Rock slope multi-mode instability probability analysis and risk evaluation method based on borehole constraint condition random field
By constructing a borehole-constrained random field and combining it with numerical simulation technology, the problems of limited borehole quantity and time-varying environmental effects in the stability analysis of rock slopes were solved, and the accuracy of multi-mode instability probability analysis and risk assessment was improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NORTHEASTERN UNIV CHINA
- Filing Date
- 2026-02-25
- Publication Date
- 2026-06-02
AI Technical Summary
In existing rock slope stability analysis, the limited number of boreholes makes it difficult to fully reflect the spatial distribution characteristics of rock mass mechanical parameters. Existing random field models fail to make full use of borehole measured data for constraints and are difficult to identify multiple potential instability modes and consider environmental time-varying effects, resulting in insufficient reliability of analysis results.
By constructing a borehole-constrained random field and combining it with numerical simulation, a fine characterization of the spatial heterogeneity of rock mass mechanical parameters is achieved, and multiple potential instability modes are identified. Data correction is performed using fast Fourier transform filtering and cokriging interpolation techniques to generate a random field model that satisfies the borehole measured data.
It improves the accuracy and engineering reliability of rock slope stability analysis, can identify the probability and risk of multiple potential instability modes, provides a scientific basis for risk assessment, and is applicable to risk management of complex rock slopes throughout their entire life cycle.
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Figure CN121725173B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of geotechnical engineering and engineering geology, specifically relating to a method for multi-mode instability probability analysis and risk assessment of rock slopes based on borehole-constrained random fields. Background Technology
[0002] Rock slopes are widely used in mining, transportation engineering, water conservancy projects, and urban construction, and their stability is directly related to project safety and the safety of people's lives and property. In the stability analysis of rock slopes, rock mechanics parameters (such as cohesion, internal friction angle, and elastic modulus) are key factors that determine the deformation and instability behavior of the slope.
[0003] In current engineering practice, rock mass mechanics parameters are typically obtained through borehole sampling and laboratory testing. However, due to limitations in exploration conditions and cost, the number of boreholes is limited, making it difficult to fully reflect the true spatial distribution characteristics of rock mass parameters. To simplify analysis, existing techniques often employ homogeneous parameters or zonal assignment methods to establish slope calculation models, neglecting the spatial heterogeneity of rock mass mechanics parameters. This leads to significant discrepancies between the analysis results and the actual engineering response. For example, in the case study of the Ulagen lead-zinc mine slope, the homogeneous model calculated a safety factor of 1.26, indicating slope stability, but this deviates significantly from the actual situation.
[0004] To overcome the aforementioned problems, random field theory has been introduced into the stability analysis of rock slopes to describe the spatial randomness and correlation of rock mass mechanical parameters. However, most existing random field modeling methods are based on unconditional random fields, which only satisfy overall statistical characteristics and spatial correlation structures, failing to fully utilize borehole measurement data to constrain the random field. This results in inconsistencies between the parameter values at the borehole location and the measured values, thus weakening the engineering reliability of the model. For example, the calculation results of a fully random field model (without borehole constraints) show that the safety factor distribution range (0.979 to 1.24) has large dispersion, and the instability probability (FoS < 1.00) reaches 6%, but its results are unreliable due to the lack of measured constraints.
[0005] Furthermore, most existing slope stability analysis methods typically use a single safety factor or a single instability mode as evaluation indicators, which fails to reflect the multiple potential instability modes that may occur under heterogeneous parameter space conditions, and also cannot systematically characterize the probability of occurrence of different instability modes. At the same time, traditional methods cannot consider the deterioration effect of environmental time-varying effects (such as freeze-thaw cycles and water softening) on rock mass strength, and cannot achieve probability-based spatial risk zoning, making it difficult to meet the actual needs of full life-cycle risk assessment and accurate engineering decision-making for complex rock slopes.
