Anti-seepage simulation method and system for dam body geomembrane

Abstract

The invention discloses an anti-seepage simulation method and system for a dam body geomembrane, and relates to the technical field of hydraulic engineering, the method comprises the following steps: integrating three-dimensional geometric data of a dam body, filling bedding information, a geomembrane laying form, a membrane-soil interface contact structure and moisture content distribution, and generating a multi-source structure feature domain; identifying a potential weak penetration area in combination with the stress memory field; the response of seepage, stress and crack energy fields is stimulated through intelligent disturbance signals, multi-stage risk subareas of the dam body are dynamically divided, and a coupling evolution field is constructed; and calculating a minimum energy permeation path in combination with an energy function, and establishing a dam body anti-seepage risk field. The method solves the technical problem that the existing dam body anti-seepage method cannot accurately identify the potential weak seepage area and evaluate the seepage risk, resulting in insufficient anti-seepage design precision and reliability, and achieves the technical effects of accurately identifying the weak seepage area and improving the dam body anti-seepage design precision and reliability through comprehensive simulation and dynamic risk evaluation.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, specifically to a method and system for simulating seepage prevention using geomembranes in dam bodies. Background Technology

[0002] Geomembranes are widely used in dam seepage control projects to prevent water leakage. However, with increased service life, geomembranes may deform or be damaged due to uneven laying, wrinkles, poor joints, etc., thus affecting the seepage control performance of the dam. Traditional dam seepage control design usually relies on static analysis and simplified assumptions, making it difficult to fully consider the complex coupling effects of various factors within the dam body, especially in terms of seepage paths and stress distribution. This limits its ability to identify potential weak seepage areas and optimize seepage control design, making it unable to accurately identify potential weak seepage areas and assess leakage risks, resulting in insufficient accuracy and reliability in seepage control design. Summary of the Invention

[0003] This application provides a method and system for simulating seepage prevention of geomembranes in dam bodies, which solves the technical problem that existing dam seepage prevention methods cannot accurately identify potential weak seepage zones and assess leakage risks, resulting in insufficient accuracy and reliability of seepage prevention design.

[0004] The first aspect of this application provides a method for simulating seepage prevention using geomembranes in dam bodies. The method includes: generating a multi-source structural feature domain for the dam body using three-dimensional geometric data, fill bedding information, geomembrane laying morphology, membrane-soil interface contact structure, and dam body moisture content distribution; after accessing residual tension, wrinkle morphology, and anchoring status during the geomembrane laying process, establishing a geomembrane stress memory field reflecting the local initial stress non-uniformity within the membrane based on the multi-source structural feature domain; and extracting the geometric change gradient features of the dam body, calculating and constructing a potential... Weak permeability zones; injecting intelligent disturbance signals into the potential weak permeability zones to trigger initial responses in the seepage field, stress field, and crack energy field; dynamically dividing the dam body into multi-level risk sub-zones based on the amplification trajectory of the initial response in the seepage pressure field, geomembrane stress field, and interface energy field; forming a coupled evolution field that amplifies with disturbance based on the multi-level risk sub-zones; constructing a joint energy function of seepage energy, membrane strain energy, and interface crack propagation energy based on the coupled evolution field; solving for the minimum energy seepage path required to cause seepage failure of the geomembrane; and establishing the dam body seepage prevention risk field.

[0005] A second aspect of this application provides a seepage prevention simulation system for a dam geomembrane. The system includes: a multi-source structural feature domain construction module, used to generate a multi-source structural feature domain for the dam body using three-dimensional geometric data, filling bedding information, geomembrane laying morphology, membrane-soil interface contact structure, and dam body moisture content distribution; a geomembrane stress memory field construction module, used to establish a geomembrane stress memory field reflecting the local initial stress non-uniformity within the membrane based on the multi-source structural feature domain after recalling residual tension, fold morphology, and anchoring state during the geomembrane laying process; and a potential weak permeability zone identification module, used to extract the geometric change gradient features of the dam body and calculate the geomembrane stress memory field. The system constructs potential weak permeability zones; an initial response triggering module injects intelligent disturbance signals into the potential weak permeability zones to trigger initial responses in the seepage field, stress field, and crack energy field; a coupled evolution field generation module dynamically divides the dam body into multi-level risk sub-zones based on the amplification trajectory of the initial response in the seepage pressure field, geomembrane stress field, and interface energy field, and forms a coupled evolution field that amplifies with disturbance based on the multi-level risk sub-zones; and a seepage prevention risk field establishment module constructs a joint energy function of seepage energy, membrane strain energy, and interface crack propagation energy based on the coupled evolution field, solves for the minimum energy seepage path required to cause seepage failure of the geomembrane, and establishes the dam body seepage prevention risk field.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application provides a method and system for simulating seepage prevention using geomembranes in dam structures, relating to the field of hydraulic engineering technology. By establishing a multi-source structural feature domain and a geomembrane stress memory field, it identifies potential weak seepage zones in the dam structure. Furthermore, it uses intelligent perturbation signals to stimulate responses in the seepage, stress, and crack energy fields, dynamically dividing risk sub-regions. Combining the coupled evolution field and joint energy function, it calculates the minimum energy seepage path, thereby accurately assessing the seepage risk of the dam structure. This solves the technical problem that existing dam seepage prevention methods cannot accurately identify potential weak seepage zones and assess leakage risks, leading to insufficient accuracy and reliability in seepage prevention design. It achieves the technical effect of accurately identifying weak seepage zones and improving the accuracy and reliability of dam seepage prevention design through comprehensive simulation and dynamic risk assessment. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1A schematic diagram of a dam geomembrane seepage prevention simulation method provided in this application embodiment;

[0010] Figure 2 This is a schematic diagram of a seepage prevention simulation system for a dam geomembrane provided in an embodiment of this application.

[0011] Figure labeling: Multi-source structural feature domain construction module 11, geomembrane stress memory field construction module 12, potential weak permeability zone identification module 13, initial response triggering module 14, coupled evolution field generation module 15, seepage prevention risk field establishment module 16. Detailed Implementation

[0012] This application provides a method and system for simulating seepage prevention of geomembranes in dam bodies, which solves the technical problem that existing dam seepage prevention methods cannot accurately identify potential weak seepage zones and assess leakage risks, resulting in insufficient accuracy and reliability of seepage prevention design.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides a method for simulating seepage prevention of geomembranes in dam bodies, the method comprising:

[0016] P10: By using the three-dimensional geometric data of the dam body, the information on the filling bedding, the geomembrane laying morphology, the membrane-soil interface contact structure, and the water content distribution of the dam body, a multi-source structural feature domain of the dam body is generated.

[0017] It should be understood that, firstly, it is necessary to generate a comprehensive and accurate multi-source structural feature domain by integrating various key information of the dam body, including the dam body's three-dimensional geometric data, fill layer information, geomembrane laying morphology, membrane-soil interface contact structure, and dam body moisture content distribution, so as to provide a basic framework for subsequent seepage prevention simulation analysis.

[0018] The three-dimensional geometric data of the dam body is the foundation for constructing the dam model, including its overall shape, dimensions, slope, and other geometric parameters. Accurate three-dimensional geometric data ensures that the model's spatial structure matches the actual dam height. For example, for a trapezoidal earth-rock dam, its three-dimensional geometric data clearly defines parameters such as the dam crest width, dam base width, dam height, and slope. This information provides an accurate spatial framework for subsequent mechanical and hydraulic analyses. In practice, high-precision measuring equipment, such as total stations and 3D laser scanners, can be used to acquire the dam's three-dimensional geometric data, which can then be imported into computer-aided design (CAD) software for modeling and analysis.

