A slope stability optimization reinforcement method based on fault slip mode

Abstract

This invention relates to the field of slope stability technology, and more particularly to a slope stability optimization and reinforcement method based on fault sliding mode. The method includes the following steps: acquiring basic information of the target slope, determining the slope's state data by combining geological conditions and geometric information, detecting the fault zone's strike trend based on geological data, performing bedding-parallel sliding detection on the slope state, analyzing the cumulative increase of sliding stress, performing reverse fault jacking detection, estimating the slope's progressive failure trend by combining sliding stress and jacking conditions, determining the slope stability decay based on the failure growth trend, tracing the source data of missing stability data, and performing reinforcement optimization calculations to derive a reinforcement scheme. This invention achieves greater slope stability through slope reinforcement.

Description

Technical Field

[0001] This invention relates to the field of slope stability technology, and in particular to a slope stability optimization and reinforcement method based on fault sliding mode. Background Technology

[0002] Existing slope stability analysis methods largely rely on geological surveys and traditional physical models, primarily calculating slope safety factors or predicting slope instability through static analysis. However, slope stability is influenced by various factors, such as geological conditions, groundwater seepage, and external loads. Traditional methods often neglect the impact of complex geological faults and their sliding modes on slope stability. Therefore, how to consider the dynamic impact of fault sliding on slopes and combine real-time monitoring data with advanced numerical simulation techniques for accurate slope analysis and reinforcement has become a current research hotspot. This paper proposes a slope stability optimization and reinforcement method based on fault sliding modes. Addressing the shortcomings of traditional analysis methods, this method proposes a more accurate slope stability analysis and reinforcement optimization approach through a comprehensive evaluation of slope geological conditions, fault sliding modes, and support structures. Summary of the Invention

[0003] Therefore, it is necessary to provide a slope stability optimization and reinforcement method based on fault sliding mode to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a slope stability optimization and reinforcement method based on fault slip mode includes the following steps: Step S1: Obtain the basic information of the target slope; determine the geological condition data of the target slope based on the basic information of the target slope; determine the geometric information of the target slope based on the basic information of the target slope; determine the state data of the target slope based on the geometric information and the geological condition data of the target slope. Step S2: Detect the strike trend data of the fault area based on the geological condition data of the target slope; use the strike trend data of the fault area to detect the bedding sliding of the target slope, and obtain the bedding sliding condition of the target slope; determine the cumulative increase of bedding sliding stress based on the bedding sliding condition of the target slope. Step S3: Use the fault zone strike trend data to perform reverse fault jacking detection on the target slope status data to obtain the reverse fault jacking status of the target slope; estimate the gradual failure growth of the slope based on the cumulative growth of slope bedding sliding stress and the reverse fault jacking status of the target slope. Step S4: Determine the target slope stability decay status based on the progressive failure growth of the slope; trace the source data of missing slope stability based on the target slope stability decay status; perform reinforcement optimization calculations based on the source data of missing slope stability to obtain slope stability reinforcement data.

[0005] This invention presents a slope stability optimization and reinforcement method based on fault sliding modes. Through comprehensive analysis of basic information, geological conditions, and geometric information of the target slope, it can effectively assess and predict slope stability, especially in the face of slope instability under complex geological conditions. This method obtains accurate slope state data based on detailed geological conditions and geometric information, and then accurately detects factors such as bedding-parallel sliding and reverse fault jacking, providing reliable data support for subsequent stability analysis. By comprehensively estimating the cumulative growth of bedding-parallel sliding stress and reverse fault jacking, it can deeply reveal the potential instability risk and progressive failure trend of the slope. Furthermore, through retrospective analysis of slope stability decay, this method further identifies the root causes of slope instability loss, thus providing a scientific basis for formulating precise reinforcement optimization strategies. Finally, by performing reinforcement optimization calculations, a reasonable slope stability reinforcement scheme is derived, thereby effectively improving slope stability, reducing the risk of geological disasters, and ensuring the safety of related engineering construction. Attached Figure Description

[0006] Figure 1 This is a schematic diagram illustrating the steps of a slope stability optimization and reinforcement method based on fault slip mode; Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2. Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0007] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0008] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0009] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0010] To achieve the above objectives, please refer to Figures 1 to 3 A slope stability optimization and reinforcement method based on fault sliding mode includes the following steps: Step S1: Obtain the basic information of the target slope; determine the geological condition data of the target slope based on the basic information of the target slope; determine the geometric information of the target slope based on the basic information of the target slope; determine the state data of the target slope based on the geometric information and the geological condition data of the target slope. In this embodiment of the invention, a high-precision digital surface model (DSM) of the target slope area is constructed using an unmanned aerial vehicle (UAV) aerial photogrammetry system. By setting a fixed flight path and image overlap rate, point cloud data of the overall spatial coordinates of the target slope is collected, and the positions of the slope toe, top, and central feature points are marked in the point cloud data. A three-dimensional laser scanning device is used to supplement and calibrate the point cloud data, ensuring that the spatial accuracy of the slope geometry reaches the centimeter level. Geometric parameters such as slope height, slope length, slope width, and slope angle are extracted from the point cloud data to obtain the geometric information of the target slope. Subsequently, geological condition data of the target slope's soil and rock mass are obtained through drilling sampling and on-site core analysis. Specifically, this includes rock layer thickness, lithological assemblage, bedding plane strike and dip angle, soil and rock mass density, water content, and groundwater level depth. Shear strength parameters of the soil and rock mass, including cohesion, are obtained through indoor triaxial shear tests and direct shear tests. and internal friction angle Rock mass strength indices were obtained through uniaxial compressive strength tests, and groundwater level temporal variations on the slope were acquired using data from groundwater monitoring wells. After determining the slope's geometric information and geological conditions, the slope state data was calculated. The slope state data includes the potential sliding force and resisting sliding force. The following simplified formula for resisting sliding stability was used: ; in, Indicates the slope safety factor; It is the cohesion of soil and rock; The length of the sliding surface; The weight of the sliding body; The inclination angle of the sliding surface; Pore ​​water pressure; It represents the internal friction angle between the rock and soil.

[0011] The slope safety factor is obtained through calculation. By combining geological parameters and geometric information, slope condition data is generated for subsequent fault impact analysis.

[0012] Step S2: Detect the strike trend data of the fault area based on the geological condition data of the target slope; use the strike trend data of the fault area to detect the bedding sliding of the target slope, and obtain the bedding sliding condition of the target slope; determine the cumulative increase of bedding sliding stress based on the bedding sliding condition of the target slope. In this embodiment of the invention, in this step, the distribution area of ​​the slope fault is identified based on the geological condition data obtained in step S1. Using geological profile mapping results and remote sensing interpretation results, the boundary line features of the fault zone are extracted, and the strike data of the fault is determined by azimuth calculation. The fault strike data is input into the geological structure trend analysis module, and the least squares method is used to fit the azimuth of multiple fault measuring points to obtain the strike trend data of the fault area. The fault strike trend data is a numerical range describing the main extension direction of the fault in space. The fault strike trend data is compared with the dip and dip angle of the bedding planes in the slope condition data, and the angle relationship discrimination formula is used: ; in, The angle between the fault strike and the bedding plane strike; The angle of the fault strike; The angle of the bedding plane orientation. When And the dip angle of the bedding plane Greater than When the condition is met, it is determined that the bedding sliding condition exists. Based on this determination, the bedding sliding condition of the slope is identified, and the spatial location and geometry of the potential sliding surface are recorded in the target slope state data.

[0013] After identifying the bedding-parallel slip condition, the stress distribution on the potential slip surface is calculated. The slip force calculation formula is used as follows:

[0014] in, This represents the sliding force along the sliding surface; The weight of the sliding body; The inclination angle of the sliding surface; Pore ​​water pressure; The length of the sliding surface.

