc.). Display the 3D model of the plastic concrete anti-seepage wall in the 3D visualization software, highlighting the cracks and pore areas. Generate stress cloud diagrams, cross-section diagrams, etc., to help engineers understand the internal structure of the wall and potential problems.

[0101] See also Figures 1 to 4 The present application provides a three-dimensional modeling method for an actual wall of a plastic concrete anti-seepage wall, the method comprising:

[0102] S1. Scan the plastic concrete anti-seepage wall by using geological radar equipment to obtain scanning data of the plastic concrete anti-seepage wall;

[0103] Specifically, the high-precision geological radar equipment is aimed at the exposed surface of the plastic concrete anti-seepage wall for a full-scale scan to obtain high-density point cloud data. The scanning equipment needs to be calibrated to ensure that the scanning range covers all structures of the wall.

[0104] Specifically, when the anti-seepage wall has not been completely poured, the geological radar equipment is used to scan from an accessible position. Regular scanning is selected during the construction process to obtain step-by-step data of the wall and obtain scanning data of the plastic concrete anti-seepage wall.

[0105] Specifically, a geological radar device including a geological radar is selected for scanning. A geological radar is a detection technology that can penetrate soil and plastic concrete. It can scan when the surface of the wall is covered with soil and detect the structure inside the wall by emitting electromagnetic waves.

[0106] S2. Gridding the scanned data of the plastic concrete anti-seepage wall to obtain gridded data of the plastic concrete anti-seepage wall; extracting wall structure features from the gridded data of the plastic concrete anti-seepage wall to obtain wall structure feature data; modeling the wall material properties of the wall structure feature data to obtain a material property model of the plastic concrete anti-seepage wall;

[0107] Specifically, the Delaunay triangulation algorithm is used to mesh the point cloud data to generate a three-dimensional mesh model. By optimizing the size and distribution of triangles, the generated mesh accurately reflects the actual shape of the wall. The gradient analysis algorithm and edge detection technology are used to identify the structural features of the wall, such as corners, edges, and irregular shapes. The extracted feature point set is used to define the geometric shape of the wall. Based on the extracted structural feature data, material property parameters (such as elastic modulus, density, tensile strength, etc.) in finite element analysis are used to model the material properties. The material property model uses multi-physical quantity modeling technology, combined with the physical parameters of the wall and experimental data, to form a material property model of the plastic concrete anti-seepage wall.

[0108] S3, fusing the grid data of the plastic concrete anti-seepage wall and the material property model of the plastic concrete anti-seepage wall to obtain a fusion model of the plastic concrete anti-seepage wall;

[0109] Specifically, the gridded data is fused with the material property model, and the weighted fusion algorithm is used to combine the geometric model with the material properties to generate a complete three-dimensional fusion model. During the fusion process, the influence of the geometric structure on the material properties is considered and the material parameters of each grid unit are adjusted. An adaptive adjustment algorithm is introduced to automatically adjust the grid density and material property parameters in the fusion model to ensure the accuracy and stability of the model under different stress conditions.

[0110] S4. Perform stress analysis on the plastic concrete anti-seepage wall fusion model to obtain a plastic concrete anti-seepage wall simulation model.

[0111] Specifically, the fused three-dimensional model is subjected to stress simulation using a stress analysis algorithm based on finite element analysis. The stress distribution of the plastic concrete cut-off wall under different working conditions is calculated by dividing the grid units and applying boundary conditions (such as load, temperature, humidity, etc.). The stress distribution and deformation are visualized using simulation software, and stress cloud maps and displacement maps are generated to show the response of the cut-off wall under different stress conditions.

[0112] Optionally, the plastic concrete anti-seepage wall scanning data includes first plastic concrete anti-seepage wall scanning data and second plastic concrete anti-seepage wall scanning data, and S1 includes:

[0113] Scanning the plastic concrete anti-seepage wall at a first angle by using a geological radar device to obtain scanning data of the first plastic concrete anti-seepage wall;

[0114] Specifically, adjust the geological radar equipment to the preset first angle (45 degrees or 60 degrees from top to bottom) to ensure that the equipment is stably placed and aimed at the surface of the plastic concrete cut-off wall. The angle threshold data should be accurately set according to the height and width of the specific wall to ensure that sufficient surface area is covered. Start scanning and obtain high-density point cloud data from the wall surface through a point cloud acquisition algorithm (such as a three-dimensional scanning algorithm based on LIDAR). The point cloud data format is a three-dimensional coordinate point set (X, Y, Z), and each point represents a measurement point on the wall surface. Perform preliminary cleaning and noise reduction on the first plastic concrete cut-off wall scanning data to remove noise points and unnecessary redundant data to ensure the accuracy and integrity of the data.

[0115] The plastic concrete anti-seepage wall is scanned at a second angle by the geological radar device to obtain second plastic concrete anti-seepage wall scanning data, wherein the angle threshold data corresponding to the first angle scan and the angle threshold data corresponding to the second angle scan are in a vertical relationship.

[0116] Specifically, rotate the geological radar equipment 90 degrees (45 degrees or 30 degrees from bottom to top) to make it perpendicular to the first angle. Make sure that the scanning device is aimed at the same wall area, but collect data from an angle perpendicular to the first scan. Repeat the scanning operation and use the same point cloud acquisition algorithm to obtain the second plastic concrete cut-off wall scanning data. Since the scanning angle is perpendicular to the first angle, these point cloud data can capture the geometric details of the wall at different angles and supplement the areas that the first scan cannot cover. The second plastic concrete cut-off wall scanning data is denoised, cleaned, and format converted to ensure the same accuracy and format as the first scanning data.

[0117] Based on the preset angle threshold, the point cloud data of the first angle and the second angle are spatially calibrated using angle conversion algorithms (such as rotation matrix and quaternion algorithm). This step ensures that the scan data at different angles are aligned in the same coordinate system. The two sets of point cloud data are accurately aligned using the ICP (Iterative Closest Point) algorithm. The ICP algorithm minimizes the difference between the two sets of point clouds by iteratively matching the closest points, ensuring the best match between the two sets of data in three-dimensional space. The point cloud data of the first angle and the second angle are fused into a complete three-dimensional point cloud model using a weighted average algorithm or a voxel-based point cloud fusion algorithm.