[0019] Next, the fill bedding information is used to further characterize the structural features and soil composition of different fill layers within the dam body. Fill bedding information refers to the distribution and interface characteristics between different layers and materials of soil or fill material (such as sand, clay, etc.) during dam construction. In practice, the physical property parameters of the fill material can be obtained through sampling analysis and laboratory testing, and combined with the thickness information of the fill layer to form complete fill bedding information data.

[0020] The geomembrane laying pattern refers to the specific method and geometric characteristics of how the geomembrane is laid within a dam. Geomembranes are typically laid in the seepage-proof areas of the dam to prevent water infiltration and ensure the stability of the dam. The laying pattern includes the membrane's laying direction, overlap method, and tension state. Generally, the membrane laying needs to take into account the deformation and stress distribution of the dam to ensure that the membrane can effectively prevent seepage during dam deformation. Therefore, the laying pattern directly affects the stress distribution and seepage-proof performance of the geomembrane. In practice, information on the geomembrane laying pattern can be obtained through on-site observation and recording, and then combined with the dam's three-dimensional geometric data to form a three-dimensional model of the geomembrane laying pattern.

[0021] The membrane-soil interface contact structure refers to the contact between the geomembrane and the dam soil, including contact strength, friction coefficient, and potential interface defects such as air bubbles and wrinkles. This contact structure determines the interaction force between the membrane and the soil, affecting membrane deformation and permeability. Good membrane-soil interface contact helps improve seepage prevention, while poor interface contact can lead to leakage. In practice, information on the membrane-soil interface contact structure can be obtained through on-site testing and non-destructive testing techniques, such as ultrasonic testing and ground-penetrating radar. This information can then be combined with the geomembrane laying pattern and dam fill layering information to form complete interface contact structure data.

[0022] The moisture content distribution of a dam reflects the water content and its variation across different layers of the dam structure. Moisture content is a crucial factor affecting seepage and soil mechanical properties. Differences in moisture content across different areas can lead to variations in permeability, thus impacting the seepage prevention effect of the geomembrane. Therefore, accurately describing the moisture content distribution at different locations within the dam is essential when simulating its seepage prevention performance. By measuring and analyzing the moisture content distribution of the dam, seepage paths and leakage risks can be predicted more accurately. In practice, moisture content sensors or geological drilling sampling analysis can be used to obtain moisture content distribution data for the dam structure. This data is then combined with the dam's three-dimensional geometric data and fill bedding information to form a complete moisture content distribution model, which is essentially the multi-source structural feature domain of the dam. This feature domain encompasses not only geometric morphology and physical properties but also key factors such as the membrane's contact with the soil, providing a comprehensive and accurate foundational model for subsequent seepage prevention simulation analysis.

[0023] Furthermore, step P10 in this embodiment of the application also includes:

[0024] A twin simulation model is constructed using the multi-source structural feature domain. In the twin simulation model, seepage prevention simulation based on multi-level risk sub-regions is performed based on potential weak permeability zones to establish a dam seepage prevention risk field.

[0025] In one possible embodiment of this application, the multi-source structural feature domain can be used to construct a twin simulation model, and seepage prevention simulation based on multi-level risk sub-regions can be performed in the twin simulation model based on potential weak permeability zones, ultimately establishing a dam seepage prevention risk field.

[0026] Specifically, firstly, the generation of the aforementioned multi-source structural feature domain provides detailed and accurate foundational data for the construction of the twin simulation model. This domain includes key information such as the dam structure's geometry, material properties, soil distribution, membrane laying, and contact state. Based on this, a digital twin simulation model is constructed. A twin simulation model is a virtual representation based on real-world data. By simulating various physical behaviors of the dam, it can reflect the dam's mechanical response, seepage behavior, temperature changes, and other characteristics, and provide real-time comparison and feedback with the actual situation.

[0027] In constructing the twin simulation model, a spatial morphology model was first created based on the dam's three-dimensional geometric data. The physical and mechanical properties of different soil layers, such as permeability, stiffness, and compressive strength, were then defined in conjunction with the fill bedding information. Next, the geomembrane's laying pattern was incorporated into the model to reflect its actual state within the dam. Factors such as residual tension, wrinkle morphology, and the membrane-soil interface contact state were considered, as these directly affect the membrane's stress distribution and seepage prevention performance. Finally, based on the dam's hydrogeological conditions, moisture content information was introduced into the simulation model to ensure that the seepage analysis accurately reflects the dam's hydrological changes.

[0028] Subsequently, seepage prevention simulation was performed in the twin simulation model based on potentially weak permeability zones. Potentially weak permeability zones refer to areas within the dam body that are relatively prone to leakage due to geometric structure, material properties, or construction factors. These areas typically become seepage sources due to uneven membrane laying, poor membrane-soil interface contact, or unstable soil layers. In the twin simulation model, these areas need to be identified and analyzed as a key focus.

[0029] Next, based on these potentially weak permeability zones, a multi-level risk sub-zone seepage prevention simulation is conducted. This process begins with dividing the dam body into risk sub-zones. Based on factors such as the dam's geometric characteristics, the geomembrane laying pattern, the physical properties of the soil layers, and the moisture content distribution, areas within the dam body that may have different levels of risk are identified. For example, in some areas, the membrane laying may be relatively loose or wrinkled, resulting in poor contact between the geomembrane and the soil; these areas have a higher seepage risk and should be classified as high-risk sub-zones. In other areas, the membrane has good contact with the soil, resulting in a lower seepage risk; these areas can be classified as low-risk sub-zones. Through this zoning, a multi-level, multi-dimensional risk assessment of the dam body can be performed.

[0030] For example, within each risk sub-zone, seepage simulations are performed to assess the seepage, stress state, and membrane stability of that area. This process requires considering the impact of external disturbances, such as water level changes and dam deformation, on each risk sub-zone, and simulating seepage behavior, stress distribution, and crack propagation under different conditions to evaluate the impact of each risk sub-zone on the overall dam's seepage control performance.

[0031] After the simulation is completed, a seepage risk field for the dam body is established through comprehensive analysis of the simulation results for all risk sub-zones. This seepage risk field is a dynamic and visualized risk assessment model that can intuitively display the seepage risk level of each part of the dam body under different working conditions. This risk field can provide important reference for the seepage design, construction optimization, and operation management of the dam body. For example, in the design phase, the geomembrane laying scheme can be optimized based on the seepage risk field; in the construction phase, reinforcement measures can be taken for high-risk areas; and in the operation phase, key monitoring and maintenance can be carried out based on the risk field.

[0032] P20: After calling the residual tension, fold morphology and anchoring state of the geomembrane laying process, a geomembrane stress memory field is established based on the multi-source structural characteristic domain to reflect the local initial stress non-uniformity within the membrane.

[0033] Optionally, by calling key construction parameters during the geomembrane laying process and combining them with multi-source structural feature domains, a geomembrane stress memory field that can reflect the non-uniformity of local initial stress inside the geomembrane can be established.

[0034] First, residual tensile data from the geomembrane laying process needs to be retrieved. During geomembrane laying, residual tensile stress is generated due to construction operations such as stretching and fixing. These stresses persist after the geomembrane is laid and may affect its subsequent mechanical behavior and impermeability. By measuring and recording the degree of stretching of the geomembrane during laying, the distribution of residual tensile stress can be determined. For example, strain sensors can be used to monitor the tensile state of the geomembrane in real time during laying, thereby obtaining accurate residual tensile data.

[0035] Furthermore, during actual installation, geomembranes may wrinkle due to uneven construction environments or incomplete installation. Wrinkles are typically caused by uneven membrane laying or irregular terrain. In dam seepage control, wrinkles can lead to uneven contact between the membrane and the soil, affecting stress distribution and even creating seepage channels in certain areas. Therefore, the potential wrinkle patterns and their impact on the overall stress state of the membrane must be considered during modeling. Accurate calculation of stress distribution in wrinkled areas provides crucial reference data for seepage control design, especially for areas that may become weak points in seepage flow.

