Tunnel lining underground water erosion simulation method, device and system and storage medium

Abstract

The invention discloses a tunnel lining underground water erosion simulation method, device and system, and a storage medium, and the method can truly represent the seepage and erosion process of underground water behind a lining on the engineering scale through building the tunnel lining underground water erosion simulation method based on a physical engine. The problems that existing simulation calculation is large in scale, boundary idealization is achieved, and experiments are disjointed from the site are solved.

Description

Technical Field

[0001] This invention belongs to the field of tunnel management and maintenance technology during service life, and specifically relates to a method, device, system, and storage medium for simulating groundwater erosion of tunnel lining. Background Technology

[0002] Numerous factors contribute to the deterioration of tunnel lining concrete, with groundwater seepage containing corrosive ions being a major one. Typical corrosive ions include sulfate, chloride, and carbonate. As seepage and diffusion progress, the lining concrete undergoes decalcification, the hydrated calcium silicate skeleton is destroyed, and the reinforcing steel corrodes, leading to cracking and a decrease in load-bearing capacity. The pore structure and permeability evolve in tandem, further accelerating deterioration. However, the flow of groundwater behind the lining is concealed and difficult to observe directly on-site. The flow field boundaries and channels are often unclear, making it difficult to accurately determine the extent and intensity of groundwater action, and even more difficult to quantify the degree and spatial distribution of concrete damage. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a method, device, system, and storage medium for simulating groundwater erosion in tunnel linings. Under real groundwater seepage conditions, it can quantitatively predict and spatially represent the erosion range and intensity of tunnel lining concrete, providing a basis for risk assessment and maintenance decisions.

[0004] To achieve the above objectives, the present invention provides the following solution: A method for simulating groundwater erosion in tunnel linings includes: Construct a lining geometry model that includes real seepage channels; Based on the lining geometry model, water pressure sensors were installed in boreholes to monitor water head at the locations identified as seepage channels behind the wall. At the same time, the wall thickness and defect identification results were verified by core sampling. Based on concrete core samples, erosion calibration experiments were conducted on the core samples under different ion types and exposure time conditions to obtain relevant parameters of Fick's second law and the strength decay relationship. The seepage channels and head boundaries are incorporated into the physics engine model to obtain the spatial positions and collision events of ion particles on the lining surface. The local apparent flux is calculated based on collision statistics and residence time, and the erosion intensity and range are mapped to the lining model in combination with the calibrated Fick parameters. Based on this, the material properties are updated and the spatiotemporal distribution of erosion and performance evaluation results are output.

[0005] As a preferred method, the geometric features behind the wall of the section of interest are obtained based on ground-penetrating radar and array ultrasound, the geometric features of the secondary lining-primary support interface, the primary support-surrounding rock interface, wall thickness, cavities and defects are identified, and a lining geometric model containing real seepage channels is constructed.

[0006] As a preferred method, a PBD-based particle method is used to simulate groundwater and ion migration, and to record the spatial position and collision events of ion particles on the lining surface.

[0007] The present invention also provides a device for simulating groundwater erosion in tunnel lining, comprising: The first processing module is used to construct a lining geometry model containing real seepage channels; The second processing module is used to install water pressure sensors and monitor water head at the locations identified as seepage channels behind the wall, based on the lining geometry model. At the same time, it verifies the wall thickness and defect identification results by core sampling. Based on concrete core samples, it conducts erosion calibration experiments on the core samples under different ion types and exposure time conditions to obtain relevant parameters of Fick's second law and the strength decay relationship. The third processing module is used to incorporate the seepage channels and water head boundaries into the physics engine model, obtain the spatial position and collision events of ion particles on the lining surface, calculate the local apparent flux based on collision statistics and residence time, and map the erosion intensity and range to the lining model in combination with the calibrated Fick parameters; thereby updating the material properties and outputting the spatiotemporal distribution of erosion and performance evaluation results.

[0008] As a preferred embodiment, the first processing module acquires the back wall geometry features of the section of interest based on ground-penetrating radar and array ultrasound, identifies the geometric features of the secondary lining-primary support interface, the primary support-surrounding rock interface, wall thickness, cavities and defects, and constructs a lining geometry model containing real seepage channels.

