A rock mass stability analysis method and system based on DFN-VR

Abstract

The present application relates to rock mass structure and mechanical analysis technical field, disclose a kind of rock mass stability analysis method and system based on DFN-VR, its method includes: obtaining the multi-source structure surface data of target rock mass region;Probability modeling is carried out to the multi-source structure surface data after pre-processing, obtain the probability distribution parameter of each structure surface, generate the discrete fracture network (DFN) of target rock mass region based on probability distribution parameter;Discrete fracture network is imported into virtual reality platform (VR) to carry out interactive check and optimization to fissure element and determine model boundary;Based on the discrete fracture network after optimization, generate rock block and build discrete element calculation network, set rock block and fissure physical and mechanical parameters;Engineering working condition is applied in discrete element solver to carry out numerical calculation, output stability analysis result.The present application improves the intuitiveness of discrete fracture network, and the degree of convenience and response speed of rock mass stability analysis.

Description

Technical Field

[0001] This invention relates to the field of rock mass structure and mechanical analysis technology, and more specifically, to a rock mass stability analysis method and system based on DFN-VR. Background Technology

[0002] Stability analysis of rock mass engineering (such as tunnels, slopes, and underground chambers) is a core topic in geotechnical engineering. Natural rock masses typically contain numerous structural planes (such as joints, faults, and fissures), which significantly control the mechanical behavior and failure modes of the rock mass. Traditional rock mass stability analysis methods are mostly based on the assumption of continuous media or simplified discrete block models, making it difficult to accurately reflect the influence of complex fracture networks on the mechanical response of rock masses.

[0003] In recent years, Discrete Fracture Network (DFN) modeling technology has gradually become an important tool for rock mass structure modeling due to its ability to explicitly characterize the spatial distribution, geometric morphology, and connectivity of fractures in rock masses. However, existing DFN modeling methods mainly rely on the random generation of statistical parameters, lacking effective integration of multi-source field measurement data, and the generated discrete fracture networks are often difficult to analyze and correct intuitively. Furthermore, the conversion process from discrete fracture networks to mechanical computational models (such as discrete element models) is complex, has low automation, and is difficult to achieve a closed-loop process of "modeling-interaction-computation-feedback" in practical engineering applications, especially failing to support rapid stability assessment during construction.

[0004] The development of Virtual Reality (VR) technology has provided a new approach for immersive interaction with 3D geological models. Combining VR with DFN (Digital Element Method) modeling holds promise for enabling experts to intuitively perceive and efficiently intervene in complex fracture systems. However, a technical solution that organically integrates DFN modeling driven by multi-source structural surface data, VR immersive interactive optimization, discrete element method (DEM) mechanics calculations, and rapid stability analysis is currently lacking. Summary of the Invention

[0005] In view of this, the present invention proposes a rock mass stability analysis method and system based on DFN-VR, aiming to solve the problem that traditional rock mass analysis relies on only a single data method and has low efficiency in analyzing complex fractures.

[0006] On the one hand, the present invention provides a rock mass stability analysis method based on DFN-VR, the method comprising:

[0007] The target rock mass area is monitored to obtain multi-source structural surface data of the target rock mass area, and the multi-source structural surface data is preprocessed.

[0008] The probability distribution parameters of each structural surface are obtained by probabilistic modeling of the preprocessed multi-source structural surface data, and the spatial location, attitude and scale characterization parameters of the fracture are randomly sampled based on the probability distribution parameters to generate a discrete fracture network.

[0009] The discrete fracture network is imported into a virtual reality platform to construct an immersive three-dimensional interactive environment. In this immersive three-dimensional interactive environment, the fracture elements are interactively checked and edited, and the model boundary for stability analysis is determined to obtain the optimized discrete fracture network.

[0010] Based on the optimized discrete fracture network, the block generation algorithm is used to obtain the rock blocks formed by fracture cutting in the target rock mass area. The optimized discrete fracture network is converted into a discrete element computation network, and physical and mechanical parameters are set for the rock blocks and fractures.

[0011] Based on the geometric and fracture mechanics parameters of the discrete fracture network, numerical calculations are performed by applying engineering working conditions in the discrete element solver to analyze the stability or unstable block composition of the engineering rock mass.

[0012] Preferably, multi-source structural surface data of the target rock mass region is acquired, and the multi-source structural surface data is preprocessed, including: the multi-source structural surface data includes the spatial location, attitude, and scale characterization parameters of the structural surfaces of the target rock mass region;

[0013] The multi-source structural surface data is preprocessed to establish multi-source structural surface data under a unified coordinate system. The preprocessing includes denoising, registration and fusion.

[0014] Preferably, probabilistic modeling is performed on the preprocessed multi-source structural surface data to obtain the probability distribution parameters of each structural surface, including:

[0015] Fisher distribution method is used to probabilistically model attitude, and the principal direction unit vector and lumped parameters are obtained;

[0016] The power-law distribution method is used to probabilistically model the trace length, and the minimum trace length and fractal dimension are obtained.

[0017] The spatial location and spacing of the structural surfaces are probabilistically modeled using uniform or non-uniform Poisson analysis methods to obtain the volume density.

[0018] Cluster analysis was used to probabilistically model the structural surface groups and obtain the weights of each group.

