An intelligent prediction method for landslide disaster risk probability based on two-phase material point method

Abstract

The present invention belongs to the technical field of geological disaster monitoring and early warning, specifically relating to an intelligent prediction method for landslide disaster risk probability based on the two-phase material point method. By treating parameters such as rock and soil properties and rainfall intensity as statistically correlated multivariate random variables, the two-phase material point method is used to numerically simulate the entire process of rainfall-induced landslide disasters. The probability distribution of response quantities such as sliding distance, collapse range, and sliding body volume is then obtained, and the risk probability is predicted based on the characteristics of the hazard-bearing body. Random samples are extracted based on the prior distribution of each parameter variable as input data sets, and the response quantity samples obtained by simulation calculations are used as output data sets. The method is trained using an XGBoost model to construct a proxy model for the two-phase material point method. Bayesian probabilistic inversion is used to perform parameter inversion on slope monitoring data, dynamically updating the probability distribution of each model response quantity. Based on the response quantity distribution, the risk probability is predicted in real time. This method not only has clear physical meaning but also improves prediction efficiency and accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological disaster monitoring and early warning, and in particular relates to an intelligent prediction method for landslide disaster risk probability based on a two-phase material point method. Background Art

[0002] Landslides are widespread, sudden, and destructive. Rainfall infiltration degrades the strength of slope rock and soil, increasing permeability and leading to decreased slope stability. This makes landslides more likely to occur, threatening economic development and the safety of people's lives and property. Predicting potential landslides has become a key and challenging issue in geological disaster prevention and control.

[0003] Currently, the main methods for predicting landslides include empirical, statistical, and nonlinear methods. Empirical methods rely on empirical evidence of landslide precursors, lack theoretical support, and have low reliability. Statistical methods use historical landslide monitoring data to predict future trends, but they struggle to reflect how landslide response data changes with influencing factors, resulting in certain limitations. Nonlinear methods treat landslides as nonlinear dynamic systems, combining various theories, including nonlinear theory, systems science theory, and neural network theory, to achieve dynamic tracking and nonlinear prediction of landslides. However, due to the complexity of landslide evolution and the variability of external influences, the physical significance of these methods is unclear.

[0004] Most current landslide prediction methods lack a complete physical understanding. Landslides involve numerous factors and complex conditions. Consequently, without the support of physical mechanics theory, most existing prediction methods and models have their own shortcomings and deficiencies. They fail to truly reveal the underlying mechanisms of landslide deformation, failure, and evolution, making them unsuitable for all types of landslides. They can only predict landslides of a certain type or stage of evolution, resulting in unsatisfactory prediction results. Therefore, it is necessary to establish a real-time prediction method for landslide catastrophic risk probability that fully considers the physical evolution mechanisms of landslides. Summary of the Invention

[0005] In view of the above-mentioned defects of the prior art, the purpose of the present invention is to provide a new landslide disaster risk probability prediction method to solve the problems of unclear physical meaning, difficulty in real-time prediction and low prediction accuracy in traditional landslide prediction methods.

[0006] To achieve the above object, the present invention provides a method for intelligently predicting landslide disaster risk probability based on a two-phase material point method, comprising the following steps:

[0007] 1) Based on landslide investigation data and local rainfall monitoring data, obtain the prior distribution of landslide rock and soil parameters such as elastic modulus E, cohesion c, internal friction angle φ, permeability coefficient k, and rainfall intensity q;

[0008] 2) Based on the prior distribution of various rock and soil parameters and rainfall intensity, the Latin hypercube sampling method (LSH) is used to extract random samples to construct the input data set;

[0009] 3) Based on the landslide stratum data, a two-phase material point numerical calculation model is constructed for each set of sample parameters in step 2), and the initial conditions, boundary conditions, and load conditions of the numerical model are set according to the actual situation;

[0010] 4) Based on the two-phase material point method, the motion equations of the landslide rock and soil and pore fluid are discretized in material point space and solved by explicit time integration, thus simulating the entire process of the destruction evolution of the unsaturated rock and soil of the landslide under the action of hydrodynamic forces;

