Fine simulation method for water-rich filling karst cave water inrush catastrophe in combination with FDEM and SPH

Abstract

The invention relates to the technical field of karst cave water inrush catastrophe simulation, in particular to a water-rich filling karst cave water inrush catastrophe refined simulation method combining FDEM and SPH. According to the technical scheme, the method comprises the steps of multi-source data collection and integration, high-precision model construction, self-adaptive grid division and material attribute assignment, water-rich filler SPH particle conversion and initialization, crustal stress balance iterative solution, tunnel excavation dynamic simulation, result fine analysis and catastrophe evaluation, and tunnel support optimization design and feedback adjustment. According to the method, the risk of water-rich filling karst cave water inrush catastrophe is accurately predicted, surrounding rock instability and water inrush hidden danger areas are locked in advance by means of a fine model and multi-parameter analysis, a safety defense line is built for construction, engineering design is powerfully optimized, support parameters are accurately determined according to simulation, resource waste is avoided, the cost is reduced, the construction period is shortened, the exploration and design efficiency is improved, and scientific decision is assisted. And industry technology progress is promoted, the method is expanded to various underground engineering, and the application prospect is wide.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation of water inrush disasters in karst caves, and in particular to a refined simulation method of water inrush disasters in water-rich filled karst caves combining FDEM and SPH. Background Art

[0002] Karst caves are a common problem in the development and construction of underground projects. As my country's rail transit enters the development stage, rail transit in more and more cities (such as Dalian, Changsha, Shenzhen, etc.) is faced with crossing complex karst areas and other areas with poor geological development. The integrity and continuity of the rock mass are destroyed due to the existence of karst caves, and the strength and stability are greatly reduced. Tunnel excavation disturbances are extremely likely to cause water inrush and cave collapse, which in turn leads to casualties in the tunnel, ground collapse and other hazards. This makes the stability and safety of water-rich karst caves one of the important considerations for tunnel excavation construction and safe tunnel operation and maintenance when excavating nearby underground structures.

[0003] Traditional engineering survey and design methods are powerless when faced with water-rich caves. Conventional geological survey methods, such as drilling, can obtain local information about the location of the borehole, but they are limited in accurately depicting the overall shape of the cave and the distribution of the fillings, making it difficult to fully grasp the overall picture of the cave system. The accuracy and reliability of geophysical exploration results under complex geological conditions also need to be improved, and it is often impossible to accurately identify the boundaries of the cave and the details of the fillings.

[0004] In terms of numerical simulation, most of the early simulation technologies were based on a single numerical method, which could not take into account the continuous-discontinuous deformation characteristics of the rock mass and the fluid behavior of the filling material at the same time. For example, the finite element method is good at dealing with continuous medium problems, but it is not good at simulating discontinuous phenomena such as cracks and fractures that occur in the rock mass during excavation; and the traditional fluid dynamics method, when coupled with rock mechanics, is difficult to accurately reflect the impact of solid-fluid interaction on engineering stability. This leads to the inability of engineers to accurately predict possible disasters that may occur during construction during the tunnel design stage, and it is difficult to formulate scientific and reasonable construction plans and support measures, which makes the project face many uncertainties during the construction process, further increasing the project risk.

[0005] Therefore, this application proposes a refined simulation method for water-rich cave flooding disasters combining FDEM and SPH. Summary of the invention

[0006] The purpose of the present invention is to propose a method for fine-tuning water inrush disaster simulation of water-rich filled caves by combining FDEM and SPH in order to address the problem that traditional engineering survey and design methods in the background technology cannot accurately identify the boundaries of caves and the details of fillings.

[0007] Technical solution of the present invention: A refined simulation method for water-rich filled karst cave water inrush disasters combining FDEM and SPH, comprising the following steps:

[0008] Multi-source data collection and integration: Use geological drilling and geophysical exploration to collect geometric, lithological, mechanical parameters of water-rich karst caves and surrounding rock masses, and physical property data of the filling materials in the karst caves;

[0009] Construct a high-precision model: Based on the collected data, build a three-dimensional fine model containing rock masses, water-rich karst caves, and tunnels in Abaqus software, and set appropriate boundary conditions;

[0010] Adaptive mesh division and material property assignment: Use adaptive encryption technology to divide the model mesh, and accurately assign material properties to rock masses and filling materials according to test results;

[0011] SPH particle conversion and initialization of water-rich filling materials: Generate SPH particles according to the volume of the filling material and the requirements of simulation accuracy, and initialize the particle mass, position, and velocity;

