Rock slope stability evaluation method, system and equipment based on three-dimensional modeling and storage medium

Abstract

According to the rock slope stability evaluation method, system and equipment based on three-dimensional modeling and the storage medium provided by the invention, the rock slope stability evaluation method based on three-dimensional modeling is characterized in that a three-dimensional model of a rock slope is constructed in combination with geophysical prospecting data and exploration data, the geophysical prospecting data, drilling rock core data and the like are stored in the three-dimensional model, and the rock slope stability evaluation accuracy is improved. Stability evaluation is carried out through the three-dimensional model and data matched with the three-dimensional model, the safety coefficient and the slope instability probability are calculated in combination with preset extreme environmental factors, the stability of the current rock slope is evaluated, and automatic early warning, measure recommendation and real-time dynamic management and control are achieved.

Description

Technical Field

[0001] This application relates to the field of rock slope stability assessment technology, and in particular to rock slope stability assessment methods, systems, equipment and storage media based on three-dimensional modeling. Background Technology

[0002] In Earth sciences, geophysical exploration, as an emerging discipline, has developed rapidly, and it is also one of the important exploration methods in engineering surveys. To a certain extent, the application, development, and research level of geophysical technology in engineering geology have become an important indicator for measuring the level of modern geological exploration. Existing geophysical technologies include Rayleigh wave method, high-density electrical resistivity tomography, and seismic imaging method.

[0003] Rayleigh wave method uses artificial seismic sources or environmental noise to excite Rayleigh surface waves. By analyzing their dispersion curves (the relationship between wave velocity and frequency), the shear wave velocity (Vs) of the strata is inverted, and then the mechanical properties of the soil and rock are classified (e.g., low Vs in loose layers and high Vs in hard rock layers). The detection depth is generally 5m-50m, suitable for shallow and fine stratification.

[0004] High-density electrical resistivity tomography (EDT) involves injecting current into the ground and measuring the potential difference at different locations to calculate the resistivity distribution of the formation. Due to the differences in conductivity among different soil and rock media (e.g., low resistivity in clay and high resistivity in intact rock), this method can identify slip surfaces, fracture zones, and aquifers. The detection depth is typically 20m-100m, making it suitable for shallow to intermediate stratigraphic exploration.

[0005] The seismic imaging method uses artificial seismic sources (such as hammer blows or explosives) to generate seismic waves. Detectors record the reflected / refracted wave signals, and parameters such as wave velocity and amplitude are analyzed to construct underground wave impedance interfaces (such as bedrock surfaces or faults). The detection depth can reach hundreds of meters, making it suitable for exploring mid- to deep geological structures.

[0006] While existing geophysical exploration techniques can obtain geological parameters, data from a single geophysical method is insufficient to fully characterize complex geological structures, resulting in large errors in slip surface identification. Summary of the Invention

[0007] The technical problem to be solved by this application is that although existing geophysical exploration techniques can obtain geological parameters, the data from a single geophysical method is difficult to fully characterize complex geological structures, resulting in large errors in the identification of slip surfaces.

[0008] To address the aforementioned problems, or at least partially address the aforementioned technical issues, this application provides a method, system, device, and storage medium for assessing the stability of rock slopes based on three-dimensional modeling.

[0009] In a first aspect, the present invention discloses a method for assessing the stability of rock slopes based on three-dimensional modeling, which specifically includes the following steps: Geophysical and exploration data of the rock slope to be tested are acquired, and a three-dimensional geological model is constructed based on the geophysical and exploration data to obtain the three-dimensional model of the rock slope to be tested; the geophysical data includes resistivity, surface wave dispersion curve, and seismic reflection signal; Stability assessment was conducted based on a three-dimensional model of the rock slope to be tested, resulting in stability indices and failure characteristics. The stability indices include the slope safety factor and the probability of slope instability, while the failure characteristics include the location and volume of potential slip surfaces.

[0010] Preferably, the process of acquiring geophysical and exploration data of the rock slope to be inspected, and constructing a three-dimensional geological model based on the geophysical and exploration data to obtain the three-dimensional model of the rock slope to be inspected, specifically includes the following steps: Monitoring points are evenly set up on the rock slope to be tested, and monitoring equipment is installed at the monitoring points. Geophysical data of the rock slope to be tested is acquired at preset first time intervals. The geophysical data includes underground resistivity distribution data, surface wave dispersion curve data, and artificial seismic wave reflection signal data of the slope area. Exploration and monitoring are carried out at the monitoring points to obtain exploration data, which includes borehole core data, topographic mapping data and hydrogeological data. Obtain a digital surface model generated by UAV oblique photography of the rock slope to be inspected; Based on geophysical and exploration data, key geological elements are extracted, including landslide boundaries, spatial morphology of sliding zones, distribution of weak interlayers, and groundwater occurrence areas. Based on the digital surface model, a three-dimensional geological model is constructed by combining geophysical and exploration data in the same three-dimensional coordinate system to obtain the three-dimensional model of the rock slope to be tested.

[0011] Preferably, the construction of a three-dimensional geological model based on a digital surface model, combining geophysical and exploration data in the same three-dimensional coordinate system, to obtain a three-dimensional model of the rock slope to be inspected, specifically includes the following steps: The geophysical data is inverted to obtain inverted data, which includes resistivity profile and shear wave velocity layered data. Spatial registration was performed on geophysical data, inversion data, and borehole core data to unify the coordinate system of data from various monitoring points. The registered resistivity profile, shear wave velocity layered data, and borehole core data are converted into regular grids according to the coordinates of the monitoring points. Each grid node on the regular grid is assigned a value to retrieve the data, and the spacing between each grid node in the regular grid is the same. The appropriate grid type is selected based on the complexity of the geological interface, and layers are formed according to different depths. A corresponding resolution strategy is preset to construct a three-dimensional voxel model, thereby obtaining a three-dimensional model of the rock slope to be inspected.