[0118] Optionally, S2 includes:

[0119] S21, performing point cloud meshing processing on the scanning data of the plastic concrete anti-seepage wall to obtain meshed data of the plastic concrete anti-seepage wall;

[0120] Specifically, the point cloud data of the plastic concrete anti-seepage wall is converted into a three-dimensional mesh model using Delaunay triangulation or Poisson surface reconstruction algorithm. Delaunay triangulation is suitable for surfaces with good regularity, while Poisson reconstruction is suitable for complex and irregular surfaces. By connecting the nearest neighbor points in the point cloud one by one, triangular facets are formed to generate preliminary meshing data. The model generated after meshing can intuitively reflect the geometric shape of the plastic concrete wall, and the number of triangular facets generated depends on the density of the point cloud.

[0121] S22, performing mesh optimization on the mesh data of the plastic concrete anti-seepage wall to obtain mesh simplified data of the plastic concrete anti-seepage wall;

[0122] Specifically, the Quadric Error Metrics (QEM) algorithm is used for mesh simplification. QEM removes unnecessary mesh points by minimizing the error after vertex merging, reducing the complexity of the mesh while still maintaining the geometric features. During the optimization process, the error matrix of each vertex is first calculated, and then the vertex pairs with the smallest error are gradually merged until the mesh reaches the desired degree of simplification. The generated mesh simplification data contains fewer triangles but retains the main geometric details.

[0123] S23, performing mesh smoothing processing on the simplified mesh data of the plastic concrete anti-seepage wall to obtain mesh smoothing data of the plastic concrete anti-seepage wall;

[0124] Specifically, the simplified mesh data is smoothed using the Laplacian smoothing algorithm. Laplacian smoothing reduces surface irregularities by iteratively moving each vertex to the geometric center of its adjacent vertices. The number of iterations and the moving factor need to be set during the smoothing process. The mesh smoothing data after smoothing will have a smoother surface while maintaining the original structural features.

[0125] S24, performing mesh normal calculation on the mesh smoothing data of the plastic concrete anti-seepage wall to obtain wall structure characteristic data;

[0126] Specifically, a normal calculation algorithm based on geometric normal vectors is used to calculate the normal vector of each mesh patch. For each triangular patch, the normal direction is calculated using the cross product according to the order of its vertices. All triangular patches are traversed to calculate the normal vector of each patch, and these normal vectors are assigned to the corresponding vertices to generate complete wall structure feature data. Normal data is used for subsequent lighting calculations and material mapping.

[0127] S25, initializing the material mapping model according to the wall structure characteristic data to obtain the material mapping model;

[0128] Specifically, the initial state of the material mapping model is generated using the UV mapping algorithm based on the wall structure feature data. UV mapping determines the texture coordinates corresponding to each vertex of the mesh by projecting the three-dimensional mesh coordinates into the two-dimensional texture coordinate system. First, the mesh is flattened, and then the corresponding UV texture coordinates are assigned to each vertex based on the flattened mesh coordinates to ensure that the material can be correctly mapped to the wall surface.

[0129] S26. Acquire physical material parameter data, and perform finite element modeling on the material mapping model according to the physical material parameter data to obtain a material property model of the plastic concrete anti-seepage wall.

[0130] Specifically, the physical parameters of plastic concrete, such as elastic modulus, Poisson's ratio, density, and compressive strength, are obtained. These parameters are provided by experimental data or industry standards. Finite element analysis (FEA) software, such as ANSYS or ABAQUS, is used to combine the material mapping model with the physical material parameters to construct a material property model of the plastic concrete cut-off wall. In finite element modeling, mesh nodes are regarded as calculation nodes for mechanical analysis, and the physical material parameters determine the behavior of these nodes under mechanical stress and deformation. Boundary conditions (such as fixed constraints and load application) are set, and then the stress, strain, and displacement responses of the material property model of the plastic concrete cut-off wall are solved to generate mechanical performance data of the wall under different working conditions.

[0131] Optionally, S3 includes:

[0132] S31, performing mesh material parameter matching according to the mesh data of the plastic concrete anti-seepage wall and the material property model of the plastic concrete anti-seepage wall to obtain initial fusion model data;

[0133] Specifically, the nearest neighbor matching algorithm or bilinear interpolation algorithm is used to match the meshed data of the plastic concrete cut-off wall with the physical parameters of the material property model. Each mesh node is compared with the corresponding point in the material property model, and the material parameters (such as elastic modulus, density, etc.) are assigned to the mesh node.

[0134] Traverse all mesh nodes and find the nearest corresponding position node in the material property model according to the geometric position of the node. Assign the parameter values ​​in the material property model to the mesh nodes through the nearest neighbor algorithm to obtain the initial fusion model data. For complex surfaces, use the bilinear interpolation algorithm to make the material parameters smoothly transition between adjacent nodes to ensure the continuity of physical parameters.

[0135] S32, performing material parameter interpolation processing on the initial fusion model data to obtain material interpolation model data;

[0136] Specifically, the cubic spline interpolation algorithm or Kriging interpolation method is used to interpolate the material parameters of the initial fusion model data. The interpolation algorithm infers the material properties of unknown nodes based on the material property values ​​of known nodes to ensure the overall smoothness of the model.

[0137] First, all mesh nodes in the model are calibrated, and the material property values ​​of the known nodes are used as constraints. The cubic spline interpolation method is used to generate a continuous material parameter distribution curve on the mesh surface. If the material parameters have significant spatial variations, the Kriging interpolation method is used. The interpolation method takes into account spatial autocorrelation and can better reflect the variability of material properties in different regions. After the interpolation is completed, the material interpolation model data is generated.

[0138] S33. Optimize the material interpolation model data to obtain a plastic concrete anti-seepage wall fusion model.

[0139] Specifically, the material interpolation model is optimized using a gradient descent method or a multi-objective optimization algorithm. The optimization goal is to minimize the error of the material property distribution while ensuring the physical consistency and accuracy of the model.