[0009] As a preferred option, the third processing module uses a PBD-based particle method to simulate groundwater and ion migration, and records the spatial position and collision events of ion particles on the lining surface.

[0010] The present invention also provides a tunnel lining groundwater erosion simulation system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a tunnel lining groundwater erosion simulation method when run by the processor.

[0011] The present invention also provides a storage medium storing a computer program, which executes a method for simulating groundwater erosion in tunnel lining when running.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention establishes a physics engine-based method for simulating groundwater erosion in tunnel linings. This method realistically reproduces the seepage and erosion process of groundwater behind the lining on an engineering scale, overcoming problems such as large simulation scale, idealized boundaries, and disconnect between experiments and field conditions in existing simulations. It has the following significant technical advantages: 1. Achieving erosion prediction through a three-layer coupling of geometry, physics, and performance. By using ground-penetrating radar and array ultrasound to reconstruct the geometry behind the tunnel wall, the simulation model achieves a realistic lining morphology and defect distribution. This model is coupled with seepage pressure monitoring and ion concentration monitoring data and input into the physics engine to achieve integrated simulation of seepage-transport-reaction processes under realistic geometry and boundary conditions, overcoming the shortcomings of previous methods that assumed boundaries or simplified geometry.

[0013] 2. Quantitative description of erosion rate and extent This invention proposes a method for calculating erosion intensity based on particle collision events, using the cumulative particle residence time as an erosion coefficient index, thus realizing the spatial distribution and temporal evolution of the erosion front. Comparison with measured core drilling results significantly improves the accuracy of engineering applications.

[0014] 3. Achieve direct mapping and updating of experimental calibration parameters. By employing IC ion analysis and UCS mechanical testing, the Fick diffusion coefficient and strength attenuation function were inverted, enabling real-time updates of material properties. The relationship between the increase in diffusion coefficient and strength attenuation caused by erosion can be dynamically input into the simulation module, giving the model a "self-updating" characteristic.

[0015] 4. Significantly improves computational efficiency and visualization capabilities. Employing position-based particle dynamics (PBD) algorithm and GPU parallel computing technology, the calculation speed is 5-10 times faster than the traditional finite element method; at the same time, it can output erosion isosurfaces and three-dimensional lining performance distribution, realizing integrated visualization analysis and maintenance decision-making.

[0016] 5. Engineering and Social Benefits This invention can predict the extent of lining deterioration and strength reduction without damaging the tunnel structure, providing a scientific basis for the inspection, diagnosis, and reinforcement design of tunnels during their service life. Engineering applications show that this method can reduce subsequent excavation verification errors to ±10% and save more than 30% in inspection and maintenance costs, demonstrating significant value for engineering promotion. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the groundwater erosion simulation method for tunnel lining according to an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1 like Figure 1 As shown, the present invention provides a method for simulating groundwater erosion in tunnel linings, comprising: S1. Geometric Reconstruction (GPR + Array Ultrasonic) S1.1 Line Scan Acquisition and Interface Recognition In areas of concern for seepage erosion, ground-penetrating radar (GPR) intensive line scanning was carried out to extract reflection characteristic signals of key interfaces such as secondary lining, primary support, surrounding rock, and construction joints. The interface locations were identified by the time-domain reflection intensity differences, and data splicing and coordinate unification were performed.

[0022] Bandpass filtering and wave velocity correction were performed on the multi-channel line scan data to obtain key geometric information of the tunnel cross-section.

[0023] S1.2 Surface Fitting and Model Construction The weighted least squares B-spline surface algorithm or thin plate spline (TPS) fitting algorithm is used to fit the multi-channel line scan data into a continuous surface, and the initial lining geometric model containing elements such as the outer surface of the secondary lining, the secondary lining-initial support interface, the initial support-surrounding rock interface, and construction joints is automatically generated.

[0024] This model is used to represent the actual lining thickness, the morphology of each interface, and the spatial distribution of potential seepage channels.

[0025] S1.3 Point Scan Refinement and Hole Correction For areas with abnormal reflections (cavities, voids, and seepage points) identified by ground-penetrating radar, an array ultrasonic phased array (Ultrasonic Phased Array) system is used for dense point scanning. The boundary morphology of the cavities behind the wall is inverted by the echo time difference and energy attenuation.

