A loose bulk layer slope stability rapid analysis system and method

Abstract

The present application relates to the technical field of slope engineering, and discloses a loose accumulation layer slope stability rapid analysis system and method, which comprises a slope measurement and modeling module, a granular body identification and modeling module, a physical engine simulation module, and a result analysis and decision module; the slope measurement and modeling module is used for acquiring slope image data and generating a slope ground point cloud model; the granular body identification and modeling module is used for forming a complete model containing loose accumulation layers; the physical engine simulation module is used for importing the complete model, configuring granular body physical parameters, adding environmental factors and instability triggering conditions, and realizing simulation of the physical effect of granular body movement; and the result analysis and decision module determines the slope stability degree and outputs an analysis result by means of a combination of qualitative analysis and quantitative analysis. The present application can shorten the analysis time from several hours or even several days to several minutes to tens of minutes.

Description

Technical Field

[0001] This invention relates to the field of slope engineering technology, and in particular to a rapid analysis system and method for the stability of loose deposit layer slopes. Background Technology

[0002] In engineering activities such as mountain tunnel construction, highway and railway construction, and open-pit mining, loose deposits of boulders are often encountered on hillsides. These deposits have a loose structure and poor stability, and are prone to causing landslides, collapses, and other engineering geological disasters when exposed to environmental factors such as heavy rainfall and vibrations. This seriously threatens the lives and property of engineering personnel and the safety of engineering construction.

[0003] Currently, stability analysis of loose deposit slopes still primarily relies on traditional theoretical calculation methods such as the limit equilibrium method and finite element numerical analysis. While these methods have relatively mature theoretical frameworks, they struggle to accurately characterize the discontinuous contact and relative motion behavior between rocks within the deposit. The core reason lies in the fact that these methods are largely based on the assumption of a continuous medium, with the failure surface being a continuous slip surface, and the safety factor is calculated through static equilibrium equations. This assumption fundamentally differs from the actual mechanical properties and failure mechanisms of loose deposit slopes, leading to analysis results that often deviate significantly from reality.

[0004] On the other hand, while discrete element method (DEM) numerical simulation can meticulously describe complex mechanical behaviors such as particle contact, motion, and force chain evolution, it demands extremely high computational resources and is relatively inefficient, requiring specialized personnel and high-performance computing equipment. Limited by the number of particles that can be generated in current DEM simulations, for large rockfill slopes, a massive number of particle units need to be constructed based on particle size distribution, leading to an exponential increase in computation time, making it difficult to meet the timeliness requirements for stability analysis in engineering survey and design phases.

[0005] In summary, existing methods for analyzing the stability of loose deposit slopes have significant limitations in terms of computational efficiency, model accuracy, and field applicability, making it impossible to achieve rapid and accurate slope stability assessment. Therefore, developing an intelligent analysis system and method that balances ease of data acquisition, model realism, and high efficiency in analysis is of significant practical importance and engineering value for improving the early warning capabilities of loose deposit slope disasters and ensuring the safety of engineering construction. Summary of the Invention

[0006] The purpose of this invention is to provide a rapid analysis system and method for the stability of loosely packed layer slopes, aiming to solve or improve at least one of the above-mentioned technical problems.

[0007] To achieve the above objectives, the present invention provides the following solution: A rapid analysis system for the stability of loosely packed layer slopes includes a slope calculation and modeling module, a particle identification and modeling module, a physics engine simulation module, and a results analysis and decision-making module. The slope measurement and modeling module uses a drone equipped with a high-definition camera, lidar scanning and ranging system to acquire slope image data, measure slope height, slope angle, slope length and platform width parameters, and generate a slope surface point cloud model. The particle identification and modeling module is used to identify the size, dimensions, shape, and gradation characteristics of particles in the loose deposit layer, generate a three-dimensional random particle model similar to the actual particle characteristics, and merge it with the slope topography point cloud model to form a complete model containing the loose deposit layer. The physics engine simulation module is used to import complete models, configure particle physical parameters, add environmental factors and instability triggering conditions, and realize the simulation of the physical effects of particle motion. The results analysis and decision-making module determines the stability of the slope and outputs the analysis results by combining qualitative and quantitative analysis.

