A method for determining a maximum weakening coefficient of a bank slope rock-soil body strength parameter

Abstract

The application discloses a kind of bank side slope rock-soil body strength parameter maximum weakening coefficient determination method, it is related to geotechnical engineering technical field, including, with structure main control area set as the main control factor of local mechanism model, the boundary condition and load condition of definition geological environment condition, set key response point observation position, construct local mechanism model with physical constraint;Multiple source sensing equipment is arranged in key response point observation position and monitoring data is collected, and difference optimization target is constructed with local mechanism model, difference optimization target is inverted, and the maximum weakening coefficient of target area is obtained;Maximum weakening coefficient is input, and numerical verification is carried out by local mechanism model, based on verification consistency, the maximum weakening coefficient of target area is extracted.The application can accurately depict the strength attenuation degree of bank side slope rock-soil body under limit working condition under the premise of keeping the consistency of geology and mechanics boundary, realizes the accurate determination and risk assessment of key unstable position.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering technology, and in particular to a method for determining the maximum weakening coefficient of the strength parameters of rock and soil on riverbank slopes. Background Technology

[0002] With the continuous advancement of water conservancy projects, port terminals, and coastal urbanization, the stability of riverbank slopes has become an important research direction in the field of geological engineering. The strength parameters of the soil and rock mass of riverbank slopes are important basic data for slope stability analysis and design. Their accurate acquisition is crucial for assessing the risk of instability of riverbank slopes under long-term operation and extreme load conditions. Existing methods usually rely on existing engineering experience and standard testing procedures, which can provide basic data for slope stability under different geological conditions and are widely used in various geological environments.

[0003] However, existing methods usually rely on fixed boundary conditions and preset load conditions. On the one hand, traditional methods are difficult to comprehensively consider complex geological features, load conditions and hydrological changes. On the other hand, they cannot be dynamically adjusted and optimized during the attenuation of strength parameters. Therefore, the strength parameters obtained based on conventional methods may not accurately reflect the actual strength attenuation under extreme working conditions, affecting the accurate determination of the stability of the bank slope. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for determining the maximum weakening coefficient of rock and soil strength parameters on riverbank slopes to solve the problem of not being able to effectively simulate the strength decay process of rock and soil under extreme working conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a method for determining the maximum weakening coefficient of the strength parameter of rock and soil mass on a riverbank slope, comprising,

[0008] Collect multi-source spatial data of remote sensing and topography of the target area, identify structural features within the target area, and obtain the set of structural control regions and the structural control scoring function value;

[0009] The set of main structural control regions is used as the main control factor of the local mechanism model. Boundary conditions and load conditions of geological environment are defined, observation locations of key response points are set, and a local mechanism model with physical constraints is constructed.

[0010] Multi-source sensing devices are deployed at key response point observation locations to collect monitoring data. Differential optimization targets are constructed with local mechanism models, and the differential optimization targets are inverted to obtain the maximum weakening coefficient of the target area.

[0011] The maximum weakening coefficient is used as input, and numerical verification is performed through a local mechanism model. Based on the consistency of the verification, the maximum weakening coefficient of the target region is extracted.

[0012] As a preferred embodiment of the method for determining the maximum weakening coefficient of the strength parameter of the riverbank slope soil and rock mass described in this invention, the following steps are taken: Collecting multi-source spatial data of the target area using remote sensing and topography, and identifying structural features within the target area.

[0013] Collect remote sensing image data, digital elevation model data and orthophoto data of the target area to form multi-source spatial data of remote sensing and topography;

[0014] By unifying the coordinates, correcting the images, and spatial registering the multi-source spatial data of remote sensing and topography, a joint representation dataset of multi-source spatial data of remote sensing and topography is formed.

[0015] Based on a multi-source spatial data joint representation dataset of remote sensing and topography, a spatial structure recognition method is used to extract candidate regions of tectonic regions with closed boundary morphology, geometric similarity and spatial coherence, and generate a set of tectonic regions.

[0016] As a preferred embodiment of the method for determining the maximum weakening coefficient of the strength parameters of the riverbank slope soil and rock mass described in this invention, the specific steps for obtaining the set of the main structural control regions and the main structural control scoring function values ​​are as follows:

[0017] In the set of structural regions, spatial morphological parameters, boundary morphological feature parameters, and spatial distribution density parameters are extracted for each candidate structural region to form a set of spatial parameters for candidate structural regions.

[0018] The spatial parameter set of the candidate regions of the construction region is used to calculate the structural control scoring function value of the candidate regions of the construction region through the structural control scoring function;

[0019] Based on the structural master control scoring function value, construct candidate regions for constructing candidate regions according to the threshold of candidate regions, and form a set of structural master control regions;

[0020] Pair the set of main structural control regions with the corresponding main structural control scoring function values ​​to obtain the set of main structural control regions and the main structural control scoring function values.

[0021] As a preferred embodiment of the method for determining the maximum weakening coefficient of the strength parameter of the rock and soil mass of the bank slope described in this invention, the boundary conditions include free surface boundary, fixed boundary and symmetrical boundary.

[0022] Load conditions include gravity load, groundwater pore pressure load, and surface distributed load.

[0023] As a preferred embodiment of the method for determining the maximum weakening coefficient of the strength parameter of the riverbank slope rock and soil mass described in this invention, the method involves: using the set of main structural control regions as the main controlling factors of the local mechanism model, and defining the boundary conditions and load conditions of the geological environment. The specific steps are as follows:

[0024] The set of main structural control regions is used as the main input data for physical modeling. Local geotechnical and lithological information of soil and rock masses is extracted from the spatial region corresponding to the set of main structural control regions.

[0025] Based on the spatial distribution of the main structural control region set and the distribution characteristics of the soil and rock mass, the boundary conditions within the control range of the main structural control region set are determined;

[0026] Based on the natural terrain slope, hydrological distribution, and groundwater level conditions within the spatial range of the main structural control area, the load conditions on the slope are set.

