Highway karst area roadbed slope stability simulation method based on digital twinning

Abstract

The invention relates to the field of data processing, in particular to a highway karst area roadbed slope stability simulation method based on digital twinning. Comprising the steps of obtaining and preprocessing multi-dimensional data to obtain preprocessed data, and performing fusion processing on the preprocessed data to obtain fused data; based on the fused data, constructing a three-dimensional virtual model of the roadbed slope for dynamic updating and simulation; and based on the three-dimensional virtual model, introducing a structural intelligent evolution graph field coupling simulation algorithm to perform stability analysis on the highway karst area roadbed slope to obtain a stability analysis result of the highway karst area roadbed slope. The technical problems of poor accuracy and poor self-adaptability of stability analysis of the roadbed slope in the karst area of the expressway are solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method for simulating the stability of roadbed slopes in karst areas of highways based on digital twins. Background Art

[0002] During highway construction, karst areas, due to their complex geological structure, strong spatial heterogeneity, and significantly variable hydrogeological conditions, present key and challenging areas for slope stability control. Karst landforms are often accompanied by unevenly developed underground caves, subsurface river systems, and variable seepage channels. Their structural uncertainty and high sensitivity to environmental factors make slopes highly susceptible to instability hazards such as slippage and collapse during long-term operation or under sudden external forces (such as heavy rain or earthquakes). Therefore, accurate, dynamic, and reliable stability assessment of highway subgrade slopes in karst areas has become a critical issue that urgently needs to be addressed in engineering design, construction, and operation and maintenance management.

[0003] Existing technologies primarily rely on traditional mechanical modeling methods, such as limit equilibrium theory, finite element numerical methods, or strength reduction methods. These methods typically assume that the soil or rock mass is a homogeneous continuous medium and perform mechanical calculations under static boundary conditions. These methods lack the ability to account for the discontinuous structure of karst strata, the evolution of seepage, and the influence of underground cavities, making them inadequate for the highly complex geological characteristics of karst regions. Furthermore, existing models are mostly based on measurement data from a single point in time, making them unable to continuously respond to dynamic environmental changes (such as rainfall variations, groundwater level fluctuations, and geothermal field disturbances). Furthermore, they are unable to embed the large amounts of heterogeneous data provided by sensors, remote sensing, and real-time monitoring systems into the models to achieve digital synchronization and dynamic prediction of physical conditions.

[0004] However, the above existing technologies still have technical problems such as poor accuracy and poor adaptability in analyzing the stability of highway embankment slopes in karst areas. Summary of the Invention

[0005] The present invention provides a simulation method for the stability of highway embankment slopes in karst areas based on digital twins, so as to solve the technical problems of poor accuracy and poor adaptability in the analysis of the stability of highway embankment slopes in karst areas.

[0006] The present invention provides a method for simulating the stability of highway karst roadbed slopes based on digital twins, which specifically includes the following technical solutions: A digital twin-based simulation method for highway embankment slope stability in karst areas includes the following steps: S1. Acquire and preprocess multi-dimensional data to obtain preprocessed data, fuse the preprocessed data to obtain fused data; construct a three-dimensional virtual model of the roadbed slope based on the fused data for dynamic updating and simulation; S2. Based on the three-dimensional virtual model, a structural intelligent evolutionary graph-field coupling simulation algorithm was introduced to conduct stability analysis on the karst area roadbed slope of the expressway, and the stability analysis results of the karst area roadbed slope of the expressway were obtained.

[0007] Preferably, the S1 specifically includes: The fused data is normalized to obtain normalized fused data, and a three-dimensional virtual model of the roadbed slope is constructed based on the normalized fused data. At the same time, combined with digital twin technology, the three-dimensional virtual model of the roadbed slope can receive and process environmental data in real time for updating and simulation.

[0008] Preferably, the S1 specifically includes: In the process of constructing the 3D virtual model of the roadbed slope, adaptive spatial mapping and reconstruction technology is introduced to dynamically adjust the grid distribution according to different geographical locations, physical properties and environmental factors.

[0009] Preferably, the S1 specifically includes: In the process of implementing the adaptive spatial mapping and reconstruction technology, the grid density of each slope sub-area is dynamically adjusted by constructing adaptive weights. The slope sub-areas are divided by an adaptive grid division method based on physical properties and environmental factors to represent the slope characteristics under different environmental conditions.

