Anchoring slope landslide instability risk evaluation method under rainfall condition

Abstract

The invention provides an anchoring slope landslide instability risk evaluation method under a rainfall condition, and belongs to the technical field of geotechnical engineering and geological disaster prevention and control, and the method comprises the steps: constructing a theoretical calculation formula of a multilayer rock-soil body anchoring slope stability coefficient under a rainfall infiltration effect based on a segmented slip plane method; based on a Monte Carlo probability analysis framework, introducing a reliability index, and establishing a slope reliability integrated evaluation model; acquiring soil body, rainfall, support and geometric parameter data of the side slope; defining various parameters as random variables, and performing large-scale sampling calculation based on the theoretical formula; counting probability distribution of the stability coefficient, and solving a reliability index and a failure probability; establishing a risk monitoring criterion, and judging whether each index exceeds a permissible value or not; and implementing risk dynamic early warning based on a judgment result. According to the method, an integrated evaluation technology system for the reliability of the anchored side slope under rainfall suitable for the multi-layer rock-soil body is constructed, and efficient quantification of the reliability of the side slope under the multi-parameter coupling effect is achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of geotechnical engineering and geological disaster prevention, and particularly relates to a method for evaluating landslide instability risk of an anchored slope under rainfall conditions. BACKGROUND

[0002] The current slope stability analysis technology system mainly focuses on the traditional deterministic analysis framework and part of the probability analysis means. The traditional method mainly uses deterministic analysis software to input fixed rock and soil parameters (such as cohesion and internal friction angle) and load conditions, calculate the safety factor, and evaluate the slope stability. Although it has become a standard tool for engineering, it cannot quantify the risk brought by parameter uncertainty. It is disconnected with the needs of engineering risk management and dynamic early warning decision-making, and engineers cannot provide key decision-making information such as parameter sensitivity and critical threshold, making it difficult to convert the analysis results into effective risk control measures.

[0003] On the basis of the deterministic model, part of the research will introduce probability and statistics theory. Although this method can solve the problem of parameter uncertainty to some extent, it still has significant limitations: first, it is based on the assumption of homogeneous soil slope, and many reliability studies are based on homogeneous soil slope or single type of slope, which cannot accurately simulate the failure mechanism of the multi-layer heterogeneous slope commonly seen in actual engineering; second, the existing research usually has a significant dimension missing problem, mainly manifested in the serious lack of consideration of the coupling effect of multiple parameters, only focusing on the variability of soil parameters or considering the influence of rainfall infiltration alone, ignoring the uncertainty of supporting and geometric parameters which are also important, and failing to systematically consider the coupling effect between parameters. This one-sided consideration leads to incomplete risk assessment results, which cannot reflect the real risk situation of the system. SUMMARY

[0004] The purpose of the present application is to provide a method for evaluating landslide instability risk of an anchored slope under rainfall conditions, which solves the above problems.

[0005] In order to achieve the above technical features, the purpose of the present application is achieved as follows: a method for evaluating landslide instability risk of an anchored slope under rainfall conditions, comprising the following steps: S1. Based on the piecewise sliding surface method, a theoretical calculation formula for the stability coefficient of a multi-layer rock and soil body anchored slope under the action of rainfall infiltration is constructed; S2. Based on the Monte Carlo probability analysis framework, a reliability index is introduced, and a slope reliability integrated evaluation model is established; S3. According to the survey data, various parameters of the slope are obtained, including soil, rainfall, support and geometric parameters; S4. Defining the various parameters as random variables, large-scale sampling calculation is carried out based on the theoretical calculation formula; S5. Calculate the probability distribution of the statistical stability coefficient to determine the reliability index and failure probability. S6. Establish risk monitoring criteria and determine whether each indicator exceeds the allowable value; if it does, proceed to step S7; otherwise, proceed to step S4. S7. Implement dynamic risk early warning.

[0006] Preferably, the method for constructing the theoretical calculation formula for the stability coefficient of a multi-layered soil and rock anchored slope under rainfall infiltration in step S1 includes the following steps: S11. The safety factor is calculated using the limit equilibrium method and is defined as the ratio of anti-skid force to sliding force, as shown in the following formula:

[0007] in, F s The stability coefficient; c i Let be the cohesive force on the bottom surface of the i-th block; For the first i The internal friction angle of the bottom surface of the strip; l i For the first i Length of the bottom surface of the strip; N i For the first i Pressure on the bottom surface of the strip, N i =W i cos ζ i ; T i For the first i Slide force of the strip, T i =W i sin ζ i ; ζ i For the first i The inclination angle of the sliding surface of the strip; W i For the first i The weight of the soil block; F N and F T The anchoring force of the anchor bolt at the th i Radial and normal components of the strip; S12. Rainfall infiltration affects the mechanical state of the slope; calculate the pore water pressure in the unsaturated zone. u and the unit weight change of soil γ The formula is as follows:

[0008]

[0009] wherein, γ w is the unit weight of water; h is the matric potential, determined by the Van Genuchten model in the soil water characteristic curve; S r represents the degree of saturation, i.e. the ratio of water volume to pore volume in the soil body; e represents the void ratio; γ d is the dry bulk density of the soil body; γ ω is the wet bulk density of the soil body; S13, according to the Mohr-Coulomb criterion, combined with the pore water pressure calculation formula, the shear strength of the i block of soil is calculated R si , the formula is as follows:

