Three-dimensional rock mass structure homogeneous region division and geological strength index identification method

Abstract

The invention discloses a three-dimensional rock mass structure homogeneous area division and geological strength index identification method, and belongs to the field of field geological logging and engineering rock mass grading, and the method comprises the following steps: obtaining point cloud data of rock mass outcrop area space information; performing structural surface identification based on the point cloud data to obtain a real rock mass structural surface; geological information calculation is carried out based on the real rock mass structural plane to obtain geological parameters of the structural plane; calculating three-dimensional space distribution information of the structural surface based on the geological parameters of the structural surface; constructing a three-dimensional model of the rock mass structure based on the three-dimensional space distribution information of the structural surface; calculating a block volume and a block shape factor based on the three-dimensional model of the rock mass structure; and carrying out geological strength index identification based on the block volume and the block shape factor to obtain a correlation analysis result between the rock mass structural characteristics and the geological strength index.

Description

Technical Field

[0001] The present invention belongs to the technical field of on-site geological logging and engineering rock mass classification, and particularly relates to a method for dividing three-dimensional rock mass structure homogeneous regions and identifying geological strength indices. Background Art

[0002] Discontinuity surfaces (such as joints, fractures, bedding planes, faults, etc.) have an important influence on the mechanical and seepage characteristics of rock masses. These discontinuity surfaces are crucial for the safety and stability of surface and underground civil engineering construction projects. Due to the uneven distribution, occurrence, and size of discontinuity surfaces in rock masses, before carrying out rock mass engineering design, it is necessary to divide the rock mass according to the development of discontinuity surfaces to identify homogeneous regions with similar geological conditions and physical properties. Different rock masses can be divided into the same homogeneous region under the condition of similar geometric properties. Currently, various methods based on statistical data, spatial distribution, numerical simulation, and cluster analysis have been developed. The most mainstream method for dividing rock mass homogeneous regions is to perform cluster analysis on the occurrence of discontinuity surfaces, and it is considered that the regions where the discontinuity surfaces in the same cluster are located belong to the same homogeneous region.

[0003] Patent CN110147526B uses the overlapping window method and the contingency table chi-square test method to divide homogeneous regions for the absolute difference in density and occurrence indices, and then combines the qualitative rock mass structure homogeneous region division method based on the mechanical origin of structural planes to determine homogeneous regions through a combination of qualitative and quantitative methods.

[0004] Patent CN111199109B uses a measuring window to collect the traces on the actual rock mass surface, draws a trace map, improves the box-counting dimension method, and proposes shape similarity to make up for the deficiency that the box-counting dimension cannot detect the occurrence of discontinuity surfaces.

[0005] Patent CN110135515B utilizes the texture characteristics of the geometric morphology of rock mass structural planes in the outcrop area. Based on obtaining the actual-size rock mass structural plane occurrence images by photogrammetry, 10 texture parameters of the gray-level co-occurrence matrix are used to describe the rock mass structure. The main control texture parameters are screened out through the sensitivity detection of the point pair distance, point pair direction angle, and pixel scale and the principal component analysis method, and homogeneous regions are divided with the main control texture parameters as the clustering index.

[0006] The above patents divide homogeneous regions based on the two-dimensional distribution statistics of rock mass structural planes in the outcrop area or the corresponding image texture analysis method, all considering only the two-dimensional angle, and there is a problem of large differences in homogeneous region division due to different selections of cross-section positions, and the results are very one-sided. Based on the above technical problems, the present invention proposes a three-dimensional model method based on measured data. Summary of the Invention

[0007] To solve the above technical problems, the present invention proposes a method for dividing homogeneous regions of a three-dimensional rock mass structure and identifying geological strength indices to solve the problems existing in the above prior art.