[0015] Step S3: Use the fault zone strike trend data to perform reverse fault jacking detection on the target slope status data to obtain the reverse fault jacking status of the target slope; estimate the gradual failure growth of the slope based on the cumulative growth of slope bedding sliding stress and the reverse fault jacking status of the target slope. In this embodiment of the invention, the fault region strike trend data and target slope state data obtained in step S2 are used to determine the reverse fault jacking influence area of ​​the slope. Reverse fault jacking refers to the pressure exerted on the slope by a fault during reverse movement, leading to slope instability. Based on slope geometry and geological conditions, the target slope region is modeled using geological profiles and a three-dimensional model, employing finite element analysis software (such as ABAQUS or FLAC3D) to simulate the impact of fault activity on the slope. Specifically, a reverse fault force model is constructed by combining fault strike trend data with slope lithology and bedding information. In this model, the magnitude and direction of the fault jacking force are jointly determined by factors such as fault strike and soil strength. In the calculation, the reverse fault jacking force... The calculation formula is: ; in, This is the thrusting force of the reverse fault; The pressure generated by fault activity; The fault contact surface area; The angle between the fault plane and the horizontal plane is given by this formula. The distribution of the reverse fault thrust is obtained through this formula, thus determining the reverse fault thrust status of the target slope area. This data can be used for subsequent slope failure trend prediction. Next, combined with the data on the cumulative growth of slope bedding sliding stress obtained in step S2, the progressive failure growth of the slope is further evaluated. Progressive failure growth refers to the process of gradual slope instability as external forces (such as reverse fault thrust) and internal sliding stress increase. By accumulating and superimposing the sliding surface stress and reverse fault thrust in the time series, a linear regression model is used to fit and obtain the accelerating trend of slope failure. Finally, the data results of progressive slope failure growth are obtained, providing a basis for the stability decay calculation in step S4.

[0016] Step S4: Determine the target slope stability decay status based on the progressive failure growth of the slope; trace the source data of missing slope stability based on the target slope stability decay status; perform reinforcement optimization calculations based on the source data of missing slope stability to obtain slope stability reinforcement data.

[0017] Using the data on the progressive failure growth of the slope obtained in step S3, and combining it with slope geological strength parameters (such as soil shear strength and internal friction angle), the stability decay of the target slope is calculated. In practice, a stability analysis model (such as the Mohr-Coulomb criterion or the effective stress method) is used to assess the stability changes of the slope under external faulting. The following stability decay calculation formula is used:

[0018] in, This represents the slope stability attenuation amount; It is the cohesion of soil and rock; The length of the sliding surface; The weight of the sliding body; The inclination angle of the sliding surface; The internal friction angle between rock and soil; This represents the decrease in the friction angle; is the initial stability coefficient.

[0019] Based on the calculation results, the slope stability attenuation was obtained, reflecting how the slope's safety factor changes over time due to factors such as reverse fault jacking and bedding-parallel sliding. Next, based on the slope stability attenuation data, causal link analysis was used to trace the source data of the missing slope stability data. These source data include: local stress accumulation caused by fault activity, rock or soil failure, and slip surface formation. Through structural sensitivity analysis, key factors affecting stability were identified, and factors leading to slope instability were determined. Finally, based on the source data of the missing stability data, slope reinforcement optimization calculations were performed. The reinforcement calculations used geotechnical engineering optimization algorithms, such as genetic algorithms (GA) or particle swarm optimization (PSO), to design slope reinforcement based on the following optimization objectives and constraints:

[0020] in, The strength coefficient of the reinforcing material; For the first The distance or thickness of the reinforcement points is determined. Through the above optimization calculations, the reinforcement scheme and required reinforcement materials are determined. Finally, based on the optimization calculation results, slope reinforcement design data is obtained, including the required support structure type, reinforcement material type, and construction method. This reinforcement data will be used to implement slope reinforcement measures, thereby effectively improving slope stability and ensuring slope safety under future changes in external factors.

[0021] Preferably, step S1 includes the following steps: Step S11: Obtain basic information of the target slope; In this embodiment of the invention, basic information about the target slope is collected. This basic information mainly includes the slope's geographical location, surrounding environment, climate conditions, historical geological activity, and initial state data. To ensure data accuracy, a combination of ground surveying and remote sensing technologies is used for information collection. Specifically, the geographical coordinates, slope, elevation, and slope area of ​​the target slope are acquired using field surveying tools (such as total stations, GPS, and laser scanners). Simultaneously, remote sensing satellite imagery and aerial photography are used to acquire regional imagery data to assess the slope's basic environment at a macroscopic level. Cross-validation from multiple data sources ensures high accuracy of the obtained basic information. During the measurement process, all data is processed using professional engineering surveying software to accurately convert and analyze the data, ensuring that each collected data point matches the actual condition of the slope.

[0022] Step S12: Determine the geological condition data of the target slope based on the basic information of the target slope; In this embodiment of the invention, after obtaining the basic information of the target slope, the geological conditions of the target slope are further analyzed based on this information. Specifically, based on the geographical location and surrounding environmental data obtained in step S11, information such as the distribution of soil and rock, stratigraphic structure, lithological characteristics, and soil type of the target slope is obtained through geological exploration methods (such as borehole exploration, ground-penetrating radar detection, seismic wave reflection, etc.). In this step, vertical profiles of the soil are obtained through borehole or ground-penetrating radar detection methods to determine the properties and strength parameters (such as cohesion, internal friction angle, void ratio, saturation, etc.) of soil and rock at different layers. Furthermore, the elastic modulus and wave velocity of the soil layers are measured using seismic wave reflection technology to further analyze the structural characteristics of the soil and rock layers. All collected geological condition data will be processed and analyzed using professional geological analysis software to ensure that the obtained data has sufficient accuracy and meets the requirements of slope stability analysis. This geological data provides the basic input for subsequent steps.

[0023] Step S13: Determine the geometric information of the target slope based on the target slope foundation information; In this embodiment of the invention, based on the geological condition data obtained in step S12, the geometric information of the target slope is further extracted from the basic information of the target slope. The geometric information of the target slope includes the slope morphology, elevation distribution, slope variation, geometric dimensions of various parts of the slope (such as height, width, slope angle, etc.), and geometric characteristics of the potential sliding surface. In this step, a geometric model of the target slope is constructed using elevation data and laser scanning data through 3D modeling technology. Three-dimensional coordinate data of the slope surface is obtained through a combination of high-precision laser scanners, UAV aerial photography, and ground control points. Subsequently, professional 3D modeling software (such as AutoCAD, ArcGIS, Civil 3D, etc.) is used for data processing to convert the point cloud data of the slope surface into a 3D digital model. By analyzing the 3D model of the slope, geometric parameters such as the location, slope, height, and width of the slope and sliding surface are calculated. This geometric data is crucial for analyzing landslide patterns, predicting the location and area of ​​potential sliding surfaces, and assessing slope stability in subsequent steps.

[0024] Step S14: Determine the target slope status data based on the target slope geometric information and target slope geological condition data.

[0025] In this embodiment of the invention, based on the target slope geometric information obtained in step S13 and the geological condition data obtained in step S12, the state data of the target slope is further determined. The purpose of this step is to combine geological conditions with geometric information to analyze the overall stability of the target slope and construct the initial state data of the slope. Through the analysis of geometric information, combined with soil and rock strength parameters, physical properties of soil layers, and the geometric dimensions of the potential sliding surface, the initial stability of the slope is evaluated. At this point, the limit equilibrium method (such as the Mohr-Coulomb criterion) or the finite element method (such as ANSYS, FLAC3D) is used for slope stability analysis. These analyses can provide the stability state of various parts of the slope and obtain the safety factor data for each area. Combining the initial stability calculation results of the slope with historical landslide records, precipitation, and other external factors of the target slope, dynamic analysis is performed to predict the stability changes of the slope under different working conditions. By summarizing these factors, the state data of the target slope is determined, including the initial safety factor of the slope, the potential sliding surface area, and its changing trend under different environmental factors.

