A refined simulation method for water inrush disasters in water-rich caves combining FDEM and SPH

Abstract

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 filling karst caves that combines FDEM and SPH. Its technical solution includes multi-source data collection and integration, construction of high-precision models, adaptive meshing and material property assignment, SPH particle transformation and initialization of water-rich fillings, iterative solution of ground stress balance, dynamic simulation of tunnel excavation, refined analysis of results and disaster assessment, and optimized design and feedback adjustment of tunnel support. The present invention accurately predicts the risk of water inrush disasters in water-rich filling karst caves, and uses refined models and multi-parameter analysis to lock in areas of surrounding rock instability and water inrush hazards in advance, building a solid safety line for construction, effectively optimizing engineering design, accurately determining support parameters based on simulation, avoiding waste of resources, reducing costs, shortening construction period, improving survey and design efficiency, assisting scientific decision-making, and promoting technological progress in the industry, expanding to multiple types of underground projects, and having broad application prospects.

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 by combining FDEM and SPH. Background Art

[0002] Karst caves are a common problem during the development and construction of underground projects. As my country's rail transit enters its development stage, rail transit in more and more cities (such as Dalian, Changsha, Shenzhen, etc.) is faced with crossing complex karst cave areas and other areas with poor geological development. The integrity and continuity of the rock mass are destroyed by the presence of karst caves, and its strength and stability are greatly reduced. Tunnel excavation disturbances can easily cause water inrush and cave collapse, leading 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 and construction, as well as safe tunnel operation and maintenance, when excavating adjacent underground structures.

[0003] Traditional engineering survey and design methods are insufficient when dealing with water-rich caves. Conventional geological survey methods, such as drilling, can obtain local information about borehole locations, but they are limited in accurately depicting the overall morphology of the cave and the distribution of the filling, making it difficult to fully grasp the overall picture of the cave system. Geophysical exploration methods also need to improve their accuracy and reliability in complex geological conditions, often failing to accurately identify cave boundaries and filling details.

[0004] In terms of numerical simulation, most early simulation technologies were based on a single numerical method, which was unable to simultaneously take into account the continuous-discontinuous deformation characteristics of the rock mass and the fluid behavior of the filling. For example, the finite element method is good at dealing with continuous medium problems, but it is not very effective in simulating discontinuous phenomena such as cracks and fractures that appear in the rock mass during excavation. Traditional fluid dynamics methods, when coupled with rock mechanics, have difficulty accurately reflecting the impact of solid-fluid interactions on engineering stability. This results in the tunnel design stage, where engineers are unable to accurately predict possible catastrophic situations that may occur during construction, making it difficult to formulate scientific and reasonable construction plans and support measures. This makes the project face many uncertainties during construction, further increasing engineering risks.

[0005] Therefore, this application proposes a refined simulation method for water-inrush disasters in water-rich filled caves by combining FDEM and SPH. Summary of the Invention

[0006] The purpose of the present invention is to address the problem in the background technology that traditional engineering survey and design methods cannot accurately identify the boundaries of caves and the details of fillings, and to propose a refined simulation method for water-rich cave inrush disasters that combines FDEM and SPH.

[0007] The technical solution of the present invention is a refined simulation method for water-rich cave flooding 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, and mechanical parameters of water-rich caves and surrounding rock masses, as well as physical properties of the cave fillings;

[0009] Build a high-precision model: Based on the collected data, build a three-dimensional detailed model of rock mass, water-rich caves, and tunnels in Abaqus software, and set adaptive boundary conditions;

[0010] Adaptive meshing and material property assignment: Adaptive meshing technology is used to divide the model mesh and accurately assign material properties to the rock mass and filling based on test results;

[0011] Water-rich filling SPH particle conversion and initialization: Generate SPH particles based on the filling volume and simulation accuracy requirements, and initialize particle mass, position and velocity;

[0012] Iterative solution of geostress equilibrium: The initial geostress is calculated by integrating the rock mass deadweight, tectonic stress, and groundwater pressure, and an iterative algorithm is used to achieve geostress equilibrium;

[0013] Dynamic simulation of tunnel excavation: Determine excavation parameters based on the actual construction plan, write subroutines linked to the Abaqus main program, and simulate tunnel excavation and groundwater seepage changes;

[0014] 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;

[0015] Tunnel support optimization design and feedback adjustment: Design tunnel supports based on disaster assessment, compare monitoring and simulation data during construction, and optimize models and supports based on feedback.