[0006] Therefore, how to construct a conditional random field model that satisfies the strict consistency of borehole measured data under the condition of limited borehole exploration data, and on this basis, comprehensively consider the environmental degradation effect to realize multi-mode instability probability analysis and spatial risk zoning of rock slopes, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention
[0007] To address the shortcomings of existing technologies in rock slope stability analysis, which generally employ homogeneous or zoned parameters and fail to reflect the spatial heterogeneity of rock mass mechanical parameters, and the limitations of existing random field methods in fully utilizing borehole measurement data for constraints, identifying multiple potential instability modes, and quantitatively evaluating instability probabilities, this invention provides a multi-mode instability probability analysis and risk assessment method for rock slopes based on borehole-constrained conditional random fields. This invention constructs a conditional random field that strictly matches measured data under limited borehole exploration data conditions and combines it with numerical simulation to achieve a reasonable expression of the spatial heterogeneity of rock mass mechanical parameters. This enables the identification of multiple instability modes that may occur in rock slopes under different parameter spatial distributions, and allows for the quantitative calculation of the probability of occurrence of each instability mode, improving the accuracy and engineering applicability of slope stability analysis and risk assessment.
[0008] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows:
[0009] A multi-mode instability probability analysis and risk assessment method for rock slopes based on borehole-constrained random fields includes:
[0010] S1: Delineate the research area of the slope, obtain borehole exploration data within the area, preprocess and statistically analyze the rock mechanics parameters in the borehole exploration data, and establish a statistical model of the rock mechanics parameters.
[0011] S2: Based on the statistical model of the rock mass mechanics parameters, an unconditional random field of rock mass mechanics parameters that satisfies the statistical characteristics obtained in S1 is constructed using the fast Fourier transform filtering method.
[0012] S3: The unconditional random field is modified using borehole survey data from the exploration lines at locations where abnormal displacements occur in the slope monitoring within the study area, and a borehole-constrained random field is constructed.
[0013] S4: Based on the borehole constraint conditional random field, generate multiple sets of conditional random field samples for different working conditions of the slope, and calculate the slope safety factor under each sample;
[0014] S5: Perform instability mode judgment and probability statistics on the conditional random field samples for each working condition, and obtain the risk level of each instability mode based on the safety factor.
[0015] Furthermore, the specific process of step S1 is as follows:
[0016] S1.1: Determine the study area of the slope to be analyzed based on the actual boundary, topographic features, and potential instability impact range of the slope project to be analyzed;
[0017] S1.2: Obtain borehole exploration data within the study area. The borehole exploration data includes the spatial location and depth of the boreholes, as well as measured values of several rock mass mechanical parameters, including cohesion, internal friction angle, bulk modulus, tensile strength, shear modulus, and rock unit weight. The measured values of the rock mass mechanical parameters are obtained by dividing the boreholes into segments at fixed depths and measuring multiple rock mass mechanical parameters in each segment.
[0018] S1.3: Preprocess the acquired rock mass mechanical parameters, including the identification and removal of abnormal data, data consistency verification and normalization.
[0019] S1.4: Perform statistical analysis on the pre-processed rock mass mechanical parameters, calculate the statistical characteristics of the rock mass mechanical parameters of each segment of each borehole, including the mean, variance, coefficient of variation and probability distribution form, thereby establishing a statistical model of the rock mass mechanical parameters.
[0020] Furthermore, in step S2, the construction process of the unconditional random field includes:
[0021] S2.1: Within the study area of the rock slope, a computational grid is established based on the slope geometry, stratigraphic structure characteristics, and numerical calculation accuracy requirements, so that the computational grid can cover the entire study area and reflect the main geological features;
[0022] S2.2: Generate a white noise field with a mean of 0 and a variance of 1 within the spatial domain corresponding to the computational grid. The power spectral density of the white noise field is calculated based on the variance of the rock mass mechanical parameters of each segment. :
[0023]
[0024] in, The coordinates of the white noise field are... Let Variance be the variance of the rock mass mechanical parameters for each segment of all boreholes. This represents the distance between each segment;
[0025] The frequency domain power spectrum is then: ;
[0026] Inverse transform of the frequency domain power spectrum yields a spatial domain random field: .
