Heterogeneous library slope instability probability intelligent quantification method based on adaptive multi-mode coupling

Abstract

The invention relates to the technical field of near-dam reservoir bank slope instability risk assessment, and discloses a heterogeneous reservoir slope instability probability intelligent quantification method based on adaptive multi-modal coupling, and the method comprises the steps: building a terrain coordinate database; determining reservoir water level and rainfall intensity characteristic values; the soil permeability characteristic and the permeability index change range thereof are detected; establishing a near-dam reservoir bank slope finite element hydraulic coupling deterministic model; constructing a non-uniform near-dam reservoir bank slope numerical model; based on the constructed non-uniform near-dam reservoir bank slope numerical model, self-adaptive hydraulic multi-mode coupling calculation is carried out; and carrying out probability intelligent quantitative analysis on slope instability. According to the technical method provided by the invention, the problem of slope instability risk assessment under multi-field coupling, spatial heterogeneity and uncertainty interleaving is solved, the precision and reliability of a calculation result are remarkably improved, and a powerful technical support is provided for intelligent assessment of non-uniform near-dam bank slope instability risk probability under complex geological conditions.

Description

Technical Field

[0001] The invention relates to the technical field of risk assessment of slope instability near a dam reservoir bank, and in particular to an intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling. Background Art

[0002] As my country's water conservancy and hydropower projects develop in-depth into high mountain and canyon areas, the stability of the slopes near dams and reservoirs has become increasingly prominent. Since the geological conditions in these areas are usually complex, the slopes often show significant non-uniformity, such as spatial heterogeneity of permeability and differential distribution of weathering layers. In addition, the frequent fluctuations in reservoir water levels and the hydraulic coupling caused by seasonal rainfall lead to a high degree of dynamic and uncertain slope instability risk. Traditional slope stability evaluation methods are mostly based on a deterministic analysis framework, which makes it difficult to quantify the probability of instability of non-uniform slopes under complex hydraulic coupling mechanisms, and has become a key bottleneck restricting the safe operation and maintenance of high dam and large reservoir projects. Existing probabilistic models do not include non-steady-state seepage fields and non-uniform parameter random fields into a unified quantitative system, and most of them are still based on the assumption of static permeability coefficients, without considering the dynamic interaction between non-steady-state seepage and inhomogeneous medium seepage response caused by reservoir water level changes and heavy rainfall coupling, resulting in limited accuracy in calculating the probability of slope instability under multi-physical field and multi-modal coupling effects. In addition, for the special working conditions of the slopes near the dam in the high mountain canyon area, such as high-frequency changes in reservoir water levels and the superposition of long-term heavy rainfall, the traditional static model cannot capture this dynamic coupling effect, lacks real-time probabilistic warning capabilities for engineering risks, and is difficult to support dynamic safety control decisions.

[0003] Based on this, existing methods are difficult to solve this type of problem. It is urgent to invent an intelligent quantification method for the probability of slope instability near the dam and reservoir bank that integrates the adaptive hydraulic multimodal coupling mechanism and the random evolution of non-uniform parameters, so as to break through the difficulty of slope instability risk assessment under the interweaving of multimodal coupling, spatial heterogeneity and uncertainty, and provide core technical support for the safety management of high dam and large reservoir projects throughout their life cycle. Summary of the invention

[0004] In order to solve the current technical problems, the main purpose of the present invention is to provide an intelligent quantification method for the instability probability of heterogeneous reservoir slopes based on adaptive multimodal coupling, so as to solve the technical problem that the existing technology is difficult to adapt to the nonlinear complex seepage boundary, resulting in the non-convergence of finite element calculations, and making it impossible to effectively explore the evolution characteristics of the mechanical state of the soil near the dam reservoir bank slope under the influence of complex transient seepage, so as to help improve the reliability of the intelligent assessment results of the instability risk probability for such slopes.