[0019] Preferably, the spatial location, attitude, and scale characterization parameters of the fractures are randomly sampled based on the probability distribution parameters to generate a discrete fracture network, including:

[0020] Define a regular geometry that encloses the target rock mass region;

[0021] Within the regular geometric body, the center position of the fracture is randomly sampled, and the fracture normal vector is randomly determined according to the probability distribution parameters of the orientation. At the same time, the fracture scale is generated according to the scale characterization parameters, and fracture geometric units are constructed. The fracture geometric units are generated repeatedly until the P32 of the discrete fracture network reaches the target density, thereby obtaining the discrete fracture network.

[0022] Preferably, the discrete fracture network is imported into a virtual reality platform to construct an immersive three-dimensional interactive environment, including:

[0023] The data format of the discrete fracture network is converted into a three-dimensional graphic intermediate format, and the fractures in the discrete fracture network are optimized.

[0024] By selecting a virtual reality platform, loading the transformed discrete fracture network into the virtual reality platform, and performing rendering and interaction design, an immersive three-dimensional interactive environment is obtained.

[0025] Preferably, the fracture elements are interactively verified and edited in the immersive 3D interactive environment, and the model boundary for stability analysis is determined to obtain an optimized discrete fracture network, including:

[0026] Based on multi-source data and interactive verification results, the discrete fracture network is selected, moved, rotated, added, deleted, and its attributes modified in an immersive 3D interactive environment using VR devices. The key locations of the model are analyzed and the model boundary for stability analysis is delineated, resulting in an optimized discrete fracture network.

[0027] Preferably, based on the optimized discrete fracture network, a block generation algorithm is used to obtain rock blocks formed by fracture cutting within the target rock mass region, including:

[0028] Represent each fracture in the discrete fracture network as a bounded planar polygon, and calculate the spatial intersection lines between all fractures:

[0029] Construct a spatial topology graph of the fracture intersection lines by using the fracture intersection points as nodes and the spatial intersection lines as edges.

[0030] A closed region search algorithm based on surface tracing is adopted. Starting from the boundary of the computational domain, the search is carried out along the fracture surface to trace the closed polyhedron enclosed by multiple fracture surfaces and obtain the boundary of the rock block.

[0031] Preferably, the optimized discrete fracture network is converted into a discrete element computation network, and physical and mechanical parameters are set for the rock block and fracture, including:

[0032] The block generation algorithm uses a cut-fill method;

[0033] The computational domain of the discrete fracture network is set as the initial complete block. The network is then divided according to the fractures in the discrete fracture network until all fractures are divided, thus obtaining the discrete element computational network.

[0034] When assigning values ​​to the physical and mechanical parameters of the rock block, the physical and mechanical parameters of the rock block include the elastic modulus, Poisson's ratio, rock block density, and rock block strength parameters, wherein the rock block strength parameters include tensile strength, cohesion, and internal friction angle;

[0035] When assigning values ​​to the physical and mechanical parameters of a crack, the physical and mechanical parameters of the crack include normal / tangential stiffness, friction coefficient, and cohesion parameter.

[0036] Preferably, based on the geometric and fracture mechanics parameters of the discrete fracture network, numerical calculations are performed in a discrete element solver using applied engineering conditions to analyze the stability or unstable block composition of the engineering rock mass, including:

[0037] The physical and mechanical parameters of the rock block and fractures are input into the discrete element solver, and the engineering working conditions are also input into the discrete element solver. The engineering working conditions include excavation unloading, support loading and seismic load.

[0038] The discrete element solver performs rapid calculations to obtain key mechanical response data, which includes: displacement field, stress field, contact surface state evolution, and post-instability accumulation morphology.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention performs noise reduction, registration and fusion preprocessing on multi-source structural surface data (including spatial location, attitude, trace length, scale characterization parameters, etc.) of the target rock mass region under a unified coordinate system, and combines statistical methods such as Fisher distribution, power law distribution, Poisson process and cluster analysis to probabilistically model the attributes of each structural surface, so that the generated discrete fracture network not only conforms to the statistical law of field observation, but also reflects the heterogeneity and phylogenetic characteristics of the rock mass structure.

[0040] By importing discrete fracture networks into a virtual reality platform and constructing an immersive 3D interactive environment, users can select, move, rotate, add, delete, and modify the attributes of fractures using VR devices. This allows for analysis of key model locations and delineation of model boundaries for stability analysis. This approach significantly improves the intuitiveness and efficiency of model correction, enabling discrete fracture networks to reflect statistical data patterns while incorporating the experience and judgment of geological experts, thus significantly enhancing the model's applicability in engineering.

[0041] Based on the optimized discrete fracture network, the rock block boundaries formed by fracture cutting are accurately identified through fracture intersection line calculation, spatial topology map construction of fracture intersection line and closed region search algorithm. Then, the discrete element calculation network is automatically generated by the cut-fill method, and the physical and mechanical parameters of rock blocks and fractures (such as elastic modulus, friction angle, cohesion, etc.) are systematically assigned, laying the foundation for subsequent high-fidelity mechanical simulation.