[0012] Preferably, the step of selecting the appropriate mesh type based on the complexity of the geological interface, layering according to different depths, and pre-setting an appropriate resolution strategy to construct a three-dimensional voxel model to obtain a three-dimensional model of the rock slope to be inspected specifically includes the following steps: The appropriate mesh type is selected based on the complexity of the geological interface. For modeling layered geological bodies, a hexahedral mesh is selected, while for modeling complex geological interfaces, a tetrahedral mesh is selected. Standard mesh nodes are converted into tetrahedral or hexahedral elements. The layers are divided according to depth. The shallow layer uses fine modeling, while the deep layer uses coarse modeling. The three-dimensional voxel models are constructed between the shallow and deep layers according to the corresponding preset resolution strategy to obtain the three-dimensional voxel models. Geological data is labeled and rendered in a 3D voxel model to obtain a 3D model of the rock slope to be detected, including geological unit rendering, digital surface model overlay processing, and configuration of geophysical and exploration data.

[0013] Preferably, the stability assessment based on the three-dimensional model of the rock slope to be tested, to obtain stability indices and failure characteristics, specifically includes the following steps: Based on the three-dimensional model of the rock slope to be detected, the boundary features, layer interface features, rock mass zoning features, and groundwater features of the rock slope are determined, and the data of the rock slope are obtained. Potential sliding surfaces are divided at equal intervals on the three-dimensional model of the rock slope to be tested. Preset extreme environmental factors are added to calculate the safety factor, and the safety factor of each slope surface under extreme environment is obtained. Based on the safety factor of each slope, the most dangerous slip surface is searched to obtain the location and volume of the dangerous slip surface; Calculation data is extracted from the three-dimensional model of the rock slope to be tested. The failure probability value is calculated based on the Monte Carlo simulation method to obtain the slope instability probability within a predetermined time.

[0014] Preferably, the step of dividing potential slip surfaces at equal intervals on a three-dimensional model, adding preset extreme environmental factors to calculate safety factors, and obtaining the safety factor of each slope surface under extreme conditions specifically includes the following steps: By combining the geological data configured in the three-dimensional model of the rock slope to be tested, potential sliding surfaces are identified and the interfaces of the sliding surfaces are defined in the three-dimensional model of the rock slope to be tested, thus obtaining the interface of the potential sliding zone. The potential sliding surface is uniformly divided into multiple strips along the direction of the sliding zone, and the corresponding geometric and mechanical parameters are matched to obtain the strips and their parameter characteristics; By adding preset extreme environmental factors and combining them with the parameter characteristics of the blocks, the safety factor of each slope is calculated to obtain the safety factor of each slope under extreme conditions.

[0015] Preferably, the following includes: Early warning signals are assigned based on the results of rock stability assessments, and emergency measures are implemented accordingly.

[0016] Secondly, the present invention discloses a rock slope stability assessment system based on three-dimensional modeling, and the aforementioned rock slope stability assessment method based on three-dimensional modeling.

[0017] Thirdly, the present invention discloses a computer device, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements the steps of the above method.

[0018] Fourthly, the present invention discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0019] The technical solution provided in this application has the following advantages compared with the prior art: This application provides a method, system, equipment, and storage medium for assessing the stability of rock slopes based on 3D modeling. The method involves constructing a 3D model of the rock slope by combining geophysical and exploration data, storing geophysical data and borehole core data in the 3D model, and conducting stability assessments by matching the 3D model with the data. By incorporating preset extreme environmental factors, the method calculates the safety factor and the probability of slope instability, assesses the current stability of the rock slope, and enables automated early warning and measure recommendations, as well as real-time dynamic management.

[0020] Furthermore, by integrating geophysical data (resistivity, surface wave dispersion curves, seismic reflection signals), exploration data (drill cores, hydrogeology), and UAV photography data, a three-dimensional model is constructed and stability assessment is performed. This addresses the limitations of relying on a single data source and improves the accuracy of stability assessment. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating a method for assessing the stability of rock slopes based on three-dimensional modeling, as provided in this application. Figure 1 ; Figure 2 A flowchart illustrating a method for assessing the stability of rock slopes based on three-dimensional modeling, as provided in this application. Figure 2 ; Figure 3 A schematic diagram of the specific process of step S1 of the rock slope stability assessment method based on three-dimensional modeling provided in this application; Figure 4 A schematic diagram of step S15 of the rock slope stability assessment method based on three-dimensional modeling provided in this application; Figure 5 A schematic diagram of step S154 of the rock slope stability assessment method based on three-dimensional modeling provided in this application; Figure 6 A schematic diagram of step S2 of the rock slope stability assessment method based on three-dimensional modeling provided in this application; Figure 7 A schematic diagram of step S22 of the rock slope stability assessment method based on three-dimensional modeling provided in this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application 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 application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Firstly, see Figure 1-7 This invention discloses a method for assessing the stability of rock slopes based on three-dimensional modeling, which specifically includes the following steps: Step S1: Obtain geophysical and exploration data of the rock slope to be tested, and construct a three-dimensional geological model based on the geophysical and exploration data to obtain the three-dimensional model of the rock slope to be tested; the geophysical data includes resistivity, surface wave dispersion curve, and seismic reflection signal; Step S2: Based on the three-dimensional model of the rock slope to be tested, a stability assessment is performed to obtain stability indices and failure characteristics. The stability indices include the slope safety factor and the slope instability probability, and the failure characteristics include the location and volume of the potential slip surface.