[0012] Iterative solution of in-situ stress balance: Calculate the initial in-situ stress by comprehensively considering the self-weight of the rock mass, tectonic stress, and groundwater pressure, and use the iterative algorithm to achieve in-situ stress balance;

[0013] Dynamic simulation of tunnel excavation: Determine the excavation parameters according to the actual construction plan, write a subroutine to connect with the Abaqus main program, and simulate the changes in tunnel excavation and groundwater seepage;

[0014] Refined analysis of results and disaster assessment: Extract the simulation result data, analyze the stability of the surrounding rock and the flow characteristics of the filling material, and judge the risks of water inrush and instability;

[0015] Optimized design and feedback adjustment of tunnel support: Design tunnel support according to the disaster assessment, compare the monitoring and simulation data during construction, and optimize the model and support according to the feedback.

[0016] Optionally, the multi-source data collection and integration specifically includes the following steps:

[0017] For geological drilling, set the grid spacing according to the engineering and geological conditions, which is 3-5 meters in complex areas, drill cores to measure their elastic modulus E r , Poisson's ratio v r , cohesion c r , friction angle mechanical parameters and record the lithological changes;

[0018] Geophysical exploration combines the seismic wave reflection method and the resistivity method to outline the contour of the karst cave;

[0019] Use special equipment to take samples of the filling materials in the water-rich karst cave, and use the drainage method to measure the density ρ of the filling material c , that is where m sample is the sample mass, V displaced is the volume of the displaced water, and the particle size distribution is determined by screening and a laser particle size analyzer.

[0020] Optionally, the construction of the high-precision model specifically includes the following steps:

[0021] Based on the collected data, a three-dimensional fine model is built in Abaqus with the measured coordinates. The karst cave boundary is fitted with a non-uniform rational B-spline curve with an accuracy of up to the millimeter level;

[0022] The model boundary conditions are as follows: the ground surface is free, the bottom is vertically fixed, and the side surfaces are subject to horizontal stress constraints according to the in-situ stress measurement results, calculated by the formula σ h = K0σ v where σ v is the vertical in-situ stress, calculated from the self-weight of the overlying rock mass, i.e., σ v = γ r H, γ r is the unit weight of the rock mass, H is the depth, and K0 is the lateral pressure coefficient, with a value range of 0.5 - 1.2 based on experience and laboratory tests.

[0023] Optionally, the adaptive mesh generation and material property assignment specifically include the following steps:

[0024] The adaptive mesh is encrypted to focus on the karst cave, the tunnel excavation face, and the surrounding area within a range of 3 - 5 times the tunnel diameter. Let the minimum mesh size be where d c is the minimum diameter of the karst cave, d t is the minimum diameter of the tunnel, d t with the unit of meters for all. The mesh in the far area is transitioned, and h max = 5h min to ensure the mesh quality, with the distortion < 0.4 and the Jacobian determinant > 0.3;

[0025] The mechanical parameters are assigned to different regions of the rock mass according to the drilling and test results. For layered rock masses including shale, the elastic modulus ratio between the bedding plane and the vertical bedding plane is set according to the test, reaching 1.5 - 2.0;

[0026] For the filling material, the Mohr-Coulomb or modified Cambridge model is used according to the particle size distribution. When the proportion of coarse particles exceeds 60%, the cohesion c c takes values of 0.5 - 2.0 kPa and the friction angle takes values of 30° - 45°. The model parameters of the viscous filling material are determined through consolidation tests.

[0027] Optionally, the conversion and initialization of SPH particles for water-rich filling materials specifically include:

[0028] According to the volume V of the water-rich karst cave filling materialc The number of SPH particles related to the simulation accuracy V SPH is the theoretical volume represented by a single SPH particle;

[0029] Particle initialization: mass m SPH = ρ c V SPH , the positions are randomly and uniformly distributed in the karst cave area, and the initial vertical velocity where g is the acceleration due to gravity, h c is the height from the center of the karst cave to the groundwater level, and the initial horizontal velocity is based on the measured water flow value or set to 0.

[0030] Optionally, in the iterative solution of the in-situ stress balance:

[0031] The initial in-situ stress combines the self-weight of the rock mass, tectonic stress, and groundwater pressure, and is applied to the model nodes as nodal forces;

[0032] Use the Abaqus Newton-Raphson iterative algorithm, and set the convergence criterion: the change in the 2-norm of the nodal displacements in adjacent iterations is less than 10 -5 and the change in the 2-norm of the stress is less than 10 -4 to determine convergence.