[0011] 5) Based on the two-phase material point method, the displacement and pore pressure values ​​of the monitoring points with time series are calculated for parameter inversion. At the same time, the final response of each sample, such as sliding distance s, collapse range S, sliding body thickness h, sliding body volume V, sliding velocity v, etc., are obtained to construct the output data set;

[0012] 6) Based on the data sets obtained in steps 2) and 5), an XGBoost model is used for training and testing to construct a proxy model of the numerical model to improve computational efficiency;

[0013] 7) Based on the prior distribution of each parameter, the Monte Carlo simulation algorithm is used in combination with the surrogate model to statistically calculate the landslide failure probability and the probability distribution characteristics of each response quantity. Combined with the characteristics of the hazard-bearing body, the initial landslide disaster risk probability prediction result is calculated;

[0014] 8) Monitoring instruments are deployed on the slope to collect real-time monitoring data such as displacement and pore pressure. Parameter inversion is performed based on the monitoring data using the Bayesian probability inversion method to calculate the posterior distribution of the rock and soil parameters. The rainfall intensity q is determined based on the measured data.

[0015] 9) Based on the posterior distribution of each rock and soil parameter obtained by inversion, the Monte Carlo simulation algorithm is used again to statistically calculate the failure probability and the probability distribution characteristics of each model response, and the risk probability prediction results are updated;

[0016] 10) Using the posterior distribution of the rock and soil parameters obtained in step 8) as the prior distribution, repeat steps 8) to 9) through the real-time collected landslide displacement, pore pressure monitoring data and rainfall data to inversely update the rock and soil parameters and achieve real-time dynamic update of the risk probability prediction results.

[0017] Furthermore, in step 2), the model parameter variable sample can be expressed as X = [x1, x2, ..., x n ], where x i Represents parameters such as elastic modulus E, cohesion c, and internal friction angle φ. n represents the dimension of model parameter variables. The number of samples required for each dimension is m. When using Latin hypercube sampling, if m n-dimensional samples that conform to the prior distribution of model parameters are to be extracted from the sample space, then the [0,1] interval needs to be divided into m intervals, and a sample is randomly extracted from each interval. The extracted values ​​are mapped to the sample space to obtain an n×m-order sample matrix A, thereby constructing the model input data set.

[0018] Furthermore, in step 3), a three-dimensional geological model of the landslide body is constructed based on the three-dimensional stratigraphic data of the landslide area, and the model is discretized into spatial material points. At the same time, a background grid covering the entire landslide sliding area is set behind the material points, and rock and soil parameters are assigned to each material point. Gravity and other external load conditions are input, displacement boundary conditions and seepage boundary conditions are set, an initial groundwater level is set, and initial ground stress balance is performed.

[0019] Furthermore, in step 4), the rock mass is described using the DP elastic-plastic constitutive model, and the pore fluid is described using Darcy's law. The Euler-Cromer explicit integration algorithm is used to solve the problem, combining the effective stress principle, the soil-water characteristic curve, and the permeability curve. The constitutive equations for the pore fluid and solid are shown in Equations (1) and (2), respectively:

[0020]

[0021]

[0022] Among them, ρ L is the liquid density, p L is the liquid pressure, v L 、v S are the absolute velocities of liquid and solid respectively, n is the porosity of the solid skeleton, S L is saturation, D ep is the tangential stiffness matrix, and h' is the constitutive tensor.

[0023] The relationship between pore water pressure and saturation is given by the soil water characteristic curve (SWRC), as shown in Equation (3). The actual permeability k and the saturated permeability k sat The relationship between the permeability curve (HCC) is obtained as shown in formula (4).

[0024]

[0025]

[0026] Among them S max 、S min are the maximum saturation and the residual saturation, respectively, p ref and λ are fitting parameters.

[0027] The nodal momentum balance equations for solid and fluid that need to be solved are shown in Equations (5) and (6).

