Dam anti-seepage effect detection method and system

Abstract

The invention relates to the technical field of geological engineering, in particular to a dam anti-seepage effect detection method and system. The method comprises the following steps: obtaining dam structure data; performing anti-seepage structure performance evaluation according to the dam structure data to determine anti-seepage weak points; performing seepage anomaly analysis based on the anti-seepage weak points to obtain seepage anomaly data; performing free surface offset identification according to the seepage abnormal data to obtain free surface offset data; performing seepage channel positioning based on the free surface offset data to obtain seepage channel data; performing seepage field numerical simulation according to the seepage channel data to obtain a seepage field simulation numerical value; dam foundation stability detection is conducted according to the seepage field simulation numerical value, and dam foundation stability data are obtained; and on the basis of the dam foundation stability data, instability critical state prediction is conducted, and instability critical state data are obtained. According to the invention, based on the geological engineering technology, the accuracy of dam anti-seepage abnormity identification and the timeliness rate of safety early warning are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological engineering, and particularly to a method and system for detecting the anti-seepage effect of a dam. Background Art

[0002] Traditional detection of the anti-seepage effect of dams relies on manual fixed-point inspections or data from single-type sensors, making it difficult to timely identify seepage anomaly areas, with incomplete spatial coverage, and the monitoring results being lagging and local; lacking the ability to capture the dynamic changes of the free surface in real time, unable to effectively track the evolution process of seepage channels. The seepage field mostly uses simplified two-dimensional steady-state models and fails to conduct high-precision numerical simulations by combining three-dimensional unsteady seepage characteristics, resulting in the simulation of seepage behavior not conforming to the actual situation; no coupled analysis path for structural parameters, material aging, seepage anomalies, and dam foundation stability has been formed, ignoring the impact of material fatigue decay on anti-seepage performance, and unable to achieve full-chain, multi-scale, and multi-physical field linkage analysis; the existing systems have low integration and scattered functions, making it difficult to achieve automated and intelligent detection and evaluation, especially with poor adaptability in the face of complex geological conditions, old dams, and extreme working conditions, and unable to meet the technical requirements of "accurate identification, dynamic prediction, and intelligent decision-making" for modern dam safety assessment. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for detecting the anti-seepage effect of a dam to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for detecting the anti-seepage effect of a dam includes the following steps: Step S1: Obtain dam structure data; evaluate the anti-seepage structure performance according to the dam structure data to determine anti-seepage weak points; conduct seepage anomaly analysis based on the anti-seepage weak points to obtain seepage anomaly data; Step S2: Identify the free surface offset according to the seepage anomaly data to obtain free surface offset data; locate the seepage channel based on the free surface offset data to obtain seepage channel data; conduct numerical simulation of the seepage field according to the seepage channel data to obtain seepage field simulation values; Step S3: Detect the stability of the dam foundation according to the seepage field simulation values to obtain dam foundation stability data; predict the critical state of instability based on the dam foundation stability data to obtain critical state of instability data; adjust the material parameters according to the critical state of instability data to obtain material parameter adjustment data; Step S4: Conduct fatigue simulation according to the material parameter adjustment data to obtain dam material fatigue data; detect the anti-seepage effect according to the dam material fatigue data to obtain anti-seepage effect data.

[0005] This invention introduces a structural data-driven performance evaluation mechanism, enabling rapid identification of potential weak points in seepage prevention structures without the need for comprehensive destructive testing, thus improving the efficiency and accuracy of early screening. Secondly, by combining free surface offset identification and seepage channel location, the spatial evolution of seepage anomalies can be dynamically captured, achieving precise tracking of seepage paths and overcoming the limitations of traditional fixed-point monitoring. Furthermore, the introduction of numerical simulation technology constructs a high-dimensional three-dimensional seepage field model, which can realistically reflect the complex seepage behavior inside the dam body, improving simulation accuracy and the reliability of decision-making. Subsequently, the seepage simulation results are used for dam foundation stability analysis and instability prediction, not only achieving… The linkage response from seepage behavior to structural safety provides quantitative support for subsequent adjustments to key parameters such as material strength and elastic modulus. Furthermore, by using fatigue simulation methods, the service status of the structure is linked to the material performance degradation process, scientifically assessing the evolution trend of permeability performance during long-term operation. Finally, by constructing a complete evaluation chain of fatigue decay function, permeability tensor, and seepage prevention performance decay coefficient, dynamic quantitative detection and intelligent evaluation of the overall seepage prevention effect of the dam are achieved. This effectively breaks through the technical bottlenecks of traditional methods in terms of identification timeliness, analysis completeness, and system integration, and is particularly suitable for safety assessment and intelligent control of seepage prevention performance of old dams under complex geological conditions.

[0006] Preferably, step S1 specifically includes: Step S11: Obtain dam structure data; Step S12: Conduct aging tests on the anti-seepage wall based on the dam structure data to obtain the anti-seepage wall aging data; Step S13: Conduct curtain grouting failure detection based on dam structural data to obtain curtain grouting failure data; Step S14: Based on the aging data of the anti-seepage wall and the failure data of the curtain grouting, conduct an evaluation of the anti-seepage structure performance to identify weak points in the anti-seepage system; Step S15: Conduct seepage anomaly analysis based on weak points in the seepage prevention system to obtain seepage anomaly data.

[0007] This invention acquires dam structural data and combines it with non-destructive analysis methods to detect aging of the anti-seepage wall and failure of curtain grouting. It enables refined state perception of key anti-seepage components without relying on manual inspections, overcoming the limitations of traditional methods in terms of spatial coverage, continuity, and real-time performance. Furthermore, by integrating aging and failure data for performance evaluation, it effectively constructs a comprehensive identification model for structural health status, laying a data foundation for downstream seepage behavior analysis. Subsequently, driven by identified weak points in the anti-seepage system, it conducts seepage anomaly analysis, shifting monitoring from fixed sensor locations to targeted area modeling. This enables precise location of anomaly zones and early warning, significantly improving the depth of anti-seepage hazard identification and pre-control capabilities during dam operation. It also provides data and spatial constraints for three-dimensional seepage simulation and subsequent stability analysis, truly forming a closed-loop correlation analysis mechanism between structural data, anti-seepage status, and seepage behavior. This provides intelligent technical support for the full life-cycle safety management of dams under complex conditions.

[0008] Preferably, step S12 specifically includes: Step S121: Identify the seepage barrier area based on the dam structure data; Step S122: Conduct wall stress testing in the seepage prevention wall area to obtain wall stress data; Step S123: Identify wall material bulging based on wall stress data and obtain bulging data; Step S124: Identify honeycomb surface based on bulging data to obtain honeycomb surface data; Step S125: Calculate the surface porosity based on the honeycomb surface data; conduct an aging assessment of the anti-seepage wall based on the surface porosity to obtain the anti-seepage wall aging data.

[0009] This invention identifies seepage barrier wall areas through structural data, ensuring spatial accuracy of the monitored objects and avoiding data overlay and omission. Subsequently, combined with wall stress detection methods, it dynamically reflects areas of abnormal wall stress, providing stress field evidence for identifying bulging phenomena. Based on bulging data, it further identifies surface honeycomb and pitted defects, enhancing the accuracy of extracting local damage morphologies and overcoming the limitations of traditional manual visual inspection or surface image analysis. Furthermore, by quantitatively calculating surface porosity using honeycomb and pitted data, it achieves high-resolution characterization of the degree of microporous degradation in the material, supporting quantitative assessment of aging. Ultimately, it achieves intelligent identification of the aging state of the seepage barrier wall, constructing a multi-level linkage detection chain from structural identification—stress diagnosis—geometric anomaly—microscopic damage—deterioration assessment. This not only improves the ability to identify early aging hazards but also provides a physical basis and evaluation indicators for subsequent seepage path analysis and seepage prevention capacity prediction, significantly enhancing the effectiveness of dam structure seepage prevention safety assessment in complex operating environments.

[0010] Preferably, step S13 specifically includes: Step S131: Identify the curtain grouting layout area based on the dam structure data; Step S132: Conduct foundation soil and rock testing based on the curtain grouting area to obtain foundation soil and rock data; Step S133: Perform joint and fracture analysis based on foundation soil and rock data to obtain joint and fracture data; Step S134: Identify fracture connectivity based on joint fracture data to determine potential seepage paths; Step S135: Detect pore water pressure based on the potential seepage path to obtain pore water pressure data; Step S136: Perform curtain grouting failure analysis based on pore water pressure data to obtain curtain grouting failure data.

[0011] This invention overcomes the technical bottlenecks of traditional methods, such as low spatial resolution, slow response, and inaccurate channel identification, by constructing a multi-level analysis chain for curtain grouting failure detection. It has significant practical value and engineering significance. First, by identifying curtain grouting areas driven by structural data, the key seepage prevention sections were accurately located, providing a foundation for subsequent zonal diagnosis. In the foundation soil and rock testing phase, multi-source geophysical parameters were integrated to comprehensively acquire the structural characteristics of foundation materials, enhancing the ability to perceive areas with high seepage potential. Further joint and fracture analysis not only identified the main fracture orientation, density, and distribution range but also deduced the spatial structure of the fracture network. Based on this, fracture connectivity was identified, overcoming the shortcomings of traditional methods in accurately identifying concealed seepage paths and clarifying potential seepage channels. Subsequently, pore water pressure monitoring was used to construct a quantitative mapping between the hydraulic driving mechanism and fracture channels, enhancing the physical and logical consistency of abnormal path identification. Finally, grouting failure analysis was conducted using pore pressure anomaly data, achieving accurate diagnosis of the overall compactness of the grouting structure and local weak points, effectively avoiding the risk of misjudging grouting quality as "surface qualified, internally failed." The entire process forms a complete diagnostic chain from structural identification to medium analysis, path modeling, hydraulic response, and failure determination, which improves the timeliness, systematicness, and intelligence of the detection, and provides data and model support for evaluating the deep seepage prevention effect of dams in complex geological environments.