[0026] This information is used to correct the initial geometric model, generate a high-precision real geometric model of the tunnel, and define the attribute labels of seepage channel nodes and void areas in the model to provide spatial boundaries for subsequent simulations.

[0027] S2. Boundary monitoring (osmotic pressure and ionic boundary conditions) S2.1 Seepage Pressure and Head Monitoring Piezometers were installed at the identified backflow channels to record changes in pore water pressure over a long period.

[0028] Calculate the head according to formula (1): in, Pore ​​water pressure, For the density of water, It is the acceleration due to gravity. This is the elevation of the measuring point.

[0029] The monitoring results are used to establish annual and seasonal head variation curves, and based on these curves, to determine the water pressure boundary conditions applied in the simulation.

[0030] S2.2 Erosion Ion Concentration Monitoring Water intakes are placed at the same or adjacent locations, and combined with online monitoring equipment and regular sampling, the main corrosive ions (such as SO42-) are determined using IC (ion chromatography) and ICP-OES (inductively coupled plasma optical emission spectrometry). 2- Cl - Mg 2+ CO3 2- The concentration variation pattern of ).

[0031] Simultaneously, online conductivity and pH sensors are deployed to record the dynamic changes in the groundwater chemical environment in real time.

[0032] Based on time-series data, a joint boundary condition database of "water head-ion concentration" is formed to drive the simulation input of the physics engine.

[0033] S3. Erosion Calibration (Experimental Parameter Acquisition and Diffusion Model Fitting) S3.1 Core Sampling and Performance Testing Core samples were taken from representative locations and cut into concrete samples of different depths along the thickness direction.

[0034] The test content includes: Ion concentration distribution (IC analysis); Residual mechanical properties (uniaxial compressive strength, UCS).

[0035] S3.2 Diffusion Model Fitting and Parameter Acquisition According to Fick's second law: in, This refers to the ion concentration. Where is the diffusion coefficient. The distance is in the thickness direction. For time.

[0036] The effective diffusion coefficient was obtained by inverting the sample test results. With concentration-intensity decay function It is used to quantify the erosion and damage patterns of concrete materials.

[0037] The calibration results serve as the basis for parameters related to material degradation and performance updates in the physics engine model.

[0038] S4. Physics Engine Simulation (PBD-based Groundwater Seepage Modeling) S4.1 Flow Field Particle Modeling The reconstructed tunnel geometry model is imported into a physics engine (such as Unity or BulletPhysics engine), and position-based dynamics (PBD) is used to simulate groundwater flow and ion migration processes.

[0039] To ensure simulation stability, constraints are imposed on the particles (volume preservation, velocity constraints, boundary bounce, etc.), and real-time performance is improved through GPU parallel computing.

[0040] S4.2 Water Pressure and Flow Rate Control Because there are seepage points behind the tunnel wall, water inlet and outlet need to be set in the simulation model.

[0041] Based on the monitored head distribution function The model defines the fluid driving force and velocity distribution, and iteratively adjusts the velocity to make the average water pressure in the simulation area consistent with the water pressure monitored on site, thus ensuring the authenticity of the physical field.

[0042] S4.3 Ion Migration and Collision Event Log In the PBD particle system, each water particle carries the attribute of ion concentration.

[0043] By statistically analyzing the ion arrival frequency and residence time of collision events between particles and grid nodes on the lining surface, a local erosion intensity matrix is ​​formed.

[0044] To balance computational efficiency, approximately 1% of the particles were randomly selected for trajectory tracking and event recording.

[0045] S5. Erosion Mapping (Isosurface Generation and Performance Update) S5.1 Collision Statistics and Erosion Coefficient Calculation At each lining mesh node, calculate the cumulative value of the particle collision duration: in, The node erosion coefficient is... For the first The duration of the collision event.

[0046] This coefficient is used to characterize the erosion intensity of a local area.

[0047] S5.2 Formation and Evolution of Erosion Isosurfaces New nodes are generated along the normal direction of the lining surface, and the node displacement distance is proportional to the local ion concentration or erosion coefficient.

[0048] Erosion isosurfaces at different time points are generated using triangular mesh reconstruction algorithms (such as MarchingCubes or PoissonSurfaceReconstruction).

[0049] These isosurfaces constitute a spatiotemporal distribution model of lining erosion.