[0042] The assigned discrete element model, along with construction conditions (such as excavation unloading, support loading, and seismic load), is input into the discrete element solver to achieve rapid mechanical calculations. The output includes displacement field, stress field, contact surface state evolution, and post-instability accumulation morphology, thereby providing dynamic, visual, and quantitative stability analysis results for engineering decision-making and significantly improving the safety and intelligence level of rock mass engineering.

[0043] On the other hand, the present invention also discloses a rock mass stability analysis system based on DFN-VR, used to apply the above-mentioned rock mass stability analysis method based on DFN-VR, the system comprising:

[0044] The data acquisition module is configured to monitor the target rock mass area, acquire multi-source structural surface data of the target rock mass area, and preprocess the multi-source structural surface data.

[0045] The DFN generation module is configured to perform probabilistic modeling on the preprocessed multi-source structural surface data to obtain the probability distribution parameters of each structural surface, and to randomly sample the spatial location, attitude, and scale characterization parameters of the fractures based on the probability distribution parameters to generate a discrete fracture network.

[0046] The DFN-VR optimization module is configured to import the discrete fracture network into a virtual reality platform, construct an immersive three-dimensional interactive environment, perform interactive verification and editing of fracture elements in the immersive three-dimensional interactive environment, and determine the model boundary for stability analysis in order to obtain the optimized discrete fracture network.

[0047] The DFN conversion module is configured to use the block generation algorithm to obtain rock blocks formed by fracture cutting within the target rock mass area based on the optimized discrete fracture network, convert the optimized discrete fracture network into a discrete element computation network, and set physical and mechanical parameters for the rock blocks and fractures.

[0048] The stability analysis module is configured to apply engineering conditions to the discrete element solver based on the geometric and fracture mechanics parameters of the discrete fracture network, and perform numerical calculations to analyze the stability of the engineering rock mass or the composition of unstable blocks.

[0049] It is understood that the rock mass stability analysis method and system based on DFN-VR provided in this application have the same beneficial effects, and will not be elaborated here. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart of a rock mass stability analysis method based on DFN-VR provided in an embodiment of the present invention;

[0052] Figure 2 A functional block diagram of a rock mass stability analysis system based on DFN-VR provided in an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] In some embodiments of this application, see Figure 1 As shown in the figure, this embodiment provides a rock mass stability analysis method based on DFN-VR, the method including:

[0056] S1, monitor the target rock mass area, acquire multi-source structural surface data of the target rock mass area, and preprocess the multi-source structural surface data.

[0057] In some embodiments of this application, multi-source structural surface data of the target rock mass region is acquired, and the multi-source structural surface data is preprocessed, including: the multi-source structural surface data includes the spatial location, attitude, and scale characterization parameters of the structural surfaces of the target rock mass region; the multi-source structural surface data is preprocessed to establish multi-source structural surface data under a unified coordinate system, and the preprocessing includes denoising, registration, and fusion.

[0058] Specifically, structural surface data of the target rock mass area can be obtained through multiple sources, including field geological surveys, borehole sampling, UAV oblique photography, LiDAR point cloud scanning, and high-resolution remote sensing image recognition. The structural surface data includes at least the spatial location, attitude parameters, and scale characterization parameters of the structural surfaces. The raw data is then denoised, registered, and fused to establish a structural surface database in a unified coordinate system.

[0059] Specifically, the preprocessing process is as follows:

[0060] Noise reduction processing removes invalid or abnormal data introduced by sensor errors, environmental interference, or human factors. For point cloud data, outlier removal methods such as statistical outlier removal, radius filtering, or density-based filtering can be used, with a preset threshold as the criterion. For image recognition results, quality control can be performed on automatically identified structural surface traces, removing traces with lengths below a preset length threshold or with significantly abnormal directional distributions. Morphological filtering can also be used to eliminate burrs and breaks in image recognition. For data verification of borehole sampling, logical errors such as exceeding dip angle or dip range limits in the structural surface attitude record can be checked, and outliers that are repeatedly entered or significantly deviate from the regional structural trend can be removed. Outliers can be detected using statistical methods such as box plots or Z-scores.

[0061] Registration processing aligns data from different devices, time periods, and coordinate systems to a unified spatial reference frame. Specifically, this includes: establishing a unified coordinate system (such as WGS-84+UTM projection or a local independent coordinate system) using the engineering area control network or existing measurement benchmarks as a reference, and unifying the elevation benchmark; spatially aligning LiDAR point clouds with UAV 3D reconstruction models and imagery results, achieving high-precision registration of point clouds and images through ICP algorithms or feature point matching (such as SIFT / SURF); projecting the locations of all borehole-exposed structural surfaces to a unified coordinate system, ensuring that borehole structural surface observations and surface structural surface results are within the same spatial reference frame; orthorectifying high-resolution remote sensing images using ground control points (GCPs) to ensure pixel positions are consistent with the actual situation; and when multiple data periods are involved, considering the effects of surface deformation or weathering, performing temporal correction on multi-temporal data to reduce geometric deviations caused by time differences.