[0008] Furthermore, in the slope measurement and modeling module, the UAV flight path is automatically planned through a preset program or remotely controlled by the operator, and the flight altitude and shooting angle are flexibly adjusted. Furthermore, the slope surface point cloud model is generated by laser radar scanning and combined with slope image data stitching.

[0009] Furthermore, the workflow of the particle recognition and modeling module includes: The structural features of the particles are obtained by taking images from the front view using a drone, and the coordinates are calibrated by the ranging system. The MATLAB software was used to process the images of granular structure features. The images were converted into grayscale images and thresholded to obtain binarized images. Thresholds were set to filter out the outlines of excessively small granular particles. When the boundaries of the particles are blurred, Photoshop software is used to sharpen, enhance contrast, and increase brightness; parameters such as the number, size, and shape of the particles are counted, and a program is written to calculate the number of particles per unit area and map it to three-dimensional space; Irregular random particles were constructed using the Voronoi polyhedron algorithm, and the thickness of the loose deposit layer was determined by combining geological survey data or field survey data to simulate the loose deposit layer. Convert the model generated in MATLAB to STL format and import it into the physics engine simulation software.

[0010] Furthermore, the physics engine simulation module adopts the Unity physics engine, adds a Rigidbody and a collider to each particle, imports the physical property parameters of rock blocks from the geotechnical engineering investigation report, and configures dynamic friction coefficient, static friction coefficient, elasticity, and key mass information; the environmental factors include resistance, gravity, and angular drag; the instability triggering conditions include external disturbance to simulate natural instability and removal of support blocks to simulate bottom excavation conditions.

[0011] Furthermore, the qualitative analysis of the result analysis and decision-making module is based on the simulation phenomenon of the Unity physics engine, and divides the slope stability state into stable state, understability state, critical instability state, and unstable state. The stable state is characterized by only minor movement of slope particles with no overall displacement trend and no surface cracks or sliding surfaces. The under-stable state is characterized by localized particle sliding and rolling downwards on the slope surface, but without the formation of through cracks or sliding surfaces. The critical state of instability is characterized by the overall sliding of slope particles along the sliding surface, forming continuous through cracks, and particle compression and accumulation at the slope toe. The unstable state is characterized by the overall collapse of the slope, with a large number of particles leaving their original positions and forming a sliding accumulation body.

[0012] Furthermore, the quantitative analysis of the result analysis and decision-making module is based on the particle motion parameters and mechanical parameters output in real time by the Unity script, and constructs displacement indexes, mechanical indexes, and structural indexes. The displacement-related indicators include cumulative displacement and displacement rate. The cumulative displacement is the cumulative displacement of the particle under load, and the displacement rate is the instantaneous velocity of the particle, i.e., the ratio of displacement to running time. The mechanical indicators are the ratio of the anti-slip force of all particles perpendicular to the sliding surface to the sliding force along the sliding surface. The structural indicators are the ratio of the effective contact number between particles to the total number of particles.

[0013] This invention also provides a rapid analysis method for the stability of loose deposit layer slopes, comprising the following steps: Step 1: Use the slope measurement and modeling module to acquire slope images and distance measurement data, and generate a slope surface point cloud model; Step 2: Extract particle feature information through the particle recognition and modeling module, generate a three-dimensional random particle model, merge it with the slope surface point cloud model to form a complete model with loose deposits, and convert it into STL format; Step 3: Import the complete model into the physics engine simulation module, configure the particle physical parameters, add environmental factors and instability triggering conditions, and run the simulation program to simulate the particle motion; Step 4: Through the results analysis and decision-making module, combined with qualitative and quantitative analysis, determine the degree of slope stability and output the analysis results.

[0014] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention can quickly acquire structural data of loose deposit slopes through drone photography or scanning. Based on a reliable image recognition module, it rapidly obtains granular structure information of the loose deposits. Combined with a physical simulation engine, it can complete the stability analysis of loose deposit slopes in a short time. The built-in decision analysis module meets the needs of human-computer interaction and rapid decision-making, greatly improving work efficiency. Compared to traditional evaluation methods that involve observation, measurement, and then numerical simulation analysis, this invention can shorten the analysis time from hours or even days to minutes or tens of minutes. Attached Figure Description

[0015] 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.