[0027] As a preferred embodiment of the method for determining the maximum weakening coefficient of the strength parameter of the riverbank slope rock and soil mass described in this invention, the specific steps include: setting the observation locations of key response points and constructing a locally constrained mechanism model.

[0028] Based on the spatial structural characteristics and potential slip paths corresponding to the set of main structural control regions, key response point observation locations are selected in the spatial region of the soil and rock mass corresponding to the set of main structural control regions.

[0029] Multi-source sensing devices are deployed at key response point observation locations to acquire real-time geological response monitoring data at these locations.

[0030] A local mechanism model with spatial structural constraints, boundary condition constraints, load condition constraints, and load condition constraints is jointly constructed by combining the set of main control regions of the structure, boundary conditions, load conditions, and observation locations of key response points.

[0031] As a preferred embodiment of the method for determining the maximum weakening coefficient of the strength parameter of the rock and soil mass of the bank slope described in this invention, the key response point observation location refers to comparing the geological response monitoring data at the key response point observation location with the simulated response value output by the local mechanism model at the key response point observation location, and constructing a difference optimization objective function based on the response error.

[0032] In the differential optimization objective function, the weakening coefficient of the soil and rock strength parameter is set as the parameter to be inverted. Based on minimizing the difference between the local mechanism model response value and the geological response monitoring data at the key response point observation location, the differential optimization objective function value is calculated, and the inversion calculation is performed using the gradient descent method.

[0033] When the objective function value of the difference optimization converges to the set tolerance range in continuous iterations, the corresponding soil and rock strength parameter weakening coefficient is output as the maximum weakening coefficient of the target area.

[0034] As a preferred embodiment of the method for determining the maximum weakening coefficient of the strength parameter of the riverbank slope soil and rock mass described in this invention, the method involves: deploying multi-source sensing devices at key response point observation locations to collect monitoring data, and constructing a difference optimization objective based on the local mechanism model. The specific steps are as follows:

[0035] The maximum weakening coefficient of the target region is assigned to the soil and rock strength parameters in the local mechanism model, while keeping the boundary conditions, load conditions and main structural control region information of the local mechanism model unchanged, to obtain the updated local mechanism model.

[0036] As a preferred embodiment of the method for determining the maximum weakening coefficient of the strength parameter of the riverbank slope soil and rock mass described in this invention, the maximum weakening coefficient is used as input, and numerical verification is performed through a local mechanism model. The specific steps are as follows:

[0037] Numerical calculations of the geological response in the target area are performed based on geological response monitoring data at key response point observation locations to obtain numerical calculation results at the key response point observation locations.

[0038] The numerical calculation results of the local mechanism model are compared point by point with the numerical calculation results at the observation locations of the key response points to determine the degree of difference between the two.

[0039] If the root mean square error between the numerical calculation results of the local mechanism model and the numerical calculation results at the observation locations of key response points is within the error tolerance range, then the local mechanism model is determined to be consistent with the geological response monitoring data.

[0040] As a preferred embodiment of the method for determining the maximum weakening coefficient of the strength parameter of the riverbank slope soil and rock mass described in this invention, the method involves: extracting the maximum weakening coefficient of the target area based on verification consistency, specifically through the following steps:

[0041] Under the condition that the local mechanism model and geological response monitoring data are consistent, the maximum weakening coefficient of the target area assigned in the local mechanism model is extracted as the final maximum weakening coefficient of the target area.

[0042] The beneficial effects of this invention are as follows: by collecting multi-source spatial data of remote sensing and topography of the target area, identifying structural features within the target area, obtaining the set of main structural control regions and the main structural control scoring function value, the invention achieves accurate acquisition of detailed spatial features of the target area, and constructs a set of main structural control regions based on these spatial data, providing key basic data and reliable structural region information for subsequent analysis. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Fig. 1 A flowchart for determining the maximum weakening coefficient of the strength parameters of rock and soil on riverbank slopes.

[0045] Fig. 2 A flowchart for identifying the main control area of ​​the structure.

[0046] Fig. 3 The flowchart for the construction and verification of the local mechanism model.

[0047] Fig. 4 The flowchart shows the inversion process and optimization of the objective function for the difference optimization. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0051] Reference Figs. 1-4 This is one embodiment of the present invention, which provides a method for determining the maximum weakening coefficient of the strength parameter of the rock and soil mass of a riverbank slope, including the following steps:

[0052] S1. Collect multi-source spatial data of remote sensing and topography of the target area, identify structural features within the target area, and obtain the set of main structural control areas and the main structural control scoring function value.

[0053] The collection of remote sensing image data, digital elevation model data, and orthophoto data of the target area constitutes multi-source spatial data of remote sensing and topography.

[0054] Furthermore, high-resolution remote sensing image data of the subtropical high-pressure target bank slope area were collected during the dry season to obtain spatial information on surface texture, structural clues, vegetation cover and weathering signs of the bank slope area. By analyzing digital elevation model data, information on elevation distribution, slope, aspect, concavity and convexity and slope polygonal geometric elements of the bank slope was obtained. Orthorectified image data of aerial or satellite remote sensing images were also collected.

[0055] Remote sensing image data, digital elevation model data, and orthophoto data are organized using a unified coordinate system to form multi-source spatial data of remote sensing and topography, represented as follows:

[0056] ;

[0057] in, This represents multi-source spatial data of remote sensing and topography within the target area. Indicates remote sensing image data in coordinates The pixel spectral value vector at that location, This indicates that the digital elevation model data is in coordinates The elevation value at that location, Indicates the orthophoto at coordinates Image grayscale values ​​or texture features at that location. This indicates the two-dimensional spatial extent of the target bank slope study area.

[0058] By unifying coordinates, correcting images, and spatially registering multi-source spatial data from remote sensing and topography, a joint representation dataset of multi-source spatial data from remote sensing and topography is generated.