[0010] wherein, μ i is the pore water pressure of the i block, σ i is the normal stress of the i block, “ σ i -μ i ” is the effective stress of the i block; S14, using the limit equilibrium method and the Van Genuchten model in the soil water characteristic curve, the stability coefficient of the anchored slope under rainfall condition is calculated, the formula is as follows:

[0011] S15, the potential slip surface is segmented according to the soil layer distribution, the shear strength is calculated separately, and then the total anti-sliding force and the sliding force are accumulated to construct the calculation formula of the stability coefficient of the multi-layer rock-soil anchored slope under the rainfall infiltration effect; assuming that the total length of the slip surface is L , according to the different soil layers it passes through, the slip surface is divided into m segments; wherein the j segment is located in the j layer of soil body, and the length is l j , j =1,2,...,m, the sum of the lengths of all segments is equal to the total length of the slip surface, satisfying ; then the calculation formula of the stability coefficient is as follows: ; wherein, F s is a stability coefficient, c ij is the first i layer bottom surface cohesion of the first j block; is the first i layer bottom surface internal friction angle of the first j block; is the first i layer sliding surface inclination angle of the first j block; is the first i layer weight of the first j block; is the first i layer pore water pressure of the first j block; l ij is the first i layer bottom surface length of the first j block.

[0012] Preferably, the method for establishing the slope reliability integrated evaluation model in step S2 comprises the following steps: S21, defining the slope instability influencing factors as a probability distribution, the probability distribution comprising a normal distribution or a lognormal distribution, randomly generating a large number of parameter samples conforming to the probability distribution, performing stability analysis on each parameter sample, calculating the stability coefficient thereof, and finally counting the proportion of failure parameter samples, i.e. the failure probability of the slope P f , and calculating the reliability index μ Fs and the standard deviation σ Fs of the stability coefficient according to the mean value β , the formula being as follows:

[0013]

[0014] wherein, the numerator part represents the gap between the mean value of the stability coefficient and the failure threshold value, the larger the gap, the higher the safety of the structure; and the denominator part represents the variability of the stability coefficient, the smaller the variability, the more concentrated the distribution of the safety coefficient, and the higher the reliability of the structure; S22, the failure probability and the reliability index have a direct mathematical relationship, the failure probability P f by the cumulative distribution function of the standard normal distribution, the formula being as follows: ; wherein, is the cumulative distribution function of the standard normal distribution; S23, calculating the stability coefficient through the theoretical calculation formula, calculating the evaluation index based on the stability coefficient, and finally establishing the slope reliability integrated evaluation model.

[0015] Preferably, the method for obtaining the soil body, rainfall, support and geometric parameters of the slope in step S3 comprises the following steps: S31, collecting the basic geographic information data of the research area, including the environmental and topographic features of the area, the terrain shape features, the ground crack distribution, the hydrological distribution features, the historical weather data set, and the slope anchoring information, and collecting the data mentioned in the calculation formula in step S1; S32, investigating the composition of each layer of rock-soil body of the slope in detail and collecting the soil body parameter data of the slope; S33, according to the precipitation, collecting the rainfall parameter data of the slope; S34, according to the detailed anchoring data, collecting the support parameter data of the slope, i.e. the design tension of the anchor rod; S35, according to the field survey data, collecting the geometric parameter data of the slope, wherein the geometric parameter data includes the height of the slope and the slope gradient.

[0016] Preferably, the soil body parameter data in S32 includes the soil body cohesion and the internal friction angle.

[0017] Preferably, the rainfall parameter in S32 includes the rainfall intensity and the rainfall duration.

[0018] Preferably, the method for performing large-scale sampling calculation in step S4 comprises the following steps: S41, according to the geological exploration data, the indoor test results and the engineering experience, determining the probability distribution model of each type of parameter; S42, for the soil body parameter, defining the cohesion and internal friction angle of each rock-soil layer as random variables subject to normal distribution or lognormal distribution; based on the historical rainfall statistical data, determining the rainfall parameter, and defining the rainfall duration and rainfall intensity as random variables; for the support parameter, taking the design tension value of the anchor rod as a random variable, considering the construction error and the prestress loss, and subjecting to normal distribution; for the geometric parameter, taking the height of the slope and the slope gradient as random variables, considering the measurement error and the construction variability, and subjecting to normal distribution; S43, using Monte Carlo sampling to determine the total sampling number, and taking 10 4 ~10 6Secondly, N samples of each random variable are generated to ensure that the value range of each random variable is fully covered, and the statistical independence between the samples of each random variable is maintained through random arrangement to form N complete parameter sample combinations; S44, batch calculation of stability coefficient, each group of parameter samples is substituted into the theoretical calculation formula for cyclic calculation to obtain the stability coefficient value; during the calculation process, the pore water pressure is calculated in real time to reflect the rainfall infiltration effect, and the anchoring force is calculated according to the actual position and is input into the corresponding block.