[0005] The technical solution adopted by the present invention is: an intelligent quantification method for the probability of instability of heterogeneous reservoir slope based on adaptive multi-modal coupling, comprising the following steps: S1. Obtain onshore and underwater geospatial data information of the project near the dam reservoir and establish a terrain coordinate database; S2. Based on the hydrological and meteorological monitoring data, select specific complex seepage conditions and determine the characteristic values ​​of reservoir water level and rainfall intensity; S3. Detect soil permeability characteristics and the range of permeability index changes; S4. Based on the topographic survey data, a finite element hydraulic coupling deterministic model of the near-dam reservoir bank slope is established; S5. The spatial non-uniform distribution characteristics of soil permeability parameters are characterized by random field method, and a numerical model of non-uniform near-dam bank slope is constructed; S6. Based on the constructed numerical model of non-uniform near-dam reservoir bank slope, adaptive hydraulic multi-modal coupling calculation is performed; S7. Monte Carlo simulation calculations are performed for different complex seepage combination conditions; S8. Obtain a cloud map of calculation results, take the slope stability safety factor as the statistical object, and conduct a probabilistic intelligent quantitative analysis of slope instability.

[0006] In S1, space remote sensing technology is used to obtain high-precision topographic information on the shore and underwater of the reservoir area near the dam and establish a coordinate database.

[0007] In S2, based on the historical hydrological and meteorological monitoring data, the complex seepage conditions under the dual effects of reservoir water level and heavy rainfall were selected, and the characteristic values ​​of reservoir water level and rainfall intensity were determined as the initial infiltration conditions of the slope hydraulic coupling numerical model.

[0008] In S3, an on-site permeability test method was used, combined with nuclear magnetic resonance and X-ray tomography, to detect the soil pore structure and permeability characteristics and determine the range of soil permeability coefficient.

[0009] In S4, based on the topographic survey data of the slope surface, a finite element hydraulic coupling deterministic model of the near-dam reservoir bank slope was established using finite element software, the saturated and unsaturated hydraulic characteristic curves of the soil were set, the complex seepage boundary conditions under the combined effects of reservoir water level and heavy rainfall were applied, the hydraulic response monitoring path was preset, the hydraulic multimodal coupling calculation and analysis steps were set, the slope model unit grid was divided, and the soil permeability coefficient was associated with the custom field variables in the subroutine in the model calculation source file.

[0010] The hydraulic characteristic curves of saturated and unsaturated soils use the van Genuchten model to describe the hydraulic function of unsaturated soils, and its mathematical expression is as follows: (1); (2); In formula (1) and formula (2): S is the soil saturation;θ w is the volumetric water content; θ s and θ r are the saturated volumetric water content and the residual volumetric water content, respectively; u w is the pore water pressure; α w , n w , m w is the material parameter; P ( S ) is the soil permeability coefficient of each area in the model; P s is the saturated permeability coefficient of soil.

[0011] In S5, secondary development was carried out based on the subroutine custom field variables of the finite element software, and the random field method was used to simulate the spatially non-uniform distribution characteristics of the saturated permeability coefficient of the soil, so as to construct a numerical model of the non-uniform near-dam reservoir bank slope.

[0012] The adopted random field obeys uniform distribution, which is used to characterize the spatial distribution characteristics of the saturated permeability coefficient of soil with obvious lower and upper bounds. Its spatial correlation is represented by the random field correlation function. The method of generating a random field obeying uniform distribution includes the following steps: 1) Generate a two-dimensional standard normal distribution random field using the modified linear estimation method R (y), where y is the space coordinate vector, R The mathematical expression of the square exponential autocorrelation function of (y) is as follows: (3); In formula (3): ρ ( x , y ) is a square exponential correlation function; Δ x and Δ y are the relative distances between any two points in space in the directions of the two coordinate axes; l x and l y are the relevant lengths of the target parameters in the coordinate axis directions respectively; 2) Based on R (y) Beta random field is obtained by equal probability transformation B (y), the transformed mathematical expression is as follows: (4); In formula (4): Φ(·) and F = FX ( x ; a , b ) are the cumulative distribution functions of the standard Gaussian variable and the target Beta variable, respectively, where a and b are the two shape parameters of the Beta distribution. a and b When both are 1, the Beta distribution is a uniform distribution; 3) Target random field of soil saturated permeability coefficient subject to uniform distribution P (y) is obtained by the following formula: ; (5); In formula (5): P min and P max are the lower and upper bounds of the variation range of the soil saturated permeability coefficient, respectively. P (y), the permeability coefficient of the soil in the unsaturated area is obtained by formula (2).