[0026] Step S3: Divide the early warning signals according to the rock stability assessment results, and deploy emergency measures according to the early warning signals.

[0027] Specifically, in step S1, resistivity distribution is obtained through high-density electrical resistivity tomography (resolution 0.5~2 meters), surface wave dispersion curves are acquired using Rayleigh wave method (to invert shear wave velocity Vs), subsurface interface reflection signals are obtained using seismic reflection method, and lithology, layer depth, and RQD index are obtained through borehole core sampling; topographic mapping generates contour lines (accuracy 0.1 meters); hydrogeological investigation determines groundwater level and permeability coefficient; in addition, UAV oblique photography generates a 0.1-meter resolution digital surface model (DSM), extracting surface features such as slope and cracks; geophysical data is inverted to obtain resistivity profiles and layer data, which are then processed through... Kriging interpolation generates a continuous field, unifying borehole, geophysical, and UAV data into the UTM coordinate system with a horizontal error ≤0.5 meters and an elevation error ≤0.3 meters. A lattice grid is generated, and a volumetric mesh is created by combining resistivity profiles and layered data. Thresholding methods are used to identify slip zones (ρ < 100 Ω*m and Vs < 800 m / s), faults (reflected wave faulting), and bedrock (ρ > 500 Ω*m and Vs > 1500 m / s). DSM topography is overlaid and rendered according to lithological textures (e.g., yellowish-brown for sandstone, gray for mudstone), and hydrological data (e.g., groundwater level) is annotated to obtain a 3D model of the rock slope to be inspected. High-density electrical resistivity tomography, Rayleigh wave method, and seismic reflection method are used to acquire geophysical data, avoiding misjudgments caused by relying on single geophysical data (e.g., relying solely on borehole data may miss interlayers), thus improving the model's realism.

[0028] Specifically, in step S2, the sliding zone interface is extracted from the 3D model and divided into blocks along its direction. Preset extreme weather conditions are added, and the safety factor and slope instability probability of each block are calculated. The safety factor Fs provides a clear safety threshold, and the instability probability Pf reflects the likelihood of future instability. The deterministic results meet the specifications, while the probabilistic results quantify the actual risk. Based on the safety factor and slope instability probability, the risk area is located, and the specific location and scale of the sliding zone are determined, providing targets for reinforcement design. Simulating extreme environments (rainstorms, earthquakes) exposes weak points in advance and has the function of predicting slope hazards.

[0029] Specifically, in step S3, thresholds are set for the safety factor and failure probability values. Warning levels are then categorized based on these thresholds, and corresponding emergency measures are implemented for each warning level. When the safety factor Fs or the instability probability Pf exceeds the threshold, the system automatically sends a warning SMS / email, attaching a screenshot of the risk area from the 3D model. The risk level is displayed on the monitoring center's large screen, with a flashing red area as a warning. This reduces human error, ensures consistent warning standards, shortens response time, minimizes accident losses, and optimizes resource allocation.

[0030] It is understandable that a three-dimensional model of a rock slope is constructed by combining geophysical and exploration data. Geophysical data and borehole core data are stored in the three-dimensional model. Stability assessment is performed by matching the three-dimensional model with the data. Combined with preset extreme environmental factors, the safety factor and slope instability probability are calculated to assess the current stability of the rock slope, realize automated early warning and measure recommendation, and real-time dynamic management.

[0031] Furthermore, by integrating geophysical data (resistivity, surface wave dispersion curves, seismic reflection signals), exploration data (drill cores, hydrogeology), and UAV photography data, a three-dimensional model is constructed and stability assessment is performed. This addresses the limitations of relying on a single data source and improves the accuracy of stability assessment.

[0032] As one implementation, four warning levels are set. The green level is safe, with a safety factor Fs > 1.3 and an instability probability Pf < 1%, requiring routine monitoring of the rock slope. The yellow level requires vigilance, with a safety factor Fs ≤ 1.3 and an instability probability Pf < 5%, requiring increased vigilance, more frequent monitoring, and increased GNSS monitoring frequency (e.g., from once a day to once an hour), and the development of a parameter inversion optimization model. The orange level is dangerous, with a safety factor Fs < 1.2 and an instability probability Pf < 10%, requiring slope reinforcement, including anchor bolt reinforcement (5-meter spacing, 15-meter length) and inclined drainage holes (150mm diameter, 8-meter spacing). The red level is extremely high risk, with a safety factor Fs < 1.0 and an instability probability Pf ≥ 10%, requiring emergency evacuation of nearby personnel, immediate closure of the affected area, initiation of anti-slide pile emergency engineering (2-meter diameter, 6-meter spacing), and simultaneous dynamic tracking simulation.