[0050] S5.3 Performance Degradation Mapping and Result Output Based on the erosion coefficient and calibration function The corresponding relationships are updated to update the material performance parameters (such as elastic modulus, strength, and permeability coefficient) of each unit, and the erosion range, erosion depth, and remaining bearing capacity distribution results are output.

[0051] The final result is a visualized 3D erosion evolution map and a structural performance assessment report, which are used for tunnel maintenance and risk warning.

[0052] Example: In-service erosion simulation of a railway tunnel (K128+320~K128+330 section) 1. Project and Survey Area Overview Location and length: Select mileage markers K128+320~K128+330 (10m section) as the demonstration section.

[0053] Test area determination: Through visual inspection and seepage trace inspection, it was determined that the seepage risk is concentrated within a range of 2m to the left and right of the construction joint, and the area from the left wall floor to 5m above (i.e., the 0-5m height zone) was selected as the key area for intensive testing and simulation.

[0054] Lining structure: secondary lining thickness 0.45m, primary support thickness 0.22m, local voids are observed at the secondary lining-primary support interface; construction joint is located at K128+325, with a width of 5–8mm (apparent).

[0055] Water chemical environment (preliminary sampling): Cl - 3.5–12.5 g / L, SO4 2- 7.0–16.0 g / L, pH 7.6–8.2, conductivity 8–18 mS / cm, water temperature 8–12℃.

[0056] 2. Equipment and electrical connections (static relationship) GPR ground-penetrating radar: center frequency 400MHz (two-dimensional line scan antenna). Array ultrasound system: The array ultrasound detection adopts a linear array mode and a B-scan scanning mode. During the detection, the velocity is first calibrated on an object with a known nominal wall thickness, and the sound velocity is adjusted between 2200-2700m / s. The analog gain is 14dB, the digital gain is 17dB, the working frequency is 50kHz, and the filter bandwidth is 25kHz. Seepage pressure monitoring: vibrating wire or fiber optic piezometer, measuring range 0–1.0 MPa (approximately 0–100 m head), accuracy ≤0.25%FS; Water intake and online sensing: Stainless steel water intake + online conductivity and pH sensors; Ion detection: IC (ion chromatography) and ICP-OES (inductively coupled plasma optical emission spectroscopy); Data Acquisition and Communication: Data acquisition unit (supports RS-485 / Modbus-RTU), 4G IoT terminal, 12VDC power supply (mains power / solar power + battery).

[0057] Electrical connection: Piezometer / online sensor → (four-core shielded cable, RS-485 bus) → data acquisition unit → (4G) → back-end server; offline import of GPR / ultrasound data into simulation workstation.

[0058] 3. S1 Geometric Reconstruction (Dynamic Process and Settings) S1.1 Line Scan Acquisition and Interface Recognition Line scanning layout: In the section from K128+320 to K128+330, horizontal line scanning is carried out at intervals of 0.2m within a range of 0-5m along the left wall.

[0059] Interface recognition: The outer surface of the secondary lining, the secondary lining-primary support, the primary support-surrounding rock, and the construction joint line are identified by the difference in reflection phase and amplitude.

[0060] S1.2 Surface Fitting and Model Construction Fitting algorithm: Weighted least squares B-spline; node spacing (u,v) ≈ 0.50 × 0.50 m, smoothing factor λ = 10 -2 .

[0061] Quality control: The residual RMS after fitting is ≤8mm; for points exceeding the limit (>15mm), the original radar traces are checked and corrected.

[0062] Output: Generates an initial 3D surface model (.stl) containing four interfaces and records the spatial alignment of construction joints.

[0063] S1.3 Point Scan Refinement and Hole Correction Array ultrasonic densification point scanning: Within a 2m range on both sides of the construction joint, densification point scanning is carried out on suspected void locations, with a point spacing of 0.1m; Inversion parameters: Void boundaries are inverted based on echo time difference / energy attenuation. The void discrimination threshold is reference volume fraction > 1.5% or equivalent porosity > 0.8%. Geometric correction: The void patches are written into the model to form a simulated geometry of "real geometry + void / channel labels"; the final mesh patch size is controlled at an average side length of 0.05m for triangles.