[0028]

[0029]

[0030] Among them, a S and a L are the nodal acceleration vectors of the solid and fluid respectively, and τ is the boundary The pulling vector at For the boundary The liquid pressure at the location, N is the node shape function matrix, B is the gradient matrix of the node shape function at the local material point MP, V MP is the volume of the material point, ρ m is the mixture density, n MP is the number of material points, n MP is the porosity of the solid skeleton of the material point, g is the acceleration vector, is the material point permeability.

[0031] Furthermore, in step 6), the specific steps of using the XGBoost model are as follows:

[0032] ① Take the input and output data obtained in step 2) and step 5) as the data set;

[0033] ② Divide the dataset into training set and test set in a ratio of 70% and 30%;

[0034] ③ Define XGBoost model parameters, including the maximum depth of the tree, learning rate, number of iterations, objective function, etc.;

[0035] ④ Use the training set data to train the XGBoost model;

[0036] ⑤ Use cross-validation method to adjust model parameters and find the optimal parameter combination;

[0037] ⑥Use the test set to evaluate the performance of the model.

[0038] Furthermore, in step 7), based on the sliding distance s, the landslide range S, etc., combined with the distribution of buildings, infrastructure, and personnel, the hazard-bearing body that may be affected by the landslide is evaluated. Then, based on the sliding body thickness h, the sliding body volume V, the sliding speed v, etc., combined with the vulnerability of buildings, infrastructure, and personnel, the loss caused by the landslide to the hazard-bearing body is estimated. Finally, risk prediction is performed based on the loss probability distribution obtained by statistical calculation.

[0039] Furthermore, in step 8), according to the Bayesian principle, the posterior probability density function can be calculated by formula (15):

[0040] f(x|y)=af(x)f(y|x) (15)

[0041] Where a is the normalization coefficient, f(x) is the prior distribution of parameter x, y is the monitoring data, and f(y|x) is the likelihood function, which can be calculated by formula (16).

[0042]

[0043] Among them, M is the number of samples, F is the theoretical value corresponding to y, ε is the residual vector, and ε i =y i -F i , i=1,2,…M. Assume that ε obeys independent equal variance Gaussian distribution, that is,

[0044] MH sampling is used to implement Markov chain Monte Carlo simulation to solve the posterior probability density function. The process is as follows Figure 6 As shown, where α(i,j) is the acceptance rate, Π(x) is the stationary distribution of the Markov chain, and C is the state transition matrix of the Markov chain.

[0045] Furthermore, in step 10), the displacement, pore pressure data and rainfall data of the landslide monitoring points are collected in real time, and the monitoring data are updated every 3 hours. Then, the rock and soil parameters are updated by inversion, the probability distribution characteristics of the landslide failure probability and the model response quantity are recalculated, and the landslide risk probability prediction results are dynamically updated.

[0046] The technical solution provided by the present invention has the following effects:

[0047] 1. The material point numerical simulation method is applied to the large deformation simulation of landslide rock and soil, taking into account the unsaturated hydrodynamic effect. The two-phase material point method is used to simulate the entire landslide failure process, fully considering the landslide catastrophic mechanism and having clear physical significance.

[0048] 2. Using the XGBoost algorithm, we construct a proxy model for the numerical model, significantly improving the efficiency of numerical calculations and thus improving the timeliness of risk probability prediction;

[0049] 3. Using the Bayesian probability inversion algorithm, combined with real-time monitoring data, the posterior distribution of uncertainty parameters is continuously inverted and calculated, reducing the uncertainty of each parameter variable, so that the calculation model continues to approach the actual situation, thereby updating and correcting the landslide probability prediction results in real time, thereby improving the accuracy of risk probability prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the intelligent prediction method for landslide disaster risk probability based on the two-phase material point method;

[0051] Figure 2 Calculation model diagram for two-phase material points of landslide;

[0052] Figure 3 This is the final simulation result of the two-phase material point method under a set of sample parameters;

[0053] Figure 4 The simulation results of monitoring point displacement and pore pressure at different times under a set of sample parameters;

[0054] Figure 5 This is the regression analysis chart of the sliding distance test set of the proxy model;

[0055] Figure 6 Solve the flowchart for the Markov chain;

[0056] Figure 7 are the prior and posterior distributions of the elastic modulus;

[0057] Figure 8 The probability distribution map of landslide sliding distance before and after the update;

[0058] Figure 9 To update the landslide disaster risk probability distribution map before and after; DETAILED DESCRIPTION

[0059] The present invention will be further described below with reference to the accompanying drawings and examples.