[0033] Optionally, in the dynamic simulation of tunnel excavation:

[0034] Determine the tunnel excavation direction and step size Δx according to the actual construction. When using the drill and blast method, D is the tunnel diameter. When using the shield method, the excavation speed v ranges from 0.5 to 2.0 m / h;

[0035] Write the Vusdfld subroutine in VisualStudio2019 to associate with the Abaqus main program. The elastic modulus of the excavated element instantaneously drops to E excavated = 0.001E r , and the Poisson's ratio is adjusted to v excavated = 0.49. According to Darcy's law where q is the seepage velocity vector, k s is the saturated permeability coefficient, is the hydraulic head gradient, and the groundwater seepage field near the excavation face is updated in real time.

[0036] Optionally, in the refined analysis of results and disaster assessment:

[0037] Extract the surrounding rock displacement, stress, strain field, and the movement trajectory and velocity of SPH particles from the Abaqus simulation results, and calculate the maximum displacement u of the surrounding rock max , set the threshold value to 0.05 - 0.1 m, and mark the potential instability area exceeding the threshold; calculate the maximum principal stress σ of the surrounding rock 1,max, the minimum principal stress σ 3,max , stress ratio R > 3 indicates possible failure;

[0038] Calculate the average flow velocity v of the filling material based on the movement and velocity of SPH particles avg , set the critical flow velocity to 0.03 - 0.08 m / s according to the characteristics of the filling material and hydrogeology. Exceeding the critical flow velocity indicates water inrush. Monitoring that the increase in pore pressure exceeds 0.1 - 0.3 MPa and is rising corroborates the risk of water inrush.

[0039] Optionally, the optimization design and feedback adjustment of the tunnel support specifically include:

[0040] According to the disaster assessment, use bolt support preferentially in potentially unstable or high-stress areas. The bolt length L bolt = 1.2 - 1.5 times the radius of the loosened circle, and the spacing S bolt is taken as 0.5 - 1.5 m; add a water-stop curtain in the possible water-inrush area, and the permeability coefficient k curtain < 10 -6 m / s, and the thickness T curtain is taken as 0.3 - 0.8 m;

[0041] During construction, set monitoring points to collect data such as the displacement, stress, and pore water pressure of the surrounding rock. If the deviation from the simulation results exceeds 10% - 15%, re-simulate and optimize the support according to the feedback adjustment model parameters. The model parameters include materials and boundaries.

[0042] Compared with the prior art, the present application includes at least one of the following beneficial technical effects:

[0043] 1. Through fine data collection and model construction, the present invention can highly restore the actual state of the water-rich filled karst cave and the surrounding rock mass. This enables the accurate capture of the changes in the stress and strain of the surrounding rock and the flow trend of the cave filling material during the simulation of tunnel excavation. It can accurately identify in advance the areas where collapse may occur, providing clear danger warnings for construction personnel, enabling them to take reinforcement measures in a timely manner, avoiding tunnel collapse accidents, and greatly ensuring the safety of construction personnel's lives;

[0044] 2. The simulation results provide accurate basis for tunnel support design. According to the stability conditions of different areas of the surrounding rock, reasonably select the support method to ensure that the support structure can effectively control the deformation of the surrounding rock and avoid waste of resources caused by over-support; in the areas where water inrush may occur, set a water-stop curtain specifically, while ensuring the waterproof effect, reducing unnecessary material input, and reducing the project cost;

[0045] 3. By adopting multi-source geological exploration means and combining with advanced numerical simulation methods, the problems of one-sided and inaccurate information acquisition in traditional exploration and design are changed. The combination of geological drilling and geophysical exploration can quickly and comprehensively obtain the information of karst caves and rock masses, providing rich and accurate data support for model construction and reducing the exploration time. At the same time, based on the simulation analysis of the accurate model, it can conduct virtual verification of multiple construction plans at one time. Designers do not need to guess the feasibility of different plans only by experience, and can compare the optimal construction plan in a short time, greatly improving the overall efficiency of exploration and design;

[0046] 4. Combining FDEM and SPH technologies fills the deficiencies of traditional numerical simulation methods in dealing with complex geological conditions such as water-rich filled karst caves, opening up a new path for the field of numerical simulation of underground engineering. It solves the long-standing problem in the industry of how to accurately simulate the continuous-discontinuous deformation of rock masses and the behavior of filling fluids at the same time, provides a powerful tool for relevant academic research, and promotes the interdisciplinary integration and development of rock mechanics, tunnel engineering and other disciplines;

[0047] In summary, the present invention accurately predicts the risk of water inrush disasters in water-rich filled karst caves. By means of a fine model and multi-parameter analysis, it can lock in advance the areas where the surrounding rock is unstable and the hidden dangers of water inrush, build a solid safety defense line for construction, effectively optimize the engineering design, accurately determine the support parameters according to the simulation, avoid waste of resources, reduce costs and shorten the construction period, improve the efficiency of exploration and design, assist in scientific decision-making, and also promote the technological progress of the industry. It is extended to various types of underground projects and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a refined simulation method for water inrush disasters in water-rich filled karst caves combining FDEM and SPH. DETAILED DESCRIPTION OF THE INVENTION

[0049] The technical solutions of the present invention will be further described below in conjunction with the drawings and specific embodiments.