[0140] Define an error function that calculates the error value of each mesh node based on the difference between the material interpolation model and the actual physical material parameters. By calculating the gradient of the error function, adjust the material parameters of each mesh node to gradually approach the actual value. The gradient descent method can effectively reduce the local error in the interpolation process. If the model needs to consider multiple material parameters (such as density, elastic modulus, compressive strength, etc.), a multi-objective optimization algorithm is used to comprehensively adjust the distribution of each material parameter so that it can perform optimally under different working conditions.

[0141] Optionally, S4 includes:

[0142] S41, performing stress validity analysis on the plastic concrete anti-seepage wall fusion model to obtain a first plastic concrete anti-seepage wall simulation model;

[0143] Specifically, the static analysis method in finite element analysis (FEA) is used to analyze the stress effectiveness of the plastic concrete cut-off wall fusion model. The purpose of this analysis is to verify whether the model meets the rationality of stress distribution and structural safety under the design conditions.

[0144] Apply real boundary conditions to the plastic concrete cut-off wall, such as load, temperature change, humidity effect, etc. Calculate the stress value of the wall at each grid node through FEA software (such as ANSYS or ABAQUS) and generate a stress cloud map. Perform a validity check on the calculated stress distribution to determine whether there is stress concentration or uneven stress distribution. If the stress distribution meets the design expectations, generate the first plastic concrete cut-off wall simulation model.

[0145] S42, performing stress multi-scale analysis on the plastic concrete anti-seepage wall fusion model to obtain a second plastic concrete anti-seepage wall simulation model;

[0146] Specifically, the hierarchical multi-scale analysis method is used to divide the structure of the plastic concrete cut-off wall into different scales (such as macro, meso and micro) for stress analysis. This method can more carefully analyze the stress distribution and deformation behavior at different scales inside the wall.

[0147] The fusion model of the plastic concrete anti-seepage wall is divided into different scale levels, and the corresponding material parameters and boundary conditions are set for each scale. An independent stress analysis is performed on the model of each scale to calculate the stress distribution at each scale. For example, the macro scale focuses on the structural stress distribution of the entire wall, and the micro scale focuses on the subtle stress changes of local materials. The stress analysis results of each scale are integrated to generate a second plastic concrete anti-seepage wall simulation model. Multi-scale analysis helps to discover local structural weaknesses and potential failure risks.

[0148] S43, integrating and screening the safety factors according to the first plastic concrete anti-seepage wall simulation model and the second plastic concrete anti-seepage wall simulation model to obtain a concrete anti-seepage wall simulation model.

[0149] Specifically, based on the stress distribution results of the first and second simulation models, a safety factor calculation method, such as the Von Mises stress criterion or the Mohr-Coulomb criterion, is used to calculate the safety factor of the wall in each area.

[0150] The stress data in the first and second simulation models are fused with the corresponding safety factors using the weighted average method or the decision tree-based fusion algorithm. The weighted average method assigns weights to each model according to the contribution of the stress distribution, fuses safety factors of different scales, and generates a global safety factor distribution. According to the fused safety factor data, set the threshold screening condition, filter out the areas with safety factors lower than the set threshold, and mark them as potential risk areas. These areas are analyzed and reinforced.

[0151] Optionally, the stress effectiveness analysis includes:

[0152] Obtaining plastic concrete cut-off wall constraint data;

[0153] Specifically, the physical constraint data of the plastic concrete anti-seepage wall is obtained through on-site surveys and design drawings. The constraint data includes fixed support points, boundary conditions, structural contact surfaces, and the connection method between the wall and the foundation. Common constraint conditions include fixed constraints (zero displacement) and sliding constraints (limiting displacement in a specific direction), which ensure that the wall meets the actual working conditions during calculation.

[0154] Applying load to the plastic concrete cut-off wall fusion model according to the plastic concrete cut-off wall constraint data to obtain load application model data;

[0155] Specifically, according to the constraint data of the plastic concrete anti-seepage wall, static and dynamic load application algorithms are used to apply loads to the wall model. Load types include self-weight load, seismic load, wind load, hydrostatic pressure, etc. Through finite element analysis software, the load intensity is gradually increased according to the actual working conditions to generate load application model data. When applying loads to each grid node, the influence of constraint conditions needs to be considered to ensure that the load effect is reasonably distributed.

[0156] Performing static stress processing and dynamic stress processing on the load application model data to obtain static stress distribution data and dynamic stress distribution data;

[0157] Specifically, the linear static analysis method is used to perform static stress processing on the load application model data. This method assumes that the material remains elastic under load and uses the equilibrium equation to calculate the stress distribution. The static stress distribution of the plastic concrete cut-off wall under load is calculated through the static solver, and static stress distribution data is generated to reflect the stress changes of the wall under static loads, which can identify weak areas under static conditions.

[0158] Use the time domain integration method or modal analysis method to perform dynamic stress processing on the load application model data. The time domain integration method is used to process transient responses, and the modal analysis method is used for frequency domain analysis. In the finite element software, considering transient loads such as earthquakes or wind loads, the stress response of the wall under dynamic loads is calculated to generate dynamic stress distribution data. This data shows the stress change amplitude and frequency characteristics of the wall under dynamic loads.

[0159] Extract stress concentration areas according to static stress distribution data and dynamic stress distribution data to obtain stress concentration area data;

[0160] Specifically, a stress concentration extraction algorithm based on stress gradient analysis is used to identify stress concentration areas in static and dynamic stress distribution data. Local gradient analysis is performed on static and dynamic stress distribution data to find stress concentrations. Stress concentration areas are areas where stress is significantly higher than the surrounding area, and occur in areas with geometric discontinuities, edges, or drastic load changes. The extracted stress concentration area data will be used for subsequent structural evaluation and annotation.

[0161] The plastic concrete cut-off wall fusion model is annotated according to the stress concentration area data, the static stress distribution data and the dynamic stress distribution data to obtain the first plastic concrete cut-off wall simulation model.

[0162] Specifically, the plastic concrete cut-off wall fusion model is annotated based on the stress concentration area data, static stress distribution data, and dynamic stress distribution data. The annotation process involves visualizing stress concentration areas and high-risk areas and marking important stress values ​​or safety factors for these areas. In the simulation model, stress concentration areas are annotated using colors or symbols to highlight potential structural weaknesses. The annotated first plastic concrete cut-off wall simulation model will be used for further safety assessment and design optimization.