[0013] In S6, based on the constructed numerical model of the non-uniform near-dam reservoir bank slope, an adaptive hydraulic multi-modal coupling calculation is performed, and the mathematical expression is as follows: (6); In formula (6): σ ʹ is effective stress; σ is the total stress; u a is the pore gas pressure, assuming that the air in the soil is connected to the atmosphere; u w is the pore water pressure, where ( u a - u w ) represents matrix suction; χ is a non-saturated parameter varying between 0 and 1, equivalent to saturation ( S ); The mathematical expressions for the shear strength of saturated and unsaturated soils are as follows: (7); In formula (7): τ f is the shear strength; c and φ ʹ are the effective cohesion and effective internal friction angle respectively; The mathematical expression of the transient control equation for flow through unsaturated soil is as follows: (8); In formula (8): P is the soil permeability coefficient; H is the water head; θ w is the volumetric water content; t For time.

[0014] In S7, Monte Carlo simulation calculation is performed, and an automatic running program is compiled using Fortran language to repeatedly execute step S6 and save the calculation results until the set number of calculations is reached. Then, the combination conditions of the complex seepage boundary are changed, and Monte Carlo simulation calculation is also performed and the calculation results are saved, providing a statistical data framework for the intelligent quantitative analysis of the slope instability probability.

[0015] In the intelligent quantitative analysis of slope instability probability, the mathematical expression of Monte Carlo simulation is as follows: (9); In formula (9): Pr( F <1) indicates the probability of slope instability; N The number of times the hydraulic coupling is calculated; I ( F i <1) is the indicator function, which is defined as follows: (10); In formula (10): F i For the i Safety factor of slope stability during secondary hydraulic coupling calculation.

[0016] In S8, the calculation results are opened to display the random seepage situation inside the soil slope and the cloud map of the slope instability mode results. The slope stability safety factor is taken as the statistical object, the calculation results are extracted, and the pore water pressure change diagram of the preset monitoring path, the slope stability safety factor statistical histogram and the corresponding empirical cumulative distribution function diagram are drawn. The probabilistic intelligent quantitative analysis of the slope instability is carried out to achieve efficient and accurate probabilistic assessment of the instability of the non-uniform near-dam reservoir bank slope under complex seepage-stress multi-modal coupling conditions.

[0017] The present invention has the following beneficial effects: 1. The present invention performs equal probability transformation based on the standard normal distribution random field to generate a target random field of soil saturated permeability coefficient that obeys uniform distribution, which effectively solves the technical difficulties in characterizing the spatial variability of soil permeability coefficient over a large span. This method significantly improves the computational efficiency of its spatial non-uniformity characterization and reduces the consumption of computing resources.

[0018] 2. The present invention adopts adaptive hydraulic multimodal coupling technology based on gradient and time step to achieve process control convergence of transient hydraulic multimodal coupling finite element calculation of near-dam reservoir bank slope under nonlinear complex seepage boundary conditions. This method breaks through the limitations of traditional technology in terms of calculation accuracy and convergence for such problems, significantly improves the accuracy and reliability of slope stability analysis results, and provides strong technical support for the probability assessment of instability risk of non-uniform near-dam reservoir bank slope under complex geological conditions.

[0019] 3. Under the framework of Monte Carlo simulation, the present invention conducts probabilistic intelligent quantitative analysis of the instability of the slope near the dam reservoir bank based on the adaptive hydraulic multimodal coupling technology. The results can be directly used to optimize the spatial layout of the monitoring equipment and the slope reinforcement design. At the same time, it supports engineering risk decision-making, improves the intelligence level of the early warning system, and realizes early warning and rapid response to landslide disasters. It has important practical significance for reducing the losses caused by landslide disasters, thereby ensuring the long-term safe operation of hydropower projects and helping to improve the economic, social and ecological benefits of the projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a flow chart of the present invention.

[0022] Figure 2 It is a schematic diagram of the slope model of the reservoir bank near the dam and the complex seepage boundary according to an embodiment of the present invention.

[0023] Figure 3 This is a cloud diagram of random seepage and instability patterns inside the slope calculated under different combined seepage conditions based on the generated random field of soil permeability coefficient in an embodiment of the present invention, where the rainfall duration is 48 h.