[0008] To achieve the above object, the present invention provides a method for dividing homogeneous regions of a three-dimensional rock mass structure and identifying geological strength indices, comprising the following steps:

[0009] Obtain point cloud data of the spatial information of the rock outcrop area;

[0010] Based on the point cloud data, identify the rock mass structural planes to obtain the true rock mass structural planes;

[0011] Based on the true rock mass structural planes, calculate the geological parameters of the structural planes through geological information measurement;

[0012] Based on the geological parameters of the structural planes, infer the three-dimensional spatial distribution information of the structural planes;

[0013] Based on the three-dimensional spatial distribution information of the structural planes, construct a three-dimensional model of the rock mass structure;

[0014] Based on the three-dimensional model of the rock mass structure, calculate the block volume and block shape factor;

[0015] Based on the block volume and block shape factor, identify the geological strength indices to obtain the correlation analysis result between the rock mass structure characteristics and the geological strength indices.

[0016] Optionally, the process of identifying potential rock mass structural planes based on the point cloud data includes:

[0017] Fill the holes in the point cloud data and thin it to obtain the thinned point cloud data;

[0018] Perform three-dimensional coordinate correction on the thinned point cloud data to obtain the corrected point cloud data;

[0019] Perform triangulation on the corrected point cloud data to obtain DEM data in TIN data format;

[0020] Based on the DEM data in TIN data format, calculate the direction, angle of the triangular grid plane normal vector and the elevation standard deviation of the three vertex coordinates, and perform clustering analysis and grouping based on the direction, angle of the triangular grid plane normal vector and the elevation standard deviation of the three vertex coordinates to obtain potential rock mass structural planes.

[0021] Optionally, the process of generating DEM data in TIN data format includes:

[0022] Based on the corrected point cloud data, generate TIN data using the triangular network growth method;

[0023] The DEM data is generated on the TIN data by using a point-by-point interpolation method to obtain the DEM data in the TIN data format.

[0024] Optionally, the process of performing cluster analysis and grouping to obtain the real rock mass structural surface based on the direction, angle and elevation standard deviation of the three vertex coordinates of the triangular mesh plane normal vector includes:

[0025] Performing ISODATA clustering on the direction, angle and elevation standard deviation of the plane normal vector of the triangular mesh and the three vertex coordinates to obtain a clustering result;

[0026] Using a moving window method to spatially group the clustering results to obtain remaining cluster groups;

[0027] Calculating the standard deviation of elevation variation of rock mass structural surfaces in the remaining cluster groups;

[0028] Based on the elevation variation standard deviation, the potential rock mass structural surface is tested to obtain the real rock mass structural surface;

[0029] Among them, the expression of elevation variation standard deviation is:

[0030]

[0031] In the formula, DEV represents the standard deviation of elevation variation, TPI represents the rock mass structural surface position index, z0 represents the center point, and R represents the predetermined radius. represents the mean elevation and SD represents the standard deviation of the elevation.

[0032] Optionally, the process of calculating geological information based on the real rock mass structural surface to obtain geological parameters of the structural surface includes:

[0033] Converting the TIN data corresponding to the real rock mass structural surface into point cloud data of the real rock mass structural surface;

[0034] Calculating the occurrence value of each structural surface by using the least square method based on the point cloud data of the real rock mass structural surface;

[0035] Obtaining the dominant grouping of rock mass structural surfaces based on the occurrence value;

[0036] The rock mass structural planes are preferentially grouped to obtain three types of structural planes: joints and fissures, rock layer planes and faults.

[0037] Optionally, the process of calculating and obtaining the three-dimensional spatial distribution information of the structural surface based on the geological parameters of the structural surface includes:

[0038] Calculating the spacing between structural surfaces based on the geometric center coordinates and geometric center point elevation values of each structural surface in the dominant grouping of the rock mass structural surfaces;

[0039] The fracture diameter is obtained based on the number of fractures per unit volume and the spacing of the structural planes;

[0040] The fracture center coordinates are obtained based on the spacing of the structural planes and the fracture diameter;

[0041] The three-dimensional spatial distribution information of the structural planes is obtained based on the spacing of the structural planes, the fracture diameter, and the fracture center coordinates.