[0062] The fusion process integrates multi-source structural surface information to generate a complete, complementary, and non-redundant set of structural surface parameters. Specifically: structural surface element extraction and standardization; extracting five structural surface elements from various data sources: location, dip, dip angle, trace length, and spacing; establishing hierarchical coding standards (such as the JRC level recommended by ISRM) for qualitative parameters such as roughness and infill material. Multi-source information complementarity fusion, overall + local fusion: remote sensing / LiDAR provides large-scale fracture distribution, while boreholes / outcrops provide local fine parameters, complementing each other; geometric + attribute fusion: combining image-identified fracture orientation with borehole-measured dip angles to reconstruct a complete three-dimensional attitude; confidence-weighted fusion: for the same structural surface identified by different methods, weights can be assigned according to the accuracy of the data source (e.g., borehole > LiDAR > remote sensing), and weighted averaging or Bayesian fusion is used to determine the final parameters. Construct a structural surface database, storing structural surface records using a relational database or spatial data engine. Each record includes fields such as ID, spatial geometry, attitude, scale parameters, data source, and confidence label to support subsequent statistical analysis and DFN probabilistic modeling input.

[0063] S2, perform probabilistic modeling on the preprocessed multi-source structural surface data to obtain the probability distribution parameters of each structural surface, and randomly sample the spatial location, attitude, and scale characterization parameters of the fractures based on the probability distribution parameters to generate a discrete fracture network.

[0064] In some embodiments of this application, probabilistic modeling is performed on the preprocessed multi-source structural surface data to obtain the probability distribution parameters of each structural surface, including: using the Fisher distribution method to probabilistically model the attitude to obtain the principal direction unit vector and lumped parameters; using the power-law distribution method to probabilistically model the trace length to obtain the minimum trace length and fractal dimension; using uniform or non-uniform Poisson analysis to probabilistically model the spatial location and spacing of the structural surfaces to obtain the volume density; and using cluster analysis to probabilistically model the structural surface groups to obtain the weights of each group.

[0065] Specifically, the Fisher distribution method is used to probabilistically model the attitude, obtaining the principal direction unit vector and lumped parameters. The Fisher distribution is a normal distribution on a sphere, suitable for describing the structural surface normal vectors concentrated around a principal direction. The probability density function is expressed as:

[0066] ;

[0067] Where X is the unit direction vector, μ is the principal direction unit vector, and κ is the lumped parameter (the larger κ is, the more concentrated the distribution). The principal direction μ is the normalized sample mean vector; κ is solved by maximum likelihood estimation (MLE) or an approximate formula (such as the Mardia & Jupp method).

[0068] The power-law distribution method is used to probabilistically model the trace length, yielding the minimum trace length and fractal dimension, specifically:

[0069] The expression for the power-law distribution is:

[0070] ;

[0071] in, L For the length of the trace, l min The minimum identifiable length is denoted by D, which is the fractal dimension (typically 1.5–2.5). The goodness of fit is verified using maximum likelihood estimation (MLE) and the Kolmogorov-Smirnov test. If the data exhibits an exponential decay trend, an exponential distribution is used. .

[0072] Uniform or non-uniform Poisson analysis methods are used to probabilistically model the spatial location and spacing of structural surfaces, obtaining fracture strength characterization parameters. Specifically, the total fracture area per unit volume, P32, and the number of fracture traces per unit area, P20, or the total length of fracture traces per unit area, P21, are defined. Based on borehole or outcrop measurements, the average linear density (e.g., traces / m) of each group is calculated and converted into three-dimensional fracture strength parameters by combining sampling bias correction (e.g., Terzaghi correction).

[0073] Cluster analysis is used to probabilistically model the structural surface groups and obtain the weight of each group. Specifically, K-means clustering or Fisher spherical clustering is used to group the structural surface groups to obtain the number of groups and the weight of each group (i.e., the proportion of the group to the total fractures).

[0074] In some embodiments of this application, the spatial location, attitude, and scale characterization parameters of the fractures are randomly sampled based on the probability distribution parameters to generate a discrete fracture network. This includes: defining a regular geometry that surrounds the target rock mass region; randomly sampling the fracture center position within the regular geometry, and randomly determining the fracture normal vector based on the probability distribution parameters of the attitude; simultaneously generating the fracture scale and constructing fracture geometric units based on the scale characterization parameters; and repeatedly generating the fracture geometric units until the P32 of the discrete fracture network reaches the target density, thereby obtaining the discrete fracture network.

[0075] Specifically, the process of generating the discrete fracture network of the target rock mass region is as follows: Define a regular geometric shape (such as a cube or cylinder) surrounding the target rock mass region, denoted as Ω. For the i-th set of structural surfaces, perform the following loop until the target density is reached: (a) Randomly generate the center point position, and uniformly randomly sample a point c=(x,y,z) within Ω. (b) Randomly generate the normal vector (direction) using the Fisher distribution sampling algorithm (such as Wood's method): generate random variables. z represents a random variable, and u1~U(0,1) represents a random variable; U(0,1) represents a standard continuous uniform distribution defined on the interval [0,1], and u1∼U(0,1) means that u1 is a real number randomly selected with equal probability between 0 and 1; generate azimuth angle ~U(0,2π), Indicates azimuth. ~U(0,2π) represents the azimuth angle. The vectors are real numbers randomly selected with equal probability between 0 and 2π; a vector is constructed in the local coordinate system. Rotate it to a global coordinate system with μi as the principal axis to obtain the final normal vector n. (c) Randomly generate the trace length (radius). Where l represents the trace length of the fracture. Although L in the above power-law distribution expression is also a trace length, L is a random variable (overall concept), while l in this formula is a specific numerical value (used for calculation or sampling). u2~U(0,1) indicates that u2 is a real number randomly selected with equal probability between 0 and 1. (d) Construct fracture geometry, representing the fracture as a circular disk with c as the center, n as the normal vector, and r=l / 2 as the radius. (e) Density control: quickly calculate the current P32 value, and stop generating when P32 reaches or exceeds the target density to avoid excessive density. If the fracture part is located outside Ω, retain its intersection with Ω (i.e., the actual visible part) to ensure that the model only contains fractures within the effective region. Output: An initial discrete fracture network consisting of N spatial fracture disks (including position, normal, and scale parameters).