[0060] See also Figure 1 This embodiment discloses a method for intelligently predicting landslide disaster risk probability based on a two-phase material point method, comprising the following steps:

[0061] 1) Taking an actual slope as an example, the prior distribution of the slope's rock and soil parameters and local rainfall intensity is obtained through exploration and monitoring, as shown in Table 1.

[0062] Table 1 Prior distribution characteristics of rock and soil parameters and rainfall intensity

[0063]

[0064] where E is the elastic modulus (kPa), c is the cohesion (kPa), φ is the internal friction angle (o), k is the permeability coefficient (m / h), and q is the rainfall intensity (m / h).

[0065] 2) Based on the prior distribution of various rock and soil parameters and rainfall intensity, the Latin hypercube sampling method (LSH) was used to extract a 5×2000 sample matrix to construct the input data set;

[0066] 3) Select a typical slope section and establish a two-phase material point model of the slope based on the stratum data and the rock and soil parameters and rainfall intensity samples in step 2). The model diagram is as follows: Figure 2 As shown in the figure, the model is 600m wide, 175m high, and has a slope of 45°. A total of 122,750 material points are used for discretization. The initial material point spacing is 1m, the background grid size is 2m*2m, each grid unit contains 4 material points, and a total of 27,391 grid nodes are assigned to each material point. Gravity is set as the external load, the bottom edge is fixed in the xz direction displacement, the two sides are fixed in the x direction displacement, the bottom edge is an impermeable interface, and the head boundaries are set on both sides according to the groundwater level. Monitoring points are arranged on the slope, and the initial ground stress balance calculation is performed.

[0067] 4) Based on the two-phase material point method, the motion equations of the landslide rock and soil and pore fluid are discretized in material point space and solved by explicit time integration. The solution time step is 1s, and the total number of steps is 80,000. The whole process of the failure evolution of the unsaturated rock and soil in the landslide is simulated for 2,000 groups of different sample parameters. Figure 3 The simulation results at the final moment under one set of sample parameters show that the landslide range is 110.3m, the sliding distance is 158.6m, the sliding body thickness is 55.2m, and the sliding body volume is 14398.9m 3 , maximum sliding speed 12.6m / s, Figure 4 The displacement and pore pressure simulation results of the monitoring points at different times under this working condition;

[0068] 5) constructing an output data set based on the 2000 sets of pore pressures, displacement values, and model response quantities such as sliding distance s, collapse range S, sliding body thickness h, sliding body volume V, and sliding velocity v calculated in step 4);

[0069] 6) Based on the dataset obtained in steps 2) and 5), the training set and test set were divided into a ratio of 7:3. The XGBoost model was used to train and test the dataset. The Optuna library was used to optimize the hyperparameters. The optimal combination of hyperparameters was obtained as follows: maximum tree depth 7, learning rate 0.06, number of decision trees 257, random sample ratio 0.7, minimum sum of child node weights 6, regularization parameter 0.5, and downsampling rate 0.9. Based on this hyperparameter combination, a surrogate model of the numerical model was constructed. Figure 5 Taking sliding distance as an example, this is the regression analysis chart of the proxy model test set;

[0070] 7) Based on the prior distribution of each parameter, random sampling was performed through the Monte Carlo simulation algorithm, and the surrogate model was used for calculation. For each group of samples, the failure criterion was the plastic zone penetration. The final statistical result was that the failure probability P of the landslide was 80.3%, and the initial probability distribution of the response (taking the sliding distance as an example) was 80.3%. Figure 8 For each group of samples, the hazard-bearing body that may be affected by the landslide is evaluated based on the sliding distance s, the landslide range S, etc., combined with the distribution of buildings, infrastructure, and personnel. Then, based on the sliding body thickness h, sliding body volume V, sliding speed v, etc., combined with the vulnerability of buildings, infrastructure, and personnel, the loss caused by the landslide to the hazard-bearing body is estimated. Finally, the initial probability distribution of human loss and economic loss is obtained statistically (as shown in Figure 2). Figure 9 shown);