[0016] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 A schematic diagram of the particle identification and modeling process; Figure 3 This is a schematic diagram of the user interface of a rapid analysis system for the stability of loose deposit slopes. Detailed Implementation

[0017] 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.

[0018] 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.

[0019] like Figure 1 As shown, this invention provides a rapid analysis method for the stability of loose deposit layer slopes, including: Step 1: Use the slope measurement and modeling module to acquire slope images and distance measurement data, and generate a slope surface point cloud model; Step 2: Extract particle feature information through the particle recognition and modeling module, generate a three-dimensional random particle model, merge it with the slope surface point cloud model to form a complete model with loose deposits, and convert it into STL format; Step 3: Import the complete model into the physics engine simulation module, configure the particle physical parameters, add environmental factors and instability triggering conditions, and run the simulation program to simulate the particle motion; Step 4: Through the results analysis and decision-making module, combined with qualitative and quantitative analysis, determine the degree of slope stability and output the analysis results.

[0020] This invention also provides a rapid analysis system for the stability of loose deposit layer slopes, the system comprising: a slope calculation and modeling module, a particle identification and modeling module, a physics engine simulation module, and a result analysis and decision-making module.

[0021] The slope measurement and modeling module uses a drone equipped with a high-definition camera, lidar scanning, and ranging system, enabling it to capture images of the slope from all angles. The drone's flight path can be automatically planned according to a preset program or remotely controlled by an operator. The flight altitude and shooting angle can be flexibly adjusted to ensure the acquisition of comprehensive and clear slope image data.

[0022] Aerial images are stitched together and combined with lidar scanning and ranging systems to calculate parameters such as slope height, slope angle, slope length, and platform width of loose deposit slopes, and generate a point cloud model of the slope surface.

[0023] The particle identification and modeling module uses close-range drone images to capture structural features of the particles, and a laser ranging system is used to calibrate the coordinates. Image recognition algorithms are then used to identify key features such as particle size, dimensions, shape, and gradation in the loosely packed layer.

[0024] The image recognition method described herein specifically utilizes MATLAB software for image processing. It reads a drone-captured image, converts it to grayscale, and performs threshold segmentation to obtain a binarized image of the accumulated particles. In a loosely packed layer, gaps exist between the particles, and there are color differences between the particles and their surroundings. During binarization, the RGB value (P) of a specific pixel within a particle to be identified is used as the basis. The RGB values ​​(X) of the remaining pixels in the image differ from P by a certain value (D). Before processing, a suitable threshold (T) needs to be set. When D is greater than T, the pixel is identified as a gap; when D is less than T, the pixel is identified as a particle. The threshold is determined through trial and error; a smaller threshold retains more particle information in the image.

[0025] (1) Setting a threshold filters out excessively small particle outlines, further improving computational efficiency.

[0026] When the boundaries of particles are blurred, Photoshop software can be used to sharpen the image, enhance contrast, and increase brightness to make it easier to obtain information such as the number, size, and shape characteristics of the particles. A program should be developed to count the number of particles per unit area in the image.

[0027] Write a loop to randomly generate particles in three-dimensional space. The size, density, and other features of the random particles are based on image recognition results. To improve the irregularity of the particles, the Voronoi polyhedron algorithm can be used.

[0028] Import the slope topographic point cloud from the previous step into MATLAB software and mesh it. Use the topographic point cloud as the lower boundary and generate several random particles of a certain thickness on the upper part to simulate the loose deposit layer.

[0029] The thickness of the loose sedimentary layer of the metamorphic layer can be based on geological survey data or data revealed by field investigation.

[0030] Convert all generated model formats in MATLAB software to the engineering mesh model and save them as "STL" or other formats for easy import into the physics engine simulation module.

[0031] The physics engine simulation module imports the generated model into the Unity physics engine, adding a Rigidbody and collider to each particle. The parameters must conform to the physical properties of the rock block and can be imported from geotechnical engineering investigation reports, etc. Further, it sets key information such as the dynamic friction coefficient, static friction coefficient, elasticity, and mass of the block. Environmental factors such as drag, gravity, and angular drag are added to enhance instability triggering and simulation control. The engine is then invoked to implement the physical effects of particle collision, friction, and motion.