[0076] S3. Import the discrete fracture network into a virtual reality platform to construct an immersive three-dimensional interactive environment. In the immersive three-dimensional interactive environment, the fracture elements are interactively checked and edited, and the model boundary for stability analysis is determined to obtain an optimized discrete fracture network. For example, based on preset scale thresholds and redundancy criteria, discrete fractures are merged or removed, and fracture attributes are modified.

[0077] In some embodiments of this application, importing the discrete fracture network into a virtual reality platform to construct an immersive three-dimensional interactive environment includes: converting the data format of the discrete fracture network into a three-dimensional graphics intermediate format and optimizing the fractures in the discrete fracture network; selecting a virtual reality platform, loading the converted discrete fracture network into the virtual reality platform, and performing rendering and interaction design to obtain an immersive three-dimensional interactive environment.

[0078] In some embodiments of this application, the crack elements are interactively verified and edited in the immersive three-dimensional interactive environment, and the model boundary for stability analysis is determined to obtain an optimized discrete crack network. This includes: selecting, moving, rotating, adding, deleting, and modifying the attributes of cracks in the discrete crack network in the immersive three-dimensional interactive environment using a VR device based on multi-source data and interactive verification results; merging or deleting some small or highly overlapping discrete cracks according to preset scale thresholds and redundancy criteria; modifying crack attributes; analyzing the key positions of the model and delineating the model boundary for stability analysis to obtain an optimized discrete crack network.

[0079] Specifically, the construction of an immersive 3D interactive environment involves the following steps: The original discrete fracture network is typically stored as text (e.g., CSV, TXT) or structured data files (e.g., HDF5). Each record contains center point coordinates, normal vectors, scale parameters (e.g., radius, or major / minor axis), and group ID. The discrete fracture network is then converted into a 3D graphics intermediate format that can be loaded by a virtual reality platform, such as OBJ / PLY, GLTF / GLB, or a custom binary format. Each fracture is typically modeled as a circular or elliptical polygon with normals (composed of triangular facets). To improve rendering efficiency, instantiation rendering is employed: for example, sharing the same mesh template and using transformation matrices (translation, rotation, scaling) to achieve batch instantiation. The virtual reality platform supports six degrees of freedom (6DoF) pose tracking and acquires the user's head and hand 6DoF pose in real time through the VR head-mounted display device (HMD) and the controller's inside-out or outside-in tracking system; it enables first-person free locomotion or teleportation to reduce the risk of motion sickness, and performs 3D visualization rendering and interactive control design.

[0080] S4, based on the optimized discrete fracture network, uses a block generation algorithm to obtain rock blocks formed by fracture cutting within the target rock mass area, converts the optimized discrete fracture network into a discrete element computation network, and sets physical and mechanical parameters for the rock blocks and fractures.

[0081] In some embodiments of this application, based on an optimized discrete fracture network, a block generation algorithm is used to obtain rock blocks formed by fracture cutting within a target rock mass region. This includes: representing each fracture in the discrete fracture network as a planar polygon with boundaries, calculating the spatial intersection lines between all fractures; constructing a spatial topology graph of fracture intersection lines by using fracture intersection points as nodes and spatial intersection lines as edges; and using a closed region search algorithm based on surface tracing to search along the fracture surfaces starting from the boundary of the computational domain, tracing the closed polyhedron enclosed by multiple fracture surfaces to obtain the rock block boundary.

[0082] Specifically, each fracture in the DFN is represented as a bounded planar polygon (usually a triangulated mesh). Spatial intersections between all fractures are calculated: for any two fractures, if their planes intersect and the intersection line passes through each other's polygonal regions, a corresponding intersection segment is generated. A spatial index structure (such as Octree or R-tree) is used to accelerate the filtering of fracture pairs, avoiding global traversal. Fracture intersection points are used as nodes, and intersection segments as edges to construct a spatial topology graph of fracture intersections. A closed region search algorithm based on surface tracing is employed: starting from the boundary of the computational domain, tracing along the fracture surface to identify closed polyhedra enclosed by multiple fracture surfaces and obtain the rock block boundaries; or a region growing method is used: seed points are placed in the space not occupied by fractures, and independent rock blocks are identified through connectivity clustering. A set of three-dimensional polyhedral rock blocks is output, each enclosed by several planar patches.