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

[0017] 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 lithologic changes;

[0018] Geophysical exploration combines seismic wave reflection and resistivity methods to outline cave contours;

[0019] 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 boiling water, sieving and laser particle size analyzer are used to determine the particle size distribution.

[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 was built in Abaqus using the measured coordinates, and the cave boundary was fitted with a non-uniform rational B-spline curve with an accuracy of millimeters.

[0022] 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, i.e. σ v =γ r H,γ r is the weight of rock mass, H is the depth, and K0 is the lateral pressure coefficient. Based on experience and indoor tests, the value range is between 0.5 and 1.2.

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

[0024] Adaptive encrypted grid focuses on caves, tunnel excavation surfaces and the surrounding area 3-5 times the tunnel diameter, with a minimum grid 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;

[0025] Mechanical parameters are assigned to different areas of the rock mass based on drilling and test results. For layered rock masses, including shale, the elastic modulus of the layer and the vertical layer is set according to the test ratio, which can reach 1.5-2.0;

[0026] 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.

[0027] Optionally, the water-rich filler SPH particle conversion and initialization specifically include:

[0028] According to the volume of water-rich cave filling Vc Determine the number of SPH particles with 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 position is randomly and evenly distributed in the cave area, the vertical initial velocity Where g is the acceleration due to gravity, h is c is the height of the cave center from the groundwater level. The initial horizontal velocity is based on the water flow measurement value or is set to 0.

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

[0031] The initial ground stress is a combination of rock mass deadweight, 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 displacement 2-norm of adjacent iteration nodes changes less than 10 -5 And the 2-norm change of stress is less than 10 -4 Determine convergence.

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

[0034] 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;

[0035] In Visual Studio 2019, 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, The water head gradient is used to update the groundwater seepage field near the excavation surface in real time.

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

[0037] 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 destructive;

[0038] Calculate the average flow velocity v of the filling material according to the SPH particle motion and velocity avg According to the filling material characteristics and hydrogeology, the critical flow velocity is set at 0.03-0.08 m / s. Supercritical flow velocity indicates water inrush, and the monitored pore pressure increase exceeds 0.1-0.3 MPa and rises, which proves the risk of water inrush.

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

[0040] According to the disaster assessment, the potential unstable or high stress areas are given priority to anchor support, and the anchor 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 curtain in the area where water may burst, and the permeability coefficient k curtain <10 -6 m / s, thickness T curtain Take 0.3-0.8 meters;

[0041] During construction, monitoring points are set up to collect data such as 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.

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

[0043] 1. Through meticulous data collection and model building, the present invention is able to highly restore the actual state of water-rich caves and surrounding rock masses. This allows for the precise capture of changes in surrounding rock stress and strain, as well as the flow trends of cave fillings, during simulated tunnel excavation. This allows for the precise identification of areas where collapse may occur, providing construction workers with clear warnings of danger, enabling them to take timely reinforcement measures to avoid tunnel collapse accidents and greatly protecting the lives of construction workers.

[0044] 2. The simulation results provide an accurate basis for tunnel support design. Based on the stability of different surrounding rock areas, a reasonable support method is selected to ensure that the support structure can effectively control surrounding rock deformation while avoiding excessive support and waste of resources. In areas where water inrush may occur, water-stop curtains are installed in a targeted manner to ensure waterproofing while reducing unnecessary material input and project costs.

[0045] 3. The use of multi-source geological survey methods combined with advanced numerical simulation methods has changed the problem of one-sided and inaccurate information acquisition in traditional survey and design. The combination of geological drilling and geophysical exploration can quickly and comprehensively obtain cave and rock mass information, providing rich and accurate data support for model construction and reducing survey time. At the same time, simulation analysis based on precise models can virtually verify multiple construction plans at one time. Designers no longer need to rely solely on experience to guess the feasibility of different plans. They can compare and determine the optimal construction plan in a short time, greatly improving the overall efficiency of survey and design.

[0046] 4. The combination of FDEM and SPH technology fills the gaps in traditional numerical simulation methods when dealing with complex geological conditions such as water-rich caves, opening up a new path for numerical simulation of underground engineering. It solves the long-standing industry challenge of accurately simulating both continuous and discontinuous deformation of rock masses 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 fields.