[0163] Optionally, the stress multi-scale analysis includes:

[0164] Initialize the stress field of the plastic concrete cut-off wall fusion model to obtain initial stress field data;

[0165] Specifically, the initial stress field generation algorithm based on finite element mesh is used to input the load conditions, boundary conditions and material properties into the plastic concrete cut-off wall fusion model to initialize the stress field data of the wall. According to the physical parameters of the plastic concrete material (such as elastic modulus, density, etc.) and its stress conditions, the initial stress value of each grid node is calculated. The initial stress field data is generated by the static solver, which reflects the overall stress distribution under the initial working condition.

[0166] Perform stress field stratification processing according to the initial stress field data to obtain stress field stratification data;

[0167] Specifically, a hierarchical algorithm based on meshing is used to stratify stress field data according to different scales. This process divides the stress field into different stress levels according to the internal structure of the material, the local area of ​​the wall, and the overall structure.

[0168] The stress field data of the plastic concrete cut-off wall is divided into different levels according to the region and stress gradient change. Each level represents a different stress area of ​​the wall. The layered processing is gradually carried out from the micro level (such as the internal stress field of the material) to the macro level (such as the overall stress field of the wall) to generate stress field layered data.

[0169] Perform multi-scale coupling analysis based on stress field layered data to obtain multi-scale coupling stress data;

[0170] Specifically, a multi-scale coupled analysis algorithm, such as multi-scale finite element analysis (MS-FEA) or multi-level reduced order modeling (ROM), is used to couple the stress fields of each layer. This algorithm combines the stress behaviors at the micro and macro scales to calculate the coupled stress distribution.

[0171] At each stress level, the influence of the local stress field on the global stress field is calculated. Through the coupled stress analysis algorithm, the interaction of stress from micro to macro is simulated to obtain multi-scale coupled stress data. Iterative calculations are performed between each level to ensure that the mutual influence of local and global stress fields is reasonably transmitted. Accurate multi-scale stress data is obtained by gradually adjusting the stress distribution of each scale.

[0172] The stress model is fused according to the multi-scale coupled stress data and the plastic concrete anti-seepage wall fusion model to obtain a second plastic concrete anti-seepage wall simulation model;

[0173] Specifically, a stress field fusion algorithm, such as a weighted average-based fusion algorithm or a layered composite algorithm, is used to fuse the multi-scale coupled stress data with the plastic concrete cut-off wall fusion model.

[0174] The multi-scale coupled stress data is mapped to the overall wall model, and the influence of microscopic stress data on the macroscopic stress field is reasonably reflected through layer-by-layer fusion. The stress data at different levels are weighted and integrated to eliminate the boundary effect in the multi-scale analysis and generate a second plastic concrete cut-off wall simulation model.

[0175] The stress models include:

[0176] Extract macro and micro stress data based on multi-scale coupled stress data to obtain macro and micro stress data;

[0177] Specifically, the finite element analysis software was used to perform numerical simulation on the plastic concrete cut-off wall, and the material parameters and loading conditions were input. Meshing was performed to ensure that the model could accurately reflect the geometric characteristics and stress state of the wall. The simulation was run to obtain the stress data of each mesh unit, including macroscopic and microscopic stress distribution. Macroscopic stress data (depth-stress): depth 0.0m: stress value 20MPa, depth 1.0m: stress value 30MPa, depth 2.0m: stress value 40MPa, depth 3.0m: stress value 45MPa, depth 4.0m: stress value 50MPa. Microscopic stress data (depth-stress): depth 0.5m: stress value 35MPa, depth 1.5m: stress value 42MPa, depth 2.5m: stress value 55MPa (stress concentration), depth 3.5m: stress value 48MPa, depth 4.5m: stress value 52MPa.

[0178] Construct a geometric model of the plastic concrete anti-seepage wall in finite element analysis software (such as ANSYS, ABAQUS, etc.) to ensure that the model size is consistent with the actual wall. Set the material properties of the plastic concrete, such as: elastic modulus (E): about 25Gpa, Poisson's ratio (ν): about 0.2, density: about 2400kg / m 3 . Set the boundary conditions of the wall according to the actual working conditions, such as the bottom is fixed (cannot move) and the side is subject to soil pressure. Calculate the deadweight of the anti-seepage wall and apply it according to the volume and density. Apply soil pressure, hydrostatic pressure, etc. For example, the height of the anti-seepage wall is 5m, the width is 1m, and the thickness is 0.5m. The volume of the wall is: V = 5m × 1m × 0.5m = 2.5m 3 The self-weight is calculated as, F 自重=V×density×g≈58860N, divide the model into a finite number of units. Use tetrahedral or hexahedral units. Perform meshing and generate nodes and elements. Record the coordinates of each node and the corresponding unit type. Start the solution process, and the software will calculate the stress, strain and other data of each node based on the input load and boundary conditions. The software automatically solves the node response and calculates the stress value of each node. The stress of each node includes: principal stress (σ1, σ2, σ3), equivalent stress (von Mises stress). After the calculation is completed, extract the stress data of each node. Macro stress records such as calculating the macro stress of the entire wall and taking the average stress value of each layer of nodes. Micro stress records such as recording the specific stress value of each node. As shown in the following table:

[0179] Stress calculation table 3:

[0180]

[0181]

[0182] Extract stress concentration areas based on macro and micro stress data to obtain stress concentration area data;

[0183] Specifically, the extracted stress data is analyzed to identify stress concentration areas. A stress threshold is set to filter out areas that exceed the threshold. The extracted stress data is sorted and a stress distribution diagram is drawn. According to material properties and engineering requirements, a stress threshold (for example, 40MPa) is set. The stress distribution data is traversed, and all nodes with stress values ​​greater than the threshold are extracted and marked as stress concentration areas. The depth and intensity of the stress concentration area are recorded. The stress concentration area is extracted as follows: stress concentration area (greater than 40MPa): depth 2.5m (stress 55MPa).

[0184] The plastic concrete cut-off wall fusion model is annotated using stress concentration area data to obtain a preliminary stress fusion model.