[0010] Preferably, the S2 specifically includes: The structural intelligent evolutionary graph-field coupling simulation algorithm is based on a topological graph structure with physical field constraints. It embeds the relationship between physical state quantities and coupled energy flows between graph nodes and edges, and uses a dynamic evolution mechanism to simulate the instability mechanism of karst slopes caused by multiple sources to conduct slope stability analysis.

[0011] Preferably, the S2 specifically includes: In the process of the structured intelligent evolutionary graph-field coupling simulation algorithm, the slope sub-region set of the entire slope area is spatially discretized, and the physical coupling paths between the slope sub-regions are represented by establishing a graph structure, where each graph node represents a discrete spatial volume unit, that is, each slope sub-region, and each edge represents the physical field information path between adjacent slope sub-regions.

[0012] Preferably, the S2 specifically includes: During the implementation of the structural intelligent evolution graph-field coupling simulation algorithm, the material properties of each slope sub-area are parametrically modeled, and the initial state of each node is initialized. The node total energy integration function is introduced, and the material density, stress-energy control coefficient, displacement gradient coupling factor, and seepage energy consumption control coefficient are combined with the displacement velocity modulus, stress tensor, local deformation rate, and seepage flux, respectively, to obtain the total energy accumulation value of the node.

[0013] Preferably, the S2 specifically includes: In the implementation process of the structural intelligent evolution graph-field coupling simulation algorithm, when the node energy continues to accumulate to a critical state, the local structure of the slope will undergo a responsive perturbation, thereby obtaining the coupling perturbation intensity of the edge. Based on the coupling perturbation intensity, the instability factor is defined and compared with the tolerance factor to determine whether the node is in an unstable state.

[0014] Preferably, the S2 specifically includes: In the implementation process of the structural intelligent evolution graph-field coupling simulation algorithm, a global stability function is introduced to characterize the total coupling tension state of the slope under the current graph structure from a macroscopic level. Based on the global stability function, the solution is obtained, and based on the solution, the energy evolution process is dynamically backtracked to finally obtain the stability result.

[0015] The beneficial effects of the technical solution of the present invention are: 1. The karst area slope is modeled as a topological graph structure with physical field information, and a node total energy integral function is constructed to obtain the total energy accumulation value of the node. The unstable evolution path of the slope is characterized by a closed-loop mechanism of "energy-disturbance-propagation-evolution". It no longer relies on the static strength theory of traditional continuous media or single fields, and has strong expression capabilities for nonlinear, multi-physical fields, and inhomogeneous geological bodies.

[0016] 2. Through the coupled analysis of instability factors and disturbance intensity, it is possible to accurately determine whether each sub-region of the slope has reached a critical failure state, and further deduce its evolution path and propagation direction in the spatial structure, thereby supporting the early identification of high-risk areas such as potential sliding surfaces and permeability damage zones, and realizing active risk warning. This method makes a macro-judgment on the slope evolution trend through the optimization solution of the global stability function, providing theoretical support for engineering control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the digital twin-based method for simulating the stability of highway embankment slopes in karst areas according to the present invention. DETAILED DESCRIPTION

[0018] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The following describes in detail a specific scheme of a method for simulating the stability of highway embankment slopes in karst areas based on digital twins provided by the present invention in conjunction with the accompanying drawings.

[0021] Refer to the attached Figure 1 , which shows a flow chart of a method for simulating the stability of highway karst area roadbed slopes based on digital twins according to an embodiment of the present invention. The method includes the following steps: S1. Acquire and preprocess multi-dimensional data to obtain preprocessed data, fuse the preprocessed data to obtain fused data; construct a three-dimensional virtual model of the roadbed slope based on the fused data for dynamic updating and simulation; According to expert experience, sensors, drones, and satellite remote sensing technology are deployed in the slope area of ​​the karst roadbed to obtain real-time multi-dimensional data of the karst roadbed of the highway, including physical and environmental parameters such as slope deformation, groundwater flow, precipitation, and seismic activity. The multi-dimensional data is preprocessed by methods such as data cleaning, denoising, time alignment, spatial alignment, data interpolation, standardization, and normalization to obtain preprocessed data. The preprocessing is a technical means well known to those skilled in the art and will not be described in detail here.

[0022] The pre-processed data is fused using existing data fusion technology such as Kalman filtering to obtain fused data. The Kalman filter can integrate the observation values ​​of each data source when fusing multi-source data (for example, sensor data and remote sensing data) and give a more accurate estimation result through weighted averaging.

