Remote non-contact intelligent identification of dangerous rock masses on steep slopes and rockfall risk assessment method

Abstract

This invention discloses a remote, non-contact, intelligent identification method for dangerous rock masses on steep slopes and rockfall risk assessment. This method uses an automated algorithm to intelligently identify dangerous rock masses by analyzing the kinematics of structural surfaces and combining the spatial position information of each structural surface in a point cloud. The goal is to identify potential dangerous rock masses in slopes and assess their potential for instability through non-contact measurement technology, reducing the risk of manual surveys. This intelligent method improves the efficiency, objectivity, accuracy, and comprehensiveness of dangerous rock mass identification, while also providing guidance for implementing appropriate preventive measures based on the level and scale of rockfall risk.

Description

Technical Field

[0001] The present invention belongs to the field of monitoring and early warning of dangerous rock masses on slopes, and specifically relates to a method for remote non-contact intelligent identification of dangerous rock masses on high and steep slopes and a rockfall risk assessment method. Background Art

[0002] Rock slopes are composed of structural bodies and structural surfaces. Dangerous rock masses with the risk of instability and collapse are prone to rockfall disasters, which are one of the main types of geological hazards in mountainous areas. As national infrastructure construction continues to penetrate mountainous areas, the safe construction of many projects is also subject to the impact of dangerous rock masses. Comprehensive investigation, identification, and proactive protection are the most effective methods for preventing and controlling dangerous rock masses. However, traditional geological survey methods make it difficult to conduct comprehensive and detailed investigations of large-scale dangerous rock masses, especially rock walls, high and steep slopes, landslide residual slopes, severely fragmented mountaintops, or areas with frequent rockfalls, making them even more difficult for geologists to access.

[0003] Remote non-contact measurement technologies such as drones and 3D laser scanning provide new technical means for the investigation and assessment of dangerous rock masses. They can remotely, quickly, and accurately obtain almost all the geometric characteristics of the slope rock mass. Such results are usually presented as point clouds or 3D models. A large number of studies at home and abroad have shown that detailed terrain slope, structural surface orientation, and other information at any position on the slope can be extracted from non-contact measurement data (Ge Yunfeng, et al. Intelligent identification and information extraction of rock mass structural surfaces based on 3D laser scanning technology [J]. Chinese Journal of Rock Mechanics and Engineering, 2017(36), 12:3050-3061.), providing a basis for judging the stability of dangerous rock masses.

[0004] Currently, point cloud data or three-dimensional model data are generally limited to the automatic or semi-automatic identification of rock structure surfaces using various algorithms, but related applications after structure surface identification, especially the monitoring and identification of dangerous rock masses and rockfall risk assessment, are rarely involved. Summary of the Invention

[0005] The present invention aims to solve the technical problems existing in the background technology and to provide a method for remote non-contact intelligent identification of dangerous rock masses on steep slopes and rockfall risk assessment. This method uses non-contact measurement technology to identify possible dangerous rock masses in the slope and assess their likelihood of instability, thereby reducing the risk of manual surveys. It also improves the efficiency, objectivity, accuracy, and comprehensiveness of dangerous rock mass identification in an intelligent manner, while providing guidance for the adoption of reasonable prevention and control measures based on their risk level and scale. This method uses an automatic algorithm to achieve intelligent identification of dangerous rock masses through kinematic analysis of structural surfaces, combined with the spatial position information of each structural surface in a point cloud. It also calculates the damage form and rockfall risk level of the dangerous rock masses to provide guidance for prevention and control measures.

[0006] In order to solve the technical problem, the technical solution of the present invention is:

[0007] A method for remote non-contact intelligent identification of dangerous rock masses on steep slopes and rockfall risk assessment, the method comprising:

[0008] Obtaining structural surface data in the slope spatial three-dimensional model;

[0009] Grouping and sorting the structural surfaces based on the structural surface data to obtain a sorting result and a classification result of the structural surfaces;

[0010] Calculating the average trace length of the structural surface based on the structural surface data to determine the impact range of the structural surface;

[0011] Based on the above-mentioned structural plane sorting results, structural plane classification results and structural plane influence range, kinematics is used to identify potential dangerous rock masses and obtain dangerous rock mass analysis results;

[0012] According to the analysis results of dangerous rock masses, kinematic analysis is used to calculate the risk level of potential dangerous rock masses and obtain the rockfall risk level information.

