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

Abstract

The present application relates to the field of data processing, especially to a highway karst area roadbed slope stability simulation method based on digital twinning. Including: obtaining and preprocessing multi-dimensional data to obtain preprocessed data, performing fusion processing on the preprocessed data to obtain fused data; based on the fused data, a three-dimensional virtual model of the roadbed slope is constructed for dynamic updating and simulation; based on the three-dimensional virtual model, a structural intelligent evolution graph field coupling simulation algorithm is introduced to analyze the stability of the highway karst area roadbed slope, and the stability analysis result of the highway karst area roadbed slope is obtained. The technical problems of poor accuracy and poor self-adaptability of highway karst area roadbed slope stability analysis are solved.

Description

Technical Field

[0001] This 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 Technology

[0002] In the construction of highway projects, karst areas, due to their complex geological structures, strong spatial heterogeneity, and significant variations in hydrogeological conditions, have become 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. The uncertainty of their structure and their high sensitivity to environmental responses make slopes highly susceptible to instability disasters such as landslides and collapses under long-term operation or sudden external forces (such as rainstorms and earthquakes). Therefore, how to conduct accurate, dynamic, and reliable stability assessments 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] In existing technologies, slope stability analysis mainly relies on traditional mechanical modeling methods such as limit equilibrium theory, finite element numerical method, or strength reduction method. These methods typically assume that the soil or rock mass is a homogeneous continuous medium and perform mechanical calculations under static boundary conditions. They lack the ability to express the discontinuous structure of karst strata, seepage evolution, and the influence of underground cavities, making them difficult to adapt to the highly complex geological features of karst regions. Furthermore, most existing models are based on measurement data at a single time point, failing to achieve continuous response to dynamic environmental changes (such as rainfall variations, groundwater level fluctuations, and geothermal field disturbances). They also cannot 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 states.

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

[0005] This invention provides a digital twin-based simulation method for the stability of roadbed slopes in karst areas of highways, in order to solve the technical problems of poor accuracy and poor adaptability in the stability analysis of roadbed slopes in karst areas of highways.

[0006] The present invention provides a method for simulating the stability of roadbed slopes in karst areas of highways based on digital twins, specifically including the following technical solutions:

[0007] A digital twin-based simulation method for roadbed slope stability in karst areas of highways includes the following steps:

[0008] S1. Acquire and preprocess multi-dimensional data to obtain preprocessed data; fuse the preprocessed data to obtain fused data; based on the fused data, construct a three-dimensional virtual model of the roadbed slope for dynamic updating and simulation;

[0009] S2. Based on a three-dimensional virtual model, a structural intelligent evolution map-field coupling simulation algorithm is introduced to conduct stability analysis on the roadbed slope in the karst area of ​​the highway, and the stability analysis results of the roadbed slope in the karst area of ​​the highway are obtained.

[0010] Preferably, S1 specifically includes:

[0011] The fused data is normalized to obtain normalized fused data. 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.

[0012] Preferably, S1 specifically includes:

[0013] In the process of constructing a three-dimensional virtual model of 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.

[0014] Preferably, S1 specifically includes:

[0015] In the process of implementing the adaptive spatial mapping and reconstruction technology, the grid density of each slope sub-region is dynamically adjusted by constructing adaptive weights. The slope sub-region is obtained by an adaptive grid division method based on physical characteristics and environmental factors, representing the slope characteristics under different environmental conditions.

[0016] Preferably, S2 specifically includes:

[0017] The structured intelligent evolution graph-field coupling simulation algorithm is based on a topological graph structure with physical field constraints. It embeds physical state variables and coupled energy flow relationships between graph nodes and edges, and uses a dynamic evolution mechanism to simulate the instability mechanism of karst slopes under multiple causes, and performs slope stability analysis.