[0071] 8) Monitoring instruments were arranged at corresponding locations on the slope. Displacement and pore pressure data of the monitoring points were collected every 3 hours after the rainfall began. The Bayesian parameter probability inversion method was used to construct the likelihood function to carry out parameter inversion calculation and solve the Markov chain of the parameters. The number of Markov chains was initially set to 10, and the maximum sample size on each chain was 5000. The posterior distribution of the rock and soil parameters was obtained. Figure 8 The comparison of the probability density curves of the prior distribution and the posterior distribution of the elastic modulus shows that the uncertainty of the rock and soil parameters is significantly reduced after the inversion;

[0072] 9) Based on the posterior distribution of each parameter obtained by inversion, the Monte Carlo simulation algorithm is used again to update the landslide failure probability P to 82.6%, and the probability distribution characteristics of each model response are recalculated. Figure 8 Compare the initial probability distribution of the sliding distance of the model response with the updated probability distribution, and update the probability distribution of human losses and economic losses (such as Figure 9 shown);

[0073] 10) Using the posterior distribution of the geotechnical parameters obtained in step 8) as the prior distribution, repeat steps 8) to 9) using the real-time collected landslide displacement, pore pressure monitoring data, and rainfall data to inversely update the geotechnical parameters, recalculate the probability distribution characteristics of the landslide failure probability and the model response quantity, and achieve real-time dynamic updating of the risk probability prediction results.

[0074] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present invention may be practiced without these specific details. Similarly, in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. The claims that follow the detailed description are hereby expressly incorporated into that detailed description, with each claim itself serving as a separate embodiment of the present invention.

[0075] It should be noted that the above embodiments illustrate rather than limit the invention, and that alternative embodiments may be devised by a person skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments should not be understood as limiting the order of execution unless otherwise specified.

Claims

1. An intelligent prediction method for landslide disaster risk probability based on a two-phase material point method, characterized in that: The following steps are involved: 1) Based on landslide investigation data and local rainfall monitoring data, the parameters of the landslide rock and soil mass and the prior distribution of rainfall intensity q are obtained. The parameters of the landslide rock and soil mass include elastic modulus E, cohesion c, internal friction angle φ, and permeability coefficient k; 2) Based on the prior distribution of various rock and soil parameters and rainfall intensity, Latin hypercube sampling method is used to extract random samples to construct the input data set; 3) Based on the landslide stratum data, a two-phase material point numerical calculation model is constructed for each set of sample parameters in step 2), and the initial conditions, boundary conditions, and load conditions of the numerical model are set according to the actual situation; 4) Based on the two-phase material point method, the motion equations of the landslide rock and soil and pore fluid are discretized in material point space and solved by explicit time integration, thus simulating the entire process of the destruction evolution of the unsaturated rock and soil of the landslide under the action of hydrodynamic forces; 5) Based on the two-phase material point method, the displacement and pore pressure values ​​of the monitoring points with time series are calculated for parameter inversion, and the final response of each sample is obtained to construct the output data set. The final response includes sliding distance s, collapse range S, sliding body thickness h, sliding body volume V, and sliding velocity v; 6) Based on the data sets obtained in steps 2) and 5), an XGBoost model is used for training and testing to construct a proxy model of the numerical model; 7) Based on the prior distribution of each parameter, the Monte Carlo simulation algorithm is used in combination with the surrogate model to statistically calculate the landslide failure probability and the probability distribution characteristics of each response quantity. Combined with the characteristics of the hazard-bearing body, the initial landslide disaster risk probability prediction result is calculated; 8) Monitoring instruments are deployed on the slope to collect real-time monitoring data, including displacement and pore pressure. Parameter inversion is performed based on the monitoring data using the Bayesian probability inversion method to calculate the posterior distribution of the geotechnical parameters. The rainfall intensity q is determined based on the measured data; 9) Based on the posterior distribution of each rock and soil parameter obtained by inversion, the Monte Carlo simulation algorithm is used again to statistically calculate the failure probability and the probability distribution characteristics of each model response, and the risk probability prediction results are updated; 10) Using the posterior distribution of the rock and soil parameters obtained in step 8) as the prior distribution, repeat steps 8) to 9) through the real-time collected landslide displacement, pore pressure monitoring data and rainfall data to inversely update the rock and soil parameters and dynamically update the risk probability prediction results in real time.