[0012] Preferably, step S2 specifically includes: Step S21: Determine the region of sudden head change based on the seepage anomaly data; Step S22: Calculate the head gradient based on the region of abrupt change in head; Step S23: Identify the position of the free surface based on the head gradient; calculate the offset distance based on the position of the free surface and the preset free surface reference data to obtain the free surface offset data; Step S24: Locate the seepage channels based on the free surface offset data to obtain seepage channel data; Step S25: Perform numerical simulation of the seepage field based on the seepage channel data to obtain the simulated seepage field values.

[0013] This invention enhances the response to seepage non-uniformity and local seepage anomalies by automatically identifying regions of abrupt head changes driven by abnormal data, avoiding the omission of hidden anomalies under traditional fixed-point inspections. The head gradient calculation further refines the hydraulic driving change characteristics of the abnormal region, making the energy gradient expression of the seepage path more accurate. Based on this, the location of the free surface is identified and the offset distance is calculated, overcoming the problem of existing methods' inability to quantify the dynamic fluctuations of the free surface in real time, providing spatial constraints for downstream seepage risk assessment. The free surface offset information drives the seepage channel positioning process in reverse, realizing information tracing from "phenomenon" to "mechanism," improving the perception of hidden high-permeability paths. Finally, the identified path is input into a numerical model to conduct a three-dimensional seepage field simulation. Compared with the traditional two-dimensional steady-state simplification method, it can more accurately reproduce the actual seepage behavior under unstable boundary conditions and spatially heterogeneous media, providing a more physically accurate input basis for subsequent dam foundation stability analysis and material aging assessment, thereby significantly improving the system's identification, prediction, and decision-making capabilities under complex geological conditions.

[0014] Preferably, step S24 specifically includes: Step S241: Calculate the hydraulic gradient based on the free surface offset data to obtain the hydraulic gradient data; Step S242: Calculate the seepage direction vector field based on the hydraulic gradient data; Step S243: Determine the seepage direction based on the seepage direction vector field; Step S244: Identify path segments with consistent flow direction based on seepage direction; Step S245: Calculate the permeability based on the path segments with consistent flow direction; identify high-permeability paths based on the permeability of the path segments with consistent flow direction to obtain high-permeability paths; Step S246: Locate the seepage channel based on the high permeability path to obtain seepage channel data.

[0015] This invention enhances the ability to capture changes in seepage dynamics by accurately calculating the hydraulic gradient based on free surface offset data, making the hydraulic driving force characteristics of the seepage field more accurate and detailed. By deriving the seepage direction vector field from the hydraulic gradient data, it scientifically characterizes the spatial distribution and flow trend of seepage, laying the foundation for accurate identification of seepage paths. Further clarifying the seepage direction helps to screen path segments with consistent flow direction, enabling quantitative analysis of seepage continuity and avoiding the fragmented understanding of seepage paths in traditional methods. Calculating permeability based on path segments with consistent flow direction accurately reflects the regional differences and heterogeneous distribution of the medium's permeability performance. This approach breaks through the limitations of previous homogeneous assumptions; by identifying and locating high-permeability paths, it accurately captures potential seepage channels, significantly improving the ability to detect hidden leakage risks and enhancing the tracking ability of dynamic evolution of seepage channels. Overall, this step-by-step process achieves multi-dimensional coupled analysis from the dynamic characteristics of free surfaces to the spatial distribution of seepage channels, overcoming the accuracy and coverage deficiencies caused by traditional two-dimensional steady-state models and single data sources. It greatly improves the system's accuracy in identifying seepage anomalies under complex geological conditions and its real-time response capability, meeting the high-standard technical requirements of modern dam safety monitoring for accurate identification, dynamic prediction, and intelligent decision-making.

[0016] Preferably, step S3 specifically includes: Step S31: Extract the head potential function based on the simulated seepage field values ​​to obtain the head distribution function; Step S32: Calculate the effective stress tensor field based on the head distribution function; Step S33: Iterate the element safety factor based on the effective stress tensor field to obtain the safety factor distribution matrix; Step S34: Identify the direction of the slip vector field based on the safety factor distribution matrix; perform dam foundation stability detection based on the direction of the slip vector field to obtain dam foundation stability data; Step S35: Based on the dam foundation stability data, predict the critical state of instability to obtain the critical state data of instability; Step S36: Adjust the material parameters based on the instability critical state data to obtain the material parameter adjustment data.

[0017] This invention extracts the head potential function from the numerical simulation of the seepage field to accurately reflect the distribution of water kinetic energy, thus improving the analytical capability for the hydraulic effects within the dam body. Based on the head distribution function, it calculates the effective stress tensor field, scientifically revealing the stress influence of hydraulic pressure on the dam foundation material and achieving a fluid-structure interaction-based mechanical state assessment. By employing an iterative method for element safety factors to construct a safety factor distribution matrix, it achieves detailed quantification of the stability of various regions of the dam foundation, enhancing the ability to identify potential instability elements. By identifying the slip vector field direction through the safety factor matrix, it accurately captures the slip trend and failure path of the dam foundation, providing crucial dynamic information for stability assessment. Based on the dam foundation stability... By using instability data to predict critical instability states, early warning and dynamic tracking of dam foundation instability risks are achieved, improving the safety protection level of the seepage prevention system. Adjusting material parameters based on instability critical state data dynamically reflects the material performance degradation process, enhancing the model's adaptive correction capability and fatigue decay simulation accuracy. Overall, the process chain enables multi-scale, multi-physics coupled analysis from the seepage field to the dam foundation's mechanical response and material evolution, breaking through the limitations of traditional simplified models and isolated data analysis. This significantly improves the accuracy, dynamism, and intelligence of dam seepage prevention safety assessment, meeting the urgent need for comprehensive risk identification and scientific decision-making under modern complex working conditions.

[0018] Preferably, step S36 specifically includes: Step S361: Extract information about unstable elements based on the instability critical state data; Step S362: Plot the stress response curve based on the information of the unstable elements; Step S363: Analyze the material degradation index based on the stress response curve; Step S364: Adjust the material strength based on the material degradation index to obtain material strength adjustment data; Step S365: Adjust the elastic modulus based on the material degradation index to obtain elastic modulus adjustment data; Step S366: Integrate the material strength adjustment data and the elastic modulus adjustment data to obtain the material parameter adjustment data.

[0019] This invention achieves precise location and quantitative analysis of potential instability zones in dam foundations by extracting instability unit information from instability critical state data, contributing to a deeper understanding of instability mechanisms. Based on the instability unit information, stress response curves are plotted to dynamically reflect the mechanical behavior of the dam body under different loads, improving the visualization and analytical accuracy of stress changes. Through material degradation index analysis of the stress response curves, the law of material performance degradation is scientifically revealed, providing a reliable basis for subsequent parameter adjustments. Adjustments to material strength and elastic modulus using material degradation indices enable dynamic simulation of material performance changes, enhancing the model's adaptability to actual aging and fatigue states. Integrating strength and elastic modulus adjustment data forms comprehensive material parameter adjustment data, improving the accuracy and practicality of seepage prevention effect assessment. The overall process achieves deep coupling between material performance degradation and dam foundation mechanical response, breaking through the limitations of traditional static and isolated analysis, significantly improving the intelligent monitoring and early warning capabilities of dam seepage prevention systems under complex working conditions, and meeting the needs of modern dam safety management for dynamic, accurate identification and scientific decision-making.

[0020] Preferably, step S4 specifically includes: Step S41: Adjust the data according to the material parameters to perform fatigue simulation and obtain fatigue data of the dam material; Step S42: Construct a material fatigue attenuation function based on dam material fatigue data; Step S43: Calculate the probability of microcrack formation based on the material fatigue attenuation function; Step S44: Perform permeability tensor analysis based on the probability of microcrack formation to obtain permeability tensor data; Step S45: Calculate the permeability performance attenuation coefficient based on the permeability tensor data; evaluate the seepage prevention effect based on the permeability performance attenuation coefficient to obtain seepage prevention effect data.

[0021] This invention utilizes fatigue simulation based on material parameter adjustment data to dynamically reflect the fatigue evolution of dam materials under long-term loading, improving the accuracy of time-series prediction of material performance degradation. It constructs a material fatigue decay function to scientifically quantify the law of material performance decay with fatigue accumulation, providing accurate mathematical model support for subsequent analysis. By calculating the probability of microcrack formation, it can sensitively capture the initial signs of potential damage within the material, achieving early risk warning. Based on the probability of microcrack formation, it conducts permeability tensor analysis, achieving a fine characterization of the spatial distribution and anisotropic features of permeability performance. This approach improves the physical realism of seepage process simulation; calculates the permeability decay coefficient to quantitatively reflect the degree of functional degradation of the seepage prevention structure, providing a reliable quantitative indicator for the evaluation of seepage prevention effectiveness; and comprehensively utilizes the above steps to achieve full-chain, multi-scale, and multi-physics field coupled analysis from material fatigue behavior to changes in permeability performance, effectively breaking through the limitations of traditional single-sensor and static models, significantly enhancing the automation and intelligence level of dam seepage prevention effect detection, improving the adaptability to complex working conditions and the state of aging dams, and meeting the high requirements of modern dam safety management for accurate identification, dynamic prediction, and scientific decision-making.

