A full probability risk quantitative assessment method for landslides in muck yards considering the three-dimensional spatial variability of muck

Abstract

The present invention discloses a full - probability risk quantitative assessment method for the landslide of a muck yard considering the three - dimensional spatial variability of muck, which includes the following steps: conducting borehole exploration of the muck yard site to obtain muck yard data; obtaining random field parameters characterizing the spatial variability of muck; simulating the three - dimensional spatial variation characteristics of muck; based on the limit equilibrium method, conducting the three - dimensional slope stability assessment of the muck yard and estimating the potential landslide volume; simulating the possible migration ranges under various landslide volumes; statistically collecting the life and property data of downstream disaster - bearing bodies, and based on the vulnerability assessment formulas for buildings and personnel, estimating the vulnerability of disaster - bearing bodies according to the landslide intensity; establishing a full - probability risk assessment formula for the muck yard landslide, and conducting quantitative risk assessment and risk rating. The present invention considers the unique spatial variation characteristics of muck, analyzes the stability, potential sliding position and volume from a three - dimensional perspective, and combines with the quantitative vulnerability assessment method to achieve accurate risk assessment.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster prevention and mitigation, and particularly relates to a method for quantitatively evaluating the full probability risk of landfill landslides considering the three-dimensional spatial variability of muck. Background Art

[0002] The rapid development of urbanization has generated a huge amount of construction muck. The types of muck are complex, and mainly landfilled. However, due to the imperfect risk control system of landfill sites, irregular disposal methods such as fast landfilling speed and over-capacity landfilling often occur, which are extremely likely to cause the instability of the landfill site. The muck landfill site brings high risks to the safety of urban life. It is urgent to evaluate the landslide risk of the landfill site, take prevention and control measures, strengthen risk control, and provide guidance for urban disaster prevention and control.

[0003] At present, few studies can comprehensively evaluate the landslide risk of landfill sites, and most of them analyze each sub-item of risk assessment such as slope stability and migration distance. A small number of comprehensive studies on the risk assessment of landfill sites only consider the disaster-causing factors from the surface environment, such as slope, runoff coefficient, catchment area, etc., as the focus of risk assessment, and fail to reflect the physical process of landslide risk assessment and the characteristics of muck itself. There are many types of muck in the landfill site, and the spatial distribution is very complex, with significant spatial variability, which cannot be ignored in the landslide risk assessment. Due to the influence of artificial landfilling, muck has unique spatial variability characteristics, such as the multi-modal edge distribution characteristics of muck. The method for characterizing its spatial variability cannot simply apply the method for characterizing the spatial variability of natural soil. In addition, compared with natural soil landslides, landfill site landslides often show the characteristics of high speed and long distance, which need to be noted when selecting the key factors of landslide intensity. Therefore, it is urgent to establish a method for quantitatively evaluating the landslide risk of landfill sites considering the characteristics of muck, landslide characteristics and the physical process of landslide response. Summary of the Invention

[0004] In view of the above reasons, the present invention provides a method for quantitatively evaluating the full probability risk of landfill landslides considering the three-dimensional spatial variability of muck. This method considers the unique spatial variability characteristics of muck, analyzes the stability, potential sliding position and volume from a three-dimensional perspective, and combines the vulnerability assessment function applicable to landfill site landslides. Compared with the traditional risk assessment method, it considers the characteristics of artificial muck more, and can achieve accurate risk assessment results.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for quantitatively evaluating the full probability risk of landfill landslides considering the three-dimensional spatial variability of muck provided by the present invention includes the following steps:

[0007] S1: Conduct borehole exploration of the landfill site to obtain the geotechnical engineering parameters, water level information and digital elevation model of the landfill site;

[0008] S2: Based on the sparse borehole data, obtain the random field parameters characterizing the spatial variability of the muck.

[0009] S3: Based on the random field theory, simulate the three-dimensional spatial variability characteristics of the muck.

[0010] S4: Based on the limit equilibrium method, conduct the three-dimensional slope stability assessment of the muck yard and estimate the potential landslide volume.

[0011] S5: Carry out numerical simulation of the landslide and estimate the possible migration range under various landslide volumes.

[0012] S6: Statistically count the life and property of the downstream disaster-bearing bodies, and based on the vulnerability assessment formulas of buildings and personnel, estimate the vulnerability of the disaster-bearing bodies according to the landslide intensity.

