Geological risk analysis method for landslide model threshold simulation

Abstract

The invention discloses a geological risk analysis method for landslide model threshold simulation. The geological risk analysis method comprises the following steps: constructing a foundation of a landslide model; scanning the surface of the target slope by using a three-dimensional laser scanner to obtain three-dimensional point cloud data of the target slope, and performing optimization processing on the three-dimensional point cloud data to obtain a complete landslide model; establishing a landslide threshold model; and constructing mapping between the water content in the soil and the cohesive force, calculating the current water content of the soil according to the current rainfall intensity of the target slope, inputting the current water content into the landslide threshold model, and evaluating the geological risk of the target slope. According to the method, the simple landslide model is constructed to be used for researching shallow soil landslide characteristics, a method is provided for research on influence of rainfall on slope stability and prevention and control of landslide geological disasters, a new thought is provided for landslide early warning, and a scientific basis is provided for early warning and prevention and control of landslide disasters; the method has important practical application value.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster analysis, and particularly to a geological risk analysis method for landslide model threshold simulation. Background Art

[0002] Landslides are common natural disasters that cause huge casualties and economic losses every year. The occurrence of landslides involves the interaction of multiple factors, including geological conditions, climate change, precipitation, terrain slope, soil moisture, vegetation coverage, etc. After rainfall infiltration, the physical and mechanical properties inside the soil layer are changed, and at the same time, the groundwater level is disturbed, and the shear strength of the soil mass is reduced, thus affecting the stability of the slope. For the prevention and mitigation of such landslides, it is crucial to establish a landslide risk meteorological warning system.

[0003] At present, the research on the initiation mechanism and reliability analysis of shallow soil landslides still does not sufficiently consider the spatial characteristics of the parameters related to soil properties in the landslide evolution process, which makes it almost rarely pay attention to the changes in slope hydro-mechanical behavior caused by soil layer properties in the construction of the initiation value of shallow soil landslides. Summary of the Invention

[0004] In view of the above deficiencies of the prior art, the present invention provides a geological risk analysis method for landslide model threshold simulation, which analyzes and evaluates geological risks based on the geological structure characteristics and rainfall characteristics of the slope.

[0005] In order to achieve the above invention purpose, the technical solution adopted by the present invention is as follows:

[0006] Provide a geological risk analysis method for landslide model threshold simulation, which includes the following steps:

[0007] S1: Calculate the slope of the target slope according to the relative positions of the top and bottom of the target slope, and combine the measured length and width of the target slope to construct the basis of the landslide model;

[0008] S2: Use a three-dimensional laser scanner to scan the surface of the target slope to obtain the three-dimensional point cloud data of the target slope, and import the three-dimensional point cloud data on the basis of the landslide model, and perform optimization processing on the three-dimensional point cloud data to obtain a complete landslide model;

[0009] S3: Calculate the resistance formed between the hard soil layer and the soft soil layer according to the relative positions and structural characteristics between the hard soil layer and the soft soil layer in the complete landslide model, and establish a landslide threshold model;

[0010] S4: Construct a mapping between the water content and cohesion in the soil, calculate the current water content of the soil according to the current rainfall intensity of the target slope, and input it into the landslide threshold model to evaluate the geological risk of the target slope.

[0011] Further, step S2 includes:

[0012] Step S2 specifically includes:

[0013] S21: Use a 3D laser scanner to scan the surface of the target slope, obtain the 3D point cloud data of the target slope, and register the 3D point cloud data; specifically including:

[0014] S211: According to all the point cloud datasets A = {Q 1 , Q 2 , …, Q n} obtained by the 3D laser scanner scanning the surface of the target slope, where n is the number of point cloud sets, and Q n is the nth point cloud set;

[0015] S212: Use the coordinate characteristics of the point clouds within the point cloud set to screen two most similar point cloud set groups;

[0016]

