CN-120706159-B - Slope stability prediction method and device under rainfall condition

CN120706159BCN 120706159 BCN120706159 BCN 120706159BCN-120706159-B

Abstract

The invention provides a slope stability prediction method and device under rainfall condition, and relates to the technical field of slope stability prediction, comprising the steps of dividing a slope by adopting a regular grid and constructing a space-time characteristic data set containing geological, hydrological and topographic parameters; the method comprises the steps of calculating a regional synergy index based on a multidimensional feature vector, dividing a side slope into a plurality of communities and isolated risk areas, calculating dynamic safety coefficients of all communities by taking a centroid subarea as a representative, dividing stability grades according to a threshold value, establishing a three-dimensional digital twin model to realize risk visualization, triggering early warning on a continuous three-period safety coefficient decrementing area and displaying an evolution track. According to the method, accurate monitoring and early warning of slope stability are achieved through multi-source data fusion and dynamic partition assessment.

Inventors

GUO LU
YANG HUILIN
DONG GUOWEI
SHI YUN
XU BAISHUN

Assignees

宿迁学院

Dates

Publication Date: 20260505
Application Date: 20250617

Claims (6)

1. A slope stability prediction method under rainfall condition is characterized by comprising the following specific steps: The method comprises the steps of 1, discretizing a target side slope into a plurality of sub-areas by adopting a regular grid, endowing each sub-area with area attributes comprising geological parameters, hydrological parameters and topographic parameters, and simultaneously fusing real-time rainfall intensity data provided by meteorological radars to form a side slope characteristic data set with space-time attributes; Step 2, according to geological parameters, hydrological parameters and topography parameters of each sub-area, using a multi-dimensional feature vector to represent slope local information of each sub-area, defining an area cooperation index between any two sub-areas based on the multi-dimensional feature vector, and carrying out clustering treatment on the sub-areas by combining a spectral clustering algorithm, so as to divide a target slope into a plurality of communities and isolated risk areas; Step 3, determining dynamic safety coefficients of each community and the isolated risk area according to the slope characteristic data set, comparing the dynamic safety coefficients with a stability threshold value, and dividing stability grades of each community and the isolated risk area according to a comparison result, wherein for each community, selecting a centroid subarea as a representative to determine the dynamic safety coefficients of the communities; Establishing a three-dimensional digital twin model of the target slope, precisely matching community division results with the model through a space mapping algorithm, marking stability levels of each community and isolated risk areas with different colors in a visual interface, triggering a risk early warning signal for the communities or isolated risk areas with monotonically decreasing dynamic safety coefficients in three continuous monitoring periods, and highlighting an evolution track of the area in the three-dimensional digital twin model; determining dynamic safety coefficients of each community and isolated risk areas according to the slope characteristic data set, wherein the formula is as follows: In the formula, Representing subareas in The dynamic safety coefficient at the moment is used for reflecting the instant stability state of the slope under the rainfall condition; Is the cohesive force of the soil body in the subarea, The sliding body is used for representing a normal stress component generated by the dead weight of the sliding body, and the sliding body refers to a soil body part which can slide along a potential sliding surface in the sub-region; for the potential depth of the sliding surface, Is the slope angle of the subarea, Is the pore water pressure of the subarea at the time t, Is the surface curvature of the sub-area, Is the curvature radius of the side slope of the subarea, Is the internal friction angle of the soil body of the subarea, For the permeability coefficient in relation to the water content, The saturated permeability coefficient is used for representing the maximum permeability of the soil body when the soil body is completely saturated, and is measured through an indoor permeability test; is the water content of the current soil body of the subarea, The residual volume water content is the water content which indicates the water content ratio which cannot be discharged by gravity in the soil body and is determined by the soil type; And The initial water content and the saturated water content are respectively, Is an empirical index and is used for controlling the change rate of the permeability coefficient along with the water content and is related to the soil pore distribution; For each community, selecting a centroid subarea as a representative to determine a dynamic safety coefficient of the community, wherein the centroid subarea is selected by firstly calculating the average coordinates of geometric centers of all subareas in the community as centroid positions, then selecting the subarea closest to the centroid positions as centroid subareas, if a plurality of subareas exist simultaneously, further comparing cosine similarity between multidimensional feature vectors and average characteristics of the community, and selecting the subarea with the highest similarity as centroid subarea, wherein the centroid subarea meets the following conditions: (1) Is located inside the community and is not adjacent to the isolated risk zone; (2) The deviation between the geological parameter, the hydrologic parameter and the average value of the community is not more than ; Comparing the dynamic safety coefficient with a stability threshold value, and dividing the stability level of each community and the isolated risk area according to the comparison result, wherein the specific logic is as follows: When (when) When the community or the isolated risk area is marked as a stable state, the anti-slip force of the side slope at the community or the isolated risk area is obviously larger than the slip force; When (when) When the community or the isolated risk area is marked as an early warning state, the monitoring is enhanced and the personnel entering is limited; When (when) When the community or the isolated risk zone is marked as a high risk state, starting an emergency plan; When (when) When the community or the isolated risk area is marked as an unstable state, the occurrence or the approach of a landslide is indicated, and an evacuation alarm is immediately triggered; In the formula, Representation of Dynamic safety coefficient of the moment of time, Is a preset stability threshold.