[0013] S7: Establish the full probability risk assessment formula for the landslide of the muck yard, and conduct quantitative assessment and risk rating of the landslide risk.

[0014] Further, the specific steps of S1 are as follows: Conduct in-door or in-situ tests to measure the geotechnical engineering parameters, where the geotechnical engineering parameters include the cohesion, internal friction angle, and unit weight of the muck; while obtaining the geotechnical engineering parameters, record the corresponding spatial positions; obtain the digital elevation model of the site through UAV mapping.

[0015] Further, the random field parameters in S2 include: marginal distribution, cross-correlation coefficient, horizontal and vertical autocorrelation distance parameters; the random field parameters characterizing the spatial variability of the muck need to consider the unique spatial variability characteristics of the muck. Due to the artificial disturbance, the spatial variability characteristics of the muck are different from those of natural soil. The main reason is that the muck yard consists of various types of muck, resulting in a multi-modal distribution of the marginal distribution of the muck parameters in the site. This multi-modal distribution is estimated by the mixture Gaussian model, and the cross-correlation between the cohesion and the internal friction angle is determined according to the Pearson correlation coefficient.

[0016] Further, the horizontal and vertical autocorrelation distance parameters need to map the soil parameters in the original space to the standard normal space using the probability transformation method, and the expression is as follows:

[0017] ;

[0018] In the formula, c and are the cohesion and the internal friction angle, X i and X i,NG are the soil parameters in the standard normal space and the original space respectively, F i is the cumulative probability density function of the non-Gaussian distribution, which is estimated by the Gaussian mixture model, is the inverse function of the standard Gaussian cumulative distribution function;

[0019] Based on the strength parameters in the standard normal space, the horizontal and vertical autocorrelation distances are estimated using the maximum likelihood method. The specific maximum likelihood function is expressed as follows:

[0020] ;

[0021] In the formula, X c and are the cohesion and internal friction angle under the standard normal distribution, δ h and δ v are the horizontal and vertical autocorrelation distances, which are hidden in the autocorrelation matrix R A . This autocorrelation matrix is determined according to the selected autocorrelation function and the spatial relative positions of the borehole samples.

[0022] Furthermore, the specific steps of S3 are as follows:

[0023] According to the random field parameters, the spatial variability of the muck parameters is simulated using the random field theory, specifically as follows:

[0024] ;

[0025] In the formula, U is -dimensional independent standard Gaussian random matrix, X U is -dimensional standard Gaussian random matrix that satisfies cross-correlation and autocorrelation. The first column is the cohesion, the second column is the internal friction angle, L A and L C are the decomposition matrices of the autocorrelation matrix R A and the cross-correlation matrix R C respectively. Subsequently, X U is transformed into the geotechnical parameters in the original space as follows:

[0026] .

[0027] Furthermore, the specific steps of S4 are as follows:

[0028] Based on the generated three-dimensional random distributions of cohesion and internal friction angle, using the three-dimensional limit equilibrium method, combined with the surface digital elevation model and the water level elevation in the landfill, calculate the slope safety factor;

[0029] Regard the soil mass between the slip surface and the surface digital elevation model as the potential sliding soil mass, and calculate the corresponding volume;

[0030] Generate multiple random fields of cohesion and internal friction angle, and based on the Monte Carlo simulation method, calculate the slope instability probability and obtain the potential sliding volume distribution.

[0031] Furthermore, the specific steps of S5 are as follows:

[0032] According to the estimated position of the slip surface in S4, calculate the pore water pressure coefficient at the base of the slip surface, and obtain the cohesion and internal friction angle of the soil at the base of the slip surface;

[0033] Regard the soil between the slip surface and the digital elevation model of the ground surface as potentially sliding soil, which serves as the landslide initiation source. Obtain the digital elevation model around the muck yard and carry out the simulation of the landslide movement path;

[0034] According to the multiple simulations of the random fields of cohesion and internal friction angle in S4 and the corresponding slip surface distributions, carry out multiple simulations of the landslide movement path, estimate the movement range, and calculate the spatial influence probability.

