Evaluation method and device for rainfall-induced landslide susceptibility based on side slope geometrical characteristics

Abstract

The invention discloses a method and device for evaluating the susceptibility of rainfall-induced landslide based on side slope geometrical characteristics, and belongs to the technical field of landslide geological disaster prevention and control. The method comprises the following steps: based on a side slope to be evaluated, establishing a standard three-dimensional side slope model, establishing simulated three-dimensional slope models with different gradients and different fluctuation degrees based on the standard three-dimensional slope model; the slope roughness coefficient of each simulated three-dimensional slope model and the safety coefficient of each simulated three-dimensional slope model in different rainfall stages are calculated, and the slope roughness coefficients are obtained based on slope geometric feature calculation and the standard three-dimensional slope model; and fitting the linear relationship between the slope roughness coefficient and the safety coefficient of each simulated three-dimensional slope model in the same rainfall stage to obtain the corresponding relationship between the slope roughness coefficient and the safety coefficient in different rainfall stages. According to the method, the rainfall-induced landslide susceptibility of the slope can be accurately and efficiently evaluated.

Description

Technical Field

[0001] This disclosure relates to the field of landslide geological disaster prevention and control technology, and in particular to an assessment method and device for assessing the susceptibility of landslides induced by rainfall based on slope geometric characteristics. Background Technology

[0002] In subtropical regions during summer, rainfall is one of the main factors inducing slope instability, such as landslides. Against the backdrop of global warming, the frequency of rainfall events is gradually increasing. Therefore, conducting landslide susceptibility assessments under heavy rainfall conditions is of great significance for preventing and controlling geological disasters such as landslides.

[0003] In the field of geological disaster prevention and mitigation, achieving high efficiency and accuracy in regional-scale slope stability analysis under rainfall conditions remains a significant challenge. Traditional GIS-based methods for assessing the susceptibility of rainfall-induced landslides often employ simplified models such as the Green-Ampt model or the one-dimensional Richards equation to simulate saturated-unsaturated seepage processes. While these methods offer significant advantages in computational efficiency, they neglect crucial seepage characteristics such as changes in matric suction during rainfall infiltration in unsaturated soil, thus limiting assessment accuracy. Furthermore, when using highly accurate numerical simulation methods such as the finite difference method (FDM) and the finite element method (FEM) for assessing rainfall-induced landslide susceptibility, limited computational resources confine these methods to single slope models with specific geometric characteristics. Even when the models are extended to a relatively large three-dimensional scale, the computation remains extremely time-consuming, hindering their widespread application at the regional scale. Summary of the Invention

[0004] This disclosure provides a method and apparatus for assessing the susceptibility of slopes to rainfall-induced landslides based on slope geometry, enabling accurate and efficient assessment of slope susceptibility to rainfall-induced landslides. The technical solution includes at least the following components: Firstly, a method for assessing the susceptibility of rainfall-induced landslides based on slope geometry is provided, comprising: establishing a standard three-dimensional slope model based on the slope to be assessed, and establishing simulated three-dimensional slope models with different slope gradients and undulations based on the standard three-dimensional slope model; calculating the surface roughness coefficient of each simulated three-dimensional slope model and the safety factor of each simulated three-dimensional slope model under different rainfall stages, wherein the surface roughness coefficient is calculated based on the slope geometry and the standard three-dimensional slope model, and the safety factor is calculated using numerical simulation; fitting the linear relationship between the surface roughness coefficient and the safety factor of each simulated three-dimensional slope model under the same rainfall stage to obtain the correspondence between the surface roughness coefficient and the safety factor under different rainfall stages, wherein the correspondence between the surface roughness coefficient and the safety factor under different rainfall stages is used to assess the susceptibility of rainfall-induced landslides of the slope to be assessed.

[0005] Optionally, the surface roughness coefficient of the simulated three-dimensional slope model is calculated using the following formula:

[0006] in, The slope roughness coefficient is mentioned above. These are the weighting coefficients. This represents the maximum slope of the simulated three-dimensional slope model. The maximum slope of the standard three-dimensional slope model is given. This represents the average slope of the simulated three-dimensional slope model. The average slope of the standard three-dimensional slope model is given. The maximum undulation height of the surface of the simulated three-dimensional slope model. The maximum undulation height of the standard three-dimensional slope model is given. This represents the average undulation height of the simulated three-dimensional slope model. The average undulation height of the standard three-dimensional slope model is given. The undulation frequency of the simulated three-dimensional slope model. The undulation frequency of the standard three-dimensional slope model is given.

[0007] Optionally, in the weighting coefficients, The value is greater than The value of , The value is greater than The value of .

[0008] Optionally, the safety factor of the simulated three-dimensional slope model is calculated as follows: the simulated three-dimensional slope model is numerically simulated using the finite difference method, and the ultimate reduction coefficient is determined using the shear strength reduction method, wherein the ultimate reduction coefficient is the safety factor; wherein, during the numerical simulation of the simulated three-dimensional slope model using the finite difference method, the unsaturated infiltration process is simulated.

[0009] Optionally, the simulation of the unsaturated infiltration process includes: numerically simulating unsaturated weathered soil using Bishop's effective stress theory; in the case of stiffness softening caused by changes in soil saturation, using Bishop's effective stress-corrected Mohr-Coulomb failure criterion to simulate the shear strength of unsaturated soil; establishing a correspondence between saturation and pore water pressure based on the relationship between volumetric water content and saturation to simulate the evolution of negative pore water pressure during the unsaturated infiltration process; and introducing a linear relationship between saturation and rock mass compressive strength and elastic modulus during the simulation of the unsaturated infiltration process to simulate the water-induced softening effect of rainfall infiltration on weathered rock mass.

[0010] Optionally, the assessment of the landslide susceptibility of the slope to be assessed based on the correspondence between the slope roughness coefficient and the safety factor under different rainfall stages can be carried out in the following manner: calculate the slope roughness coefficient of the slope to be assessed; determine the safety factor of the slope to be assessed under different rainfall stages based on the slope roughness coefficient of the slope to be assessed and the correspondence between the slope roughness coefficient and the safety factor under different rainfall stages.

[0011] Secondly, an assessment device for assessing the susceptibility of rainfall-induced landslides based on slope geometric characteristics is also provided, comprising: a modeling module for establishing a standard three-dimensional slope model based on the slope to be assessed, and establishing simulated three-dimensional slope models with different slope gradients and undulations based on the standard three-dimensional slope model; a parameter calculation module for calculating the surface roughness coefficient of each simulated three-dimensional slope model and the safety factor of each simulated three-dimensional slope model under different rainfall stages, wherein the surface roughness coefficient is calculated based on the slope geometric characteristics and the standard three-dimensional slope model, and the safety factor is calculated using numerical simulation; and a fitting module for fitting the linear relationship between the surface roughness coefficient and the safety factor of each simulated three-dimensional slope model under the same rainfall stage to obtain the correspondence between the surface roughness coefficient and the safety factor under different rainfall stages, wherein the correspondence between the surface roughness coefficient and the safety factor under different rainfall stages is used to assess the susceptibility of rainfall-induced landslides of the slope to be assessed.

[0012] Optionally, in the parameter calculation module, the surface roughness coefficient of the simulated three-dimensional slope model is calculated using the following formula:

[0013] in, The slope roughness coefficient is mentioned above. These are the weighting coefficients. This represents the maximum slope of the simulated three-dimensional slope model. The maximum slope of the standard three-dimensional slope model is given. This represents the average slope of the simulated three-dimensional slope model. The average slope of the standard three-dimensional slope model is given. The maximum undulation height of the surface of the simulated three-dimensional slope model. The maximum undulation height of the standard three-dimensional slope model is given. This represents the average undulation height of the simulated three-dimensional slope model. The average undulation height of the standard three-dimensional slope model is given. The undulation frequency of the simulated three-dimensional slope model. The undulation frequency of the standard three-dimensional slope model is given.

[0014] Optionally, in the parameter calculation module, the weighting coefficients The value is greater than The value of , The value is greater than The value of .

[0015] Optionally, the parameter calculation module is further configured to perform numerical simulation of the simulated three-dimensional slope model using the finite difference method, and to determine the ultimate reduction coefficient using the shear strength reduction method, wherein the ultimate reduction coefficient is the safety factor; wherein, during the numerical simulation of the simulated three-dimensional slope model using the finite difference method, the unsaturated infiltration process is simulated.