[0024] Figure 4 It is a pore water pressure distribution curve inside the slope along the left preset monitoring path I calculated under different combined seepage conditions based on the generated soil permeability random field in an embodiment of the present invention.

[0025] Figure 5 It is a pore water pressure distribution curve inside the slope along the preset monitoring path II on the right side calculated under different combined seepage conditions based on the generated random field of soil permeability coefficient in an embodiment of the present invention.

[0026] Figure 6It is a histogram of the safety factor of the non-uniform near-dam reservoir bank slope stability calculated based on the Monte Carlo simulation framework according to an embodiment of the present invention.

[0027] Figure 7 The embodiment of the present invention corresponds to Figure 6 Empirical cumulative distribution function diagram of safety factor for non-uniform near-dam bank slope stability. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 The embodiment of the present invention provides an intelligent quantification method for the probability of instability of a heterogeneous reservoir slope based on adaptive multi-modal coupling, comprising the following steps: S1. Obtain onshore and underwater geographic spatial data information of the project near the dam reservoir area and establish a terrain coordinate database.

[0030] In S1, advanced space remote sensing technologies such as satellite remote sensing, ground-based SAR, laser scanner, and multi-wave sonar are used to obtain high-precision terrain information on the shore and underwater near the dam reservoir area and establish a coordinate database, providing geospatial data support for the accurate construction of the near-dam reservoir shore slope model under complex terrain and geological conditions.

[0031] S2. Based on the hydrological and meteorological monitoring data, select specific complex seepage conditions and determine the characteristic values ​​of reservoir water level and rainfall intensity.

[0032] In S2, based on the historical hydrological and meteorological monitoring data, big data analysis methods such as machine learning, deep learning, and time series analysis are used to fully explore the data change patterns, consider the complex seepage conditions under the dual effects of reservoir water level and heavy rainfall, and determine the reservoir water level. W (m) and rainfall intensity q The characteristic value (m / h) is used as the initial infiltration condition of the slope hydraulic coupling numerical model.

[0033] S3. Detect soil permeability characteristics and the range of variation of its permeability index.

[0034] In S3, double ring and drilling field permeability test methods are used, combined with advanced technical means such as nuclear magnetic resonance and X-ray tomography, to detect the pore structure and permeability characteristics of the soil, determine the range of soil permeability coefficient, and express the lower and upper limits of the range as P min and P max .

[0035] S4. Based on the topographic survey data, a finite element hydraulic coupling deterministic model of the reservoir bank slope near the dam is established.

[0036] In S4, based on the slope surface topographic survey database, see Figure 2 The finite element software ABAQUS is used to establish a finite element hydraulic coupling deterministic model of the reservoir bank slope near the dam, the saturated and unsaturated hydraulic characteristic curves of the soil are set, the complex seepage boundary conditions under the combined action of reservoir water level and heavy rainfall are applied, the hydraulic response monitoring path is preset, the transient hydraulic multimodal coupling calculation Soils type analysis step is set, the plane strain four-node quadrilateral pore pressure unit CPE4P is used to divide the slope model unit grid, the model is exported as an INP calculation source file, and the soil permeability coefficient is associated with the custom field variable TEMP in the subroutine UTEMP in the model calculation source file.

[0037] The hydraulic characteristic curves of saturated and unsaturated soils use the van Genuchten model to describe the hydraulic function of unsaturated soils, and its mathematical expression is as follows: (1); (2); In formula (1) and formula (2): S is the soil saturation; θ w is the volumetric water content; θ s and θ r are the saturated volumetric water content and the residual volumetric water content, respectively; u w is the pore water pressure; α w , n w , m w is the material parameter, where α w It is the inverse of the air intake value and is related to the air intake pressure of the soil; n w is the pore distribution parameter, which describes the uniformity of soil pore size distribution; m w is the shape parameter, m w =1 1 / n w ; P ( S ) is the soil permeability coefficient of each area in the model; P sis the saturated permeability coefficient of the soil. In the embodiment of the present invention, P s Characterized as a random field with a uniform distribution and a lower and upper bound.

[0038] From formula (2), we can see that in the process of external seepage boundary change, the permeability coefficient of unsaturated soil will change with the change of saturation through k s Obtained by reduction.