[0042] Optionally, the calculation process of the block volume includes:

[0043] The surface of the irregular block is decomposed into several triangles by the Delauany algorithm;

[0044] Several of the triangles are respectively connected to the centroid of the block to form several tetrahedrons;

[0045] The volume of each tetrahedron is calculated and the sum of the volumes of several tetrahedrons is obtained to get the block volume;

[0046] Among them, the calculation formula for the volume of a tetrahedron is:

[0047]

[0048] In the formula, (x1,y1,z1), (x2,y2,z2), (x3,y3,z3), (x4,y4,z4) represent the coordinates of the four vertices of the tetrahedron, and V represents the volume of the tetrahedron.

[0049] Optionally, the calculation expression for the block shape factor is:

[0050]

[0051] In the formula, β is the shape factor, l max represents the maximum distance between the two end points of the block, and l min represents the minimum distance between the two end points of the block.

[0052] Compared with the prior art, the present invention has the following advantages and technical effects:

[0053] The present invention provides a method for dividing homogeneous regions of a three-dimensional rock mass structure and identifying the geological strength index. Through a series of innovative steps, it realizes the identification of the geological strength index from the acquisition of spatial information in the rock outcrop area. First, 3D laser scanning technology is used to obtain point cloud data, ensuring the high precision and comprehensiveness of the data. Then, through the structural plane identification technology, the real rock mass structural planes are accurately extracted, laying a solid foundation for subsequent analysis. The geological information calculation step further refines the geological parameters of the structural planes, and the calculation of the three-dimensional spatial distribution information pushes the analysis to the three-dimensional level, enhancing the depth and breadth of the analysis. Finally, based on the calculation of the block volume and shape factor of the three-dimensional model, combined with the identification of the geological strength index, the correlation analysis between the rock mass structure characteristics and the geological strength index is realized, providing a scientific and accurate basis for the stability assessment and design of rock mass engineering, and significantly improving the accuracy and efficiency of rock mass structure analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0055] Figure 1 Schematic diagram of a fractured rock mass according to an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of block volume calculation according to an embodiment of the present invention;

[0057] Figure 3 Flowchart of the method for dividing homogeneous regions of a three-dimensional rock mass structure and identifying the geological strength index according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine the embodiments to detail this application.

[0059] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0060] Embodiment 1

[0061] As Figure 1As shown, it aims to solve the problem that the traditional rock mass homogeneous zone division is based on the two-dimensional distribution statistics of the rock mass structural surface in the outcrop area or the corresponding image texture analysis method, and there is a large difference in the homogeneous zone division due to the different section position selection. This embodiment provides a three-dimensional rock mass structure homogeneous zone division and geological strength index identification method. The present invention advances the rock mass homogeneous zone division method from a two-dimensional perspective to a three-dimensional perspective based on measured data, reduces the error caused by a single section, realizes the visualization of geological conditions, and improves the accuracy of prediction and analysis.

[0062] The present invention discloses a method of using a three-dimensional laser scanner to obtain point cloud data of spatial information of a rock outcrop area. On the basis of converting the point cloud data into DEM data, the rock structure surface attitude, trace length, spacing and three-dimensional coordinates of the trace are measured and calculated, and the three-dimensional spatial distribution information of joints, fissures, rock stratum layers and faults in the rock outcrop area is deduced. A three-dimensional fracture network model of the rock structure is established, and the ISODATA clustering method is used to divide the rock structure homogeneous areas using shape factors and block volumes as clustering indicators. The correlation between the rock block size and the geological strength index in the RMR index is compared to construct the correlation between the characteristics of the rock structure homogeneous area and the geological strength index. The present invention advances the rock homogeneous area division method from a two-dimensional perspective to a three-dimensional perspective based on measured data, establishes a quantitative representation of the geological strength index, and provides a key node for the digital-numerical integrated analysis of rock engineering. The specific steps are as follows: Figure 3 shown.

[0063] Obtaining point cloud data of the spatial information of the rock outcrop area specifically includes the following steps (1):

[0064] (1) Point cloud data acquisition: A 3D laser scanner is set up 5 m in front of the selected tunnel face. 3D laser scanning technology is used to obtain point cloud data describing the geometric morphology of the rock surface of the tunnel face.