[0059] Furthermore, remote sensing image data, digital elevation model data, and orthophoto data are projected and transformed to the same geographic coordinate system or projected coordinate system. While ensuring spatial consistency, geometric and radiometric corrections are performed on the remote sensing image data to eliminate spatial distortions caused by terrain undulations and sensor attitude. The remote sensing image data, digital elevation model data, and orthophoto data are then overlaid and fused in the same coordinate system to achieve spatial registration, forming a spatially complete multi-source joint dataset. After image and spatial registration processing, the remote sensing and terrain multi-source spatial data are corrected, ultimately generating a joint representation dataset of remote sensing and terrain multi-source spatial data.

[0060] Based on a multi-source spatial data joint representation dataset of remote sensing and topography, a spatial structure recognition method is used to extract candidate regions of tectonic regions with closed boundary morphology, geometric similarity and spatial coherence, and generate a set of tectonic regions.

[0061] Furthermore, based on the multi-source spatial data joint representation dataset of remote sensing and topography, the dataset is spatially divided using a boundary closure extraction algorithm, and candidate structural regions with complete boundaries and clear shapes are initially extracted.

[0062] Geometric similarity measures are used to classify candidate tectonic regions. These classified candidate regions are then merged with regions exhibiting similar spatial morphology to generate a set of tectonic regions with stable structural characteristics. Each element possesses structural independence and geological significance. Based on a multi-source spatial data jointly represented by remote sensing and topographic data, spatial structure identification methods are employed to extract regions with closed boundary morphology, geometric similarity, and spatial coherence. Combining graph theory and boundary morphology analysis, a set of structurally dominant regions is obtained. A structural dominance scoring function is defined as follows:

[0063] ;

[0064] in, Indicates an index variable. This represents the structural control score of the candidate region for constructing the region. This indicates the connectivity between the candidate region of the normalized construction region and its surrounding regions. This represents the geometric complexity and shape stability of the candidate regions for the normalized construction region. This represents the burial depth or relative elevation difference of the candidate structural region after normalization, which affects the stress concentration degree of the candidate structural region. This represents the candidate region for constructing the normalized topological influence factor. Weight coefficients representing the connectivity between the candidate region and its surrounding regions after normalization. The weighting coefficients represent the geometric complexity and shape stability of the candidate structural regions after normalization. This represents the burial depth or relative elevation difference of the candidate structural region after normalization, which affects the stress concentration degree of the candidate structural region. This represents the weight coefficient of the candidate region for constructing the topological influence factor after normalization.

[0065] It should be noted that, The spatial distribution and connectivity of candidate regions within the target area are analyzed and defined. The setting is based on the geometric complexity of the candidate regions for construction. The settings are based on the distribution of soil and rock layers and burial depth conditions in the target area. The weighting coefficients are set based on the topological relationships of the structural units within the target area. These coefficients are adjusted according to the specific geological characteristics, engineering requirements, and stability analysis results of the target area, and must satisfy the following conditions: .

[0066] It should be noted that, based on the multi-source spatial data jointly expressed by remote sensing and topography, spatial connectivity analysis is performed on each candidate structural region and other surrounding structural regions using spatial structure identification methods. The connectivity between each candidate structural region and its surrounding regions is evaluated, and the connectivity between the candidate structural region and its surrounding regions is obtained. The connectivity between the candidate structural region and its surrounding regions is then normalized to obtain the normalized connectivity between the candidate structural region and its surrounding regions.

[0067] It should be noted that by combining remote sensing image data, digital elevation model data, and orthophoto data with spatial structure recognition methods and graph theory analysis, feature parameters related to geometric complexity and shape stability are extracted to obtain the geometric complexity and shape stability of candidate regions of the structural region. The geometric complexity and shape stability of the candidate regions of the structural region are then normalized to obtain the normalized geometric complexity and shape stability of the candidate regions of the structural region.

[0068] It should be noted that, based on the multi-source spatial data jointly expressed by remote sensing and topography, a spatial structure recognition method is used to extract candidate structural regions with closed boundary morphology, geometric similarity, and spatial coherence. By analyzing the spatial morphological characteristics of the candidate structural regions, the relative elevation difference of the candidate structural regions is further calculated using digital elevation model data to obtain the burial depth or relative elevation difference of the candidate structural regions. The burial depth or relative elevation difference of the candidate structural regions is then normalized to obtain the normalized burial depth or relative elevation difference of the candidate structural regions, which affects the stress concentration degree of the candidate structural regions.

[0069] It should be noted that, based on the spatial topological relationship between the candidate region of the constructed region and the adjacent structural region, the relative position, connectivity and potential slip path influence of the constructed region and the adjacent region are evaluated by the spatial structure identification method to obtain the topological relationship of the constructed region, and finally obtain the candidate region of the topological influence factor constructed region. The candidate region of the topological influence factor constructed region is then normalized to obtain the normalized candidate region of the topological influence factor constructed region.

[0070] The structural control score is calculated for each element in the set of constructed regions using the structural control score function. Based on the structural control score of each element, a mapping set between candidate regions and structural control scores is constructed.

[0071] In the set of tectonic regions, spatial morphological parameters, boundary morphological feature parameters, and spatial distribution density parameters are extracted from each candidate tectonic region to form a set of spatial parameters for the candidate tectonic regions.

[0072] Furthermore, in the set of constructed regions, each candidate region is extracted from the multi-source spatial data joint representation dataset of remote sensing and topography using image segmentation and boundary extraction algorithms, morphological analysis methods, and spatial statistical analysis methods to extract spatial morphological parameters, boundary morphological feature parameters, and spatial distribution density parameters.

[0073] It should be noted that spatial morphological parameters include area, major axis length, minor axis length, compactness, and extensibility; boundary morphological characteristic parameters include boundary complexity, edge clarity, and boundary direction distribution; and spatial distribution density parameters include the number of candidate regions for tectonic regions per unit area and clustering index.