[0047] In summary, the present invention accurately predicts the risk of water inrush disasters in water-rich filled caves. By using sophisticated models and multi-parameter analysis, it can lock in the areas of surrounding rock instability and water inrush hazards in advance, build a solid safety line for construction, effectively optimize engineering design, and accurately determine support parameters based on simulation, thereby avoiding waste of resources, reducing costs, shortening construction period, improving survey and design efficiency, assisting scientific decision-making, and promoting technological progress in the industry. It has been expanded to various types of underground projects and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a refined simulation method for water-rich cave flooding disasters combining FDEM and SPH. DETAILED DESCRIPTION

[0049] The technical solution of the present invention is further described below with reference to the accompanying drawings and specific embodiments.

[0050] like Figure 1 As shown in the figure, the present invention proposes a refined simulation method for water-rich filling cave water inrush disasters combining FDEM and SPH, including multi-source data collection and integration, high-precision model construction, adaptive meshing and material property assignment, SPH particle transformation and initialization of water-rich filling, iterative solution of ground stress balance, dynamic simulation of tunnel excavation, refined analysis of results and disaster assessment, tunnel support optimization design and feedback adjustment. Each step is described in detail below.

[0051] 1. Multi-source data collection and integration

[0052] Using geological drilling technology, drilling is carried out according to a pre-designed grid layout. The grid spacing is determined by the project scale and geological complexity, and is set at 3-5 meters in complex areas. Rock samples are obtained from the drill core and their elastic modulus E is measured. r , Poisson's ratio v r , cohesion c r , friction angle Mechanical parameters and record the lithologic changes at different depths.

[0053] Geophysical exploration methods, such as seismic reflection and resistivity, are used in combination. The seismic reflection method, which stimulates seismic waves on the surface and infers the location and general shape of the cave based on the reflection time and amplitude changes of the waves, has a depth resolution of 0.5-1 meter. The resistivity method, which utilizes the differences in electrical conductivity between different geological bodies, measures the resistivity distribution of the underground medium and further delineates the cave's contours, with a positioning accuracy of ±2 meters.

[0054] For filling materials in water-rich caves, representative samples are obtained through special sampling equipment. In the laboratory, the filling density ρ is measured using the drainage method based on Archimedes' principle. c ,Right now where m sample is the sample mass, V displaced is the volume of water displaced; the particle gradation is determined by combining the screening method with the laser particle size analyzer to obtain the proportion of particles of different particle sizes.

[0055] 2. High-precision model construction

[0056] Based on the collected geological data, a detailed 3D model of the rock mass, water-rich caves, and tunnels was created in Abaqus software. Using actual measured coordinate data as a benchmark, the model's geometric dimensions were accurate to the millimeter level. Non-uniform rational B-spline (NURBS) curves were used to fit the cave boundaries, ensuring a highly consistent shape with the measured results.

[0057] In terms of model boundary condition setting, the surface is set as a free boundary, allowing vertical displacement; a vertical fixed constraint is imposed on the bottom boundary of the model to simulate the support of the earth; the side boundary is based on the ground stress measurement results and is calculated by 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, i.e. σ v =γ r H,γ r is the weight of rock mass, H is the depth, and K0 is the lateral pressure coefficient. Based on experience and indoor tests, the value range is between 0.5 and 1.2.

[0058] 3. Adaptive Meshing and Material Property Assignment

[0059] The grid division adopts adaptive encryption technology, focusing on the cave area, tunnel excavation surface and its surrounding area within 3-5 times the tunnel diameter. Here, the minimum grid size h min , according to Saint-Venant's principle and empirical formula 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; for the rock mass away from the critical area, the grid size is transitioned in a gradient, with the maximum grid size h max =5h min , ensure that the mesh quality meets the convergence requirements of numerical simulation, the distortion is controlled below 0.4, and the Jacobian value is greater than 0.3.

[0060] Assign rock material properties, and input the rock mechanical parameters of different regions into the model according to the drilling and laboratory test results. For layered rock with obvious anisotropy, the elastic modulus E along the layer direction is parallel Elastic modulus E in the direction perpendicular to the plane perpendicular The ratio is set according to test data. For common shales, the ratio is 1.5-2.0.