[0035] Furthermore, the downstream disaster-bearing bodies statistically analyzed in S6 are mainly divided into two categories: one is the building disaster-bearing bodies valued by economic losses, including houses, transportation facilities, and important projects; the other is the personnel disaster-bearing bodies valued by the number of casualties;

[0036] Statistically analyze the economic value of the building disaster-bearing bodies and the population of the personnel disaster-bearing bodies;

[0037] Take the landslide intensity factors obtained from the numerical simulation in S5: depth, velocity, and impact force, as the intensity input for vulnerability assessment; the building vulnerability assessment formula needs to be determined according to the building type and the characteristics of the high-speed long-distance movement of the muck yard landslide, and the personnel vulnerability is determined according to the indicators of the corresponding building vulnerability.

[0038] Furthermore, the full probability risk assessment formula for the muck yard landslide established in S7 is as follows:

[0039] ;

[0040] In the formula, P f is the probability of slope instability, is the probability of the spatial influence range of the landslide, is the exposure probability of the disaster-bearing body. The exposure probability of the building disaster-bearing body is 1, and the exposure probability of the personnel disaster-bearing body is determined by the daily routine of the personnel within the landslide influence range. is the vulnerability of the disaster-bearing body, where I is the landslide intensity, E is the value of the disaster-bearing body, n is the number of Monte Carlo simulations, and m is the number of downstream disaster-bearing bodies.

[0041] A quantitative assessment device for the total probability risk of a muck yard landslide considering the three-dimensional spatial variability of muck provided by the present invention. The device includes a memory and a processor. Among them, when the processor executes the computer program stored in the memory, it realizes the process of the quantitative assessment method for the total probability risk of a muck yard landslide considering the three-dimensional spatial variability of muck. The calculation program is written in Python language, which realizes the estimation of spatial variability parameters and the simulation of the strength parameter random field, calls the Scoops3D software to complete the three-dimensional slope stability analysis, calls the Massflow software to complete the landslide numerical simulation, uses the ArcGIS Pro software to incorporate the downstream disaster-bearing bodies into a vector file, converts the vector file into matrix data through Python, obtains the landslide intensity borne by each disaster-bearing body, and combines with the vulnerability assessment formula to complete the vulnerability assessment and the final quantitative assessment of the total probability risk.

[0042] Based on the above technical solutions, the embodiments of the present invention can at least produce the following technical effects:

[0043] The present invention considers the unique spatial variation characteristics of muck, analyzes the stability, potential slip positions and volumes from a three-dimensional perspective, and combines with the quantitative vulnerability assessment method to establish a quantitative risk assessment method for the total probability of muck yard landslides for the first time. This method takes more into account the characteristics of artificial muck than traditional risk assessment models and can achieve accurate risk assessment results. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the structures shown in these drawings.

[0045] Figure 1 It is a flow chart of the quantitative assessment method for the total probability risk of a muck yard landslide considering the three-dimensional spatial variability of muck;

[0046] Figure 2 Part (a) shows the cohesion of the strength parameters of a certain muck yard obtained by borehole exploration, Figure 2 Part (b) shows the internal friction angle of a certain muck yard obtained by borehole exploration;

[0047] Figure 3 It is the surface elevation model of a certain muck yard obtained by UAV aerial survey;

[0048] Figure 4 It is the bimodal edge distribution model of the strength parameters in a certain muck yard;

[0049] Figure 5Part (a) shows the three-dimensional random field of the cohesion parameter within a certain muck yard generated; Figure 5 Part (b) shows the three-dimensional random field of the friction angle parameter within a certain muck yard generated;

[0050] Figure 6 Part (a) shows the three-dimensional slope safety factor distribution map of a certain muck yard; Figure 6 Part (b) shows the potential sliding volume distribution map of a certain muck yard;

[0051] Figure 7 Part (a) shows the potential sliding position of a certain muck yard; Figure 7 Part (b) shows the potential sliding migration range of a certain muck yard;

[0052] Figure 8 Part (a) shows the evaluation result of the landslide personnel risk of a certain muck yard; Figure 8 Part (b) shows the evaluation result of the economic risk of a certain muck yard. Specific implementation manners

[0053] Next, the technical solutions in the embodiments of the present invention will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement it. When the combination of the technical solutions appears to be contradictory or unable to be implemented, it should be considered that such a combination of the technical solutions does not exist and is not within the protection scope required by the present invention.