[0036] Next, the anchorage status of the geomembrane is evaluated. Anchorage status refers to the fixation between the geomembrane and the dam soil. In dam design, the geomembrane is typically anchored at the edges and bottom of the dam to prevent displacement or loosening during dam deformation. The stability of the anchorage directly affects the membrane's seepage control capability. Poor anchorage can lead to slippage, tearing, and other problems, affecting seepage control within the dam. During modeling, the geomembrane's anchorage strength must be fully considered, especially the stress concentration effect in the anchorage area during dam deformation. These stress concentrations can cause localized failure of the membrane near the anchorage points, affecting the overall seepage control effect. Therefore, the model needs to simulate the stress distribution of the membrane under different anchorage states, particularly the stress state near the anchorage points.

[0037] After acquiring parameters such as residual tension, wrinkle morphology, and anchorage status during the geomembrane laying process, a geomembrane stress memory field is established by combining the dam's three-dimensional geometric data, fill bedding information, membrane-soil interface contact structure, and dam's moisture content distribution from the multi-source structural feature domain. This process utilizes finite element analysis (FEA) technology, inputting geometric and physical parameters from the multi-source structural feature domain, as well as geomembrane laying parameters, into the model. By establishing appropriate mesh generation and boundary conditions, the initial stress state of the geomembrane after laying is simulated. Finite element analysis can accurately calculate the stress distribution inside the geomembrane, including tensile stress, compressive stress, and shear stress.

[0038] Furthermore, based on the calculated stress distribution data, a geomembrane stress memory field is constructed. This stress memory field reflects the non-uniformity of local initial stress within the geomembrane, meaning that the magnitude and direction of stress may differ significantly at different locations. For example, stress concentration is more pronounced in folded areas and near anchorage points, while the stress distribution is relatively uniform in smooth areas. By establishing the stress memory field, the deformation process of the geomembrane can be dynamically simulated, especially how the membrane's stress response changes under external hydrological or mechanical disturbances. This process not only provides necessary input data for subsequent seepage analysis but also guides the optimized design of dam seepage prevention.

[0039] P30: After extracting the geometric change gradient features of the dam body, a potential weak permeability zone is constructed based on the geomembrane stress memory field.

[0040] Furthermore, step P30 in this embodiment of the application also includes:

[0041] P31: After identifying the joint line location based on the multi-source structural feature domain, perform joint line segmentation processing; P32: Taking each joint line segment as a unit, statistically analyze the differences in stiffness and density of the soil on both sides of each segment, and establish a material inhomogeneity identifier based on the statistical results; P33: Perform stress concentration correlation analysis on each joint line segment based on the geomembrane stress memory field, and establish a correlated stress anomaly identifier; P34: Use the material inhomogeneity identifier and the correlated stress anomaly identifier to identify weak joint areas of the geomembrane and establish potential weak joint areas; P35: Construct potential permeability weak areas based on the potential weak joint areas.

[0042] It should be understood that the first step is to extract the geometric change gradient characteristics of the dam body. These gradient characteristics refer to the rate of geometric change of various parts of the dam structure, particularly the deformation of the dam surface or interior. For example, the dam body may undergo local deformation due to external loads, water pressure, or temperature changes; the geometric change gradient reflects the spatial distribution characteristics of these deformations. In numerical simulations, these gradient characteristics are typically obtained by analyzing the geometric and deformation data of the dam body. Extracting these characteristics helps identify potential stress concentration areas and weak deformation zones within the dam body, providing crucial information for subsequent identification of seepage-prone areas and seepage prevention design.

[0043] Subsequently, potential weak permeability zones were constructed based on the geomembrane stress memory field calculation. The geomembrane stress memory field reflects the initial stress distribution within the geomembrane, including areas of stress concentration and areas of lower stress. In areas of stress concentration, the geomembrane may be more prone to deformation or failure, leading to an increased risk of leakage. By combining the dam's geometric gradient characteristics with the geomembrane stress memory field, potential weak permeability zones within the dam can be calculated and identified. These weak zones are the focus of subsequent seepage prevention simulations and risk assessments.

[0044] Specifically, the location of geomembrane joints is first identified within the multi-source structural feature domain. Joints are unavoidable connection points during geomembrane installation, and these areas are often high-risk zones for leakage. After identifying the joint locations, the joints are segmented, meaning longer joints are divided into multiple shorter segments. This segmentation allows for a more detailed analysis of the characteristics of each joint segment, improving the accuracy of the analysis.

[0045] Next, taking each joint line segment as a unit, the differences in stiffness and density of the soil on both sides of each segment are statistically analyzed. Stiffness measures the soil's ability to resist deformation, while density reflects the soil's compaction. These factors directly affect the seepage path and the stability of the dam. By statistically analyzing the differences in stiffness and density of the soil on both sides of each joint line segment, the inhomogeneity of soil properties can be identified. These inhomogeneities may lead to a decrease in the seepage prevention performance of the geomembrane. Based on the statistical results, a material inhomogeneity indicator can be established to identify the differences in soil on both sides of each joint line segment, that is, to mark the joint line segments with significant differences in material properties on both sides.

[0046] Furthermore, each segment of the joint line may exhibit different stress distributions due to factors such as differences in soil stiffness and membrane laying patterns. Therefore, by performing stress concentration correlation analysis based on the stress memory field, stress concentration areas around the joint line can be identified. Stress concentration refers to areas where the geomembrane may bear significant stress due to tension, wrinkling, or poor anchoring. By analyzing the stress state of each joint line segment, areas of stress anomalies can be identified, which may become weak points in seepage. The stress concentration analysis results will generate associated stress anomaly value identifiers to help identify areas where excessive stress concentration may occur.

[0047] Next, using the aforementioned material inhomogeneity and associated stress anomaly indicators, weak joint areas of the geomembrane are identified. Weak joint areas refer to regions where the geomembrane's seepage prevention performance is affected due to the combined effects of differences in soil physical properties, such as stiffness and density, and membrane stress concentration. Identifying these weak joint areas allows for precise location of areas within the dam with poor seepage prevention, providing a basis for subsequent reinforcement or repair measures.

[0048] Finally, based on the identified potential weak joint areas, and combined with the overall analysis results of the dam's geometric gradient characteristics and the geomembrane stress memory field, potential seepage weak areas are constructed. These potential seepage weak areas not only include weak areas at the joint lines but may also include other high-risk areas caused by geometric changes or stress concentration. Through this comprehensive analysis, possible seepage risk points in the dam can be identified more comprehensively, thus providing a crucial basis for the dam's seepage prevention design and optimization.

[0049] Furthermore, step P35 in this embodiment of the application also includes:

[0050] P35-1: Calculate the curvature value point by point on the geomembrane surface and construct the neighborhood average value of the curvature; P35-2: Mark the curvature abrupt change points based on the curvature value and the neighborhood average value; P35-3: Using the curvature abrupt change points, trace the continuous distribution line of residual strain along the fold direction. If the residual strain continuously meets a preset threshold along a linear or curved direction and intersects with the curvature abrupt change point, then construct the fold main axis; P35-4: Perform a region search along the fold main axis to detect local discontinuities in fold axial displacement and thickness variation, and construct fold concentration areas based on the detection results; P35-5: Classify the fold concentration areas according to the degree of fold superposition and the cumulative axial strain along the fold main axis to establish potential fold concentration weak areas; P35-6: Construct potential permeability weak areas based on the potential joint weak areas and potential fold concentration weak areas.

[0051] Specifically, the construction process of potential weak permeability zones can be further refined, especially the detailed analysis of geomembrane fold areas, in order to comprehensively assess the weak areas of the geomembrane in the dam body and provide more precise support for the seepage prevention design of the dam body.