[0022] Preferably, this specification also provides a dam seepage prevention effect testing system for performing the dam seepage prevention effect testing method described above. The dam seepage prevention effect testing system includes: The seepage anomaly analysis module is used to acquire dam structural data; to evaluate the performance of the seepage prevention structure based on the dam structural data in order to identify weak points in the seepage prevention; and to perform seepage anomaly analysis based on the weak points in the seepage prevention to obtain seepage anomaly data. The seepage field numerical simulation module is used to identify free surface offsets based on seepage anomaly data to obtain free surface offset data; locate seepage channels based on the free surface offset data to obtain seepage channel data; and perform seepage field numerical simulation based on the seepage channel data to obtain seepage field simulation values. The material parameter adjustment module is used to perform dam foundation stability testing based on seepage field simulation values ​​to obtain dam foundation stability data; predict the instability critical state based on the dam foundation stability data to obtain instability critical state data; and adjust the material parameters based on the instability critical state data to obtain material parameter adjustment data. The seepage prevention effect detection module is used to perform fatigue simulation based on the material parameter adjustment data to obtain dam material fatigue data; and to perform seepage prevention effect detection based on the dam material fatigue data to obtain seepage prevention effect data. Attached Figure Description

[0023] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1This is a schematic diagram of the steps in the method for detecting the seepage prevention effect of a dam according to the present invention; Figure 2 This is a detailed flowchart of step S1 in the present invention; Figure 3 This is a detailed flowchart of step S12 in the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0025] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0026] 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 merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0027] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for detecting the seepage prevention effect of a dam, the method comprising the following steps: Step S1: Obtain dam structural data; conduct seepage control performance evaluation based on dam structural data to identify weak points in seepage control; perform seepage anomaly analysis based on seepage weak points to obtain seepage anomaly data; In this embodiment, a high-precision 3D laser scanner is used to comprehensively scan the dam structure, acquiring detailed geometric data of the dam's surface and internal structure. The scanning equipment should have millimeter-level spatial resolution, and the point density of the point cloud data collected during the scanning process should be no less than 10,000 points per square meter to ensure sufficient detail. Subsequently, the collected point cloud data is imported into professional 3D modeling software for preprocessing, including noise reduction, filtering, and smoothing operations, to ensure data integrity and accuracy. By constructing a finite element structural model of the dam, the stress distribution of the anti-seepage wall and related structures is calculated based on structural mechanics principles, using material mechanical parameters such as the elastic modulus of concrete (typically 10 ... The pressure (MPa), Poisson's ratio (0.2), and ultimate tensile strength (e.g., compressive strength of 40 MPa) are used for calculation. Weak points in the seepage control system are identified by comparing the calculation results with preset safety thresholds (e.g., stress must not exceed 70% of the material strength). Subsequently, in the identified weak point areas, seepage anomalies are collected using seepage pressure sensors and resistivity imagers. The sensor accuracy should reach 0.01 MPa, and the sampling frequency should be once per minute. Resistivity imaging uses a multi-point electrode array with an electrode spacing of 0.5 meters to measure resistivity changes, reflecting the seepage path and abnormal areas. The sensor and resistivity data are processed using time series analysis and spatial interpolation methods to obtain seepage anomaly data.

[0028] Step S2: Identify free surface offset based on seepage anomaly data to obtain free surface offset data; locate seepage channels based on free surface offset data to obtain seepage channel data; perform seepage field numerical simulation based on seepage channel data to obtain seepage field simulation values. In this embodiment, image recognition and numerical analysis methods are used based on seepage anomaly data to determine the dynamic changes of the free water surface inside the dam. Water level sensors, with a resolution of at least 1 mm, are deployed at key seepage sections to monitor the free water surface position in real time. A laser rangefinder is used to perform a three-dimensional scan of the seepage free surface boundary to obtain two-dimensional cross-sectional coordinate data. By comparing the current free surface data with a preset reference free surface position (the reference free surface position is the average water level line under historical normal operating conditions, with an allowable deviation range of ±5 cm), the offset distance is calculated. An offset distance exceeding 3 cm is considered a significant offset. Based on the free surface offset data, a path tracking algorithm is used to identify seepage channels. Channel positioning is based on the hydraulic gradient direction, combined with the continuity of the seepage channel and the permeability distribution; the channel width threshold is set to be above 0.1 meters. Subsequently, the flow velocity and pressure within the seepage channel are numerically simulated using the finite difference method or the finite volume method. The simulation time step is set to 10 seconds, and the spatial grid size is 0.05 meters to ensure numerical stability and calculation accuracy. Boundary conditions include the dam crest water level, downstream spillway level, and dam pore pressure, with data obtained from historical hydrological data and real-time sensor monitoring. The seepage field simulation includes velocity field, pressure field, and pore water distribution; the numerical results must meet the convergence condition, i.e., the residual must be less than [value missing]. .

[0029] Step S3: Perform dam foundation stability testing based on the seepage field simulation values ​​to obtain dam foundation stability data; predict the critical instability state based on the dam foundation stability data to obtain critical instability state data; adjust material parameters based on the critical instability state data to obtain material parameter adjustment data. In this embodiment, a three-dimensional finite element model of the dam foundation soil and structure is constructed. The model mesh size is controlled within 0.1 meters to ensure the accuracy of stress and deformation field calculations. Material parameters include the soil's internal friction angle (usually taken as 30°), cohesion (taken as 50 kPa), and elastic modulus (taken as...). kPa), and related mechanical parameters of the structural materials. By calculating the effective stress tensor field, the Mohr-Coulomb failure criterion was used to iterate the element with a safety factor of 1.5 initially, and iterated until the convergence accuracy reached [value missing]. The safety factor distribution matrix, composed of element safety factor values, reflects the stability state of different regions of the dam foundation. Based on the safety factor matrix, numerical gradient analysis is applied to identify the potential slip surface direction and slip vector field. Subsequently, critical state theory is used, combined with a safety factor threshold (usually 1.0), to predict the critical instability state and extract information of critical instability elements. Based on the stress and deformation data of the critical state elements, material parameters, including elastic modulus and strength parameters, are adjusted using a multivariate fitting algorithm. The adjustment range is based on the cumulative stress overload percentage, with a maximum adjustment range not exceeding 20% ​​of the initial material parameters. The adjusted material parameters are used for subsequent calculations and simulations.

[0030] Step S4: Perform fatigue simulation based on the adjusted material parameters to obtain dam material fatigue data; conduct seepage prevention effect testing based on the dam material fatigue data to obtain seepage prevention effect data.

[0031] In this embodiment, fatigue simulation of the dam material is performed using a time-stress coupling-based fatigue analysis method, adjusted based on material parameter data. The Miner linear cumulative damage method is employed, utilizing historical load data with load amplitudes ranging from 10% to 90% of the dam's maximum operating pressure. Fatigue life is calculated based on the material's SN curve, with parameters collected from standard laboratory fatigue tests. The fatigue limit is set at 40% of the material's maximum stress. During the simulation, the material fatigue decay function is linked to macroscopic elastic modulus and strength degradation through microscopic damage parameters, with the cumulative damage variable accuracy controlled within a specified range. Based on fatigue simulation results, the probability of microcrack formation was calculated using the Weibull distribution function, with parameters determined through fitting of extensive material fracture experimental data. Based on the microcrack probability, permeability tensor analysis was conducted, employing a statistical description of the pore structure. The tensor components were dynamically adjusted according to the crack direction and size distribution. The permeability degradation coefficient was calculated by comparing the initial permeability with the permeability after fatigue, with a threshold set at a degradation exceeding 15% to be considered significant degradation. Finally, the seepage prevention effect was quantitatively tested based on the permeability degradation coefficient to ensure the test data is available for further structural safety assessment.

[0032] Preferably, step S1 specifically includes: Step S11: Obtain dam structure data; In this embodiment, a 3D laser scanner is used to scan the dam surface and key structural parts, with scanning accuracy controlled within 1 mm and a point cloud density of over 10,000 points per square meter to ensure spatial resolution and integrity of the data. Secondly, ground-penetrating radar (GPR) technology is used to probe the internal structure of the dam, with the operating frequency set between 400 MHz and 900 MHz to obtain information on the internal layered structure and defects of the anti-seepage wall and curtain grouting body. During data acquisition, laser scanning and ground-penetrating radar data are collected synchronously, with sampling frequencies of 300,000 points per second for laser scanning and 10 lines per meter for ground-penetrating radar scanning. All acquired data undergoes spatial registration and temporal synchronization using data fusion technology to eliminate noise and measurement errors, forming a comprehensive 3D dataset containing information on the dam's shape, internal structure, and materials.

