Geological risk analysis method using landslide model threshold simulation

Abstract

The application discloses a landslide model threshold simulation geological risk analysis method, including the following steps: constructing the basis of the landslide model; using a three-dimensional laser scanner to scan the surface of the target slope, obtaining three-dimensional point cloud data of the target slope, optimizing the three-dimensional point cloud data, and obtaining a complete landslide model; establishing a landslide threshold model; constructing a mapping between the water content in the soil and the cohesion, 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. The application constructs a simple landslide model for studying the characteristics of shallow soil landslide, provides a method for studying the influence of rainfall on the stability of the slope and preventing and treating landslide geological disasters, provides a new idea for landslide early warning, provides a scientific basis for the early warning and prevention of landslide disasters, and has important practical application value.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geological disaster analysis, in particular to a landslide model threshold simulation geological risk analysis method. BACKGROUND

[0002] Landslide is a common natural disaster, causing huge casualties and economic losses every year. The occurrence of landslide 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 of the soil inside are changed, and the groundwater level is disturbed, reducing the shear strength of the soil, thereby affecting the stability of the slope. For disaster prevention and mitigation of such landslides, it is crucial to establish a landslide risk meteorological warning system.

[0003] Currently, the start-up mechanism and reliability analysis of shallow soil landslides still do not adequately consider the spatial characteristics of soil property-related parameters during the landslide evolution process, which makes it almost impossible to focus on the changes in slope water and soil mechanical behavior caused by soil properties in the construction of shallow soil landslide start-up values. SUMMARY

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

[0005] To achieve the above-mentioned application purposes, the technical solutions adopted by the present application are as follows:

[0006] A landslide model threshold simulation geological risk analysis method is provided, which comprises the following steps:

[0007] S1: Calculate the slope of the target slope according to the relative position 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 three-dimensional point cloud data of the target slope, and import the three-dimensional point cloud data into the basis of the landslide model to optimize the three-dimensional point cloud data and 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 position and structural characteristics of 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 soil moisture content and cohesion, calculate the current soil moisture content 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] Furthermore, step S2 includes:

[0012] Step S2 specifically includes:

[0013] 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:

[0014] S211: All point cloud datasets A = {Q1, Q2, ..., Q n}, n is the number of point cloud sets, Q n is the nth point cloud set;

[0015] S212: using the coordinate features of the point clouds in the point cloud set to select the two most similar point cloud set groups;

[0016]

[0017] 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, 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;

[0018] S213: The most similar point cloud group (Q e ,Q u ) then, according to the point cloud group (Q e ,Q u ) to perform point cloud matching based on the coordinate features of the point cloud; specifically:

[0019] According to the point cloud Q e The coordinate feature T of any point cloud e in e , in Point Cloud Q u The point cloud u closest to the point cloud e is selected internally for closest point cloud matching;

[0020]

[0021] Point Cloud 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 after the nearest point cloud matching is successful is calculated. e～u ;

[0022]

[0023] The intermediate coordinate feature T e～u As the point cloud features matched by the two nearest point clouds, the point cloud set Q e The point cloud set Q u The point cloud set Q0 matched by the point cloud matching;

[0024] S22: Form the three-dimensional point cloud map formed by the point cloud set Q0 as the accurate three-dimensional point cloud map of the target slope, and establish a columnar region below each point cloud data according to the distribution density of the point cloud in the accurate three-dimensional point cloud map, one point cloud data corresponding to one columnar region, the columnar region vertically downward and being a cubic column structure, and the columnar regions being uniformly distributed in the accurate three-dimensional point cloud map;

[0025] S23: Input the accurate three-dimensional point cloud map with the columnar regions into the basis of the landslide model, set the height of each columnar region according to the average thickness of the soft soil layer on the target slope, and make the height of the columnar region the same as the average thickness of the soft soil layer, and the blank area between the columnar region and the basis of the landslide model as the hard soil layer of the target slope; form a complete landslide model.