[0065] The structural surface is identified based on the point cloud data to obtain the real rock mass structural surface, which specifically includes steps (2) to (4):

[0066] (2) Data denoising and correction: remove the noise in the point cloud data caused by the interference of the tunnel face engineering; repair the local voids in the point cloud data after denoising, and fill the point cloud data according to the trend distribution of the point cloud data in the neighborhood of the void; thinning: the uneven distribution density of the point cloud data causes local data redundancy, and the density of the over-dense data distribution area is thinned according to the trend distribution of the point cloud data; correction matching is based on 3 calibration points and compass measurement to correct the point cloud data to the geological coordinate system. The specific implementation steps are as follows:

[0067] (a) Import the point cloud data into cloudcompare, fill the point cloud holes and perform thinning;

[0068] (b) Import the thinned point cloud data into GIS software. During the import process, perform three-dimensional coordinate correction, where the horizontal coordinate is the Y coordinate, the vertical coordinate is the X coordinate, and the Z coordinate is the elevation coordinate.

[0069] (3) DEM data conversion. Based on the corrected point cloud data, perform triangulation to obtain the TIN data format of DEM. The specific implementation steps are as follows:

[0070] (a) Generate TIN data using the triangulation growth method. The steps are as follows:

[0071] Step 1: Select any point in the corrected point cloud data as the initial point;

[0072] Step 2: Detect the point closest to the initial point and connect them as a reference edge of a triangle. Determine the third endpoint according to the Delaunay triangulation discrimination rule;

[0073] Step 3: Connect the three endpoints in Step 2 to form two new reference edges;

[0074] Step 4: Iterate Step 2 and Step 3 until all reference edges are processed.

[0075] (b) Generate DEM data using the point-by-point interpolation method based on the TIN data. The steps of the point-by-point interpolation method are as follows:

[0076] Step 1: Determine the neighborhood size of the interpolation point;

[0077] Step 2: Select the sampling points included in the neighborhood;

[0078] Step 3: Select the interpolation method model;

[0079] Step 4: Solve the elevation of the interpolation point using the interpolation method model based on the sampling points in the neighborhood.

[0080] (4) Structural plane identification. Based on the three vertex coordinates of each triangular grid in the digital elevation model of the TIN data, calculate three indicators: the direction, angle, and elevation standard deviation of the normal vector of the triangular grid plane; use cluster analysis to perform ISODATA cluster analysis on the direction, angle, and elevation standard deviation of the normal vectors of all triangular grid units; use the moving window method to filter out smaller cluster groups, and the remaining cluster groups are potential rock mass structural planes. Calculate the elevation variation standard deviation of the vertex coordinates of the TIN data triangular grids composed of the potential rock mass structural planes to check whether the potential rock mass structural plane is a real rock mass structural plane. The specific implementation steps are as follows:

[0081] (a) Based on the digital elevation model, the slope, aspect, and elevation standard deviation data are statistically analyzed. The slope and aspect represent the inclination angle and direction of the grid plane normal vector respectively, and the elevation standard deviation represents the undulation degree of the surface.

[0082] The aspect α and slope β are expressed as the elevation change rates of the surface function H = f(x, y) in the east-west and north-south directions. The calculation formulas are as follows:

[0083]

[0084] The elevation standard deviation σ is used to reflect the undulation degree of the rock mass structural plane and is related to the elevation value. The formula is as follows:

[0085]

[0086] where z(i, j) is the elevation value, is the average elevation, D(z) is the elevation variance, and m and n are the number of grids in the calculation window.

[0087] (b) Using the slope, aspect, and elevation standard deviation as clustering indicators, ISODATA clustering is performed. The specific steps of ISODATA clustering are as follows:

[0088] Step 1: Select the initial parameters.

[0089] Step 2: Calculate the distance metric function for each cluster.

[0090] Step 3: Merge or split clusters according to the given requirements.

[0091] Step 4: Repeat the iteration. Calculate the new metrics and determine whether the results meet the clustering requirements

[0092] (c) The moving window method is used to group the clustering results spatially. According to the area of the on-site rock mass structural plane and the density of the point cloud data, a suitable number of grouped triangular grids is set as the threshold to filter out the clustering groups smaller than the threshold.