2. The method for predicting slope stability under rainfall condition of claim 1, wherein the target slope is discretized into a plurality of sub-areas by adopting a regular grid, and the grid size is determined as follows according to the slope scale and the monitoring precision requirement To the point of Assigning region attributes comprising geological parameters, hydrologic parameters and topographic parameters to each sub-region, wherein the geological parameters comprise permeability coefficients, internal friction angles and cohesive forces, the hydrologic parameters comprise initial water content, saturated water content and real-time pore water pressure, and the topographic parameters comprise gradient and surface curvature; Fusing real-time rainfall intensity data provided by the weather radar, matching the space-time resolution with the grid division scale, and finally forming a slope characteristic data set with time-space-attribute multidimensional characteristics, wherein each parameter is stored in a matrix form and establishes a space-time index relation; Geological parameters, hydrologic parameters and topography parameters are all obtained through on-site investigation, sensor monitoring and remote sensing measurement and serve as regional attributes of each sub-region.
3. The method for predicting slope stability under rainfall condition according to claim 2, wherein a multidimensional feature vector is constructed for each sub-area according to geological parameters, hydrological parameters and topographic parameters of each sub-area, and the expression of the multidimensional feature vector is as follows: In the formula, Is a subarea Is used to determine the multi-dimensional feature vector of (a), Is the index of the sub-region, 、 And Representing sub-regions respectively Is a combination of the geological, hydrographic and topographical parameters, 、 And In turn sub-regions The permeability coefficient, the internal friction angle and the cohesive force, 、 And In turn sub-regions The initial water content, the saturated water content and the real-time pore water pressure, And In turn sub-regions Slope and surface curvature of the earth; The regional synergy index between any two sub-regions is defined based on the multidimensional feature vector, and the formula is as follows: In the formula, Representing sub-regions And subregions The region co-ordination index between them, And Index of sub-region, and ; 、 And Representing sub-regions respectively And subregions Geological feature difference, hydrologic feature difference and topographic feature difference among the two; 、 And Representing sub-regions respectively Is a combination of the geological, hydrographic and topographical parameters, 、 And In turn sub-regions The permeability coefficient, the internal friction angle and the cohesive force, 、 And In turn sub-regions The initial water content, the saturated water content and the real-time pore water pressure, And In turn sub-regions Is defined by the slope of (a) and the curvature of the earth's surface, 、 And For the preset weight to be given, And meet the following 。
4. The method for predicting slope stability under rainfall condition according to claim 3, wherein the clustering treatment is carried out on the sub-areas by combining a spectral clustering algorithm, and the specific logic is as follows: The regional synergy index matrix is constructed based on regional synergy indexes among all the subregions, and the sub-regions are subjected to community division by adopting a spectral clustering algorithm, wherein the specific process comprises the steps of calculating a Laplacian matrix of the regional synergy index matrix, and determining the optimal community number through eigenvalue decomposition And dividing the subareas into subareas by using a K-means algorithm Setting dynamic similarity threshold So that the average regional synergy index between the subareas in the community is smaller than While the average regional synergy index of the subareas between communities is greater than For any sub-region, if the region synergy index of the sub-region and all sub-regions is greater than or equal to the dynamic similarity threshold value Determining an isolated risk area; and repeating the similarity judgment until the community attributions of all the subareas are not changed, so as to form a final community division result.
5. The method for predicting the stability of the slope under the rainfall condition, which is characterized by comprising the steps of constructing a three-dimensional digital twin model of a target slope based on a BIM platform, precisely matching community division results with model grids through space coordinate mapping, displaying stability grades of communities and isolated risk areas in real time by adopting a four-color gradient rendering scheme in a visual interface, and displaying real-time rainfall intensity distribution in a superposition manner, wherein green, yellow, orange and red represent a stable state, an early warning state, a high risk state and a destabilizing state respectively; For the area with monotonically decreasing dynamic safety coefficient in three continuous monitoring periods, automatically triggering an audible and visual early warning signal, marking the area with a pulse flickering effect in a model, generating an early warning report containing position coordinates, the change rate of the dynamic safety coefficient and the potential sliding direction, synchronously recording the stability evolution track of each area by the system, and supporting the display of the landslide development process in a time axis playback mode.
6. A slope stability prediction device under rainfall conditions is characterized in that the slope stability prediction device under rainfall conditions is used for executing the slope stability prediction method under rainfall conditions according to any one of claims 1-5, and comprises the following steps: The slope gridding modeling module is used for discretizing the target slope into a plurality of sub-areas by adopting a regular grid, endowing each sub-area with area attributes comprising geological parameters, hydrological parameters and topographic parameters, and simultaneously fusing real-time rainfall intensity data provided by meteorological radars to form a slope characteristic data set with space-time attributes; The intelligent community dividing module is used for representing slope local information of each sub-area by using a multi-dimensional feature vector according to geological parameters, hydrological parameters and topographic parameters of each sub-area, defining an area coordination index between any two sub-areas based on the multi-dimensional feature vector, and clustering the sub-areas by combining a spectral clustering algorithm so as to divide a target slope into a plurality of communities and isolated risk areas; The dynamic security assessment module is used for determining the dynamic security coefficient of each community and the isolated risk area according to the slope characteristic data set, comparing the dynamic security coefficient with a stability threshold value, and dividing the stability grade of each community and the isolated risk area according to the comparison result; The three-dimensional early warning visualization module is used for establishing a three-dimensional digital twin model of the target slope, precisely matching community division results with the model through a space mapping algorithm, marking stability grades of each community and isolated risk areas with different colors in a visual interface, triggering a risk early warning signal for the communities or isolated risk areas with monotonically decreasing dynamic safety coefficients in three continuous monitoring periods, and highlighting the evolution track of the areas in the three-dimensional digital twin model.