[0026] Preferably, step S14 includes the following steps: Step S141: Measure the elevation data of the target slope based on the geometric information of the target slope to obtain the elevation data of the target slope; In this embodiment of the invention, based on the geometric information of the target slope obtained in step S13, the elevation data of the target slope is obtained through precise elevation measurement. To ensure the accuracy of the measurement results, high-precision measuring tools such as total stations, laser scanners, and UAV LiDAR are used. During the measurement process, measurement points are set up to acquire elevation data at different locations on the target slope, ensuring that the measurement covers the entire range of the slope. An elevation distribution map of the slope is constructed using these measurement data. When using a total station, the elevation of each measurement point is calculated by setting a reference point and recording the horizontal and vertical angles of multiple measurement points. For areas that are difficult to access, a UAV carrying a LiDAR scanning device is used to obtain point cloud data of the slope surface. Using the point cloud data, computer-aided design software (such as AutoCAD and ArcGIS) is used to post-process the data and calculate the elevation data of the target slope. Through data analysis, the specific elevation value of each measurement point is obtained, and combined with the actual terrain conditions of the slope, complete slope elevation information is formed.

[0027] Step S142: Calculate the target slope volume data based on the target slope elevation data; In this embodiment of the invention, based on the elevation data obtained in step S141, the volumetric data of the target slope is further calculated. The volumetric data includes the total volume of the slope, the volume distribution of the slope body, and the soil volume of each region. The elevation data is input into a digital terrain model (DTM), and the topographic features of the slope are presented in the form of contour lines. Then, volume calculation is performed using two-dimensional or three-dimensional modeling software (such as Civil 3D or AutoCAD). During volumetric calculation, the spacing and elevation changes between each contour line segment in the digital terrain model are used to calculate the volume at each level. The slope volume is calculated in detail using a layer-by-layer integration method. The volumetric data reflects the overall scale of the slope and the volume of the potential sliding surface, and is an important basis for subsequent slope stability calculations. The calculated target slope volumetric data will provide important physical parameters for subsequent stability analysis.

[0028] Step S143: Determine the target slope morphology data based on the target slope geometry information; In this embodiment of the invention, this step further analyzes the morphological data of the target slope based on the geometric information of the target slope obtained in step S13. Specifically, the morphological data of the target slope is obtained by calculating the overall morphological characteristics of the slope, including slope gradient, slope shape, excavation method, and geometric features of the slope toe and crest. To improve the accuracy of the morphological data, three-dimensional modeling technology is used to transform the slope's geometric information into a three-dimensional model and extract the key geometric parameters of the slope. In practice, three-dimensional modeling software such as AutoCAD or Revit is used to establish a three-dimensional model of the target slope based on elevation and geometric data. Then, by analyzing the slope morphology, slope changes, and response to external forces in each part of the model, morphological data related to stability are extracted. This morphological data includes slope distribution, slope undulation, etc., which can provide important basis for subsequent landslide prediction and stability analysis.

[0029] Step S144: Based on the target slope morphology data and target slope volume data, assess the potential sliding force growth of the target slope to obtain the potential sliding force growth data of the slope. In this embodiment of the invention, in this step, the potential sliding force growth of the target slope is assessed by combining the target slope morphology data from step S143 and the target slope volume data calculated in step S142. This assessment aims to infer the sliding force growth by analyzing the slope's geometry and soil volume changes. For this purpose, the sliding force needs to be calculated based on the slope volume data and slope morphology data. The sliding force is inferred through the accumulation of vertical loads on the sliding surface. In specific operation, the limit equilibrium method (such as the Mohr-Coulomb criterion) and the sliding surface shear stress method are used to calculate the potential sliding force in each region based on the slope's geometric data, the physical and mechanical properties of the soil, and slope changes. By progressively analyzing the stability of each sliding surface, the distribution of sliding force in different parts of the slope is assessed. This assessment can identify landslide areas and potential sliding force growth trends, thus providing a basis for subsequent stability decay analysis.

[0030] Step S145: Evaluate the slope's soil and rock strength parameters based on the target slope's geological condition data; In this embodiment of the invention, the soil and rock strength parameters of the slope are evaluated based on the geological condition data of the target slope obtained in step S12. These parameters mainly include cohesion, internal friction angle, elastic modulus, and void ratio, which directly affect the slope's stability. In practice, the mechanical parameters of the slope soil are obtained through laboratory tests (such as triaxial shear tests, direct shear tests, and uniaxial compression tests). Secondly, regional analysis is conducted based on the soil mechanical properties of different rock layers to ensure that the strength parameters of each part of the slope accurately reflect the physical properties of different strata. Through the analysis of different soil and rock layers, the strength parameters of each part of the slope are obtained and input into the slope stability analysis model to calculate the slope's safety factor and the stability of the sliding surface.

[0031] Step S146: Determine the initial stability of the target slope based on the slope soil and rock strength parameters and the potential sliding force growth data; In this embodiment of the invention, the initial stability of the target slope is determined based on the slope soil and rock strength parameters obtained in step S145 and the potential sliding force growth data assessed in step S144. During this process, the stability of the target slope is analyzed using limit equilibrium methods (such as the Mohr-Coulomb criterion) or finite element methods (such as FLAC3D). The safety factor of the target slope is calculated by combining the soil and rock strength parameters and the sliding force data. In operation, professional engineering analysis software (such as PLAXIS, FLAC3D) is used to input soil and rock strength parameters, sliding force distribution data, slope geometry, and volume data to perform initial stability analysis of the slope. The calculated safety factor value determines whether the target slope is at risk of landslide and further predicts the behavior of the potential sliding surface. This analysis provides a technical basis for the design of slope reinforcement measures.

[0032] Step S147: Determine the target slope state data based on the initial stability of the target slope.

[0033] In this embodiment of the invention, based on the initial stability status of the target slope obtained in step S146, the state data of the target slope is further determined. By assessing the initial stability of the slope and combining it with historical monitoring data (such as precipitation, seismic activity, temperature changes, etc.), the dynamic state data of the target slope is updated. This data includes the real-time stability of the slope, changes in the location of the slip surface, and changes in the slope morphology. Continuous monitoring and data analysis ensure the dynamic updating of the target slope state data. This data will provide crucial support for subsequent slope stability optimization and reinforcement design, ensuring the stability of the slope under different environmental changes.

[0034] Preferably, step S2 includes the following steps: Step S21: Monitor the location information of the fault area based on the geological condition data of the target slope to obtain the location information of the fault area; In this embodiment of the invention, the location information of the fault area is monitored based on the geological condition data of the target slope obtained in step S1. To ensure the accuracy and comprehensiveness of the location data, multiple methods such as ground-penetrating radar, seismic reflection detection (seismic waves), geological drilling, and laser scanning are used for comprehensive monitoring of the slope area. Specifically, multiple detection points (including drilling points and seismic reflection points) are deployed to collect geological data related to the target slope. The location and orientation of the fault plane are obtained using lidar or three-dimensional ground-penetrating radar technology to further determine the existence area and location of the fault. During this process, the depth and orientation of the fault are analyzed by the propagation time difference of seismic reflection waves through different strata, which can effectively identify the fault structure in and around the target slope. By post-processing the collected data (using geological software such as RockWorks or GeoGraph), the fault area can be spatially located, and the location data of the fault can be accurately extracted to obtain the location information data of the fault area. This information provides a reliable basis for subsequent fault orientation trend analysis.

[0035] Step S22: Detect the fault strike trend based on the fault region location information to obtain fault region strike trend data; In this embodiment of the invention, the fault region location information data obtained in step S21 is used to detect and analyze the strike trend of the fault. To ensure the accuracy of the analysis, methods such as geological structural line method, vector calculation method, and spatial cluster analysis are used. Based on the location information of the fault region, a line feature map of the fault region is drawn on the geological map and field data. Spatial analysis is performed using GIS software such as ArcGIS to connect multiple fault points, and the directional relationship between each fault point is analyzed using vector calculation method to calculate the strike data of the fault. To improve accuracy, spatial cluster analysis is used to group multiple fault points according to their geographical location and identify the strike trend of each group of faults. By performing regression analysis and fitting on the fault strike trend, the strike trend data of the fault region is obtained. The specific data includes the main strike direction of the fault, the strike dispersion, and the density distribution of the fault. This data is crucial for subsequent bedding-parallel slip detection because the strike of the fault directly affects the position and direction of the slip surface.