[0093] (d) Check whether the elevation variation standard deviation is between +1 and -1. If so, it is determined that the point cloud data formed by this cluster presents a planar feature; if not, this cluster is a general rock exposure surface. If so, it is determined that the potential rock mass structural plane is a real rock mass structural plane. The calculation formula for the elevation variation standard deviation DEV is as follows:

[0094]

[0095] The Topographic Position Index (TPI) of the rock mass structural plane represents the difference between the elevation of the center point z0 and the average elevation within a predetermined radius R around it The SD represents the standard deviation of the elevation.

[0096] Geological parameters of the structural plane are calculated based on the actual rock mass structural plane, specifically including step (5):

[0097] (5) Geological information calculation: Convert the TIN data corresponding to the rock mass structural plane into point cloud data. Based on the point cloud data of the actual rock mass structural plane, use the least squares method to calculate the attitude values of each structural plane through the point cloud coordinates, including dip direction, dip angle, two-dimensional trace length, and the position of the midpoint of the trace length. Cluster and group the dip angles and dip directions of the rock mass structural planes to obtain the dominant grouping of the rock mass structural planes, and classify them into joint fissures, rock layer bedding planes, and faults according to the dominant grouping. The specific steps are as follows:

[0098] (a) Based on the extracted actual structural plane TIN data, convert it into point cloud data, and use the least squares method to calculate the attitude and trace length of each structural plane point cloud set, and count the number of trace lines; the dip direction α i and dip angle β i of each structural plane are calculated as follows:

[0099]

[0100] where a i and b i are the direction vectors of the plane fitted by the least squares method.

[0101] (b) Based on the attitude data analysis, distinguish the fracture types of the structural plane species into three categories: joint fissures, rock layer bedding planes, and faults;

[0102] (c) Sort all types of structural planes according to their areas, and eliminate the structural planes with smaller areas through the area screening method, and retain the larger structural planes. To ensure that the structural planes have a certain representativeness, retain the top 20 structural planes in terms of area as the 20 major structural areas.

[0103] The three-dimensional spatial distribution information of the structural plane is deduced based on the geological parameters of the structural plane, specifically including step (6):

[0104] (6) Deduction of three-dimensional spatial information: Calculate the inter-group spacing of each dominant grouping through the geometric center coordinates and elevation values of the geometric center points of the joint planes, and combine the number of fractures per unit volume to calculate the three-dimensional fracture diameter and the position of the center point. The specific steps are as follows:

[0105] (a) Use GIS software to statistically analyze the geometric center coordinates of each structural plane in each structural plane grouping and the average elevation value of the DEM data corresponding to each structural plane. Based on the average elevation value, the geometric center coordinates of the structural plane, and the attitude data corresponding to each structural plane, calculate the structural plane spacing in ascending order of the horizontal coordinates. The formula for calculating the spacing is as follows:

[0106] The first major category, when a < b and the inclination is between 0 degrees and 180 degrees:

[0107] l = (b - a)cosα - csinα ctanα < b - a

[0108] l = csinα - (b - a)cosα ctanα > b - a

[0109] The second major category, when a < b and the inclination is between 180 degrees and 360 degrees:

[0110]

[0111] The third major category, when a > b and the inclination is between 0 degrees and 180 degrees:

[0112]

[0113] The fourth major category, when a > b and the inclination is between 180 degrees and 360 degrees:

[0114] l = (a - b)cosα - csinα ctanα < a - b

[0115] l = csinα - (a - b)cosα ctanα > a - b

[0116] Where l represents the spacing between joint sets; α represents the dip angle; a represents the average elevation of the left side of two adjacent joint planes; b represents the average elevation of the right side; c represents the absolute value of the difference in the Y coordinates of the center points of the two joint planes.

[0117] (b) Calculate the fracture surface density based on the number of traces in the tunnel face, infer its bulk density by combining the surface density and the occurrence of each structural plane, and finally infer the diameter of the nodular joints in space through the relationship between the bulk density, spacing, and diameter, and calculate the center coordinates of the joint fractures.