[0033] Step S12: Conduct aging tests on the anti-seepage wall based on the dam structure data to obtain the anti-seepage wall aging data; In this embodiment, an infrared thermal imager is used to measure the surface temperature distribution of the cutoff wall. The infrared camera wavelength range is set to 8-14 micrometers, with a temperature resolution of 0.05℃, to detect abnormal thermal responses in internal voids, cracks, and moist areas of the wall. Subsequently, an ultrasonic detector is used to measure the sound wave propagation velocity of the concrete structure of the cutoff wall. The ultrasonic frequency is set to 50kHz, and the detection depth reaches 500 mm. Areas of aging and damage are located by detecting areas where the wave velocity decreases. Combined with surface deformation data obtained from laser scanning, digital image correlation (DIC) technology is used to analyze minute deformations of the wall, with a deformation detection resolution of 0.01 mm. A strain threshold of 0.2% is used to determine areas of material aging or crack propagation. By integrating thermal imaging, ultrasonic, and deformation data, aging data of the cutoff wall is generated, with specific indicators including crack length (millimeter level), crack width (millimeter level), temperature anomaly range (degrees Celsius), and ultrasonic velocity reduction percentage (%).

[0034] Step S13: Conduct curtain grouting failure detection based on dam structural data to obtain curtain grouting failure data; In this embodiment, a multi-point pore pressure sensor array is deployed in the curtain grouting area. The sensor accuracy reaches 0.01 MPa, and the sampling frequency is once per minute to monitor changes in pore water pressure in real time. Secondly, resistivity imaging technology is employed, with an electrode spacing of 0.5 meters and a frequency range of 1 kHz to 100 kHz. Resistivity changes are measured to reflect the integrity of the grouting body and the failure area. Combined with ground-penetrating radar to detect the distribution and fractures of the grouting body, the radar frequency is set to 600 MHz, and the detection depth reaches 10 meters to identify the interface between the grouting body and the surrounding soil. Based on the thresholds for pore water pressure anomalies and resistivity changes (pore water pressure anomalies exceeding 15% of the background value and resistivity reduction exceeding 20%), the curtain grouting failure area is determined. The comprehensive data generates curtain grouting failure data, including specific parameters such as the failure range (square meters), the magnitude of pore water pressure anomalies (MPa), and the percentage change in resistivity.

[0035] Step S14: Based on the aging data of the anti-seepage wall and the failure data of the curtain grouting, conduct an evaluation of the anti-seepage structure performance to identify weak points in the anti-seepage system; In this embodiment, the cracks and aging indicators of the cutoff wall are spatially overlaid with the failed areas of the curtain grouting. Using GIS spatial analysis technology, a crack density threshold of more than 5 cracks per square meter and a grouting failure area threshold of more than 10 square meters are set to identify structurally weak areas. Subsequently, for these weak areas, pressure distribution is acquired using seepage pressure sensors with an accuracy of 0.005 MPa and a monitoring duration of at least 7 days. The data is used to verify structural performance. Based on comprehensive indicators, such as crack width exceeding 0.5 mm, pore water pressure anomaly exceeding 0.1 MPa, and resistivity change exceeding 25%, the seepage prevention performance is graded and assessed to identify key weak points. The assessment results are output in graphical and numerical form, including weak point coordinates, spatial range, and relevant performance indicators, for subsequent seepage anomaly analysis.

[0036] Step S15: Conduct seepage anomaly analysis based on weak points in the seepage prevention system to obtain seepage anomaly data.

[0037] In this embodiment, high-density water pressure sensors and conductivity sensors are deployed around the weak point, with accuracies of 0.001 MPa and 1 µS / cm, respectively. Sampling is performed every 5 minutes to continuously collect data on water pressure and water quality changes. Next, resistivity imaging technology is used to refine the seepage channel structure at the weak point, and the electrode spacing is adjusted to 0.3 meters to improve resolution. Combining water pressure and resistivity data, multivariate statistical analysis is applied to calculate abnormal head and conductivity thresholds (outliers are defined as exceeding three times the standard deviation of the mean), locating the abnormal seepage area and channel. Through time-series trend analysis, the time points and trends of seepage anomalies are identified, forming a seepage anomaly dataset containing the anomaly intensity, range, and duration, detailing the spatial distribution and change process of the anomaly events.

[0038] Preferably, step S12 specifically includes: Step S121: Identify the seepage barrier area based on the dam structure data; In this embodiment, the cutoff wall area is extracted using a boundary recognition algorithm. First, based on laser scanning point cloud data, spatial clustering technology is employed to extract the set of boundary points of the cutoff wall. The boundary recognition error is controlled within ±5 mm to ensure the accuracy of the area extraction. Then, combined with ground-penetrating radar (GPR) imaging data, the thickness and depth range of the cutoff wall are located. The GPR uses a center frequency of 600 MHz, a vertical resolution of no less than 10 cm, and a detection depth covering the entire thickness of the cutoff wall. The spatial coordinate system transformation adopts the WGS84 standard of the International Geodetic System to ensure that the spatial position of the cutoff wall area matches the overall dam structure data. The boundary coordinates of the area are accurate to four decimal places, and the error in the length and width of the range is controlled within 5 cm. After extraction, a three-dimensional spatial data file of the cutoff wall is generated as the basis for subsequent detection.

[0039] Step S122: Conduct wall stress testing in the seepage prevention wall area to obtain wall stress data; In this embodiment, fiber optic strain sensing technology is used for wall stress detection. The sensor spacing is set to 1 meter, and the sensor measurement accuracy is ±1 micro-strain, covering key structural units of the anti-seepage wall. During sensor installation, the sensors are fixed to the wall through drilled holes with a diameter controlled at 12 mm. The drilling depth is determined according to the wall thickness to ensure that the sensors fit the inner surface of the wall. Wall stress data is collected in real time at a sampling frequency of 10 times per second, and data transmission is carried out via fiber optics to ensure signal integrity. The stress data includes the principal stress direction and magnitude. During data processing, a digital filtering algorithm is used to filter out high-frequency noise, and the filter cutoff frequency is set to 50 Hz. The data recording time is no less than 72 hours, generating a wall stress time-series curve and stress distribution cloud map, with values ​​accurate to 0.01 MPa.

[0040] Step S123: Identify wall material bulging based on wall stress data and obtain bulging data; In this embodiment, a stress-strain inversion algorithm is used, combined with a preset material constitutive relationship, to calculate the local volumetric expansion rate. Using the normal stress state of the wall as a benchmark, a bulging threshold is set when the local stress exceeds the benchmark value by more than 15%, and the corresponding strain shows a positive expansion trend. Ultrasonic detection technology is further used to confirm the bulging area. The ultrasonic frequency is set to 100kHz, the detection depth is no less than 300 mm, and a decrease in sound wave propagation speed of more than 5% is used as the criterion for material bulging. The bulging area is spatially located, with the bulging volume accurate to the cubic centimeter level, generating a bulging data file containing the bulging volume, location, and morphological parameters.

[0041] Step S124: Identify honeycomb surface based on bulging data to obtain honeycomb surface data; In this embodiment, a method combining high-definition optical imaging and laser scanning is used to identify honeycomb-like pitted surfaces based on bulging data. The laser scanning employs a 1064nm wavelength laser, achieving a point cloud density of 20,000 points per square meter, combined with a high-definition camera image resolution of 0.1 mm / pixel, to scan the wall surface. Honeycomb-like pitted surface features are extracted using image texture analysis technology, with a surface texture roughness threshold set at 0.5 mm. Areas with texture roughness exceeding this threshold are used as the determination range for honeycomb-like pitted surfaces. Spatial registration technology is used during region identification to fuse the laser point cloud with the image data, ensuring the location accuracy of the honeycomb-like pitted surfaces is within 5 mm. The analyzed and output honeycomb-like pitted surface data includes parameters such as area, depth, and distribution density.

[0042] Step S125: Calculate the surface porosity based on the honeycomb surface data; conduct an aging assessment of the anti-seepage wall based on the surface porosity to obtain the anti-seepage wall aging data.

[0043] In this embodiment, digital image processing technology is used to binarize the honeycomb-patterned area, with a threshold set to a grayscale value of 150 (0-255 scale). The percentage of the void area to the total detected area is calculated using pixel statistics, achieving an accuracy of 0.1%. Combined with depth information obtained from laser scanning, the two-dimensional area calculation error is corrected, and the three-dimensional void volume ratio is calculated. The specific calculation formula is as follows: Porosity data is used as an aging indicator in the aging assessment of the cutoff wall. A porosity threshold of 15% is used as the aging judgment boundary; areas exceeding this threshold are considered severely aged. The cutoff wall aging data includes a porosity distribution map, aging level, and related spatial coordinate information.

[0044] Preferably, step S13 specifically includes: Step S131: Identify the curtain grouting layout area based on the dam structure data; In this embodiment, based on the 3D scanning data of the dam structure and the design blueprint data, spatial coordinate mapping technology is used to accurately delineate the curtain grouting area. Multi-band UAV aerial imagery and ground-penetrating radar (GPR) data are overlaid on the area, and combined with the building foundation outline, Geographic Information System (GIS) software is used to generate a 2D plan view and a 3D model of the grouting area. The boundary accuracy of the grouting area is controlled within ±10 cm, and the spatial range covers the designed depth of the curtain grouting and a 5-meter perimeter to ensure complete coverage for subsequent inspections.