Description

Slope stability prediction method and device under rainfall condition Technical Field The invention relates to the technical field of slope stability prediction, in particular to a slope stability prediction method and device under rainfall conditions. Background In slope stability analysis, conventional methods typically rely on static data such as physical and mechanical properties of the soil, slope, vegetation coverage, and the like. These methods mostly employ empirical formulas and simple limit balance analysis, and cannot cover the potential risks caused by dynamic changes. For example, the intensity and duration of rainfall can significantly affect the water content of the soil and the pore water pressure, resulting in a change in soil mass intensity. However, the prior art often fails to monitor in real time the rainfall conditions and their impact on slope stability. Therefore, static evaluation often cannot accurately reflect the actual slope safety state, and particularly under extreme weather conditions, the occurrence rate of landslide accidents is increased, and serious threat is brought to personnel and property safety. In addition, conventional slope stability assessment lacks comprehensive consideration of space-time dynamics, is usually based on isolated analysis of local features, and is difficult to effectively identify and divide potential risk areas. In many cases, complex interrelationships exist between the different subregions of the side slope, which are often ignored in conventional approaches. In the process of acquiring and processing the characteristic data of the side slope, the prior art cannot integrate information from different sources in time, such as actual rainfall intensity, geological parameters, hydrologic state and the like, and the real-time evaluation of the overall safety and stability of the side slope is affected. In addition, the lack of effective intelligent monitoring means limits the reaction speed and accuracy of the side slope risk assessment, and an efficient early warning mechanism cannot be formed. Therefore, a dynamic monitoring and evaluating method for integrating multidimensional data is urgently needed to cope with complex and changeable natural environment conditions, so that scientificity and effectiveness of slope management are improved. The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art. Disclosure of Invention The invention aims to provide a slope stability prediction method and device under rainfall conditions, so as to solve the problems in the background technology. In order to achieve the above purpose, the present invention provides the following technical solutions: a slope stability prediction method under rainfall condition comprises the following specific steps: The method comprises the steps of 1, discretizing a target side slope into a plurality of sub-areas by adopting a regular grid, endowing each sub-area with area attributes comprising geological parameters, hydrological parameters and topographic parameters, and simultaneously fusing real-time rainfall intensity data provided by meteorological radars to form a side slope characteristic data set with space-time attributes; Step 2, according to geological parameters, hydrological parameters and topography parameters of each sub-area, using a multi-dimensional feature vector to represent slope local information of each sub-area, defining an area cooperation index between any two sub-areas based on the multi-dimensional feature vector, and carrying out clustering treatment on the sub-areas by combining a spectral clustering algorithm, so as to divide a target slope into a plurality of communities and isolated risk areas; Step 3, determining dynamic safety coefficients of each community and the isolated risk area according to the slope characteristic data set, comparing the dynamic safety coefficients with a stability threshold value, and dividing stability grades of each community and the isolated risk area according to a comparison result, wherein for each community, selecting a centroid subarea as a representative to determine the dynamic safety coefficients of the communities; And 4, establishing a three-dimensional digital twin model of the target slope, precisely matching community dividing results with the model through a space mapping algorithm, marking stability levels of each community and isolated risk areas with different colors in a visual interface, triggering a risk early warning signal for the communities or isolated risk areas with monotonically decreasing dynamic safety coefficients in three continuous monitoring periods, and highlighting an evolution track of the area in the three-dimensional digital twin model. Further, a target slope is discr