[0013] Furthermore, the slope data is measured using non-contact measurement technology and converted into a three-dimensional slope spatial model after data preprocessing.

[0014] Furthermore, the structural surface data in the three-dimensional slope spatial model includes specific spatial position and size information, occurrence information and trace length geological information of the structural surface required for intelligent identification of dangerous rock masses.

[0015] Furthermore, the grouping and sorting of the structural surfaces specifically includes:

[0016] Calculate the external normal vector n(x, y, z) of the structural surface according to the occurrence information;

[0017]

[0018] Among them, the dip of the occurrence is φ and the inclination is θ;

[0019] Calculate the center of the structural surface nc(x0, y0, z0) according to the structural surface position information;

[0020] Sort the structural surfaces by D value;

[0021] D=-n·nc=-(xx0+yy0+zz0);

[0022] According to the different occurrences of structural surfaces, cluster analysis method is used to divide the structural surfaces into j groups of dominant structural surfaces and random structural surfaces.

[0023] Furthermore, the structural surface influence range R is:

[0024] k is the number of structural surfaces in this group, and L is the trace length.

[0025] Furthermore, the use of kinematics to identify potential dangerous rock masses specifically includes:

[0026] Find the structural surface nc* affected by the structural surface nc. The judgment conditions are:

[0027]

[0028] To determine whether plane instability failure occurs, if nc* satisfies:

[0029] Located on the upper part of the nc plane, that is, D>D*=-n·nc*=-(xx0 * +yy0 * +zz0 * ),

[0030] It belongs to a normal slope, that is, z*>0, and:

[0031] θ0 is the rock friction angle;

[0032] Or it belongs to an overhanging slope, that is, z*<0, and

[0033] Then the block composed of the structural planes nc* and nc may suffer from plane instability failure;

[0034] iii. Determine whether collapse and instability failure occurs. If nc* satisfies:

[0035] Located on the upper part of the nc plane, that is, D>D*=-n·nc*=-(xx0 * +yy0 * +zz0 * );

[0036] It belongs to a normal slope, that is, z*>0, and:

[0037] θ0 is the rock friction angle;

[0038] Or it belongs to an overhanging slope, that is, z*<0, and

[0039] Then the block composed of the structural surfaces nc* and nc may suffer from overturning and unstable failure;

[0040] To determine whether wedge-shaped body failure occurs, if multiple structural surfaces nc* satisfy:

[0041] Located at the bottom of the nc plane, that is, D<D*=-n·nc*=-(xx0 * +yy0 * +zz0 * );

[0042] The normal vector nc of the intersection line of any two structural surfaces i and j ij for:

[0043]

[0044] Corresponding tendency φ ij and the inclination angle θ ij for:

[0045]

[0046] If nc ij Belong to the normal intersection line, that is, z ij >0, and:

[0047] θ0 is the rock friction angle;

[0048] Or it belongs to the hanging intersection line, that is, z ij <0, and

[0049] Then the structural surface nc* i ,nc* j The blocks combined with nc may suffer from wedge-shaped instability failure.

[0050] Furthermore, kinematic analysis is used to calculate the rockfall risk level of potential dangerous rock masses, including:

[0051] The risk factor pf of planar instability failure of a block composed of structural surfaces nc* and nc is calculated as:

[0052]

[0053] The risk factor tf of the block with the combination of structural surfaces nc* and nc that may collapse and fail is calculated as:

[0054]

[0055] Structural surface nc* i 、nc* j The risk factor wf of wedge-shaped instability failure of the block combined with nc is calculated as:

[0056]

[0057] The total instability risk f of the structural surface is:

[0058] f = 1-(1-pf)(1-tf)(1-wf).