[0017] Among them, Q i , Q j are any two point cloud sets within the point cloud dataset A respectively, Q j is the reference point cloud set, Q i is the target point cloud set, T i is the coordinate characteristic of the point cloud within the target point cloud set, T j is the coordinate characteristic of the point cloud within the reference point cloud set, i is the number of the point cloud within the target point cloud set, j is the number of the point cloud within the target point cloud set, and I is the number of the point clouds within the target point cloud set;

[0018] After the most similar point cloud set group (Q e , Q u ) is screened out through step S212, perform point cloud matching according to the coordinate characteristics of the point clouds within the point cloud set group (Q e , Q u ); specifically:

[0019] According to the coordinate characteristic T e of any point cloud e within the point cloud set Q e , screen the point cloud u closest to the point cloud e within the point cloud set Q u for nearest point cloud matching;

[0020]

[0021] After the point clouds within the point cloud set Q e and the point clouds within the point cloud set Q u complete the nearest point cloud matching, calculate the intermediate coordinate characteristic T e～u after the nearest point cloud matching is successful;

[0022]

[0023] Take the intermediate coordinate feature T e～u As the point cloud feature after the matching of two nearest point clouds, generate a point cloud set Q e And the point cloud set Q u The point cloud set Q after point cloud matching 0 ;

[0024] S22: Take the three-dimensional point cloud map formed by the point cloud set Q 0 As the precise three-dimensional point cloud map of the target slope, establish a cylindrical area below each point cloud data according to the distribution density of the point clouds in the precise three-dimensional point cloud map. One point cloud data corresponds to one cylindrical area. The cylindrical area is vertically downward and has a cubic cylinder structure. The cylindrical areas are evenly distributed in the precise three-dimensional point cloud map;

[0025] S23: Input the precise three-dimensional point cloud map with cylindrical areas established into the landslide model, and set the height of each cylindrical area according to the average thickness of the soft soil layer on the target slope, and make the height of the cylindrical area the same as the average thickness of the soft soil layer. The blank area between the cylindrical area and the foundation of the landslide model is used as the hard soil layer of the target slope; form a complete landslide model.

[0026] Furthermore, step S3 includes:

[0027] S31: Calculate the resistance F provided by the hard soil layer for the soft soil layer according to the contact area between the area where the hard soil layer is located and the lower part of the soft soil layer in the complete landslide model 0 ;

[0028]

[0029] Among them, ρ 0 Is the average density of the soft soil layer, v 0 Is the volume of the soft soil layer on the target slope, g is the acceleration due to gravity, θ is the inclination angle of the target slope, w w Is the contact area in the vertical direction between the hard soil layer area and the soft soil layer, S is the cross-sectional area of the soft soil layer on the target slope, h w Is the average height of the hard soil layer area, k is the number of cylindrical areas included in the hard soil layer area, γ 0 Is the width of the cylindrical area;

[0030] S32: Calculate the friction force f generated between the soft soil layer and the hard soil layer; f = μ·ρ 0 v 0 gcosθ, where μ is the friction coefficient of the soft soil layer;

[0031] S33: Utilize soil cohesion, frictional force f, and resistance F 0 to establish a landslide threshold model for the target slope;

[0032] ε·z + f + F 0 ≥ρ 0 v 0 gcosθ;

[0033] where z is the soil cohesion and ε is the soil cohesion coefficient.

[0034] Furthermore, step S4 includes:

[0035] S41: Establish a corresponding mapping condition according to the water content range and cohesion value condition in the soil of the soft soil layer where κ is the water content range of the soil, f(κ) is the mapping function with respect to the water content range κ, and α κ is the soil cohesion z mapped according to the water content range κ;

[0036] S42: Collect the rainfall of the target slope, calculate the infiltration amount of the soil during the rainfall process, and calculate the water content of the soil on the soft soil layer according to the infiltration amount;

[0037] I′t - ξ - R = (M 1 - M 2 )DH 0 ;