[0050] As Figure 1 shown, a refined simulation method for water inrush disasters in water-rich filled karst caves combining FDEM and SPH proposed by the present invention includes multi-source data collection and integration, construction of a high-precision model, adaptive grid division and material property assignment, SPH particle conversion and initialization of water-rich filling materials, iterative solution of in-situ stress balance, dynamic simulation of tunnel excavation, refined analysis of results and disaster assessment, and optimized design and feedback adjustment of tunnel support. Each step will be described in detail below.

[0051] I. Multi-source data collection and integration

[0052] Adopt geological drilling technology to drill holes according to the pre-designed grid layout. The grid spacing depends on the project scale and geological complexity, and is set to 3 - 5 meters in complex areas. Obtain rock samples from the drill cores and measure their elastic modulus E r , Poisson's ratio v r , cohesion c r , friction angle mechanical parameters, and record the lithological changes at different depths simultaneously.

[0053] Apply geophysical exploration methods, such as the combination of seismic wave reflection method and resistivity method. The seismic wave reflection method excites seismic waves on the ground surface and infers the location and approximate shape of the karst cave according to the reflection time and amplitude change of the waves. Its depth resolution can reach 0.5 - 1 meter; the resistivity method uses the conductivity differences of different geological bodies to measure the resistivity distribution of the underground medium and further outline the contour of the karst cave. The positioning accuracy is within the range of ±2 meters.

[0054] For the filling materials in the water-rich karst cave, obtain representative samples through special sampling equipment. In the laboratory, according to Archimedes' principle, use the drainage method to measure the density ρ of the filling materials c , that is where m sample is the sample mass, and V displaced is the volume of the displaced water; use the sieving method combined with a laser particle size analyzer to determine the particle size distribution and obtain the proportion of particles with different particle sizes.

[0055] II. High-precision model construction

[0056] Based on the collected geological data, create a three-dimensional fine model including rock mass, water-rich karst cave, and tunnel in Abaqus software. Based on the actually measured coordinate data, ensure that the geometric dimensions of the model are accurate to the millimeter level. For the karst cave boundary, use non-uniform rational B-spline (NURBS) curves for fitting to make its shape highly consistent with the measured results.

[0057] Regarding the setting of the model boundary conditions, the ground surface is set as a free boundary, allowing vertical displacement; a vertical fixed constraint is applied to the bottom boundary of the model to simulate the support of the earth; the side boundaries are subject to horizontal stress constraints according to the in-situ stress measurement results through the formula σ h = K0σ v , where σ v is the vertical in-situ stress, calculated from the self-weight of the overlying rock mass, that is σ v = γ r H, γ r is the unit weight of the rock, H is the depth, and K0 is the coefficient of lateral pressure. According to experience and laboratory tests, its value range is between 0.5 - 1.2.

[0058] III. Adaptive Mesh Generation and Material Property Assignment

[0059] Adaptive refinement technology is adopted for mesh generation, with key attention paid to the karst cave area, the tunnel excavation face, and the area within 3 - 5 times the tunnel diameter around it. Let the minimum mesh size be h min , according to Saint-Venant's principle and empirical formula where d c is the minimum diameter of the karst cave, d t is the minimum diameter of the tunnel, and d t are all in meters; for the rock mass part far from the key area, the mesh size transitions gradually in a gradient manner, and the maximum mesh size h max = 5h min is ensured to meet the requirements of numerical simulation convergence, with the distortion controlled below 0.4 and the Jacobian determinant value greater than 0.3

[0060] For the assignment of rock mass material properties, according to the results of drilling and laboratory tests, the rock mechanics parameters of different regions are input into the model correspondingly. For the layered rock mass with obvious anisotropy, the elastic modulus E parallel along the bedding plane and the elastic modulus E perpendicular perpendicular to the bedding plane are set in proportion according to the test data. For common shale, this ratio reaches 1.5 - 2.0

[0061] For the filling material of water-rich karst caves, before converting it into SPH particles, the material parameters in different particle size ranges are determined according to its particle size distribution first. Taking the Mohr-Coulomb constitutive model as an example, the cohesion c c and the friction angle are adjusted according to the proportion of coarse particles (such as sand grains, gravel) in the filling material. When the proportion of coarse particles exceeds 60%, the value range of c c is 0.5 - 2.0 kPa and is between 30° - 45°; for the filling material with a higher viscous component, the modified Cambridge model is introduced, considering the compression characteristics of cohesive soil, and the model parameters are determined through laboratory consolidation tests