[0027] Furthermore, in step S3, the construction process of the borehole constraint random field includes:
[0028] S3.1: Obtain borehole survey data from the exploration lines at locations where abnormal displacements occur in the slope monitoring within the study area as a constraint condition;
[0029] S3.2: Use the unconditional random field constructed in S2 as the initial background field. Borehole survey data at the exploration line serves as the observation field. Then conditional random fields This can be expressed as:
[0030]
[0031] in, This is a conditional correction term used to adjust for the bias of the random field at the observation points; this correction term can be achieved through co-Kriging interpolation.
[0032]
[0033] in, The weights are represented by the variance matrix, and the conditional expected value is ensured to be satisfied. ; Indicates the borehole segment index. This represents the total number of segments of all boreholes along the exploration line.
[0034] Furthermore, step S4 specifically includes:
[0035] S4.1: Based on the factors affecting slope stability (such as blasting vibration, slope angle, precipitation, freeze-thaw cycles, earthquakes, etc.) and the calculated constitutive equations, different engineering conditions are classified for slopes;
[0036] S4.2: Using the borehole constraint conditional random field generated in S3, multiple sets of conditional random field samples are generated for each engineering working condition. Each conditional random field sample corresponds to a set of spatially distributed rock mechanics parameters.
[0037] S4.3: The stability of the generated conditional random field samples is calculated using the strength reduction method to obtain the safety factor of the slope under each conditional random field sample for each engineering condition.
[0038] Furthermore, step S5 specifically includes:
[0039] S5.1: Based on the safety factor of each conditional random field sample under each engineering condition, and in accordance with the provisions on landslide risk level and its safety factor, the risk level with the largest proportion of all conditional random field samples under each condition shall be taken as the risk level under that condition.
[0040] S5.2: Based on the scale of failure, characteristics of the sliding surface, and failure mechanism, statistics are compiled on the failure modes, failure locations, and probabilities of slopes under various engineering conditions.
[0041] Furthermore, the scale of the damage includes overall instability or local instability; the characteristics of the sliding surface include shallow sliding, deep sliding, or combined sliding; and the damage mechanism includes arc-shaped sliding or multi-arc sliding.
[0042] The beneficial effects of this invention are:
[0043] (1) This invention significantly improves the accuracy and engineering reliability of slope stability analysis: This invention achieves a fine quantitative characterization of the spatial heterogeneity of rock mass mechanical parameters by constructing a borehole constrained conditional random field (CRF). The random field generated by this method introduces local rock mass mechanical parameters at the borehole location, thereby greatly improving the realism of the numerical model and the reliability of the analysis results, effectively overcoming the defect of insufficient reliability of unconditional random fields due to the lack of measured constraints.
[0044] (2) This invention achieves accurate identification and probabilistic evaluation of multi-mode instability risks of complex slopes: Traditional methods can usually only provide a single safety factor or instability mode. This invention, through a large number of multi-sample numerical simulations, can systematically identify multiple potential instability modes that may occur on slopes under parameter space variation conditions, and quantitatively calculate the probability of occurrence of each mode. It can comprehensively grasp the various risks that may exist on slopes, rather than focusing only on the most dangerous single situation, providing richer scientific basis for risk assessment and prevention and control decisions. Attached Figure Description
[0045] Figure 1 This is a map of the study area in an embodiment of the present invention.
[0046] Figure 2 This is a model diagram of the mining area in an embodiment of the present invention; where (a) shows the lithological distribution and (b) shows the boundary before and after slope cutting.
[0047] Figure 3 This is a three-dimensional mesh model diagram of the mining area according to an embodiment of the present invention.
[0048] Figure 4 The diagram shows an unconditional random field model according to an embodiment of the present invention; where (a) is a cohesive random field and (b) is an internal friction angle random field.
[0049] Figure 5 The diagram shows the borehole constraint position and the random field diagram after constraint in an embodiment of the present invention; wherein (a) is the borehole constraint position diagram, (b) is the random field of cohesion after constraint, and (c) is the random field of internal friction angle after constraint.