[0016] Optionally, the parameter calculation module is also used to perform numerical simulation of unsaturated weathered soil using Bishop's effective stress theory; in the case of stiffness softening caused by changes in soil saturation, Bishop's effective stress is used to correct the Mohr-Coulomb failure criterion to simulate the shear strength of unsaturated soil; based on the relationship between volumetric water content and saturation, a correspondence between saturation and pore water pressure is established to simulate the evolution of negative pore water pressure during unsaturated infiltration; in the process of simulating the unsaturated infiltration process, a linear relationship between saturation and rock mass compressive strength and elastic modulus is introduced to simulate the water-induced softening effect of rainfall infiltration on weathered rock mass.

[0017] Optionally, the fitting module is further configured to calculate the surface roughness coefficient of the slope to be evaluated; and based on the surface roughness coefficient of the slope to be evaluated and the correspondence between the surface roughness coefficient and the safety factor under different rainfall stages, determine the safety factor of the slope to be evaluated under different rainfall stages.

[0018] Thirdly, a computer device is also provided, comprising: a memory and a processor, wherein the memory stores at least one computer program, the at least one computer program being loaded and executed by the processor to perform the assessment method for rainfall-induced landslide susceptibility based on slope geometry characteristics described in the above embodiments.

[0019] Fourthly, a computer-readable storage medium is also provided, wherein at least one computer program is stored in the computer-readable storage medium, the at least one computer program being loaded and executed by a processor to perform the assessment method for rainfall-induced landslide susceptibility based on slope geometry characteristics described in the above embodiments.

[0020] Fifthly, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the method described in the first aspect.

[0021] The beneficial effects of the technical solutions provided in this disclosure include at least the following: In this embodiment, a standard three-dimensional slope model is established based on the slope to be evaluated, and simulated three-dimensional slope models with different slope gradients and undulations are established based on the standard three-dimensional slope model. The surface roughness coefficient of each simulated three-dimensional slope model and the safety factor of each simulated three-dimensional slope model under different rainfall stages are calculated. The surface roughness coefficient is calculated based on the slope geometric characteristics and the standard three-dimensional slope model, and the safety factor is calculated using numerical simulation. The linear relationship between the surface roughness coefficient and the safety factor of each simulated three-dimensional slope model under the same rainfall stage is fitted to obtain the correspondence between the surface roughness coefficient and the safety factor under different rainfall stages. The correspondence between the surface roughness coefficient and the safety factor under different rainfall stages is used to assess the susceptibility of the slope to be evaluated to rainfall-induced landslides. In this way, only the surface roughness coefficient of the slope to be evaluated needs to be calculated, and the safety factor of the slope to be evaluated can be accurately and efficiently determined based on the correspondence between the surface roughness coefficient and the safety factor, and then the susceptibility of rainfall-induced landslides can be assessed based on this safety factor.

[0022] Compared with the numerical simulation method used in related calculations to calculate the safety factor of each slope to be evaluated separately, the present invention only needs to use numerical simulation to calculate the safety factor of each simulated three-dimensional slope model to establish the correspondence between the slope roughness coefficient and the safety factor, without using numerical simulation to calculate the safety factor of each slope to be evaluated. This is particularly suitable for scenarios with a large number of slopes to be evaluated, and can achieve rapid determination of slope safety factors in a large batch. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart is shown below illustrating an exemplary embodiment of the present disclosure of a method for assessing rainfall-induced landslide susceptibility based on slope geometry. Figure 2 These are schematic diagrams of a standard 3D slope model and a simulated 3D slope model; Figure 3 These are schematic diagrams of the top and side views of the intermediate three-dimensional slope model P2; Figure 4 This is a schematic diagram of pore water pressure in a standard three-dimensional slope model under conditions of 24 hours and 48 hours of rainfall. Figure 5 This is a cloud map showing the maximum shear strain increment of the intermediate 3D slope model P4 before rainfall and 48 hours after rainfall. Figure 6 This is a schematic diagram showing the correspondence between the slope roughness coefficient and the safety factor under different rainfall stages, which was finally fitted. Figure 7 This is a schematic diagram of the three-dimensional model of the slope to be evaluated, modeled using Rhino. Figure 8 It is a cross-sectional view of the three-dimensional model of the slope to be evaluated; Figure 9 These are schematic diagrams of several typical two-dimensional cross-sections; Figure 10 This is a schematic diagram of the ArcGIS grid and the six selected sections; Figure 11 This is a schematic diagram of a two-dimensional section 1 of the study region 1 in ArcGIS; Figure 12 A flowchart is shown below illustrating another exemplary embodiment of the present disclosure of a method for assessing rainfall-induced landslide susceptibility based on slope geometry. Figure 13 This illustration shows a schematic diagram of the structure of an assessment device for assessing the susceptibility of rainfall-induced landslides based on slope geometry, provided in an exemplary embodiment of this disclosure. Figure 14 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0025] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, but do not exclude other elements or objects.

[0026] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0027] Figure 1 A flowchart illustrating an exemplary embodiment of this disclosure provides a method for assessing rainfall-induced landslide susceptibility based on slope geometry, a method executable by a computer device. See also... Figure 1 The method includes: In step 101, a standard three-dimensional slope model is established based on the slope to be evaluated, and simulated three-dimensional slope models with different slopes and different degrees of undulation are established based on the standard three-dimensional slope model.

[0028] In this embodiment of the disclosure, the model size of the slope to be evaluated and the standard three-dimensional slope model are the same. For example, if the model size of the slope to be evaluated is 140m by 140m by 80m, then the model size of the standard three-dimensional slope model is also 140m by 140m by 80m.

[0029] Figure 2 These are schematic diagrams of a standard 3D slope model and a simulated 3D slope model. For example... Figure 2 As shown, the standard three-dimensional slope model is P1. The standard three-dimensional slope model P1 has a size of 140m by 140m by 80m and a slope angle of 50°. The slope surface of this standard three-dimensional slope model has multiple undulations, and the degree of undulation of each undulation is constant at 5m. The soil and rock parameters of the standard three-dimensional slope model adopt a homogeneous weathered soil and rock layer.

[0030] In actual terrain, slopes exhibit variations in both surface curvature (i.e., the degree of slope undulation) and slope angle. Therefore, the simulated three-dimensional slope model needs to consider both these variations. To facilitate the creation of a simulated three-dimensional slope model, a set of intermediate three-dimensional slope models with the same slope angle but different degrees of slope undulation can be first established based on the standard three-dimensional slope model. Then, based on the standard three-dimensional slope model and the intermediate three-dimensional slope models, simulated three-dimensional slope models with different slope angles and surface curvatures can be created.

[0031] like Figure 2 The intermediate three-dimensional slope models P2 to P4 are shown in the figure. The only difference between the intermediate three-dimensional slope models P2 to P4 and the standard three-dimensional slope model P1 is the slope curvature. All other parameters (such as slope angle and soil parameters) are the same.

[0032] Figure 2 In the intermediate 3D slope model P2, the undulation increases linearly, ranging from 5 m to 11 m with 2 m intervals; the undulation increases linearly in intermediate 3D slope model P3, ranging from 5 m to 14 m with 3 m intervals; and the undulation increases linearly in intermediate 3D slope model P4, ranging from 5 m to 17 m with 4 m intervals. That is, from the standard 3D slope model P1 to the intermediate 3D slope models P2 to P4, the increments of slope curvature are 0 m, 2 m, 3 m, and 4 m respectively. In other words, the slope curvature differs between the standard 3D slope model P1 and the intermediate 3D slope models P2 to P4, while the slope angle remains the same.

[0033] Then, adjust the slope angles of the standard three-dimensional slope model P1 and the intermediate three-dimensional slope models P2 to P4 to obtain the corresponding simulated three-dimensional slope models.

[0034] like Figure 2 As shown, in the simulated three-dimensional slope model P1.1 corresponding to the standard three-dimensional slope model P1, based on the standard three-dimensional slope model P1, the slope angle in each convex and concave element is changed from... Gradually increase to The slope angle changes for each segment as follows: In each convex element, the slope angle starts from the inflection point. Increase to the peak Then gradually fell back to In the concave element, the slope angle starts from the inflection point. Increase to the trough , then drop back This alternation creates a continuous sequence of slope angle changes.