[0036] Step S23: Use the fault zone strike trend data to perform bedding-parallel sliding detection on the target slope status data to obtain the bedding-parallel sliding condition of the target slope; In this embodiment of the invention, the bedding-parallel sliding of the target slope is further detected using the fault strike trend data obtained in step S22. At this point, the sliding potential of different areas is assessed by combining the geometric structure data of the target slope with the fault strike trend data. A sliding surface model is established using a finite element analysis (FEA) model, incorporating the slope's elevation, slope, soil properties, and fault strike trend. By analyzing the bedding dip angle and dip direction of the sliding surface, and combining this with soil information of the slope (such as soil cohesion and friction angle), the distribution of potential sliding surfaces and the sliding risk are calculated. Specifically, by analyzing multiple sliding surfaces on the slope, the angular relationship between the fault strike trend and the bedding-parallel sliding surfaces is assessed, and its sliding dynamics are calculated. In operation, three-dimensional modeling is performed using software (such as FLAC3D or PLAXIS), and potential sliding areas are determined using the fault strike and bedding information of the slope. In the three-dimensional finite element model, the bedding structure data of the slope and the fault strike trend data are coupled to obtain the bedding-parallel sliding condition data of the target slope. By analyzing the bedding-parallel sliding behavior of the target slope, landslide-prone areas were identified, and the sliding risk in these areas was estimated. This data is of great significance for subsequent assessments of cumulative stress growth.

[0037] Step S24: Determine the cumulative increase of bedding-parallel sliding stress in the target slope based on the bedding-parallel sliding condition.

[0038] In this embodiment of the invention, combining the target slope bedding-sliding data obtained in step S23, the cumulative growth of slope bedding-sliding stress is further analyzed. This requires stress transfer and accumulation analysis using a sliding surface model based on the initial stress distribution of the bedding-sliding surface. Specifically, based on the stress distribution of the bedding-sliding surface, a shear stress calculation method for the sliding surface is used. The shear stress distribution of the sliding surface is calculated using finite element software (such as ANSYS or ABAQUS), thereby obtaining the stress values ​​at various points on the bedding-sliding surface. Stress accumulation increases over time; therefore, it is necessary to simulate the stress change process at different time points. Using a stress time-series accumulation analysis method, the stress values ​​of various parts of the slope are analyzed over time to obtain the cumulative stress growth data of the slope bedding-sliding. The cumulative stress growth is usually described by the interaction of shear stress, friction, and gravity on the sliding surface. Over time, when the stress in certain areas of the slope reaches a critical value, a landslide will occur. Therefore, in this step, by analyzing the shear stress of the sliding surface, the time point of slope failure and the scale of the landslide can be determined. Finally, based on the data on the cumulative stress growth, the cumulative stress growth of the bedding sliding of the target slope was determined.

[0039] Preferably, step S22 includes the following steps: Step S221: Extract geological structural line features from the location information of the fault area to obtain the boundary line data of the fault area; In this embodiment of the invention, geological information of the target slope area is analyzed, and the geological structure of the fault area is divided in detail using remote sensing imagery, geological maps, and field exploration data. Specifically, GIS software (such as ArcGIS) is used to perform spatial data analysis on the remote sensing images and geological exploration data of the target slope area. Fault structures in the slope are identified by extracting the line features of the geological structures. During the extraction process, boundary detection algorithms (such as Canny edge detection and Sobel operator) are used to identify the boundary lines of the fault area on the geological map. For complex boundary lines, image segmentation technology is used to further optimize the accuracy of fault line identification. Boundary line data, containing the accurate location, shape, and boundary features of the fault, is extracted using these methods. Through post-processing, combined with spatial data models and field verification data, the final fault area boundary line data is obtained. This data not only accurately marks the fault boundaries but also provides detailed geological structure information for subsequent steps, ensuring the accuracy of subsequent analysis.

[0040] Step S222: Calculate the fault azimuth angle of the fault region boundary line data to obtain the fault region azimuth angle data; In this embodiment of the invention, the fault boundary line data obtained in step S221 is used to calculate the azimuth angle of each fault line using a geometric calculation method. Specifically, this involves using a vector analysis method to determine the start and end points of the fault boundary, and then calculating the azimuth angle of the fault based on the direction of the line connecting the two points. The calculation formula is as follows:

[0041] in,( , )and( , These are the coordinates of the two endpoints of the fault line; This refers to the azimuth of the fault. This calculation is automated using the built-in analysis tools of GIS software (such as ArcGIS and QGIS). Using this method, the accurate azimuth of each fault is obtained. The purpose of this step is to convert the fault azimuth information into standardized angle data, which is crucial for subsequent fault strike dispersion analysis and consistent clustering.

[0042] Step S223: Perform fault strike dispersion analysis based on fault region azimuth data to obtain fault strike dispersion data; In this embodiment of the invention, based on the fault azimuth data obtained in step S222, a statistical analysis method is used to analyze the dispersion of the fault strike. The specific steps are as follows: collect the azimuth data of all faults and standardize them to ensure data uniformity and comparability. Then, calculate the standard deviation of all azimuths. The larger the standard deviation, the more dispersed the fault strike distribution; the smaller the standard deviation, the stronger the consistency of the fault strike. The specific calculation method is as follows: ; in, For the azimuth angle of each fault, The azimuth angle of all faults is the average. This represents the number of faults. By calculating this dispersion value, the consistency of the fault strike in the fault region can be determined.

[0043] Step S224: Perform fault strike consistency clustering based on the fault strike dispersion data to obtain fault strike clustering result data; In this embodiment of the invention, based on the fault strike dispersion data obtained in step S223, a clustering algorithm is used to perform consistency analysis on the fault strike. Common clustering methods (such as K-means clustering, DBSCAN, etc.) are used to group the fault azimuth data. The goal of clustering is to group faults with similar strikes into one class and separate faults with large strike differences. In specific operations, the fault azimuth data is preprocessed according to the dispersion data (such as noise reduction, smoothing, etc.), and then a suitable clustering algorithm is selected to group the fault strikes. By setting appropriate clustering parameters (such as distance metrics, thresholds, etc.), clustering of faults with similar strikes is achieved. The clustering results classify each fault into one or more classes, obtaining the clustering result data of the fault strikes. This operation helps to identify faults with similar strikes in the same region, thereby providing reliable data for subsequent trend fitting.

[0044] Step S225: Fit the fault strike trend data to the fault strike clustering results data to obtain the fault region strike trend data.

[0045] In this embodiment of the invention, based on the fault strike clustering results obtained in step S224, linear regression or least squares method is used to fit the fault strike trend of each cluster group. Specifically, regression analysis is performed on the azimuth data of each type of fault to fit the main direction of each cluster. At this time, a linear regression model is used, or when the fault strike data exhibits nonlinearity, a more complex curve fitting method is used. The goal of the fitting is to find the main direction trend of the fault region, i.e., the collective strike of all faults. The fault region strike trend data obtained after fitting provides an important basis for subsequent analysis. Finally, by weighted averaging the main directions of each cluster, the overall strike trend data of the region is obtained. The following linear regression formula is used in the calculation: ; in, To fit the trend angle, For regression coefficients, The intercept is... The x-axis represents the coefficients obtained through fitting. and It describes the main trend of the fault region.

[0046] Preferably, step S23 includes the following steps: Step S231: Extract the dip and dip angle of the bedding planes from the target slope state data to obtain the slope bedding structure parameters; In this embodiment of the invention, the dip direction and dip angle of the bedding planes of the slope are extracted by analyzing the target slope state data. Specifically, the following steps are taken: Three-dimensional spatial data of the target slope is obtained based on field survey data or by using remote sensing images, LiDAR scanning, etc. Based on this spatial data, geological analysis software (such as ArcGIS, Surfer) is used to extract the bedding planes of the slope. The dip direction of the bedding plane refers to the inclination direction of the bedding plane, while the dip angle refers to the angle between the bedding plane and the horizontal plane. The location of each bedding plane is extracted by cross-sectioning the three-dimensional data of the slope, and the dip angle of the bedding plane is calculated based on its geometric characteristics. The calculation method uses the following formula:

[0047] in, For the height difference, The horizontal distance is used to obtain the dip angle of each bedding plane of the target slope and the bedding structure parameters of the slope, including the dip direction, dip angle and distribution of each layer.