[0050] Figure 6The graph shows the functional relationship between parameters such as peak shear strength and peak strain and the number of freeze-thaw cycles N in an embodiment of the present invention; where (a) is peak strength, (b) is peak strain, (c) is residual strength, (d) is characteristic parameter a, (e) is characteristic parameter b, and (f) is characteristic parameter 𝛾.
[0051] Figure 7 This is a safety factor distribution diagram for one of the operating conditions in an embodiment of the present invention.
[0052] Figure 8 The diagram shows the failure mode of the working condition in the embodiment of the present invention; wherein (a) is shallow multi-step circular arc sliding, (b) is shallow large-range circular arc sliding, (c) is shallow overall circular arc sliding, (d) is composite overall multi-circular arc sliding, (e) is composite large-range multi-circular arc sliding, and (f) is shallow overall multi-circular arc sliding.
[0053] Figure 9 This is a safety factor distribution diagram for working condition two in an embodiment of the present invention.
[0054] Figure 10 The diagram shows the failure modes under condition two in the embodiment of the present invention; where (a) is shallow overall circular arc sliding, (b) is shallow large-scale circular arc sliding, (c) is shallow multi-step circular arc sliding, (d) is composite large-scale multi-circular arc sliding, and (e) is shallow continuous multi-step circular arc sliding.
[0055] Figure 11 This is a safety factor distribution diagram for three working conditions in an embodiment of the present invention.
[0056] Figure 12 The diagram shows the three failure modes under the working conditions of the present invention; where (a) is shallow large-scale circular arc sliding, (b) is shallow overall circular arc sliding, (c) is composite overall multi-circular arc sliding, and (d) is composite large-scale multi-circular arc sliding.
[0057] Figure 13 This is a safety factor distribution diagram for operating condition four in an embodiment of the present invention.
[0058] Figure 14 The diagram shows the failure modes under condition four in the embodiment of the present invention; where (a) is deep overall circular arc sliding, (b) is shallow large-scale circular arc sliding, (c) is shallow multi-step circular arc sliding, and (d) is shallow continuous multi-step circular arc sliding.
[0059] Figure 15 This is a comparison chart of the safety factor distribution under various working conditions in embodiments of the present invention. Detailed Implementation
[0060] The specific embodiments of the present invention are described in detail below with reference to the technical solutions and accompanying drawings.
[0061] The Wulagen lead-zinc mine is characterized by sharply dissected terrain, crisscrossing gullies, well-exposed bedrock, and extremely sparse vegetation, with only scattered drought-resistant plants such as camel thorn. The mining area experiences scarce rainfall, intense evaporation, and extreme temperature variations. These unique cold and arid climatic conditions subject the mine slopes to intense physical weathering, particularly the freeze-thaw cycle effect, which significantly degrades the mechanical properties of the rock mass. This application uses the Wulagen lead-zinc mine as an example to detail a multi-mode instability analysis method for rock slopes under random field constraints in open-pit metal mine boreholes.
[0062] S1: Delineate the study area of the slope, obtain borehole exploration data within this area, perform preprocessing and statistical analysis, and establish a statistical model for rock mass mechanical parameters; the specific process is as follows:
[0063] S1.1: Since construction began in 2012, the open-pit mine at the Wulagen lead-zinc mine has expanded its mining area to cover exploration lines 47 to 72, and the pit's shape has gradually changed from a hillside open-pit to a sunken open-pit. As of the end of 2023, the lowest mining elevation in the pit had decreased to 1969m, and the highest was 2344m. Among the surrounding slopes of the mine, the highest elevations of the south, north, east, and west slopes are 2284m, 2209m, 2344m, and 2314m, respectively, resulting in maximum slope height differences of 315m, 240m, 375m, and 345m, respectively.