[0039] The complex seepage boundary conditions under the combined effects of reservoir water level and heavy rainfall are applied. The reservoir water level effect is considered to be a hydrostatic pore pressure boundary that increases linearly with depth on the boundaries below the water level on the left and right sides of the model. The rainfall effect is considered to be applied through rainfall intensity, with unit flow rate. q (m / h) is used to express the

[0040] Preset hydraulic response monitoring path, see Figure 2 That is, monitoring path I is preset in the middle of the slope, and monitoring path II is preset in the middle of the slope top to monitor the real-time changes in the pore water pressure of unsaturated soil during the infiltration of reservoir water and precipitation.

[0041] S5. The spatially non-uniform distribution characteristics of soil permeability parameters are characterized by the random field method, and a numerical model of the non-uniform near-dam reservoir bank slope is constructed.

[0042] In S5, secondary development is carried out based on the custom field variables of the subroutine UTEMP of the finite element software ABAQUS. In the custom area of ​​the subroutine, Fortran is used for programming, and the random field method is used to simulate the spatial non-uniform distribution characteristics of the saturated permeability coefficient of soil. While characterizing the spatial non-uniformity of the parameters, it can ensure that they have a certain spatial correlation, so as to construct a numerical model of the non-uniform near-dam reservoir bank slope.

[0043] The random field used follows a uniform distribution and is used to characterize the saturated permeability of soil with obvious lower and upper bounds. P s The spatial distribution characteristics, whose spatial correlation is represented by the random field correlation function, the method of generating a random field that obeys uniform distribution includes the following steps: 1) First, use the modified linear estimation method to generate a two-dimensional standard normal distribution random field R (y), where y is the spatial coordinate vector. R The mathematical expression of the square exponential autocorrelation function of (y) is as follows: (3); In formula (3): ρ ( x , y) is a square exponential correlation function; Δ x and Δ y are the relative distances between any two points in space in the directions of the two coordinate axes; l x and l y are the relevant lengths of the target parameters in the coordinate axis directions respectively.

[0044] 2) Based on R (y) Beta random field is obtained by equal probability transformation B (y), the transformed mathematical expression is as follows: (4); In formula (4): Φ(·) and F = F X ( x ; a , b ) are the cumulative distribution functions of the standard Gaussian variable and the target Beta variable, respectively, where a and b are the two shape parameters of the Beta distribution. a and b When both are 1, the Beta distribution is a uniform distribution.

[0045] 3) Target random field of soil saturated permeability coefficient subject to uniform distribution P (y) can be obtained by the following formula: (5); In formula (5): P min and P max are the lower and upper limits of the variation range of the soil saturated permeability coefficient, respectively. In the embodiment of the present invention, k min and k max 1 respectively × 10 –6 m / s and 1 × 10 –4 m / s.

[0046] In the generated random field P (y), the permeability coefficient of the soil in the unsaturated area is obtained by formula (2).

[0047] S6. Based on the constructed numerical model of the non-uniform near-dam reservoir bank slope, adaptive hydraulic multi-modal coupling calculation is performed.

[0048] In S6, based on the constructed numerical model of the non-uniform near-dam reservoir bank slope, an efficient adaptive multi-modal hydraulic coupling calculation is performed. Due to the complexity of unsaturated soil, effective stress state variables are used to describe it in the hydraulic multi-modal coupling calculation and analysis. The mathematical expression is as follows: (6); In formula (6): σ ʹ is effective stress; σ is the total stress; u a is the pore air pressure, assuming that the air in the soil is connected to the atmosphere; u w is the pore water pressure, where ( u a - u w ) represents matrix suction; χ is a non-saturated parameter that varies between 0 and 1, generally equivalent to saturation ( S ).

[0049] Based on the effective stress principle, the modified Mohr-Coulomb criterion is used to describe the shear strength of saturated and unsaturated soils. The mathematical expression is as follows: (7); In formula (7): τ f is the shear strength; c and φ ʹ are the effective cohesion and the effective internal friction angle respectively.

[0050] According to the law of mass conservation and Darcy's law, the mathematical expression of the transient control equation for flow through unsaturated soil is as follows: (8); In formula (8): P is the soil permeability coefficient; H is the water head; θ w is the volumetric water content; t For time.