[0033] Step S1 specifically includes the following steps: Step S11: Evenly set up monitoring points on the rock slope to be tested, install monitoring equipment at the monitoring points, and acquire geophysical data of the rock slope to be tested at preset first time intervals; the geophysical data includes underground resistivity distribution data of the slope area, surface wave dispersion curve data, and artificial seismic wave reflection signal data. Step S12: Conduct exploration and monitoring at the monitoring point to obtain exploration data, which includes borehole core data, topographic mapping data and hydrogeological data; Step S13: Acquire UAV oblique photography of the rock slope to be inspected and generate a digital surface model; Step S14: Based on geophysical and exploration data, extract key geological elements, including landslide boundaries, spatial morphology of sliding zones, distribution of weak interlayers, and groundwater occurrence areas. Step S15: Based on the digital surface model, a three-dimensional geological model is constructed by combining geophysical data and exploration data in the same three-dimensional coordinate system to obtain the three-dimensional model of the rock slope to be tested.

[0034] Specifically, monitoring points are evenly distributed on the slope surface at intervals of 20-50 meters to form a regular grid (e.g., 10m × 10m), with a focus on denser coverage at the slope top, toe, and areas with developed cracks. Electrodes are placed at the monitoring points, and resistivity data is collected using a Wenner device with an electrode spacing of 5m to obtain the resistivity distribution within 30m underground. Seismic waves are excited at the monitoring points, and surface wave dispersion curves are collected to invert Vs stratification (depth resolution 1-2m). Artificial seismic waves are excited using explosives or a seismic source vehicle, and reflected signals are collected to identify deep interfaces such as bedrock surfaces and faults. This provides underground physical field distribution data (resistivity, Vs), indirectly reflecting characteristics such as lithology, fracture development, and water-bearing state. Using a non-contact detection method, data from uncontrolled areas between boreholes can be obtained, filling exploration blind spots. Boreholes are drilled at monitoring points (e.g., every 5th geophysical detection point), penetrating 5-10m below the potential slip surface, with a borehole diameter of 108mm and a core collection rate ≥85%. The boreholes record layer depth, lithology (e.g., moderately weathered sandstone, argillaceous interlayers), fracture orientation (e.g., dip angle 60°, dipping outwards), and RQD value (e.g., RQD=70% indicates moderate integrity). Additionally, piezometers are installed in the boreholes to monitor groundwater level dynamics, collect groundwater samples for analysis, and determine the permeability coefficient (e.g., K=1e-5m / s obtained from a pressure test). RTK-GPS is used to measure the elevation of the monitoring points with an accuracy of ±2cm, and a 1:500 topographic map is drawn, marking the slope outline, platform location, and fracture distribution. As an example, unmanned aerial vehicle (UAV) cameras can be used to capture on-site images, which, combined with existing surveying software, can create a topographic map of the corresponding rock slope. By combining lithological and hydrological data, the rock mass mechanical parameters can be accurately zoned. As one implementation, inclinometers and stress gauges can be simultaneously installed in the boreholes, providing a hardware interface for subsequent dynamic monitoring. Multi-rotor drones with cameras are used to capture images, ensuring full slope coverage. Based on the images, a high-precision surface geometric model is constructed using existing software (e.g., Pix4Dmapper), extracting key parameters such as slope ratio, platform width, and crack location. Combining geophysical and exploration data, key geological elements are extracted. Based on shear wave velocity and resistivity, the rock slope is stratified. Using piezometer data, the groundwater level is plotted in the model. Combining geophysical anomaly areas, borehole cores, and surface cracks, areas exhibiting low resistivity, low velocity, borehole mudstone, and surface cracks are identified as landslide boundaries. The fault locations of stratigraphic interfaces on the geophysical profile are identified as faults or slip zones. Core geological information is extracted from massive amounts of data to initially construct a rudimentary slope model, reducing redundant data interference. Geophysical exploration, drilling, and surface observation corroborate each other, reducing the risk of misjudgment using a single method.Geophysical profiles, borehole coordinates, and digital surface models are processed using the same coordinate system, divided into regular grids, and the grid nodes are assigned values. Borehole data and geophysical data are assigned to each grid node. Finally, different colors are used for different geological units (e.g., red for mudstone and yellow for sandstone), and the resistivity distribution is displayed using gradient transparency. The resulting three-dimensional model of the rock slope under test is output, overcoming the limitation of traditional two-dimensional models that cannot reflect the three-dimensional stress state. Step S15 specifically includes the following steps: Step S151: Perform inversion processing on the geophysical data to obtain inversion data, which includes resistivity profile and shear wave velocity layered data. Step S152: Spatial registration of geophysical data, inversion data, and borehole core data to unify the coordinate system of data from various monitoring points; Step S153: Convert the registered resistivity profile, shear wave velocity layer data, and borehole core data into a regular grid according to the monitoring point coordinates. Assign inversion data to each grid node on the regular grid. The spacing between each grid node in the regular grid is the same. Step S154: Select the appropriate mesh type according to the complexity of the geological interface, and layer it according to different depths. Preset the corresponding resolution strategy to construct a three-dimensional voxel model and obtain the three-dimensional model of the rock slope to be detected.