[0026] Further, step S3 includes:

[0027] S31: Calculate the resistance F0 provided by the hard soil layer to the soft soil layer according to the contact area between the region where the hard soil layer is located and the soft soil layer below in the complete landslide model;

[0028]

[0029] Wherein, 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 acceleration of gravity, θ is the inclination angle of the target slope, w is the number of the hard soil layer region in the complete landslide model, W is the number of the hard soil layer regions in the complete landslide model, s w is the contact area of the vertical direction of the hard soil layer region 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 region, k is the number of columnar regions contained by the hard soil layer region, γ0 is the width of the columnar region;

[0030] S32: Calculate the friction force f between the soft soil layer and the hard soil layer; f = μ·ρ0v0gcosθ, wherein μ is the friction coefficient of the soft soil layer;

[0031] S33: Establish the landslide threshold model of the target slope by using the soil cohesion, the friction force f and the resistance F0;

[0032] ε·z+f+F0≥ρ0v0gcosθ;

[0033] Wherein, z is soil cohesion, and epsilon is soil cohesion coefficient.

[0034] Further, the step S4 comprises:

[0035] S41: according to the soil moisture content range of the soft soil layer and the soil cohesion value condition, a corresponding mapping condition is established Wherein, kappa is the soil moisture content range, f(kappa) is the mapping function of the soil moisture content range kappa, alpha κ is the soil cohesion z mapped according to the soil moisture content range kappa;

[0036] S42: the rainfall of the target slope is collected to calculate the soil infiltration of the rainfall process, and the soil moisture content of the soft soil layer is calculated according to the infiltration;

[0037] I't-ξ-R=(M1-M2)DH0;

[0038] Wherein, I' is the rainfall intensity, t is the rainfall time, R is the runoff generated by the target slope in the rainfall process, xi is the rainfall loss, M1 is the saturated moisture content of the soil, M2 is the current soil moisture content, D is the soil density, and H is the average thickness of the soft soil layer;

[0039] S43: the current soil moisture content M1 calculated in step S42 is input into the mapping function f(kappa) to obtain the corresponding soil cohesion z value, and the landslide threshold model is input to verify whether the landslide threshold model is established, if not, the target slope has a landslide risk under the current rainfall condition, and if yes, there is no landslide risk.

[0040] The beneficial effects of the present application are: the present application 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, which is used to establish a landslide model, by analyzing the structural characteristics between the soft soil layer and the hard soil layer in the landslide model, the 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 force in the soft soil layer, a landslide threshold model is constructed, the landslide threshold model is analyzed by the real-time rainfall of the slope, and then whether the soft soil layer of the slope has a geological disaster is evaluated. The present application constructs a simple landslide model to study the characteristics of shallow soil landslide, provides a method for studying the influence of rainfall on the stability of the slope and preventing and treating landslide geological disasters, provides a new idea for landslide warning, and provides a scientific basis for landslide warning and prevention, which has important practical application value. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 The flow chart of the geological risk analysis method of the landslide model threshold simulation. DETAILED DESCRIPTION

[0042] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

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

[0044] S1: Calculate the slope of the target slope based on the relative positions of the top and bottom of the target slope, and construct the basis of the landslide model in combination with the measured length and width of the target slope. In this embodiment, the basis of the established landslide model can be a slope surface structure, which is used as the basis of the landslide model, and the slope surface structure serves as the rock layer below the hard soil layer.

[0045] S2: Scan the surface of the target slope using a 3D laser scanner to obtain 3D point cloud data of the target slope, import the 3D point cloud data into the landslide model, and optimize the 3D point cloud data to obtain a complete landslide model. Step S2 specifically includes:

[0046] Step S2 specifically includes:

[0047] 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:

[0048] S211: All point cloud datasets A = {Q1, Q2, ..., Q n}, n is the number of point cloud sets, Q n is the nth point cloud set;

[0049] S212: using the coordinate features of the point clouds in the point cloud set to select the two most similar point cloud set groups;

[0050]

[0051] 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, T i is the coordinate feature of the point cloud in the target point cloud set, T j, 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, I is the number of the point cloud in the target point cloud set;

[0052] S213: After the most similar point cloud set group (Q e ,Q u ) is screened out by step S212, the point cloud matching is performed according to the coordinate features of the point clouds in the point cloud set group (Q e ,Q u );