[0051] Adaptive hydraulic coupling calculation is based on the adaptive technology of gradient and time step, which can automatically adjust the grid density and time step according to the calculation results, ensuring sufficient accuracy in key areas while reducing the calculation burden in non-critical areas.

[0052] 1) Local mesh refinement: The mesh density is adjusted according to the gradient distribution of physical quantities such as stress, strain, pore water pressure, etc. The mesh is automatically refined in areas with larger gradients, while a coarser mesh is maintained in areas with smaller gradients, thereby optimizing computing resources.

[0053] 2) Time step adjustment: According to the changes in the transient response of hydraulic multimodal coupling, the time step is automatically adjusted to ensure that a smaller step size is used when the changes are rapid and a larger step size is used when the changes are slow, thereby improving the calculation efficiency.

[0054] S7. Monte Carlo simulation calculations are performed for different complex seepage combination conditions.

[0055] In S7, a Monte Carlo simulation is performed, and an automated program is compiled using the Fortran language to repeatedly execute step S6 and save the calculation results of each time until the set number of calculations is reached. N , and then change the combination conditions of the complex seepage boundary, perform Monte Carlo simulation calculations and save the calculation results, and provide a statistical data framework for the intelligent quantitative analysis of the slope instability probability. In the embodiment of the present invention, the different combination conditions for the complex seepage boundary, that is, keep the rainfall duration unchanged for 48 hours, consider three reservoir water levels of 20 m, 25 m, and 30 m respectively.

[0056] In the intelligent quantitative analysis of slope instability probability, the mathematical expression of the Monte Carlo simulation is as follows: (9); In formula (9): Pr( F <1) indicates the probability of slope instability; N The number of times the hydraulic coupling is calculated; I ( F i <1) is the indicator function, which is defined as follows: (10); In formula (10): F i For the i Safety factor of slope stability during secondary hydraulic coupling calculation.

[0057] S8. Obtain a cloud map of calculation results, take the slope stability safety factor as the statistical object, and conduct a probabilistic intelligent quantitative analysis of slope instability.

[0058] In S8, open the calculation results, see Figure 3 , showing the random seepage inside the soil slope under different combinations of complex seepage boundary conditions, including random seepage pattern, soil permeability, saturation cloud map, and slope instability pattern cloud map. The results in the figure are calculated based on a typical sample of the random field of soil saturated permeability coefficient in Monte Carlo simulation to ensure the consistency of the site and facilitate the comparison of calculation results under different working conditions. F For statistical objects, Python program is used to automatically extract calculation results, see Figures 4 to 7, use the code to draw the pore water pressure change diagram of preset monitoring path I and path II, the statistical histogram of slope stability safety factor and the corresponding empirical cumulative distribution function diagram, etc., to conduct probabilistic intelligent quantitative analysis of slope instability, and realize efficient and accurate probabilistic assessment of instability of non-uniform near-dam reservoir bank slopes under complex seepage-stress multi-modal coupling conditions.

[0059] Specifically, from Figures 3 to 5 It can be seen that for the case where the reservoir water level is relatively low, such as 20 m, the area of ​​the slope surface affected by rainfall infiltration is large, the accumulated precipitation continues to increase and flows along the slope surface along the area with larger permeability coefficient to the vicinity of the infiltration line, resulting in an increase in the pore water pressure of the soil near this position, while the seepage in the saturated soil below the infiltration line is more likely to flow along the area with larger permeability coefficient, thereby generating irregular permeability acting on the soil skeleton. This phenomenon becomes more obvious with the increase of reservoir water level. The seepage inside the slope at this stage is mainly driven by the head difference at both ends of the slope. In this case, the strength of the soil near the toe of the slope decreases due to the increase in pore water pressure, which in turn affects the overall stability of the slope. See Figures 6-7 , when the reservoir water level is relatively high, such as 25 m and 30 m, the corresponding slope stability safety factor is reduced accordingly. The above analysis results reveal the variation characteristics and main differences of non-uniform near-dam reservoir bank slopes under different complex seepage boundary combination conditions from the perspective of seepage characteristics and instability modes, and clarify the key role of spatial non-uniformity of soil permeability coefficient in the analysis of near-dam reservoir bank slope stability under water conditions, which provides a basic basis for revealing the uncertainty propagation mechanism of instability of such slopes under complex seepage boundary conditions. At the same time, the probabilistic intelligent quantitative analysis results calculated based on the Monte Carlo simulation framework can be directly used to optimize the spatial layout of monitoring equipment and slope reinforcement design, while supporting engineering risk decision-making, improving the intelligence level of the early warning system, and realizing early warning and rapid response to landslide disasters, which has important practical significance for reducing landslide disaster losses.