[0048] Step S232: Compare the angle relationship between the slope bedding structure parameters and the fault strike trend data to obtain the matching relationship data between the bedding plane and the fault strike; In this embodiment of the invention, the slope bedding structure parameters obtained in step S231 are compared with the fault strike trend data obtained in step S22 to analyze the angular relationship between the two. Specifically, based on the dip angle and dip direction data of the slope bedding surfaces, the direction information of each layer is matched with the fault region strike trend data, and the normal vector of each layer is calculated based on the dip direction and dip angle of the bedding surfaces. Then, by comparing the normal vector of the bedding surfaces with the strike trend of the fault, the angle between the two is calculated. A smaller angle indicates that the bedding surfaces and the fault strike are consistent, while a larger angle indicates that the strikes of the two are significantly different. The calculation method is as follows: ; in, and These are the normal vectors of the bedding plane and the fault, respectively. The angle between the two is given. This method yields matching data between each bedding plane and the fault strike. This data helps identify potential bedding-parallel slip surfaces and unstable regions.

[0049] Step S233: Based on the matching relationship data, determine the dip angle of the slope bedding to obtain the slope bedding dip angle determination result data; In this embodiment of the invention, bedding plane and fault strike matching relationship data obtained in step S232 are used to determine the bedding dip angle. Specifically, the degree of matching between each bedding plane and the fault strike is ranked according to the calculated angle values. Bedding planes with smaller angles are identified as areas with smaller bedding dip angles. The dip angle distribution of these areas is further analyzed to determine if there is a potential risk of bedding slip. By comprehensively analyzing the angles and dip angles between different bedding planes and the fault strike, the presence of a bedding dip angle and its stability information for each bedding plane are obtained. Finally, the output is a slope bedding dip angle discrimination dataset, containing the dip angle of each layer and its stability risk assessment. The key to this step is to assess the potential slip risk by accurately calculating the angular relationship between the dip angle of each layer and the fault strike.

[0050] Step S234: Identify potential sliding surfaces based on the slope bedding angle discrimination results to obtain potential sliding surface data of the slope; In this embodiment of the invention, potential sliding surfaces are identified based on the bedding angle discrimination results obtained in step S233. Specifically, based on the dip angle discrimination results of each layer, bedding planes with larger or nearly horizontal dip angles are selected as candidate areas for potential sliding surfaces. Then, further screening and analysis are performed by combining information such as the geological conditions of the slope, soil and rock strength, and seismic data. Mechanical calculations are performed on these potential sliding surfaces using a sliding dynamics model to analyze the sliding trend and deformation of each surface. The deformation behavior of the slope under different loads is simulated using geological stability analysis software (such as FLAC3D, GeoStudio, etc.). The identification results of the sliding surfaces will output a set of data indicating the spatial location, geometric characteristics, and stability of each potential sliding surface. The key to this step is to identify potential sliding surfaces by analyzing the dip angle and stability of each layer, combined with comprehensive factors of the slope, and to provide the location and stability analysis results of the sliding surfaces.

[0051] Step S235: Determine the bedding sliding condition of the target slope based on the potential sliding surface data of the slope.

[0052] In this embodiment of the invention, the bedding-parallel sliding condition of the target slope is determined based on the potential sliding surface data obtained in step S234. Specifically, the following steps are performed: Based on the potential sliding surface data, a finite element analysis method is used to numerically simulate the entire slope, analyzing the deformation behavior of the sliding surface under different working conditions. Based on the stability assessment results of each potential sliding surface, parameters such as sliding force, sliding depth, and sliding velocity are further calculated. During the analysis, slope stability analysis tools (such as GeoStudio, FLAC3D, etc.) are used for numerical calculation to obtain the slope stability coefficient (FS). Based on the calculated FS value, it is determined whether bedding-parallel sliding has occurred on the slope, and the degree of sliding risk. If the FS is less than a critical value, it indicates that the slope has a significant risk of bedding-parallel sliding.

[0053] Preferably, step S24 includes the following steps: Step S241: Extract the slope structural mechanical parameters based on the bedding sliding condition of the target slope to obtain slope mechanical parameter data; In this embodiment of the invention, the mechanical parameters of the slope structure are extracted based on the bedding-parallel sliding condition of the target slope. Specifically, based on the potential sliding surface and bedding-parallel sliding condition identified in the previous steps, the mechanical analysis of the slope is performed using finite element analysis or discrete element methods (e.g., FLAC3D or UDEC). These analysis software programs can perform detailed calculations based on the slope's geometric information, soil and rock properties, and external loads. In the model, each region of the slope is divided into multiple small units, and appropriate mechanical parameters are assigned to each unit, such as the compressive strength, shear strength, elastic modulus, and Poisson's ratio of the soil and rock. By calculating the stress distribution in the slope structure, the mechanical parameters of each region are extracted, specifically including shear stress, normal stress, and deformation. Finally, the obtained slope mechanical parameter data includes the stress state and deformation of each unit, providing data support for subsequent sliding surface force analysis.

[0054] Step S242: Decompose the sliding surface force into the slope mechanical parameter data to obtain the distribution data of the sliding surface force along the bedding plane; In this embodiment of the invention, the slope mechanical parameter data extracted in step S241 is used to decompose the forces acting on the bedding sliding surface of the slope. Specifically, based on the slope mechanical parameter data, the total force acting on the sliding surface is calculated, including the normal force perpendicular to the sliding surface and the tangential force parallel to the sliding direction. The normal force is generated by the weight of the slope and the pressure of the overlying soil layer, while the tangential force is mainly caused by shear stress. The normal and tangential forces at each point on the sliding surface are calculated using the following formulas: ; ; in, For normal stress, For shear stress, The area of ​​the force application is then considered. Next, taking into account the geometric characteristics of the sliding surface, these forces are decomposed into forces acting in different directions, yielding the distribution data of the forces acting along the bedding plane. This data includes the magnitude and direction of the forces acting on the sliding surface at different locations and is used for subsequent shear stress calculations.

[0055] Step S243: Calculate the shear stress of the sliding surface based on the force distribution data of the bedding sliding surface to obtain the shear stress data of the sliding surface; In this embodiment of the invention, the shear stress on the sliding surface is calculated using the force distribution data of the bedding-parallel sliding surface obtained in step S242. Specifically, based on the magnitude of the force in each sliding surface region, the shear stress at each location is calculated using the shear stress formula. The formula for calculating the shear stress is as follows:

[0056] in, The tangential force acting on the sliding surface. Let be the area of ​​the sliding surface region. By averaging and integrating the tangential forces in each small region of the sliding surface, the shear stress distribution across the entire sliding surface is obtained. These data represent the magnitude of the shear stress on different regions of the sliding surface, thus helping to determine whether the sliding surface is overloaded or in a critical sliding state.

[0057] Step S244: Evaluate the exceedance of shear stress on the bedding sliding surface based on the shear stress data of the sliding surface; In this embodiment of the invention, based on the shear stress data of the sliding surface obtained in step S243, it is assessed whether the shear stress of the bedding sliding surface exceeds the limit. Specifically, the critical value of the shear stress of the sliding surface is calculated based on the soil and rock strength parameters of the slope. The critical shear stress is determined by the material strength of the slope and the friction coefficient between the sliding surface and the soil, and the calculation formula is as follows:

[0058] in, This represents the maximum shear stress on the sliding surface. For normal stress, The friction coefficient is used. Next, the shear stress of the sliding surface obtained in step S243 is compared with the critical value to determine whether there is an over-limit situation. If the calculated shear stress is greater than the critical value, it indicates that the sliding surface has undergone shear failure, that is, the risk of bedding plane sliding is high.