[0074] By using image processing and spatial statistical methods, standardized calculations are performed in a unified coordinate system to ultimately obtain a set of spatial parameters for candidate regions of the constructed region.

[0075] The spatial parameter set of the candidate regions of the constructed region is used to calculate the structural control score function value of the candidate regions of the constructed region through the structural control score function.

[0076] Furthermore, based on the spatial parameter set of candidate structural regions, the structural control score value of each candidate structural region is calculated using the defined structural control scoring function. Specifically, the structural control scoring function calculates the score value of each candidate structural region by weighting and normalizing the spatial morphological parameters, boundary morphological characteristic parameters, and spatial distribution density parameters of each candidate structural region.

[0077] The set of constructed regions is prioritized based on the structural master control scoring function value, and candidate constructed regions with structural master control scoring function values ​​greater than the screening threshold are selected to form a set of structural master control regions.

[0078] Furthermore, based on the structural master control scoring function value, the set of constructed regions is sorted from largest to smallest, a screening threshold is set, and candidate constructed regions with structural master control scoring function values ​​greater than the screening threshold are selected to form a set of structural master control regions.

[0079] It should be noted that the screening threshold is set by statistically analyzing the structural control scores of candidate structural regions, based on the specific engineering requirements of project safety, stability, and load-bearing capacity. The screening threshold is adjusted according to the geological background of the structural region in the target region, and further adjusted based on the preliminary screening results and feedback information in the subsequent optimization process, to ensure that the screened structural regions maintain a balance between accuracy and engineering feasibility.

[0080] Pair the set of main structural control regions with the corresponding main structural control scoring function values ​​to obtain the set of main structural control regions and the main structural control scoring function values.

[0081] Furthermore, the structural control region set obtained through screening is paired one by one with the structural control scoring function value to generate a complete structural control region set and scoring value, and finally obtain the structural control region set and structural control scoring function value.

[0082] For each candidate construction region in the set of main structural control regions, generate a main structural control region scoring data structure. The main structural control region scoring data structure is in the form of an ordered mapping or a two-dimensional attribute table, containing fields: main structural control region number, spatial range description, and main control scoring function value.

[0083] S2. The set of main control regions of the structure is used as the main control factor of the local mechanism model. The boundary conditions and load conditions of the geological environment are defined, the observation positions of key response points are set, and a local mechanism model with physical constraints is constructed.

[0084] The set of main structural control regions is used as the main input data for physical modeling. Local geotechnical and lithological information of soil and rock masses is extracted within the spatial region corresponding to the set of main structural control regions.

[0085] Furthermore, the set of main structural control regions and the main structural control scoring function values ​​are used as the main control input data for local physical modeling to determine the spatial boundary range of the main structural control regions. Based on multi-source spatial data from remote sensing and topography, geological three-dimensional data within the corresponding spatial regions are extracted to obtain a set of local rock and soil geometric structure information and a preliminary set of lithological attribute information that correspond one-to-one with the spatial range of the main structural control regions.

[0086] Specifically, by utilizing the spatial boundary of the main structural control area, the geological database or three-dimensional geological model within the target area is spatially clipped to extract the stratigraphic interfaces, rock and soil distribution, and contact relationships within each main structural control area; based on the multi-source data fusion method, the geometric morphological parameters of the local rock and soil are determined, lithological information is extracted, and a preliminary rock and soil zoning structure is constructed within the main structural control area.

[0087] It should be noted that the preliminary soil and rock zoning structure is a set of zoning and interface subdivisions for each structurally controlled region according to stratigraphic interfaces, lithology, and contact relationships. The preliminary soil and rock zoning structure itself is not used as the final output index, but rather serves as the foundation for the subsequent construction of local mechanism models, including geometric and physical property assignments, load mapping, selection of key response points, and inversion and verification criteria.

[0088] Based on the spatial distribution of the main structural control region set and the distribution characteristics of the soil and rock mass, the boundary conditions within the control range of the main structural control region set are determined.

[0089] Furthermore, considering the spatial distribution of each structural control area within the target area and the characteristics of soil and rock mass distribution, the boundary conditions within the control range of each structural control area within the target area are determined. Based on the spatial location of each structural control area within the target area, the geological background and engineering environment characteristics of each structural control area are analyzed, generating specific physical and mathematical boundary conditions for each structural control area. The physical boundaries include the natural interface constraints of the candidate structural regions in terms of hydrology, topography, and geological structure. The mathematical boundaries include displacement constraint boundaries, force boundaries, and contact boundaries set in numerical simulation calculations. Specifically, a comprehensive analysis is conducted on the topographic slope, contact relationship between adjacent rock layers, range of groundwater level changes, and rock mass exposure conditions at the location of the structural control area to determine a boundary condition configuration scheme reflecting the actual working conditions. Finally, a set of physical and mathematical boundary condition configurations corresponding to each structural control area is generated, determining the boundary conditions within the control range of the set of structural control areas.

[0090] It should be noted that a comprehensive analysis of the topographic slope, contact relationship between adjacent rock strata, range of groundwater level variation, and rock mass exposure conditions of the main structural control area is conducted to determine a boundary condition configuration scheme that reflects the actual working conditions. Specifically, the four elements of topographic slope, contact relationship between adjacent rock strata, range of groundwater level variation, and rock mass exposure conditions of the main structural control area are quantified and normalized to obtain an element list. A classification framework is then established based on boundary type, including free surface boundaries, displacement-constrained boundaries, symmetrical boundaries, and contact boundaries. Based on groundwater level distribution, river and lake locations, and recharge and discharge conditions, known water level boundaries are set for the water-adjacent surface and known water level lines, and known flow boundaries are set for the underground side boundaries. Simultaneously, through mechanical calculations and balancing of pore water pressure generated by the water level, combined with the element list and engineering threshold set, an interpretable category determination is made for each boundary location. Boundary segments with high exposure and slopes exceeding the set threshold are classified as free surface boundaries; those far from the calculation core area, Boundary segments that can be considered as far-field constraints are classified as displacement constraint boundaries; boundary segments with obviously symmetrical loads are classified as symmetrical boundaries; interfaces with abrupt changes in material properties and potential slip signs are classified as contact boundaries, and reasonable friction and separation conditions are assigned to them. Candidate boundary combinations are obtained, and consistency checks and optimizations are performed on the candidate boundaries. The consistency check includes checking whether the kinematics are compatible, whether the hydraulics are conserved, and whether the mechanics are in equilibrium. Candidate configurations are obtained, and the monitoring data of key response points are compared with the trial calculation results of the candidate configurations point by point. Candidate configurations with a weighted root mean square error between the model output and the monitoring value at the key response point lower than the boundary configuration verification threshold are selected. For candidate configurations with a weighted root mean square error between the model output and the monitoring value at the tolerance key response point higher than the boundary configuration verification threshold, local boundary correction processing is performed, and the comparison is performed again until the weighted root mean square error between the model output and the monitoring value at the tolerance key response point reaches the tolerance limit. The boundary condition configuration set is then statistically analyzed and generated.