[0054] Figure 1 The flow chart of the quantitative assessment method for the full probability risk of muck yard landslides considering the three-dimensional spatial variability of muck mainly consists of 7 steps as follows:

[0055] S1: Conduct borehole exploration of the muck yard site to obtain the geotechnical engineering parameters, water level information and digital elevation model of the muck yard;

[0056] S2: Based on the sparse borehole data, obtain the random field parameters characterizing the spatial variability of muck;

[0057] S3: Simulate the three-dimensional spatial variation characteristics of muck based on the random field theory;

[0058] S4: Based on the limit equilibrium method, conduct the three-dimensional slope stability assessment of the muck yard and estimate the potential landslide volume;

[0059] S5: Conduct landslide numerical simulation to estimate the possible migration range under various landslide volumes;

[0060] S6: Statistically analyze the life and property of downstream disaster-bearing bodies, and estimate the vulnerability of disaster-bearing bodies based on the vulnerability assessment formulas of buildings and personnel according to the landslide intensity.

[0061] S7: Establish a full probability risk assessment formula for the muck yard landslide, and conduct quantitative assessment and risk rating of the landslide risk.

[0062] In the above-mentioned S1, borehole exploration of the muck yard site is carried out to obtain geotechnical engineering parameters and water level information of the muck yard. This site exploration can obtain undisturbed soil samples by drilling, and carry out indoor tests to measure geotechnical engineering parameters such as the cohesion, internal friction angle, and unit weight of the muck; while obtaining geotechnical engineering parameters, record the corresponding spatial positions. The digital elevation model of the site is obtained through UAV mapping. Figure 2 Data of cohesion and internal friction angle obtained from borehole exploration of a certain muck yard. Figure 3 Surface model of the muck yard obtained by UAV mapping.

[0063] In S2, based on the sparse borehole data, the random field parameters characterizing the spatial variability of the muck are obtained. The random field parameters include: marginal distribution, cross-correlation coefficient, horizontal and vertical autocorrelation distances. The random field parameters characterizing the spatial variability of the muck need to consider the unique spatial variability characteristics of the muck. Due to artificial disturbance, the spatial variability characteristics of the muck are different from those of natural soil. The main reason is that the muck yard is composed of various mucks, so that the marginal distribution of the muck parameters in the site shows a multi-modal distribution. This multi-modal distribution can be estimated by the Gaussian mixture model. The cross-correlation between cohesion and internal friction angle can be determined according to the Pearson correlation coefficient. Figure 4 Bimodal marginal distribution model of cohesion and internal friction angle of the landfill obtained by using the Gaussian mixture model.

[0064] In the above-mentioned S2, based on the sparse borehole data, the random field parameters characterizing the spatial variability of the muck are obtained. The horizontal and vertical autocorrelation distance parameters need to map the soil parameters in the original space to the standard normal space by the equiprobability transformation method. The expression is as follows:

[0065] ;

[0066] In the formula, c and are cohesion and internal friction angle, X i and X i,NG are soil parameters in the standard normal space and the original space respectively, F i is the cumulative probability density function of non-Gaussian distribution, which can be estimated by the Gaussian mixture model, is the inverse function of the standard Gaussian cumulative distribution function. Based on the strength parameters in the standard normal space, the horizontal and vertical autocorrelation distances are estimated by the maximum likelihood method. The specific maximum likelihood function is expressed as follows:

[0067] ;

[0068] Wherein, X c and are the cohesion and internal friction angle under the standard normal distribution, and are the horizontal and vertical autocorrelation distances, which are hidden in the autocorrelation matrix R A . The autocorrelation matrix is determined according to the selected autocorrelation function and the spatial relative positions of the borehole samples. The exponential squared autocorrelation function is selected as the autocorrelation function within the site, as follows:

[0069] ;

[0070] Wherein, , , and are the relative distances between two positions in the x, y, and z directions, is the autocorrelation coefficient in the autocorrelation matrix R A . According to the exploration data of the landfill, the horizontal and vertical autocorrelation distances can be estimated to be 34 m and 3 m respectively by using the maximum likelihood method.