[0059] Step S245: Perform time-series cumulative analysis on the data of excessive shear stress on the slope to obtain the time-series cumulative data of stress on the slope sliding surface; In this embodiment of the invention, time-series cumulative analysis is performed on the shear stress exceedance data obtained in step S244 to predict the growth trend of stress on the slope sliding surface. Specifically, for each sliding surface region, the shear stress exceedance at different time points is recorded and accumulated. By calculating the time-series data of shear stress exceedance on the sliding surface, the changing trend of shear stress exceedance is analyzed. The following formula is used to accumulate the time-series data:

[0060] in, Let be the shear stress exceeding the limit at time i. The number of time points, This represents the total accumulated shear stress. Using this method, the time-series accumulated shear stress data for each sliding surface region is obtained, providing a basis for subsequent stress growth trend analysis.

[0061] Step S246: Determine the stress growth trend based on the cumulative stress time series data of the slope sliding surface to obtain the cumulative growth of slope bedding sliding stress.

[0062] In this embodiment of the invention, the stress growth trend of the slope sliding surface is determined based on the cumulative stress time series data obtained in step S245. Specifically, the growth trend of shear stress is predicted using trend analysis methods (such as linear regression or polynomial fitting) based on the accumulated shear stress time series data. This analysis method identifies the growth rate and development trend of the shear stress on the slope sliding surface. For example, if the cumulative stress growth rate is rapid, it indicates that the slope is in an unstable state and will experience sliding failure. If the growth rate is slow, the slope is in a stable state. The determination of the stress growth trend will provide data support for subsequent slope reinforcement design. Ultimately, the result is the cumulative growth of the slope sliding stress, indicating whether the slope is in a critical state of sliding failure and whether the stress continues to increase.

[0063] Preferably, step S3 includes the following steps: Step S31: Use the fault zone strike trend data to perform reverse fault jacking detection on the target slope status data to obtain the reverse fault jacking status of the target slope; In this embodiment of the invention, the fault strike trend data obtained from step S2 is used to perform reverse fault pushing detection on the target slope's condition data. Specifically, the following steps are performed: A three-dimensional spatial model of the slope's fault condition is created based on the fault strike trend data of the slope area. The fault strike, depth, and active fault zone are determined using geological exploration data and seismic wave reflection data. Numerical calculation methods, such as the finite difference method (FDM) or finite element analysis (FEA), are used to simulate the stress state of the slope within the fault area. Next, the fault strike trend data is used to simulate the impact of reverse fault pushing on the target slope. Reverse fault pushing refers to the process in which rock layers above and below a fault plane are pushed towards the surface during crustal movement. This force affects slope stability, especially near the fault contact point. The distribution of reverse fault pushing on the slope is calculated using the finite element model, thus revealing the reverse fault pushing condition of the slope within the fault area. This process can identify the fault's movement trend and the resulting surface deformation, providing a basis for subsequent slope stability assessment.

[0064] Step S32: Determine the abnormal bearing condition of the slope support structure based on the reverse fault jacking condition of the target slope; In this embodiment of the invention, based on the reverse fault jacking condition of the target slope obtained in step S31, the bearing anomaly of the slope support structure is further determined. Specifically, the support structure of the target slope (such as support piles, anchor bolts, arch supports, etc.) is modeled. Numerical calculation software, such as ANSYS or ABAQUS, combined with the geological conditions of the slope and fault activity data, is used to perform a mechanical analysis of the support structure. This analysis includes various aspects such as stress, deformation, and stability of the support structure. By inputting the reverse fault jacking force into the support structure model, the influence of the jacking force on the support structure is simulated, especially the pressure distribution, deformation, and displacement of the support portion. Using stress-strain curves and slope stability analysis methods, the maximum bearing capacity of the support structure under fault jacking is calculated. If the calculation results show that the stress on the support structure exceeds its bearing capacity, or excessive deformation occurs, it is determined that the support structure has a bearing anomaly. Based on this, data on the bearing anomaly of the support structure are obtained for subsequent slope reinforcement design.

[0065] Step S33: Estimate the accelerating trend of dynamic deformation of the slope structure based on the cumulative increase of slope bedding sliding stress and the abnormal bearing condition of the slope support structure; In this embodiment of the invention, the dynamic deformation acceleration trend of the slope structure is estimated by combining the cumulative growth of bedding-parallel sliding stress obtained in step S24 and the abnormal bearing condition of the slope support structure obtained in step S32. Specifically, based on the time-series cumulative data of the bedding-parallel sliding stress, a dynamic model of the slope is established using numerical simulation methods (e.g., the finite element method). This model needs to consider the nonlinear behavior of the slope material, external loads (such as climate change and construction disturbance), and the interaction of internal structures. In the model, the cumulative bedding-parallel sliding stress data of the slope is coupled with the abnormal bearing condition of the support structure to simulate the combined effect of both on slope deformation. Using dynamic analysis methods, the stress distribution and deformation of the slope at different time points are calculated. At this time, the dynamic deformation acceleration trend is caused by two factors: the increase of bedding-parallel sliding stress and the mechanical anomalies of the support structure (such as local structural failure or support displacement). Based on the simulation results, the dynamic deformation trend of the slope structure is obtained, and it is determined whether there is a risk of accelerated failure. In this step, it is crucial to pay close attention to early signs of accelerated slope deformation, such as a rapid increase in deformation rate and an excessive increase in local stress. These signals indicate that the slope has entered an unstable state, thus providing early warning for subsequent damage assessment and reinforcement measures.

[0066] Step S34: Estimate the gradual failure growth of the slope based on the accelerating trend of dynamic deformation of the slope structure.

[0067] In this embodiment of the invention, the progressive failure growth of the slope is estimated based on the accelerated dynamic deformation trend of the slope structure obtained in step S33. Specifically, based on the accelerated deformation trend obtained in step S33, the rate of slope deformation and its impact on slope stability are analyzed. Combining the mechanical properties of the slope, material strength, and the stability of the support structure, the progressive failure process of the slope is modeled using numerical analysis methods (such as time-domain analysis or frequency-domain analysis). Progressive slope failure typically manifests as stress concentration, crack propagation, and surface subsidence. In the numerical model, based on the accelerated dynamic deformation trend, the failure development at different locations on the slope is calculated, with particular attention paid to the stress distribution on the sliding surface and the support structure. By simulating the failure process under different conditions, the speed and extent of failure propagation are evaluated, thereby predicting the probability of slope failure.

[0068] Based on the progression of damage, numerical simulations were used to calculate the time required for the slope to reach its failure state, and the failure trend was used to predict whether the slope would become completely unstable within a certain period of time. Ultimately, the gradual failure growth of the slope was obtained, providing a basis for subsequent reinforcement measures.

[0069] Preferably, step S32 includes the following steps: Step S321: Extract the force distribution characteristics based on the reverse fault jacking condition of the target slope to obtain reverse fault jacking force distribution data; In this embodiment of the invention, the force distribution characteristics are extracted based on the reverse fault jacking condition of the target slope obtained in step S31. Specifically, the slope is modeled in three dimensions using a fault jacking analysis model (such as finite element analysis), and the location information of the fault and the stress data caused by fault activity are input. Stress analysis is performed on different areas of the slope using the reverse fault jacking data to calculate the influence range of the fault jacking force on the target slope. During this process, the spatial distribution of the fault jacking force is calculated by combining field survey data, seismic wave reflection images, geological cross-section maps, and soil mechanical parameters, obtaining the force distribution data at different locations on the slope. Specifically, the magnitude, direction, and distribution of the reverse fault jacking force are accurately extracted using the finite element analysis method. These data characterize the changes in the mechanical properties of the slope under the influence of reverse fault activity, providing fundamental data for subsequent support structure analysis.