[0019] Preferably, the specific steps of solving the reliability index and failure probability in step S5 are as follows: S51, statistical analysis is performed based on the stability coefficient samples obtained through Monte Carlo simulation; S52, a stability coefficient probability distribution histogram is drawn to observe the distribution form; S53, according to the theoretical calculation formula and the reliability integrated evaluation model, the slope failure probability and the reliability index are solved according to the probability distribution; the total number of unstable samples is counted, the slope failure probability and the estimated value of the failure probability point are calculated; and the reliability index is calculated based on the first-order second-moment method; S54, the change curves of the failure probability and the reliability index under the influence of each parameter are drawn, the results are visualized, and the reliability is evaluated to lay the foundation for subsequent risk warning; S55, the whole process from stability coefficient sample statistics to reliability index quantification is completed, and probability basis is provided for dynamic risk warning of slope instability.

[0020] Preferably, the step of judging whether each index exceeds the allowable value in step S6 is as follows: S61, the sensitive region of each index of slope reliability to parameter change is identified, especially the inflection point of the index value with parameter change, and the parameter critical threshold value causing the reliability to sharply decrease is determined to provide basis for risk warning; S62, based on the reliability analysis results and the engineering risk acceptance criteria, the following warning threshold system is established; S621, normal monitoring state: reliability index≥3.2, failure probability<0.1%, proving that the slope is in a highly reliable state, and the monitoring is controlled according to the conventional monitoring frequency; S622, risk attention state: 2.7≤reliability index<3.2, 0.1%≤failure probability<1%, proving that the slope reliability starts to decrease, and the monitoring frequency needs to be strengthened; S623, risk alert state: reliability index<2.7, failure probability≥1%, proving that the slope is in a high-risk state, and the emergency monitoring and disposal plan is started; S63, a dynamic correlation mechanism of monitoring data and reliability evaluation is established, and various parameter data are collected in real time: pore water pressure sensor data, surface displacement GPS monitoring data, and real-time rainfall intensity monitoring data; S64, real-time calculation of monitoring data corresponding to reliability index, and judgment of whether the allowable value of the early warning threshold system is exceeded.

[0021] Preferably, the step of dynamically warning the risk in step S7 is as follows: S71, if the reliability index exceeds the allowable value, triggering a warning, issuing a warning signal, identifying the critical threshold of the parameter, and implementing engineering reinforcement measures; S72, the following conditions are met, and the slope triggering a warning is relieved: a. The monitoring data for 7 consecutive days shows that the reliability index returns to the safe range; b. The induced factors are completely eliminated; c. The engineering reinforcement measures are confirmed to be effective through evaluation.

[0022] The present application has the following beneficial effects: 1. Since the present application uses reliability index and failure probability as core evaluation standards, and deeply integrates reliability analysis results with real-time monitoring data, the practicality of the engineering is solved, and accurate decision-making for the slope risk is realized; 2. Since the present application uses a segmented slip surface theoretical model, the limitations of the traditional homogeneous soil slope assumption are overcome, and the accuracy and authenticity of the slope stability analysis are significantly improved; 3. Since the present application establishes a multi-parameter random variable coupling analysis framework of soil body and rainfall, the uncertainty of various parameters and their interaction are systematically considered, and the overall evaluation of the slope system risk is realized; 4. As described above, the present application breaks through the limitations of traditional deterministic analysis, and through the coupling of theoretical calculation formula and Monte Carlo probability analysis framework, an integrated reliability evaluation technology system for anchored slope under rainfall is constructed, which is suitable for multi-layer rock-soil body, realizes efficient quantization of slope reliability under multi-parameter coupling, and related results can adapt to the dynamic risk warning of slope under different geological conditions and rainfall working conditions, and provide a theoretical basis and practical tool for reliability design, risk classification prevention and control, and monitoring strategy optimization of anchored slope. BRIEF DESCRIPTION OF DRAWINGS

[0023] The present application will be further described below in combination with the drawings and examples.

[0024] Figure 1 is a slope landslide instability risk evaluation process schematic diagram provided by the present application under rainfall conditions; Figure 2 is a finite element numerical model diagram for showing an embodiment provided by the present application; Figure 3 is a probability density distribution diagram of the stability coefficient calculated by theoretical analysis and calculation provided by the present application; Figure 4 is an influence analysis diagram of soil parameters on reliability evaluation indexes in an embodiment of the present application; Figure 4 in the figure (a) is an influence analysis diagram of cohesion variability on reliability evaluation indexes in an embodiment of the present application; Figure 4 in the figure (b) is an influence analysis diagram of internal friction angle variability on reliability evaluation indexes in an embodiment of the present application; Figure 5 is an influence analysis diagram of rainfall parameters on reliability evaluation indexes in an embodiment of the present application; Figure 5 in the figure (a) is an influence analysis diagram of rainfall intensity on reliability evaluation indexes in an embodiment of the present application; Figure 5 in the figure (b) is an influence analysis diagram of rainfall duration on reliability evaluation indexes in an embodiment of the present application; Figure 6 is an influence analysis diagram of geometric parameters on reliability evaluation indexes in an embodiment of the present application; Figure 6 in the figure (a) is an influence analysis diagram of slope gradient on reliability evaluation indexes in an embodiment of the present application; Figure 6 in the figure (b) is an influence analysis diagram of slope height on reliability evaluation indexes in an embodiment of the present application; Figure 7 is an influence analysis diagram of supporting parameter anchor rod tension on reliability evaluation indexes in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The present application will be further described in detail by specific embodiments. The following embodiments can make the professional technical personnel more fully understand the present application, but do not limit the present application in any way.