[0052] First, the curvature value is calculated point-by-point on the geomembrane surface. Curvature is a crucial parameter for measuring the degree of surface bending; for geomembranes, changes in curvature reflect surface wrinkles and deformation. Curvature values ​​can be calculated using Principal Component Analysis (PCA). For any point on the geomembrane surface and its neighbors, the covariance matrix is ​​calculated, and its eigenvalues ​​and eigenvectors are solved. The eigenvector corresponding to the smallest eigenvalue is the normal vector at that point, and the curvature value at that point is the ratio of the smallest eigenvalue to the sum of all eigenvalues. In this way, the curvature value at each point on the geomembrane surface can be obtained. Simultaneously, to reduce the influence of local noise, the neighborhood average of the curvature is also calculated—the average curvature within a certain range around each calculation point. This helps smooth the data and avoids the interference of outliers from individual points on the overall analysis results.

[0053] Subsequently, curvature abrupt change points are marked based on the calculated curvature values ​​and their neighborhood averages. Curvature abrupt change points are regions where the curvature value changes significantly; these regions typically correspond to abrupt changes in membrane surface morphology, such as wrinkles or other geometric irregularities. These curvature abrupt change points can be identified by comparing the curvature values ​​with the neighborhood averages. The appearance of curvature abrupt change points usually indicates potential stress concentration problems in the geomembrane, and these areas may become potential weak permeability zones. Therefore, marking these points is to further track strain distribution and membrane deformation paths.

[0054] Next, using the marked curvature abrupt change points, the continuous distribution line of residual strain is traced along the fold direction. Residual strain refers to the permanent deformation of the geomembrane caused by construction operations such as stretching and folding during installation. During the tracing process, if the residual strain is continuously distributed along a certain linear or curved direction and intersects with the curvature abrupt change point, it indicates that there is a significant strain concentration in this direction. In this case, the fold principal axis, i.e., the axis along the line of residual strain concentration, can be constructed as the dominant direction of the fold morphology.

[0055] Subsequently, a regional search is conducted along the main axis of the folds to identify local discontinuities in the fold direction. Specifically, changes in the folded region can be detected by analyzing the axial displacement of the membrane along the main axis of the folds, i.e., the degree of membrane deformation and the changes in membrane thickness, i.e., local thinning or thickening of the membrane. These discontinuous areas often indicate irregular membrane deformation, which may lead to a reduction in the membrane's impermeability and therefore require close attention. Based on these detection results, concentrated fold zones can be constructed. These areas are potential weak points in the dam body and are prone to becoming seepage channels.

[0056] Next, these regions are classified based on the degree of fold overlap and the accumulation of axial strain along the principal axis of the folds. The degree of fold overlap refers to the density of folds within the folded region, while the axial strain accumulation classification is based on the strain accumulation along the principal axis of the folds. The accumulation of axial strain reflects the stress concentration in a given region; higher strain accumulation typically indicates a greater susceptibility to failure or leakage. Therefore, by analyzing these factors, potential weak fold concentration zones can be identified, posing a higher risk in dam seepage control.

[0057] Finally, the potential joint weak zones and potential fold concentration weak zones identified in the above analysis are integrated to construct the final potential seepage weak zone. This potential seepage weak zone includes not only the weak zones at the joint lines but also high-risk areas in the fold regions caused by stress concentration and significant deformation. Through this comprehensive analysis, potential seepage risk points in the dam body can be identified more comprehensively, providing accurate input for subsequent seepage prevention simulation and risk assessment.

[0058] Furthermore, steps P35-6 in the embodiments of this application also include:

[0059] P35-61: Extract the membrane-soil interface roughness, local fit, and moisture distribution characteristics within the multi-source structural feature domain; P35-62: Based on the extraction results, combine the identification of sudden drops and reverse distributions in the geomembrane stress memory field to establish potential interface void weak zones; P35-63: Utilize the stress gradient peak points of the geomembrane stress memory field, combined with the material stiffness differences at the dam topographic deformation location and abrupt changes in building stratification, to identify potential high-stress concentration weak zones; P35-64: Construct potential permeability weak zones from the aforementioned potential joint weak zones, potential fold concentration weak zones, potential interface void weak zones, and potential high-stress concentration weak zones.

[0060] In one possible embodiment of this application, the process of constructing potential weak permeability zones can be further refined by identifying potential weak permeability zones in the dam body through detailed analysis of the membrane-soil interface.

[0061] First, the membrane-soil interface roughness, local fit, and moisture distribution characteristics within the multi-source structural feature domain were extracted. Membrane-soil interface roughness refers to the microscopic morphology of the membrane-soil contact surface. A rougher interface may lead to incomplete contact between the membrane and soil, thus affecting the seepage prevention effect. Local fit reflects the uniformity of the membrane-soil contact; poor fit may result in local contact gaps, forming potential seepage channels. Moisture distribution characteristics reflect the distribution of moisture in the soil; concentrated or uneven moisture distribution directly affects the membrane's stress state and its seepage prevention capacity. Therefore, the extraction of these characteristics provides fundamental data for subsequent weak zone analysis.

[0062] Subsequently, based on the extraction results and combined with the identification of sudden drops and reverse distributions in the geomembrane stress memory field, potential interface void weak zones were established. Specifically, by analyzing the sudden drop and reverse distribution identifications in the stress memory field, areas of poor contact between the membrane and the soil can be identified. Sudden drop identification in the stress memory field refers to the significant changes in the contact state between the membrane and the soil in certain areas of the membrane due to abrupt changes in stress or stress concentration, forming voids or areas of poor contact. Reverse distribution identification refers to the irregular changes in stress distribution in certain areas between the geomembrane and the soil, leading to the breakdown of the membrane-soil contact and the formation of voids. These areas are usually weak points in seepage. By establishing potential interface void weak zones, areas of poor membrane-soil contact in the dam body can be clearly identified, providing support for subsequent seepage prevention reinforcement or repair work.

[0063] Next, by utilizing the stress gradient peak points of the geomembrane stress memory field, combined with the material stiffness differences at the dam's topographic deformation locations and abrupt changes in structural bedding, potential high-stress concentration zones are identified. Stress gradient peak points typically appear on the geomembrane surface or at the membrane-soil interface; these points represent the areas of maximum stress variation in the membrane under external loads or environmental changes. Based on these areas of maximum stress variation, combined with the material stiffness differences at the dam's topographic deformation locations and abrupt changes in structural bedding, stress concentration zones can be further located. Topographic deformation and abrupt changes in bedding are usually accompanied by drastic changes in the physical properties of the soil within the dam, leading to differences in soil stiffness and thus stress concentration. By identifying these potential high-stress concentration zones, potential structural weaknesses in the dam can be detected in advance, providing data support for subsequent design and repair.

[0064] Finally, the identified potential joint weak zones, potential fold concentration weak zones, potential interface void weak zones, and potential high stress concentration weak zones are combined to form the final potential seepage weak zones. These weak zones cover areas within the dam body that are at risk of leakage due to various factors such as joints, folds, interface voids, and high stress concentration, providing accurate input for subsequent seepage prevention simulation and risk assessment.

[0065] P40: Inject intelligent perturbation signals into the potential weak permeability zone to trigger the initial response of the seepage field, stress field, and crack energy field.

[0066] Furthermore, step P40 in this embodiment of the application also includes:

[0067] P41: Based on the spatial boundary, type label, geomembrane stress memory field, and local material inhomogeneity of the potential weak permeability zones, generate a set of perturbation target parameters for each weak zone; P42: Using the set of perturbation target parameters as the matching target, perform perturbation signal type matching to trigger seepage, stress, and crack energy responses, and establish matching results. The matching results include local pressure gradient fluctuations, mechanical displacements, temperature disturbances, or interface contact conditions; P43: Based on the initial stress peak value, membrane-soil interface stiffness, and fold morphology of the potential weak permeability zones, adaptively optimize the amplitude, direction of action, and time series of the matching results to establish intelligent perturbation signals.