[0071] Simulating the three-dimensional spatial variability characteristics of muck based on the random field theory in S3. According to the estimated random field parameters, the spatial variability of muck parameters is simulated by using the random field theory. The specific steps are as follows:

[0072] ;

[0073] Wherein, U is dimensional independent standard Gaussian random matrix, X U is dimensional standard Gaussian random matrix satisfying cross-correlation and autocorrelation. The first column is the cohesion, the second column is the internal friction angle, L A and L C are the decomposition matrices of the autocorrelation matrix R A and the cross-correlation matrix R C respectively, which can be determined according to Cholesky decomposition or eigenvalue decomposition. Figure 5 is the random field of cohesion and internal friction angle generated based on the borehole data of the muck site. Subsequently, the simulated data X U (including cohesion X C and internal friction angle ) in the standard space is transformed into the geotechnical parameters in the original space, as follows:

[0074] ;

[0075] In S4, based on the limit equilibrium method, the three-dimensional slope stability of the muck yard is evaluated and the potential landslide volume is estimated. Based on the generated three-dimensional spatial random distribution of cohesion and internal friction angle, the three-dimensional limit equilibrium method, such as the Bishop method, is used to calculate the slope safety factor in combination with the surface digital elevation model and the water level elevation in the landfill. The soil between the slip surface and the surface digital elevation model is regarded as the potential sliding soil, and the corresponding volume is calculated. Multiple random fields of cohesion and internal friction angle are generated, and based on the Monte Carlo simulation method, the slope instability probability is calculated to obtain the potential sliding volume distribution. The slope stability analysis is carried out by Python calling the Scoops3D software. Figure 6 This is the distribution map of the safety factor of the muck yard, and the calculated instability probability is 0.9%.

[0076] In S5, based on the depth-integrated continuum model, the possible migration range under various landslide volumes is estimated. According to the slip surface position estimated in S4, the pore water pressure coefficient at the base of the slip surface is calculated, and the cohesion and internal friction angle of the soil at the base of the slip surface are obtained. The soil between the slip surface and the surface digital elevation model is regarded as the potential sliding soil, which is used as the landslide initiation source. The digital elevation model around the muck yard is obtained, and based on the depth-integrated continuum model, the landslide migration path simulation is carried out. According to the multiple simulations of the random fields of cohesion and internal friction angle in S4 and the corresponding slip surface distribution, multiple landslide migration path simulations are carried out to estimate the migration range and calculate the spatial influence probability. The landslide numerical simulation is carried out by Python calling the Massflow software. Figure 6 This is the distribution map of the potential sliding volume of the muck yard landslide, and the volume is between 50,000 cubic meters and 600,000 cubic meters. Figure 7 This is the potential sliding position of the muck yard landslide under a certain working condition. Figure 7 This is the final migration range of the landslide under the corresponding working condition.

[0077] In S6, the downstream disaster-bearing bodies counted are mainly divided into two categories. One category is the building disaster-bearing bodies valued by economic losses, including houses, transportation facilities and important projects; the other category is the personnel disaster-bearing bodies valued by the number of casualties. The economic value of the building disaster-bearing bodies and the population of the personnel disaster-bearing bodies are counted. Figure 7 The types of disaster-bearing bodies downstream of the muck yard are identified, including ponds, farmland and buildings. According to the high-speed long-distance migration characteristics of the muck yard landslide, the vulnerability assessment formula for reinforced concrete buildings is selected as follows:

[0078] ;

[0079] In the formula, V rc represents the vulnerability value of the reinforced concrete building, I D , I V and I PThe landslide intensity acting on the building, which are depth, velocity, and impact force respectively, can be obtained through numerical simulation in S5. Based on the vulnerability values obtained from the three landslide intensities, the maximum value is taken as the vulnerability value of the impacted building. The vulnerability assessment formula for non-reinforced concrete buildings is as follows:

[0080] ;

[0081] In the formula, V non-rc represents the vulnerability value of non-reinforced concrete buildings. Once farmland and ponds are buried by the landslide, their vulnerability values are regarded as 1. The vulnerability estimation formula for the people in the building is as follows:

[0082] ;

[0083] In the formula, V H is the corresponding vulnerability value of the people, and V B is the vulnerability value of the building where the people are located.