[0053] According to the coordinate feature T e of any point cloud e in the point cloud set Q e , the point cloud u closest to the point cloud e in the point cloud set Q u is screened out, and the nearest point cloud matching is performed;

[0054]

[0055] After the nearest point cloud matching between the point clouds in the point cloud set Q e and the point clouds in the point cloud set Q u is completed, the intermediate coordinate feature T e～u after the nearest point cloud matching is successful is calculated;

[0056]

[0057] The intermediate coordinate feature T e～u is taken as the point cloud feature after the nearest point cloud matching of the two, and the point cloud set Q0 after the point cloud matching between the point cloud set Q e and the point cloud set Q u is generated;

[0058] S22: The three-dimensional point cloud diagram formed by the point cloud set Q0 is taken as the accurate three-dimensional point cloud diagram of the target slope, and a columnar region is established below each point cloud data according to the distribution density of the point clouds in the accurate three-dimensional point cloud diagram, one point cloud data corresponds to one columnar region, the columnar region is vertically downward and is a cubic column structure, and the columnar regions are uniformly distributed in the accurate three-dimensional point cloud diagram;

[0059] S23: The accurate three-dimensional point cloud diagram with the columnar regions is input into the basis of the landslide model, and the height of each columnar region is set according to the average thickness of the soft soil layer on the target slope, and the height of the columnar region is the same as the average thickness of the soft soil layer, and the blank area between the columnar region and the basis of the landslide model is taken as the hard soil layer of the target slope; and a complete landslide model is formed.

[0060] The three-dimensional point cloud map embodies the complete slope surface structure, and the soft soil layer is simplified into an ideal and uniform soil layer in the embodiment, which is uniformly laid 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 support force on the soft soil layer of the slope, thereby reducing the risk of soft soil landslide.

[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, the resistance formed between the hard soil layer and the soft soil layer is calculated, and a landslide threshold model is established. Step S3 specifically includes:

[0062] S31: According to the contact area between the region where the hard soil layer is located and the underlying soft soil layer in the complete landslide model, the resistance F0 provided by the hard soil layer for the soft soil layer is calculated;

[0063]

[0064] wherein, 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 acceleration of gravity, θ is the inclination angle of the target slope, w is the number of the hard soil layer region in the complete landslide model, W is the number of the hard soil layer regions in the complete landslide model, s w is the contact area between the vertical direction of the hard soil layer region 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 region, k is the number of the cylindrical regions contained by the hard soil layer region, γ0 is the width of the cylindrical region;

[0065] S32: The friction force f between the soft soil layer and the hard soil layer is calculated; f = μ·ρ0v0gcosθ, wherein μ is the friction coefficient of the soft soil layer;

[0066] S33: The landslide threshold model of the target slope is established by using the soil cohesion, the friction force f and the resistance F0;

[0067] ε·z+f+F0≥ρ0v0gcosθ;

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

[0069] S4: A mapping between the water content in the soil and the cohesion is constructed, and the current water content of the soil is calculated according to the current rainfall intensity of the target slope, and is input into the landslide threshold model to evaluate the geological risk of the target slope. Step S4 specifically includes:

[0070] S41: A corresponding mapping condition is established according to the water content range of the soft soil layer and the cohesion value condition wherein κ is the water content range of the soil, f(κ) is the mapping function of the water content range κ, and ακ The soil cohesion z is mapped according to the water content range K; the soil cohesion is different at different water contents, and generally varies according to the different properties of the rock and soil, and the mapping function is established by establishing the relationship between the water content and the cohesion value.

[0071] S42: Collecting rainfall of the target slope, calculating the infiltration amount of the soil in the rainfall process, and calculating the water content of the soil on the soft soil layer according to the infiltration amount;

[0072] I't-ξ-R=(M1-M2)DH0;

[0073] Wherein, I' is the rainfall intensity, t is the rainfall time, R is the runoff generated by the target slope in 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;

[0074] S43: inputting the current soil water content M1 calculated in step S42 into the mapping function f(K) to obtain the corresponding soil cohesion z value, and inputting the soil cohesion z value 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 condition, and if yes, the landslide risk does not exist.