[0064] 4. S2 Boundary Monitoring (Data and Functions) S2.1 Seepage Pressure and Head Placement: Install one piezometer at three depths of 0.5m, 1.5m, and 2.5m along the normal direction behind the midpoint of the construction joint; Sampling and conversion: Sampling interval 1 hour, according to Convert water head.

[0065] Representative functions obtained from one year of monitoring (fitted and used for simulation): in Time is measured in days.

[0066] S2.2 ion concentration Sampling frequency: once a week; during high water periods (before and after the flood season), sampling frequency is increased to once every 3 days; Online recording: conductivity and pH every 1 day; Boundary concentration sequence (annual mean ± amplitude): Cl - : SO4 2- : Joint Boundary: Formation The time function library is used as the input / output conditions for S4 simulation.

[0067] 5. S3 Erosion Calibration (Experimental-Model Mapping) S3.1 Core Drilling and Testing Core sampling: Take one hole with a diameter of 70mm at the midpoint of the construction joint and one hole at 1.0m on each side; cut sections at 75mm intervals along the lining thickness direction; test: IC: Cl at various depths - SO4 2- concentration; UCS: The size of the micro cylindrical sample is φ75×75mm; and the moisture content is recorded.

[0068] S3.2 Diffusion Model Fitting Fick's Second Law: Inversion results: Cl - : ; SO4 2- : Temperature correction: ,Pick ; Strength decay function: fitting the uniaxial compressive strength curve with normalized concentration. in , (SO4) 2- (Master control region fitting).

[0069] 6. S4 Physics Engine Simulation (PBD Setup and Solving) S4.1 Particle Field and Time Step Engine: PBD framework (GPU parallelism, 32-bit floating point); Computational domain: 10m in the mileage direction × 6m in the circumferential direction × 2m behind the wall; number of particles: 1 × 10 5 Time step Δt = 0.002s; Constraint iterations 8 times per step.

[0070] S4.2 Boundary and Steady-State Calibration Inlet / Outlet: The channel face marked S1 is designated as the inlet, and the far field of the surrounding rock and the surface (here, the construction joint) are designated as the outlets; Water head drive: Apply The pressure boundary of the transformation is adjusted iteratively to ensure that the deviation between the average water pressure in the computational domain and the monitored value is ≤0.05m; Ion properties: particle-carrying Scalar, moving with the flow.

[0071] S4.3 Collision and Sampling Collision monitoring: Enable particle-wall collision monitoring for the lining triangular mesh nodes; Sampling: Randomly select 1% of particles to record their trajectories and dwell times, accumulating one frame in a 1-second time window; Output: Obtain the node-level erosion intensity coefficient. 7. S5 Erosion Mapping and Performance Update (Results and Effects) S5.1 Isosurface Generation Grid scale: Average side length of lining face piece is 0.05m; Threshold: with Generate erosion isosurfaces (MarchingCubes); Geometric output: Obtain the geometry of the erosion front at three time points: 6 months, 12 months, and 24 months.

[0072] S5.2 Result Example (Normal Direction of Construction Joint Midpoint) 6 months: Erosion front depth approximately 12mm; 12 months: Approximately 22mm; 24 months: Approximately 38mm; Corresponding UCS attenuation (top layer 0–20 mm): from 35 MPa to 25–29 MPa (SO4) 2- Main controller, associated Cl - ).

[0073] S5.3 Performance Updates and Visualization in accordance with and Update unit parameters (elastic modulus, permeability coefficient, diffusion coefficient); Output a 3D visualization (erosion isosurface + residual bearing capacity heat map), the geometric model and parameters are used for subsequent finite element performance analysis, and maintenance recommendations are generated.

[0074] Example 2 The present invention also provides a device for simulating groundwater erosion in tunnel lining, comprising: The first processing module is used to construct a lining geometry model containing real seepage channels; The second processing module is used to install water pressure sensors and monitor water head at the locations identified as seepage channels behind the wall, based on the lining geometry model. At the same time, it verifies the wall thickness and defect identification results by core sampling. Based on concrete core samples, it conducts erosion calibration experiments on the core samples under different ion types and exposure time conditions to obtain relevant parameters of Fick's second law and the strength decay relationship. The third processing module is used to incorporate the seepage channels and water head boundaries into the physics engine model, obtain the spatial position and collision events of ion particles on the lining surface, calculate the local apparent flux based on collision statistics and residence time, and map the erosion intensity and range to the lining model in combination with the calibrated Fick parameters; thereby updating the material properties and outputting the spatiotemporal distribution of erosion and performance evaluation results.