[0083] Suppose we have a simple discrete fracture network containing three main fractures, A, B, and C. These three fractures intersect in three-dimensional space, forming several intersection points and lines. Following the steps above: First, depict fractures A, B, and C as planar polygons with boundaries. Then, calculate all possible fracture intersection lines (e.g., the intersection between A and B, A and C, and B and C), and determine the intersection points based on these lines. Next, using these intersection points as nodes and the intersection lines as edges, construct a spatial topological graph of the fracture intersection lines. Finally, apply a closed region search algorithm, starting from the model's boundaries and gradually exploring until one or more closed regions enclosed by fractures A, B, and C are found. These closed regions represent different rock blocks.

[0084] In some embodiments of this application, the optimized discrete fracture network is converted into a discrete element computation network, and physical and mechanical parameters are set for the rock blocks and fractures, including: the block generation algorithm adopts the cut-fill method; the computational domain of the discrete fracture network is set as an initial complete block, and the network is divided according to the fractures in the discrete fracture network until all fractures are divided to obtain the discrete element computation network; when assigning values ​​to the physical and mechanical parameters of the rock blocks, the physical and mechanical parameters of the rock blocks include elastic modulus, Poisson's ratio, rock block density, and rock block strength parameters, wherein the rock block strength parameters include tensile strength, cohesion, and internal friction angle; when assigning values ​​to the physical and mechanical parameters of the fractures, the physical and mechanical parameters of the fractures include normal / tangential stiffness, friction coefficient, and cohesion parameters.

[0085] Specifically, using a block generation algorithm, the rock mass is divided into a discrete element computational mesh: First, the entire computational domain is initialized as a complete block; then, based on the fracture surfaces (boundary patches) in the DFN, an iterative cutting operation is performed on the block, dividing the fracture-cut block into multiple sub-blocks, and recording the newly generated contact surfaces. The iterative cutting can be implemented using spatial partitioning or Boolean operation methods, such as binary spatial partitioning (BSP) or constructed solid geometry (CSG). This process is repeated until the target fracture is processed. For fractures that do not penetrate the entire rock mass, only the local blocks within their influence range are cut to form internal fracture surfaces. This results in a discrete element computational network composed of polyhedral blocks and their contact surfaces, which can be directly imported into a discrete element solver for computation (such as 3DEC).

[0086] When setting physical and mechanical parameters for rock blocks and fractures, corresponding material mechanical properties can be assigned to each mechanical unit (rock block, fracture, contact surface) in the discrete element model, making it physically computable. When assigning rock block properties, if the rock block is of a single lithology (such as granite), each rock block can use the same set of material parameters, such as elastic modulus, Poisson's ratio, rock block density, and strength parameters. If it is a multi-lithological geological condition, the properties can be assigned according to the region in combination with the geological model. When assigning fracture properties, it can be based on the structural surface group mapping rules: each group of structural surfaces has an identification in the DFN (such as "Group 1: NE-trending joint"); a set of mechanical parameters (from laboratory tests, empirical formulas, or inversion) is preset for each group, including the normal / tangential stiffness, friction coefficient, and cohesion parameters of the fracture; the system automatically retrieves the corresponding values ​​from the parameter library and assigns them to the corresponding contact surface according to the "group ID" of each fracture in the DFN.

[0087] S5, based on the geometric and fracture mechanics parameters of the discrete fracture network, applies engineering working conditions to the discrete element solver for numerical calculation, and analyzes the stability or unstable block composition of the engineering rock mass.

[0088] In some embodiments of this application, based on the geometric and fracture mechanics parameters of the discrete fracture network, engineering working conditions are applied in the discrete element solver for numerical calculation to analyze the stability or unstable block composition of the engineering rock mass. This includes: inputting the physical and mechanical parameters of the rock block and fractures into the discrete element solver, and inputting the engineering working conditions into the discrete element solver. The engineering working conditions include excavation unloading, support loading, and seismic load. The discrete element solver performs rapid calculations to obtain key mechanical response data, which includes: displacement field, stress field, contact surface state evolution, and post-instability accumulation morphology.

[0089] Specifically, when applying construction conditions in the discrete element solver, the following methods can be used: excavation unloading can be simulated by deleting blocks in a specified area to simulate blasting or mechanical excavation; or by gradually releasing boundary stress to simulate step-by-step excavation, and can be combined with initial ground stress balance and boundary load release processing; support loading can be achieved by adding structural elements, including but not limited to anchor bolts, shotcrete layers and steel arches, where anchor bolts can be simulated using cable elements and prestressed and bond strength can be set, and lining can be fitted to the tunnel wall using shell or beam elements and given stiffness and strength; seismic loads can be applied to the bottom boundary of the model by applying acceleration time history records and using viscous damped boundaries to achieve equivalent infinite domain radiation damping, thereby reducing the influence of reflected waves. To improve computational efficiency, a discrete element solver that supports high-speed parallel computing (such as the GPU-accelerated version of 3DEC 9.0+) can be used, along with explicit dynamic integration and time step control based on numerical stability conditions. Meanwhile, by defining the boundaries between critical analysis regions and computational domains to reduce irrelevant regions, non-critical regions can be simplified by applying preset thresholds (e.g., merging small blocks), and adaptive time step or event-driven output strategies can be employed. Furthermore, shared memory or inter-process communication (IPC) mechanisms can be used to decouple the computation process from the VR visualization process to reduce interaction latency.