[0035] The construction method of the simulated three-dimensional slope model P2.1 corresponding to the intermediate three-dimensional slope model P2 is similar to that of the simulated three-dimensional slope model P1.1. Based on the intermediate three-dimensional slope model P2, the slope angle in each convex and concave unit is determined by... Gradually increase to The slope angle changes for each segment as follows: .

[0036] The slope angle of the simulated three-dimensional slope model P3.1 corresponding to the intermediate three-dimensional slope model P3 is from... to Step size is The slope angle of the simulated three-dimensional slope model P4.1 corresponding to the intermediate three-dimensional slope model P4 is from... to Step size is Always lower than .

[0037] The simulated three-dimensional slope models P1.1 to P4.1 are used to compare the relative effects of slope angle variation and undulation on slope stability under conditions where the slope surface undulation is significant but the slope angle variation is limited.

[0038] Figure 3 These are schematic diagrams of the top and side views of the intermediate three-dimensional slope model P2. Figure 3 Part (a) is a top view of the intermediate three-dimensional slope model P2. Figure 3 Part (b) is a side view of the intermediate three-dimensional slope model P2. For example... Figure 3 As shown in section (a), the EF and GH lines are defined as reference surfaces used to assess the undulation characteristics of the slope. The EF line is the reference surface at the bottom of the slope, and the GH line is the reference surface at the top of the slope. The intermediate 3D slope model P2 exhibits a continuous alternating convex-concave shape along the longitudinal direction. The undulation height of the slope is defined as the vertical distance between the undulation peak (or trough) and the reference line. Taking the reference line GH as an example, the reference line GH is divided into four equal segments, each representing the length of a complete convex or concave portion. In this intermediate 3D slope model P2, the degree of undulation increases linearly at 2-m intervals, from 5 m to 11 m.

[0039] like Figure 3As shown in section (b), in the intermediate three-dimensional slope model P2, slope 301 consists of two layers of weathered rock, each 3 meters thick, for a total thickness of 6 meters, and a slope height of 80 meters. AB represents the groundwater level, which is set relatively low based on the field survey results of the slope to be evaluated. ABCD represents the unsaturated zone above the groundwater level, with a maximum matrix suction of -50 kPa. Given a fixed slope and slope height, the length of the slope crest has a relatively small impact on slope stability; therefore, selecting this two-dimensional profile (i.e., the side view) as the key profile of the three-dimensional slope model is reasonable.

[0040] In step 102, the surface roughness coefficient of each simulated three-dimensional slope model and the safety factor of each simulated three-dimensional slope model under different rainfall stages are calculated.

[0041] The slope roughness coefficient is calculated based on the slope geometry and a standard three-dimensional slope model, while the safety factor is calculated using numerical simulation.

[0042] In this embodiment, the rainfall phase is divided by time, for example, based on 24 hours, dividing the rainfall phase into three stages: before rainfall, 24 hours after rainfall, and 48 hours after rainfall. In implementation, the base duration can be adjusted according to actual needs; the base duration is not limited to 24 hours.

[0043] The slope surface roughness coefficient (SSRC) in this embodiment is used to quantitatively describe the geometric characteristics of a three-dimensional slope (including slope and slope undulation).

[0044] Optionally, the surface roughness coefficient of the simulated three-dimensional slope model is calculated using the following formula (1).

[0045] (1) In formula (1), This is the surface roughness coefficient. These are the weighting coefficients. This represents the maximum slope of the simulated 3D slope model. The maximum slope of a standard 3D slope model. This represents the average slope of the simulated 3D slope model. The average slope of a standard three-dimensional slope model. To simulate the maximum undulation height on the surface of a three-dimensional slope model, This represents the maximum undulation height of a standard 3D slope model. This represents the average undulation height of the simulated 3D slope model. The average undulation height of a standard three-dimensional slope model. To simulate the undulation frequency of a three-dimensional slope model, This represents the undulation frequency of a standard three-dimensional slope model.

[0046] Optionally, in the weighting coefficients of the slope roughness coefficient, The value is greater than The value of , The value is greater than The value of .

[0047] exist Figure 3 In part (a), the four endpoints EFHG of the two baselines can be connected in sequence to form a reference surface. The part of the slope that protrudes from the reference surface is defined as positive undulation, and the part of the slope that is concave to the reference surface is defined as negative undulation.

[0048] Existing research has shown that local concave and convex slopes, as well as geomorphic features such as bends, significantly affect slope stability. Concave slopes, due to their geometric shape, provide lateral restraint, thus improving slope stability, and their Factor of Safety (FoS) is typically higher than that of conventional slope models. Conversely, convex slopes are prone to stress concentration in the crest region, resulting in a higher risk of sliding and a relatively lower safety factor in three-dimensional analysis. To facilitate comparison of slope roughness coefficients among different three-dimensional slope models, the calculation of the slope roughness coefficient only considers the adverse effects of convex regions on the safety factor in continuously undulating slopes; data from concave regions are not included in the calculation. Considering the significant impact of slope angle on the three-dimensional slope safety factor (especially when the slope angle is greater than...), ... Therefore, slope-related indicators are given a high weight in SSRC calculations (i.e., ...). The value is greater than The value of . In addition to the degree of undulation, another key geometric feature affecting the safety factor is the undulation frequency, that is, the number of turns within a certain length range. The research results indicate that when the slope surface undulation exceeds 10 meters, its impact on the safety factor is greater than that of the undulation frequency. Therefore, in the weighting of the slope roughness coefficient, the weight related to the degree of undulation is higher than the weight related to the undulation frequency (i.e., the value of ). The value is greater than (The value of ).

[0049] Under the above conditions, the weight of the slope roughness coefficient can be taken as an empirical value. This embodiment only provides an exemplary value, but it is not intended to be limiting. For example, , , , , In this example, it satisfies... The value is greater than The value of , The value is greater than The value of .

[0050] This slope roughness coefficient comprehensively describes the slope, undulation amplitude, and irregularity of a three-dimensional slope. Under rainfall conditions, it exhibits a good linear correlation with the safety factor. This parameter provides a new, quantifiable geometric index for rapidly identifying high-risk slopes within a region. Based on this, numerous three-dimensional numerical simulations under different conditions can be conducted, yielding rich simulation datasets. These datasets can then be used to extract the dominant control relationships of factors such as slope height, slope, soil parameters, groundwater depth, and slope roughness on the safety factor. This strategy effectively promotes the transformation of high-precision simulation results into low-cost, large-scale, rapid assessment models, providing technical support for the rapid identification and classification of rainfall-induced landslide risks at the regional scale.

[0051] For the slope to be evaluated, the above formula (1) can also be used to calculate the slope roughness coefficient. Just replace the relevant parameters of the simulated three-dimensional slope model with the parameters of the slope to be evaluated during the calculation. Details are omitted here.

[0052] Existing research largely relies on simplified assumptions to assess the susceptibility of regional rainfall-induced landslides. Most methods divide the rainfall-induced landslide mechanism into two steps: infiltration analysis and slope stability calculation. The most commonly used infiltration model is the Green-Ampt model, which assumes a one-dimensional wetting front advance. Due to its low computational cost, it is widely used in large-scale landslide assessments. For example, some researchers have used the Green-Ampt model to calculate rainfall infiltration depth and used the results as input parameters for slope stability analysis. However, the Green-Ampt model does not consider the dynamic changes in matrix suction and permeability under unsaturated soil conditions, nor does it reflect the influence of soil-water characteristic curves on slope shear strength, thus limiting its applicability in terms of accuracy. Furthermore, existing research focuses primarily on the stability analysis of weathered soil layers. However, in some highly permeable rock layers, even short-duration rainfall can rapidly infiltrate into the weathered rock layer, further weakening slope stability. Unlike weathered soil layers, which are mainly affected by matrix suction, the strength parameters of weathered rock layers are more significantly controlled by the water weakening effect, and the weakening coefficient has an exponential relationship with porosity. Furthermore, due to the difficulty in accurately measuring the initial groundwater level at the regional scale, some studies have simplified the process by using a steady-state flow model, assuming that saturated pore water flows parallel to the slope surface. While this simplifies the calculations, it neglects the impact of dynamic changes in groundwater on the accuracy of the analysis. Therefore, accurately assessing the rainfall seepage behavior of unsaturated soils and considering the dynamic changes in the physical parameters of the soil and rock mass are crucial for improving the accuracy of slope stability analysis.