[0075] As a preferred embodiment, the first processing module acquires the back wall geometry features of the section of interest based on ground-penetrating radar and array ultrasound, identifies the geometric features of the secondary lining-primary support interface, the primary support-surrounding rock interface, wall thickness, cavities and defects, and constructs a lining geometry model containing real seepage channels.

[0076] As a preferred option, the third processing module uses a PBD-based particle method to simulate groundwater and ion migration, and records the spatial position and collision events of ion particles on the lining surface.

[0077] Example 3 The present invention also provides a tunnel lining groundwater erosion simulation system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a tunnel lining groundwater erosion simulation method when run by the processor.

[0078] Example 4 The present invention also provides a storage medium storing a computer program, which executes a method for simulating groundwater erosion in tunnel lining when running.

[0079] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for simulating groundwater erosion in tunnel linings, characterized in that, include: Construct a lining geometry model that includes real seepage channels; Based on the lining geometry model, water pressure sensors were installed in boreholes to monitor water head at the locations identified as seepage channels behind the wall. At the same time, the wall thickness and defect identification results were verified by core sampling. Based on concrete core samples, erosion calibration experiments were conducted on the core samples under different ion types and exposure time conditions to obtain relevant parameters of Fick's second law and the strength decay relationship. The seepage channels and head boundaries are incorporated into the physics engine model to obtain the spatial positions and collision events of ion particles on the lining surface. The local apparent flux is calculated based on collision statistics and residence time, and the erosion intensity and range are mapped to the lining model in combination with the calibrated Fick parameters. Based on this, the material properties are updated and the spatiotemporal distribution of erosion and performance evaluation results are output.

2. The method for simulating groundwater erosion in tunnel lining as described in claim 1, characterized in that, Based on the acquisition of the back wall geometry features of the section of interest by ground-penetrating radar and array ultrasound, the geometric features of the secondary lining-primary support interface, the primary support-surrounding rock interface, wall thickness, cavities and defects are identified, and a lining geometry model containing real seepage channels is constructed.

3. The method for simulating groundwater erosion in tunnel lining as described in claim 2, characterized in that, The PBD-based particle method was used to simulate groundwater and ion migration, and the spatial position and collision events of ion particles on the lining surface were recorded.

4. A device for simulating groundwater erosion in tunnel lining, characterized in that, include: The first processing module is used to construct a lining geometry model containing real seepage channels; The second processing module is used to install water pressure sensors and monitor water head at the locations identified as seepage channels behind the wall, based on the lining geometry model. At the same time, it verifies the wall thickness and defect identification results by core sampling. Based on concrete core samples, it conducts erosion calibration experiments on the core samples under different ion types and exposure time conditions to obtain relevant parameters of Fick's second law and the strength decay relationship. The third processing module is used to incorporate the seepage channels and water head boundaries into the physics engine model, obtain the spatial position and collision events of ion particles on the lining surface, calculate the local apparent flux based on collision statistics and residence time, and map the erosion intensity and range to the lining model in combination with the calibrated Fick parameters; thereby updating the material properties and outputting the spatiotemporal distribution of erosion and performance evaluation results.

5. The tunnel lining groundwater erosion simulation device as described in claim 4, characterized in that, The first processing module acquires the back wall geometry features of the section of interest based on ground-penetrating radar and array ultrasound, identifies the secondary lining-primary support interface, primary support-surrounding rock interface, wall thickness, voids and defects, and constructs a lining geometry model containing real seepage channels.

6. The tunnel lining groundwater erosion simulation device as described in claim 5, characterized in that, The third processing module uses a PBD-based particle method to simulate groundwater and ion migration, recording the spatial position and collision events of ion particles on the lining surface.

7. A groundwater erosion simulation system for tunnel lining, characterized in that, include: A memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program performing the tunnel lining groundwater erosion simulation method as described in any one of claims 1-3 when executed by the processor.

8. A storage medium, characterized in that, The storage medium stores a computer program that, when running, executes the method for simulating groundwater erosion in tunnel lining as described in any one of claims 1-3.

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