[0059] Furthermore, after obtaining the risk level information of the dangerous rock mass, the method further includes: outputting the dangerous rock mass identification results and rockfall risk assessment diagrams under various failure modes.

[0060] Compared with the prior art, the advantages of the present invention are:

[0061] By analyzing the kinematics of structural surfaces and combining the spatial position information of each structural surface in the point cloud, an automated algorithm is employed to intelligently identify dangerous rock masses. This approach aims to identify potential dangerous rock masses in slopes and assess their potential for instability through non-contact measurement technology, reducing the risk of manual surveys. This intelligent approach improves the efficiency, objectivity, accuracy, and comprehensiveness of dangerous rock mass identification, while also providing guidance for the adoption of appropriate preventive measures based on their risk level and scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 , rockfall risk map for slope collapse and instability;

[0063] Figure 2 , rockfall risk map for wedge-shaped instability of the slope;

[0064] Figure 3 : Rockfall risk map for slope instability;

[0065] Figure 4 : Rockfall risk map for slope rock mass instability and failure. DETAILED DESCRIPTION

[0066] The specific implementation of the present invention is described below in conjunction with embodiments:

[0067] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0068] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0069] Example 1:

[0070] (1) The data source for the slope model in this embodiment is a point cloud acquired by a 3D laser scanner. After necessary noise reduction, splicing, and coordinate conversion, the point cloud is converted into a slope triangulation model using the Delaunay triangulation algorithm. The slope model mainly has four groups of dominant structural surfaces.

[0071] (2) An automated algorithm is used to obtain all the structural surface information in the slope, and the structural surface corresponds one-to-one with the structural surface of the slope triangulation model.

[0072] (3) Use mean clustering to divide the structural surfaces into four groups, calculate the D value of the structural surfaces, and sort the structural surfaces of each group from small to large according to the D value. The algorithm will traverse all structural surfaces in sequence starting from the lower left corner.

[0073] (4) Calculate the average trace length of each group of structural surfaces and determine the influence range R of each group of structural surfaces;

[0074] (5) Use kinematic analysis to identify potential dangerous rock masses. During the analysis, dangerous rock masses are divided into three categories according to the initiation form of block instability: plane instability, wedge instability, and tipping instability. At the same time, the structural surface is also divided into normal structural surface and overhanging structural surface.

[0075] (6) Kinematic analysis is used to calculate the risk level of potential dangerous rock masses. The risk of structural surface instability is calculated based on the three forms of plane instability, wedge instability and tipping instability.

[0076] (7) Output the dangerous rock mass identification results and rockfall risk map under various damage forms (see Appendix) Figure 1 , the dangerous rock mass that may collapse and the corresponding rockfall risk of this mode, attached Figure 2 , the dangerous rock mass that may cause wedge failure and the corresponding rockfall risk of this mode, attached Figure 3 , the dangerous rock mass that may cause plane failure and the corresponding rockfall risk of this mode, the overall distribution of dangerous rock mass on the slope and the rockfall risk map, attached Figure 4 ).

[0077] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0080] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