[0038] where I′ is the rainfall intensity, t is the rainfall time, R is the runoff generated by the target slope during the rainfall process, ξ is the rainfall loss, M 1 is the saturated water content of the soil, M 2 is the current water content of the soil, D is the soil density, and H is the average thickness of the soft soil layer;

[0039] S43: Input the calculated current water content M 1 of the soil in step S42 into the mapping function f(κ) to obtain the corresponding value of the soil cohesion z, and input it into the landslide threshold model to verify whether the landslide threshold model holds. If it does not hold, there is a landslide risk for the target slope under the current rainfall condition. If it holds, there is no landslide risk.

[0040] The beneficial effects of the present invention are as follows: The present invention uses three-dimensional scanning technology to scan the three-dimensional point cloud data of the slope, and optimizes the point cloud data to obtain accurate three-dimensional point cloud data of the slope for establishing a landslide model. By analyzing the structural characteristics between the soft soil layer and the hard soil layer in the slope model, the acting force of the hard soil layer on the soft soil layer is analyzed. At the same time, by analyzing the influence of rainfall on the acting force in the soft soil layer, a landslide threshold model is constructed. The landslide threshold model is analyzed through the real-time rainfall of the slope, and then it is evaluated whether a geological disaster occurs in the soft soil layer of the slope. The present invention constructs a simple landslide model for studying the characteristics of shallow soil landslides, provides a method for studying the influence of rainfall on slope stability and the prevention and control of landslide geological disasters, provides a new idea for landslide early warning, and provides a scientific basis for the early warning and prevention and control of landslide disasters, having important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flowchart of a geological risk analysis method for landslide model threshold simulation. DETAILED DESCRIPTION OF THE INVENTION

[0042] The following describes the specific embodiments of the present invention to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are within the scope of protection.

[0043] As Figure 1 shown, a geological risk analysis method for landslide model threshold simulation includes the following steps:

[0044] S1: Calculate the slope of the target slope according to the relative positions of the top and bottom of the target slope, and combine the measured length and width of the target slope to construct the basis of the landslide model; in this embodiment, the basis of the established landslide model can be the slope surface structure, which is used as the basis of the landslide model, and the slope surface structure is the rock layer below the hard soil layer.

[0045] S2: Use a three-dimensional laser scanner to scan the surface of the target slope to obtain the three-dimensional point cloud data of the target slope, and import the three-dimensional point cloud data on the basis of the landslide model, and perform optimization processing on the three-dimensional point cloud data to obtain a complete landslide model. Step S2 specifically includes:

[0046] Step S2 specifically includes:

[0047] S21: Use a three-dimensional laser scanner to scan the surface of the target slope to obtain the three-dimensional point cloud data of the target slope, and register the three-dimensional point cloud data; specifically including:

[0048] S211: According to all point cloud data sets A = {Q 1 , Q 2 , …, Q n} scanned by a three-dimensional laser scanner on the surface of the target slope, where n is the number of point cloud sets, and Q n is the nth point cloud set;

[0049] S212: Screen two most similar point cloud set groups by using the coordinate characteristics of the point clouds within the point cloud sets;

[0050]

[0051] Among them, Q i , Q j are any two point cloud sets within the point cloud data set A respectively, Q j is the reference point cloud set, Q i is the target point cloud set, T i is the coordinate characteristic of the point cloud within the target point cloud set, T j is the coordinate characteristic of the point cloud within the reference point cloud set, i is the number of the point cloud within the target point cloud set, j is the number of the point cloud within the target point cloud set, and I is the number of the point clouds within the target point cloud set;

[0052] S213: After the most similar point cloud set group (Q e , Q u ) is screened out through step S212, perform point cloud matching according to the coordinate characteristics of the point clouds within the point cloud set group (Q e , Q u ); Specifically:

[0053] According to the coordinate characteristic T e of any point cloud e within the point cloud set Q e , screen the point cloud u closest to the point cloud e within the point cloud set Q u to perform the closest point cloud matching;