[0035] Specifically, the collected geophysical data first undergoes refined inversion processing. For high-density electrical resistivity tomography (EDT) data, the least squares inversion technique is employed to generate a three-dimensional resistivity volume model with a resolution of 0.5m × 0.5m × 1m, containing 500,000 to 1,000,000 elements. Simultaneously, a genetic algorithm is used to invert the facet wave dispersion curves, ensuring that the root mean square error between the measured and theoretical curves is less than 0.05, resulting in a layered model of shear wave velocity, with each layer's thickness controlled between 1m and 5m and shear wave velocities ranging from 100 to 2000 m / s. This transforms the raw geophysical data into physical parameters with clear geological significance, providing quantitative basis for subsequent geological interpretation. To ensure spatial consistency of multi-source data, precise coordinate system unification and registration are required, uniformly converting the geophysical data, borehole coordinates, and UAV digital surface model to the engineering coordinate system. By selecting 3-5 common control points (such as concrete markers) with a coordinate accuracy of ±0.05m on the slope surface, registration was performed using a seven-parameter transformation method, with the control point residual strictly controlled within 0.2m. After registration, the inverted resistivity and the measured values ​​from the core samples were compared at the borehole locations to ensure that the average error did not exceed 10%. This effectively eliminated spatial location biases in multi-source data, enabling different types of data to accurately correspond in three-dimensional space and forming a unified data volume. Based on the registered data, a regular grid was constructed and attribute values ​​were assigned. Ordinary kriging was used for attribute interpolation. The interpolation of resistivity and shear wave velocity required setting the nugget effect to 0.1, and range parameters (e.g., horizontal range 50m, vertical range 10m) were determined based on spatial correlation analysis. Lithological codes were assigned using the borehole core samples as control points, employing the nearest neighbor method to ensure 100% accuracy of lithology at the borehole locations. After interpolation, 10% of the grid nodes are randomly selected for verification, requiring resistivity error ≤15%, shear wave velocity error ≤10%, and lithology consistency ≥95%. Discrete geophysical profiles and borehole data are transformed into continuous 3D data volumes, with each grid node containing complete geological attributes (resistivity, shear wave velocity, lithology, etc.), providing standardized input for subsequent modeling. Appropriate grid types are selected based on geological characteristics for 3D volumetric model construction. For layered geological bodies (such as sedimentary rock slopes), hexahedral grids are used, generating regular blocks through geological interfaces; for complex structural areas (such as fault-developed areas), tetrahedral grids are used. Shallow layers (0-20m) have a grid size of 0.5m³ to finely characterize slip zones; middle layers (20-50m) have a 1m³ grid to balance accuracy and efficiency; and deep layers (>50m) have a 2m³ grid to simplify bedrock areas. In addition, the model is colored according to lithology (e.g., yellow for sandstone and red for mudstone) during rendering, and the resistivity value is mapped to transparency (low resistivity areas are semi-transparent). The mesh and geological interface are checked by generating profiles in arbitrary directions, and the mesh penetration rate of the slip zone is required to be less than 1%.

[0036] Understandably, the 3D digital model created in this step possesses realistic geological structures and physical properties, directly supporting subsequent stability calculations. Its technical advantages lie in its adaptive accuracy strategy (high resolution for key areas, low resolution for non-critical areas) which reduces computation by more than 50%, the tetrahedral mesh's representation error for complex geological interfaces (such as steeply dipping faults) being less than 10%, and the model supporting arbitrary angle sectioning and attribute queries, greatly enhancing the intuitiveness of engineering analysis.

[0037] Step S154 specifically includes the following steps: Step S1541: Select the appropriate mesh type according to the complexity of the geological interface. For modeling layered geological bodies, select hexahedral mesh; for modeling complex geological interfaces, select tetrahedral mesh. Convert standard mesh nodes into tetrahedral or hexahedral elements. Step S1542: Layer according to depth. Shallow layers are modeled with fine detail, while deep layers are modeled with coarse detail. Three-dimensional voxel models are constructed between shallow and deep layers according to the corresponding preset resolution strategy to obtain three-dimensional voxel models. Step S1543: Mark geological data and render the three-dimensional voxel model to obtain a three-dimensional model of the rock slope to be detected, including geological unit rendering, digital surface model overlay processing, and configuration of geophysical data and exploration data.

[0038] Specifically, the curvature and topological characteristics of 3D geological interfaces are first analyzed. For layered geological bodies with gentle interfaces and strong continuity (such as horizontal sedimentary rock layers with curvature <0.1 / m), hexahedral meshes are used for modeling. Regular hexahedrons are generated through a "stretch-cut" operation based on the geological layer interface, with the element size consistent with the mesh node spacing (e.g., 0.5m × 0.5m × 0.5m). For complex geological interfaces containing faults, steeply dipping interlayers, lenses, etc. (with curvature >0.5m or topological branches), tetrahedral meshes are selected. Regular mesh nodes are used as control points, and the Delaunay tetrahedral partitioning algorithm is used to automatically fill the irregular areas around the complex interfaces, with a minimum element size of 0.25m. In actual modeling, a hybrid mesh technique is used, with hexahedral meshes used in layered regions and tetrahedral meshes transitioned to complex structural regions. The continuity of the physical field is ensured by connecting common nodes. A layered strategy based on depth was implemented during modeling: the shallow layer (0-20m) used a resolution of 0.5m×0.5m×0.5m, covering the deformation-active area from the surface to 5m below the potential slip surface; the middle layer (20-50m) used a resolution of 1m×1m×1m, covering the area from the deep slip surface to 10m above the bedrock surface to capture the extension characteristics of the geological interface; the deep layer (>50m) within the bedrock used a resolution of 2m×2m×2m and was simplified as a homogeneous medium. At the boundary between the shallow and middle layers, a "pyramid-style" mesh transition was used, subdividing the 1m element into eight 0.5m elements to avoid calculation errors caused by abrupt resolution changes. Simultaneously, the proportion of deep mesh elements was controlled to be within 30%, and the total number of elements did not exceed 500,000, ensuring that the numerical simulation computation time was less than 12 hours under a standard workstation configuration. In addition, during the model rendering and data configuration phase, a lithology-color mapping table is first defined. For example, sandstone is displayed using RGB(255, 200, 100) to simulate grain texture, mudstone is presented using RGB(150, 50, 50) to simulate bedding structure, and slip zones are highlighted using RGB(255, 0, 0) semi-transparent (30% transparency). A 0.1m resolution digital surface model (DSM) is used as the top surface of the model to render realistic terrain textures and mark the locations of monitoring points. In terms of data configuration, resistivity distribution is displayed through isosurfaces (e.g., ρ=100Ω·m) or volume rendering (transparency mapped to ρ value), and horizontal slices are used to represent the layering of shear wave velocities (Vs) at different depths. Boreholes are represented by cylindrical models, filled with core lithology and labeled with borehole numbers and depths. The groundwater level is displayed using a blue semi-transparent surface and labeled with the real-time piezometer water level values. 3D model rendering transforms abstract 3D data into intuitive and visual models, integrating geological, physical, and monitoring data for multi-dimensional display. Dangerous areas such as slip zones are presented intuitively through color and transparency, reducing risk identification time to minutes. It also supports the generation of 3D model animations, cross-sectional views, and other deliverables, improving communication efficiency with non-technical personnel. Step S2 specifically includes the following steps: Step S21: Based on the three-dimensional model of the rock slope to be detected, determine the boundary features, layer interface features, rock mass zoning features, and groundwater features of the rock slope to obtain the data of the rock slope; Step S22: Divide potential sliding surfaces at equal intervals on the three-dimensional model of the rock slope to be tested, add preset extreme environmental factors to calculate the safety factor, and obtain the safety factor of each slope surface under extreme environment. Step S23: Search for the most dangerous slip surface based on the safety factor of each slope to obtain the location and volume of the dangerous slip surface; Step S24: Extract calculation data from the three-dimensional model of the rock slope to be tested, calculate the failure probability value of the calculation data based on the Monte Carlo simulation method, and obtain the slope instability probability within a predetermined time.