[0062] IV. Conversion and Initialization of SPH Particles for Water-Rich Karst Cave Fillings

[0063] According to the volume of the water-rich karst cave filling (unit: cubic meters) and the required simulation accuracy, the number of SPH particles is determined. Using the formula where V SPH is the theoretical volume represented by a single SPH particle, combined with the minimum mesh size, take

[0064] Each SPH particle is initialized, and the mass is calculated through the formula m SPH = ρ c V SPHCalculated; the initial positions of the particles are randomly and uniformly distributed within the karst cave area. Considering the influence of gravity, the initial velocity is set in the vertical direction as where g is the acceleration due to gravity, and h c is the height from the center of the karst cave to the groundwater level, both in meters. The initial velocity in the horizontal direction is set according to the measured value of the regional water flow velocity. If the water flow is not measured, the initial velocity is 0.

[0065] V. Iterative solution for in-situ stress balance

[0066] The initial in-situ stress is calculated by comprehensively considering the self-weight of the rock mass, tectonic stress, and groundwater pressure. The vertical in-situ stress has been mentioned in the boundary condition setting; the tectonic stress is estimated based on the regional geological structure background. By using the geomechanics analysis method and combining the in-situ stress measurement data on-site, the horizontal tectonic stress component is estimated. The final expression of the horizontal in-situ stress is σ h = K0σ v + σ t . The groundwater pressure is calculated using the hydrostatic pressure formula p w = ρ w gh w , where ρ w is the density of water, g is the acceleration due to gravity, and h w is the vertical distance from the calculation point to the groundwater level, all in meters), and it is applied to the corresponding nodes in the form of nodal forces in the model.

[0067] The Newton-Raphson iterative algorithm built into Abaqus is used to solve the in-situ stress balance. The iterative convergence criterion is set. When the change in the 2-norm of the nodal displacements in two adjacent iterative processes is less than 10 -5 and the change in the 2-norm of the stress is less than 10 -4 , it is determined that the in-situ stress balance reaches the convergence state, ensuring the stability of the initial state of the model and providing a reliable basis for the subsequent tunnel excavation simulation.

[0068] VI. Dynamic simulation of tunnel excavation

[0069] According to the actual construction plan, determine the excavation direction, step size Δx, and excavation speed of the tunnel. The step size Δx is set according to the tunnel diameter D (unit: meters) and the requirements of the construction technology. When using the drill and blast method, to ensure the stability of the excavation process; the excavation speed v is determined according to the project progress plan and the performance of the on-site equipment. For example, when using the shield tunneling method, the value ranges between 0.5 - 2.0 meters per hour.

[0070] Write the Vusdfld subroutine in VisualStudio2019, which is closely associated with the Abaqus main program. In the subroutine, the excavation process of the soil mass is simulated by changing the material properties of the elements. When a certain element enters the excavation step, its elastic modulus is instantly reduced to E excavated= 0.001E r , the Poisson's ratio is adjusted to v excavated = 0.49 to make it close to the fluid state and simulate the mechanical response after soil excavation; meanwhile, according to Darcy's law where q is the seepage velocity vector and k s is the saturated permeability coefficient, is the hydraulic head gradient, and the groundwater seepage field near the excavation surface is updated in real time, considering the influence of seepage on soil strength and stability.

[0071] VII. Refined result analysis and catastrophe assessment

[0072] Extract the surrounding rock displacement field u(x, y, z), stress field σ(x, y, z), strain field ε(x, y, z), and the motion trajectories and velocity information of SPH particles from the Abaqus simulation results. Calculate the maximum displacement of the surrounding rock (unit: meter). Based on experience and similar engineering cases, set a displacement threshold. When u max > u t h res h old When, u t h res h old The value range is between 0.05 - 0.1 meter, and mark this area as the potential instability area; calculate the maximum principal stress σ 1,max (unit: MPa) and the minimum principal stress σ 3,max (unit: MPa), and evaluate the stability of the surrounding rock through the stress ratio When R > 3, 3 is the lower limit of the empirical safety stress ratio, which can be adjusted according to the actual project. Judge that this area may be damaged.