[0054]

[0055] After the point clouds within the point cloud set Q e and the point clouds within the point cloud set Q u complete the closest point cloud matching, calculate the intermediate coordinate characteristic T e～u after the closest point cloud matching is successful;

[0056]

[0057] Take the intermediate coordinate characteristic T e～u as the point cloud characteristic after the two closest point cloud matchings, and generate the point cloud set Q e after the point cloud matching between the point cloud set Q u and the point cloud set Q0 ;

[0058] S22: Take the three-dimensional point cloud map formed by the point cloud set Q 0 as the accurate three-dimensional point cloud map of the target slope. According to the distribution density of the point clouds in the accurate three-dimensional point cloud map, establish a cylindrical area below each point cloud data. One point cloud data corresponds to one cylindrical area. The cylindrical area is perpendicular downward and has a cubic columnar structure. The cylindrical areas are evenly distributed in the accurate three-dimensional point cloud map;

[0059] S23: On the basis of inputting the accurate three-dimensional point cloud map with cylindrical areas established into the landslide model, and according to the average thickness of the soft soil layer on the target slope, set the height of each cylindrical area and make the height of the cylindrical area the same as the average thickness of the soft soil layer. The blank area between the cylindrical area and the base of the landslide model is used as the hard soil layer of the target slope; thus forming a complete landslide model.

[0060] The three-dimensional point cloud map reflects the complete surface structure of the slope. In this embodiment, the soft soil layer is simplified into an ideal and uniform soil layer, and the soft soil layer is evenly spread on the underlying hard soil layer. Therefore, the structural characteristics of the hard soil layer can be reflected through the structural characteristics of the three-dimensional point cloud map. According to the uneven structural characteristics of the hard soil layer, the protrusions and depressions of the hard soil layer will generate a supporting force on the soft soil layer of the slope, reducing the risk of landslide of the soft soil layer.

[0061] S3: According to the relative position and structural characteristics between the hard soil layer and the soft soil layer in the complete landslide model, calculate the resistance formed between the hard soil layer and the soft soil layer, and establish a landslide threshold model. Step S3 specifically includes:

[0062] S31: Calculate the resistance F provided by the hard soil layer for the soft soil layer according to the contact area between the area where the hard soil layer is located and the lower part of the soft soil layer in the complete landslide model 0 ;

[0063]

[0064] where ρ 0 is the average density of the soft soil layer, v 0 is the volume of the soft soil layer on the target slope, g is the acceleration due to gravity, θ is the inclination angle of the target slope, w is the number of the hard soil layer area in the complete landslide model, W is the number of the hard soil layer areas in the complete landslide model, s w is the contact area in the vertical direction between the hard soil layer area and the soft soil layer, S is the cross-sectional area of the soft soil layer on the target slope, h w is the average height of the hard soil layer area, k is the number of cylindrical areas included in the hard soil layer area, γ 0 is the width of the cylindrical area;

[0065] S32: Calculate the frictional force f generated between the soft soil layer and the hard soil layer; f = μ·ρ0 v 0 gcosθ, where μ is the friction coefficient of the soft soil layer;

[0066] S33: Establish a landslide threshold model for the target slope using soil cohesion, frictional force f, and resistance F 0 Establish a landslide threshold model for the target slope;

[0067] ε·z + f + F 0 ≥ρ 0 v 0 gcosθ;

[0068] where z is the soil cohesion and ε is the soil cohesion coefficient.

[0069] S4: Construct a mapping between the water content and cohesion in the soil, calculate the current water content of the soil based on the current rainfall intensity of the target slope, and input it into the landslide threshold model to evaluate the geological risk of the target slope. Step S4 specifically includes:

[0070] S41: Establish corresponding mapping conditions based on the water content range and cohesion value conditions in the soft soil layer where κ is the water content range of the soil, f(κ) is the mapping function with respect to the water content range κ, and α κ is the soil cohesion z mapped according to the water content range κ; the cohesion of the soil varies with different water contents. Generally, according to the different properties of the rock and soil, the cohesion will also change. In the present invention, a mapping function is established by establishing the relationship between the water content and the cohesion value.