[0018] Preferably, S2 specifically includes:

[0019] In the process of structural intelligent evolution graph-field coupling simulation algorithm, the set of slope sub-regions of the entire slope area is spatially discretized. A graph structure is established to represent the physical coupling path between slope sub-regions. 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.

[0020] Preferably, S2 specifically includes:

[0021] In the implementation of the structural intelligent evolution field coupling simulation algorithm, the material properties of each slope sub-region are parametrically modeled, and the initial state of each node is initialized. The node total energy integral 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.

[0022] Preferably, S2 specifically includes:

[0023] In the implementation of the structural intelligent evolution graph field coupling simulation algorithm, when the node energy accumulates to the critical state, the local structure of the slope will undergo responsive disturbance, thereby obtaining the coupling disturbance intensity of the edge. Based on the coupling disturbance intensity, an instability factor is defined, and the instability factor is compared with the tolerance factor to determine whether the node is in an unstable state.

[0024] Preferably, S2 specifically includes:

[0025] In the implementation 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 results are obtained, and based on the solution results, the energy evolution process is dynamically backtracked to finally obtain the stability results.

[0026] The beneficial effects of the technical solution of the present invention are:

[0027] 1. The karst slope is modeled as a topological graph structure with physical field information, and the total energy integral function of the nodes is constructed to obtain the total energy accumulation value of the nodes. 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 field, and has a strong ability to express nonlinear, multi-physics field and heterogeneous geological bodies.

[0028] 2. By coupling analysis of instability factors and disturbance intensity, it is possible to accurately determine whether each sub-region of the slope has reached the critical failure state, and further deduce its evolution path and propagation direction in the spatial structure. This supports the early identification of high-risk areas such as potential sliding surfaces and seepage failure zones, and realizes proactive risk warning. This method makes a macro-judgment of the slope evolution trend through the optimization solution of the global stability function, and provides theoretical support for engineering regulation. Attached Figure Description

[0029] Figure 1 This is a flowchart of a method for simulating the stability of roadbed slopes in karst areas of highways based on digital twins, as described in this invention. Detailed Implementation

[0030] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Unless otherwise defined, 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 pertains.

[0032] The following description, in conjunction with the accompanying drawings, details the specific scheme of the simulation method for roadbed slope stability in karst areas of highways based on digital twins provided by this invention.

[0033] See attached document Figure 1 The diagram illustrates a flowchart of a digital twin-based simulation method for roadbed slope stability in karst areas of highways, according to an embodiment of the present invention. The method includes the following steps:

[0034] S1. Acquire and preprocess multi-dimensional data to obtain preprocessed data; fuse the preprocessed data to obtain fused data; based on the fused data, construct a three-dimensional virtual model of the roadbed slope for dynamic updating and simulation;

[0035] Based on expert experience, multi-dimensional data of highway karst roadbeds are acquired in real time by deploying sensors, drones, and satellite remote sensing technology on the slope areas of the roadbed. This data includes physical and environmental parameters such as slope deformation, groundwater flow, precipitation, and seismic activity. The multi-dimensional data is then preprocessed using methods such as data cleaning, noise reduction, time alignment, spatial alignment, data interpolation, standardization, and normalization to obtain preprocessed data. These preprocessing techniques are well-known to those skilled in the art and will not be elaborated upon here.

[0036] The preprocessed data is fused using existing data fusion techniques such as Kalman filtering to obtain fused data. When fusing multi-source data (e.g., sensor data and remote sensing data), Kalman filtering can combine the observations from various data sources and provide a more accurate estimation result through weighted averaging.