[0073] For the water-rich karst filling, analyze its flow characteristics according to the motion trajectories and velocities of SPH particles. Calculate the average flow velocity of the filling (unit: m / s). Combine the particle characteristics of the filling and the on-site hydrogeological conditions to set the critical flow velocity v critical . When v avg > v critical When, v critical The value range is between 0.03 - 0.08 m / s, depending on the filling type and pore structure, and predict the possible occurrence of water inrush disasters; meanwhile, monitor the change of pore pressure inside the filling. When the increase in pore pressure exceeds 0.1 - 0.3 MPa and continues to rise, it further corroborates the intensification of the water inrush risk.

[0074] VIII. Optimization design and feedback adjustment of tunnel support

[0075] According to the catastrophe assessment of the simulation results, carry out the optimization design of tunnel support. If potential instability areas or high-stress areas appear, give priority to considering the use of bolt support. The bolt length L boltDetermined according to the range of the surrounding rock loosening zone, it can be calculated by the formula L bolt = 1.2 - 1.5 times the radius of the loosening zone. The bolt spacing S bolt Based on the surrounding rock strength and stress distribution, the value ranges from 0.5 to 1.5 meters; for areas where water inrush may occur, a water-stop curtain is added. The permeability coefficient k of the curtain curtain should satisfy k curtain < 10 -6 m / s, and the thickness T curtain According to the water pressure and geological conditions, the value ranges from 0.3 to 0.8 meters.

[0076] During the tunnel construction process, monitoring points are arranged to collect data such as the displacement, stress, and pore water pressure of the surrounding rock in real time. The monitoring data is compared and analyzed with the simulation results. If the deviation between the two exceeds 10% - 15%, the model parameters (such as material properties, boundary conditions) are adjusted based on the feedback information, and re-simulation is carried out to optimize the support design to ensure the safe and smooth progress of tunnel construction.

[0077] In this embodiment, through fine data collection and model construction, the actual state of the water-rich filled karst cave and the surrounding rock mass can be highly restored. This enables the accurate capture of the changes in the stress and strain of the surrounding rock and the flow trend of the cave filling materials during the simulation of tunnel excavation. For example, when judging the stability of the surrounding rock, based on the maximum displacement and principal stress data obtained from accurate simulation and compared with the set scientific thresholds (such as when the maximum displacement of the surrounding rock exceeds 0.05 - 0.1 meters, it is marked as a potentially unstable area, and a stress ratio greater than 3 indicates possible failure), the areas that may collapse can be accurately identified in advance, providing clear danger warnings for construction workers so that they can take reinforcement measures in time to avoid the occurrence of tunnel collapse accidents, greatly ensuring the safety of construction workers' lives.

[0078] Among them, for the water inrush disaster caused by the water-rich cave filling materials, the average flow velocity and pore pressure changes of the filling materials are accurately calculated based on SPH particle simulation. Once the flow velocity exceeds the critical value (0.03 - 0.08 m / s) and the pore pressure increase is abnormal (exceeds 0.1 - 0.3 MPa and continues to rise), the water inrush risk can be predicted in advance, and the construction team can prepare emergency plans accordingly, such as arranging drainage equipment in advance and strengthening water-stop measures, etc., to effectively reduce the losses caused by water inrush accidents.

[0079] In addition, the simulation results provide an accurate basis for tunnel support design. According to the stability of different areas of the surrounding rock, the support method is reasonably selected. For example, the anchor length (1.2-1.5 times the radius of the loose circle) and spacing (0.5-1.5 meters) are accurately determined in the potential instability area to ensure that the support structure can effectively control the deformation of the surrounding rock and avoid excessive support causing waste of resources; in areas where water inrush may occur, water-stop curtains are set up in a targeted manner, and the permeability coefficient (less than 10 -6 m / s) and thickness (0.3-0.8 m), while ensuring the waterproof effect, reducing unnecessary material input and lowering project costs. Since the problems that may be faced during the construction process can be fully understood through simulation during the design phase, repeated design changes during the construction process are avoided. Compared with the traditional model of relying on experience-based design and frequent trial and error adjustments during construction, rework and shutdowns caused by unreasonable design are greatly reduced, the construction period is shortened, and manpower, material resources and time costs are further saved.

[0080] In this embodiment, multi-source geological survey means are used in combination with advanced numerical simulation methods to change the problem of one-sided and inaccurate information acquisition in traditional survey and design. Geological drilling is combined with geophysical exploration to quickly and comprehensively obtain cave and rock mass information, provide rich and accurate data support for model construction, and reduce survey time; at the same time, simulation analysis based on accurate models can virtually verify multiple construction plans at one time. Designers do not need to guess the feasibility of different plans based on experience alone, and can compare the optimal construction plan in a short time, which greatly improves the overall efficiency of survey and design.