[0032] Add instability triggering and simulation control, for example, by breaking the initial equilibrium through external disturbances to simulate natural instability (such as earthquakes, rain erosion or gravity overload), or by removing support blocks (simulating bottom excavation conditions).

[0033] To improve the visual effect, texture rendering and dust particles can be added to the loose deposit slope.

[0034] The results analysis and decision-making module determines the slope stability by combining qualitative and quantitative analysis.

[0035] The qualitative analysis is based on simulation phenomena from the Unity physics engine to determine the stability of the slope.

[0036] Physics engine simulations can visualize the macroscopic deformation characteristics of slopes through animation, allowing on-site engineers to make a preliminary assessment of slope stability. Slope stability can be categorized as follows: Stable state: Slope particles undergo only minor movement with no overall displacement trend, and the surface has no cracks or sliding surfaces; Understable state: Localized particle sliding and rolling downwards occurs on the slope surface, but no through cracks or sliding surfaces form; Critical instability state: Slope particles slide along the sliding surface as a whole, forming continuous through cracks, and particle compression and accumulation occur at the slope toe; Unstable state: The entire slope collapses, with a large number of particles detaching from their original positions, forming a sliding deposit.

[0037] The quantitative analysis allows Unity to output particle motion parameters (displacement, velocity, acceleration) and mechanical parameters (contact force, constraint force) in real time via scripts. Based on these data, quantitative indicators are constructed to quantitatively analyze the slope stability.

[0038] The quantitative indicators include displacement indicators, mechanical indicators, and structural indicators. These quantitative indicators are displayed in real time during the simulation. The critical threshold for slope instability can be determined based on the quantitative indicators. There is a default recommended value for the critical threshold, which can also be manually set. The selection of the critical threshold can be determined through engineering specifications, research reports, etc.

[0039] The displacement indicators include the cumulative displacement of the particles and the displacement rate.

[0040] The cumulative displacement refers to the cumulative displacement of particles under load. Before instability, the cumulative displacement will suddenly increase. For example, it can be set to be considered as the critical threshold for slope instability when it exceeds 20% of the slope height.

[0041] The displacement rate is used to calculate the instantaneous velocity of the particles. Under steady conditions, the rate of change of the instantaneous velocity of the particles is very small and close to 0. Before instability, the displacement rate of a large number of particles will show a step increase.

[0042] The instantaneous velocity of the particle is the ratio of the displacement of the particle to the running time.

[0043] The mechanical indicators are obtained by using Unity to obtain the contact force between particles and to calculate the ratio (F) of the anti-slip force (normal force) of all particles perpendicular to the sliding surface / the sliding force (tangential force) along the sliding surface. If the ratio is less than 1.0, it indicates that the slope has a tendency to slide, and the larger the ratio is, the more unstable the slope is.

[0044] The structural index is the ratio of the number of particles in effective contact (excluding particles that have broken contact) to the total number of particles. A ratio > 0.8 indicates tight interlocking between particles and slope stability; a ratio < 0.4 indicates that a large number of particles have broken contact and the structure has collapsed.

[0045] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0046] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A rapid analysis system for the stability of loose deposit layer slopes, characterized in that, It includes modules for slope calculation and modeling, particle identification and modeling, physics engine simulation, and results analysis and decision-making. The slope measurement and modeling module uses a drone equipped with a high-definition camera, lidar scanning and ranging system to acquire slope image data, measure slope height, slope angle, slope length and platform width parameters, and generate a slope surface point cloud model. The particle identification and modeling module is used to identify the size, dimensions, shape, and gradation characteristics of particles in the loose deposit layer, generate a three-dimensional random particle model similar to the actual particle characteristics, and merge it with the slope topography point cloud model to form a complete model containing the loose deposit layer. The physics engine simulation module is used to import complete models, configure particle physical parameters, add environmental factors and instability triggering conditions, and realize the simulation of the physical effects of particle motion. The results analysis and decision-making module determines the stability of the slope and outputs the analysis results by combining qualitative and quantitative analysis.