2. The method for intelligent prediction of landslide disaster risk probability based on the two-phase material point method according to claim 1, characterized in that: In step 2), the model parameter variable sample is represented as X = [x1, x2, ..., x n ], where x i Represents the elastic modulus E, cohesion c, and internal friction angle φ parameters, n represents the dimension of the model parameter variable, and the number of samples required for each dimension is m. When using the Latin hypercube sampling method, if m n-dimensional samples that conform to the prior distribution of the model parameters are to be extracted from the sample space, then the [0,1] interval needs to be divided into m intervals, and a sample is randomly extracted from each interval. The extracted values ​​are mapped to the sample space to obtain an n×m-order sample matrix A, thereby constructing the model input data set.

3. The method for intelligent prediction of landslide disaster risk probability based on the two-phase material point method according to claim 1, characterized in that: In step 3), a three-dimensional geological model of the landslide body is constructed based on the three-dimensional stratigraphic data of the landslide area, and the model is discretized into spatial material points. At the same time, a background grid covering the entire landslide sliding area is set behind the material points. The rock and soil parameters are assigned to each material point, gravity and external load conditions are input, displacement boundary conditions and seepage boundary conditions are set, the initial groundwater level is set, and the initial ground stress balance is performed.

4. The method for intelligent prediction of landslide disaster risk probability based on the two-phase material point method according to claim 1, characterized in that: In step 4), the rock and soil are described by the DP elastic-plastic constitutive model, and the pore fluid is described by Darcy's law. Combined with the effective stress principle, soil-water characteristic curve and permeability curve, the Euler-Cromer explicit integration algorithm is used to solve the problem.

5. The method for intelligent prediction of landslide disaster risk probability based on the two-phase material point method according to claim 1, characterized in that: In step 6), the specific steps of using the XGBoost model are as follows: ① Take the input and output data obtained in step 2) and step 5) as the data set; ② Divide the dataset into training set and test set; ③ Define XGBoost model parameters, including the maximum depth of the tree, learning rate, number of iterations, and objective function; ④ Use the training set data to train the XGBoost model; ⑤ Use cross-validation method to adjust model parameters and find the optimal parameter combination; ⑥Use the test set to evaluate the performance of the model.

6. The method for intelligent prediction of landslide disaster risk probability based on the two-phase material point method according to claim 1, characterized in that: In step 7), the hazard-bearing objects that may be affected by the landslide are assessed based on the sliding distance s, the landslide range S, and the distribution of buildings, infrastructure, and personnel. The losses caused by the landslide to the hazard-bearing objects are estimated based on the sliding thickness h, the sliding volume V, and the sliding velocity v, combined with the vulnerability of buildings, infrastructure, and personnel. Finally, a risk prediction is performed based on the loss probability distribution obtained through statistical calculations.

7. The method for intelligent prediction of landslide disaster risk probability based on the two-phase material point method according to claim 1, characterized in that: In step 8), based on the prior distribution of geotechnical parameters, the Bayesian parameter probability inversion method is used to construct the likelihood function to carry out parameter inversion calculation, and the Kalkov chain Monte Carlo simulation is used to solve the posterior probability density function to calculate the posterior distribution of geotechnical parameters and reduce the uncertainty of geotechnical parameters.

8. The method for intelligent prediction of landslide disaster risk probability based on the two-phase material point method according to claim 1, characterized in that: In step 10), the displacement, pore pressure data and rainfall data of the landslide monitoring point are collected in real time, and the monitoring data are updated every 3 hours. Then, the rock and soil parameters are updated by inversion, the probability distribution characteristics of the landslide failure probability and the model response quantity are recalculated, and the landslide risk probability prediction results are dynamically updated.

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