[0023] Based on the fused data, digital twin technology is used to construct a 3D virtual model of the embankment slope, updating the slope's spatial position, stress distribution, groundwater flow, and other environmental changes in real time. Specifically, to eliminate dimensionality effects, the fused data is normalized to produce normalized fused data. Using geographic information systems and 3D modeling software (such as Revit, ArcGIS, and AutoCAD), a 3D virtual model of the embankment slope is constructed based on this normalized fused data, accurately reflecting key parameters such as the slope's spatial position, stress distribution, and groundwater flow. Furthermore, the integration of digital twin technology enables the 3D virtual model of the embankment slope to not only receive and process environmental data in real time but also update and simulate as external conditions (such as precipitation, groundwater changes, and seismic activity) dynamically change, providing engineers with real-time, accurate slope stability analysis and prediction.

[0024] In the process of constructing the 3D virtual model of the roadbed slope, in order to make the network modeling of the slope adaptively adjusted with time and environmental changes, so as to maintain the accuracy and dynamic response capability of the 3D virtual model of the roadbed slope, the adaptive spatial mapping and reconstruction technology is introduced, which can dynamically adjust the grid distribution according to different geographical locations, physical properties and environmental factors. Specifically, by constructing the adaptive weight The grid density of each slope sub-area is dynamically adjusted. The slope sub-area is divided by an adaptive grid division method based on physical characteristics and environmental factors to represent the slope characteristics under different environmental conditions. The adaptive grid adjustment formula is as follows: , in, Indicates at time Lower, slope sub-area The grid size, in digital twin simulation, refers to the division accuracy of the slope area in 3D modeling, while using different design variables To determine the slope sub-area Material distribution, design variables Indicates whether each slope sub-area is filled with material. The value between 0 and 1 indicates that the slope sub-region is a blank area and the slope sub-region is completely filled with materials. Indicates at time Lower, slope sub-area The grid size; Indicates at time Lower, slope sub-area The adaptive adjustment factor is an adaptive weight dynamically calculated based on environmental conditions, reflecting the impact of factors such as precipitation and temperature changes on grid density. It is obtained by statistical analysis of environmental historical data obtained from the historical database, such as linear regression analysis or multiple regression analysis.

[0025] Through the above process, the grid of each sub-area of ​​the slope will automatically adjust according to environmental changes, making the slope modeling more refined and able to more accurately reflect geological changes and dynamic environments.

[0026] S2. Based on the three-dimensional virtual model, a structural intelligent evolutionary graph-field coupling simulation algorithm was introduced to conduct stability analysis on the karst area roadbed slope of the expressway, and the stability analysis results of the karst area roadbed slope of the expressway were obtained.

[0027] Based on a three-dimensional virtual model of the roadbed slope, a structural intelligent evolutionary graph-field coupling simulation algorithm was introduced to conduct a stability analysis of the roadbed slope in the karst area of ​​the expressway, obtaining the stability analysis results of the roadbed slope in the karst area of ​​the expressway. The structural intelligent evolutionary graph-field coupling simulation algorithm is based on a topological graph structure with physical field constraints. The relationship between physical state quantities and coupled energy flow is embedded between graph nodes and edges. The dynamic evolution mechanism is used to simulate the instability mechanism of the karst area slope caused by multiple sources, realizing the slope stability analysis. The specific process is as follows: The slope sub-region set of the entire slope area is spatially discretized, and each slope sub-region is recorded as , by building a graph structure , characterizes the physical coupling path between slope sub-regions, where each graph node represents a discrete spatial volume unit, that is, each slope sub-region, and each edge represents the physical field information path between adjacent slope sub-regions. In the initialization stage, each slope sub-region needs to be The material properties of the material are parametrically modeled, including material density, stress-energy control coefficient, displacement gradient coupling factor, seepage energy consumption control coefficient, etc., and the initial state of each node is initialized according to the expert experience method, including displacement velocity modulus, stress tensor, local deformation rate, seepage flux, etc.