[0026] Embodiment 1: Figure 1 is a slope landslide instability risk evaluation process schematic diagram under rainfall condition provided by the present application, for reference Figure 1This invention provides a method for assessing the risk of slope landslide instability under rainfall conditions, illustrated in the following embodiments: Based on the segmented slip surface method, a theoretical calculation formula for the stability coefficient of a multi-layered soil-rock anchored slope under rainfall infiltration is constructed; based on the Monte Carlo probability analysis framework, a reliability index is introduced to establish an integrated slope reliability assessment model; based on survey data, soil, rainfall, support, and geometric parameter data of the slope are obtained; various parameters are defined as random variables, and large-scale sampling calculations are performed based on the theoretical formula; the probability distribution of the stability coefficient is statistically analyzed to solve for the reliability index and failure probability; dynamic risk warning is implemented, and monitoring criteria are used to determine whether each index exceeds the allowable value; based on the judgment results, dynamic risk warning is implemented.

[0027] To systematically study the reliability of anchored slopes under rainfall conditions with varying geological conditions and to clarify the impact of soil parameter variability and other relevant parameters on slope reliability, this study takes a slope in the Three Gorges Reservoir area of ​​the Yangtze River as an example. Considering the differences in shear strength among multiple layers of soil and rock, a theoretical calculation model for the reliability of anchored slopes with multiple layers of soil and rock under rainfall is developed based on the segmented slip surface method, coupled with theoretical calculations and the Monte Carlo probabilistic framework. The influence of various relevant parameters on slope reliability indices and failure probabilities is also explored. The results show that the theoretical calculation model has good versatility and can more realistically and efficiently evaluate the safety level of slopes, providing a comprehensive and effective analytical tool for the reliability of complex slopes under different working conditions. This embodiment takes a reservoir bank slope project in the Three Gorges Reservoir area of ​​the Yangtze River as the background. Based on the segmented slip surface method, a formula for calculating the stability coefficient of multi-layer rock and soil anchored slopes under rainfall was established. An integrated evaluation system was built by combining a probabilistic analysis framework. The slope failure probability and reliability index were calculated based on the Monte Carlo method. The influence of the correlation of various parameters on the evaluation index was also discussed. The relevant results can provide a reference for studying the stability and disaster prevention and mitigation of multi-layer rock and soil anchored slopes under rainfall conditions. Constructing the stability coefficient of anchored slopes under rainfall conditions F s :

[0028] in, F s The stability coefficient; c i Let be the cohesive force on the bottom surface of the i-th block; For the first i The internal friction angle of the bottom surface of the strip; l i For the first i Length of the base of the strip; N i For the first i Pressure on the bottom surface of the strip, N i=W i cos ζ i ; T i For the first i Slide force of the strip, T i =W i sin ζ i ; ζ i For the first i The inclination angle of the sliding surface of the strip; W i For the first i The weight of the soil block; F N and F T These are the radial and normal components of the anchoring force in the i-th block, respectively; μ i It is the first i Pore ​​water pressure in the strip.

[0029] The potential slip surface is segmented according to soil layer distribution, and the shear strength is calculated separately. Then, the total anti-sliding force and sliding force are summed to construct a formula for calculating the stability coefficient of multi-layered soil and rock anchored slopes under rainfall infiltration. The total length of the slip surface is assumed to be... L Based on the different soil layers it traverses, the slip surface is divided into m segments; where the first segment is... j The section is located in the first j Within the soil layer, the length is l j , j =1,2,...,m, where the sum of the lengths of all segments equals the total length of the slip surface, satisfying... The formula for calculating the stability coefficient is as follows: ; in, F s The stability coefficient, c ij For the first i The first in the strip j The cohesive force at the bottom surface of the layer; For the first i The first in the strip j The internal friction angle of the bottom surface of the layer; For the first i The first in the strip j The dip angle of the sliding surface of the soil layer; For the first i The first of the blocks j The weight of the soil layer; For the firsti the first j the pore water pressure of the soil of the first l ij the first i the length of the bottom surface of the soil of the first j

[0030] wherein, F s the stability coefficient, c ij the cohesion of the bottom surface of the first i the first j the first i the first j the internal friction angle of the bottom surface of the first ζ i the sliding surface inclination angle of the soil of the first i the first j the weight of the soil of the first i the first j the pore water pressure of the soil of the first i the first j

[0031] The influencing factors of slope instability are defined as probability distribution, which includes normal distribution or lognormal distribution, a large number of parameter samples are randomly generated, stability analysis is performed on each parameter sample, the stability coefficient is calculated, and finally the proportion of failure parameter samples is calculated, i.e. the failure probability of the slope P f and the mean value of the stability coefficient μ Fs and the standard deviation σ Fs The reliability index β is calculated, and the formula is as follows:

[0032]

[0033] wherein the numerator part μ Fs - 1 represents the gap between the mean value of the stability coefficient and the failure threshold, the larger the gap, the higher the safety of the structure; the denominator part σ Fs represents the variability of the stability coefficient, the smaller the variability, the more concentrated the distribution of the safety coefficient, and the higher the reliability of the structure; ​​​​​Based on the stability coefficient obtained from the theoretical formula, the evaluation index is calculated, and finally an integrated evaluation system for slope reliability is established to evaluate slope reliability. Furthermore, basic geographic information data of the study area are collected, including the region's environmental geomorphological features, topographic shape features, distribution of ground cracks, hydrological distribution features, historical weather datasets, and slope anchoring information, as well as the data mentioned in the calculation formula described in S1. Table 1. Key Data Values ​​for Slope

[0034] The slope studied is located in the Three Gorges Reservoir area of ​​the Yangtze River. The slope is 155m long and 70m high. According to the engineering geological survey and investigation report, the uppermost layer of the slope is a Quaternary deposit, mainly composed of grayish-yellow gravelly silty clay interspersed with gravel and angular stones, formed during the early landslide process. The second layer is weathered bedrock, containing clay minerals such as montmorillonite. The lowest layer mainly consists of sand, gravel, and a small amount of clay. Rainfall in the Three Gorges area is abundant and seasonally distributed, with annual rainfall generally between 1000-1400 mm. A detailed investigation was conducted on the composition of the soil and rock layers of the slope, and soil parameter data, including soil cohesion and internal friction angle, were collected. Specific data are shown in Table 1. Rainfall type was differentiated based on precipitation, and rainfall parameter data, including rainfall intensity and duration, were collected. Specific data are shown in Table 2. Based on detailed anchoring data, slope support parameters were collected; the design tension of the anchor bolts for this slope is 500 kN / m. Geometric parameter data were collected based on field survey data; the slope height is 70 m and the slope angle is 40°. Table 2 shows the rainfall data for this slope area.

[0035] A numerical stability analysis model for slopes was employed using Geo-studio software. Slope material properties were defined, and the Mohr-Coulomb yield criterion was used to simulate the soil and rock mass. Different soil layers were defined to reflect the true geological structure of the heterogeneous slope. The upper layer of the slope was set as a permeable slope to simulate rainfall infiltration, allowing rainwater to freely infiltrate. The slope surface flow rate was set according to rainfall conditions, and rainfall intensity and duration were defined. The grid size on the slope surface was set to 1m, with the grid density increased in potential sliding areas and near anchor bolts to improve calculation accuracy; the remaining grid size was controlled at 2m. Figure 2 The image shows the slope described in the embodiment; Furthermore, large-scale sampling calculations are performed: Based on geological exploration data, laboratory test results, and engineering experience, the probability distribution models for the various parameters are determined. For soil parameters, the cohesion and internal friction angle of each rock-soil layer are defined as random variables subject to normal distribution or lognormal distribution; based on historical rainfall statistical data, the rainfall duration and intensity are defined as random variables; for support parameters, the design tension value of the anchor rod is taken as a random variable subject to normal distribution, considering construction errors and prestress loss; for geometric parameters, the slope height and slope are taken as random variables subject to normal distribution, considering measurement errors and construction variability; Monte Carlo sampling is used to determine the total sampling number, and 10 4 ~10 6 times are taken for each random variable to generate N samples, ensuring that the value range of each random variable is fully covered. By random arrangement, the statistical independence between the samples of each random variable is maintained, forming N complete parameter sample combinations; Batch calculation of stability coefficient: each group of parameter samples is substituted into the theoretical calculation formula for cyclic calculation to obtain the stability coefficient value. During the calculation process, the pore water pressure is calculated in real time to reflect the rainfall infiltration effect, and the anchoring force is calculated according to the actual position of the corresponding block; Further, the reliability index and failure probability are solved: Based on the stability coefficient samples obtained by Monte Carlo simulation, the probability distribution of the parameters is established; the stability coefficient probability distribution histogram is drawn to observe its distribution form, as shown in Figure 3 ; According to the theoretical calculation formula and the reliability integrated evaluation model, the slope failure probability and reliability index are solved based on the probability distribution. The total number of unstable samples is counted, and the estimated value of the failure probability and failure probability point is calculated; and the reliability index is calculated based on the first-order second-moment method; Draw the change curve of failure probability and reliability index under the influence of each parameter, visualize the results, and perform reliability evaluation to lay the foundation for subsequent risk warning. The evaluation process includes the following specific steps: Analysis of the influence of soil parameters on the reliability index: Figure 4 is a graph showing the influence of soil parameters on the reliability evaluation index. Combined with Figure 4 (a) and Figure 4 (b), it is shown that as the cohesion variability increases, the internal friction angle has a greater impact on the reliability index, but when it reaches 30%, the reliability index of different internal friction angle variabilities tends to be the same; as the internal friction angle variability increases, the reliability index under different cohesion variabilities will decrease to a gentle trend, and the reliability index of low variability cohesion changes greatly. Therefore, in the analysis of the stability of this slope, the variability of cohesion has a more critical impact on the overall reliability.