[0053] Optionally, the safety factor of the simulated three-dimensional slope model is calculated as follows: the simulated three-dimensional slope model is numerically simulated using the finite difference method, and the ultimate reduction coefficient is determined using the shear strength reduction method, with the ultimate reduction coefficient being the safety factor; wherein, during the numerical simulation of the simulated three-dimensional slope model using the finite difference method, the unsaturated infiltration process is simulated.

[0054] Numerical simulation of unsaturated infiltration processes can accurately simulate the impact of rainfall on slope strength. It can calculate the changes in shear strength of soil and weathered rock under different saturation conditions, and reflect the potential sliding surface and instability risk of slopes during rainfall. This provides a reliable theoretical basis and engineering application reference for slope stability analysis and engineering safety assessment.

[0055] In simulating unsaturated infiltration, the movement of water within the soil alters its mechanical properties, leading to soil deformation. This deformation, in turn, changes the channels and space for water flow, resulting in variations in soil porosity. Therefore, it is necessary to first model the seepage field and stress field separately, and then couple them to simulate the unsaturated infiltration process. The seepage field simulates the water flow within the soil, while the stress field simulates the shear strength of the unsaturated soil, which reflects the changes in porosity.

[0056] In this embodiment of the disclosure, The numerical simulation process is illustrated using a three-dimensional fast Lagrange continuum analysis software as an example. The numerical simulation employs an elastic-ideal plastic material model, assuming the constitutive relations of the soil and rock mass satisfy the Mohr-Coulomb strength criterion. It also assumes the shear dilatation angle is zero and ignores the influence of tensile strength to simplify the simulation of discontinuous fracture failure. For the matrix suction of unsaturated soil, based on existing research, the matrix suction is controlled within a specific upper limit during the analysis, i.e., it is assumed to be a constant value, for example, not exceeding -100 kPa. This approach effectively simplifies the complex nonlinear terms in the unsaturated seepage-mechanism coupling model, improving computational efficiency while maintaining simulation accuracy, and facilitating a series of parameter sensitivity analyses and instability mechanism identification studies.

[0057] Optionally, when modeling the seepage field, the water flow within the soil is simulated using Darcy's law, and this process is represented by formula (2).

[0058] (2) In formula (2), A unit flow vector, Let be the permeability coefficient, where The relative permeability coefficient, For saturation, Pore ​​water pressure, For fluid density, For Descartes components, The gravity vector, subscript The value range is from 1 to 3.

[0059] Formula (2) is used to describe the direction and rate of water flow in slope soil or rock mass, while taking into account the influence of the permeability coefficient with saturation under unsaturated conditions, as well as the driving effect of gravity on the flow.

[0060] Among them, relative permeability coefficient It is based on saturation For functions of variables, empirical models can be used. In the middle, relative permeability coefficient It is expressed using formula (3).

[0061] (3) The meanings of the parameters in formula (3) are the same as those in formula (2), and will not be elaborated here. Formula (3) indicates that the permeability of soil or rock will change nonlinearly with the change of saturation, and can reflect the adjustment of seepage characteristics with water state under unsaturated conditions.

[0062] Optionally, when modeling the stress field, Bishop's effective stress theory is used to numerically simulate unsaturated weathered soil; in the case of stiffness softening caused by changes in soil saturation, Bishop's effective stress is used to modify the Mohr-Coulomb failure criterion to simulate the shear strength of unsaturated soil.

[0063] Bishop's effective stress theory can accurately reflect the evolution characteristics of matrix suction during rainfall infiltration. Changes in matrix suction directly affect the shear strength, volumetric strain, and density of unsaturated soil, thereby affecting the overall stability of the slope.

[0064] Rainfall infiltration leads to an increase in pore water pressure, reducing the effective stress of the soil. Simultaneously, changes in soil saturation cause stiffness softening. Therefore, the Bishop effective stress-corrected Mohr-Coulomb failure criterion is used to accurately simulate the strength evolution of soil under rainfall. In the numerical simulation, a single-phase flow condition with zero pore air pressure is assumed to simplify the calculation while still capturing the main hydraulic and mechanical responses. In this case, the Bishop effective stress-corrected Mohr-Coulomb failure criterion is expressed as Equation (4).

[0065] (4) In formula (4), The shear strength of unsaturated soil. For the effective internal friction angle, For effective cohesion, For the total normal stress, The pore air pressure is given. The meanings of the other parameters in formula (4) are the same as those in formula (2), and will not be detailed here. Single-phase calculation is used in the numerical simulation, i.e., the pore air pressure is assumed to be... It is 0.

[0066] Because rainfall infiltration has a water-induced softening effect on weathered rock masses, the numerical simulation also needs to analyze the attenuation of rock mechanical parameters under saturation (the attenuation of rock mechanical parameters under saturation also affects the stress field). In implementation, a linear relationship between saturation and the compressive strength and elastic modulus of the rock mass is introduced to simulate the water-induced softening effect of rainfall infiltration on weathered rock masses.

[0067] The uniaxial compressive strength of weathered rock decreases linearly with saturation, meaning that an increase in saturation leads to a decrease in rock strength. Based on a sandstone porosity of 0.16, the compressive strength of saturated rock is approximately 80% of that in the dry state, and the elastic modulus also decreases linearly with saturation. By introducing the linear relationship between saturation and rock mass compressive strength and elastic modulus into the numerical model, the weakening effect of rock mass caused by rainfall infiltration can be dynamically reflected, and the mechanical behavior of slopes under rainfall conditions can be realistically simulated. Typically, the reduction factor for saturated rock relative to the dry state is taken as 0.8. The uniaxial compressive strength (UCS) of standard rock samples is linearly related to saturation, meaning that UCS decreases linearly with increasing saturation. For ease of calculation, the strength and Young's modulus of wet rock samples can be calculated using the following formulas (5) to (6), respectively.

[0068] (5) (6) In formulas (5) to (6), For saturation Uniaxial compressive strength of rock materials The uniaxial compressive strength of dry rock. The uniaxial compressive strength of saturated rock; For saturation Young's modulus of rock materials Young's modulus of dry rock. It represents the Young's modulus of saturated rock.

[0069] During seepage, the density of the soil-water mixture automatically updates with saturation, thus taking into account the influence of the soil skeleton and pore water on the overall density and inertial effects. In the calculation of unsaturated soil seepage, the density is automatically updated according to the degree of saturation. The density of the soil-water mixture is determined by a combination of soil porosity, soil skeleton density, pore water density, and degree of saturation, which can more accurately reflect the overall density change caused by the change in water content during rainfall infiltration. This process is expressed by formula (7).

[0070] (7) In formula (7), The density of the soil-water mixture, This refers to the density of the soil skeleton. The density of pore water, The porosity of the soil. This represents saturation.

[0071] In seepage analysis, the porosity, saturation, and volumetric strain of the soil are coupled into a unified governing equation to describe the interaction between hydraulics and soil mechanics. Ignoring thermal expansion, the fully coupled fluid-solid interaction employed in this embodiment is described by a single constitutive equation, thereby coupling the seepage field and the stress field. This governing equation is expressed by equation (8).

[0072] (8) In formula (8), Pore ​​water pressure, The value is the Biot modulus (Pa). The porosity of the soil. For saturation, Moisture content, moisture content Obtained through seepage field calculations; For volumetric strain, volumetric strain The volumetric strain was obtained through stress field calculations. Changes in soil porosity can lead to changes in soil porosity. Changes; The Biot coefficient is the Biot coefficient in the embodiments of this disclosure. The value of is 1, meaning that the compressibility of soil particles is not considered. For time.

[0073] The above governing equations actually contain two variables: saturation. and pore water pressure Therefore, saturation needs to be established. and pore water pressure The relationship function is established between volumetric water content and saturation. In implementation, based on the relationship between volumetric water content and saturation, a correspondence between saturation and pore water pressure is established to simulate the evolution of negative pore water pressure during unsaturated infiltration.

[0074] Optionally, saturation can be established using the VG (van Genuchten) model. and pore water pressure The nonlinear relationship between them is realized and updated in real time through the FISH function, thereby dynamically reflecting the impact of rainfall infiltration on matrix suction and soil hydraulic state.