[0075] The three-dimensional scanning technology is used to scan the three-dimensional point cloud data of the slope, and the point cloud data is optimized to obtain accurate three-dimensional point cloud data of the slope, which is used to establish a landslide model, the force of the hard soil layer on the soft soil layer is analyzed according to the structural characteristics between the soft soil layer and the hard soil layer in the landslide model, and the landslide threshold model is constructed by analyzing the influence of the rainfall on the force in the soft soil layer. The landslide threshold model is analyzed by the real-time rainfall of the slope, and then the geological disaster of the soft soil layer of the slope is evaluated. The simple landslide model is constructed to study the characteristics of the shallow soil landslide, which provides a method for the influence of the rainfall on the stability of the slope and the prevention and control of the landslide geological disaster, provides a new idea for the landslide warning, and provides a scientific basis for the warning and prevention of the landslide disaster, and has 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 combine it with the measured length and width of the target slope to build the basis of the landslide model; S2: Scan the surface of the target slope using a 3D laser scanner to obtain 3D point cloud data of the target slope, import the 3D point cloud data into the landslide model, and optimize the 3D point cloud data to obtain a complete landslide model; S3: Based on the relative position and structural characteristics of 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 a 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; 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 datasets obtained by scanning the target slope surface with a 3D laser scanner , n is the number of point cloud sets, For the n Point cloud collection; S212: using the coordinate features of the point clouds in the point cloud set to select the two most similar point cloud set groups; ； in, Point cloud datasets Any two point clouds in For the reference point cloud, is the target point cloud, is the coordinate feature of the point cloud in the target point cloud set, 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, I is the number of point clouds in the target point cloud set; S213: The most similar point cloud group selected by step S212 Afterwards, according to the point cloud group Point cloud matching is performed based on the coordinate features of the inner point cloud; specifically: According to point cloud Any point cloud e Coordinate characteristics of , in point cloud collection Internal screening and point cloud e The closest point cloud u , perform nearest point cloud matching; ； Point Cloud Collection Point cloud and point cloud set After the nearest point cloud matching is completed, the intermediate coordinate features after the nearest point cloud matching is successfully calculated ; ； The intermediate coordinate feature As the point cloud features after matching the two closest point clouds, generate a point cloud set With point cloud collection Point cloud set after point cloud matching ; S22: Gathering Point Clouds The formed 3D point cloud image is used as the accurate 3D point cloud image of the target slope. According to the distribution density of the point cloud in the precise 3D point cloud image, a columnar area is established below each point cloud data. One point cloud data corresponds to one columnar area. The columnar area is vertically downward and has a cubic column structure. The columnar area is evenly distributed in the precise 3D 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. The blank area between the columnar 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.

2. The geological risk analysis method of landslide model threshold simulation according to claim 1, characterized in that: The step S3 comprises: S31: Calculated based on the contact area between the hard soil layer and the soft soil layer within the complete landslide model The resistance provided by the hard soil layer to the soft soil layer ; ； in, is the average density of the soft soil layer, is the volume of the soft soil layer on the target slope, g is the acceleration due to gravity, 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 hard soil areas in the complete landslide model, 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, h w is the average height of the hard soil layer area, k is the number of columnar regions contained in the hard soil layer area, is the width of the columnar area; S32: Calculate the friction between soft and hard soil layers f ; ,in, μ is the friction coefficient of the soft soil layer; S33: Using soil cohesion and friction f and resistance Establish a landslide threshold model for the target slope; ； in, z is soil cohesion, is the soil cohesion coefficient.

3. The geological risk analysis method of landslide model threshold simulation according to claim 2, characterized in that: The step S4 comprises: S41: Establish corresponding mapping conditions based on the soil moisture content range and cohesion value conditions in the soft soil layer ,in, is the soil moisture content range, About the water content range The mapping function, According to the water content range Mapped soil cohesion z ; 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 based on the infiltration; ； in, is the rainfall intensity, t For rainfall time, R is the runoff generated by the target slope during rainfall, For rainfall losses, M 1 is the saturated water content of the soil, M 2 is the current soil moisture content, D is the soil density, H is the average thickness of the soft soil layer; S43: The current soil moisture content calculated in step S42 is M 1 Input mapping function The corresponding soil cohesion is obtained z The value is taken and input 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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