[0084] The full probability risk assessment formula for the muck yard landslide established in S7 is as follows:

[0085] ;

[0086] In the formula, P f is the probability of slope instability, is the probability of the spatial influence range of the landslide, is the exposure probability of the disaster-bearing body. The exposure probability of the building disaster-bearing body is 1, and the exposure probability of the people disaster-bearing body is determined by the daily routine of the people within the landslide influence range. is the vulnerability of the disaster-bearing body, where I is the landslide intensity, E is the value of the disaster-bearing body, n is the number of Monte Carlo simulations, and m is the number of downstream disaster-bearing bodies.

[0087] Figure 8 is the estimated risk and economic loss of the people. Since the landslide migration distance is short and it will not impact the downstream villages, the risk of the people is 0. The economic loss is mainly the farmland and ponds buried by the landslide, and the potential economic loss is 89,000 yuan.

[0088] In the S7, the obtained quantitative risk assessment results are rated for the landslide risk of the muck yard according to the risk levels in the Regulations on the Prevention and Control of Geological Disasters. If the number of deaths is greater than or equal to 30, or the direct economic loss is greater than or equal to 10 million yuan, the risk level is extremely high; if the number of deaths is less than 30 and greater than or equal to 10, or the direct economic loss is less than 10 million yuan and greater than or equal to 5 million yuan, the risk level is high; if the number of deaths is less than 10 and greater than or equal to 3, or the direct economic loss is less than 5 million yuan and greater than or equal to 1 million yuan, the risk level is medium; if the number of deaths is less than 3, or the direct economic loss is less than 1 million yuan, the risk level is low. According to the quantitative risk assessment results of the landslide of the muck yard, the risk level is low.

[0089] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A quantitative assessment method for the full probability risk of muck yard landslides considering the three-dimensional spatial variability of muck, characterized in that, It includes the following steps: S1: Conduct on-site drilling exploration of the muck yard to obtain geotechnical engineering parameters, water level information, and digital elevation model of the muck yard; S2: Based on the sparse drilling data, obtain the random field parameters characterizing the spatial variability of the muck; S3: Simulate the three-dimensional spatial variability characteristics of the muck based on the random field theory; S4: Based on the limit equilibrium method, conduct three-dimensional slope stability assessment of the muck yard and estimate the potential landslide volume; S5: Conduct numerical simulation of the landslide to estimate the possible migration range under various landslide volumes; S6: Statistically analyze the life and property of the downstream disaster-bearing bodies. Based on the vulnerability assessment formulas for buildings and personnel, estimate the vulnerability of the disaster-bearing bodies according to the landslide intensity; The downstream disaster-bearing bodies statistically analyzed in S6 are mainly divided into two categories: one is the building disaster-bearing bodies valued by economic losses, including houses, transportation facilities, and important projects; the other is the personnel disaster-bearing bodies valued by the number of casualties; Statistically analyze the economic value of the building disaster-bearing bodies and the population of the personnel disaster-bearing bodies; Use the landslide intensity factors obtained from the numerical simulation in S5: depth, velocity, and impact force, as the intensity input for vulnerability assessment; the building vulnerability assessment formula needs to be determined according to the building type and the characteristics of the high-speed long-distance movement of the muck yard landslide, and the personnel vulnerability is determined according to the indicators of the corresponding building vulnerability; S7: Establish a full probability risk assessment formula for the muck yard landslide, conduct quantitative risk assessment of the landslide and risk rating; The full probability risk assessment formula for the muck yard landslide established in S7 is as follows: ; Wherein, P f is the probability of slope instability, is the probability of the spatial influence range of landslide, is the exposure probability of the disaster-bearing body. The exposure probability of the building disaster-bearing body is 1, and the exposure probability of the human disaster-bearing body is determined by the daily routine of the people within the influence range of the landslide. is the vulnerability of the disaster-bearing body, where I is the landslide intensity, E is the value of the disaster-bearing body, n is the number of Monte Carlo simulations, and m is the number of downstream disaster-bearing bodies.

2. The method for quantitatively evaluating the full probability risk of spoil ground landslide considering the three-dimensional spatial variability of spoil, according to claim 1, is characterized in that The specific steps of S1 are as follows: Conduct indoor or in-situ tests to measure geotechnical engineering parameters, and the geotechnical engineering parameters include the cohesion, internal friction angle, and unit weight of the muck; While obtaining the geotechnical engineering parameters, record the corresponding spatial positions; Obtain the digital elevation model of the site through UAV mapping.