[0185] Specifically, the fused model of the cut-off wall is annotated using the data of the stress concentration area to clearly identify the potential risk area. The fused cut-off wall model is imported into the visualization software. Using the extracted stress concentration area data, different colors or symbols are applied to mark in the model. Generate a corresponding label for each marked area, including stress value, depth and other information. For example, in the 3D model, the area with a depth of 2.5m is marked as a "stress concentration area" and a label with a stress value of 55MPa is attached.

[0186] According to the macro and micro stress data and the preliminary stress fusion model, hierarchical stress fusion is performed to obtain the second plastic concrete anti-seepage wall simulation model;

[0187] Specifically, the extracted macro-micro stress data are combined with the preliminary stress fusion model to perform hierarchical stress analysis. The model is divided into several levels, such as the upper layer, the middle layer and the lower layer, to ensure that each layer can reflect different stress states. According to the macro-micro stress data, the stress values ​​of each layer are summarized and the average stress of each layer is calculated. The stress of each layer is weighted to ensure that the influence of each layer is reflected in the overall model. The stress data of all layers are integrated to obtain the second plastic concrete anti-seepage wall simulation model. Upper layer (0.0m-1.0m): average stress 25MPa, middle layer (1.0m-3.0m): average stress 40MPa, lower layer (3.0m-4.5m): average stress 50MPa, calculate the weighted, obtain the second plastic concrete anti-seepage wall simulation model, and form a stress cloud map.

[0188] The macro-micro stress data include macro stress data and micro stress data, and the stress concentration area extraction includes:

[0189] Perform graph neural network encoding on macro stress data and micro stress data, and obtain macro stress characteristic graph data and micro stress characteristic graph data;

[0190] Specifically, the feature map is constructed by collecting macro-stress and micro-stress data. The extracted macro-stress and micro-stress data are organized into a two-dimensional matrix form, where each matrix element corresponds to the stress value of a measurement point. The macro-stress data and micro-stress data are input into the encoding module (calculated through the graph convolution layer) to generate feature maps. These feature maps will reflect the spatial distribution of stress.

[0191] Specifically, each node and its stress value are constructed as nodes in the graph, and the edges between nodes can be established according to the spatial position or stress relationship. Edges are established for adjacent nodes to form a graph structure. The macroscopic and microscopic feature graph data are input into the encoding module of the graph neural network. A graph node is created for each node, the stress value is used as the node feature, and edges are established to connect adjacent nodes (such as between node 1 and node 2, between node 2 and node 3). Stress data of the node (macroscopic and microscopic). Feature extraction is performed through the graph convolution layer, and the convolution operation is performed using the combination of node features and the features of its adjacent nodes. After graph convolution, an activation function (such as ReLU) is used for nonlinear transformation. The features of each node will be updated according to the features of its neighbors. After multiple layers of graph convolution, macroscopic stress feature graph data and microscopic stress feature graph data are generated, and these feature graphs will reflect the spatial distribution of stress.

[0192] Performing distance calculation on the macroscopic stress characteristic map data and the microscopic stress characteristic map data to obtain macroscopic stress map characteristic clustering data and microscopic stress map characteristic clustering data respectively;

[0193] Specifically, distance calculation is performed based on the feature map data to identify feature similarity. For each point in the feature map, its distance to other points is calculated to generate a distance matrix. The distance calculation can be based on the Euclidean distance. Based on the distance matrix, feature clustering is performed. Points with similar features are clustered together to form macro stress map feature clustering data and micro stress map feature clustering data.

[0194] Specifically, the distance between each point in the feature map is calculated to generate a distance matrix. For example, the Euclidean distance is used. The distance matrix is ​​used to cluster the macroscopic and microscopic features. Clustering is performed based on a distance threshold (e.g., 10 MPa) to cluster similar stress values ​​together.

[0195] Dynamic stress threshold control is performed according to the macroscopic stress map characteristic clustering data and the microscopic stress map characteristic clustering data, and macroscopic stress threshold matrix data and microscopic stress threshold matrix data are obtained respectively;

[0196] Specifically, stress thresholds are set and dynamically adjusted to suit different conditions. Preliminary macro and micro stress thresholds are set (e.g., based on material properties). Clustering results are analyzed and thresholds are dynamically adjusted. Thresholds can be reset based on the changing trend of local stress and the distribution of clustering data to improve extraction accuracy. Based on the adjusted thresholds, macro stress threshold matrix data and micro stress threshold matrix data are generated.

[0197] Specifically, the macro threshold is set to 40 MPa and the micro threshold is set to 45 MPa.

[0198] After obtaining the preliminary stress data, cluster analysis was performed to identify stress hot spots. The clustering results show that some nodes show an obvious concentration trend in the stress distribution. According to the cluster analysis results, the number of nodes in each cluster was counted. A large number of clusters indicates that these nodes have a high degree of concentration in the stress distribution. The center of gravity (mean or median) of each cluster was calculated. If the center of gravity position is concentrated in a specific area, it means that there is stress concentration in this area. The macroscopic stress concentration area was identified, and the stress values ​​of the following nodes exceeded the threshold of 40MPa: node 3 (depth 2m, stress 40MPa), node 4 (depth 3m, stress 45MPa). The microscopic stress concentration area was identified, and the stress values ​​of the following nodes exceeded the threshold of 45MPa: node 2 (depth 1.5m, stress 50MPa), node 3 (depth 2.5m, stress 55MPa).

[0199] The adjustment basis is that if a node appears multiple times in the clustering results and its stress value is close to or higher than the current threshold, the threshold of the node is increased. If a node appears multiple times in the clustering results and its stress value is much lower than the current threshold, the threshold of the node can be considered to be lowered. If the mean stress value of a layer increases significantly (such as greater than 10%), the threshold is increased as a whole. If the mean stress value of a layer decreases significantly (such as less than 10%), the threshold is lowered as a whole.

[0200] After each stress data update, monitor the clustering results and record the frequency of occurrence of each node and its stress value. For each node, adjust the stress value assessment threshold based on its frequent occurrence in the cluster: If the stress value of a node exceeds 50MPa in multiple analyses and the clustering results show that the node appears frequently, increase the macro threshold to 45MPa. If the stress value of a node is less than 35MPa in multiple analyses, reduce the macro threshold to 35MPa. If the stress value of a node exceeds 55MPa in multiple analyses and the clustering results show that the node appears frequently, increase the micro threshold to 50MPa. If the stress value of a node is less than 40MPa in multiple analyses, reduce the micro threshold to 40MPa. Update the macro and micro stress thresholds to ensure that the new thresholds are used when locating the next stress hotspot.