[0075] The equations of the VG model describe the relationship between the volumetric water content and pore water pressure of soil. This is achieved by utilizing the relationship between volumetric water content and saturation (…). , This refers to the volumetric moisture content. The porosity of the soil. (For saturation), saturation can be derived. and pore water pressure The correspondence between them is shown in formula (9).

[0076] (9) In formula (9), To participate in saturation, and These are the material parameters in the VG model. Indicates the intake air value. The value is updated according to the FISH function; Used to describe the non-uniformity of soil pore size distribution.

[0077] Through The FISH function can be used for secondary development to perform unsaturated seepage calculations based on the pore water pressure-saturation relationship established by the equation. Furthermore, this method can effectively capture the changes in matrix suction caused by variations in the saturation of unsaturated soil during rainfall infiltration, thus more accurately simulating the seepage characteristics and stability of slopes under rainfall conditions.

[0078] The numerical simulation process described above can be understood as the change in saturation caused by simulated rainfall infiltration, which in turn affects matrix suction and pore water pressure. Changes in matrix suction and pore water pressure reduce the soil's shear strength parameters. Therefore, this simulation process can yield more accurate and realistic matrix suction and pore water pressure, leading to more accurate shear strength parameters. Subsequent safety factor simulations are based on these shear strength parameters, and the improved accuracy of the shear strength parameters also leads to an improved accuracy of the final safety factor.

[0079] In related technologies, regional landslide assessment often employs simplified infiltration models (such as the Green-Ampt model) to approximate rainfall infiltration and pore pressure changes. However, such methods struggle to reflect the true evolution of matrix suction and shear strength degradation in unsaturated soils, exhibiting significant biases, particularly in strongly unsaturated strata such as loess and sandy soils, leading to inaccurate landslide susceptibility assessments. The numerical simulation framework described above comprehensively considers various key influencing factors of unsaturated seepage on slope stability, including the dynamic evolution of matrix suction and unsaturated soil density, as well as the weakening effect of rainwater on rock strength caused by rainfall infiltration into weathered soft rock layers.

[0080] By combining the above methods, numerical simulation can comprehensively capture the changes in matrix suction, stiffness softening, pore water pressure evolution, and rock mass attenuation effects caused by rainfall infiltration into unsaturated soil. This method can not only calculate the shear strength changes of soil and weathered rock under different saturation conditions, but also reflect the potential sliding surface and instability risk of slopes during rainfall, providing a reliable theoretical basis and engineering application reference for slope stability analysis and engineering safety assessment. It enables regional-scale landslides to not only possess higher physical realism but also dynamically respond to changes in key factors such as rainfall intensity, duration, and initial soil moisture content, thus improving the scientific rigor and accuracy of landslide prediction.

[0081] Figure 4 This is a schematic diagram of pore water pressure in a standard three-dimensional slope model under conditions of 24 hours and 48 hours of rainfall. Figure 4 As shown, in the initial stage of rainfall (24 hours), the pore pressure of the upper soil of the slope gradually increases, with water mainly remaining in the surface soil. The negative pore pressure zone still occupies a large area, indicating that the soil is still in a partially saturated state. As rainfall continues to 48 hours, the pore pressure increases significantly further, and water gradually infiltrates into the middle and lower soil, causing the overall negative pore pressure zone of the slope to shrink, while the positive pore pressure zone expands to deeper layers. This reflects the advancing wetting front and pore pressure accumulation effect of unsaturated soil during rainfall. This trend in pore pressure variation has a significant impact on slope stability and provides a key basis for subsequent safety factor analysis.

[0082] This disclosure employs the Finite Difference Method (FDM) as the primary numerical simulation method, mainly due to its significant advantages in handling complex geological conditions and large-scale three-dimensional simulations. FDM excels in dealing with heterogeneous soil and rock materials, complex boundary conditions, and multiphysics coupling problems, and is particularly suitable for simulating water-rock interactions. Its high computational efficiency makes it ideal for large-scale three-dimensional simulations requiring high spatial and temporal resolution. Furthermore, FDM exhibits good numerical stability, effectively avoiding convergence problems caused by complex boundary conditions and nonlinear responses. During the FDM numerical simulation, the soil and rock mass is set as an elastic-ideal plastic material and follows the Mohr-Coulomb yield criterion. The relevant flow law is adopted, which assumes that the shear dilatation angle of the material is equal to the internal friction angle, while the tensile strength is not considered. The convergence criterion in this simulation is based on the unbalanced forces at each node, i.e., the resultant force exerted on the node by adjacent elements. A node is considered to be in equilibrium when the sum of the forces acting on it is zero. To ensure simulation accuracy, this study normalizes the unbalanced forces at each node, using the gravitational force acting on the node as a benchmark. Normalization is performed when the unbalanced forces at all nodes are less than [a certain value]. When the simulation converges, it is considered to have converged.

[0083] Among them, the Shear Strength Reduction Method (SSRM) determines the ultimate reduction factor by simultaneously reducing the cohesion and internal friction angle of the soil until the model fails. The value of the ultimate reduction factor is the safety factor.

[0084] The reduction calculation uses a binary search method for fast approximation, with the initial reduction coefficient set. The upper and lower bounds are defined as follows: the lower bound is set to 1.0, which is a trial value to ensure convergence; the upper bound is set to 2.0, which is the critical value that would lead to non-convergence. Subsequently, calculations are performed using the midpoint between the upper and lower bounds. If convergence is achieved, the lower bound is updated; if convergence fails, the upper bound is updated, until the difference between the upper and lower bounds is less than a preset threshold (e.g., ...). When ), the limit reduction factor is reached.

[0085] The relationship between the reduction factor and the shear strength is expressed by the following formula (10).

[0086] (10) In formula (10), Shear strength, For soil cohesion, It is the internal friction angle. For effective normal stress, This is the reduction factor.

[0087] Based on the above formula (10), the shear strength parameters of the material are reduced synchronously until the slope fails. The final determined limit reduction coefficient is the safety factor.

[0088] The above method can be used to calculate the safety factor for any slope in the simulated three-dimensional slope.

[0089] Considering the complexity of models and limitations of computational resources in practical engineering, the tools used in the above numerical simulation process can be replaced by various numerical tools, such as Plaxis 3D for coupled simulation of unsaturated infiltration processes, or three-dimensional limit equilibrium analysis based on software such as GeoStudio SLOPE / W. Under simplified conditions, two-dimensional models can also be used to perform representative calculations on typical profiles. These different numerical methods can be flexibly replaced under different accuracy and efficiency requirements to achieve the regional-scale landslide risk identification objective of this invention.

[0090] Figure 5 This is a cloud map showing the maximum shear strain increment of the intermediate 3D slope model P4 before and 48 hours after rainfall. The map clearly shows that the potential sliding surface of the slope is mainly concentrated in the boundary area between the undulating slope surfaces, especially at the location with the most significant undulations, where the shear strain increment reaches its maximum. This indicates that these areas with significant local topographic changes are prone to stress concentration, becoming weak points for sliding failure. Simultaneously, 48 hours after rainfall, the maximum shear strain increment shows a clear expanding trend; the previously localized high shear strain area is concentrated in the boundary area between the undulating surfaces, indicating a decrease in slope stability due to rainfall infiltration.

[0091] In step 103, the linear relationship between the slope roughness coefficient and the safety factor of each simulated three-dimensional slope model under the same rainfall stage is fitted to obtain the corresponding relationship between the slope roughness coefficient and the safety factor under different rainfall stages.

[0092] The relationship between the slope roughness coefficient and the safety factor under different rainfall stages is used to assess the susceptibility of rainfall-induced landslides.

[0093] The surface roughness coefficient and safety factor of each simulated 3D slope model obtained under the same rainfall stage are equivalent to a point to be fitted. Fitting all the points to be fitted under the same rainfall stage yields the correspondence between the surface roughness coefficient and the safety factor under that rainfall stage. Different rainfall stages can yield different correspondences between the surface roughness coefficient and the safety factor.

[0094] In implementation, the least squares method can be used to fit the linear relationship between the slope roughness coefficient and the safety factor, resulting in a relationship expression in the form of formula (11).

[0095] (11) In formula (11), For safety reasons, This is the surface roughness coefficient. These are the coefficients obtained by fitting using the least squares method.