[0036] Analysis of the influence of rainfall parameters on the reliability index:Figure 5 is the analysis diagram of the influence of rainfall parameters on reliability evaluation index. Rainfall infiltration can significantly change the physical and mechanical properties of the slope soil, and the soil parameters themselves have significant variability and uncertainty, so the statistical properties of the safety factor calculated will be different. Observing Figure 5 (a) and Figure 5 (b) the influence of rainfall intensity and rainfall duration on the slope reliability index presents nonlinearity and randomness.

[0037] Analysis of the influence of geometric parameters on reliability index: slope geometry and support parameters are also important factors in the theoretical calculation formula, and their changes will also affect the slope reliability. Figure 6 is the analysis diagram of the influence of geometric parameters on reliability evaluation index. From Figure 6 (a) and Figure 6 (b) analysis, when the slope gradient increases, the reliability index decreases, and the failure probability increases. When the slope gradient exceeds 45°, the failure probability rises sharply from 0.0007 to 0.0410, and the risk of slope disasters such as landslides increases significantly. When the slope height exceeds 40m, the same trend appears. Therefore, when designing and supporting the slope, special attention should be paid to the influence of the slope geometric parameters. For high and steep slopes, adequate support measures should be designed to ensure their stability and safety.

[0038] Analysis of the influence of support parameters on reliability index: anchor rod tension is the tensile force transmitted to the stratum, which is the core mechanical parameter of the anchor rod system. It determines the maximum load it can withstand, and from which the spacing and row spacing of the anchor rod can be calculated. Figure 7 is the analysis diagram of the influence of support parameters on reliability evaluation index. When the anchor rod tension increases, the reliability index of the slope shows an upward trend, and the risk of slope disasters such as landslides decreases. However, too high anchor rod tension will significantly increase the engineering cost. Therefore, the slope support measures need to be considered comprehensively, and the best balance point between economy and safety should be found when arranging the anchor rod.

[0039] Complete the whole process from stability coefficient sample statistics to reliability index quantification, and provide probability basis for dynamic early warning of slope instability risk.

[0040] Perform dynamic risk warning, judge whether each index exceeds the allowable value based on monitoring criteria. Identify the sensitive area of the slope reliability index to parameter changes, especially the inflection point of the index value with parameter changes, determine the critical threshold of the parameter that leads to a sharp decline in reliability, and provide a basis for risk warning.

[0041] Based on the reliability analysis result and the engineering risk acceptance criterion, a multi-level early warning threshold system is established: the reliability index of normal monitoring state is greater than or equal to 3.2, and the failure probability is less than 0.1%, which proves that the slope is in a highly reliable state, and the monitoring is carried out according to the conventional monitoring frequency; the reliability index of risk attention state is 2.7 to less than 3.2, and the failure probability is 0.1% to less than 1%, which proves that the reliability of the slope begins to decline, and the monitoring frequency needs to be strengthened; the reliability index of risk warning state is less than 2.7, and the failure probability is greater than or equal to 1%, which proves that the slope is in a high-risk state, and the emergency monitoring and disposal plan is started. According to the monitoring criterion and the early warning system, the indexes of the embodiment do not exceed the allowable value, and are in a normal monitoring state.

[0042] A dynamic correlation mechanism between monitoring data and reliability evaluation is established, and various parameter data are collected in real time: pore water pressure sensor data, surface displacement GPS monitoring data, real-time rainfall intensity monitoring data, etc. The corresponding reliability index of the monitoring data is calculated in real time to determine whether it exceeds the allowable value of the early warning threshold system.

[0043] If the reliability index exceeds the allowable value, the early warning is triggered, the early warning signal is issued, the critical threshold of the parameter is identified, and the engineering reinforcement measures are implemented. The early warning slope is provided with early warning release principles, that is, one of the following conditions is met: the reliability index is restored to the safety range for 7 consecutive days of monitoring data; the induced factors are completely eliminated; the engineering reinforcement measures are confirmed to be effective after evaluation; The present application can adapt to the slope reliability analysis under different geological conditions and rainfall working conditions, and provides a scientific basis for the reliability design and risk assessment of slope engineering, realizes the dynamic early warning of the slope, and carries out more detailed risk assessment and monitoring to prevent potential safety hazards.

[0044] Although the preferred embodiments of the present application are described above in combination with the drawings, the present application is not limited to the above specific embodiments, and the above specific embodiments are only illustrative and not restrictive, and those skilled in the art can make many specific changes under the inspiration of the present application without departing from the purpose of the application and the scope protected by the claims, which are all within the protection scope of the present application.