[0068] Specifically, intelligent perturbation signals are injected into potentially weak permeability zones to trigger the initial responses of the seepage field, stress field, and crack energy field, thereby simulating the dam's response under different working conditions and evaluating the effectiveness of the seepage prevention design.

[0069] First, based on the spatial boundaries, type labels, geomembrane stress memory field, and local material inhomogeneities of potential weak permeability zones, a set of perturbation target parameters is generated for each weak zone. The spatial boundaries define the extent of the weak zone, the type labels distinguish its nature (e.g., joint weak zone, folded weak zone), the geomembrane stress memory field provides stress distribution information, and the local material inhomogeneities reflect changes in material properties within the weak zone. These information collectively constitute the set of perturbation target parameters, providing the foundation for subsequent perturbation signal design. For example, for a folded weak zone, its perturbation target parameter set might include the stress peak value of the folded region, fold morphology parameters, and the stiffness difference between the materials on both sides of the fold.

[0070] Subsequently, using the set of perturbation target parameters as the matching target, the matching of perturbation signal types that trigger seepage, stress, and crack energy responses is performed, and the matching results are established. For example, by analyzing the physical characteristics of potential weak permeability zones, suitable perturbation signal types are selected, and matching is performed based on the characteristics of the weak zones. The types of perturbation signals may include local pressure gradient fluctuations, mechanical displacements, temperature disturbances, or changes in interfacial contact conditions. These signals can simulate the dam's response under different external conditions, such as water flow, soil settlement, and temperature changes. Through matching, perturbation signals are generated, and the initial response modes of these signals in the seepage field, stress field, and crack energy field are determined. This process helps to understand the behavior and potential risks of the dam under different perturbation conditions.

[0071] Finally, based on the initial peak stress, membrane-soil interface stiffness, and fold morphology of the potentially weak permeability zone, the amplitude, direction of action, and time series of the matching results are adaptively optimized to establish an intelligent perturbation signal. The initial peak stress determines the intensity of the perturbation signal, the membrane-soil interface stiffness affects the signal's action mode, and the fold morphology determines the signal's propagation path. By comprehensively considering these factors and optimizing the amplitude, direction of action, and time series of the perturbation signal, it can be ensured that the signal can accurately act on the target weak zone and trigger the initial responses of the seepage field, stress field, and crack energy field. For example, for a folded weak zone with a high initial peak stress, it may be necessary to design a perturbation signal with a large amplitude, an action direction along the fold principal axis, and a gradually increasing time series to fully excite the response of the weak zone.

[0072] By optimizing the perturbation signal, an intelligent perturbation signal can be obtained. This signal can accurately simulate the initial response of the dam body in a potentially weak permeability zone and trigger changes in the seepage field, stress field, and crack energy field. The optimization of the intelligent perturbation signal not only considers the internal physical characteristics of the dam body but also dynamically adjusts the intensity and mode of action of the perturbation signal according to actual conditions, thereby providing more accurate and reliable simulation results for assessing the dam's seepage prevention performance.

[0073] P50: Based on the amplification trajectory of the initial response in the seepage pressure field, geomembrane stress field and interface energy field, the dam body is dynamically divided into multi-level risk sub-regions, and a coupled evolution field amplified by disturbance is formed based on the multi-level risk sub-regions.

[0074] Furthermore, in the embodiment of this application, step P50 further includes dynamically dividing the dam body into multi-level risk sub-zones:

[0075] P51: Extract the multidimensional response trajectory dataset from the initial response, calculate the seepage sensitivity, stress sensitivity, and interface energy sensitivity along the spatial grid cells of the dam body, and statistically analyze the local sensitivity peak and gradient changes based on the calculation results to form a local risk index; P52: Based on the spatial distribution and sensitivity gradient of the local risk index, divide the dam body into multiple levels of multi-level risk sub-regions, including high-risk sub-regions, medium-risk sub-regions, and low-risk sub-regions.

[0076] Optionally, based on the amplified trajectory of the initial response in the seepage pressure field, geomembrane stress field, and interface energy field, the dam body is dynamically divided into multi-level risk sub-zones, and a coupled evolution field is formed based on these sub-zones. This process aims to identify and delineate different risk areas in the dam body by analyzing its response to different disturbances, thereby providing accurate risk assessment for subsequent seepage control design.

[0077] First, a multidimensional response trajectory dataset is extracted from the initial response and calculated along the spatial grid cells of the dam body. These response trajectory datasets originate from the initial responses of the seepage field, stress field, and interfacial energy field. Each grid cell represents a local region of the dam body, and its response trajectory describes the changing trend of the dam body under specific disturbances. Next, seepage sensitivity, stress sensitivity, and interfacial energy sensitivity are calculated along the spatial grid cells of the dam body as important indicators for evaluating the response of different regions of the dam body to different disturbance intensities. Seepage sensitivity reflects the dam body's responsiveness under seepage pressure; more sensitive areas indicate larger seepage changes, potentially leading to seepage problems. Stress sensitivity reflects the strain response of the geomembrane under external forces; areas with high sensitivity may be areas of stress concentration or large deformation, easily leading to rupture or instability. Interfacial energy sensitivity is related to the contact strength between the membrane and the soil; areas with high sensitivity may experience interfacial voids or poor contact, forming seepage channels.

[0078] Subsequently, based on the calculation results, the local sensitivity peak and gradient changes are statistically analyzed to form a local risk index. The local sensitivity peak represents the maximum sensitivity reached in a specific region, while the gradient change reflects the rate of change of sensitivity in space. These statistical results can be used to assess the risk level of each spatial grid cell. For example, a region with a high seepage sensitivity peak and rapid gradient change may be considered a high-risk region because it responds more drastically to changes in seepage pressure, potentially leading to more severe leakage or structural damage.

[0079] Next, based on the spatial distribution and sensitivity gradient of the local risk index, the dam body is divided into multiple levels of multi-level risk sub-zones. These multi-level risk sub-zones include high-risk, medium-risk, and low-risk sub-zones. High-risk sub-zones refer to areas with high local risk indices and large sensitivity gradients; these areas are most sensitive to the amplification trajectory of the initial response and may rapidly develop into serious leakage or structural problems under disturbance. Medium-risk sub-zones are areas with moderate risk indices and sensitivity gradients; these areas require attention and appropriate protective measures. Low-risk sub-zones are areas with low risk indices and small sensitivity gradients; these areas are relatively stable but still require routine monitoring.

[0080] Ultimately, based on these multi-level risk sub-zones, a coupled evolution field amplified by disturbance can be constructed. This field demonstrates the interaction and evolution of stress, seepage, and energy changes in various regions of the dam body under different disturbances. By simulating the dynamic response of the dam body under actual working conditions, the coupled evolution field can intuitively show the behavioral changes of the dam body in different risk sub-zones, providing a reference for seepage prevention design.

[0081] P60: Based on the coupled evolution field, construct the joint energy function of seepage energy, membrane strain energy, and interface crack propagation energy, solve the minimum energy seepage path required for the geomembrane to leak and fail, and establish the dam seepage prevention risk field.

[0082] The joint energy function is as follows:

[0083] ;in, Characterizing the joint energy, Characterizes potential seepage pathways or dam unit volume regions. Characterizing osmotic pressure, Characterizing the local drag modulus, Characterizing the local stress of the geomembrane, Characterizing the elastic modulus of geomembranes, Characterizing the volume of a geomembrane unit, Characterizes the critical energy release rate of interfacial cracks. The actual area representing the propagation of the interface crack. These are the weighting coefficients.