[0090] Specifically, this step imports the previously constructed discrete element method (DEM) computational network (including the geometry of rock blocks and the topological relationship of fractures) and its pre-set physical and mechanical parameters (such as the elastic modulus, density, and tensile strength of rock blocks, and the normal / tangential stiffness, friction angle, and cohesion of fractures) into the DEM solver, and applies engineering working conditions for numerical calculation. These working conditions include, but are not limited to, excavation unloading, application of support structures, blasting vibration, seismic loads, and changes in groundwater pressure. Based on Newton's laws of motion and contact constitutive relations, the DEM solver dynamically simulates the relative motion between rock blocks and the opening, sliding, and closing behavior of fractures, and outputs key mechanical response data. This mechanical response data includes at least the overall and local displacement field, stress field, and contact surface state evolution information of the rock mass, and may further include velocity field, energy dissipation, and post-instability accumulation morphology. Finally, based on the mechanical response data, stability evaluation indicators, possible instability forms, and their impact ranges are obtained, and the risk areas are visualized.

[0091] See Figure 2 As shown, this invention also discloses a rock mass stability analysis system based on DFN-VR, used to apply the above-mentioned rock mass stability analysis method based on DFN-VR. The system includes:

[0092] The data acquisition module is configured to monitor the target rock mass area, acquire multi-source structural surface data of the target rock mass area, and preprocess the multi-source structural surface data.

[0093] The DFN generation module is configured to perform probabilistic modeling on the preprocessed multi-source structural surface data to obtain the probability distribution parameters of each structural surface, and to randomly sample the spatial location, attitude, and scale characterization parameters of the fractures based on the probability distribution parameters to generate a discrete fracture network.

[0094] The DFN-VR optimization module is configured to import the discrete fracture network into a virtual reality platform, construct an immersive three-dimensional interactive environment, interactively verify and edit fracture elements in the immersive three-dimensional interactive environment, and determine the model boundary for stability analysis to obtain the optimized discrete fracture network.

[0095] The DFN conversion module is configured to use the block generation algorithm to obtain rock blocks formed by fracture cutting within the target rock mass area based on the optimized discrete fracture network, convert the optimized discrete fracture network into a discrete element computation network, and set physical and mechanical parameters for the rock blocks and fractures.

[0096] The stability analysis module is configured to apply engineering conditions to the discrete element solver based on the geometric and fracture mechanics parameters of the discrete fracture network, and perform numerical calculations to analyze the stability of the engineering rock mass or the composition of unstable blocks.

[0097] This invention generates a discrete fracture network through multi-source data fusion and probabilistic statistical modeling. This network can be used to characterize the distribution characteristics of natural fractures in a target rock mass, providing a geometric model foundation for subsequent stability analysis. The discrete fracture network is then imported into a virtual reality platform to construct an immersive 3D interactive environment. Engineers can verify and edit structural surface elements within this interactive environment, thereby improving model consistency and reducing manual verification workload. Furthermore, the optimized discrete fracture network is transformed into a discrete element computation network, and numerical calculations are performed under construction conditions to obtain mechanical responses such as displacement, stress, and contact surface state evolution. Based on this, stability analysis results and a visual representation of risk areas are generated, providing analytical basis for engineering design and construction.

[0098] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A rock mass stability analysis method based on DFN-VR, characterized in that, The method includes: The target rock mass area is monitored to obtain multi-source structural surface data of the target rock mass area, and the multi-source structural surface data is preprocessed. The probability distribution parameters of each structural surface are obtained by probabilistic modeling of the preprocessed multi-source structural surface data, and the spatial location, attitude and scale characterization parameters of the fracture are randomly sampled based on the probability distribution parameters to generate a discrete fracture network. The discrete fracture network is imported into a virtual reality platform to construct an immersive three-dimensional interactive environment. In this immersive three-dimensional interactive environment, the fracture elements are interactively checked and edited, and the model boundary for stability analysis is determined to obtain the optimized discrete fracture network. Based on the optimized discrete fracture network, the block generation algorithm is used to obtain the rock blocks formed by fracture cutting in the target rock mass area. The optimized discrete fracture network is converted into a discrete element computation network, and physical and mechanical parameters are set for the rock blocks and fractures. Based on the geometric and fracture mechanics parameters of the discrete fracture network, numerical calculations are performed by applying engineering working conditions in the discrete element solver to analyze the stability or unstable block composition of the engineering rock mass. The preprocessed multi-source structural surface data is probabilistically modeled to obtain the probability distribution parameters of each structural surface, including: using the Fisher distribution method to probabilistically model the attitude, obtaining the principal direction unit vector and lumped parameters; using the power-law distribution method to probabilistically model the trace length, obtaining the minimum trace length and fractal dimension; using uniform or non-uniform Poisson analysis methods to probabilistically model the spatial location and spacing of the structural surfaces, obtaining the volume density; and using cluster analysis methods to probabilistically model the structural surface groups, obtaining the weights of each group. Based on the probability distribution parameters, the spatial location, attitude, and scale characterization parameters of the fractures are randomly sampled to generate a discrete fracture network. This includes: defining a regular geometry that surrounds the target rock mass region; randomly sampling the fracture center position within the regular geometry, and randomly determining the fracture normal vector based on the probability distribution parameters of the attitude; simultaneously generating the fracture scale and constructing fracture geometric units based on the scale characterization parameters; and repeatedly generating the fracture geometric units until the P32 of the discrete fracture network reaches the target density, thereby obtaining the discrete fracture network. Based on the optimized discrete fracture network, a block generation algorithm is used to obtain rock blocks formed by fracture cutting within the target rock mass region. This includes: representing each fracture in the discrete fracture network as a planar polygon with boundaries, calculating the spatial intersection lines between all fractures; constructing a spatial topology graph of fracture intersection lines by using fracture intersection points as nodes and spatial intersection lines as edges; and using a closed region search algorithm based on surface tracing to search along the fracture surfaces starting from the boundary of the computational domain, tracing the closed polyhedrons enclosed by multiple fracture surfaces to obtain the rock block boundaries.