[0045] Step S132: Conduct foundation soil and rock testing based on the curtain grouting area to obtain foundation soil and rock data; In this embodiment, based on the curtain grouting area, foundation soil and rock testing is conducted using borehole sampling and in-situ geophysical exploration techniques. The borehole depth is extended by 10% from the designed grouting depth, typically 20 to 30 meters, with the borehole diameter controlled within 100 millimeters. Soil and rock samples are obtained through standard penetration tests (SPT), with a 63.5 kg hammer and a free fall height of 760 mm. The number of penetrations is used as an indicator of foundation compaction. In-situ testing utilizes the seismic reflection wave method, with an excitation frequency of 10 Hz to 100 Hz, to collect stratigraphic layering and joint data. Geomechanical parameters include porosity, permeability coefficient, and elastic modulus. The permeability coefficient is determined within a certain range. to m / s.

[0046] Step S133: Perform joint and fracture analysis based on foundation soil and rock data to obtain joint and fracture data; In this embodiment, by analyzing the fracture density and distribution characteristics in the foundation soil and rock testing results, a fault identification algorithm is used to perform spatial three-dimensional reconstruction of joint fractures. Combined with high-resolution ground-penetrating radar data, the fracture width threshold is set to 0.5 mm, and the length threshold is set to 1 meter or more. The fracture distribution direction is statistically represented using polar coordinates, and the fracture dip angle and strike accuracy are controlled within ±3 degrees. The fracture data includes fracture length, width, strike, and density, and computer-aided soil and rock analysis software is used for data integration and visualization output.

[0047] Step S134: Identify fracture connectivity based on joint fracture data to determine potential seepage paths; In this embodiment, based on joint and fracture data, a fluid dynamics connectivity algorithm is used to determine the connectivity between fractures. A fracture network graph is constructed using graph theory, where nodes represent fracture intersections and edges represent fracture pathways. The connectivity threshold is set to consider fractures connected if the distance between them does not exceed 5 cm. Potential seepage paths are identified in the connected network, and the path length, width, and flow area are quantitatively calculated, with the spatial coordinates of the seepage paths accurate to 1 cm. The seepage path information is exported as structured data for subsequent pressure testing and seepage simulation.

[0048] Step S135: Detect pore water pressure based on the potential seepage path to obtain pore water pressure data; In this embodiment, a pore water pressure sensor array is deployed according to the potential seepage path. The sensors are designed for a pressure range of 0-1 MPa and an accuracy of ±0.005 MPa. The sensor installation depth is determined longitudinally along the seepage path, with a spacing not exceeding 2 meters to ensure continuous pressure data acquisition. The pressure signal is transmitted in real time via a wired digital acquisition system at a sampling frequency of 1 Hz. During data processing, a filter is used to remove high-frequency noise, and the data acquisition time window is no less than 72 hours. The pore water pressure data includes time-series pressure values, spatial distribution, and variation trends, and the data format meets the standardized hydrological monitoring data specifications.

[0049] Step S136: Perform curtain grouting failure analysis based on pore water pressure data to obtain curtain grouting failure data.

[0050] In this embodiment, based on pore water pressure data, a time-series analysis method is used to identify abnormal pressure fluctuations, and a pressure mutation threshold of 0.02 MPa / hour is set as the failure warning critical point. Combining grouting design parameters and on-site pressure data, the effective permeability change of the curtain grouting layer is calculated; a permeability change exceeding 10% of the initial design value is considered grouting failure. The pressure field is numerically simulated using the finite difference method, with a simulation time step of 10 minutes and a spatial discretization accuracy of 0.1 meters. The failure analysis results output includes the failure location coordinates, failure degree, and trend assessment. The data format is compatible with structured databases, facilitating comprehensive analysis and the formulation of subsequent protective measures.

[0051] Preferably, step S2 specifically includes: Step S21: Determine the region of sudden head change based on the seepage anomaly data; In this embodiment, continuous time-series seepage pressure data must first be acquired, with a sampling frequency of once per second and a collection period of no less than 72 hours to ensure complete recording of dynamic changes. The pressure difference between adjacent monitoring points is calculated using the spatial difference method, with a threshold set at 0.05 MPa. Areas exceeding this threshold are identified as regions of sudden head change. The spatial difference calculation formula is as follows: Where H_i and H_j are the head values ​​of adjacent monitoring points, respectively. The abrupt change area is located using GIS software with an accuracy requirement of 0.1 meters.

[0052] Step S22: Calculate the head gradient based on the region of abrupt change in head; In this embodiment, the head gradient is calculated based on the determined abrupt change in head region. The three-point difference method is used to estimate the head gradient, and the calculation formula is as follows: ; Δx represents the distance between adjacent measuring points, typically 0.5 meters; ▽H represents the head gradient, in MPa / m; Hi+1 represents the head value at position xi+1 (i.e., a measuring point to the right of the current point xi); Hi-1 represents the head value at position xi-1 (i.e., a measuring point to the left of the current point xi); the gradient direction and magnitude are expressed through a vector field, with a gradient threshold set to 0.02 MPa / m, and regions exceeding this value are marked as high gradient regions. The gradient data is interpolated using Kriging interpolation to generate a continuous head gradient distribution map with a resolution of 0.1 meters.

[0053] Step S23: Identify the position of the free surface based on the head gradient; calculate the offset distance based on the position of the free surface and the preset free surface reference data to obtain the free surface offset data; In this embodiment, the free surface position is identified based on the head gradient data. The calculation formula is: free surface head H_f = total head minus pressure head. The pressure head is measured by a pore water pressure gauge, and the unit is MPa. The free surface position is compared with preset reference free surface data (collected in the initial stable state, with an accuracy of ±0.01 meters) to calculate the offset distance Δd, using the following formula: ; Δd is the Free Surface Deviation Distance, in meters (m). It represents the change in the current free surface position relative to the baseline state and is used to reflect water level fluctuations or seepage evolution. These are the spatial height coordinates of the current free surface position, in meters (m), obtained using a high-precision laser rangefinder or acoustic rangefinder. The sampling frequency is once per minute, for real-time monitoring. The baseline data for the free surface, measured in meters (m), was collected at the free surface elevation when the dam was in its initial stable state, with a measurement accuracy of ±0.01 meters. This data serves as a reference value for evaluating free surface offset. The free surface position was acquired in real time using a high-precision laser rangefinder or acoustic rangefinder, with a sampling frequency of once per minute. The offset data is stored in a time-series database for subsequent trend analysis.

[0054] Step S24: Locate the seepage channels based on the free surface offset data to obtain seepage channel data; In this embodiment, seepage channels are located based on free surface offset data. A path tracing algorithm is used to connect the locations of continuously increasing free surface offsets into a path. Path identification is performed using threshold filtering, with an offset distance threshold set at 0.05 meters; nodes below this value are discarded. The final seepage channel data is stored as a spatial coordinate sequence with an accuracy of 0.1 meters. This data can be used for visualization and analysis in a 3D Geographic Information System (GIS).

[0055] Step S25: Perform numerical simulation of the seepage field based on the seepage channel data to obtain the simulated seepage field values.

[0056] In this embodiment, a numerical simulation of the seepage field is performed based on seepage channel data. A three-dimensional unsteady-state seepage numerical solution method is adopted, and the computational domain is divided into a cubic grid with a side length of 0.1 meters to ensure simulation accuracy. The time step is set to 5 minutes to ensure numerical stability during the simulation process. Boundary conditions include upstream water level and pressure, downstream pressure boundaries, and lateral no-flow boundaries. The permeability parameter is assigned based on the permeability measurement results of the seepage channel. The finite volume method is used for the solution, and the iterative convergence error is controlled within a certain range. Within this range. The simulation results include pressure field, head distribution, and velocity vector, in standard VTK format for easy visualization and analysis.

[0057] Preferably, step S24 specifically includes: Step S241: Calculate the hydraulic gradient based on the free surface offset data to obtain the hydraulic gradient data; In this embodiment, hydraulic gradient calculation is performed based on free surface offset data. First, the spatial difference method is used to calculate the spatial rate of change of head. The free surface offset data is input in the form of spatial coordinates and corresponding offset values, with a fixed sampling point spacing of 0.1 meters. The formula for calculating the hydraulic gradient is: ; Where Δh represents the elevation difference of the free surface between two adjacent points, in meters. The horizontal distance between two points is expressed in meters. The calculation process is performed on all adjacent points to obtain the hydraulic gradient vector at each point, including the gradient magnitude and direction. The hydraulic gradient data is output as a two-dimensional vector field in a standard grid data structure, with an accuracy controlled to 0.01 meters of head.

[0058] Step S242: Calculate the seepage direction vector field based on the hydraulic gradient data; In this embodiment, the seepage direction vector field is calculated based on hydraulic gradient data. Following the principle that water flows from high head to low head, the hydraulic gradient direction at each calculation point is taken as the seepage direction. The direction of each vector in the vector field is determined by calculating the opposite direction of the gradient components, and the vector magnitude is the magnitude of the hydraulic gradient. Using numerical calculation methods, the gradient vector field is mapped to a two-dimensional spatial grid with a grid resolution of 0.1 meters. The direction vectors are normalized to ensure that all vectors have a length of 1, so that the subsequent path tracing algorithm can accurately identify the flow direction.