[0201] Using the macro stress threshold matrix data to locate the macro stress hotspot of the macro stress map feature clustering data, and obtaining the macro stress hotspot location map data, and using the micro stress threshold matrix data to locate the micro stress hotspot of the micro stress map feature clustering data, and obtaining the micro stress hotspot location map data;

[0202] Specifically, the threshold matrix data is used to locate the stress hotspot area. According to the macro stress threshold matrix, the area where the stress value exceeds the threshold is screened out and marked as the macro stress hotspot. Similarly, the micro stress threshold matrix is ​​used to locate the micro stress hotspot. The located hotspot area is recorded in the stress hotspot location map data, including information such as position and stress value.

[0203] Perform multi-modal stress layered mapping based on the macroscopic stress hotspot location map data and the microscopic stress hotspot location map data to obtain layered stress mapping matrix data;

[0204] Specifically, the threshold matrix data is used to locate the stress hotspot area. According to the macro stress threshold matrix, the area where the stress value exceeds the threshold is screened out and marked as the macro stress hotspot. Similarly, the micro stress threshold matrix is ​​used to locate the micro stress hotspot. The located hotspot area is recorded in the stress hotspot location map data, including information such as position and stress value.

[0205] According to the depth of the plastic concrete anti-seepage wall, the entire wall is divided into multiple layers (such as the upper layer, the middle layer, and the lower layer), and each layer should be able to reflect the stress state at different depths. According to the located macroscopic and microscopic stress hotspot data, multimodal stress stratification mapping is performed. The stratification principle is such as dividing the depth into three equal parts to perform multimodal stress stratification mapping, or, according to the plastic concrete data query, according to the depth of the plastic concrete anti-seepage wall, the entire wall is divided into multiple layers (such as the upper layer, the middle layer, and the lower layer), and each layer should be able to reflect the stress state at different depths to perform multimodal stress stratification mapping, or, according to the soil layer data (through geological exploration or historical data query), stratification is performed according to the soil layer data and the plastic concrete depth data in the plastic concrete anti-seepage wall scanning data (obtained previously through geological radar scanning), and plastic concrete stratification data is obtained, and multimodal stress stratification mapping is performed according to the located macroscopic and microscopic stress hotspot data and plastic concrete stratification data. Determine the stress distribution at different depth levels: first layer (0-1m): 20-30MPa, second layer (1-2m): 30-40MPa, third layer (2-3m): 40-45MPa (including hot spots).

[0206] The concentrated area synthesis is performed according to the layered stress mapping matrix data to obtain the stress concentration area data.

[0207] Specifically, the layered stress mapping matrix is ​​synthesized to identify stress concentration areas. Based on the layered stress mapping matrix, overlapping stress hotspot areas are identified and the final stress concentration area data is synthesized. The synthesized stress concentration area data is recorded, including the characteristics, location and corresponding stress value of each area.

[0208] Specifically, according to the layered stress mapping matrix data, the overlapping hot spots are identified and the stress concentration area data is synthesized. The data of the stress concentration area is recorded and marked as a high-risk area. For example, the synthesized stress concentration area data includes: the concentration area depth of 1.5m (45MPa) is marked as a high-risk area.

[0209] Optionally, the stress field layering process includes:

[0210] Material properties are layered according to the initial stress field data to obtain stress field material layering data;

[0211] Specifically, the stratification algorithm based on physical properties stratifies the initial stress field data according to the physical parameters of the material. The algorithm divides the regions in the stress field according to the different elastic modulus, density, tensile strength and other properties of the material.

[0212] Extract material properties from the initial stress field data, including elastic modulus, Poisson's ratio, compressive strength, etc. Use clustering algorithms (such as K-means or hierarchical clustering) to divide the stress field into several layers based on the similarity of material properties, each corresponding to different material properties. The resulting stress field material layering data will mark different areas and reflect the differences in physical material properties in these areas.

[0213] Perform stress gradient stratification according to the initial stress field data to obtain stress field gradient stratification data;

[0214] Specifically, a gradient stratification algorithm based on stress change rate is used to divide the initial stress field data into different levels according to the stress gradient change. This method is used to identify stress concentration and sharp change areas in the stress field.

[0215] The stress distribution in the initial stress field data is calculated by the spatial derivative calculation through the derivation algorithm to obtain the stress gradient data. The area with a larger gradient is the stress concentration area, and the area with a smaller gradient is the stress gentle area. According to the size of the stress gradient, the stress field is divided into several gradient layers using the threshold method or multi-level hierarchical algorithm. Each level represents a different stress gradient strength. The stress field gradient hierarchical data is generated to show the stress change rate and concentration in each area.

[0216] The stress field material stratification data and the stress field gradient stratification data are cross-labeled to obtain the stress field stratification data.

[0217] Specifically, a cross-labeling algorithm is used to combine material property layered data and stress gradient layered data to generate composite labeled stress field layered data.

[0218] The material property layered data and the stress gradient layered data are cross-fused, and the material properties and stress gradients of each grid node are annotated using a cross-labeling algorithm based on logical operations. In the fused stress field layered data, each area is annotated with its material properties and stress gradient information at the same time. Combining the two layered results, it is possible to identify weak points that exist not only in stress concentration areas but also in special material areas, generate stress field layered data, and mark the intersection areas of material properties and stress gradients. These intersection areas represent critical locations in the structure, have higher safety risks, and require further analysis and reinforcement.

[0219] Optionally, the stress gradient layering comprises:

[0220] Perform gradient calculation based on initial stress field data to obtain stress field gradient data;

[0221] Specifically, a numerical differential algorithm (such as a finite difference method or a finite element method) is used to perform gradient calculation on the initial stress field data, and the stress change rate of each grid node is obtained by derivative calculation.

[0222] According to the distribution of stress in three-dimensional space, the main direction of stress change is determined, and the stress change vector of each node is calculated. The stress gradient value of all mesh nodes is calculated to generate stress field gradient data. A larger gradient value means that there is stress concentration or sharp change in the area.