[0091] Among them, the free surface boundary refers to the exposed surface that is not constrained by external forces, the displacement constrained boundary refers to the bedrock side boundary with fixed tangential slip, the symmetric boundary refers to the boundary with mirror symmetry of the load, and the contact boundary refers to the contact surface between soft and hard rocks and the potential slip surface. The engineering threshold set is set based on the initial value of the cross-constraint of historical and on-site monitoring data, field survey and standard limits, and the value is determined by the calibration search to minimize the verification error. The engineering threshold set can be adjusted according to field data and by project. The boundary configuration verification threshold is set by taking the maximum value of the uncertainty assessment of the residual statistics of the benchmark data and reference configuration and the synthesis of measurement noise and model discretization error.

[0092] Boundary conditions include free surface boundaries, fixed boundaries, and symmetric boundaries.

[0093] Furthermore, by combining remote sensing technology, geological surveys, and digital elevation model data, the areas of the main structural control region exposed to air or water are identified, and the boundaries are dynamically adjusted according to water level changes and flow conditions. Free surface boundaries are obtained by setting no external force constraints in the normal direction.

[0094] The free surface boundary is used to represent the natural free surface of the main structural control area of ​​the bank slope region exposed to air or water, which is not constrained by external forces in the normal direction.

[0095] By analyzing the area where the bottom of the main structural control region is in contact with stable bedrock and foundation, and combining geological survey data and soil profile information, the area in contact with immovable bedrock or foundation is identified. Displacement restrictions in each direction are set in the area in contact with immovable bedrock or foundation to ensure that the main structural control region in the area in contact with immovable bedrock or foundation cannot be displaced in the simulation, thereby generating fixed boundary conditions in actual working conditions and obtaining fixed boundaries.

[0096] Fixed boundaries are used to indicate the bottom of the main control area of ​​the structure or the area in contact with stable bedrock or base, and displacement in all directions is restricted.

[0097] By analyzing the geometric distribution and stress state of the main structural region, combined with the geological background and rock strata structure, symmetrical geometric features or structural regions within the area are identified. Based on the geometric symmetry, the specific location of the symmetry plane is determined, and normal displacement constraints are applied to the symmetry plane while maintaining freedom in the tangential direction. This ensures that the simulation can accurately reflect the deformation and stress characteristics of the structure under symmetry, thus obtaining the symmetry boundary.

[0098] Symmetric boundaries are used to indicate the positions where the subsequent local mechanism model has symmetry in geometry and stress state, with displacement restricted in the normal direction of the symmetry plane and free in the tangential direction.

[0099] Based on the natural terrain slope, hydrological distribution, and groundwater level conditions within the spatial range of the main structural control area, the load conditions on the slope are set.

[0100] Furthermore, based on the natural topographic slope, hydrological distribution, and groundwater level conditions of the bank slope area where the main structural control area set is located, and combined with the relevant environmental parameters extracted from the multi-source spatial data jointly expressed by remote sensing and topography, the load conditions borne by the slope rock and soil in the local mechanism model are set.

[0101] Load conditions include gravity load, groundwater pore pressure load, and surface distributed load.

[0102] Furthermore, the load conditions include gravity loads formed by the self-weight of the soil and rock mass, determined based on the topographic and hydrological environmental parameters of the region where the multi-source spatial data jointly expresses the dataset and the main control area of ​​the structure, pore water pressure loads caused by groundwater level conditions, and planar distributed external loads acting on the slope surface.

[0103] It should be noted that by combining digital elevation model data to analyze the topographic slope and elevation information of the target area, determining the density of the soil and rock mass through geological survey data, and calculating the self-weight load of the main structural control area based on the thickness of the soil and rock mass and topographic changes, the distribution of gravity load is obtained.

[0104] It should be noted that by combining groundwater level monitoring data with multi-source spatial data to jointly express the dataset, the hydrological conditions and permeability of the target area are analyzed, and the distribution of groundwater pore pressure load affected by groundwater pressure in the main structural control area is determined based on the groundwater level change and pore water pressure calculation formula.

[0105] It should be noted that by combining the topographic slope, hydrological environment and regional engineering needs of the target area, external load sources are identified, and the surface distributed load acting on the slope surface is determined by calculating the load distribution function.

[0106] Based on the spatial structural characteristics and potential slip paths corresponding to the set of main structural control regions, key response point observation locations are selected in the spatial region of the soil and rock mass corresponding to the set of main structural control regions.

[0107] Furthermore, within the three-dimensional spatial region of the soil and rock mass covered by the main structural control area set, based on the spatial morphological parameters, boundary morphological feature parameters, and spatial distribution density parameters extracted from the multi-source spatial data joint representation dataset, and combined with the potential slip paths obtained from analysis in a unified coordinate system, key locations that may experience strength weakening and instability are identified.

[0108] In the identification results, the locations of potential slip path intersection areas, high boundary complexity areas, and high clustering index areas are preferentially selected as key response points.