[0096] Figure 6 This is a schematic diagram illustrating the correspondence between the slope roughness coefficient and the safety factor under different rainfall stages, obtained through final fitting. For example... Figure 6 As shown, the fitting results indicate a significant negative correlation between the slope roughness coefficient and the safety factor. Specifically, as the slope roughness coefficient increases, the geometric undulations and structural complexity of the slope surface intensify, leading to increased uncertainty in the location of the sliding surface. This makes the slope more prone to instability under disturbances such as rainfall, resulting in a significant decrease in the safety factor. Figure 6 Taking the fitted curve shown as an example, the method is verified to have a good characterization ability of the three-dimensional slope stability trend, which fully demonstrates that the proposed slope roughness coefficient construction method and its weight allocation have certain scientific and engineering applicability.

[0097] Optionally, step 103 includes steps 1031 to 1032.

[0098] Step 1031: Calculate the surface roughness coefficient of the slope to be evaluated.

[0099] The surface roughness coefficient of the slope to be evaluated can be calculated using the same method as that used in simulating a three-dimensional slope model.

[0100] Calculating the surface roughness coefficient of a slope to be evaluated essentially requires obtaining the slope and parameters related to the degree of undulation (including maximum undulation height, average undulation height, and undulation frequency). Since the undulation height of a slope is defined as the vertical distance between the undulation crest (or trough) and the reference surface, it is equivalent to first determining the reference surface before determining the parameters related to the degree of undulation based on the reference surface.

[0101] In summary, when calculating the surface roughness coefficient of the slope to be evaluated, it is necessary to first obtain parameters such as the slope and reference surface of the slope to be evaluated.

[0102] However, while parameters such as slope and reference surface can be directly obtained from a simulated three-dimensional slope model, the slope to be evaluated is a real slope. To obtain parameters such as slope and reference surface of the slope to be evaluated, it is necessary to first model the slope to be evaluated, and then obtain parameters such as slope and reference surface from the model, so as to calculate the slope roughness coefficient.

[0103] Different modeling software produces different models of the slope to be evaluated, and the corresponding methods for calculating the slope roughness coefficient also differ. The following sections explain the process of calculating the slope roughness coefficient using Rhino and ArcGIS modeling software, respectively.

[0104] (1) The surface roughness coefficient of the slope to be evaluated is calculated using Rhino.

[0105] Rhino is a powerful 3D modeling software. When using Rhino to calculate the surface roughness coefficient of a slope to be evaluated, the elevation data of the slope to be evaluated is first extracted using ArcGIS, and the contour data of the slope to be evaluated is then imported into Rhino to construct a 3D terrain model. Figure 7 This is a schematic diagram of the 3D model of the slope to be evaluated, generated by Rhino. (Example) Figure 7 As shown, the dimensions of the constructed 3D model of the slope to be evaluated are 420m x 200m x 100m. Once this 3D model is obtained, the slope value of the slope to be evaluated can be determined.

[0106] because Figure 7 The resolution of the DEM elevation data of the slope to be evaluated is 5m by 5m. Therefore, within the 420-meter longitudinal range of the slope to be evaluated, a two-dimensional profile is extracted every 5 meters.

[0107] Figure 8 This is a cross-sectional view of the three-dimensional model of the slope to be evaluated. Figure 8 Part (a) is an oblique view in the cross-sectional view of the three-dimensional model of the slope to be evaluated. Figure 8 Part (b) is the right view of the cross-sectional view of the three-dimensional model of the slope to be evaluated. For example... Figure 8 As shown in section (a), the slope to be evaluated is divided into three sub-regions, namely Region 1, Region 2 and Region 3, for subsequent calculation of the slope roughness coefficient. Each sub-region is 140 meters by 200 meters in size and contains 28 two-dimensional profile lines.

[0108] To quantitatively calculate the degree of undulation of a slope, a reference surface needs to be established. Compared to ArcGIS, Rhino has a significant advantage in this process, as it can more easily establish reference surfaces and generate the two-dimensional profile maps required by the user.

[0109] The method for constructing the datum plane is as follows: Select the profile at the starting point of the longitudinal direction of the region (the two-dimensional profile where MP is located) and the profile at the ending point (the two-dimensional profile where NQ is located) as the datum (e.g., Figure 8 (as shown in part (b)).

[0110] Taking the two-dimensional profile where MP is located as an example, in this profile, point O is selected at the toe of the slope and point R is selected at the crest of the slope, and a baseline OR is formed by connecting them, representing the slope of the reference surface of this profile. In order to improve the accuracy of statistical analysis, this baseline OR is designed to divide the slope undulation as evenly as possible, thereby minimizing the deviation between the actual profile line and the standard slope.

[0111] The same operation applies to the two-dimensional profile where NQ is located. By processing it in the same way as the two-dimensional profile where MP is located, the baseline in the two-dimensional profile where NQ is located can be obtained.

[0112] The baselines in both the 2D profiles containing MP and NQ are set to have a consistent slope angle during the construction process. These two baselines are then connected to form a plane, which serves as the reference surface for evaluating slope undulation (e.g., ...). Figure 7 (As shown).

[0113] Figure 9 These are several typical two-dimensional cross-sectional schematic diagrams. For example... Figure 9 As shown, four representative two-dimensional profile lines can be further extracted from regions 2 and 3, among which... Figure 9 Part (d) shows a profile line in region 2. As can be seen from the figure, the standard slope of this region is defined by a reference plane, and its slope makes an angle with the horizontal plane of θ. Although a uniform division between the datum plane and the section can be achieved through manual control ( Figure 9 (a) and Figure 9 (c)), but some cross-sectional lines still show the following characteristics. Figure 9 (b) and Figure 9 (d) illustrates the non-uniform state. This phenomenon further highlights the necessity of the slope roughness coefficient proposed in this study. The slope roughness coefficient provides a standardized method for describing the degree of undulation of a three-dimensional slope along the longitudinal direction, thus providing a reliable basis for slope stability analysis under complex terrain conditions.

[0114] The reference surface of the slope to be evaluated can be determined by the above method, and then the surface roughness coefficient of the slope to be evaluated can be calculated.

[0115] (2) The surface roughness coefficient of the slope to be evaluated is calculated using ArcGIS.

[0116] ArcGIS has powerful terrain information extraction capabilities, enabling the acquisition of 3D slope and surface undulation information from selected areas. In ArcGIS, slope information can be easily extracted based on divided grid cells.

[0117] Figure 10 This is a schematic diagram of the ArcGIS grid and the six selected sections. (Example) Figure 10As shown, based on the accuracy of the DEM data of the slope to be evaluated, the slope was divided into a regular grid of 5m by 5m, and the study area 1 (1060 meters long) and study area 2 (900 meters long) were analyzed separately. In ArcGIS, elevation and slope information of any two-dimensional profile can be extracted. To ensure the reliability of the slope roughness coefficient calculation, only slope areas with elevation differences within 10 meters were analyzed, as excessive height changes would significantly affect the safety factor of the three-dimensional slope. Subsequently, each grid cell was numbered, and an xOy coordinate system was established in the profile dimension, thus allowing the extraction of slope and elevation data for any two-dimensional profile along the Ox direction. Based on the extracted data, the average and maximum values ​​of the slope and surface undulation of each profile were calculated. In the Ox direction, merging all grid cells yielded the elevation and slope information of a two-dimensional profile; while in the Oy direction, combining the data from each profile yielded the statistical index of the three-dimensional slope roughness coefficient for the study area.

[0118] Unlike Rhino, it is difficult to establish a unified reference plane in ArcGIS to calculate the undulation of a three-dimensional slope. Therefore, this embodiment of the disclosure uses a method of setting a separate reference line for each two-dimensional profile to perform statistical calculations of the slope undulation height.

[0119] Figure 11 This is a schematic diagram of a two-dimensional section 1 of the study region 1 in ArcGIS. (Example) Figure 11 As shown, a reference line OR is selected in this two-dimensional cross-section to estimate the slope undulation height. This profile covers grid cells numbered from 57 to 84, with a total elevation difference of 80 meters. The slope angle of the reference line OR is... , used to calculate the surface undulation of the profile. Divide the OR line segment OH into 14 segments, and then calculate the degree of slope undulation using the following formulas (12) to (13). Since the calculation of the slope roughness coefficient only considers the area on the slope that protrudes from the reference line (i.e., the convex section), the focus is on the slope area above the reference line, which is the size of AD.