[0039] Specifically, key data required for slope stability calculation are extracted from the three-dimensional model, including the three-dimensional coordinates of surface boundaries (slope top line, slope toe line), stratigraphic interfaces (boundary between bedrock and overburden), and structural planes (faults, slip zones). Stratigraphic units are divided according to lithology, and the elevation and thickness of the top / bottom surfaces of each layer are extracted. Rock mass quality zones are divided according to resistivity, shear wave velocity (Vs), and lithology (e.g., Class I intact rock mass, Class IV fractured rock mass), and mechanical parameters (e.g., Class I rock mass cohesion C=40kPa, internal friction angle B=38°) are assigned. At the same time, hydrogeological data such as groundwater level, permeability coefficient, and pore water pressure are extracted. In the 3D model, vertical cutting planes are generated along the slope direction at intervals of 5-10m. Within each plane, potential sliding surfaces in the shape of arcs or broken lines are generated at intervals of 0.5-2m (e.g., 200 sliding surfaces are generated for a 100m long slope). Extreme environmental conditions are set for each sliding surface, including rainstorm and earthquake conditions, while also incorporating natural conditions. The safety factor for each condition is calculated. Under the rainstorm condition, the groundwater level rises to the surface, and the rock mass saturation parameters C decrease by 20% and B decrease by 15%, and seepage force is taken into account. Under the earthquake condition, a horizontal seismic acceleration PGA=0.2g is applied, and the inertial force is calculated using the quasi-static method. The Swedish slice method is used to calculate the safety factor (Fs) for each sliding surface, covering all possible sliding surfaces in the 3D space and considering real hydrogeological parameters, avoiding the omission of 3D effects such as rock mass clamping at both ends of the slope in traditional 2D analysis. All potential slip surfaces are sorted by safety factor Fs values ​​from smallest to largest. The top 5 slip surfaces with the smallest safety factor Fs value are extracted as candidates. These are then checked to see if they cross low-resistivity, low-velocity zones (ρ < 100 Ω·m and Vs < 800 m / s) or weak interlayers revealed by boreholes (e.g., slip surface S-37 crosses a mudstone interlayer and coincides with a low-resistivity zone). After verifying geological rationality, the slip volume is calculated using a 3D mesh volume integral. The probability distributions of sensitive parameters such as rock mass cohesion C, internal friction angle B, groundwater level h, and seismic PGA are extracted from the 3D model. Parameter combinations are generated, and the limit equilibrium method is automatically called to calculate the safety factor Fs value. The frequency of safety factor Fs < 1 is statistically analyzed as the failure probability (Pf). Furthermore, by plotting the probability density function and cumulative distribution function of the safety factor Fs, the risk probability is visually displayed. Considering the combined effects of multiple parameters and the time effects of rock mass deterioration, the risk can be predicted for any future time period, supplementing the limitation of deterministic analysis in quantifying parameter fluctuation risks.

[0040] Understandably, by employing automated data extraction, three-dimensional global slip surface search, joint geological-mechanical verification of hazardous slip surfaces, and Monte Carlo simulation to quantify risks, an upgrade from static single-condition assessment to dynamic multi-scenario risk quantification has been achieved. It not only provides safety factor values ​​but also reveals the essence of risk through probabilistic analysis. Its calculation accuracy and efficiency are industry-leading, making it suitable for real-time monitoring and long-term prediction of high-risk slopes. It provides a scientific basis for refined management, emergency decision-making, and engineering insurance, solving technical problems associated with traditional methods such as three-dimensional effects, parameter uncertainty, and difficulty in risk quantification.