[0070] Step S322: Calculate the stress transmission of the slope support structure based on the distribution data of the reverse fault jacking force to obtain the stress transmission data of the slope support structure; In this embodiment of the invention, the stress transmission of the slope support structure is further calculated using the reverse fault jacking force distribution data obtained in step S321. Specifically, the slope support structure (such as anchor bolts, retaining piles, foundation reinforcement, etc.) is modeled in three dimensions, and the reverse fault jacking force distribution data is input to establish a mechanical analysis model of the slope support structure. The stress response of the support structure under reverse fault jacking is calculated using the analysis model. Computer-aided engineering analysis tools (such as ABAQUS or ANSYS) are used to solve for the stress transmission of the support structure, obtaining the stress distribution within the support structure. When calculating stress transmission, the nonlinear properties of the slope material and the mechanical characteristics of the slope's interior should be considered. Through the process of dynamically loading the reverse fault jacking force, the stress state of different parts of the support structure under various loads is obtained. This stress transmission data provides necessary input for subsequent judgment of whether the slope support structure has any bearing anomalies.

[0071] Step S323: Identify local stress concentration areas based on the stress transmission data of the slope support structure to obtain local stress concentration data of the support structure; In this embodiment of the invention, based on the stress transmission data of the slope support structure obtained in step S322, local stress concentration areas are identified. Specifically, the stress of each part of the support structure is assessed through numerical calculations (e.g., stress analysis or stress-strain curves). By comparing the stress distribution in different areas, stress gradient algorithms (e.g., gradient constraint method or Poisson's ratio analysis method) are used to identify areas with significant stress concentration. Stress concentration detection algorithms are used to determine which parts of the support structure are in areas of maximum stress concentration. These areas are often weak points with low bearing capacity and therefore require special attention. During the analysis, computational tools (e.g., the stress analysis toolbox in MATLAB) are used to further identify these stress concentration areas and mark their specific locations and stress values. This data provides a basis for subsequent analysis of the bearing capacity of the support structure.

[0072] Step S324: Perform load-bearing deviation analysis on the local stress concentration data of the support structure to obtain the load-bearing deviation data of the support structure; In this embodiment of the invention, the bearing capacity deviation analysis of the support structure is performed based on the local stress concentration area data obtained in step S323. Specifically, the bearing capacity deviation of the support structure in the maximum stress area is calculated by combining the slope geological characteristics and the design parameters of the support structure. The core of the bearing capacity deviation analysis is to compare the known design bearing capacity of the support structure with the actual stress data. Based on the material parameters of the support structure (such as strength, compressive strength, and flexural strength) and the stress data of the local stress concentration area, the deviation value of the support structure under different loads is calculated. Numerical analysis methods (such as the finite element method) are used to simulate the stress state of the support structure under different load conditions, and the model is used to verify whether the deviation exceeds its design bearing capacity range. This step, through a detailed calculation process, determines the deviation data of the support structure and provides a basis for the next step of structural safety assessment.

[0073] Step S325: Calculate the structural bearing safety margin based on the bearing deviation data of the support structure to obtain the safety margin data of the support structure; In this embodiment of the invention, the structural bearing capacity safety margin is calculated based on the bearing deviation data of the support structure obtained in step S324. Specifically, based on the structural bearing deviation data, the safety margin is calculated using the design safety factor method, combined with the material strength of the support structure and external forces. By comparing the bearing capacity of the support structure with the actual stress, the safety margin of the support structure under the current forces is determined. This process requires calculating the safety margin of the support structure under various loads using the safety factor formula and verifying whether the structure has sufficient bearing capacity through stress analysis. Through calculations for each structural unit, the safety margin data of the overall structure is obtained. These data provide a clear basis for judging whether the support structure can continue to bear load stably.

[0074] Step S326: Determine the bearing anomaly based on the safety margin data of the support structure to obtain the bearing anomaly status of the slope support structure.

[0075] In this embodiment of the invention, based on the safety margin data of the support structure obtained in step S325, a load-bearing anomaly determination is performed to obtain the load-bearing anomaly status of the slope support structure. Specifically, the safety margin data of the support structure is analyzed to determine whether it meets the design requirements. If the safety margin is less than the standard value of the design safety factor, the support structure is considered to have a load-bearing anomaly. Through systematic analysis of the load-bearing capacity and safety margin of the support structure under different stresses, combined with geological survey data, a final determination of load-bearing anomaly is made. If the calculation results show that the load-bearing capacity of the support structure is lower than the standard requirements, it is determined to be a load-bearing anomaly, thereby triggering subsequent reinforcement measures or reconstruction plans.

[0076] Preferably, step S4 includes the following steps: Step S41: Calculate the stability decay trend of the progressive failure growth of the slope to obtain the stability decay status of the target slope; In this embodiment of the invention, based on the dynamic deformation acceleration trend data of the slope structure obtained in step S33, the stability decay trend of the progressive failure growth of the slope is calculated. Specifically, a three-dimensional physical model of the slope is constructed using nonlinear finite element analysis (such as ABAQUS), and the dynamic deformation data of the slope is input. By calculating the slope stability under multiple loading cycles, the stability decay trend of the slope under different working conditions is calculated. By simulating the effects of external factors such as earthquakes, rainwater infiltration, and mining, the stability decay process of the slope structure over time is observed, and the stability change trend of the slope is evaluated based on the calculation results. Key parameters of stability decay include the slope's shear strength, sliding resistance, and stress distribution. Using these data, the degree of stability decay of the target slope is quantitatively calculated, and the stability decay status of the slope is obtained. These data provide the basic data for subsequent reinforcement optimization calculations.

[0077] Step S42: Perform causal link analysis on the target slope stability attenuation status to obtain missing causal data on slope stability; In this embodiment of the invention, causal link analysis is performed based on the target slope stability degradation status obtained in step S41. Specifically, causal analysis is conducted based on multiple indicators of slope stability degradation (such as slope deformation, stress change, soil strength, etc.) to construct a causal link model for stability degradation. This process can employ graph theory analysis, causal inference, or Bayesian networks to reveal the potential factors of slope stability degradation and their interactions. Through quantitative analysis of different causes of stability degradation, key factors leading to reduced slope stability (such as rising groundwater levels, fault activity, and deterioration of soil and rock properties) are identified, and the causal relationships between these factors are modeled. The output of the causal link will reveal the root cause and mechanism of slope stability loss, forming causal data on slope stability loss. This data provides a basis for subsequent source identification and reinforcement strategies.

[0078] Step S43: Based on the missing causal data of slope stability, identify the key sources and obtain the missing source data of slope stability; In this embodiment of the invention, key sources of slope stability loss are identified based on the causal data obtained in step S42. Specifically, the causal link data is further analyzed, and combined with slope geological survey data, historical monitoring data, and groundwater flow data, to screen out the factors that have the most significant impact on slope stability. These key sources include local soil changes, rock fissures, and external geological activities. Multivariate analysis methods (such as principal component analysis (PCA) and canonical correlation analysis (CCA)) are used to screen and weight each influencing factor to identify the main sources of stability loss. For example, if increased groundwater infiltration is a key factor leading to slope stability decay, this factor will be identified as a source of stability loss. This key source data provides targeted optimization directions for slope reinforcement.

[0079] Step S44: Determine slope stability reinforcement strategies based on the source data and causal data of missing slope stability data; In this embodiment of the invention, slope stability reinforcement strategies are determined based on the source data and causal data of slope stability loss obtained in step S43. Specifically, the target area for reinforcement is determined through analysis of key source data. For example, if groundwater seepage is the main factor causing slope stability degradation, reinforcement strategies include guiding groundwater drainage or installing drainage pipes. By calculating the implementation effects of different reinforcement methods (such as anchor reinforcement, thickening the protective layer, and installing a drainage system), and comprehensively considering factors such as slope stability and construction feasibility, the most suitable reinforcement scheme is finally determined. The formulation of reinforcement strategies is based on mathematical programming models (such as optimal reinforcement design models and analysis models) to optimize the selection of reinforcement schemes, ensuring that the stability of the reinforced slope is significantly improved.

[0080] Step S45: Perform reinforcement optimization calculations based on the slope stability reinforcement strategy to obtain slope stability reinforcement data.