[0061] For water-rich cave fillings, before converting them into SPH particles, the material parameters of different particle size ranges are determined based on their particle grading. Taking the Mohr-Coulomb constitutive model as an example, the cohesion c c and friction angle Adjust according to the proportion of coarse particles (such as sand and gravel) in the filling. When the proportion of coarse particles exceeds 60%, c c The value range is 0.5-2.0kPa, Between 30°-45°; for fillings with higher viscosity, the modified Cambridge model is introduced, considering the compression characteristics of clay, and the model parameters are determined through indoor consolidation tests.

[0062] 4. SPH particle transformation and initialization of water-rich cave fillings

[0063] The number of SPH particles is determined based on the volume of the water-rich cave filling (unit: cubic meter) and the required simulation accuracy. Where V SPH is the theoretical volume represented by a single SPH particle, combined with the minimum grid size,

[0064] Initialize each SPH particle and its mass is calculated by the formula m SPH =ρ c V SPHThe calculation shows that the initial position of the particles is randomly and evenly distributed in the cave area. Taking the influence of gravity into account, the initial velocity in the vertical direction is set to Where g is the acceleration due to gravity, h is c is the height of the cave center from the groundwater level, in meters. The initial horizontal velocity is set according to the measured value of the regional water flow velocity. If no water flow is measured, the initial velocity is 0.

[0065] 5. Iterative Solution of Geostress Equilibrium

[0066] The calculation of initial geostress comprehensively considers the deadweight of the rock mass, tectonic stress, and groundwater pressure. The vertical geostress has been mentioned in the boundary condition setting; the tectonic stress is estimated based on the regional geological structure background through geomechanical analysis methods combined with on-site stress measurement data. The final expression of horizontal geostress 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, h is the w It is calculated as the vertical distance from the calculation point to the groundwater level (unit is meter) and applied to the corresponding node in the model in the form of nodal force.

[0067] The Newton-Raphson iterative algorithm built into Abaqus is used to solve the ground stress equilibrium. The iterative convergence criterion is set, and the 2-norm change of the node displacement between two adjacent iterations is less than 10 -5 And the 2-norm change of stress is less than 10 -4 When the ground stress balance reaches the convergence state, the model's initial state is ensured to be stable, providing a reliable basis for subsequent tunnel excavation simulation.

[0068] 6. Dynamic simulation of tunnel excavation

[0069] According to the actual construction plan, determine the direction of tunnel excavation, step length Δx and excavation speed. Step length Δx is set according to the tunnel diameter D (unit: meter) and construction process requirements. When using the drilling and blasting method, To ensure the stability of the excavation process; the excavation speed v is determined according to the project schedule and the performance of the on-site equipment. For example, when using the shield method for construction, the value is between 0.5-2.0 meters per hour.

[0070] The Vusdfld subroutine is written in Visual Studio 2019 and is closely linked to the Abaqus main program. In the subroutine, the excavation process of the soil is simulated by changing the material properties of the unit. When a unit enters the excavation step, its elastic modulus is instantly reduced to E excavated=0.001E r , Poisson's ratio is adjusted to v excavated =0.49, making it close to the fluid state, simulating the mechanical response of soil after excavation; at the same time, according to Darcy's law Where q is the seepage velocity vector, k s is the saturated permeability coefficient, The groundwater seepage field near the excavation surface is updated in real time based on the water head gradient, and the impact of seepage on soil strength and stability is considered.

[0071] 7. Refined Analysis of Results and Disaster 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 trajectory 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 the 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 meters, marking the area as a potential unstable area; calculate the maximum principal stress σ of the surrounding rock 1,max (unit: MPa) and minimum principal stress σ 3,max (Unit: MPa), through stress ratio When evaluating the stability of the surrounding rock, when R>3, 3 is the lower limit of the empirical safety stress ratio, which can be adjusted according to the actual project, to judge whether the area may be damaged.

[0073] For water-rich cave fillings, the flow characteristics are analyzed based on the SPH particle motion trajectory and velocity. The average flow velocity of the filling (unit: m / s) is calculated, and the critical flow velocity v is set based on the filling particle characteristics and on-site hydrogeological conditions. critical , when v avg >v critical When v critical The value range is between 0.03 and 0.08 m / s, depending on the filling type and pore structure, and predicts the possibility of water inrush disasters; at the same time, the changes in pore pressure inside the filling are monitored. When the pore pressure increase exceeds 0.1-0.3 MPa and continues to rise, it further proves that the risk of water inrush is increasing.