2. The rapid analysis system for the stability of loose deposit layer slopes according to claim 1, characterized in that, In the slope measurement and modeling module, the UAV flight path is automatically planned through a preset program or remotely controlled by the operator, and the flight altitude and shooting angle can be flexibly adjusted.

3. The rapid analysis system for the stability of loose deposit layer slopes according to claim 1, characterized in that, The slope surface point cloud model is generated by stitching together LiDAR and slope image data.

4. The rapid analysis system for the stability of loose deposit layer slopes according to claim 1, characterized in that, The workflow of the particle identification and modeling module includes: The structural features of the particles are obtained by taking images from the front view using a drone, and the coordinates are calibrated by the ranging system. The MATLAB software was used to process the images of granular structure features. The images were converted into grayscale images and thresholded to obtain binarized images. Thresholds were set to filter out the outlines of excessively small granular particles. When the boundaries of the particles are blurred, Photoshop software is used to sharpen, enhance contrast, and increase brightness; the number, size, and shape parameters of the particles are counted, the number of particles per unit area is calculated, and mapped to three-dimensional space; Irregular random particles were constructed using the Voronoi polyhedron algorithm, and the thickness of the loose deposit layer was determined by combining geological survey data or field survey data to simulate the loose deposit layer. Convert the model generated in MATLAB to STL format and import it into physics engine simulation software or geotechnical engineering numerical simulation software.

5. The rapid analysis system for the stability of loose deposit layer slopes according to claim 1, characterized in that, The physics engine simulation module uses the Unity physics engine, adds a Rigidbody and a collider to each particle, imports the physical property parameters of rock blocks from the geotechnical engineering investigation report, and configures the dynamic friction coefficient, static friction coefficient, elasticity, and mass. The environmental factors include resistance, gravity, and angular drag; the instability triggering conditions include external disturbance simulating natural instability and removing support blocks simulating bottom excavation conditions.

6. The rapid analysis system for the stability of loose deposit layer slopes according to claim 1, characterized in that, The qualitative analysis of the results analysis and decision-making module is based on the simulation phenomena of the Unity physics engine, and divides the slope stability state into stable state, understability state, critical instability state, and unstable state. The stable state is characterized by only minor movement of slope particles with no overall displacement trend and no cracks or sliding surfaces on the surface; the under-stable state is characterized by localized particle sliding and rolling downwards on the slope surface, but without the formation of through cracks or sliding surfaces; the critical state of instability is characterized by the overall sliding of slope particles along the sliding surface, forming continuous through cracks, and particle compression and accumulation at the slope toe; the unstable state is characterized by the overall collapse of the slope, with a large number of particles leaving their original positions and forming a sliding accumulation body.

7. The rapid analysis system for the stability of loose deposit layer slopes according to claim 1, characterized in that, The quantitative analysis of the results analysis and decision-making module is based on the particle motion parameters and mechanical parameters output in real time by Unity scripts, and constructs displacement indicators, mechanical indicators, and structural indicators. The displacement-related indicators include cumulative displacement and displacement rate. The cumulative displacement is the cumulative displacement of the particle under load, and the displacement rate is the instantaneous velocity of the particle, i.e., the ratio of displacement to running time. The mechanical indicators are the ratio of the anti-slip force of all particles perpendicular to the sliding surface to the sliding force along the sliding surface. The structural indicators are the ratio of the effective contact number between particles to the total number of particles.

8. A rapid analysis method for the stability of loose deposit layer slopes, characterized in that, Includes the following steps: Step 1: Use the slope measurement and modeling module to obtain slope images and distance measurement data, and generate a slope surface point cloud model; Step 2: Extract particle feature information through the particle recognition and modeling module, generate a three-dimensional random particle model, merge it with the slope surface point cloud model to form a complete model with loose deposits, and convert it into STL format; Step 3: Import the complete model into the physics engine simulation module, configure the particle physical parameters, add environmental factors and instability triggering conditions, and run the simulation program to simulate the particle motion; Step 4: Through the results analysis and decision-making module, combined with qualitative and quantitative analysis, determine the degree of slope stability and output the analysis results.

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