[0037] Based on the fused data and combined with digital twin technology, a three-dimensional virtual model of the roadbed slope is constructed, which is updated in real time with various environmental changes such as the slope's spatial location, stress distribution, and groundwater flow. Specifically, to eliminate the influence of dimensions, the fused data is normalized to obtain normalized fused data. Using geographic information systems and 3D modeling software (such as Revit, ArcGIS, AutoCAD, etc.), a three-dimensional virtual model of the roadbed slope is constructed based on the normalized fused data, accurately reflecting key parameters such as the slope's spatial location, stress distribution, and groundwater flow. At the same time, combined with digital twin technology, the three-dimensional virtual model of the roadbed slope can not only receive and process environmental data in real time, but also update and simulate with the dynamic changes of external conditions (such as precipitation, groundwater changes, seismic activity, etc.), thereby providing engineers with real-time and accurate slope stability analysis and prediction.

[0038] In the construction of a 3D virtual model of a roadbed slope, to enable the slope's network model to adaptively adjust with time and environmental changes, thus maintaining the accuracy and dynamic response capability of the 3D virtual model, adaptive spatial mapping and reconstruction technology is introduced. This technology can dynamically adjust the mesh distribution based on different geographical locations, physical properties, and environmental factors. Specifically, this is achieved by constructing adaptive weights... The grid density of each slope sub-region is dynamically adjusted. These sub-regions are obtained through an adaptive gridding method based on physical characteristics and environmental factors, representing slope features under different environmental conditions. The adaptive grid adjustment formula is as follows:

[0039] ,

[0040] in, Indicates at time Below, slope area In digital twin simulation, mesh size refers to the precision of the slope area division in 3D modeling, while also utilizing different design variables. To determine the slope sub-region Material distribution, design variables Indicating whether each slope sub-area is filled with material is a... The values ​​are between 0 and 1, where 0 indicates that the slope area is a blank area and 1 indicates that the slope area is completely filled with material. Indicates at time Below, slope area Grid size; Indicates at time Below, slope area The adaptive adjustment factor, which is an adaptive weight dynamically calculated based on environmental conditions, reflects the influence of factors such as precipitation and temperature changes on grid density. It is obtained by statistical analysis of historical environmental data obtained from historical databases, such as linear regression analysis or multiple regression analysis.

[0041] Through the above process, the grid of each sub-region of the slope will be automatically adjusted according to environmental changes, making the slope modeling more refined and able to more accurately reflect geological changes and dynamic environment.

[0042] S2. Based on a three-dimensional virtual model, a structural intelligent evolution map-field coupling simulation algorithm is introduced to conduct stability analysis on the roadbed slope in the karst area of ​​the highway, and the stability analysis results of the roadbed slope in the karst area of ​​the highway are obtained.

[0043] Based on a three-dimensional virtual model of the roadbed slope, a structural intelligent evolutionary graph-field coupling simulation algorithm is introduced to analyze the stability of the roadbed slope in the karst area of ​​a highway, obtaining the stability analysis results. The structural intelligent evolutionary graph-field coupling simulation algorithm is based on a topological graph structure with physical field constraints. It embeds physical state variables and coupled energy flow relationships between graph nodes and edges, and uses a dynamic evolution mechanism to simulate the instability mechanism of the karst slope under multiple causes, thus realizing the slope stability analysis. The specific process is as follows:

[0044] Spatially discretize the set of slope subregions for the entire slope region, and denote each slope subregion as . By establishing a graph structure This represents the physical coupling paths between slope sub-regions, where each graph node represents a discrete spatial volume unit, i.e., each slope sub-region, and each edge represents the physical field information path between adjacent slope sub-regions. During the initialization phase, each slope sub-region needs to be initialized... The material properties 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.

[0045] Further, define nodes The total cumulative energy is expressed by the following composite integral expression: ,