[0120] (12) (13) In formulas (12) and (13), i represents the i-th mesh element on the two-dimensional section. This represents the elevation difference between the slope surface at the i-th grid cell and the baseline in the two-dimensional section (i.e., Figure 11 AC in the middle). The elevation value of the slope surface at the i-th grid cell (i.e. Figure 11 (AB in the middle) The elevation value of the baseline at the i-th grid cell (i.e. Figure 10 (BC in the middle) That is, the undulation height of the slope at the i-th grid cell, expressed as the vertical distance between the slope at the i-th grid cell and the baseline (i.e., Figure 11 (AD in the middle) The slope angle of the baseline.

[0121] The same method can be used to calculate the slope undulation height of each two-dimensional section, and this method can be extended to calculate the slope undulation of any other two-dimensional profile. In ArcGIS, the slope information of the slope to be evaluated can be obtained directly. By combining the slope undulation height of each two-dimensional section and the slope information of the slope to be evaluated, the slope roughness coefficient can be calculated.

[0122] In practical applications, different alternative methods can be selected based on accuracy requirements and data availability. For example, in scenarios requiring high precision, 3D models generated from LiDAR point cloud data or UAV oblique photography can be used for slope geometry reconstruction; while when data resources are limited, local geometric indices can be extracted based on 2D DEM profiles. Both extraction methods can quantify the spatial morphology of the slope, thereby calculating the slope roughness coefficient.

[0123] Step 1032: Based on the slope roughness coefficient of the slope to be evaluated and the correspondence between the slope roughness coefficient and the safety factor under different rainfall stages, determine the safety factor of the slope to be evaluated under different rainfall stages.

[0124] Here, after obtaining the surface roughness coefficient of the slope to be evaluated, it is substituted into the correspondence between the surface roughness coefficient and the safety factor under different rainfall stages to determine the safety factor corresponding to the surface roughness coefficient of the slope to be evaluated under different rainfall stages. This safety factor is the safety factor of the slope to be evaluated. The safety factor reflects the rainfall-induced landslide susceptibility of the slope to be evaluated; the higher the safety factor, the less likely rainfall is to induce a landslide.

[0125] A safety factor threshold can be preset. If, among the safety factors of the slope to be evaluated in different rainfall stages, there is a safety factor that is less than the safety factor threshold, it indicates that the slope to be evaluated has a risk of landslide.

[0126] In this embodiment, a standard three-dimensional slope model is established based on the slope to be evaluated, and simulated three-dimensional slope models with different slope gradients and undulations are established based on the standard three-dimensional slope model. The surface roughness coefficient of each simulated three-dimensional slope model and the safety factor of each simulated three-dimensional slope model under different rainfall stages are calculated. The surface roughness coefficient is calculated based on the slope geometric characteristics and the standard three-dimensional slope model, and the safety factor is calculated using numerical simulation. The linear relationship between the surface roughness coefficient and the safety factor of each simulated three-dimensional slope model under the same rainfall stage is fitted to obtain the correspondence between the surface roughness coefficient and the safety factor under different rainfall stages. The correspondence between the surface roughness coefficient and the safety factor under different rainfall stages is used to assess the susceptibility of the slope to be evaluated to rainfall-induced landslides. In this way, only the surface roughness coefficient of the slope to be evaluated needs to be calculated, and the safety factor of the slope to be evaluated can be accurately and efficiently determined based on the correspondence between the surface roughness coefficient and the safety factor, and then the susceptibility of rainfall-induced landslides can be assessed based on this safety factor.

[0127] Compared with the numerical simulation method used in related calculations to calculate the safety factor of each slope to be evaluated separately, the present invention only needs to use numerical simulation to calculate the safety factor of each simulated three-dimensional slope model to establish the correspondence between the slope roughness coefficient and the safety factor, without using numerical simulation to calculate the safety factor of each slope to be evaluated. This is particularly suitable for scenarios with a large number of slopes to be evaluated, and can achieve rapid determination of slope safety factors in a large batch.

[0128] Figure 12 This is a flowchart illustrating another exemplary embodiment of the present disclosure of a method for assessing rainfall-induced landslide susceptibility based on slope geometry, a method that can be executed by a computer device. See also Figure 12 The method includes: In step 1201, based on the slope to be evaluated, multiple standard three-dimensional slope models are established, and based on the standard three-dimensional slope models, simulated three-dimensional slope models with different slopes and different degrees of undulation are established.

[0129] The multiple standard three-dimensional slope models in step 1201 can be the sum of the standard three-dimensional slope models in step 101 and the multiple intermediate three-dimensional slope models. That is, the multiple intermediate three-dimensional slope models in step 101 can also be used as standard three-dimensional slope models.

[0130] The implementation methods for the standard three-dimensional slope model and multiple intermediate three-dimensional slope models are described in step 101 above, and are omitted here in detail.

[0131] In step 1202, one of the multiple standard three-dimensional slope models is selected as the target standard three-dimensional slope model.

[0132] In one possible implementation, a model is randomly selected from multiple standard three-dimensional slope models as the target standard three-dimensional slope model.

[0133] In another possible implementation, the standard three-dimensional slope model that is closest to the slope to be evaluated is selected from multiple standard three-dimensional slope models as the target standard three-dimensional slope model.

[0134] Subsequently, when calculating the slope roughness coefficient, the standard three-dimensional slope model in formula (1) will be replaced with the target standard three-dimensional slope model.

[0135] In step 1203, the surface roughness coefficient of each simulated three-dimensional slope model and the safety factor of each simulated three-dimensional slope model under different rainfall stages are calculated.

[0136] The slope roughness coefficient is obtained based on the calculation of slope geometric characteristics and the target standard three-dimensional slope model, while the safety factor is calculated using numerical simulation. In step 1204, the linear relationship between the slope roughness coefficient and the safety factor of each simulated three-dimensional slope model under the same rainfall stage is fitted to obtain the corresponding relationship between the slope roughness coefficient and the safety factor under different rainfall stages.

[0137] The relationship between the slope roughness coefficient and the safety factor under different rainfall stages is used to assess the susceptibility of the slope to be evaluated to rainfall-induced landslides.

[0138] The implementation methods for steps 1203 and 1204 are the same as those for steps 102 to 103 above. In implementation, it is only necessary to replace the standard three-dimensional slope model in steps 102 to 103 above with the target standard three-dimensional slope model. Detailed descriptions are omitted here.

[0139] In this embodiment, multiple standard three-dimensional slope models are established, and one of them is selected as the target standard three-dimensional slope model. Subsequently, the slope roughness is calculated and the susceptibility to rainfall-induced landslides is assessed based on this target standard three-dimensional slope model. Thus, the standard three-dimensional slope model used in calculating the slope roughness coefficient is not unique; a suitable standard three-dimensional slope model can be selected as the target standard three-dimensional slope model according to the slope to be evaluated, further improving the accuracy of the calculated slope roughness and thus improving the accuracy of the finally determined safety factor.

[0140] The following are device embodiments of this application. For details not described in detail in the device embodiments, please refer to the above method embodiments.

[0141] Figure 13A schematic diagram of an assessment device for assessing rainfall-induced landslide susceptibility based on slope geometry, provided in an exemplary embodiment of this disclosure, is shown. See also Figure 13 The assessment device 1300 for assessing the susceptibility of landslides induced by rainfall based on slope geometry features includes: a modeling module 1301, a parameter calculation module 1302, and a fitting module 1303.

[0142] The modeling module 1301 is used to build a standard three-dimensional slope model based on the slope to be evaluated, and to build simulated three-dimensional slope models with different slopes and different degrees of undulation based on the standard three-dimensional slope model.

[0143] The parameter calculation module 1302 is used to calculate the surface roughness coefficient of each simulated three-dimensional slope model and the safety factor of each simulated three-dimensional slope model under different rainfall stages. The surface roughness coefficient is calculated based on the slope geometric characteristics and the standard three-dimensional slope model, and the safety factor is calculated by numerical simulation.

[0144] The fitting module 1303 is used to fit the linear relationship between the slope roughness coefficient and the safety factor of each simulated three-dimensional slope model under the same rainfall stage, so as to obtain the corresponding relationship between the slope roughness coefficient and the safety factor under different rainfall stages. The corresponding relationship between the slope roughness coefficient and the safety factor under different rainfall stages is used to assess the susceptibility of the slope to be evaluated to rainfall-induced landslides.