[0081] The simulation process takes into account many factors such as rock mass anisotropy, complex characteristics of filling materials, and groundwater seepage, and the simulation results fully reflect the actual situation of the project. This enables project decision makers to make reasonable decisions based on detailed data and scientific simulation results, such as choosing appropriate construction technology (drilling and blasting or shield method and corresponding parameters), determining reasonable excavation steps and speeds, avoiding risks caused by blind construction, and ensuring the smooth progress of the project.

[0082] It is worth noting that the simulation method of this application innovatively combines FDEM and SPH technology, filling the gaps in traditional numerical simulation methods in dealing with complex geological conditions such as water-rich cave fillings, and opening up a new path in the field of numerical simulation of underground engineering. It solves the long-standing problem of how to accurately simulate the continuous-discontinuous deformation of rock mass and the behavior of filling fluids, providing a powerful tool for related academic research and promoting the cross-disciplinary integration of rock mechanics, tunnel engineering and other disciplines.

[0083] With the application and popularization of this method, it can not only be used in tunnel engineering, but also for other similar underground projects such as mine exploitation and the construction of underground powerhouse in water conservancy and hydropower projects. As long as it involves water-rich karst caves or similar complex geological structures, it has extremely high application value and is expected to be widely used in various underground engineering fields, contributing to the high-quality development of global underground engineering construction.

[0084] The above specific embodiments are only several alternative embodiments of the present invention. Based on the technical solution of the present invention and the relevant revelations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A refined simulation method for flood disasters in water-rich filled caves combining FDEM and SPH, characterized in that: The following steps are involved: Multi-source data collection and integration: Use geological drilling and geophysical exploration to collect geometric, lithological, and mechanical parameters of water-rich caves and surrounding rock masses, as well as physical characteristics of the fillings in the caves; Build a high-precision model: Build a three-dimensional fine model of rock mass, water-rich caves, and tunnels in Abaqus software based on the collected data, and set adaptive boundary conditions; Adaptive meshing and material property assignment: Adaptive encryption technology is used to divide the model mesh and construct the material properties of rock mass and filling according to the test results; Conversion and initialization of SPH particles of water-rich filling: Generate SPH particles according to the filling volume and simulation accuracy requirements, and initialize particle mass, position and velocity; Iterative solution of geostress balance: The initial geostress is calculated by integrating the rock mass deadweight, tectonic stress and groundwater pressure, and the geostress balance is achieved by iterative algorithm; Dynamic simulation of tunnel excavation: Determine excavation parameters based on the actual construction plan, write subroutines to link with the Abaqus main program, and simulate tunnel excavation and groundwater seepage changes; Refined analysis of results and disaster assessment: Extract simulation result data, analyze surrounding rock stability and filling flow characteristics, and determine water inrush and instability risks; Tunnel support optimization design and feedback adjustment: Based on disaster assessment and tunnel support, compare monitoring and simulation data during construction, and optimize the model and the tunnel support based on feedback.

2. According to claim 1, a method for fine-tuning simulation of flood disasters in water-rich filled karst caves by combining FDEM and SPH, characterized in that: The multi-source data collection and integration specifically includes the following steps: The grid spacing of geological drilling is set according to the engineering and geological conditions. The grid spacing in complex areas is 3-5 meters. The core is drilled to measure its elastic modulus E r , Poisson's ratio v r , cohesion c r , friction angle Mechanical parameters and record lithology changes; Geophysical exploration combines seismic reflection and resistivity methods to outline cave contours; Use special equipment to take samples of water-rich cave fillings and use the drainage method to measure the filling density ρ c ,Right now Where m sample is the sample mass, V displaced To displace the volume of water, sieving and laser particle size analyzer are used to determine the particle size distribution.

3. According to claim 1, a method for fine-tuning simulation of flood disasters in water-rich filled karst caves by combining FDEM and SPH, characterized in that: The construction of the high-precision model specifically includes the following steps: Based on the collected data, a three-dimensional fine model was built in Abaqus with measured coordinates, and the cave boundary was fitted with a non-uniform rational B-spline curve; The boundary conditions of the model are: the surface is free, the bottom is vertically fixed, and the side is based on the ground stress measurement results, according to the formula σ h =K0σ v Apply horizontal stress constraint, where σ v is the vertical stress, which is calculated from the deadweight of the overlying rock mass, that is, σ v =γ r H, γ r is the weight of rock mass, H is the depth, K0 is the lateral pressure coefficient. According to experience and indoor tests, the value range is between 0.5 and 1.