[0046] in, It is a node exist Total accumulated energy at any given time; It is a node The material density of the slope sub-area is obtained from geological surveys or geotechnical data tables and determined based on the specific material. It is a node In time The displacement velocity modulus is calculated through simulation of displacement gradient; It is a node The stress energy control coefficient is used to amplify or suppress the weight of stress energy contribution in the material element. It is set according to expert experience, with a reference value of [value missing]. ; It is a node In time The stress tensor (or equivalent stress) is measured by sensors, such as strain gauges, and inferred. It is a node The displacement gradient coupling factor, used to adjust its contribution to the deformation energy, is determined based on expert experience, with a reference value of [value missing]. ; It is a node Displacement in three-dimensional space caused by external loads, geological changes, or environmental changes is obtained by solving the displacement field equations. It is an approximate representation of the volumetric strain of a material element in three-dimensional space, reflecting the local deformation rate of the nodes in the three-axis directions, and is calculated based on the nodal coordinate interpolation function; It is a node The seepage energy consumption control coefficient is used to adjust the influence of seepage on the balance of total energy accumulation. It is determined based on fluid-solid coupling experiments, and a reference value is taken as follows: ; It is a node In time The permeation flux was obtained based on Darcy's law; It is a node The seepage divergence per unit volume represents whether fluid flows out (positive) or in (negative), and is used in the seepage field. Calculation of divergence;

[0047] The formation process of the above formula includes four energy descriptions: the first term The kinetic energy per unit mass of the node; the second term The third term is the square of the stress tensor per unit volume multiplied by the stress energy regulation coefficient, representing the elastic strain energy; The fourth term is the square of the sum of displacement gradients in all directions multiplied by the displacement gradient coupling factor, characterizing the viscoplastic deformation energy; The seepage divergence multiplied by the seepage energy consumption control coefficient represents the internal energy carried away by seepage. The upper limit of the integral function of the total energy at the above nodes is the value at the current moment. The lower limit is the initial time. This ensures that energy changes throughout the entire process are taken into account.

[0048] When the nodal energy accumulates to a critical state predetermined by expert experience, 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 Coupled perturbation strength at time t ;

[0049] By introducing the aforementioned coupling perturbation strength, the abrupt change in physical state is automatically mapped to the dynamic evolution of the topology. A greater coupling perturbation strength indicates stronger coupling instability between the two nodes and a higher evolutionary potential. To further quantify the instability tendency of a single node, an instability factor is defined based on structural energy analysis and the coupled network model. : ,

[0050] in, It is a node exist The instability factor at any given time is an evaluation index used to measure whether a critical state of destruction has been reached. At this point, the node is in an unstable state and enters the evolutionary stage. This is the tolerance factor, determined based on fitting experiments; the reference value is [value missing]. ; It is a node It is the set of adjacent nodes, representing the set of nodes connected to it. All directly connected neighboring nodes are determined based on expert experience. It is the edge exist The coupling perturbation strength at any given moment; It is a node It is the index of the adjacent node; It is the edge exist The weighting coefficient at time point reflects the strength of the interaction between adjacent nodes. It is determined based on expert experience, with a reference value of [value missing]. 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, while the numerator is the current energy state of the node.

[0051] Furthermore, in order to determine the overall evolution trend and risk diffusion path of the slope, a global stability function is introduced based on the existing map-field coupled stability assessment algorithm. From a macroscopic perspective, the total coupling tension state of the slope under the current graph structure is characterized as follows:

[0052] ,

[0053] in, It is the edge The temperature field intensity between the two nodes represents 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 propagation of cracks, thereby affecting slope stability. It is calculated from the changes in the temperature field and can be obtained through numerical simulation or on-site temperature sensors. It is the total number of nodes;

[0054] In the above global stability function, the first term The energy-disturbance comprehensive expression for 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 larger coupled disturbance intensities do not dominate the overall trend, which is more consistent with engineering practice. The second term... The term is a suppression term for the squared value of the temperature field intensity on all sides. Since excessive temperature difference can induce new cracks and reduce stability, the reciprocal function is used to weaken the overall stability contribution of thermal anomalies.