[0060] This embodiment further illustrates the effectiveness of the technical method proposed in the present invention in evaluating the internal random seepage and failure mode uncertainty of non-uniform near-dam reservoir bank slopes under complex seepage boundaries, solves the technical difficulties in characterizing the spatial variability of soil permeability coefficient over large spans, and the adaptive hydraulic multimodal coupling technology used significantly improves the accuracy and reliability of the calculation results, providing strong technical support for the intelligent assessment of the instability risk probability of non-uniform near-dam reservoir bank slopes under complex geological conditions.

[0061] The above embodiments are only used to illustrate the present invention, wherein the structure, connection mode and manufacturing process of each component may be changed. Any equivalent transformations and improvements based on the technical solution of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. An intelligent quantification method for the probability of instability of heterogeneous reservoir slopes based on adaptive multi-modal coupling, characterized by: The following steps are involved: S1. Obtain onshore and underwater geospatial data information of the project near the dam reservoir and establish a terrain coordinate database; S2. Based on the hydrological and meteorological monitoring data, select specific complex seepage conditions and determine the characteristic values ​​of reservoir water level and rainfall intensity; S3. Detect soil permeability characteristics and the range of permeability index changes; S4. Based on the topographic survey data, a finite element hydraulic coupling deterministic model of the near-dam reservoir bank slope is established; S5. The spatial non-uniform distribution characteristics of soil permeability parameters are characterized by random field method, and a numerical model of non-uniform near-dam bank slope is constructed; S6. Based on the constructed numerical model of non-uniform near-dam reservoir bank slope, adaptive hydraulic multi-modal coupling calculation is performed; S7. Monte Carlo simulation calculations are performed for different complex seepage combination conditions; S8. Obtain a cloud map of calculation results, take the slope stability safety factor as the statistical object, and conduct a probabilistic intelligent quantitative analysis of slope instability.

2. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 1 is characterized in that: In S1, space remote sensing technology is used to obtain high-precision topographic information on the shore and underwater of the reservoir area near the dam and establish a coordinate database.

3. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 1 is characterized in that: In S2, based on the historical hydrological and meteorological monitoring data, the complex seepage conditions under the dual effects of reservoir water level and heavy rainfall were selected, and the characteristic values ​​of reservoir water level and rainfall intensity were determined as the initial infiltration conditions of the slope hydraulic coupling numerical model.

4. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 1 is characterized in that: In S3, an on-site permeability test method was used, combined with nuclear magnetic resonance and X-ray tomography, to detect the soil pore structure and permeability characteristics and determine the range of soil permeability coefficient.

5. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 1 is characterized in that: In S4, based on the topographic survey data of the slope surface, a finite element hydraulic coupling deterministic model of the near-dam reservoir bank slope was established using finite element software, the saturated and unsaturated hydraulic characteristic curves of the soil were set, the complex seepage boundary conditions under the combined effects of reservoir water level and heavy rainfall were applied, the hydraulic response monitoring path was preset, the hydraulic multimodal coupling calculation and analysis steps were set, the slope model unit grid was divided, and the soil permeability coefficient was associated with the custom field variables in the subroutine in the model calculation source file.

6. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 5 is characterized in that: The saturated and unsaturated hydraulic characteristic curves of soil use the van Genuchten model to describe the hydraulic function of unsaturated soil, and its mathematical expression is as follows: （1）； （2）； In formula (1) and formula (2): S is the soil saturation; θ w is the volumetric water content; θ s and θ r are the saturated volumetric water content and the residual volumetric water content, respectively; u w is the pore water pressure; α w , n w , m w is the material parameter; P ( S ) is the soil permeability coefficient of each area in the model; P s is the saturated permeability coefficient of soil.

7. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 1 is characterized in that: In S5, secondary development was carried out based on the subroutine custom field variables of the finite element software, and the random field method was used to simulate the spatially non-uniform distribution characteristics of the saturated permeability coefficient of the soil, so as to construct a numerical model of the non-uniform near-dam reservoir bank slope.

8. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 7 is characterized in that: The adopted random field obeys uniform distribution, which is used to characterize the spatial distribution characteristics of the saturated permeability coefficient of soil with obvious lower and upper bounds. Its spatial correlation is represented by the random field correlation function. The method of generating a random field obeying uniform distribution includes the following steps: 1) Generate a two-dimensional standard normal distribution random field using the modified linear estimation method R (y), where y is the space coordinate vector, R The mathematical expression of the square exponential autocorrelation function of (y) is as follows: （3）； In formula (3): ρ ( x , y ) is a square exponential correlation function; Δ x and Δ y are the relative distances between any two points in space in the directions of the two coordinate axes; l x and l y are the relevant lengths of the target parameters in the coordinate axis directions respectively; 2) Based on R (y) Beta random field is obtained by equal probability transformation B (y), the transformed mathematical expression is as follows: （4）； In formula (4): Φ(·) and F = F X ( x ; a , b ) are the cumulative distribution functions of the standard Gaussian variable and the target Beta variable, respectively, where a and b are the two shape parameters of the Beta distribution. a and b When both are 1, the Beta distribution is a uniform distribution; 3) Target random field of soil saturated permeability coefficient subject to uniform distribution P (y) is obtained by the following formula: （5）； In formula (5): P min and P max They are the lower and upper limits of the variation range of soil saturated permeability coefficient respectively.

9. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 1 is characterized in that: In S6, based on the constructed numerical model of the non-uniform near-dam reservoir bank slope, an adaptive hydraulic multi-modal coupling calculation is performed, and the mathematical expression is as follows: （6）； In formula (6): σ ʹ is effective stress; σ is the total stress; u a is the pore gas pressure, assuming that the air in the soil is connected to the atmosphere; u w is the pore water pressure, where ( u a - u w ) represents matrix suction; χ is a non-saturated parameter varying between 0 and 1, equivalent to saturation ( S ); The mathematical expressions for the shear strength of saturated and unsaturated soils are as follows: （7）； In formula (7): τ f is the shear strength; c and φ ʹ are the effective cohesion and effective internal friction angle respectively; The mathematical expression of the transient control equation for flow through unsaturated soil is as follows: （8）； In formula (8): P is the soil permeability coefficient; H is the water head; θ w is the volumetric water content; t For time.

10. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 1 is characterized in that: In S7, Monte Carlo simulation calculation is performed, and an automatic running program is compiled using Fortran language to repeatedly execute step S6 and save the calculation results until the set number of calculations is reached. Then, the combination conditions of the complex seepage boundary are changed, and Monte Carlo simulation calculation is also performed and the calculation results are saved, providing a statistical data framework for the intelligent quantitative analysis of the slope instability probability.

11. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 10 is characterized in that: In the intelligent quantitative analysis of slope instability probability, the mathematical expression of Monte Carlo simulation is as follows: （9）； In formula (9): Pr( F <1) indicates the probability of slope instability; N The number of times the hydraulic coupling is calculated; I ( F i <1) is the indicator function, which is defined as follows: （10）； In formula (10): F i For the i Safety factor of slope stability during secondary hydraulic coupling calculation.

12. The intelligent quantification method for heterogeneous reservoir slope instability probability based on adaptive multi-modal coupling according to claim 1 is characterized in that: In S8, the calculation results are opened to display the random seepage situation inside the soil slope and the cloud map of the slope instability mode results. The slope stability safety factor is taken as the statistical object, the calculation results are extracted, and the pore water pressure change diagram of the preset monitoring path, the slope stability safety factor statistical histogram and the corresponding empirical cumulative distribution function diagram are drawn. The probabilistic intelligent quantitative analysis of the slope instability is carried out to achieve efficient and accurate probabilistic assessment of the instability of the non-uniform near-dam reservoir bank slope under complex seepage-stress multi-modal coupling conditions.

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