[0064] Mining practice shows that the overall stability of the north slope's anti-dip slope is relatively good, with no large-scale instability incidents. However, with the continuous increase in the free face from mining, the south slope's bedding slope has experienced several wedge-shaped landslides, planar sliding, and rock collapses of varying sizes in recent years, triggered by unfavorable lithological combinations, tectonic fissures, blasting vibrations, atmospheric precipitation, and snowmelt. These instabilities are mostly concentrated at the lithological interface between mudstone and sandstone. Figure 1 As shown, areas S1 and S4 of the southern slope have been designated as key landslide monitoring and prevention zones. In particular, the potential unstable body in area S1, if it were to slide, could very likely cut off the mine's critical transportation route, posing a serious threat to the mine's normal production and operation. Therefore, this embodiment selects the slope section at elevations of 2134m to 2209m within area S1, near exploration line 27, as the specific study area. The original designed step slope angle in this area was approximately 63°, and the plan is to reduce it to 55° through slope reduction engineering to significantly improve the overall stability of the slope. The typical engineering geological profile used in the study is shown below. Figure 2As shown in (a), the stratigraphic structure revealed in this profile, from top to bottom, consists of feldspathic sandstone, gravelly sandstone, ore body, conglomerate, and slightly weathered mudstone and moderately weathered gypsum layers. This profile clearly shows significant vertical variations in lithology, well-developed structural planes, and the presence of localized weak interlayers. These geological conditions have a significant adverse impact on slope stability. It is particularly noteworthy that the mechanical strength of the slightly weathered mudstone and gypsum layers decreases sharply under water immersion and repeated freeze-thaw cycles, often constituting potential sliding surfaces that control slope stability.
[0065] S1.2: Using a Matrice 300 RTK drone equipped with a PSDK 102S oblique camera, oblique photogrammetry was performed to collect surface building parameter data, realistically reflecting the appearance, location, height and other attributes of the features. Combined with the PC application software DJI Terra, surface modeling of the Ulagan lead-zinc mine was achieved.
[0066] S1.3: Obtain borehole exploration data within the study area. The borehole exploration data includes the spatial location of the borehole, the borehole depth, and several measured values of rock mass mechanical parameters, including cohesion, internal friction angle, bulk modulus, tensile strength, shear modulus, and rock unit weight. The measured values of the rock mass mechanical parameters are obtained by dividing the borehole into segments of 10m each and measuring several rock mass mechanical parameters in each segment.
[0067] S1.4: Preprocess the acquired rock mass mechanics parameters, including the identification and removal of abnormal data, data consistency verification, and normalization.
[0068] S1.5: Statistical analysis of the preprocessed rock mass mechanics parameters is performed using Python to calculate the statistical characteristics of the rock mass mechanics parameters for each segment of each borehole. The statistical characteristics include the mean, variance, coefficient of variation, and probability distribution form, thereby establishing a statistical model of the rock mass mechanics parameters.
[0069] S2: An unconditional random field of rock mass mechanical parameters satisfying the statistical characteristics obtained in S1 is constructed using the Fast Fourier Transform filtering method; the specific process is as follows:
[0070] S2.1: Within the study area of the rock slope, a computational grid is established based on the slope's geometric morphology, stratigraphic structure characteristics, and numerical calculation accuracy requirements. This computational grid should cover the entire study area and reflect the main geological features. Specifically:
[0071] In Rhino software, a cube model is built according to the dimensions of the mining area. Boolean operations are then performed on the cube model using the point cloud data obtained in S1.2 to cut it, resulting in an initial model with surface morphology. Based on the exploration lines, geological longitudinal profiles, and geological cross profiles in the mining area exploration report, the boundary lines of faults, surrounding rocks, interbedded rocks, and ore groups are extracted to determine the spatial orientation and location of the initial model. In Rhino software, the cross-sectional lines of a specific surrounding rock or interbedded rock are connected, and the "lofting" function is used to construct solid surfaces for cutting. Following the basic principle of "from large to small, from outside to inside," Boolean operations are used to cut the initial model sequentially, completing the Rhino 3D refined calculation model of the mining area, as shown in the attached figure. Figure 2 As shown in (a), the cut lithological blocks are combined into a non-flow model. The mesh is then created using the meshing function with a side length of 2, resulting in a mesh size of 95818. The 3D mesh model is shown below. Figure 3 As shown.