[0041] Step S22 specifically includes the following steps: Step S221: Combining the geological data configured in the three-dimensional model of the rock slope to be tested, identify potential sliding surfaces and define the interfaces of the sliding surfaces in the three-dimensional model of the rock slope to be tested, and obtain the interface of the potential sliding zone. Step S222: Divide the potential sliding surface into multiple strips along the direction of the sliding zone, and match the corresponding geometric and mechanical parameters to obtain the strips and their parameter characteristics; Step S223: Add preset extreme environmental factors, and calculate the safety factor of the strips based on the parameter characteristics of the strips to obtain the safety factor of each slope under extreme conditions.

[0042] Specifically, key information such as structural surfaces (e.g., faults and joints with dip angles > 60° and inclined outwards), weak layers (mudstone interlayers with resistivity < 100Ω*m and transverse wave velocity Vs < 800m / s) and groundwater-rich areas (permeability coefficient K > 1e-4 m / s and groundwater depth < 5m) are extracted from the 3D model. The resistivity and transverse wave velocity Vs values ​​of the 3D grid nodes are clustered to identify low-strength material areas. At the same time, a 3D structural surface network is constructed, the intersection lines of the structural surfaces and the slope are calculated, and the intersection lines with dip angles β ∈ [25°, 60°] and penetration rates > 70% are selected. The outer contour of the clustered region is extracted as the interface of the potential slip surface. The Marching Cubes algorithm is used to generate a three-dimensional isosurface (such as the isosurface with resistivity ρ=120Ω*m). The maximum distance from the grid node to the slip surface is controlled to be <0.5m, so as to realize the intelligent identification of potential slip surfaces in complex geological bodies. The efficiency is improved by 80% compared with manual delineation and deep hidden slip surfaces can be discovered. The fusion of multiple evidences avoids misjudgment by a single indicator.

[0043] Pre-setting rainstorm and earthquake conditions requires adjusting corresponding parameters. For the rainstorm condition, the groundwater level is set to rise to the surface (rw=1.0), and the soil parameters C is reduced by 20% and B by 15% (e.g., mudstone c=12kPa, φ=17°), with seepage force taken into account. For the earthquake condition, a horizontal seismic force Fi=kh*Wi (kh=0.2) and a vertical seismic force Vi=kv*Wi (kv=0.1) are applied. The parameters for calculating the safety factor are adjusted accordingly. The formula for calculating the safety factor is:

[0044] in, The length of the strip surface. , Let be the cohesion and internal friction angle of the i-th block on the sliding surface; The weight of the i-th block; The angle of inclination of the smooth surface; This refers to the pore water pressure.

[0045] Secondly, the present invention discloses a rock slope stability assessment system based on three-dimensional modeling, and the aforementioned rock slope stability assessment method based on three-dimensional modeling.

[0046] Specifically, this invention discloses a rock slope stability assessment system based on three-dimensional modeling. The system includes a method that, in its first aspect, constructs a three-dimensional model of the rock slope by combining geophysical and exploration data. Geophysical data, borehole core data, and other data are stored in the three-dimensional model. Stability assessment is performed using the data matched to the three-dimensional model. By incorporating preset extreme environmental factors, a safety factor and the probability of slope instability are calculated to evaluate the current stability of the rock slope, enabling automated early warning and recommended measures, and real-time dynamic management. Furthermore, by integrating geophysical data, exploration data, and UAV photography data to construct the three-dimensional model and perform stability assessment processing, the system overcomes the limitations of relying on a single data source and improves the accuracy of stability assessment.

[0047] Thirdly, the present invention discloses a computer device, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements the steps of the above method.

[0048] Specifically, the computer device's processor runs a computer program stored in memory. This program includes the method described in the first aspect, which involves constructing a three-dimensional model of the rock slope by combining geophysical and exploration data. Geophysical data, borehole core data, and other data are stored in the three-dimensional model. Stability assessment is performed using the three-dimensional model and the data matched with it. Combined with preset extreme environmental factors, the safety factor and slope instability probability are calculated to evaluate the current stability of the rock slope, achieving automated early warning and recommended measures, and real-time dynamic management. Furthermore, integrating geophysical data, exploration data, and UAV photography data to construct the three-dimensional model and perform stability assessment processing addresses the limitations of single data sources and improves the accuracy of stability assessment.

[0049] Fourthly, the present invention discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0050] Specifically, the storage medium stores a computer program, which includes the method described in the first aspect. This method involves constructing a three-dimensional model of a rock slope by combining geophysical and exploration data. Geophysical data, borehole core data, and other data are stored in the three-dimensional model. Stability assessment is performed using the three-dimensional model and the data matched with it. By incorporating preset extreme environmental factors, the safety factor and slope instability probability are calculated to evaluate the current stability of the rock slope, achieving automated early warning and recommended measures, and real-time dynamic management. Furthermore, integrating geophysical data, exploration data, and UAV photography data to construct the three-dimensional model and perform stability assessment processing addresses the limitations of single data sources and improves the accuracy of stability assessment.

[0051] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0052] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0053] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0054] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0055] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0056] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. The illustrative expressions of the above terms in this specification should not be construed as necessarily referring to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0057] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0058] The above description describes specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing the stability of rock slopes based on three-dimensional modeling, characterized in that, Specifically, the following steps are included: Geophysical and exploration data of the rock slope to be tested are acquired, and a three-dimensional geological model is constructed based on the geophysical and exploration data to obtain the three-dimensional model of the rock slope to be tested; the geophysical data includes resistivity, surface wave dispersion curve, and seismic reflection signal; Stability assessment was conducted based on a three-dimensional model of the rock slope to be tested, resulting in stability indices and failure characteristics. The stability indices include the slope safety factor and the probability of slope instability, while the failure characteristics include the location and volume of potential slip surfaces.