[0059] Step S243: Determine the seepage direction based on the seepage direction vector field; In this embodiment, the seepage direction is determined based on the seepage direction vector field. Specifically, the spatial continuity of the seepage direction vector at each sampling point is checked. A threshold of 15° is used for the angle difference between adjacent points; points with a direction difference less than this threshold are considered to have the same direction. For discontinuous points, an interpolation method is used to correct the direction. Linear interpolation is used to ensure a smooth and continuous direction field. The seepage direction data is stored in a two-dimensional vector field format, with each point containing azimuth information. The azimuth ranges from 0° to 360°, with an accuracy of 0.1°.

[0060] Step S244: Identify path segments with consistent flow direction based on seepage direction; In this embodiment, path segments with consistent flow direction are identified based on the seepage direction. First, a region growing algorithm is used. Starting from a seed point, adjacent points are searched along the seepage direction vector. Points whose direction differs from the current point's direction by less than 15° are added to the path. Path growth continues until the direction consistency condition can no longer be met. The minimum path segment length is set to 1 meter; paths shorter than this are discarded to avoid invalid paths. All path segments are stored as spatial coordinate sequences, with a 0.1-meter spacing between path points, and the data format is compatible with GIS path data standards.

[0061] Step S245: Calculate the permeability based on the path segments with consistent flow direction; identify high-permeability paths based on the permeability of the path segments with consistent flow direction to obtain high-permeability paths; In this embodiment, the permeability is calculated based on the path segment with consistent flow direction, and the permeability is obtained through Darcy's law inversion. The flow rate data Q (unit: m³) from the measurement points along the path segment is used. 3 / s), cross-sectional area A (unit: m) 2 The permeability k is calculated using the head difference Δh (in meters) and path length L (in meters), with the following formula: ; Flow data is acquired through a flow meter installed on-site, with a measurement accuracy of ±0.01m. 3 / s. The cross-sectional area is measured based on the actual geometric dimensions of the path segment, with an error controlled within 5%. The head difference is calculated based on head sensor data, with an accuracy of ±0.01 meters. After calculating the permeability, it is compared with the design permeability threshold of 0.001 m / s. Path segments exceeding the threshold are identified as high-permeability paths. All calculation results are saved in tabular form, including path coordinates, permeability values, and determination results.

[0062] Step S246: Locate the seepage channel based on the high permeability path to obtain seepage channel data.

[0063] In this embodiment, seepage channels are located based on high-permeability paths, and all path segments identified as high-permeability are connected to form a seepage channel network. Path connections are based on spatial proximity, with path segments less than 0.15 meters apart being merged into a single channel. Channel data is stored in polyline format, including spatial coordinate sequences and corresponding permeability attributes. This channel data can be imported into a Geographic Information System (GIS) for visualization and further analysis, conforming to the Shapefile or GeoJSON standard. Channel boundaries are generated through buffer analysis, with a buffer radius of 0.2 meters representing the seepage influence range. The seepage channel location results are stored in a structured database for easy correlation analysis with other monitoring data.

[0064] Preferably, step S3 specifically includes: Step S31: Extract the head potential function based on the simulated seepage field values ​​to obtain the head distribution function; In this embodiment, the head potential function is extracted based on the numerical simulation of the seepage field. First, the head value of each discrete unit in the numerical simulation results of the seepage field is obtained. The data exists in the form of a two-dimensional or three-dimensional grid, with a grid unit side length of 0.1 meters and a time step of 10 minutes. By traversing each unit node in the grid, the head data at the node is mapped to the head distribution function H(x,y,z). Specifically, interpolation is used to smooth the node head values. The interpolation method is three-dimensional linear interpolation to ensure the continuity and smoothness of the function. Finally, the head distribution function is expressed in a spatial coordinate system, providing the head potential function value at any location, with a data accuracy of 0.001 meters of head.

[0065] Step S32: Calculate the effective stress tensor field based on the head distribution function; In this embodiment, the effective stress tensor field is calculated based on the hydraulic head distribution function. First, according to the effective stress theory of soil, the pore water pressure u in each unit is calculated. The pore water pressure is calculated by multiplying the hydraulic head value H by the water density ρ (taken as 1000 kg / m³). 3 ) and gravitational acceleration g (9.81 m / s²) 2 The calculation yields u = ρgH. Then, the total stress tensor σ provided in the geological structure parameters is used. The total stress includes the stress caused by self-weight and external loads, and its unit is megapascals (MPa). The formula for calculating the effective stress tensor σ' is... , where I is the unit tensor. During calculation, the effective stress tensor is calculated for each mesh element, and the results are stored in tensor matrix form with numerical precision controlled within 0.001 MPa.

[0066] Step S33: Iterate the element safety factor based on the effective stress tensor field to obtain the safety factor distribution matrix; In this embodiment, the element safety factor is iterated based on the effective stress tensor field. The safety factor definition used in landslide stability analysis is adopted, where the safety factor FS is equal to the ratio of shear strength to shear stress. The shear strength is calculated based on the Mohr-Coulomb criterion, and the formula is as follows: Where c is the cohesion (in MPa) and φ is the internal friction angle (in degrees). The values ​​of cohesion and internal friction angle are based on the geological survey report, with typical values ​​ranging from c=0.1-0.5 MPa and φ=25°-35°. Shear stress is obtained from the tangential component of the effective stress tensor. An iterative method is used to adjust the estimated value of the safety factor FS for each element until FS satisfies the equilibrium condition, with an error threshold set to 0.01. During the iteration process, the finite difference method is used to calculate the stress distribution, with a maximum iteration count limited to 100 times. The safety factor distribution matrix is ​​stored in two-dimensional matrix form, with each element corresponding to the safety factor of the grid element, and an accuracy of 0.01.

[0067] Step S34: Identify the direction of the slip vector field based on the safety factor distribution matrix; perform dam foundation stability detection based on the direction of the slip vector field to obtain dam foundation stability data; In this embodiment, the slip vector field direction is identified based on the safety factor distribution matrix. First, units with a safety factor below 1.0 are considered potential slip units, and the safety factor gradient for that region is extracted. The gradient direction is calculated using the difference in safety factors between adjacent units. The partial derivative is obtained using the central difference method to calculate the gradient vector ▽FS, with units of dimensionless per meter. The slip vector field direction is opposite to the safety factor gradient, pointing towards the point of lowest safety factor. The slip vector is normalized, with a direction angle accuracy of 0.1 degrees. Dam foundation stability is assessed based on the slip vector field direction. Energy release rate and force balance analysis are used to calculate the moment and resisting moment on the slip surface, obtaining stability evaluation parameters. The data is stored in tabular form, including coordinates, slip vectors, and moment values.

[0068] Step S35: Based on the dam foundation stability data, predict the critical state of instability to obtain the critical state data of instability; In this embodiment, the critical state of instability is predicted based on dam foundation stability data. Critical state theory is used, defining the critical state as the mechanical state when the safety factor FS = 1.0. The critical position in the safety factor distribution is calculated using interpolation, with cubic spline interpolation used to achieve spatial positioning of the critical state, achieving a spatial accuracy of 0.01 meters. The prediction process includes analysis of safety factor variations under different loading conditions, with a loading step of 0.1 MPa, until the critical state is reached. The prediction results include the coordinates of the critical safety factor position, the critical stress value, and the geometric characteristics of the slip surface. The results are stored in a structured data table for easy subsequent analysis.

[0069] Step S36: Adjust the material parameters based on the instability critical state data to obtain the material parameter adjustment data.

[0070] In this embodiment, material parameters are adjusted based on the instability critical state data. The main adjustments are to the cohesion (c) and the internal friction angle (φ). The adjustments are based on the critical state analysis results, using a reverse calculation of the material parameters that would cause the safety factor to drop to the critical value. The calculation formula employs the limit equilibrium method, and the adjustment range is limited to no more than 10% of the original parameters to ensure reasonable adjustment. The cohesion adjustment step is set to 0.01 MPa, and the internal friction angle adjustment step is 0.1°. The adjusted parameters are stored in the material parameter database in key-value pair format for easy integration with other modules. The adjustment process incorporates dam monitoring data to ensure the timeliness and accuracy of the parameters.

[0071] Preferably, step S36 specifically includes: Step S361: Extract information about unstable elements based on the instability critical state data; In this embodiment, unstable element information is extracted based on the critical instability state data. First, the safety factor distribution matrix is ​​scanned, and elements with a safety factor FS ≤ 1.0 are selected as unstable elements. The spatial location, number, and related stress state data of the unstable elements are extracted and stored. Specifically, the three-dimensional mesh data is traversed, and a threshold determination method is used to identify unstable elements. The threshold is strictly set to 1.0 to ensure accurate identification. The storage structure is a two-dimensional table, containing element ID, three-dimensional coordinates (x, y, z), current stress state (principal stresses σ1, σ2, σ3), and instability type identifier (slippage, fracture, etc.). Database indexing technology is used during the extraction process to improve processing efficiency, ensuring that data access time does not exceed 0.5 seconds.

[0072] Step S362: Plot the stress response curve based on the information of the unstable elements; In this embodiment, stress-response curves are plotted based on the information of the unstable elements. Specifically, stress-strain relationship data of the unstable elements are collected within the range of loading conditions. The loading conditions are gradually increased in increments of 0.1 MPa until the safety factor FS drops below 1.0. For each loading step, the principal stress and corresponding strain value of the element are recorded. The data are obtained using strain gauges and stress sensors with an accuracy of 0.001 MPa and 0.0001 strain units. Based on the collected data, stress-strain curves are plotted using a coordinate system of strain (horizontal axis) and stress (vertical axis). Cubic spline interpolation is used for interpolation between curve points to ensure a smooth curve that reflects the true response of the material. The plotting results are stored as vector graphics files and numerical tables for subsequent analysis.