[0084] Specifically, as shown in the above equation, a joint energy function is constructed based on the coupled evolution field. This function comprehensively considers seepage energy, membrane strain energy, and interface crack propagation energy, and can be used to solve for the minimum energy seepage path required to cause leakage failure of the geomembrane, thereby establishing the dam seepage prevention risk field. The characterization represents the comprehensive energy of the dam body under various physical actions, encompassing the energy of seepage, stress, and crack propagation. Characterizes potential seepage pathways or dam unit volume regions, i.e., regions or volume regions within the dam body where seepage may occur. It represents the seepage pressure, that is, the magnitude of the water flow pressure inside the dam body, and is measured in Pa. The local resistance modulus, which represents the local resistance of the geomembrane to seepage flow, is expressed in Pa. Characterizes the local stress of the geomembrane, that is, the stress state of the geomembrane at different points, and the unit is Pa. The elastic modulus of a geomembrane is a measure of the elastic properties of the membrane material under stress, expressed in Pa. Characterizing the volume of a geomembrane unit, The critical energy release rate of interfacial cracks is the critical energy required for crack propagation between the membrane and the soil, expressed in J / m². The actual area representing the propagation of the interface crack. These are weighting coefficients used to adjust the degree of influence of each energy element in the joint energy function.

[0085] Among them, the first item This represents seepage energy, expressed through osmotic pressure. and local resistance modulus The ratio is used to describe the energy conversion during the seepage process; the second term This represents the strain energy within the membrane, combined with the local stress of the membrane. elastic modulus and volume This reflects the deformation energy of the geomembrane after being subjected to stress; the third item This represents the crack energy, combined with the critical energy release rate of the interfacial crack. and the actual area of ​​crack propagation This reflects the energy release during the propagation of cracks in the geomembrane.

[0086] This joint energy function comprehensively considers the energy from seepage, stress, and crack propagation, thus fully reflecting the mechanical and seepage behavior of the geomembrane within the dam body. By calculating these energy terms, potential seepage paths within the dam body can be identified, the minimum energy seepage path leading to membrane leakage and failure can be found, and a seepage risk field can be established accordingly.

[0087] Furthermore, in solving for the minimum energy permeation path required to cause leakage and failure of the geomembrane, step P60 of this application embodiment also includes:

[0088] P61: Discretize the dam area where the coupled evolution field is located into several volumetric units and interface units in three-dimensional space, and adaptively adjust the unit density according to the spatial distribution of energy gradient in the coupled evolution field. Calculate the energy contribution of volumetric units and interface units using the joint energy function to form accumulative unit energy labels. P62: Map the center of the volumetric unit or the node of the interface unit to the vertex of the graph. Establish the graph edges using spatially adjacent unit nodes or those that may be connected through cracks or joints. Configure the energy cost weight of the edges using the accumulative unit energy labels to construct an energy-weighted graph. P63: Perform a global shortest path search on a coarse grid scale based on the energy-weighted graph to construct N candidate paths and record the total energy and path topology features of each candidate path. P64: Perform grid refinement on the neighborhood of the N candidate paths. Calculate the joint energy of the volumetric units and interface units in the refined local area and update the corresponding edge weights. Re-execute the path search on the refined grid to construct the minimum energy penetration path.

[0089] It should be understood that the process of finding the minimum energy seepage path required for geomembrane leakage and failure can be further refined. The coupled evolution field of the dam area is discretized into multiple volumetric and interface elements, and the energy contribution of each element is calculated to establish an energy weighted graph. Finally, the minimum energy seepage path is found through the shortest path search method.

[0090] First, the dam area is discretized into several volumetric elements and interface elements in three-dimensional space. This step is fundamental to numerical simulation; by dividing the continuous physical space into a finite number of small elements, each element can be calculated and analyzed independently. Volumetric elements represent the volumetric elements within the dam body, while interface elements represent the contact surfaces between the geomembrane and the soil, or between geomembranes. This discretization transforms the complex dam structure into a computable finite element model, suitable for numerical simulation and energy analysis. During discretization, the dam's geometry, material properties, and potential seepage paths must be considered to ensure the rationality of the element division and the accuracy of the calculations.

[0091] Next, the element density is adaptively adjusted based on the spatial distribution of the energy gradient in the coupled evolution field. In regions with high energy gradients, the element density is increased to improve computational accuracy; while in regions with low energy gradients, the element density is appropriately reduced to decrease computational complexity. This adaptive adjustment ensures accurate calculations in critical areas of the penetration path and stress concentration.

[0092] Then, based on the joint energy function, the energy contribution of each volume element and interface element is calculated separately. The joint energy function combines seepage energy, strain energy, and crack propagation energy, and forms an accumulative element energy label by calculating the energy contribution of each element. These labels reflect the impact of each element on the overall energy, providing energy data support for subsequent path optimization.

[0093] Next, the discretized volumetric or interface elements are mapped to vertices of a graph, where each vertex represents the spatial location of the element. Then, edges are established between the element nodes using spatial adjacency relationships or based on the connectivity of cracks and joints, and the energy cost weights of these edges are configured using accumulative element energy tags. These edges connect adjacent or potentially connected elements, forming an energy-weighted graph. The energy weight of each edge, determined by the aforementioned element energy tags, reflects the energy required for seepage, stress transfer, or crack propagation from one element to another; these weights will be used for subsequent shortest path searches.

[0094] Next, based on the constructed energy-weighted graph, a global shortest path search is performed at a coarse grid scale to find N candidate penetration paths, and the total energy and path topology characteristics of each candidate path are recorded. This step involves finding the paths with the minimum energy cost on the graph model; these paths are selected from numerous possible paths and represent potential minimum energy penetration paths.

[0095] Next, mesh refinement is performed on the neighborhoods of the N candidate paths. This refinement process improves computational accuracy by increasing the density of computational cells in the regions containing the candidate paths, resulting in more detailed energy calculations in local areas and capturing subtle changes in seepage paths, stress distribution, and crack propagation. Then, on the refined mesh, the joint energy of each cell is recalculated, and the weights of the corresponding edges are updated. Subsequently, the path search is re-performed based on the refined mesh. This step ensures that paths are re-evaluated and optimized on the finer, refined mesh to find the true minimum-energy seepage path, which represents the path requiring the minimum energy consumption for dam leakage failure. This process not only helps engineers identify the most vulnerable seepage channels in the dam but also provides an optimization basis for dam seepage prevention design, thereby ensuring the long-term stability and safety of the dam.

[0096] In summary, the embodiments of this application have at least the following technical effects:

[0097] This application, through the establishment of multi-source structural feature domains and geomembrane stress memory fields, can accurately identify potential weak permeability zones in dam bodies. By analyzing the initial responses of seepage, stress, and crack energy fields, it dynamically divides the dam body into multi-level risk sub-zones and assesses the seepage risk of different areas in real time. This provides a reliable basis for subsequent design optimization, repair, and reinforcement, avoiding significant impacts of potential leakage problems on the dam body. Based on coupled evolution fields and joint energy functions, it solves for the minimum energy seepage path, thereby optimizing the dam's seepage prevention design and improving its seepage prevention performance and long-term stability. By exciting the initial response of the dam body with intelligent perturbation signals, it can effectively predict the seepage risk of the dam body under different working conditions, achieving a more refined assessment of seepage prevention effects and improving the reliability and long-term effectiveness of the seepage prevention system.

[0098] This technology achieves the goal of accurately identifying weak seepage zones through comprehensive simulation and dynamic risk assessment, thereby improving the accuracy and reliability of dam seepage prevention design.