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4. According to claim 1, a method for fine-tuning simulation of flood disasters in water-rich filled karst caves by combining FDEM and SPH, characterized in that: The adaptive meshing and material property assignment specifically include the following steps: Adaptive mesh encryption focuses on caves, tunnel excavation surfaces and the surrounding area 3-5 times the tunnel diameter, with a minimum mesh size where d c is the minimum diameter of the cave, d t is the minimum diameter of the tunnel, d t The unit is meter, far zone grid transition, h max =5h min , ensure the mesh quality, distortion < 0.4, Jacobian > 0.3; Mechanical parameters are assigned to different areas of the rock mass based on drilling and test results. For layered rock mass, including shale, the elastic modulus of the layer and vertical layer is set at a ratio of 1.5-2.0 based on the test; The filling material is based on the particle size distribution using the Mohr-Coulomb or modified Cambridge model. When the coarse particles account for more than 60%, the cohesion c c Take 0.5-2.0kPa, friction angle Take 30°-45°, and determine the model parameters through consolidation test of viscous filling.

5. According to claim 1, a method for fine-tuning simulation of flood disasters in water-rich filled karst caves by combining FDEM and SPH, characterized in that: The water-rich filler SPH particle conversion and initialization specifically include: According to the volume V of water-rich cave filling c Determine the number of SPH particles with simulation accuracy V SPH is the theoretical volume represented by a single SPH particle; Particle initialization: mass m SPH =ρ c V SPH , the position is randomly and evenly distributed in the cave area, and the vertical initial velocity Where g is the acceleration due to gravity, h is c It is the height of the cave center from the groundwater level. The initial horizontal velocity is based on the measured value of water flow or is set to 0.

6. The method for fine-tuning the simulation of flood disasters in water-rich filled karst caves by combining FDEM and SPH according to claim 1, characterized in that: In the iterative solution of the geostress equilibrium: The initial geostress combines the deadweight of the rock mass, tectonic stress, and groundwater pressure, and is applied to the model nodes as nodal forces; Use the Abaqus Newton-Raphson iterative algorithm and set the convergence criterion: the displacement 2-norm change of adjacent iteration nodes is less than 10 -5 And the 2-norm change of stress is less than 10 -4 Determine convergence.

7. The method for fine-tuning the simulation of flood disasters in water-rich filled karst caves by combining FDEM and SPH according to claim 1, characterized in that: In the tunnel excavation dynamic simulation: The tunnel excavation direction and step length Δx are determined according to the actual construction. When using the drilling and blasting method, D is the tunnel diameter. When using the shield method, the excavation speed v is 0.5-2.0 m / h; In VisualStudio2019, write the Vusdfld subroutine to associate with the Abaqus main program, and the elastic modulus of the excavation unit instantly drops to E excavated =0.001E r , Poisson's ratio is adjusted to v excavated =0.49, according to Darcy's law Where q is the seepage velocity vector, k s is the saturated permeability coefficient, It is the water head gradient and updates the groundwater seepage field near the excavation surface in real time.

8. The method for fine-tuning simulation of flood disasters in water-rich filled karst caves by combining FDEM and SPH according to claim 1, characterized in that: The above results are being refined for analysis and disaster assessment: Extract the surrounding rock displacement, stress, strain field and SPH particle motion trajectory and velocity from the Abaqus simulation results to calculate the maximum displacement u of the surrounding rock max , set the threshold value to 0.05-0.1 m, and mark the potential instability zone if the threshold value is exceeded; calculate the maximum principal stress σ of the surrounding rock 1,max , minimum principal stress σ 3,max , stress ratio R>3 is considered to be potentially damaged; Calculate the average flow velocity v of the filling material according to the SPH particle movement and velocity avg The critical flow velocity is set at 0.03-0.08 m / s according to the filling material characteristics and hydrogeology. Supercritical flow velocity indicates water inrush. The monitored pore pressure increase exceeds 0.1-0.3 MPa and rises, which proves the risk of water inrush.

9. The method for fine-tuning simulation of flood disasters in water-rich filled karst caves by combining FDEM and SPH according to claim 1, characterized in that: The tunnel support optimization design and feedback adjustment specifically include: According to the disaster assessment, the potential unstable or high stress area should be supported by anchor bolts first, and the anchor bolt length L bolt =1.2-1.5 times the radius of the loose circle, spacing S bolt Take 0.5-1.5 meters; add water-stop curtains in the possible water inrush area, and the permeability coefficient k curtain <10 -6 m / s, thickness T curtain Take 0.3-0.8 meters; During construction, monitoring points are set up to collect data on surrounding rock displacement, stress, and pore water pressure. If the deviation from the simulation results exceeds 10%-15%, the model parameters are adjusted based on the feedback and the support is re-simulated and optimized. The model parameters include materials and boundaries.

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