[0109] The three-dimensional coordinates of the selected key response points and the candidate region identifiers of the constructed region are encoded and stored to generate a set of key response point observation locations.

[0110] The key response point observation locations in the key response point observation location set are used to obtain the comparison response index between the numerical simulation output results and the measured monitoring data.

[0111] A local mechanism model with spatial structural constraints, boundary condition constraints, load condition constraints, and load condition constraints is jointly constructed by combining the set of main control regions of the structure, boundary conditions, load conditions, and observation locations of key response points.

[0112] Furthermore, based on the geotechnical spatial region corresponding to the set of main structural control regions in a unified spatial coordinate system, the set of spatial structural characteristic parameters matched by the set of main structural control regions is used as structural constraints. The set of boundary conditions determined by the natural topographic slope, hydrological distribution, and groundwater level conditions within the geotechnical spatial region is combined with the set of load conditions composed of gravity load, groundwater pore pressure load, and surface distribution load within the same spatial range as load constraints. The set of key response point observation locations corresponding to the set of main structural control regions in three-dimensional space is used as the output response acquisition nodes of the local mechanism model. In the numerical simulation model of geotechnical and structural engineering, a local mechanism model with spatial structural constraints, boundary condition constraints, and load condition constraints is jointly constructed.

[0113] Furthermore, the set of key response point observation locations is input into the local mechanism model, and the boundary conditions and load conditions are set to run the numerical simulation. The displacement, stress and pore water pressure of the key response points in a unified coordinate system are obtained, and the response value set is calculated.

[0114] It should be noted that the calculation of the response value set specifically involves inputting the set of key response point observation locations into the local mechanism model, while simultaneously loading corresponding matching boundary conditions and load conditions. Within the control range of the main structural control area set, numerical calculations are performed through mechanical solutions. At each key response point observation location, displacement and pore water pressure are extracted from the numerical calculation results. Based on the numerical calculation results and constitutive relations, point interpolation and a consistent elastoplastic nearest-point return algorithm are used to obtain the stress corresponding to each key response point observation location. The displacement, pore water pressure, and stress corresponding to each key response point observation location extracted from the numerical calculation results are converted into standard components in a unified coordinate system according to the definition in the specification. The observation locations of each key response point are organized according to a fixed field order, and single-point response records are obtained. The single-point response records include three types of quantities: location identifier, time identifier, and displacement, stress, and pore water pressure in a unified coordinate system. All single-point response records are statistically analyzed to generate the calculated response value set.

[0115] Furthermore, the sensor data of the actual monitoring points deployed at the corresponding three-dimensional spatial locations are strictly corresponding to the observation locations of the key response points in three-dimensional space, forming a set of actual monitoring response values ​​that correspond one-to-one with the key response points.

[0116] Under a unified time series and spatial index, the difference between the calculated response value set of the local mechanism model and the measured monitoring response value set is calculated to generate a comparison response index set, thereby obtaining a quantitative assessment of the fitting accuracy of the local mechanism model to the deformation and instability behavior of the soil and rock mass in the main control area of ​​the structure.

[0117] Multi-source sensing devices are deployed at key response point observation locations to acquire geological response monitoring data at these locations in real time.

[0118] Furthermore, at the three-dimensional spatial locations corresponding to the key response point observation locations, multi-source sensing and monitoring equipment matching the spatial coordinates of remote sensing image data, digital elevation model data, and orthophoto data is deployed. This includes high-precision deformation sensors for acquiring displacement response, pore water pressure sensors for acquiring groundwater dynamic response, and meteorological and hydrological sensors for acquiring surface environmental change response. Through the multi-source sensing and monitoring network, geological response monitoring datasets of key response points are collected in real time. These datasets are then mapped one-to-one with the numerical simulation outputs of local mechanism models under a unified spatial reference system to obtain geological response monitoring data at the key response point observation locations.

[0119] S3. Deploy multi-source sensing devices at key response point observation locations to collect monitoring data, construct differential optimization targets with local mechanism models, perform inversion on differential optimization targets, and obtain the maximum weakening coefficient of the target area.

[0120] In the differential optimization objective function, the weakening coefficient of the soil and rock strength parameter is set as the parameter to be inverted. The inversion calculation is carried out based on the gradient descent method by minimizing the difference between the response value of the local mechanism model and the geological response monitoring data of the observation location of the key response point.

[0121] Furthermore, based on the calculated responses and corresponding monitoring data of the key response point observation locations in the local mechanism model, spatiotemporal alignment and dimensional unification processing are performed. The difference optimization objective function is obtained through the weighted least squares method. In the difference optimization objective function, the weakening coefficient of the soil and rock strength parameter is set as the parameter to be inverted. The numerical simulation response values ​​of the local mechanism model under the given set of main structural control regions, boundary condition constraints, and load condition constraints are matched one by one with the geological response monitoring dataset of the corresponding key response point observation locations under a unified spatiotemporal reference. The difference between the two is calculated and the difference optimization objective function value is formed. Based on the gradient descent iterative strategy, the value of the weakening coefficient of the soil and rock strength parameter is continuously adjusted to minimize the difference optimization objective function value.

[0122] When the objective function for difference optimization converges within the set tolerance range during continuous iteration, the corresponding weakening coefficient of the soil and rock strength parameter is output as the maximum weakening coefficient of the target area.

[0123] Furthermore, after completing multiple rounds of differential optimization iteration calculations based on the gradient descent method, the changing trend of the objective function value is continuously monitored. When the relative rate of change of the objective function value is less than the set convergence tolerance in at least five consecutive iterations, the optimization process is determined to have converged, and the iterative result of the weakening coefficient of the soil and rock strength parameter tends to be stable. The weakening parameter under the optimal fitting of the soil and rock strength under the convergence iteration is extracted and defined as the maximum weakening coefficient.