[0223] Cluster calculation is performed based on stress field gradient data to obtain stress field gradient cluster characteristic data;

[0224] Specifically, K-means or density-based clustering algorithm (DBSCAN) is used to perform clustering calculations on the stress field gradient data to identify characteristic regions in the stress field.

[0225] The gradient value and spatial position of each node are extracted from the stress field gradient data to form a feature vector. The stress gradient data are divided into several categories through the K-means or DBSCAN algorithm, each category represents a different stress change characteristic area, and the stress field gradient clustering feature data is generated.

[0226] The initial stress field data are partitioned into adjacent areas according to the stress field gradient clustering feature data to obtain the initial stress field gradient stratified data;

[0227] Specifically, according to the stress field gradient clustering feature data, the Voronoi diagram segmentation method or the proximity-based partitioning algorithm is used to segment the initial stress field data into multiple adjacent areas.

[0228] The Voronoi diagram segmentation method is used to segment the entire stress field into multiple adjacent regions according to the cluster center point. Each region contains grid nodes close to the cluster center point. The initial stress field gradient stratification data is generated, and each stratification region corresponds to a stress gradient characteristic cluster region.

[0229] Calculate the local gradient change rate according to the initial stress field gradient stratification data to obtain the gradient stratification local change rate data;

[0230] Specifically, the stress gradient change rate is calculated for a local area of ​​the initial stress field gradient layered data using a local difference method or a curvature-based change rate calculation method.

[0231] Select a local area within each partition and calculate its gradient change rate, especially focusing on the area where the stress gradient changes sharply. By calculating the derivative of the gradient curve, the local gradient change rate data is obtained. Areas with high change rates indicate that the stress changes sharply and there is stress concentration.

[0232] According to the local change rate data of gradient stratification, the gradient sensitive areas are classified according to the initial stress field gradient stratification data to obtain the stress field gradient stratification data.

[0233] Specifically, based on the support vector machine (SVM) or decision tree algorithm, the sensitive areas of the stress field gradient stratification data are classified according to the local gradient change rate data. Sensitive features such as gradient change rate and change amplitude are extracted from the local change rate data. The sensitive areas in the stress field gradient stratification data are classified using the SVM or decision tree algorithm. Sensitive areas are areas where stress changes are most significant and may face the risk of structural failure. Stress field gradient stratification data are generated and each gradient sensitive area is marked for subsequent structural optimization and safety assessment.

[0234] Specifically, a threshold value of the gradient change rate is set. The threshold value is determined by engineering experience or preliminary simulation results. For example, an area where the gradient change rate is greater than a specific threshold value is defined as a sensitive area.

[0235] Optionally, the present invention further provides a plastic concrete anti-seepage wall actual wall 3D modeling system, which is used to execute the plastic concrete anti-seepage wall actual wall 3D modeling method as described above, and the plastic concrete anti-seepage wall actual wall 3D modeling system comprises:

[0236] The plastic concrete anti-seepage wall scanning module is used to scan the plastic concrete anti-seepage wall through geological radar equipment to obtain the plastic concrete anti-seepage wall scanning data;

[0237] The plastic concrete anti-seepage wall material property model building module is used to perform grid processing on the plastic concrete anti-seepage wall scanning data to obtain the plastic concrete anti-seepage wall grid data; perform wall structure feature extraction on the plastic concrete anti-seepage wall grid data to obtain the wall structure feature data; perform wall material property modeling on the wall structure feature data to obtain the plastic concrete anti-seepage wall material property model;

[0238] The plastic concrete anti-seepage wall model fusion module is used to fuse the plastic concrete anti-seepage wall grid data and the plastic concrete anti-seepage wall material property model to obtain the plastic concrete anti-seepage wall fusion model;

[0239] The plastic concrete anti-seepage wall stress analysis module is used to perform stress analysis on the plastic concrete anti-seepage wall fusion model to obtain a plastic concrete anti-seepage wall simulation model.

[0240] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0241] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. 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 invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A three-dimensional modeling method for an actual wall of a plastic concrete anti-seepage wall, characterized in that: The method comprises: S1. Scan the plastic concrete anti-seepage wall by using geological radar equipment to obtain scanning data of the plastic concrete anti-seepage wall; S2. Gridding the scanned data of the plastic concrete anti-seepage wall to obtain gridded data of the plastic concrete anti-seepage wall; extracting wall structure features from the gridded data of the plastic concrete anti-seepage wall to obtain wall structure feature data; modeling the wall material properties of the wall structure feature data to obtain a material property model of the plastic concrete anti-seepage wall; S3, fusing the grid data of the plastic concrete anti-seepage wall and the material property model of the plastic concrete anti-seepage wall to obtain a fusion model of the plastic concrete anti-seepage wall; S4. Perform stress analysis on the plastic concrete anti-seepage wall fusion model to obtain a plastic concrete anti-seepage wall simulation model.

2. The method according to claim 1, characterized in that The plastic concrete anti-seepage wall scanning data includes the first plastic concrete anti-seepage wall scanning data and the second plastic concrete anti-seepage wall scanning data, and S1 includes: Scanning the plastic concrete anti-seepage wall at a first angle by using a geological radar device to obtain scanning data of the first plastic concrete anti-seepage wall; The plastic concrete anti-seepage wall is scanned at a second angle by the geological radar device to obtain second plastic concrete anti-seepage wall scanning data, wherein the angle threshold data corresponding to the first angle scan and the angle threshold data corresponding to the second angle scan are in a vertical relationship.

3. The method according to claim 1, characterized in that S2 include: Performing point cloud meshing processing on the scanning data of the plastic concrete anti-seepage wall to obtain meshing data of the plastic concrete anti-seepage wall; Optimize the mesh data of the plastic concrete anti-seepage wall to obtain simplified mesh data of the plastic concrete anti-seepage wall; Performing mesh smoothing processing on the simplified mesh data of the plastic concrete anti-seepage wall to obtain the mesh smoothing data of the plastic concrete anti-seepage wall; Calculate the mesh normal of the plastic concrete anti-seepage wall mesh smoothing data to obtain the wall structure characteristic data; Initialize the material mapping model according to the wall structure characteristic data to obtain the material mapping model; Physical material parameter data is obtained, and finite element modeling is performed on the material mapping model according to the physical material parameter data to obtain a material property model of the plastic concrete anti-seepage wall.