[0118] Tunnel face trace surface density The calculation formula is as follows:

[0119]

[0120] W is the side length of the square tunnel face, N (k) represents the total number of traces

[0121] The expectation of the cosine of the acute angle γ between the discontinuous normal vector and the sampling window plane is approximately calculated using the direction samples as follows:

[0122]

[0123] Where E (k)[cos(γ)] represents the expectation of cos(γ) formed by the k-th joint grouping. ) and respectively represent the dip direction and dip angle of the i-th sample of the k-th discontinuity set.

[0124] Combined with the trace center plane density and the expectation of the cosine of the acute angle γ, the bulk density formula can be obtained

[0125]

[0126] From this, the relationship between the fracture volume density and the fracture diameter can be obtained. Combining the relationship table of volume density, spacing and diameter, the average size of the diameter can be obtained. As shown in Table 1:

[0127] Table 1

[0128]

[0129] The calculation formula for the central coordinates of the joint fracture is as follows:

[0130]

[0131] Where: R is the radius size of the fracture, L is the length of the trace, (a, b, c) is the direction vector from the fracture center to the trace center, (X1, Y1, Z1) is the trace center coordinate, and (X2, Y2, Z2) is the fracture center coordinate.

[0132] Based on the three-dimensional spatial distribution information of the structural plane, a three-dimensional model of the rock mass structure is constructed, specifically including step (7):

[0133] (7) Three-dimensional modeling of the rock mass structure. According to the three-dimensional trace lengths and center point positions of joint fractures, rock layer surfaces and faults, a discrete fracture network model is established, the volume of the blocks cut by the three types of structural planes in the model is calculated, and the blocks are clustered according to the center of gravity points of the blocks to divide the three-dimensional structure of the rock mass. The intersection lines of 1 group of fractures and 3 groups of fractures are 1, 2, 3, 4 respectively, the intersection lines of 2 groups of fractures and 3 groups of fractures are 5, 6, 7, 8, and the intersection lines of 1 group of fractures and 2 groups of fractures are 9, 10, 11, 12. As Figure 2 shown, red is for 1 group, blue is for 2 group, and green is for 3 group. The specific steps are as follows:

[0134] (a) Combine the joint fracture diameter, occurrence, and central coordinates to establish a three-dimensional model of joint fractures, and its manifestation is disc-shaped fractures distributed inside the rock mass; combine the two-dimensional trace position and occurrence to establish a rock layer surface and fault model, and its manifestation is fractures cutting across the rock mass.

[0135] (b) In the GeneralBlock software, couple the joint fissures, rock bedding planes, and fault models with the rock mass, calculate the volume of the blocks formed by the fissures cutting the rock mass, calculate the degree of blockification, evaluate the surrounding rock classification, and compare with the actual exposed results. The corresponding relationship between the degree of blockification and the surrounding rock classification is shown in Table 2.

[0136] Table 2

[0137]

[0138] Calculate the block volume and block shape factor based on the three-dimensional model of the rock mass structure, specifically including steps (8) - step (10):

[0139] (8) Block calculation: Use the Convex Hull algorithm to find the polyhedron formed by the fissures cutting the rock blocks and calculate the coordinates of its end points, as Figure 1 shown; the specific steps are as follows:

[0140] (a) Calculate the intersection points of each fissure with the rock blocks;

[0141] (b) Use the Convex Hull algorithm to find the polyhedron formed by all the intersection points;

[0142] (c) Use the calculate_end_points function to expand the convex hull vertices and remove duplicates to obtain the end points of each polyhedron;

[0143] (d) Use Matplotlib to draw the rock blocks, fissures, intersection points, and end points of the polyhedron.