[0072] S2.2: Import the 3D mesh model constructed in S2.1 into FLAC3D, and generate a white noise field with a mean of 0 and a variance of 1 in the spatial domain corresponding to the computational mesh. The power spectral density of each grid cell was calculated using Python computation code. :
[0073]
[0074] in, The coordinates of the white noise field are... Let Variance be the variance of the rock mass mechanical parameters for each segment. This represents the distance between each segment;
[0075] The frequency domain power spectrum is then: ;
[0076] Inverse transform of the frequency domain power spectrum yields a spatial domain random field: .
[0077] The generated unconditional random field, such as Figure 4 As shown.
[0078] S3: Error correction is applied to the unconditional random field constructed in S2, and a borehole-constrained random field is constructed to ensure that the rock mechanics parameter values of the borehole-constrained random field at each borehole location are consistent with the measured parameters at the corresponding depth; the specific process includes:
[0079] S3.1: Using the cohesion and internal friction angle of the borehole exploration data of SZK27-1, SZK27-2, and SZK27-3 along the 27 exploration lines within the study area as constraints, and dividing the area into 10m segments, the borehole locations are as follows: Figure 5 As shown in (a), the data is shown in Table 1.
[0080] Table 1. Mean values of cohesion and internal friction angle of boreholes in exploration line 27
[0081]
[0082] S3.2: Use the unconditional random field constructed in S2 as the initial background field. Borehole survey data at the exploration line serves as the observation field. Then conditional random fields This can be expressed as:
[0083]
[0084] in, This is a conditional correction term used to adjust for the bias of the random field at the observation points; this correction term can be achieved through co-Kriging interpolation.
[0085]
[0086] in, The weights are represented by the variance matrix, and the conditional expected value satisfies the following conditions. ; Indicates the borehole segment index. This represents the total number of segments of all boreholes along the exploration line.
[0087] S3.3: Calculate the observation field using the method in S2.2. The power spectral density is calculated, and the correction term is calculated using Python according to the method in S3.2. The background field is then corrected using the correction term to obtain the constrained random field, such as... Figure 5 As shown in (b) and (c).
[0088] S4: Multi-sample numerical simulation and safety factor calculation, including:
[0089] S4.1: Based on freeze-thaw cycles and slope angles, the constitutive equations are used to classify slopes into four engineering conditions:
[0090] Working condition 1: Before slope cutting, the borehole constraint random field is applied using the Mohr-Coulomb constitutive model;
[0091] Working condition 2: Before slope cutting, a random field of borehole constraint conditions is used, which takes into account the effects of water immersion and freeze-thaw.
[0092] Working condition 3: After slope cutting, a random field with borehole constraint conditions using the Mohr-Coulomb constitutive model;
[0093] Working condition 4: After slope cutting, a borehole constraint condition random field is adopted using a strain softening constitutive model that considers the effects of water immersion and freeze-thaw.
[0094] S4.2: Establishing Parameters for the Softening Constitutive Model: The strata on the slopes of the Wulagen lead-zinc mine all contain clay minerals. Under freeze-thaw cycles and water immersion, the rock's mechanical properties will significantly deteriorate. Therefore, core samples were taken from the exploration line. Based on triaxial shear tests of saturated (7.4%) freeze-thawed feldspar sandstone, using data from 0, 5, 10, 15, and 20 freeze-thaw cycles under a confining pressure of 1.5 MPa as examples, the functional relationships between parameters such as peak shear strength and peak strain and the number of freeze-thaw cycles N were established. Figure 6 As shown.
[0095] The peak strength parameters, residual strength parameters, and calibrated softening law parameters obtained through experiments are systematically input into the constitutive model of FLAC3D to complete the correction of the Mohr-Coulomb constitutive model and obtain a strain softening constitutive model that considers the effects of water immersion and freeze-thaw.
[0096] S4.3: Using the borehole constraint conditional random field method generated in S3, generate no less than 200 conditional random field samples for each of the four engineering conditions to ensure the reliability of the statistical analysis results.
[0097] S4.4: The strength reduction method was used to perform stability calculations on 200 conditional random field samples for each of the four engineering conditions, and the safety factor FoS of the slope under each conditional random field sample for the four engineering conditions was obtained, and the distribution is as follows: Figure 7 , Figure 9 , Figure 11 , Figure 13 As shown, the safety factors for four engineering conditions are compared. Figure 15 As shown.