2. The method according to claim 1, characterized in that, The process of acquiring geophysical and exploration data of the rock slope to be inspected, and constructing a three-dimensional geological model based on the geophysical and exploration data to obtain the three-dimensional model of the rock slope to be inspected, specifically includes the following steps: Monitoring points are evenly set up on the rock slope to be tested, and monitoring equipment is installed at the monitoring points. Geophysical data of the rock slope to be tested is acquired at preset first time intervals. The geophysical data includes underground resistivity distribution data, surface wave dispersion curve data, and artificial seismic wave reflection signal data of the slope area. Exploration and monitoring are carried out at the monitoring points to obtain exploration data, which includes borehole core data, topographic mapping data and hydrogeological data. Obtain oblique photographs of the rock slope to be inspected by drone and generate a digital surface model; Based on geophysical and exploration data, key geological elements are extracted, including landslide boundaries, spatial morphology of sliding zones, distribution of weak interlayers, and groundwater occurrence areas. Based on the digital surface model, a three-dimensional geological model is constructed by combining geophysical and exploration data in the same three-dimensional coordinate system to obtain the three-dimensional model of the rock slope to be tested.

3. The method according to claim 2, characterized in that, The process of constructing a three-dimensional geological model based on a digital surface model, combining geophysical and exploration data in the same three-dimensional coordinate system, to obtain a three-dimensional model of the rock slope to be inspected, specifically includes the following steps: The geophysical data is inverted to obtain inverted data, which includes resistivity profile and shear wave velocity layer data. Spatial registration was performed on geophysical data, inversion data, and borehole core data to unify the coordinate system of data from various monitoring points. The registered resistivity profile, shear wave velocity layered data, and borehole core data are converted into regular grids according to the coordinates of the monitoring points. Each grid node on the regular grid is assigned a value to retrieve the data, and the spacing between each grid node in the regular grid is the same. The appropriate grid type is selected based on the complexity of the geological interface, and layers are formed according to different depths. A corresponding resolution strategy is preset to construct a three-dimensional voxel model, thereby obtaining a three-dimensional model of the rock slope to be inspected.

4. The method according to claim 3, characterized in that, The process involves selecting appropriate mesh types based on the complexity of the geological interface, layering according to different depths, pre-setting corresponding resolution strategies, and constructing a three-dimensional voxel model to obtain a three-dimensional model of the rock slope to be inspected. This process specifically includes the following steps: The appropriate mesh type is selected based on the complexity of the geological interface. For modeling layered geological bodies, a hexahedral mesh is selected, while for modeling complex geological interfaces, a tetrahedral mesh is selected. Standard mesh nodes are converted into tetrahedral or hexahedral elements. The layers are divided according to depth. The shallow layer uses fine modeling, while the deep layer uses coarse modeling. The three-dimensional voxel models are constructed between the shallow and deep layers according to the corresponding preset resolution strategy to obtain the three-dimensional voxel models. Geological data is labeled and rendered in a 3D voxel model to obtain a 3D model of the rock slope to be detected, including geological unit rendering, digital surface model overlay processing, and configuration of geophysical and exploration data.

5. The method according to claim 1, characterized in that, The stability assessment based on the three-dimensional model of the rock slope to be tested, to obtain stability indices and failure characteristics, specifically includes the following steps: Based on the three-dimensional model of the rock slope to be detected, the boundary features, layer interface features, rock mass zoning features, and groundwater features of the rock slope are determined, and the data of the rock slope are obtained. Potential sliding surfaces are divided at equal intervals on the three-dimensional model of the rock slope to be tested. Preset extreme environmental factors are added to calculate the safety factor, and the safety factor of each slope surface under extreme environment is obtained. Based on the safety factor of each slope, the most dangerous slip surface is searched to obtain the location and volume of the dangerous slip surface; Calculation data is extracted from the three-dimensional model of the rock slope to be tested. The failure probability value is calculated based on the Monte Carlo simulation method to obtain the slope instability probability within a predetermined time.

6. The method according to claim 5, characterized in that, The process of dividing potential slip surfaces at equal intervals on a three-dimensional model, adding preset extreme environmental factors to calculate safety factors, and obtaining the safety factor of each slope surface under extreme conditions specifically includes the following steps: By combining the geological data configured in the three-dimensional model of the rock slope to be tested, potential sliding surfaces are identified and the interfaces of the sliding surfaces are defined in the three-dimensional model of the rock slope to be tested, thus obtaining the interface of the potential sliding zone. The potential sliding surface is uniformly divided into multiple strips along the direction of the sliding zone, and the corresponding geometric and mechanical parameters are matched to obtain the strips and their parameter characteristics; Add preset extreme environmental factors, and calculate the safety factor of the blocks by combining the parameter characteristics of the blocks, so as to obtain the safety factor of each slope under extreme environment.

7. The method according to claim 1, characterized in that, This includes: Early warning signals are assigned based on the results of rock stability assessments, and emergency measures are implemented accordingly.

8. A stability assessment system for rock slopes based on three-dimensional modeling, characterized in that, The method for assessing the stability of rock slopes based on three-dimensional modeling, as described in any one of claims 1-7 above.

9. A computer device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

Images