Claims

1. A method for assessing the risk of landslide instability of anchored slopes under rainfall conditions, characterized in that, Includes the following steps: S1. Based on the segmented slip surface method, a theoretical calculation formula for the stability coefficient of anchored slopes in multi-layered rock and soil under rainfall infiltration is constructed. S2. Based on the Monte Carlo probability analysis framework, a reliability index is introduced to establish an integrated evaluation model for slope reliability; S3. Based on the survey data, obtain various parameters of the slope, including soil, rainfall, support and geometric parameters; S4. Define the various parameters as random variables and perform large-scale sampling calculations based on the theoretical calculation formulas. S5. Calculate the probability distribution of the statistical stability coefficient to determine the reliability index and failure probability. S6. Establish risk monitoring criteria to determine whether each indicator exceeds the permissible value; If it exceeds the limit, proceed to step S7; Otherwise, proceed to step S4; S7. Implement dynamic risk early warning.

2. The method for assessing the risk of landslide instability of anchored slopes under rainfall conditions according to claim 1, characterized in that, The method for constructing the theoretical calculation formula for the stability coefficient of a multi-layered soil and rock anchored slope under rainfall infiltration, as described in step S1, includes the following steps: S11. The safety factor is calculated using the limit equilibrium method and is defined as the ratio of anti-skid force to sliding force, as shown in the following formula: in, F s The stability coefficient; c i Let be the cohesive force on the bottom surface of the i-th block; For the first i The internal friction angle of the bottom surface of the strip; l i For the first i Length of the base of the strip; N i For the first i Pressure on the bottom surface of the strip, N i =W i cosζ i ; T i For the first i Slide force of the strip, T i =W i sinζ i ; ζ i For the first i The inclination angle of the sliding surface of the strip; W i For the first i The weight of the soil block; F N and F T The anchoring force of the anchor bolt at the th i Radial and normal components of the strip; S12. Rainfall infiltration affects the mechanical state of the slope; calculate the pore water pressure in the unsaturated zone. u and the unit weight change of soil γ The formula is as follows: in, γ w The unit weight of water; h The matrix potential is determined by the Van Genuchten model in the soil moisture characteristic curve; S r It indicates saturation, which is the proportion of water volume to pore volume in the soil. e Indicates the void ratio; γ d The dry unit weight of the soil; γ ω This refers to the wet unit weight of the soil. S13. According to the Mohr-Coulomb criterion and the formula for calculating pore water pressure, calculate the first... i Shear strength of soil in strip blocks R si The formula is as follows: in, μ i It is the first i Pore ​​water pressure of the strip σ i No. i Normal stress of a strip, σ i -μ i "Is the first" i Effective stress of the strip; S14. Using the limit equilibrium method and the Van Genuchten model in the soil moisture characteristic curve, the stability coefficient of the anchored slope under rainfall conditions is calculated, as follows: S15. Divide the potential sliding surface into segments according to soil layer distribution, calculate the shear strength separately, and then sum the total anti-sliding force and sliding force to construct a formula for calculating the stability coefficient of multi-layered soil and rock anchored slopes under rainfall infiltration; assuming the total length of the sliding surface is... L Based on the different soil layers it traverses, the slip surface is divided into m segments; where the first segment is... j The section is located in the first j Within the soil layer, the length is l j , j =1,2,...,m, where the sum of the lengths of all segments equals the total length of the slip surface, satisfying... The formula for calculating the stability coefficient is as follows: ; in, F s The stability coefficient, c ij For the first i The first in the strip j The cohesive force at the bottom surface of the layer; For the first i The first in the strip j The internal friction angle of the bottom surface of the layer; For the first i The first in the strip j The dip angle of the sliding surface of the soil layer; For the first i The first of the blocks j The weight of the soil layer; For the first i The first of the blocks j Pore ​​water pressure in the soil layer; l ij For the first i The first in the strip j The length of the bottom surface of the soil layer.

3. The method for assessing the risk of landslide instability of anchored slopes under rainfall conditions according to claim 1, characterized in that, The method for establishing an integrated assessment model for slope reliability described in step S2 includes the following steps: S21. Define the influencing factors of slope instability as a probability distribution, including a normal distribution or a log-normal distribution. Randomly generate a large number of parameter samples that follow the probability distribution, perform stability analysis on each parameter sample, calculate its stability coefficient, and finally statistically analyze the proportion of failed parameter samples, which is the failure probability of the slope. P f And based on the mean of the stability coefficient μ Fs and standard deviation σ Fs Calculate reliability index β The formula is as follows: Among them, the molecular part This represents the difference between the mean stability coefficient and the failure threshold; the larger the difference, the higher the structural safety. The denominator is... This indicates the variability of the stability coefficient. The smaller the variability, the more concentrated the distribution of the safety factor, and the higher the reliability of the structure. S22. There is a direct mathematical relationship between failure probability and reliability index. Failure probability P f The formula is defined using the cumulative distribution function of the standard normal distribution as follows: ； in, It is the cumulative distribution function of the standard normal distribution; S23. Calculate the stability coefficient using the theoretical calculation formula, calculate the evaluation index based on the stability coefficient, and finally establish an integrated evaluation model for slope reliability.