[0124] It should be noted that the convergence tolerance is based on the accuracy requirements and reliability standards of soil and rock strength parameters in actual engineering applications, and is set by analyzing historical geological survey data, monitoring data, and data from similar engineering projects.

[0125] S4. Using the maximum weakening coefficient as input, perform numerical verification through a local mechanism model. Based on the consistency of the verification, extract the maximum weakening coefficient of the target region.

[0126] The maximum weakening coefficient of the target region is assigned to the soil and rock strength parameters in the local mechanism model, while keeping the boundary conditions, load conditions and main structural control region information of the local mechanism model unchanged, to obtain the updated local mechanism model.

[0127] Furthermore, after obtaining the weakening coefficient of the maximum soil and rock strength parameter in the target area, the maximum weakening coefficient obtained by the differential optimization objective function inversion is assigned to the soil and rock strength parameter in the local mechanism model. Within the three-dimensional space region of the soil and rock corresponding to the set of main structural control regions, the strength parameter of the soil and rock in the target area is updated. While updating the soil and rock strength parameter, the boundary condition constraints, load condition constraints and main structural control region information in the local mechanism model remain unchanged, thus obtaining the updated local mechanism model that reflects the changes in the response and stability of the soil and rock in the target area.

[0128] Numerical calculations of the geological response in the target area are performed based on geological response monitoring data from key response point observation locations to obtain numerical calculation results at the key response point observation locations.

[0129] Furthermore, after obtaining the updated local mechanism model, based on the established set of key response point observation locations, the geological response monitoring dataset of each key response point is analyzed. Combined with the updated rock and soil strength parameters and load constraints in the local mechanism model, the geological response of the target area is numerically calculated. The local mechanism model is used to solve the problem under the existing boundary constraints, and the numerical calculation results at each key response point observation location are obtained.

[0130] The numerical calculation results of the local mechanism model are compared point by point with the numerical calculation results at the observation locations of the key response points to determine the degree of difference between the two.

[0131] Furthermore, in the updated local mechanism model, the numerical calculation results obtained by the local mechanism model are compared point by point with the geological response monitoring dataset collected in real time at the key response point observation location set. Based on the unified spatiotemporal benchmark, the measured data of each key response point observation location are matched with the corresponding local mechanism model calculation results, and the difference index between the two is calculated.

[0132] If the root mean square error between the numerical calculation results of the local mechanism model and the numerical calculation results at the observation locations of key response points is within the error tolerance range, then the local mechanism model is determined to be consistent with the geological response monitoring data.

[0133] Furthermore, based on the comparison and difference analysis of the numerical calculation results, the error of the numerical calculation results of the local mechanism model and the geological response monitoring data at the observation locations of the key response points are compared. The root mean square error between the numerical calculation results of the local mechanism model and the numerical calculation results at the observation locations of the key response points is used as the evaluation index. The error values ​​are calculated point by point for all key response points and summarized.

[0134] It should be noted that the root mean square error between the numerical calculation results of the local mechanism model and the numerical calculation results at the observed locations of the key response points is expressed as:

[0135] ;

[0136] in, This represents the root mean square error. Indicates the number of critical response points. This represents the calculated value from the local mechanism model. This represents the measured monitoring value, which is obtained by centrally extracting the measured monitoring response value. An error tolerance is set, and the root mean square error is... When the error tolerance is within the acceptable range, the verification results between the local mechanism model and the geological response monitoring data are consistent, indicating that the local mechanism model can accurately reflect the geological response of the target area. When the root mean square error exceeds the error tolerance, numerical optimization and model correction need to be performed again to improve the accuracy and fit of the local mechanism model.

[0137] It should be noted that the error tolerance is based on the accuracy requirements in actual engineering applications and the reliability of geological response monitoring data. It is set through analysis of historical geological survey data, monitoring data, and data from similar engineering projects. The value of the error tolerance is adjusted according to the measurement accuracy of key response points, the complexity of regional geology, and the accuracy and reliability standards of local mechanism model calculations.

[0138] Under the condition that the local mechanism model and geological response monitoring data are consistent, the maximum weakening coefficient of the target area assigned in the local mechanism model is extracted as the final maximum weakening coefficient of the target area.

[0139] Furthermore, under the condition that the local mechanism model and geological response monitoring data are consistent, the maximum weakening coefficient of the target area assigned in the updated local mechanism model is extracted, and the optimal value is obtained by inversion calculation through the difference optimization objective function, thereby determining the maximum weakening coefficient.

[0140] This embodiment also provides a computer device applicable to the method for determining the maximum weakening coefficient of the strength parameters of rock and soil on bank slopes, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for determining the maximum weakening coefficient of the strength parameters of rock and soil on bank slopes as proposed in the above embodiment.

[0141] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0142] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for determining the maximum weakening coefficient of the strength parameters of the riverbank slope soil and rock mass as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0143] In summary, this invention achieves accurate acquisition of detailed spatial features of the target area by: collecting multi-source spatial data of remote sensing and topography of the target area, identifying structural features within the target area, obtaining a set of key structural control regions and structural control scoring function values, and constructing a set of key structural control regions based on these spatial data, providing crucial basic data and reliable structural region information for subsequent analysis.

[0144] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for determining the maximum weakening coefficient of rock and soil strength parameters on a riverbank slope, characterized in that: include, Collect multi-source spatial data of remote sensing and topography of the target area, identify structural features within the target area, and obtain the set of structural control regions and the structural control scoring function value; The set of main structural control regions is used as the main control factor of the local mechanism model. Boundary conditions and load conditions of geological environment are defined, observation locations of key response points are set, and a local mechanism model with physical constraints is constructed. Multi-source sensing devices are deployed at key response point observation locations to collect monitoring data. Differential optimization targets are constructed with local mechanism models, and the differential optimization targets are inverted to obtain the maximum weakening coefficient of the target area. The maximum weakening coefficient is used as input, and numerical verification is performed through a local mechanism model. Based on the consistency of the verification, the maximum weakening coefficient of the target region is extracted.