[0081] Many other changes and modifications can be made without departing from the spirit and scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A remote non-contact intelligent identification method for dangerous rock masses on steep slopes and a rockfall risk assessment method, characterized by: The method comprises: Obtaining structural surface data in the slope spatial three-dimensional model; Grouping and sorting the structural surfaces based on the structural surface data to obtain a sorting result and a classification result of the structural surfaces; Calculating the average trace length of the structural surface based on the structural surface data to determine the impact range of the structural surface; Based on the above-mentioned structural plane sorting results, structural plane classification results and structural plane influence range, kinematics is used to identify potential dangerous rock masses and obtain dangerous rock mass analysis results; According to the analysis results of dangerous rock mass, the risk level of potential dangerous rock mass is calculated by kinematic analysis to obtain the risk level information of dangerous rock mass; The kinematic analysis is used to identify potential dangerous rock masses, specifically including: Find the structural surface nc* affected by the structural surface nc. The judgment conditions are: To determine whether plane instability failure occurs, if nc* satisfies: Located on the upper part of the nc plane, that is, D>D*=-n·nc*=-(xx0 * +yy0 * +zz0 * ), It belongs to a normal slope, that is, z*>0, and: θ*>θ0 tanθ`>tanθ θ0 is the rock friction angle; Or it belongs to an overhanging slope, that is, z*<0, and Then the block composed of the structural planes nc* and nc may suffer from plane instability failure; iii. Determine whether collapse and instability failure occurs. If nc* satisfies: Located on the upper part of the nc plane, that is, D>D*=-n·nc*=-(xx0 * +yy0 * +zz0 * ); It belongs to a normal slope, that is, z*>0, and: θ`+θ>90°+θ0 θ0 is the rock friction angle; Or it belongs to an overhanging slope, that is, z*<0, and Then the block composed of the structural surfaces nc* and nc may suffer from overturning and unstable failure; To determine whether wedge-shaped body failure occurs, if multiple structural surfaces nc* satisfy: Located at the bottom of the nc plane, that is, D<D*=-n·nc*=-(xx0 * +yy0 * +zz0 * ); The normal vector nc of the intersection line of any two structural surfaces i and j ij for: Corresponding tendency φ ij and the inclination angle θ ij for: If nc ij Belong to the normal intersection line, that is, z ij >0, and: i ij >θ0 tanθ` ij >tanθ θ0 is the rock friction angle; Or it belongs to the hanging intersection line, that is, z ij <0, and Then the block composed of the structural surfaces nc*i, nc*j and nc may suffer wedge-shaped instability failure.

2. The method for remote non-contact intelligent identification of dangerous rock masses on high and steep slopes and rockfall risk assessment according to claim 1 is characterized in that: The slope data is measured using non-contact measurement technology and converted into a three-dimensional slope model after data preprocessing.

3. The method for remote non-contact intelligent identification of dangerous rock masses on high and steep slopes and for assessing rockfall risk according to claim 1 is characterized in that: The structural surface data in the three-dimensional slope spatial model includes the specific spatial position and size information, occurrence information and trace length geological information of the structural surface required for intelligent identification of dangerous rock masses.

4. The method for remote non-contact intelligent identification of dangerous rock masses on high and steep slopes and for assessing rockfall risk according to claim 3 is characterized in that: The grouping and sorting of structural surfaces specifically includes: Calculate the external normal vector n(x, y, z) of the structural surface according to the occurrence information; Among them, the dip of the occurrence is φ and the inclination is θ; Calculate the center of the structural surface nc(x0, y0, z0) according to the structural surface position information; Sort the structural surfaces by D value; D=-n·nc=-(xx0+yy0+zz0); According to the different occurrences of structural surfaces, cluster analysis method is used to divide the structural surfaces into j groups of dominant structural surfaces and random structural surfaces.

5. The method for remote non-contact intelligent identification of dangerous rock masses on high and steep slopes and for assessing rockfall risk according to claim 4 is characterized in that: The structural surface influence range R: k is the number of structural surfaces in this group, and L is the trace length.

6. The method for remote non-contact intelligent identification of dangerous rock masses on high and steep slopes and for assessing rockfall risk according to claim 1 is characterized in that: Kinematic analysis is used to calculate the potential risk level of dangerous rock masses, including: The risk factor pf of planar instability failure of a block composed of structural surfaces nc* and nc is calculated as: The risk factor tf of the block with the combination of structural surfaces nc* and nc that may collapse and fail is calculated as: Structural surface nc* i 、nc* j The risk factor wf of wedge-shaped instability failure of the block combined with nc is calculated as: The overall rockfall risk f of dangerous rock masses controlled by structural surfaces is: f = 1-(1-pf)(1-tf)(1-wf).

7. The method for remote non-contact intelligent identification of dangerous rock masses on high and steep slopes and for assessing rockfall risk according to claim 1 is characterized in that: After obtaining the rockfall risk level information of the dangerous rock mass, the method further includes: outputting dangerous rock mass identification results and rockfall risk assessment diagrams under various damage modes.

Images