[0074] 8. Tunnel support optimization design and feedback adjustment

[0075] Based on the catastrophic assessment of the simulation results, the tunnel support optimization design is carried out. If potential unstable areas or high stress areas appear, anchor support is given priority, and the anchor length L boltAccording to the range of the surrounding rock loosening zone, it can be determined by the formula L bolt = 1.2-1.5 times the radius of the loosening circle, anchor spacing S bolt According to the surrounding rock strength and stress distribution, the value is between 0.5-1.5 meters; for areas where water inrush may occur, a water-stop curtain is added, and the curtain permeability coefficient k curtain Should satisfy k curtain <10 -6 m / s, thickness T curtain Depending on the water pressure and geological conditions, the value is between 0.3-0.8 meters.

[0076] During tunnel construction, monitoring points are deployed to collect real-time data on surrounding rock displacement, stress, and pore water pressure. This data is then compared and analyzed with simulation results. If the two deviate by more than 10%-15%, model parameters (such as material properties and boundary conditions) are adjusted based on this feedback, and the simulation is repeated to optimize the support design and ensure safe and smooth tunnel construction.

[0077] This embodiment can highly restore the actual state of the water-rich filled cave and the surrounding rock mass through precise data collection and model construction. This makes it possible to accurately capture the changes in surrounding rock stress and strain, as well as the flow trend of the cave filling during the simulated tunnel excavation process. For example, when judging the stability of the surrounding rock, the maximum displacement and principal stress data obtained by precise simulation are compared with the set scientific thresholds (such as the maximum displacement of the surrounding rock exceeding 0.05-0.1 meters is marked as a potential unstable area, and the stress ratio greater than 3 indicates possible damage). The area where collapse may occur is accurately identified in advance, providing construction personnel with clear danger warnings, enabling them to take reinforcement measures in time to avoid the occurrence of tunnel collapse accidents, greatly protecting the lives of construction personnel.

[0078] For water inrush hazards caused by water-rich cave fillings, SPH particle simulation accurately calculates the average flow velocity and pore pressure changes of the filling. Once the flow velocity exceeds a critical value (0.03-0.08 m / s) and the pore pressure increases abnormally (exceeding 0.1-0.3 MPa and continuing to rise), the risk of water inrush can be predicted in advance. Construction teams can then prepare emergency plans in advance, such as deploying drainage equipment and strengthening water-stopping measures, effectively reducing 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, in the potential instability area, the anchor length (1.2-1.5 times the radius of the loose circle) and spacing (0.5-1.5 meters) are accurately determined 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 in a targeted manner, and the permeability coefficient (less than 10) is accurately set according to the water pressure and geological conditions. -6 m / s) and thickness (0.3-0.8 m), while ensuring waterproofing effectiveness, it reduces unnecessary material input and lowers project costs. By enabling a full understanding of potential construction challenges through simulation during the design phase, repeated design changes during construction are avoided. Compared to the traditional model of design relying on experience and frequent trial-and-error adjustments during construction, this significantly reduces rework and downtime caused by design errors, shortens the construction cycle, and further saves manpower, material resources, and time.

[0080] In this embodiment, multi-source geological surveys, combined with advanced numerical simulation methods, address the one-sided and inaccurate information acquisition issues inherent in traditional survey and design. The combination of geological drilling and geophysical exploration rapidly and comprehensively captures cave and rock mass information, providing rich and accurate data support for model construction and reducing survey time. Simulation analysis based on precise models also enables virtual verification of multiple construction options simultaneously. Designers no longer need to rely solely on empirical assumptions about the feasibility of different options; they can quickly compare and identify the optimal construction plan, significantly improving overall survey and design efficiency.

[0081] The simulation process takes into account numerous factors, including rock mass anisotropy, complex fill characteristics, and groundwater seepage, resulting in a comprehensive reflection of the actual project situation. This enables project decision-makers to make informed decisions based on detailed data and scientific simulation results, such as selecting the appropriate construction method (drill and blast or shield tunneling and corresponding parameters) and determining the appropriate excavation step length and speed, thus avoiding the risks associated with blind construction and ensuring smooth project progress.