[0028] Further, define the node The total energy accumulation value is as follows: , in, is a node exist Total energy accumulation value at the moment; is a node The material density of the slope sub-area is obtained from geological surveys or geotechnical data tables and is determined based on the specific materials; is a node In time The displacement velocity modulus is calculated by simulating the displacement gradient; is a node The stress energy control coefficient is used to amplify or suppress the weight of the stress energy contribution in the material unit. It is set according to the expert experience method and the reference value is ; is a node In time The stress tensor (or equivalent stress) is measured by a sensor, such as a strain gauge; is a node The displacement gradient coupling factor is used to regulate the contribution to deformation energy and is determined according to expert experience. The reference value is ; is a node The displacements that occur in three-dimensional space due to external loads, geological changes, or environmental changes are obtained by solving the displacement field equations; It is an approximate representation of the volume strain of a material element in three-dimensional space, reflecting the local deformation rate of the node in the three-axis direction, and is calculated based on the nodal coordinate interpolation function; is a node The seepage energy consumption control coefficient is used to adjust the effect of seepage on the balance of total energy accumulation value. It is determined based on the fluid-solid coupling test and the reference value is ; is a node In time The seepage flux is obtained based on Darcy's law; is a node The seepage divergence per unit volume, which means fluid outflow (positive) or fluid inflow (negative), is calculated using the seepage field. Divergence calculation of ; The formation process of the above formula includes four energy descriptions: the first is the kinetic energy per unit mass of the node; the second term The square of the stress tensor per unit volume multiplied by the stress energy control coefficient represents the elastic strain energy; the third term The fourth term is the square of the sum of the displacement gradients in each direction multiplied by the displacement gradient coupling factor, which represents the viscoplastic deformation energy; It is the seepage divergence multiplied by the seepage energy consumption control coefficient, which represents the internal energy carried away by the seepage. The integral upper limit of the total energy integral function of the above node is the current moment , the lower limit is the initial time , ensuring that the energy changes of the entire process are considered.

[0029] When the node energy accumulates to the critical state preset according to the expert experience method, the local structure of the slope will undergo responsive disturbance. Therefore, based on the existing stress-seepage-thermal three-field coupling model, the slope exist The coupling perturbation intensity at the moment ; By introducing the above-mentioned coupling perturbation intensity, the sudden change of physical state is automatically mapped to the dynamic evolution of topological structure. The greater the coupling perturbation intensity, the stronger the coupling instability between the two nodes and the higher the evolution potential. To further quantify the instability trend of a single node, the instability factor is defined based on structural energy analysis and coupling network model. : , in, is a node exist The instability factor at the moment is used to measure whether the critical state of destruction has been reached. When , the node is in an unstable state and enters the evolution stage. is the tolerance factor, which is determined based on fitting experiments and the reference value is ; is a node Is a set of adjacent nodes, indicating the nodes All directly connected neighboring nodes are determined based on expert experience; It's the edge exist The coupling perturbation intensity at the moment; is a node is the index of the adjacent node; It's the edge exist The weight coefficient at the moment reflects the strength of the interaction between adjacent nodes. It is determined according to the expert experience method, and the reference value is ; The physical meaning of the denominator in the above formula is the total local coupling strength, that is, the total unstable propagation capability between the node and its neighborhood, and the numerator is the current energy state of the node.

[0030] Furthermore, in order to judge the evolution trend and risk diffusion path of the slope as a whole, a global stability function is introduced based on the existing graph-field coupled stability assessment algorithm. , characterizes the total coupling tension state of the slope under the current graph structure from a macroscopic level and is defined as follows: , in, It's the edge The temperature field intensity between the two nodes above indicates the degree of influence of temperature changes on the physical properties of soil and rock. Slope areas with large temperature differences may promote the formation or expansion of cracks, thereby affecting slope stability. It is calculated from the change in temperature field and can be obtained through numerical simulation or on-site temperature sensors. is the total number of nodes; In the above global stability function, the first term The energy-disturbance comprehensive expression of all nodes quantifies the instability potential driven by the triple coupling of "stress-seepage-thermal field" in the local area of ​​the slope. The cube root ensures that the larger coupling disturbance intensity will not dominate the overall trend, which is more in line with engineering practice. is the suppression term of the square value of the temperature field intensity on all edges. Since excessive temperature difference will induce new cracks and reduce stability, the reciprocal function is used to weaken the overall stability contribution brought by thermal anomalies.

[0031] The optimization direction of the above-mentioned global stability function is maximization, reflecting the evolution trend of the slope in a certain state, which is the state most likely to form a coupled instability channel. Taking the global stability function as the objective function, the existing adaptive search algorithm is used to obtain the solution. Based on the solution, the energy evolution process is dynamically traced by combining existing time series simulation and stability safety factor inversion analysis techniques. At the same time, the existing limit equilibrium theory or strength reduction method is introduced to solve the physical indicators. The final stability results include the local safety factor of each slope sub-region (node), the critical sliding surface area, the potential failure mode (such as shear failure, seepage failure), the stability time evolution curve, the instability chain path distribution, and the overall slope stability margin index (such as the instability energy threshold ratio).