[0144] (9) Block volume calculation: Decompose the surface of the irregular block into multiple triangles through the Delauany algorithm, connect the triangle patches with the center of gravity of the block to form a series of tetrahedrons, and calculate the volume of each tetrahedron, as Figure 2 shown; the specific steps are as follows:

[0145] (a) Construct a triangular mesh: Decompose the surface of the irregular block into multiple triangles through the Delauany algorithm;

[0146] (b) Divide into tetrahedrons: Connect these triangle patches with the center of gravity to form a series of tetrahedrons;

[0147] (c) Calculate the volume of the tetrahedron: The volume of each tetrahedron can be calculated by the following formula:

[0148]

[0149] Among them, (x1, y1, z1), (x2, y2, z2), (x3, y3, z3), and (x4, y4, z4) are the coordinates of the four vertices of the tetrahedron.

[0150] (10) Calculate the block shape factor. Based on the block endpoint coordinates obtained in step (8), calculate the shape factor of each block. The formula is as follows:

[0151]

[0152] Among them, β is the shape factor, and l max represents the maximum distance between the two endpoints of the block, and l min represents the minimum distance between the two endpoints of the block.

[0153] Based on the block volume and the block shape factor, conduct geological strength index identification to obtain the correlation analysis result between the rock mass structure characteristics and the geological strength index, specifically including steps (11) - step (12):

[0154] (11) Divide the clustering homogeneous area. Use the ISODATA clustering method, with the shape factor and the block volume as the clustering indicators, to divide the rock mass structure homogeneous area;

[0155] The specific steps of ISODATA clustering are as follows:

[0156] Step 1: Select the initial parameters;

[0157] Step 2: Calculate the distance metric function for each cluster;

[0158] Step 3: Merge or split the clusters according to the given requirements;

[0159] Step 4: Repeat the iteration. Calculate the new metrics and determine whether the results meet the clustering requirements.

[0160] (12) GSI correlation estimation. Refer to the correlation between the rock mass block size and the geological strength index in the RMR index to construct the correlation between the characteristics of the rock mass structure homogeneous area and the geological strength index.

[0161] Conduct GSI evaluation based on the homogeneous area division result. The specific steps are as follows:

[0162] (a) Load data: Use Pandas to read the data;

[0163] (b) Calculate the basic GSI value: Determine the basic GSI value according to the rock type;

[0164] (c) Adjust the GSI value: Adjust the GSI value according to factors such as joint density and joint roughness;

[0165] (d) Calculate the final GSI value: Combine the adjusted GSI value with other geological features to obtain the final GSI value;

[0166] (e) Visualize the results: Use Matplotlib for visualization to display the GSI values of different homogeneous zones;

[0167] (f) Correlation analysis: Analyze the correlation between the rock mass structure characteristics and the geological strength index.

[0168] The method for dividing the homogeneous zones of the rock mass in the present invention advances from a two-dimensional perspective to a three-dimensional perspective based on measured data. The block volume and shape factor are used as three-dimensional indicators to divide the homogeneous zones, reducing the error caused by a single section, establishing a quantitative characterization of the geological strength index, and providing a key node for the digital-numerical integrated analysis of rock mass engineering.

[0169] The above are only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for dividing homogeneous regions of a three-dimensional rock mass structure and identifying geological strength indices, characterized in that, It includes the following steps: Obtain the point cloud data of the spatial information in the rock mass outcrop area; Identify the structural planes based on the point cloud data to obtain the true rock mass structural planes; Calculate the geological information of the structural planes based on the true rock mass structural planes to obtain the geological parameters of the structural planes; Deduce the three-dimensional spatial distribution information of the structural planes based on the geological parameters of the structural planes; Construct a three-dimensional model of the rock mass structure based on the three-dimensional spatial distribution information of the structural planes; Calculate the block volume and block shape factor based on the three-dimensional model of the rock mass structure; Identify the geological strength index based on the block volume and block shape factor to obtain the correlation analysis result between the rock mass structure characteristics and the geological strength index.

2. The method for dividing homogeneous regions of a three-dimensional rock mass structure and identifying geological strength index according to claim 1, characterized in that, The process of identifying the potential rock mass structural planes based on the point cloud data includes: Fill the holes in the point cloud data and thin it to obtain the thinned point cloud data; Perform three-dimensional coordinate correction on the thinned point cloud data to obtain the corrected point cloud data; Perform triangulation on the corrected point cloud data to obtain the DEM data in the TIN data format; Calculate the direction, angle, and elevation standard deviation of the three vertex coordinates of the triangular grid plane normal vector based on the DEM data in the TIN data format, and perform clustering analysis and grouping based on the direction, angle, and elevation standard deviation of the triangular grid plane normal vector to obtain the potential rock mass structural planes.