[0099] Example 2, based on the same inventive concept as the seepage prevention simulation method for a dam geomembrane in the aforementioned examples, such as... Figure 2 As shown, this application provides a seepage prevention simulation system for geomembranes in dam bodies. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0100] The multi-source structural feature domain construction module 11 is used to generate the multi-source structural feature domain of the dam body by using the three-dimensional geometric data of the dam body, the filling layer information, the geomembrane laying morphology, the membrane-soil interface contact structure and the water content distribution of the dam body.

[0101] The geomembrane stress memory field construction module 12 is used to call the residual tension, fold morphology and anchoring state of the geomembrane laying process, and then establish a geomembrane stress memory field that reflects the local initial stress non-uniformity within the membrane based on the multi-source structural characteristic domain.

[0102] The potential permeability weak zone identification module 13 is used to extract the geometric change gradient features of the dam body and then construct the potential permeability weak zone based on the geomembrane stress memory field.

[0103] The initial response triggering module 14 is used to inject intelligent disturbance signals into the potential weak permeability zone to trigger the initial response of the seepage field, stress field and crack energy field.

[0104] The coupled evolution field generation module 15 is used to dynamically divide the dam body into multi-level risk sub-regions based on the amplified trajectory of the initial response in the seepage pressure field, geomembrane stress field and interface energy field, and to form a coupled evolution field that amplifies with disturbance based on the multi-level risk sub-regions.

[0105] The seepage risk field establishment module 16 is used to construct a joint energy function of seepage energy, membrane strain energy, and interface crack propagation energy based on the coupled evolution field, solve the minimum energy seepage path required for the geomembrane to leak and fail, and establish the seepage risk field of the dam body.

[0106] Furthermore, the multi-source construction feature domain construction module 11 is also used to perform the following steps:

[0107] A twin simulation model is constructed using the multi-source structural feature domain. In the twin simulation model, seepage prevention simulation based on multi-level risk sub-regions is performed based on potential weak permeability zones to establish a dam seepage prevention risk field.

[0108] Furthermore, the potential weak penetration area identification module 13 is also used to perform the following steps:

[0109] After identifying the joint line location based on the multi-source structural feature domain, joint line segmentation is performed. Using each joint line segment as a unit, the differences in stiffness and density of the soil on both sides are statistically analyzed segment by segment, and a material inhomogeneity identifier is established based on the statistical results. Stress concentration correlation analysis is performed on each joint line segment based on the geomembrane stress memory field, and a correlated stress anomaly identifier is established. The material inhomogeneity identifier and the correlated stress anomaly identifier are used to identify weak joint areas of the geomembrane, establishing potential weak joint areas. Potential permeability weak areas are then constructed based on these potential weak joint areas.

[0110] Furthermore, the potential weak penetration area identification module 13 is also used to perform the following steps:

[0111] Curvature values ​​are calculated point by point on the geomembrane surface, and a neighborhood average of the curvature is constructed. Curvature abrupt change points are marked based on the curvature values ​​and neighborhood averages. Using the curvature abrupt change points, the continuous distribution line of residual strain is traced along the fold direction. If the residual strain continuously meets a preset threshold along a linear or curved direction and intersects with the curvature abrupt change point, the fold main axis is constructed. A region search is performed along the fold main axis to detect local discontinuities in fold axial displacement and thickness variation, and fold concentration areas are constructed based on the detection results. The fold concentration areas are classified according to the degree of fold superposition and the cumulative axial strain along the fold main axis to establish potential fold concentration weak areas. Potential permeability weak areas are constructed based on the potential joint weak areas and potential fold concentration weak areas.

[0112] Furthermore, the potential weak penetration area identification module 13 is also used to perform the following steps:

[0113] The roughness, local fit, and moisture distribution characteristics of the membrane-soil interface within the multi-source structural feature domain are extracted. Based on the extraction results, and combined with the identification of sudden drops and reverse distributions in the geomembrane stress memory field, potential interface void weak zones are established. Using the stress gradient peak points of the geomembrane stress memory field, and combined with the material stiffness differences at the dam topographic deformation location and abrupt changes in building stratification, potential high-stress concentration weak zones are identified. The potential joint weak zones, potential fold concentration weak zones, potential interface void weak zones, and potential high-stress concentration weak zones are used to construct potential permeability weak zones.

[0114] Furthermore, the initial response triggering module 14 is also used to perform the following steps:

[0115] Based on the spatial boundaries, type labels, geomembrane stress memory field, and local material inhomogeneities of the potential weak permeability zones, a set of perturbation target parameters is generated for each weak zone. Using the set of perturbation target parameters as the matching target, perturbation signal type matching that triggers seepage, stress, and crack energy responses is performed to establish matching results. The matching results include local pressure gradient fluctuations, mechanical displacements, temperature perturbations, or interface contact conditions. Based on the initial stress peak value, membrane-soil interface stiffness, and fold morphology of the potential weak permeability zones, the amplitude, direction of action, and time series of the matching results are adaptively optimized to establish intelligent perturbation signals.

[0116] Furthermore, the coupled evolution field generation module 15 is also used to perform the following steps:

[0117] Extract the multidimensional response trajectory dataset from the initial response, calculate the seepage sensitivity, stress sensitivity, and interface energy sensitivity along the spatial grid cells of the dam body, and statistically analyze the local sensitivity peak and gradient changes based on the calculation results to form a local risk index; based on the spatial distribution and sensitivity gradient of the local risk index, divide the dam body into multiple levels of multi-level risk sub-regions, including high-risk sub-regions, medium-risk sub-regions, and low-risk sub-regions.

[0118] Furthermore, in the seepage prevention risk field establishment module 16:

[0119] The joint energy function is as follows:

[0120] ;in, Characterizing the joint energy, Characterizes potential seepage pathways or dam unit volume regions. Characterizing osmotic pressure, Characterizing the local drag modulus, Characterizing the local stress of the geomembrane, Characterizing the elastic modulus of geomembranes, Characterizing the volume of a geomembrane unit, Characterizes the critical energy release rate of interfacial cracks. The actual area representing the propagation of the interface crack. These are the weighting coefficients.

[0121] Furthermore, the seepage prevention risk field establishment module 16 is also used to perform the following steps:

[0122] The coupled evolution field is discretized into several volumetric and interface units in three-dimensional space. The unit density is adaptively adjusted according to the spatial distribution of the energy gradient in the coupled evolution field. The energy contribution of the volumetric and interface units is calculated using the joint energy function to form an accumulative unit energy label. The center of the volumetric unit or the node of the interface unit is mapped as the vertex of the graph. Edges of the graph are established between spatially adjacent unit nodes or those that may be connected through cracks or joints. The energy cost weight of the edges is configured using the accumulative unit energy label to construct an energy-weighted graph. Based on the energy-weighted graph, a global shortest path search is performed at a coarse grid scale to construct N candidate paths. The total energy and path topology features of each candidate path are recorded. The grid is refined in the neighborhood of the N candidate paths. The joint energy of the volumetric and interface units in the local area after refinement is calculated and the corresponding edge weights are updated. The path search is re-performed on the refined grid to construct the minimum energy penetration path.

[0123] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0124] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0125] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for simulating seepage prevention using geomembrane in dam bodies, characterized in that, The method includes: By using the three-dimensional geometric data of the dam body, the information on the filling bedding, the geomembrane laying morphology, the membrane-soil interface contact structure, and the water content distribution of the dam body, a multi-source structural feature domain of the dam body is generated. After invoking the residual tension, fold morphology and anchorage status of the geomembrane laying process, a geomembrane stress memory field is established based on the multi-source structural characteristic domain to reflect the local initial stress non-uniformity within the membrane. After extracting the geometric gradient features of the dam body, a potential weak permeability zone is constructed based on the geomembrane stress memory field. Injecting intelligent perturbation signals into the potential weak permeability zone to trigger the initial response of the seepage field, stress field, and crack energy field; Based on the amplification trajectory of the initial response in the seepage pressure field, geomembrane stress field and interface energy field, the dam body is dynamically divided into multi-level risk sub-zones, and a coupled evolution field amplified by disturbance is formed based on the multi-level risk sub-zones. Based on the coupled evolution field, a joint energy function of seepage energy, membrane strain energy, and interface crack propagation energy is constructed. The minimum energy seepage path required to cause leakage failure of the geomembrane is solved, and a seepage prevention risk field for the dam body is established.