[0081] In this embodiment of the invention, based on the reinforcement strategy obtained in step S44, reinforcement optimization calculations are performed to obtain slope stability reinforcement data. Specifically, based on the reinforcement strategy, engineering simulation software (such as PLAXIS, FLAC, etc.) is used to optimize the reinforcement scheme and analyze the improvement effect of different reinforcement methods on slope stability. Through numerical calculations, the changes in mechanical parameters such as shear strength and anti-sliding force of the reinforced slope are evaluated to determine the final reinforcement effect. By comparing the calculation results before and after reinforcement, slope stability reinforcement data is obtained. This data includes the safety factor of the reinforced slope, stability changes, and the bearing capacity of the reinforced structure. Finally, based on this data, the effectiveness of the reinforcement measures is determined, providing a basis for subsequent construction design.

[0082] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A slope stability optimization and reinforcement method based on fault sliding mode, characterized in that, Includes the following steps: Step S1: Obtain the basic information of the target slope; Determine the geological conditions of the target slope based on its basic information; Determine the geometric information of the target slope based on the basic information of the target slope; The target slope status data is determined based on the target slope's geometric information and geological conditions. Step S2: Detect the trend data of the fault zone based on the geological condition data of the target slope; By using fault zone strike trend data to detect bedding-parallel sliding of the target slope, the bedding-parallel sliding status of the target slope is obtained. Determine the cumulative increase of bedding-parallel sliding stress in the target slope based on the bedding-parallel sliding condition. Step S3: Use the fault zone strike trend data to perform reverse fault jacking detection on the target slope status data to obtain the reverse fault jacking status of the target slope; Estimate the progressive failure growth of the slope based on the cumulative increase of slope bedding sliding stress and the jacking condition of the reverse fault of the target slope; Step S4: Determine the target slope stability decay status based on the progressive failure growth of the slope; trace the source data of missing slope stability based on the target slope stability decay status; perform reinforcement optimization calculations based on the source data of missing slope stability to obtain slope stability reinforcement data.

2. The slope stability optimization and reinforcement method based on fault sliding mode according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain basic information of the target slope; Step S12: Determine the geological condition data of the target slope based on the basic information of the target slope; Step S13: Determine the geometric information of the target slope based on the target slope foundation information; Step S14: Determine the target slope status data based on the target slope geometric information and target slope geological condition data.

3. The slope stability optimization and reinforcement method based on fault sliding mode according to claim 2, characterized in that, Step S14 includes the following steps: Step S141: Measure the elevation data of the target slope based on the geometric information of the target slope to obtain the elevation data of the target slope; Step S142: Calculate the target slope volume data based on the target slope elevation data; Step S143: Determine the target slope morphology data based on the target slope geometry information; Step S144: Based on the target slope morphology data and target slope volume data, assess the potential sliding force growth of the target slope to obtain the potential sliding force growth data of the slope. Step S145: Evaluate the slope's soil and rock strength parameters based on the target slope's geological condition data; Step S146: Determine the initial stability of the target slope based on the slope soil and rock strength parameters and the potential sliding force growth data; Step S147: Determine the target slope state data based on the initial stability of the target slope.

4. The slope stability optimization and reinforcement method based on fault sliding mode according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Monitor the location information of the fault area based on the geological condition data of the target slope to obtain the location information of the fault area; Step S22: Detect the fault strike trend based on the fault region location information to obtain fault region strike trend data; Step S23: Use the fault zone strike trend data to perform bedding-parallel sliding detection on the target slope status data to obtain the bedding-parallel sliding condition of the target slope; Step S24: Determine the cumulative increase of bedding-parallel sliding stress in the target slope based on the bedding-parallel sliding condition.

5. The slope stability optimization and reinforcement method based on fault sliding mode according to claim 4, characterized in that, Step S22 includes the following steps: Step S221: Extract geological structural line features from the location information of the fault area to obtain the boundary line data of the fault area; Step S222: Calculate the fault azimuth angle of the fault region boundary line data to obtain the fault region azimuth angle data; Step S223: Perform fault strike dispersion analysis based on fault region azimuth data to obtain fault strike dispersion data; Step S224: Perform fault strike consistency clustering based on the fault strike dispersion data to obtain fault strike clustering result data; Step S225: Fit the fault strike trend data to the fault strike clustering results data to obtain the fault region strike trend data.

6. The slope stability optimization and reinforcement method based on fault sliding mode according to claim 4, characterized in that, Step S23 includes the following steps: Step S231: Extract the dip and dip angle of the bedding planes from the target slope state data to obtain the slope bedding structure parameters; Step S232: Compare the angle relationship between the slope bedding structure parameters and the fault strike trend data to obtain the matching relationship data between the bedding plane and the fault strike; Step S233: Based on the matching relationship data, determine the dip angle of the slope bedding to obtain the slope bedding dip angle determination result data; Step S234: Identify potential sliding surfaces based on the slope bedding angle discrimination results to obtain potential sliding surface data of the slope; Step S235: Determine the bedding sliding condition of the target slope based on the potential sliding surface data of the slope.

7. The slope stability optimization and reinforcement method based on fault sliding mode according to claim 4, characterized in that, Step S24 includes the following steps: Step S241: Extract the slope structural mechanical parameters based on the bedding sliding condition of the target slope to obtain slope mechanical parameter data; Step S242: Decompose the sliding surface force into the slope mechanical parameter data to obtain the distribution data of the sliding surface force along the bedding plane; Step S243: Calculate the shear stress of the sliding surface based on the force distribution data of the bedding sliding surface to obtain the shear stress data of the sliding surface; Step S244: Evaluate the exceedance of shear stress on the bedding sliding surface based on the shear stress data of the sliding surface; Step S245: Perform time-series cumulative analysis on the data of excessive shear stress on the slope to obtain the time-series cumulative data of stress on the slope sliding surface; Step S246: Determine the stress growth trend based on the cumulative stress time series data of the slope sliding surface to obtain the cumulative growth of slope bedding sliding stress.

8. The slope stability optimization and reinforcement method based on fault sliding mode according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Use the fault zone strike trend data to perform reverse fault jacking detection on the target slope status data to obtain the reverse fault jacking status of the target slope; Step S32: Determine the abnormal bearing condition of the slope support structure based on the reverse fault jacking condition of the target slope; Step S33: Estimate the accelerating trend of dynamic deformation of the slope structure based on the cumulative increase of slope bedding sliding stress and the abnormal bearing condition of the slope support structure; Step S34: Estimate the gradual failure growth of the slope based on the accelerating trend of dynamic deformation of the slope structure.

9. The slope stability optimization and reinforcement method based on fault sliding mode according to claim 8, characterized in that, Step S32 includes the following steps: Step S321: Extract the force distribution characteristics based on the reverse fault jacking condition of the target slope to obtain reverse fault jacking force distribution data; Step S322: Calculate the stress transmission of the slope support structure based on the distribution data of the reverse fault jacking force to obtain the stress transmission data of the slope support structure; Step S323: Identify local stress concentration areas based on the stress transmission data of the slope support structure to obtain local stress concentration data of the support structure; Step S324: Perform load-bearing deviation analysis on the local stress concentration data of the support structure to obtain the load-bearing deviation data of the support structure; Step S325: Calculate the structural bearing safety margin based on the bearing deviation data of the support structure to obtain the safety margin data of the support structure; Step S326: Determine the bearing anomaly based on the safety margin data of the support structure to obtain the bearing anomaly status of the slope support structure.

10. The slope stability optimization and reinforcement method based on fault sliding mode according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Calculate the stability decay trend of the progressive failure growth of the slope to obtain the stability decay status of the target slope; Step S42: Perform causal link analysis on the target slope stability attenuation status to obtain missing causal data on slope stability; Step S43: Based on the missing causal data of slope stability, identify the key sources and obtain the missing source data of slope stability; Step S44: Determine slope stability reinforcement strategies based on the source data and causal data of missing slope stability data; Step S45: Perform reinforcement optimization calculations based on the slope stability reinforcement strategy to obtain slope stability reinforcement data.

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