[0071] S42: Collect the rainfall of the target slope, calculate the infiltration amount of the soil during the rainfall process, and calculate the water content of the soil on the soft soil layer based on the infiltration amount;

[0072] I′t - ξ - R = (M 1 - M 2 )DH 0 ;

[0073] where I′ is the rainfall intensity, t is the rainfall time, R is the runoff generated by the target slope during the rainfall process, ξ is the rainfall loss, M 1 is the saturated water content of the soil, M 2 is the current water content of the soil, D is the soil density, and H is the average thickness of the soft soil layer;

[0074] S43: Input the current water content M 1 of the soil calculated in step S42 into the mapping function f(κ) to obtain the corresponding value of the soil cohesion z, and input it into the landslide threshold model to verify whether the landslide threshold model holds. If it does not hold, the target slope has a landslide risk under the current rainfall conditions. If it holds, there is no landslide risk.

[0075] The present invention utilizes three-dimensional scanning technology to scan the three-dimensional point cloud data of a slope, optimize the point cloud data, and obtain accurate three-dimensional point cloud data of the slope for establishing a landslide model. By analyzing the structural characteristics between the soft soil layer and the hard soil layer in the slope model, the acting force of the hard soil layer on the soft soil layer is analyzed. At the same time, by analyzing the influence of rainfall on the acting force in the soft soil layer, a landslide threshold model is constructed. The landslide threshold model is analyzed through the real-time rainfall of the slope, and then it is evaluated whether a geological disaster occurs in the soft soil layer of the slope. The present invention constructs a simple landslide model for studying the characteristics of shallow soil landslides, provides a method for studying the influence of rainfall on slope stability and the prevention and control of landslide geological disasters, provides a new idea for landslide early warning, and provides a scientific basis for the early warning and prevention and control of landslide disasters, having important practical application value.

Claims

1. A geological risk analysis method for landslide model threshold simulation, characterized in that: The following steps are involved: S1: Calculate the slope of the target slope based on the relative positions of the top and bottom of the target slope, and build the basis of the landslide model by combining the measured length and width of the target slope; S2: Scan the surface of the target slope with a 3D laser scanner to obtain 3D point cloud data of the target slope, import the 3D point cloud data into the landslide model, optimize the 3D point cloud data, and obtain a complete landslide model; S3: According to the relative position and structural characteristics between the hard soil layer and the soft soil layer in the complete landslide model, the resistance formed between the hard soil layer and the soft soil layer is calculated to establish the landslide threshold model; S4: Construct a mapping between soil moisture content and cohesion, calculate the current soil moisture content based on the current rainfall intensity of the target slope, and input it into the landslide threshold model to assess the geological risk of the target slope.