[0032] In summary, a digital twin-based simulation method for highway embankment slope stability in karst areas was completed.

[0033] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0034] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0035] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for simulating the stability of highway embankment slopes in karst areas based on digital twins, characterized in that: The following steps are involved: S1. Acquire and preprocess multi-dimensional data to obtain preprocessed data, and fuse the preprocessed data to obtain fused data; Based on the fused data, a 3D virtual model of the roadbed slope is constructed for dynamic updating and simulation; S2. Based on the three-dimensional virtual model, a structural intelligent evolutionary graph-field coupling simulation algorithm was introduced to conduct stability analysis on the karst area roadbed slope of the expressway, and the stability analysis results of the karst area roadbed slope of the expressway were obtained.

2. The method for simulating the stability of highway karst roadbed slopes based on digital twins according to claim 1, characterized in that: Said S1 specifically includes: The fused data is normalized to obtain normalized fused data, and a three-dimensional virtual model of the roadbed slope is constructed based on the normalized fused data. At the same time, combined with digital twin technology, the three-dimensional virtual model of the roadbed slope can receive and process environmental data in real time for updating and simulation.

3. The method for simulating the stability of highway karst roadbed slopes based on digital twins according to claim 2, characterized in that: Said S1 specifically includes: In the process of constructing the 3D virtual model of the roadbed slope, adaptive spatial mapping and reconstruction technology is introduced to dynamically adjust the grid distribution according to different geographical locations, physical properties and environmental factors.

4. The method for simulating the stability of highway karst roadbed slopes based on digital twins according to claim 3 is characterized in that: Said S1 specifically includes: In the process of implementing the adaptive spatial mapping and reconstruction technology, the grid density of each slope sub-area is dynamically adjusted by constructing adaptive weights. The slope sub-areas are divided by an adaptive grid division method based on physical properties and environmental factors to represent the slope characteristics under different environmental conditions.

5. The method for simulating the stability of highway karst roadbed slopes based on digital twins according to claim 1, characterized in that: Said S2 specifically includes: The structural intelligent evolutionary graph-field coupling simulation algorithm is based on a topological graph structure with physical field constraints. It embeds the relationship between physical state quantities and coupled energy flows between graph nodes and edges, and uses a dynamic evolution mechanism to simulate the instability mechanism of karst slopes caused by multiple sources to conduct slope stability analysis.

6. The method for simulating the stability of highway karst roadbed slopes based on digital twins according to claim 5, characterized in that: Said S2 specifically includes: In the process of the structured intelligent evolutionary graph-field coupling simulation algorithm, the slope sub-region set of the entire slope area is spatially discretized, and the physical coupling paths between the slope sub-regions are represented by establishing a graph structure, where each graph node represents a discrete spatial volume unit, that is, each slope sub-region, and each edge represents the physical field information path between adjacent slope sub-regions.

7. The method for simulating the stability of highway karst roadbed slopes based on digital twins according to claim 6, characterized in that: Said S2 specifically includes: During the implementation of the structural intelligent evolution graph-field coupling simulation algorithm, the material properties of each slope sub-area are parametrically modeled, and the initial state of each node is initialized. The node total energy integration function is introduced, and the material density, stress-energy control coefficient, displacement gradient coupling factor, and seepage energy consumption control coefficient are combined with the displacement velocity modulus, stress tensor, local deformation rate, and seepage flux, respectively, to obtain the total energy accumulation value of the node.

8. The method for simulating the stability of highway karst roadbed slopes based on digital twins according to claim 7, characterized in that: Said S2 specifically includes: In the implementation process of the structural intelligent evolution graph-field coupling simulation algorithm, when the node energy continues to accumulate to a critical state, the local structure of the slope will undergo a responsive perturbation, thereby obtaining the coupling perturbation intensity of the edge. Based on the coupling perturbation intensity, the instability factor is defined and compared with the tolerance factor to determine whether the node is in an unstable state.

9. The method for simulating the stability of highway karst roadbed slopes based on digital twins according to claim 1, characterized in that: Said S2 specifically includes: In the implementation process of the structural intelligent evolution graph-field coupling simulation algorithm, a global stability function is introduced to characterize the total coupling tension state of the slope under the current graph structure from a macroscopic level. Based on the global stability function, the solution is obtained, and based on the solution, the energy evolution process is dynamically backtracked to finally obtain the stability result.

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