2. The rock mass stability analysis method based on DFN-VR according to claim 1, characterized in that, Acquire multi-source structural surface data of the target rock mass region and preprocess the multi-source structural surface data, including: the multi-source structural surface data includes the spatial location, attitude, and scale characterization parameters of the structural surfaces in the target rock mass region; The multi-source structural surface data is preprocessed to establish multi-source structural surface data under a unified coordinate system. The preprocessing includes denoising, registration and fusion.

3. The rock mass stability analysis method based on DFN-VR according to claim 1, characterized in that, The discrete fracture network is imported into a virtual reality platform to construct an immersive three-dimensional interactive environment, including: The data format of the discrete fracture network is converted into a three-dimensional graphic intermediate format, and the fractures in the discrete fracture network are optimized. By selecting a virtual reality platform, loading the transformed discrete fracture network into the virtual reality platform, and performing rendering and interaction design, an immersive three-dimensional interactive environment is obtained.

4. The rock mass stability analysis method based on DFN-VR according to claim 3, characterized in that, In the immersive 3D interactive environment, fracture elements are interactively verified and edited, and the model boundary for stability analysis is determined to obtain an optimized discrete fracture network, including: Based on multi-source data and interactive verification results, the discrete fracture network is selected, moved, rotated, added, deleted, and its attributes modified in an immersive 3D interactive environment using VR devices. The key locations of the model are analyzed and the model boundary for stability analysis is delineated, resulting in an optimized discrete fracture network.

5. The rock mass stability analysis method based on DFN-VR according to claim 1, characterized in that, The optimized discrete fracture network is converted into a discrete element computation network, and physical and mechanical parameters are set for the rock block and fractures, including: The block generation algorithm uses a cut-fill method; The computational domain of the discrete fracture network is set as the initial complete block. The network is then divided according to the fractures in the discrete fracture network until all fractures are divided, thus obtaining the discrete element computational network. When assigning values ​​to the physical and mechanical parameters of the rock block, the physical and mechanical parameters of the rock block include the elastic modulus, Poisson's ratio, rock block density, and rock block strength parameters, wherein the rock block strength parameters include tensile strength, cohesion, and internal friction angle; When assigning values ​​to the physical and mechanical parameters of a crack, the physical and mechanical parameters of the crack include normal / tangential stiffness, friction coefficient, and cohesion parameter.

6. The rock mass stability analysis method based on DFN-VR according to claim 1, characterized in that, Based on the geometric and fracture mechanics parameters of discrete fracture networks, numerical calculations are performed in a discrete element solver using applied engineering conditions to analyze the stability or unstable block composition of engineering rock masses, including: The physical and mechanical parameters of the rock block and fractures are input into the discrete element solver, and the engineering working conditions are also input into the discrete element solver. The engineering working conditions include excavation unloading, support loading and seismic load. The discrete element solver performs rapid calculations to obtain key mechanical response data, which includes: displacement field, stress field, contact surface state evolution, and post-instability accumulation morphology.

7. A rock mass stability analysis system based on DFN-VR, used to apply the rock mass stability analysis method based on DFN-VR as described in any one of claims 1-6, characterized in that, The system includes: The data acquisition module is configured to monitor the target rock mass area, acquire multi-source structural surface data of the target rock mass area, and preprocess the multi-source structural surface data. The DFN generation module is configured to perform probabilistic modeling on the preprocessed multi-source structural surface data to obtain the probability distribution parameters of each structural surface, and to randomly sample the spatial location, attitude, and scale characterization parameters of the fractures based on the probability distribution parameters to generate a discrete fracture network. The DFN-VR optimization module is configured to import the discrete fracture network into a virtual reality platform, construct an immersive three-dimensional interactive environment, perform interactive verification and editing of fracture elements in the immersive three-dimensional interactive environment, and determine the model boundary for stability analysis in order to obtain the optimized discrete fracture network. The DFN conversion module is configured to use the block generation algorithm to obtain rock blocks formed by fracture cutting within the target rock mass area based on the optimized discrete fracture network, convert the optimized discrete fracture network into a discrete element computation network, and set physical and mechanical parameters for the rock blocks and fractures. The stability analysis module is configured to apply engineering conditions to the discrete element solver based on the geometric and fracture mechanics parameters of the discrete fracture network, and perform numerical calculations to analyze the stability of the engineering rock mass or the composition of unstable blocks.

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