[0098] S5: Perform multi-mode instability identification and reliability-based risk probability assessment on the conditional random field samples for each operating condition, as detailed below:
[0099] S5.1: Based on the provisions of the "Technical Specification for Safety Monitoring of High and Steep Slopes in Metal and Non-metal Mines" in Table 2 regarding landslide risk levels and their safety factors, and combined with the safety factor FoS of 800 conditional random field samples for four engineering conditions, the risk levels of the four engineering conditions are statistically analyzed, as shown in Table 3.
[0100] Table 2 Landslide Risk Classification as Stipulated in the "Technical Specification for Safety Monitoring of High and Steep Slopes in Metal and Non-metal Mines"
[0101]
[0102] Table 3 Evaluation Results of Landslide Risk Level Index for Each Working Condition
[0103]
[0104] S5.2: The main criteria for classifying damage modes include:
[0105] Damage scale: Overall instability (sliding surface penetrates the slope) and local instability (multi-step, large-scale or continuous multi-step);
[0106] Sliding surface characteristics: shallow sliding, deep sliding, and combined sliding;
[0107] Damage mechanisms: circular arc sliding, multi-circular arc sliding, etc.
[0108] Table 4 shows the slope failure modes and their probabilities under four engineering conditions, categorized according to their failure modes. The failure modes for each condition are as follows: Figure 8 , Figure 10 , Figure 12 , Figure 14 As shown.
[0109] Table 4. Failure Modes and Probabilities of Slopes under Various Working Conditions
[0110]
[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.
Claims
1. A rock slope multi-mode instability probability analysis and risk evaluation method based on borehole constraint condition random field, characterized in that, include: S1: Delineate the research area of the slope, obtain borehole exploration data within the area, preprocess and statistically analyze the rock mechanics parameters in the borehole exploration data, and establish a statistical model of the rock mechanics parameters. S2: Based on the statistical model of the rock mass mechanics parameters, an unconditional random field of rock mass mechanics parameters that satisfies the statistical characteristics obtained in S1 is constructed using the fast Fourier transform filtering method. S3: The unconditional random field is modified using borehole survey data from the exploration lines at locations where abnormal displacements occur in the slope monitoring within the study area, and a borehole-constrained random field is constructed. S4: Based on the borehole constraint conditional random field, generate multiple sets of conditional random field samples for different working conditions of the slope, and calculate the slope safety factor for each sample; specifically including: S4.1: Based on freeze-thaw cycles, slope angles, and the calculated constitutive equations, slopes are classified into different engineering conditions: Working condition 1: Before slope cutting, the borehole constraint random field is applied using the Mohr-Coulomb constitutive model; Working condition 2: Before slope cutting, a random field of borehole constraint conditions is used, which takes into account the effects of water immersion and freeze-thaw. Working condition 3: After slope cutting, a random field with borehole constraint conditions using the Mohr-Coulomb constitutive model; Working condition 4: After slope cutting, a borehole constraint condition random field is used, which takes into account the effects of water immersion and freeze-thaw. S4.2: Establish parameters for the softening constitutive model: Take rock cores at the exploration line and, based on the triaxial shear test of saturated freeze-thawed feldspathic sandstone, take the test data of saturated feldspathic sandstone under a confining pressure of 1.5 MPa and 0, 5, 10, 15 and 20 freeze-thaw cycles as examples, establish the functional relationship between parameters including peak shear strength and peak strain and the number of freeze-thaw cycles. The peak strength parameters, residual strength parameters, and calibrated softening law parameters obtained through the above experiments are systematically input into the constitutive model of FLAC3D to complete the correction of the Mohr-Coulomb constitutive model and obtain a strain softening constitutive model that considers the effects of water immersion and freeze-thaw. S4.3: Using the borehole constraint conditional random field generated in S3, generate no less than 200 conditional random field samples for each of the four engineering conditions. Each conditional random field sample corresponds to a set of spatially distributed rock mechanics parameters. S4.4: The strength reduction method is used to perform stability calculations on 200 conditional random field samples under each of the four engineering conditions to obtain the safety factor of the slope under each conditional random field sample under the four engineering conditions. S5: Perform instability mode judgment and probability statistics on the conditional random field samples for each working condition, and obtain the risk level of each instability mode based on the safety factor.