4. The method for assessing the risk of landslide instability of anchored slopes under rainfall conditions according to claim 1, characterized in that, The method for obtaining the soil, rainfall, support, and geometric parameters of the slope described in step S3 includes the following steps: S31. Collect basic geographic information data of the study area, including the environmental and geomorphological features of the area, topographic features, distribution of ground cracks, hydrological distribution features, historical weather datasets, and slope anchorage information. Collect the data mentioned in the calculation formula described in step S1. S32. Conduct a detailed investigation of the composition of each layer of rock and soil on the slope and collect slope soil parameter data. S33. Differentiate rainfall types based on precipitation amount and collect slope rainfall parameter data; S34. Based on detailed anchoring data, collect slope support parameter data, i.e., anchor bolt design tension; S35. Based on the field survey data, collect slope geometric parameter data, including slope height and slope gradient.

5. The method for assessing the risk of landslide instability of anchored slopes under rainfall conditions according to claim 4, characterized in that: The soil parameter data described in S32 includes soil cohesion and internal friction angle.

6. The method for assessing the risk of landslide instability of anchored slopes under rainfall conditions according to claim 4, characterized in that: The rainfall parameters described in S32 include rainfall intensity and rainfall duration.

7. The method for assessing the risk of landslide instability of anchored slopes under rainfall conditions according to claim 1, characterized in that: Step S4, which involves large-scale sampling calculations, includes the following steps: S41. Based on geological exploration data, indoor test results, and engineering experience, determine the probability distribution model of the various parameters; S42. For soil parameters, the cohesion and internal friction angle of each soil layer are defined as random variables following a normal or log-normal distribution; rainfall parameters are determined based on historical rainfall statistics, with rainfall duration and intensity defined as random variables; for support parameters, the design tension value of the anchor bolt is used as a random variable, taking into account construction errors and prestress losses, and follows a normal distribution; for geometric parameters, the slope height and slope are used as random variables, taking into account measurement errors and construction variability, and follow a normal distribution. S43. Using Monte Carlo sampling, determine the total number of samplings, and round it to a precision of 10. 4 ~10 6 Next, N samples are generated for each random variable to ensure that the range of values ​​of each random variable is fully covered; by random permutation, statistical independence is maintained among the random variable samples, forming N complete parameter sample combinations; S44. Calculate the stability coefficient in batches. Substitute each set of parameter samples into the theoretical calculation formula and perform iterative calculations to obtain the stability coefficient value. During the calculation process, pore water pressure is calculated in real time to reflect the rainfall infiltration effect, and the anchoring force is included in the corresponding block according to the actual location of action.

8. The method for assessing the risk of landslide instability of anchored slopes under rainfall conditions according to claim 1, characterized in that: The specific steps for solving the reliability index and failure probability in step S5 are as follows: S51. Statistical analysis of stability coefficient samples obtained from Monte Carlo simulation; S52. Draw a histogram of the probability distribution of the stability coefficient and observe its distribution pattern; S53. Based on the theoretical calculation formula and reliability integrated evaluation model, solve the slope failure probability and reliability index according to the probability distribution; count the total number of unstable samples, calculate the slope failure probability and the estimated value of the failure probability point; and calculate the reliability index based on the first second moment method. S54. Plot the change curves of failure probability and reliability index under the influence of each parameter, visualize the results, conduct reliability assessment, and lay the groundwork for subsequent risk warning. S55. Complete the entire process from stability coefficient sample statistics to reliability index quantification, providing probabilistic basis for dynamic early warning of slope instability risk.

9. The method for assessing the risk of landslide instability of anchored slopes under rainfall conditions according to claim 1, characterized in that: Step S6, which involves determining whether each indicator exceeds the allowable value, is as follows: S61. Identify the sensitive areas of various slope reliability indicators to parameter changes, especially the inflection points of indicator values ​​as parameters change, determine the critical threshold of parameters that lead to a sharp decline in reliability, and provide a basis for risk warning. S62. Based on the reliability analysis results and engineering risk acceptance criteria, the following early warning threshold system is established; S621. Normal monitoring status: Reliability index ≥ 3.2, failure probability < 0.1%, proving that the slope is in a highly reliable state and should be monitored at the regular monitoring frequency; S622. Risk concern status: 2.7≤reliability index<3.2, 0.1%≤failure probability<1%, indicating that the reliability of the slope has begun to decline and the monitoring frequency needs to be increased; S623. Risk Alert Status: Reliability index < 2.7, failure probability ≥ 1%, indicating that the slope is in a high-risk state, and the emergency monitoring and response plan should be activated. S63. Establish a dynamic correlation mechanism between monitoring data and reliability assessment, and collect data of various parameters in real time: pore water pressure sensor data, surface displacement GPS monitoring data, and real-time rainfall intensity monitoring data. S64. Calculate the reliability index corresponding to the monitoring data in real time to determine whether it exceeds the allowable value of the early warning threshold system.

10. The method for assessing the risk of landslide instability of anchored slopes under rainfall conditions according to claim 1, characterized in that: The steps for dynamic risk warning described in step S7 are as follows: S71. If a reliability index exceeds the allowable value, an early warning will be triggered, an early warning signal will be issued, the critical threshold of the parameter will be identified, and engineering reinforcement measures will be implemented. S72. The warning for a slope that triggered the warning shall be lifted if one of the following conditions is met: a) Monitoring data for 7 consecutive days showed that the reliability index had returned to a safe range; b. The triggering factors have been completely eliminated; c. The engineering reinforcement measures have been assessed and confirmed to be effective.

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