2. The geological risk analysis method of landslide model threshold simulation according to claim 1 is characterized in that: The step S2 comprises: S21: Scanning the surface of the target slope using a 3D laser scanner to obtain 3D point cloud data of the target slope, and registering the 3D point cloud data; specifically including: S211: All point cloud data sets A = {Q1, Q2, ..., Q n }, n is the number of point cloud sets, Q n is the nth point cloud set; S212: using the coordinate features of the point clouds in the point cloud set to select two most similar point cloud set groups; Among them, Q i , Q j are any two point cloud sets in the point cloud dataset A, Q j As the reference point cloud, Q i is the target point cloud set, T i is the coordinate feature of the point cloud in the target point cloud set, T j is the coordinate feature of the point cloud in the reference point cloud set, i is the number of the point cloud in the target point cloud set, j is the number of the point cloud in the target point cloud set, and I is the number of point clouds in the target point cloud set; S213: The most similar point cloud group (Q e ,Q u ) and then, according to the point cloud group (Q e ,Q u ) to match the point cloud based on the coordinate features of the point cloud; specifically: According to the point cloud Q e The coordinate feature T of any point cloud e in e , in the point cloud Q u The point cloud u that is closest to the point cloud e is selected internally to perform the closest point cloud matching; Point Cloud Collection Q e Point cloud and point cloud set Q u After the point cloud in the nearest point cloud is matched, the intermediate coordinate feature T is calculated after the nearest point cloud is successfully matched. e～u ; The intermediate coordinate feature T e～u As the point cloud features after the two closest point clouds are matched, the point cloud set Q is generated e Q u Point cloud set Q0 after point cloud matching; S22: using the three-dimensional point cloud image formed by the point cloud set Q0 as the accurate three-dimensional point cloud image of the target slope, and establishing a columnar area below each point cloud data according to the distribution density of the point cloud in the accurate three-dimensional point cloud image. One point cloud data corresponds to one columnar area, and the columnar area is vertically downward and has a cubic column structure. The columnar areas are evenly distributed in the accurate three-dimensional point cloud image; S23: The precise three-dimensional point cloud map with columnar areas is input into the landslide model, and the height of each columnar area is set according to the average thickness of the soft soil layer on the target slope, and the height of the columnar area is made the same as the average thickness of the soft soil layer, and the blank area between the columnar area and the foundation of the landslide model is used as the hard soil layer of the target slope; thus forming a complete landslide model.

3. The geological risk analysis method of landslide model threshold simulation according to claim 2 is characterized in that: The step S3 comprises: S31: Calculate the resistance F0 provided by the hard soil layer to the soft soil layer based on the contact area between the hard soil layer and the soft soil layer in the complete landslide model; s w =h w kγ0cosθ; Where ρ0 is the average density of the soft soil layer, v0 is the volume of the soft soil layer on the target slope, g is the gravitational acceleration, θ is the inclination angle of the target slope, w is the number of the hard soil layer area in the complete landslide model, W is the number of the hard soil layer area in the complete landslide model, and s w is the vertical contact area between the hard soil layer and the soft soil layer, S is the cross-sectional area of ​​the soft soil layer on the target slope, and h w is the average height of the hard soil layer area, k is the number of columnar areas contained in the hard soil layer area, and γ0 is the width of the columnar area; S32: Calculate the friction force f generated between the soft soil layer and the hard soil layer; f = μ·ρ0v0gcosθ, where μ is the friction coefficient of the soft soil layer; S33: Establish a landslide threshold model of the target slope using soil cohesion, friction f and resistance F0; ε·z+f+F0≥ρ0v0gcosθ; Where z is soil cohesion and ε is soil cohesion coefficient.

4. The geological risk analysis method of landslide model threshold simulation according to claim 3 is characterized in that: The step S4 comprises: S41: Establish corresponding mapping conditions according to the soil moisture range and cohesion value conditions in the soft soil layer Where κ is the soil moisture range, f(κ) is the mapping function of the moisture range κ, and α κ is the soil cohesion z mapped according to the water content range κ; S42: collecting the rainfall of the target slope to calculate the infiltration of the soil during the rainfall process, and calculating the water content of the soil on the soft soil layer according to the infiltration; I′t-ξ-R=(M1-M2)DH0; Among them, I′ is the rainfall intensity, t is the rainfall time, R is the runoff generated by the target slope during the rainfall process, ξ is the rainfall loss, M1 is the saturated water content of the soil, M2 is the current soil water content, D is the soil density, and H is the average thickness of the soft soil layer; S43: Input the current soil moisture content M1 calculated in step S42 into the mapping function f(κ) to obtain the corresponding soil cohesion z value, and input it into the landslide threshold model to verify whether the landslide threshold model is established. If not, the target slope has a landslide risk under the current rainfall conditions. If established, there is no landslide risk.

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