2. The method for determining the maximum weakening coefficient of the strength parameters of the rock and soil mass on the bank slope as described in claim 1, characterized in that: The process involves collecting multi-source spatial data of the target area, including remote sensing and topographic features, and identifying structural characteristics within the target area. The specific steps are as follows: Collect remote sensing image data, digital elevation model data and orthophoto data of the target area to form multi-source spatial data of remote sensing and topography; By unifying coordinates, correcting images, and spatially registering multi-source spatial data of remote sensing and terrain, a joint representation dataset of multi-source spatial data of remote sensing and terrain is formed. Based on a multi-source spatial data joint representation dataset of remote sensing and topography, a spatial structure recognition method is used to extract candidate regions of tectonic regions with closed boundary morphology, geometric similarity and spatial coherence, and generate a set of tectonic regions.

3. The method for determining the maximum weakening coefficient of the strength parameters of the rock and soil mass on the bank slope as described in claim 2, characterized in that: The specific steps for obtaining the set of structural master control regions and the structural master control scoring function value are as follows: In the set of tectonic regions, spatial morphological parameters, boundary morphological feature parameters, and spatial distribution density parameters are extracted for each candidate tectonic region to form a set of spatial parameters for candidate tectonic units. The spatial parameter set of candidate regions of the structural unit is used to calculate the structural master score function value of the candidate regions of the structural unit through the structural master score function; The set of constructed regions is sorted according to the structural master control scoring function value, and candidate constructed regions with structural master control scoring function values ​​greater than the screening threshold are selected to form a set of structural master control regions. Pair the set of main structural control regions with the corresponding main structural control scoring function values ​​to obtain the set of main structural control regions and the main structural control scoring function values.

4. The method for determining the maximum weakening coefficient of the rock and soil strength parameters of a bank slope as described in claim 3, characterized in that: The boundary conditions include free surface boundaries, fixed boundaries, and symmetric boundaries; The load conditions include gravity load, groundwater pore pressure load, and surface distributed load.

5. The method for determining the maximum weakening coefficient of the strength parameters of the rock and soil mass on the bank slope as described in claim 4, characterized in that: The set of main structural control regions is used as the main controlling factor of the local mechanism model. Boundary conditions and load conditions of geological environment are defined. The specific steps are as follows: The set of main structural control regions is used as the main input data for physical modeling. Local geotechnical and lithological information of soil and rock masses is extracted from the spatial region corresponding to the set of main structural control regions. Based on the spatial distribution of the main structural control region set and the distribution characteristics of the soil and rock mass, the boundary conditions within the control range of the main structural control region set are determined; Based on the natural terrain slope, hydrological distribution, and groundwater level conditions within the spatial range of the main structural control area, the load conditions on the slope are set.

6. The method for determining the maximum weakening coefficient of the strength parameters of the rock and soil mass on the bank slope as described in claim 5, characterized in that: The specific steps for setting the observation locations of key response points and constructing a locally constrained mechanism model are as follows: Based on the spatial structural characteristics and potential slip paths corresponding to the set of main structural control regions, key response point observation locations are selected in the spatial region of the soil and rock mass corresponding to the set of main structural control regions. Multi-source sensing devices are deployed at key response point observation locations to acquire geological response monitoring data at these locations in real time. A local mechanism model with spatial structural constraints, boundary condition constraints, load condition constraints, and load condition constraints is jointly constructed by combining the set of main control regions of the structure, boundary conditions, load conditions, and observation locations of key response points.

7. The method for determining the maximum weakening coefficient of the strength parameters of the rock and soil mass on the bank slope as described in claim 6, characterized in that: The key response point observation location refers to comparing the geological response monitoring data at the key response point observation location with the simulated response values ​​output by the local mechanism model at the key response point observation location, and constructing a difference optimization objective function based on the response error. In the differential optimization objective function, the weakening coefficient of the soil and rock strength parameter is set as the parameter to be inverted. Based on minimizing the difference between the local mechanism model response value and the geological response monitoring data at the key response point observation location, the differential optimization objective function value is calculated, and the inversion calculation is performed using the gradient descent method. When the objective function value of the difference optimization converges to the set tolerance range in continuous iterations, the corresponding soil and rock strength parameter weakening coefficient is output as the maximum weakening coefficient of the target area.

8. The method for determining the maximum weakening coefficient of the strength parameters of the rock and soil mass on the bank slope as described in claim 7, characterized in that: The specific steps for deploying multi-source sensing devices at key response point observation locations to collect monitoring data, and constructing a difference optimization objective based on the local mechanism model, are as follows: The maximum weakening coefficient of the target region is assigned to the soil and rock strength parameters in the local mechanism model, while keeping the boundary conditions, load conditions and main structural control region information of the local mechanism model unchanged, to obtain the updated local mechanism model.

9. The method for determining the maximum weakening coefficient of the strength parameters of the rock and soil mass on the bank slope as described in claim 8, characterized in that: The step of using the maximum weakening coefficient as input and performing numerical verification through a local mechanism model is as follows: Numerical calculations of the geological response in the target area are performed based on geological response monitoring data at key response point observation locations to obtain numerical calculation results at the key response point observation locations. The numerical calculation results of the local mechanism model are compared point by point with the numerical calculation results at the observation locations of the key response points to determine the degree of difference between the two. If the root mean square error between the numerical calculation results of the local mechanism model and the numerical calculation results at the observation locations of key response points is within the error tolerance range, then the local mechanism model is determined to be consistent with the geological response monitoring data.

10. The method for determining the maximum weakening coefficient of the strength parameters of the rock and soil mass on the bank slope as described in claim 9, characterized in that: The specific steps for extracting the maximum weakening coefficient of the target region based on verification consistency are as follows: Under the condition that the local mechanism model and geological response monitoring data are consistent, the maximum weakening coefficient of the target area assigned in the local mechanism model is extracted as the final maximum weakening coefficient of the target area.

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