[0055] The optimization direction of the aforementioned global stability function is to maximize it, reflecting the evolution trend of the slope under a certain state as the state most likely to form a coupled instability channel. Using the global stability function as the objective function, existing adaptive search algorithms are employed to obtain the solution. Based on the solution, combined with existing time-series simulation and stability safety factor inversion analysis techniques, the energy evolution process is dynamically backtracked. Simultaneously, existing limit equilibrium theory or strength reduction methods are introduced to calculate physical indices, ultimately yielding stability results, including the local safety factor of each slope sub-region (node), critical sliding surface region, potential failure modes (such as shear failure and seepage failure), stability time-history evolution curve, instability chain path distribution, and overall slope stability margin indices (such as the instability energy threshold ratio).

[0056] In summary, a digital twin-based simulation method for the stability of roadbed slopes in karst areas of highways has been developed.

[0057] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0058] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0059] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A highway karst area roadbed slope stability simulation method based on digital twinning, characterized by, Comprise the following steps: S1. Obtain and preprocess multi-dimensional data to obtain preprocessed data, fuse the preprocessed data to obtain fused data; Based on the fused data, a three-dimensional virtual model of the subgrade slope is constructed for dynamic updating and simulation; S2. Based on the three-dimensional virtual model, a structural intelligent evolution graph field coupling simulation algorithm is introduced, a topological graph structure with physical field constraints is used to embed physical state quantities and coupling energy flow relationships between graph nodes and edges, and a dynamic evolution mechanism is used to simulate the instability mechanism of the slope in the karst area under the action of multiple sources, to analyze the stability of the subgrade slope in the karst area of the expressway, and obtain the stability analysis result of the subgrade slope in the karst area of the expressway; In the process of the structural intelligent evolution graph field coupling simulation algorithm, the slope sub-region set of the whole slope region is spatially dispersed, and the physical coupling path between the slope sub-regions is represented by establishing a graph structure, wherein each graph node represents a discrete spatial volume unit, i.e. each slope sub-region, and each edge represents the physical field information path between adjacent slope sub-regions; The material properties of each slope sub-region are parameterized modeled, and the initial state of each node is initialized, a node total energy integral function is introduced, the material density, stress energy regulation coefficient, displacement gradient coupling factor and seepage energy consumption regulation 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; When the node energy continuously accumulates to the critical state, the local structure of the slope will respond to the disturbance, thereby obtaining the coupling disturbance intensity of the edge, based on the coupling disturbance intensity, defining the instability factor, comparing the instability factor with the tolerance factor to determine whether the node is in an unstable state.

2. The expressway karst area roadbed slope stability simulation method based on digital twinning according to claim 1, characterized in that, The S1 specifically comprises: The fused data is normalized to obtain normalized fused data, and a three-dimensional virtual model of the subgrade slope is constructed based on the normalized fused data, and the three-dimensional virtual model of the subgrade slope is updated and simulated in real time by combining with the digital twin technology.

3. The expressway karst area roadbed slope stability simulation method based on digital twinning according to claim 2, characterized in that, The S1 specifically comprises: In the process of constructing the three-dimensional virtual model of the subgrade slope, an adaptive spatial mapping and reconstruction technology is introduced, and the grid distribution is dynamically adjusted according to different geographical positions, physical properties and environmental factors.

4. The expressway karst area roadbed slope stability simulation method based on digital twinning according to claim 3, characterized in that, The S1 specifically comprises: In the process of implementing the adaptive spatial mapping and reconstruction technology, the grid density of each slope sub-region is dynamically adjusted by constructing an adaptive weight, and the slope sub-region is divided by an adaptive grid division method based on physical properties and environmental factors, representing the slope characteristics under different environmental conditions.

5. The expressway karst area roadbed slope stability simulation method based on digital twinning according to claim 1, characterized in that, The S2 specifically comprises: In the process of implementing the structural intelligent evolution graph field coupling simulation algorithm, a global stability function is introduced to represent the total coupling tension state of the slope under the current graph structure from a macroscopic perspective; based on the global stability function, the solution result is obtained, and based on the solution result, the energy evolution process is dynamically traced back to finally obtain the stability result.

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