2. The method for multi-mode instability probability analysis and risk assessment of rock slopes based on borehole-constrained random fields according to claim 1, characterized in that, The specific process of step S1 is as follows: S1.1: Determine the study area of the slope to be analyzed based on the actual boundary, topographic features, and potential instability impact range of the slope project to be analyzed; S1.2: Obtain borehole exploration data within the study area. The borehole exploration data includes the spatial location and depth of the boreholes, as well as measured values of several rock mass mechanical parameters, including cohesion, internal friction angle, bulk modulus, tensile strength, shear modulus, and rock unit weight. The measured values of the rock mass mechanical parameters are obtained by dividing the boreholes into segments at fixed depths and measuring multiple rock mass mechanical parameters in each segment. S1.3: Preprocess the acquired rock mass mechanical parameters, including the identification and removal of abnormal data, data consistency verification and normalization. S1.4: Perform statistical analysis on the pre-processed rock mass mechanical parameters, calculate the statistical characteristics of the rock mass mechanical parameters of each segment of each borehole, including the mean, variance, coefficient of variation and probability distribution form, thereby establishing a statistical model of the rock mass mechanical parameters.
3. The method for multi-mode instability probability analysis and risk assessment of rock slopes based on borehole-constrained random fields according to claim 2, characterized in that, In step S2, the construction process of the unconditional random field includes: S2.1: Within the study area of the rock slope, a computational grid is established based on the slope geometry, stratigraphic structure characteristics, and numerical calculation accuracy requirements, so that the computational grid covers the entire study area and reflects the geological characteristics; S2.2: Generate a white noise field with a mean of 0 and a variance of 1 within the spatial domain corresponding to the computational grid. The power spectral density of the white noise field is calculated based on the variance of the rock mass mechanical parameters of each segment. : in, The coordinates of the white noise field are... Let Variance be the variance of the rock mass mechanical parameters for each segment of all boreholes. This represents the distance between each segment; The frequency domain power spectrum is then: ; Inverse transform of the frequency domain power spectrum yields a spatial domain random field: .
4. The method for multi-mode instability probability analysis and risk assessment of rock slopes based on borehole-constrained random fields according to claim 3, characterized in that, In step S3, the construction process of the borehole constraint random field includes: S3.1: Obtain borehole survey data from the exploration lines at locations where abnormal displacements occur in the slope monitoring within the study area as a constraint condition; S3.2: Use the unconditional random field constructed in S2 as the initial background field. Borehole survey data at the exploration line serves as the observation field. Then conditional random fields Expressed as: in, This is a conditional correction term used to adjust for the bias of the random field at the observation points; this correction term is implemented through cokriging interpolation. in, The weights are represented by the variance matrix, and the conditional expected value is ensured to be satisfied. ; Indicates the borehole segment index. This represents the total number of segments of all boreholes along the exploration line.
5. The method for multi-mode instability probability analysis and risk assessment of rock slopes based on borehole-constrained random fields according to claim 4, characterized in that, Step S5 specifically includes: S5.1: Based on the safety factor of each conditional random field sample under each engineering condition, and in accordance with the provisions on landslide risk level and its safety factor, the risk level with the largest proportion of all conditional random field samples under each condition shall be taken as the risk level under that condition. S5.2: Based on the scale of failure, characteristics of the sliding surface, and failure mechanism, statistics are compiled on the failure modes, failure locations, and probabilities of slopes under various engineering conditions.
6. The method for multi-mode instability probability analysis and risk assessment of rock slopes based on borehole-constrained random fields according to claim 5, characterized in that, The scale of the damage includes overall instability or local instability; the characteristics of the sliding surface include shallow sliding, deep sliding, or combined sliding; the damage mechanism includes arc-shaped sliding or multi-arc sliding.