[0145] Optionally, in the parameter calculation module 1302, the surface roughness coefficient of the simulated three-dimensional slope model is calculated using the following formula:

[0146] in, This is the surface roughness coefficient. These are the weighting coefficients. This represents the maximum slope of the simulated 3D slope model. The maximum slope of a standard 3D slope model. This represents the average slope of the simulated 3D slope model. The average slope of a standard three-dimensional slope model. To simulate the maximum undulation height on the surface of a three-dimensional slope model, This represents the maximum undulation height of a standard 3D slope model. This represents the average undulation height of the simulated 3D slope model. The average undulation height of a standard three-dimensional slope model. To simulate the undulation frequency of a three-dimensional slope model, This represents the undulation frequency of a standard three-dimensional slope model.

[0147] Optionally, in the parameter calculation module 1032, the weighting coefficients... The value is greater than The value of , The value is greater than The value of .

[0148] Optionally, the parameter calculation module 1302 is also used to perform numerical simulation of the simulated three-dimensional slope model using the finite difference method, and to determine the ultimate reduction coefficient using the shear strength reduction method, wherein the ultimate reduction coefficient is a safety factor; wherein, in the process of performing numerical simulation of the simulated three-dimensional slope model using the finite difference method, the unsaturated infiltration process is simulated.

[0149] Optionally, the parameter calculation module 1302 is also used to perform numerical simulation of unsaturated weathered soil using Bishop's effective stress theory; in the case of stiffness softening caused by changes in soil saturation, Bishop's effective stress is used to correct the Mohr-Coulomb failure criterion to simulate the shear strength of unsaturated soil; based on the relationship between volumetric water content and saturation, a correspondence between saturation and pore water pressure is established to simulate the evolution of negative pore water pressure during unsaturated infiltration; in the process of simulating the unsaturated infiltration process, a linear relationship between saturation and rock mass compressive strength and elastic modulus is introduced to simulate the water-induced softening effect of rainfall infiltration on weathered rock mass.

[0150] Optionally, the fitting module 1303 is also used to calculate the surface roughness coefficient of the slope to be evaluated; based on the surface roughness coefficient of the slope to be evaluated and the correspondence between the surface roughness coefficient and the safety factor under different rainfall stages, the safety factor of the slope to be evaluated under different rainfall stages is determined.

[0151] It should be noted that the above-described embodiments of the assessment device for assessing rainfall-induced landslide susceptibility based on slope geometric characteristics are only illustrative examples of the division of functional modules. In practical applications, the functions described above can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the assessment device for assessing rainfall-induced landslide susceptibility based on slope geometric characteristics and the method embodiment for assessing rainfall-induced landslide susceptibility based on slope geometric characteristics are based on the same concept, and their specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0152] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods are possible. Furthermore, the functional modules in the various embodiments of this disclosure can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0153] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a terminal device (which may be a personal computer, mobile phone, or communication device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0154] Figure 14 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. For example... Figure 14 As shown, the computer device 1400 includes a processor 1401 and a memory 1402.

[0155] Processor 1401 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1401 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1401 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1401 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 1401 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0156] The memory 1402 may include one or more computer-readable storage media, which may be non-transitory. The memory 1402 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1402 is used to store at least one instruction, which is executed by the processor 1401 to implement the assessment method for rainfall-induced landslide susceptibility based on slope geometry provided in this disclosure embodiment.

[0157] Those skilled in the art will understand that Figure 14 The structure shown does not constitute a limitation on the computer device 1400, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0158] This disclosure also provides a non-transitory computer-readable storage medium, wherein when the instructions in the storage medium are executed by a processor of a computer device, the computer device is able to execute the assessment method for rainfall-induced landslide susceptibility based on slope geometry provided in this disclosure.

[0159] This disclosure also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the assessment method for rainfall-induced landslide susceptibility based on slope geometric characteristics provided in this disclosure.

[0160] The above description is merely an optional embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A method for assessing the susceptibility of rainfall-induced landslides based on slope geometry, characterized in that, The method includes: Based on the slope to be evaluated, a standard three-dimensional slope model is established, and based on the standard three-dimensional slope model, simulated three-dimensional slope models with different slopes and different degrees of undulation are established. The surface roughness coefficient of each simulated three-dimensional slope model and the safety factor of each simulated three-dimensional slope model under different rainfall stages are calculated. The surface roughness coefficient is calculated based on the slope geometric characteristics and the standard three-dimensional slope model, and the safety factor is calculated by numerical simulation. The linear relationship between the slope roughness coefficient and the safety factor of each simulated three-dimensional slope model under the same rainfall stage is fitted to obtain the correspondence between the slope roughness coefficient and the safety factor under different rainfall stages. The correspondence between the slope roughness coefficient and the safety factor under different rainfall stages is used to assess the rainfall-induced landslide susceptibility of the slope to be evaluated.

2. The method according to claim 1, characterized in that, The surface roughness coefficient of the simulated three-dimensional slope model is calculated using the following formula: in, The slope roughness coefficient is mentioned above. These are the weighting coefficients. This represents the maximum slope of the simulated three-dimensional slope model. The maximum slope of the standard three-dimensional slope model is given. This represents the average slope of the simulated three-dimensional slope model. The average slope of the standard three-dimensional slope model is given. The maximum undulation height of the surface of the simulated three-dimensional slope model. The maximum undulation height of the standard three-dimensional slope model is given. This represents the average undulation height of the simulated three-dimensional slope model. The average undulation height of the standard three-dimensional slope model is given. The undulation frequency of the simulated three-dimensional slope model. The undulation frequency of the standard three-dimensional slope model is given.

3. The method according to claim 1, characterized in that, In the weighting coefficients, The value is greater than The value of , The value is greater than The value of .

4. The method according to any one of claims 1 to 3, characterized in that, The safety factor of the simulated three-dimensional slope model is calculated in the following way: The simulated three-dimensional slope model is numerically simulated using the finite difference method, and the ultimate reduction coefficient is determined using the shear strength reduction method. The ultimate reduction coefficient is the safety factor. In the process of numerically simulating the simulated three-dimensional slope model using the finite difference method, the unsaturated infiltration process is simulated.

5. The method according to claim 4, characterized in that, The simulation of the unsaturated infiltration process includes: Numerical simulation of unsaturated weathered soil was performed using Bishop's effective stress theory. In the case of stiffness softening caused by changes in soil saturation, the Bishop effective stress modified Mohr-Coulomb failure criterion is adopted to simulate the shear strength of unsaturated soil. Based on the relationship between volumetric water content and saturation, a correspondence between saturation and pore water pressure is established to simulate the evolution of negative pore water pressure during unsaturated infiltration. In simulating the unsaturated infiltration process, a linear relationship between saturation and rock mass compressive strength and elastic modulus is introduced to simulate the water-induced softening effect of rainfall infiltration on weathered rock mass.

6. The method according to any one of claims 1 to 3, characterized in that, The following method is used to assess the susceptibility of the slope to be evaluated to rainfall-induced landslides, based on the correspondence between the slope roughness coefficient and the safety factor under different rainfall stages: Calculate the surface roughness coefficient of the slope to be evaluated; Based on the slope roughness coefficient of the slope to be evaluated and the correspondence between the slope roughness coefficient and the safety factor under different rainfall stages, the safety factor of the slope to be evaluated under different rainfall stages is determined.

7. An assessment device for the susceptibility of landslides induced by rainfall based on slope geometry, characterized in that, The device includes: The modeling module is used to build a standard three-dimensional slope model based on the slope to be evaluated, and to build simulated three-dimensional slope models with different slopes and different degrees of undulation based on the standard three-dimensional slope model. The parameter calculation module is used to calculate the slope roughness coefficient of each simulated three-dimensional slope model and the safety factor of each simulated three-dimensional slope model under different rainfall stages. The slope roughness coefficient is calculated based on the slope geometric characteristics and the standard three-dimensional slope model, and the safety factor is calculated by numerical simulation. The fitting module is used to fit the linear relationship between the slope roughness coefficient and the safety factor of each simulated three-dimensional slope model under the same rainfall stage, so as to obtain the correspondence between the slope roughness coefficient and the safety factor under different rainfall stages. The correspondence between the slope roughness coefficient and the safety factor under different rainfall stages is used to assess the rainfall-induced landslide susceptibility of the slope to be evaluated.

8. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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