[0082] It is worth noting that the simulation method proposed in this application innovatively combines FDEM and SPH techniques, addressing the shortcomings of traditional numerical simulation methods in dealing with complex geological conditions such as water-rich caves, and opening up a new path for numerical simulation of underground engineering. It solves the long-standing industry challenge of accurately simulating both continuous and discontinuous deformation of rock masses 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 fields.

[0083] With the application and promotion of this method, it can not only be used in tunnel engineering, but also in other similar underground projects such as mining, water conservancy and hydropower underground power plant construction, etc., as long as it involves water-rich 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 merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art may make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A refined simulation method for water-rich cave flooding disasters combining FDEM and SPH, characterized by: 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 properties of the cave fillings; Build a high-precision model: Based on the collected data, build a three-dimensional detailed model of rock mass, water-rich caves, and tunnels in Abaqus software, and set adaptive boundary conditions; Adaptive meshing and material property assignment: Adaptive meshing technology is used to divide the model mesh and construct the rock mass and filling material properties based on test results; Water-rich filling SPH particle conversion and initialization: Generate SPH particles based on the filling volume and simulation accuracy requirements, and initialize particle mass, position and velocity; Iterative solution of geostress equilibrium: The initial geostress is calculated by integrating the rock mass deadweight, tectonic stress, and groundwater pressure, and the geostress equilibrium is achieved using an iterative algorithm; Dynamic simulation of tunnel excavation: Determine excavation parameters based on the actual construction plan, write subroutines linked to 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 tunnel support based on feedback.

2. The method for fine-tuning water inrush disaster simulation of water-rich filled caves by combining FDEM and SPH according to claim 1 is 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 lithologic changes; Geophysical exploration combines seismic wave 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 boiling water, sieving and laser particle size analyzer are used to determine the particle size distribution.

3. The method for fine-tuning water inrush disaster simulation of water-rich filled caves by combining FDEM and SPH according to claim 1 is 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 using the 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, i.e. σ v =γ r H,γ r is the weight of rock mass, H is the depth, and K0 is the lateral pressure coefficient. Based on experience and indoor tests, the value range is between 0.5 and 1.

2.

4. The method for fine-tuning the simulation of water-rich cave flooding disasters by combining FDEM and SPH according to claim 1 is characterized in that: The adaptive meshing and material property assignment specifically include the following steps: Adaptive encrypted grid focuses on caves, tunnel excavation surfaces and the surrounding area 3-5 times the tunnel diameter, with a minimum grid 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 masses, including shale, the elastic modulus of the layer and the 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. The method for fine-tuning water inrush disaster simulation of water-rich filled caves by combining FDEM and SPH according to claim 1 is characterized in that: The water-rich filler SPH particle conversion and initialization specifically include: According to the volume of water-rich cave filling V 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, the vertical initial velocity Where g is the acceleration due to gravity, h is c is the height of the cave center from the groundwater level. The initial horizontal velocity is based on the water flow measurement value or is set to 0.

6. The method for fine-tuning the simulation of water-rich cave flooding disasters by combining FDEM and SPH according to claim 1 is characterized in that: In the iterative solution of the in-situ stress equilibrium: The initial ground stress is a combination of rock mass deadweight, 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 of adjacent iteration nodes changes less than 10 -5 And the 2-norm change of stress is less than 10 -4 Determine convergence.

7. The method for fine-tuning water inrush disaster simulation of water-rich filled caves by combining FDEM and SPH according to claim 1 is 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 Visual Studio 2019, 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, The water head gradient is used to update the groundwater seepage field near the excavation surface in real time.

8. The method for fine-tuning water inrush disaster simulation of water-rich filled caves by combining FDEM and SPH according to claim 1 is characterized in that: In the detailed analysis and disaster assessment of the above results: 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 destructive; Calculate the average flow velocity v of the filling material according to the SPH particle motion and velocity avg According to the filling material characteristics and hydrogeology, the critical flow velocity is set at 0.03-0.08 m / s. Supercritical flow velocity indicates water inrush, and 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 water-inrush disaster in water-rich filled caves by combining FDEM and SPH according to claim 1 is characterized in that: The tunnel support optimization design and feedback adjustment specifically include: According to the disaster assessment, the potential unstable or high stress areas are given priority to anchor support, and the anchor 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 curtain in the area where water may burst, 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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