3. The method for dividing homogeneous regions of a three-dimensional rock mass structure and identifying geological strength indices according to claim 2, characterized in that, The process of generating the DEM data in the TIN data format includes: Generate TIN data based on the corrected point cloud data using the triangular network growth method; Generate DEM data on the TIN data using the point-by-point interpolation method to obtain the DEM data in the TIN data format.

4. The method for dividing homogeneous regions of three-dimensional rock mass structures and identifying geological strength indices according to claim 2, characterized in that, The process of performing clustering analysis and grouping based on the direction, angle, and elevation standard deviation of the triangular grid plane normal vector to obtain the true rock mass structural planes includes: Perform ISODATA clustering on the direction, angle, and elevation standard deviation of the triangular grid plane normal vector to obtain the clustering result; Use the moving window method to group the clustering result in terms of spatial position to obtain the remaining clustering groups; Calculate the elevation variation standard deviation of the rock mass structural planes in the remaining clustering groups; Verify the potential rock mass structural planes based on the elevation variation standard deviation to obtain the true rock mass structural planes; Among them, the expression of the elevation variation standard deviation is: Where DEV represents the standard deviation of elevation variation, TPI represents the rock mass structural plane position index, z0 represents the center point, R represents the predetermined radius, represents the average elevation, and SD represents the standard deviation of elevation.

5. The method for dividing homogeneous regions of a three-dimensional rock mass structure and identifying geological strength indices according to claim 4, wherein The process of calculating the geological information of the structural planes based on the true rock mass structural planes to obtain the geological parameters of the structural planes includes: Convert the TIN data corresponding to the true rock mass structural planes into the point cloud data of the true rock mass structural planes; Calculate the attitude value of each structural plane using the least squares method based on the point cloud data of the true rock mass structural planes; Obtain the dominant grouping of the rock mass structural planes based on the attitude value; Group the dominant grouping of the rock mass structural planes to obtain three types of structural planes: joint fissures, bedding planes, and faults.

6. The method for dividing homogeneous regions of a three-dimensional rock mass structure and identifying geological strength indices according to claim 5, characterized in that, The process of deducing the three-dimensional spatial distribution information of the structural planes based on the geological parameters of the structural planes includes: Calculate the structural plane spacing based on the geometric center coordinates and geometric center point elevation values of each structural plane in the dominant grouping of the rock mass structural planes; Obtain the fracture diameter based on the number of fractures per unit volume and the structural plane spacing; Obtain the fracture center coordinates based on the structural plane spacing and the fracture diameter; Obtain the three-dimensional spatial distribution information of the structural plane based on the spacing of the structural planes, the diameter of the fissures, and the central coordinates of the fissures.

7. The method for dividing homogeneous regions of three-dimensional rock mass structures and identifying geological strength indices according to claim 1, characterized in that, The calculation process of the block volume includes: Decompose the surface of the irregular block into a number of triangles through the Delauany algorithm; Connect the centers of gravity of the block with a number of the said triangles respectively to form a number of tetrahedrons; Calculate the volume of each tetrahedron and sum the volumes of a number of tetrahedrons to obtain the block volume; Among them, the calculation formula for the volume of the tetrahedron is: In the formula, (x1, y1, z1), (x2, y2, z2), (x3, y3, z3), (x4, y4, z4) represent the coordinates of the four vertices of the tetrahedron, and V represents the volume of the tetrahedron.

8. The method for dividing homogeneous regions of three-dimensional rock mass structures and identifying geological strength indices according to claim 7, characterized in that, The calculation expression for the block shape factor is: In the formula, β is the shape factor, and l max represents the maximum distance between the two end points of the block, and l min represents the minimum distance between the two end points of the block.

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