2. The seepage prevention simulation method for dam geomembrane as described in claim 1, characterized in that, After extracting the geometric gradient features of the dam body, a potential weak permeability zone is constructed based on the geomembrane stress memory field, including: After identifying the joint line position based on the multi-source structural feature domain, joint line segmentation processing is performed. Taking each joint line segment as a unit, the difference in stiffness and density of the soil on both sides of each segment is statistically analyzed, and a material heterogeneity indicator is established based on the statistical results. Based on the geomembrane stress memory field, stress concentration correlation analysis is performed on each joint line segment to establish a correlation stress anomaly value identifier. Weak areas at the joints of geomembranes are identified by using material inhomogeneity indicators and associated stress anomaly indicators, and potential weak areas at the joints are established. Construct potential weak penetration zones based on the potential weak seam zones.

3. The seepage prevention simulation method for dam geomembrane as described in claim 2, characterized in that, Constructing potential penetration weak zones based on the potential joint weak zones also includes: The curvature value is calculated point by point on the surface of the geomembrane, and the neighborhood average value of the curvature is constructed. Curvature abrupt change points are marked based on the curvature value and the neighborhood average value; Using the curvature abrupt change point, the continuous distribution line of residual strain is traced along the fold direction. If the residual strain continuously meets the preset threshold along a linear or curved direction and intersects with the curvature abrupt change point, then the main axis of the fold is constructed. A regional search is performed along the main axis of the fold to detect local discontinuities in the axial displacement and thickness variation of the fold, and a fold concentration area is constructed based on the detection results. The fold concentration areas are classified according to the degree of fold superposition and the cumulative axial strain along the main fold axis to establish potential fold concentration weak areas. Based on the potential weak areas of seams and the potential weak areas of concentrated folds, potential weak areas of penetration are constructed.

4. The seepage prevention simulation method for dam geomembrane as described in claim 3, characterized in that, Based on the potential weak areas of seams and potential weak areas of concentrated wrinkles, potential weak areas of penetration are constructed, including: Extract membrane-soil interface roughness, local adhesion, and moisture distribution characteristics within the multi-source tectonic feature domain; Based on the extraction results, combined with the identification of sudden drops and reverse distributions of the geomembrane stress memory field, potential interface void weak zones are established. By utilizing the stress gradient peak points of the geomembrane stress memory field, and combining the material stiffness differences at the dam topographic deformation location and the abrupt changes in building bedding, potential high-stress concentration weak areas can be identified. The potential weak areas of joints, potential weak areas of wrinkle concentration, potential weak areas of interface voids, and potential weak areas of high stress concentration are used to construct potential weak areas of penetration.

5. The seepage prevention simulation method for dam geomembrane as described in claim 1, characterized in that, Injecting intelligent perturbation signals into the potentially weak penetration areas includes: Based on the spatial boundary, type label, geomembrane stress memory field, and local material inhomogeneity of the potential weak permeability zones, a set of perturbation target parameters for each weak zone is generated. Using the set of disturbance target parameters as the matching target, perform disturbance signal type matching that triggers seepage, stress and crack energy response, and establish matching results. The matching results include local pressure gradient fluctuations, mechanical displacements, temperature disturbances or interface contact conditions. Based on the initial stress peak value, membrane-soil interface stiffness, and fold morphology of the potential weak permeability zone, the matching results are adaptively optimized in terms of amplitude, direction of action, and time series to establish an intelligent disturbance signal.

6. The seepage prevention simulation method for dam geomembrane as described in claim 1, characterized in that, Dynamically divide the dam body into multi-level risk sub-zones, including: Extract the multidimensional response trajectory dataset from the initial response, calculate the seepage sensitivity, stress sensitivity, and interface energy sensitivity along the spatial grid cells of the dam body, and statistically analyze the local sensitivity peak and gradient changes based on the calculation results to form a local risk index; Based on the spatial distribution and sensitivity gradient of the local risk index, the dam body is divided into multiple levels of multi-level risk sub-zones, including high-risk sub-zones, medium-risk sub-zones, and low-risk sub-zones.

7. The seepage prevention simulation method for dam geomembrane as described in claim 1, characterized in that, The joint energy function is as follows: ; in, Characterizing the joint energy, Characterizes potential seepage pathways or dam unit volume regions. Characterizing osmotic pressure, Characterizing the local drag modulus, Characterizing the local stress of the geomembrane, Characterizing the elastic modulus of geomembranes, Characterizing the volume of a geomembrane unit, Characterizes the critical energy release rate of interfacial cracks. The actual area representing the propagation of the interface crack. These are the weighting coefficients.

8. The seepage prevention simulation method for dam geomembrane as described in claim 7, characterized in that, Find the minimum energy seepage path required to cause leakage and failure of the geomembrane, including: The coupled evolution field is discretized into several volumetric units and interface units in three-dimensional space. The unit density is adaptively adjusted according to the spatial distribution of the energy gradient in the coupled evolution field. The energy contribution of the volumetric units and interface units is calculated using the joint energy function to form an accumulative unit energy label. Map the center of the volume element or the interface unit node to the vertex of the graph, establish the edges of the graph by using spatially adjacent unit nodes or those that may be connected through cracks or seams, configure the energy cost weight of the edges by using the energy labels of the accumulative unit nodes, and construct an energy-weighted graph. Based on the energy-weighted graph, a global shortest path search is performed at a coarse grid scale to construct N candidate paths, and the total energy and path topology features of each candidate path are recorded. Calculate the joint energy of the volume elements and interface elements in the local area after encryption and update the corresponding edge weights. Then, perform path search again on the encrypted grid to construct the minimum energy penetration path.

9. The seepage prevention simulation method for dam geomembrane as described in claim 1, characterized in that, The multi-source structural feature domains that generate the dam body include: A twin simulation model is constructed using the multi-source structural feature domain. In the twin simulation model, seepage prevention simulation based on multi-level risk sub-regions is performed based on potential weak permeability zones to establish a dam seepage prevention risk field.

10. A seepage prevention simulation system for dam geomembranes, characterized in that, The system includes: The multi-source structural feature domain construction module is used to generate the multi-source structural feature domain of the dam body by using the 3D geometric data of the dam body, the filling layer information, the geomembrane laying morphology, the membrane-soil interface contact structure and the water content distribution of the dam body. The geomembrane stress memory field construction module is used to call the residual tension, fold morphology and anchoring status of the geomembrane laying process, and then establish a geomembrane stress memory field that reflects the local initial stress non-uniformity within the membrane based on the multi-source structural characteristic domain. The potential weak permeability zone identification module is used to extract the geometric change gradient features of the dam body and then calculate and construct the potential weak permeability zone based on the geomembrane stress memory field. An initial response triggering module is used to inject intelligent disturbance signals into the potential weak permeability zone to trigger the initial response of the seepage field, stress field and crack energy field; The coupled evolution field generation module is used to dynamically divide the dam body into multi-level risk sub-regions based on the amplified trajectory of the initial response in the seepage pressure field, geomembrane stress field and interface energy field, and to form a coupled evolution field that amplifies with disturbance based on the multi-level risk sub-regions. The seepage risk field establishment module is used to construct a joint energy function of seepage energy, membrane strain energy, and interface crack propagation energy based on the coupled evolution field, solve for the minimum energy seepage path required to cause seepage failure of the geomembrane, and establish the seepage risk field of the dam body.

Images