4. The method according to claim 1, characterized in that: S3 includes: According to the mesh data of the plastic concrete anti-seepage wall and the material property model of the plastic concrete anti-seepage wall, mesh material parameters are matched to obtain initial fusion model data; Perform material parameter interpolation processing on the initial fusion model data to obtain material interpolation model data; The material interpolation model data is optimized to obtain the fusion model of plastic concrete anti-seepage wall.

5. The method according to claim 1, characterized in that S4 include: The stress validity analysis of the plastic concrete anti-seepage wall fusion model is carried out to obtain the first plastic concrete anti-seepage wall simulation model; Perform stress multi-scale analysis on the fusion model of plastic concrete anti-seepage wall to obtain the second simulation model of plastic concrete anti-seepage wall; The safety factors are integrated and screened according to the first plastic concrete anti-seepage wall simulation model and the second plastic concrete anti-seepage wall simulation model to obtain a concrete anti-seepage wall simulation model.

6. The method according to claim 5, characterized in that The stress effectiveness analysis includes: Obtaining plastic concrete cut-off wall constraint data; Applying load to the plastic concrete cut-off wall fusion model according to the plastic concrete cut-off wall constraint data to obtain load application model data; Performing static stress processing and dynamic stress processing on the load application model data to obtain static stress distribution data and dynamic stress distribution data; Extract stress concentration areas according to static stress distribution data and dynamic stress distribution data to obtain stress concentration area data; The plastic concrete cut-off wall fusion model is annotated according to the stress concentration area data, the static stress distribution data and the dynamic stress distribution data to obtain the first plastic concrete cut-off wall simulation model.

7. The method according to claim 5, characterized in that The stress multi-scale analysis includes: Initialize the stress field of the plastic concrete cut-off wall fusion model to obtain initial stress field data; Perform stress field stratification processing according to the initial stress field data to obtain stress field stratification data; Perform multi-scale coupling analysis based on stress field layered data to obtain multi-scale coupling stress data; The stress model is fused according to the multi-scale coupled stress data and the plastic concrete anti-seepage wall fusion model to obtain a second plastic concrete anti-seepage wall simulation model; The stress models include: Extract macro and micro stress data based on multi-scale coupled stress data to obtain macro and micro stress data; Extract stress concentration areas based on macro and micro stress data to obtain stress concentration area data; The plastic concrete cut-off wall fusion model is annotated using stress concentration area data to obtain a preliminary stress fusion model. According to the macro and micro stress data and the preliminary stress fusion model, hierarchical stress fusion is performed to obtain the second plastic concrete anti-seepage wall simulation model; The macro-micro stress data include macro stress data and micro stress data, and the stress concentration area extraction includes: Perform graph neural network encoding on macro stress data and micro stress data, and obtain macro stress characteristic graph data and micro stress characteristic graph data; Performing distance calculation on the macroscopic stress characteristic map data and the microscopic stress characteristic map data to obtain macroscopic stress map characteristic clustering data and microscopic stress map characteristic clustering data respectively; Dynamic stress threshold control is performed according to the macroscopic stress map characteristic clustering data and the microscopic stress map characteristic clustering data, and macroscopic stress threshold matrix data and microscopic stress threshold matrix data are obtained respectively; Using the macro stress threshold matrix data to locate the macro stress hotspot of the macro stress map feature clustering data, and obtaining the macro stress hotspot location map data, and using the micro stress threshold matrix data to locate the micro stress hotspot of the micro stress map feature clustering data, and obtaining the micro stress hotspot location map data; Perform multi-modal stress layered mapping based on the macroscopic stress hotspot location map data and the microscopic stress hotspot location map data to obtain layered stress mapping matrix data; The concentrated area synthesis is performed according to the layered stress mapping matrix data to obtain the stress concentration area data.

8. The method according to claim 7, characterized in that The stress field layering process includes: Material properties are layered according to the initial stress field data to obtain stress field material layering data; Perform stress gradient stratification according to the initial stress field data to obtain stress field gradient stratification data; The stress field material stratification data and the stress field gradient stratification data are cross-labeled to obtain the stress field stratification data.

9. The method according to claim 8, characterized in that Stress gradient stratification includes: Perform gradient calculation based on initial stress field data to obtain stress field gradient data; Cluster calculation is performed based on the stress field gradient data to obtain stress field gradient cluster characteristic data; The initial stress field data are partitioned into adjacent areas according to the stress field gradient clustering feature data to obtain the initial stress field gradient stratified data; Calculate the local gradient change rate according to the initial stress field gradient stratification data to obtain the gradient stratification local change rate data; According to the local change rate data of gradient stratification, the gradient sensitive areas are classified according to the initial stress field gradient stratification data to obtain the stress field gradient stratification data.

10. A three-dimensional modeling system for actual wall of plastic concrete anti-seepage wall, characterized in that: Used to execute the actual wall three-dimensional modeling method of the plastic concrete anti-seepage wall according to claim 1, the actual wall three-dimensional modeling system of the plastic concrete anti-seepage wall comprises: The plastic concrete anti-seepage wall scanning module is used to scan the plastic concrete anti-seepage wall through geological radar equipment to obtain the plastic concrete anti-seepage wall scanning data; The plastic concrete anti-seepage wall material property model building module is used to perform grid processing on the plastic concrete anti-seepage wall scanning data to obtain the plastic concrete anti-seepage wall grid data; perform wall structure feature extraction on the plastic concrete anti-seepage wall grid data to obtain the wall structure feature data; perform wall material property modeling on the wall structure feature data to obtain the plastic concrete anti-seepage wall material property model; The plastic concrete anti-seepage wall model fusion module is used to fuse the plastic concrete anti-seepage wall grid data and the plastic concrete anti-seepage wall material property model to obtain the plastic concrete anti-seepage wall fusion model; The plastic concrete anti-seepage wall stress analysis module is used to perform stress analysis on the plastic concrete anti-seepage wall fusion model to obtain a plastic concrete anti-seepage wall simulation model.

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