3. The full probability risk quantitative assessment method for the landslide of the muck yard considering the three-dimensional spatial variability of muck according to claim 1, characterized in that The random field parameters in S2 include: marginal distribution, cross-correlation coefficient, horizontal and vertical autocorrelation distance parameters; the random field parameters characterizing the spatial variability of the muck need to consider the unique spatial variability characteristics of the muck. Due to the influence of artificial disturbance, the spatial variability characteristics of the muck are different from those of natural soil. The muck yard is mainly composed of various mucks, resulting in a multi-modal distribution of the marginal distribution of the muck parameters in the site. This multi-modal distribution is estimated by the mixture Gaussian model, and the cross-correlation between the cohesion and the internal friction angle is determined according to the Pearson correlation coefficient.

4. The method for quantitatively evaluating the full probability risk of the soil dump landslide considering the three-dimensional spatial variability of the muck according to claim 3, characterized in that The horizontal and vertical autocorrelation distance parameters need to map the soil parameters in the original space to the standard normal space using the probability transformation method, and the expression is as follows: ; where c and are the cohesion and the angle of internal friction, X i and X i,NG are the soil parameters in the standard normal space and the original space respectively, F i is the cumulative probability density function of non-Gaussian distribution, which is estimated by the Gaussian mixture model, is the inverse function of the standard Gaussian cumulative distribution function; Based on the strength parameters in the standard normal space, use the maximum likelihood method to estimate the horizontal and vertical autocorrelation distances. The specific maximum likelihood function is expressed as follows: ; Wherein, X c and are the cohesion and internal friction angle under the standard normal distribution, δ h and δ v are the horizontal and vertical autocorrelation distances, which are hidden in the autocorrelation matrix R A . The autocorrelation matrix is determined according to the selected autocorrelation function and the spatial relative positions of the borehole samples.

5. The full probability risk quantitative assessment method for the landslide of the muck yard considering the three-dimensional spatial variability of the muck according to claim 1, characterized in that The specific steps of S3 are as follows: According to the random field parameters, use the random field theory to simulate the spatial variability of the muck parameters, specifically as follows: ; where U is a matrix of independent standard Gaussian random variables of dimension U , X is a standard Gaussian random matrix of dimension A satisfying cross-correlation and auto-correlation, with the first column being the cohesion and the second column being the internal friction angle, and L C and L A are the decomposition matrices of the auto-correlation matrix R C and the cross-correlation matrix R U respectively. Subsequently, X U is transformed into geotechnical parameters in the original space as follows: 。 6. The quantitative full probability risk assessment method for the landslide of the muck yard considering the three-dimensional spatial variability of the muck as claimed in claim 1, characterized in that The specific steps of S4 are as follows: Based on the generated three-dimensional random distribution of cohesion and internal friction angle, use the three-dimensional limit equilibrium method, combined with the surface digital elevation model and the water level elevation in the landfill, to calculate the slope safety factor; Regard the soil mass between the slip surface and the surface digital elevation model as the potential sliding soil mass, and calculate the corresponding volume; Generate multiple random fields of cohesion and internal friction angle. Based on the Monte Carlo simulation method, calculate the probability of slope instability and obtain the distribution of potential sliding volume.

7. The quantitative full probability risk assessment method for the landslide of the muck yard considering the three-dimensional spatial variability of the muck as claimed in claim 1, characterized in that The specific steps of S5 are as follows: According to the estimated position of the slip surface in S4, calculate the pore water pressure coefficient at the base of the slip surface and obtain the cohesion and internal friction angle of the soil at the base of the slip surface. Regard the soil between the slip surface and the digital elevation model of the ground surface as potential sliding soil, which serves as the landslide initiation source. Obtain the digital elevation model around the muck yard and conduct simulation of the landslide movement path. According to the simulation of multiple random fields of cohesion and internal friction angle in S4 and the corresponding distribution of slip surfaces, conduct multiple simulations of the landslide movement path, estimate the movement range, and calculate the probability of the spatial influence range of the landslide.

8. A quantitative assessment device for the full probability risk of a muck yard landslide considering the three-dimensional spatial variability of muck, the device includes a memory and a processor, wherein, When the processor executes the computer program stored in the memory, it realizes the process of the quantitative assessment method for the full probability risk of muck yard landslide considering the three-dimensional spatial variability of muck as described in any one of claims 1-7.

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