[0073] Step S363: Analyze the material degradation index based on the stress response curve; In this embodiment, material degradation indices are analyzed based on the stress response curve. First, the boundary point between the elastic and plastic stages of the material is determined. Using an inflection point detection algorithm, the second derivative of the stress-strain curve is calculated, with a threshold set at a curvature change greater than 0.05. Degradation indices include the rate of decrease in elastic modulus and the magnitude of the decrease in yield stress. The elastic modulus is obtained by linearly fitting the slope of the elastic stage curve, with a calculation accuracy of 0.1%. The yield stress is determined by the point corresponding to the maximum rate of change of stress in the curve, with an accuracy of 0.01 MPa. The cumulative degree of fatigue damage under cyclic loading is calculated using the curve integral method. The damage variable D is calculated using the Miner method, with a threshold range of 0-1; values ​​exceeding 0.6 are considered significant degradation. The material degradation indices are stored numerically, including the percentage of elastic modulus degradation, yield stress change, and fatigue damage value.

[0074] Step S364: Adjust the material strength based on the material degradation index to obtain material strength adjustment data; In this embodiment, material strength is adjusted based on material degradation indices, primarily by reducing cohesion c and internal friction angle φ. The adjustment range is linearly correlated with the degradation index D. The specific adjustment formula is as follows: , ; Where α and β are 0.3 and 0.2 respectively, representing the weights of material degradation on the parameters. c_original represents the original cohesion, in MPa, used to measure the shear strength of a material under no external force, with a value range of 0.2–0.5 MPa, and the data source is indoor direct shear or triaxial shear tests in geotechnical engineering sites; φ_original represents the original internal friction angle, in degrees (°), used to reflect the frictional characteristics between soil or rock particles, with a typical value range of 28°–35°, also obtained through geological exploration tests; D is the material degradation index, a dimensionless parameter representing the degree of material deterioration under stress, with a value range of 0–1, where D=0 indicates no material performance degradation, and D=1 indicates complete degradation. The value of D can be obtained through analysis of the slope change of the descending segment of the stress-strain curve, cumulative acoustic emission counting, or statistical inversion of microcrack density; α is the cohesion adjustment weight coefficient, fixed at 0.3, meaning that for every 1.0 increase in D, c decreases by 30°. %; β is the internal friction angle adjustment weight coefficient, set to 0.2, indicating that for every 1.0 increase in D, φ decreases by 20%; c_new and φ_new are the new values ​​of cohesion and internal friction angle after degradation, respectively, serving as inputs for subsequent stability analysis and seepage coupling calculations; the parameter adjustment process adopts a step method, with the adjustment step size for c set to 0.01 MPa and the adjustment step size for φ set to 0.1°. Each iteration compares the current parameters with the calculated values ​​of the above formulas. If not, the iteration continues until the error between c_new and φ_new and the theoretical value is less than 1e-4. All calculation results are numbered according to material unit number and stored in the form of a structured data table. Fields include unit number, location coordinates, original parameters, degradation index, adjustment coefficient, final parameters, and iteration step number, etc. The table output format is uniformly CSV, which is convenient for interfacing with structural analysis systems or material performance evolution modules. The original parameters c and φ are taken from geological exploration data, with c ranging from 0.2 to 0.5 MPa and φ ranging from 28° to 35°. The adjustment process uses a step-by-step method, with adjustment steps of 0.01 MPa and 0.1°, until the parameter changes satisfy the above formula. The adjustment results are saved in a structured data table for easy access by subsequent modules.

[0075] Step S365: Adjust the elastic modulus based on the material degradation index to obtain elastic modulus adjustment data; In this embodiment, the elastic modulus is adjusted based on the material degradation index D. The adjustment range is determined by the degradation index D, and the adjusted value of the elastic modulus E is calculated using a linear reduction method. The formula is: ; Where γ is 0.4. The original elastic modulus E depends on the material type and ranges from 5 × 10^3 MPa to 1 × 10^4 MPa. The adjustment process uses a point-by-point update method with a step size of 50 MPa. After each adjustment, the material mechanical response is recalculated to verify the validity of the parameters and ensure numerical stability. The adjustment data is stored in the material parameter database, and the fields include timestamp, original elastic modulus, new elastic modulus, and corresponding degradation index values.

[0076] Step S366: Integrate the material strength adjustment data and the elastic modulus adjustment data to obtain the material parameter adjustment data.

[0077] In this embodiment, material strength adjustment data and elastic modulus adjustment data are integrated to construct a complete material parameter adjustment dataset. The integration process includes data verification, parameter consistency checks, and format standardization. The verification steps ensure that the adjusted c, φ, and E are all within reasonable physical ranges, with c not less than 0.1 MPa, φ not less than 20°, and E not less than... MPa. The data is stored in key-value pair format, where the key contains "cohesion adjustment", "internal friction angle adjustment", and "elastic modulus adjustment", and the value corresponds to the specific value and adjustment time. The integrated parameter data is transmitted to the dam mechanics analysis module through an interface to realize dynamic parameter updates and feedback control.

[0078] Preferably, step S4 specifically includes: Step S41: Adjust the data according to the material parameters to perform fatigue simulation and obtain fatigue data of the dam material; In this embodiment, fatigue simulation is performed based on adjusted material parameters. A numerical calculation method based on finite element analysis is used. The adjusted material parameters (including cohesion c, internal friction angle φ, and elastic modulus E) are input into the structural element model for cyclic loading simulation. The cyclic loading stress level is set from 0.5 MPa to 3.0 MPa, the loading frequency is fixed at 1 Hz, and the total number of simulation cycles is 10^6 to reflect long-term service conditions. The fatigue calculation adopts Miner's linear cumulative damage theory, progressively adding the damage value of each cycle. The stress-strain relationship adopts an elastoplastic constitutive model, and the fatigue life calculation formula for the material is: Where σ_f is the fatigue strength, Δσ is the stress amplitude, and m is a material constant, with values ​​of σ_f = 50 MPa and m = 5, respectively. The simulation results output includes the cumulative fatigue damage value and remaining life of each structural element, saved in tabular form, with fields including element ID, cycle number, damage value, and remaining life.

[0079] Step S42: Construct a material fatigue attenuation function based on dam material fatigue data; In this embodiment, a material fatigue decay function is constructed based on dam material fatigue data. An exponential decay model is used to represent the relationship between fatigue damage and the number of cycles N. The function form is as follows: Where D is the fatigue damage variable and k is the material fatigue rate constant, taken as 0.00001. The parameter k was determined by least squares fitting based on laboratory fatigue test data. The experiment used standard geotechnical specimens for three-point bending fatigue testing, with a cyclic stress amplitude of 2.0 MPa and a test frequency of 1 Hz. The number of cycles corresponding to fatigue failure was recorded. The constructed attenuation function was verified using a curve fitting tool to ensure a goodness of fit R^2 of not less than 0.95. The numerical results of the attenuation function were stored as a function table or parameter file for subsequent fatigue damage calculations.

[0080] Step S43: Calculate the probability of microcrack formation based on the material fatigue attenuation function; In this embodiment, the probability of microcrack initiation is calculated based on the material fatigue attenuation function, and the fatigue damage D value is mapped to the microcrack initiation probability P using the logistic function. Where 'a' is the steepness coefficient, set to 15, and 'D_0' is the threshold damage, set to 0.6. The threshold D_0 is determined by material microstructure failure tests to ensure the lower limit of fatigue damage at the initiation of microcracks. During the calculation, the fatigue damage D value of each element is input, and the corresponding probability P is calculated point by point. The probability value is expressed as a percentage with an accuracy of 0.01%. The results are stored in a three-dimensional array, with the array index corresponding to the spatial location of the element, facilitating subsequent spatial distribution analysis.

[0081] Step S44: Perform permeability tensor analysis based on the probability of microcrack formation to obtain permeability tensor data; In this embodiment, permeability tensor analysis is performed based on the probability of microcrack formation, mapping the microcrack probability to local permeability changes, and constructing a three-dimensional permeability tensor K. The tensor calculation employs anisotropic flow theory, establishing a direct proportionality between the microcrack probability P and the increase in permeability, as expressed in the following formula: Where K_0 is the original permeability tensor, I is the unit tensor, α is the amplification factor of 0.8, and T is the crack direction tensor, the direction of which is determined by geological fault and fracture direction data. The original permeability K_0 ranges from [value missing]. m / s to The permeability tensor is determined based on measured groundwater flow data and soil porosity inversion. The tensor calculation steps include: first, mapping the microcrack probability distribution to each spatial cell; second, calculating the directional influence based on crack direction data; and third, calculating the local permeability tensor based on the amplification coefficient. The permeability tensor is stored in matrix form, with each spatial cell corresponding to a 3×3 matrix, achieving a high data accuracy. m / s.

[0082] Step S45: Calculate the permeability performance attenuation coefficient based on the permeability tensor data; evaluate the seepage prevention effect based on the permeability performance attenuation coefficient to obtain seepage prevention effect data.

[0083] In this embodiment, the permeability performance attenuation coefficient is calculated based on permeability tensor data. First, eigenvalue decomposition is performed on the permeability tensor, and the largest eigenvalue λ_max is extracted to represent the permeability in the direction of maximum permeation. The attenuation coefficient β is defined as... Where λ_0 is the original maximum permeability characteristic value, taken from the initial crack-free state. During the calculation, the difference between the current permeability tensor and the initial state is compared point by point, and the calculation accuracy of the attenuation coefficient is controlled within 0.001. Subsequently, the permeability performance attenuation coefficient is input into the seepage prevention effect evaluation module. The evaluation results generate seepage prevention effect data, including a spatial distribution map and a numerical report. The report lists the areas of permeability performance change, the numerical range, and the corresponding location. The seepage prevention effect data is stored in a standard database format for easy long-term monitoring and dynamic updates.

[0084] Preferably, this specification also provides a dam seepage prevention effect testing system for performing the dam seepage prevention effect testing method described above. The dam seepage prevention effect testing system includes: The seepage anomaly analysis module is used to acquire dam structural data; to evaluate the performance of the seepage prevention structure based on the dam structural data in order to identify weak points in the seepage prevention; and to perform seepage anomaly analysis based on the weak points in the seepage prevention to obtain seepage anomaly data. The seepage field numerical simulation module is used to identify free surface offsets based on seepage anomaly data to obtain free surface offset data; locate seepage channels based on the free surface offset data to obtain seepage channel data; and perform seepage field numerical simulation based on the seepage channel data to obtain seepage field simulation values. The material parameter adjustment module is used to perform dam foundation stability testing based on seepage field simulation values ​​to obtain dam foundation stability data; predict the instability critical state based on the dam foundation stability data to obtain instability critical state data; and adjust the material parameters based on the instability critical state data to obtain material parameter adjustment data. The seepage prevention effect detection module is used to perform fatigue simulation based on the material parameter adjustment data to obtain dam material fatigue data; and to perform seepage prevention effect detection based on the dam material fatigue data to obtain seepage prevention effect data.

[0085] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0086] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily 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 invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for detecting the seepage prevention effect of a dam, characterized in that, Includes the following steps: Step S1: Obtain dam structural data; conduct seepage control performance evaluation based on dam structural data to identify weak points in seepage control; perform seepage anomaly analysis based on seepage weak points to obtain seepage anomaly data; Step S2: Based on the seepage anomaly data, perform free surface offset identification to obtain free surface offset data; Seepage channel location is performed based on free surface offset data to obtain seepage channel data; Numerical simulation of the seepage field is performed based on the seepage channel data to obtain the simulated seepage field values. Step S3: Perform dam foundation stability testing based on the seepage field simulation values ​​to obtain dam foundation stability data; predict the critical instability state based on the dam foundation stability data to obtain critical instability state data; adjust material parameters based on the critical instability state data to obtain material parameter adjustment data. Step S4: Perform fatigue simulation based on the adjusted material parameters to obtain dam material fatigue data; conduct seepage prevention effect testing based on the dam material fatigue data to obtain seepage prevention effect data.

2. The method for detecting the seepage prevention effect of a dam according to claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain dam structure data; Step S12: Conduct aging tests on the anti-seepage wall based on the dam structure data to obtain the anti-seepage wall aging data; Step S13: Conduct curtain grouting failure detection based on dam structural data to obtain curtain grouting failure data; Step S14: Based on the aging data of the anti-seepage wall and the failure data of the curtain grouting, conduct an evaluation of the anti-seepage structure performance to identify weak points in the anti-seepage system; Step S15: Conduct seepage anomaly analysis based on weak points in the seepage prevention system to obtain seepage anomaly data.

3. The method for detecting the seepage prevention effect of a dam according to claim 2, characterized in that, Step S12 is as follows: Step S121: Identify the seepage barrier area based on the dam structure data; Step S122: Conduct wall stress testing in the seepage prevention wall area to obtain wall stress data; Step S123: Identify wall material bulging based on wall stress data and obtain bulging data; Step S124: Based on the bulging data, perform honeycomb surface identification to obtain honeycomb surface data; Step S125: Calculate the surface porosity based on the honeycomb surface data; conduct an aging assessment of the anti-seepage wall based on the surface porosity to obtain the anti-seepage wall aging data.

4. The method for detecting the seepage prevention effect of a dam according to claim 2, characterized in that, Step S13 is as follows: Step S131: Identify the curtain grouting layout area based on the dam structure data; Step S132: Conduct foundation soil and rock testing based on the curtain grouting area to obtain foundation soil and rock data; Step S133: Perform joint and fracture analysis based on foundation soil and rock data to obtain joint and fracture data; Step S134: Identify fracture connectivity based on joint fracture data to determine potential seepage paths; Step S135: Detect pore water pressure based on the potential seepage path to obtain pore water pressure data; Step S136: Perform curtain grouting failure analysis based on pore water pressure data to obtain curtain grouting failure data.

5. The method for detecting the seepage prevention effect of a dam according to claim 1, characterized in that, Step S2 is as follows: Step S21: Determine the region of sudden head change based on the seepage anomaly data; Step S22: Calculate the head gradient based on the region of abrupt change in head; Step S23: Identify the position of the free surface based on the head gradient; calculate the offset distance based on the position of the free surface and the preset free surface reference data to obtain the free surface offset data; Step S24: Locate the seepage channels based on the free surface offset data to obtain seepage channel data; Step S25: Perform numerical simulation of the seepage field based on the seepage channel data to obtain the simulated seepage field values.

6. The method for detecting the seepage prevention effect of a dam according to claim 5, characterized in that, Step S24 is as follows: Step S241: Calculate the hydraulic gradient based on the free surface offset data to obtain the hydraulic gradient data; Step S242: Calculate the seepage direction vector field based on the hydraulic gradient data; Step S243: Determine the seepage direction based on the seepage direction vector field; Step S244: Identify path segments with consistent flow direction based on seepage direction; Step S245: Calculate the permeability based on the path segments with consistent flow direction; identify high-permeability paths based on the permeability of the path segments with consistent flow direction to obtain high-permeability paths; Step S246: Locate the seepage channel based on the high permeability path to obtain seepage channel data.

7. The method for detecting the seepage prevention effect of a dam according to claim 1, characterized in that, Step S3 is as follows: Step S31: Extract the head potential function based on the simulated seepage field values ​​to obtain the head distribution function; Step S32: Calculate the effective stress tensor field based on the head distribution function; Step S33: Iterate the element safety factor based on the effective stress tensor field to obtain the safety factor distribution matrix; Step S34: Identify the direction of the slip vector field based on the safety factor distribution matrix; perform dam foundation stability detection based on the direction of the slip vector field to obtain dam foundation stability data; Step S35: Based on the dam foundation stability data, predict the critical state of instability to obtain the critical state data of instability; Step S36: Adjust the material parameters based on the instability critical state data to obtain the material parameter adjustment data.

8. The method for detecting the seepage prevention effect of a dam according to claim 7, characterized in that, Step S36 is as follows: Step S361: Extract information about unstable elements based on the instability critical state data; Step S362: Plot the stress response curve based on the information of the unstable elements; Step S363: Analyze the material degradation index based on the stress response curve; Step S364: Adjust the material strength based on the material degradation index to obtain material strength adjustment data; Step S365: Adjust the elastic modulus based on the material degradation index to obtain the elastic modulus adjustment data; Step S366: Integrate the material strength adjustment data and the elastic modulus adjustment data to obtain the material parameter adjustment data.

9. The method for detecting the seepage prevention effect of a dam according to claim 1, characterized in that, Step S4 is as follows: Step S41: Adjust the data according to the material parameters to perform fatigue simulation and obtain fatigue data of the dam material; Step S42: Construct a material fatigue attenuation function based on dam material fatigue data; Step S43: Calculate the probability of microcrack formation based on the material fatigue attenuation function; Step S44: Perform permeability tensor analysis based on the probability of microcrack formation to obtain permeability tensor data; Step S45: Calculate the permeability performance attenuation coefficient based on the permeability tensor data; evaluate the seepage prevention effect based on the permeability performance attenuation coefficient to obtain seepage prevention effect data.

10. A system for detecting the seepage prevention effect of a dam, characterized in that, The method for detecting the seepage prevention effect of a dam as described in claim 1, wherein the detection system for the seepage prevention effect of a dam comprises: The seepage anomaly analysis module is used to acquire dam structural data; to evaluate the performance of the seepage prevention structure based on the dam structural data in order to identify weak points in the seepage prevention; and to perform seepage anomaly analysis based on the weak points in the seepage prevention to obtain seepage anomaly data. The seepage field numerical simulation module is used to identify free surface offsets based on seepage anomaly data to obtain free surface offset data; locate seepage channels based on the free surface offset data to obtain seepage channel data; and perform seepage field numerical simulation based on the seepage channel data to obtain seepage field simulation values. The material parameter adjustment module is used to perform dam foundation stability testing based on seepage field simulation values ​​to obtain dam foundation stability data; predict the instability critical state based on the dam foundation stability data to obtain instability critical state data; and adjust the material parameters based on the instability critical state data to obtain material parameter adjustment data. The seepage prevention effect detection module is used to perform fatigue simulation based on the material parameter adjustment data to obtain dam material fatigue data; and to perform seepage prevention effect detection based on the dam material fatigue data to obtain seepage prevention effect data.

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