Method for predicting and evaluating the breakage of a tunnel in a fault and related device

Abstract

The present disclosure provides a fault tunnel damage prediction evaluation method and related equipment. Specifically, the prediction evaluation method comprises: constructing a fault tunnel model; wherein the fault tunnel model comprises at least one solid particle and at least one fluid particle; the parameters of the solid particle and the fluid particle are respectively assigned to corresponding initial values; wherein the parameters include target parameters; according to a preset time step and a preset target parameter control equation, the target parameters are updated cyclically until a predetermined number of times; and according to the updated target parameters of the fluid particle, the impact pressure of the water body is determined. Such a scheme provides an effective basis for disaster prevention and disaster warning in tunnel construction.

Description

Technical Field

[0001] This disclosure relates to the field of predictive assessment technology, and in particular to a predictive assessment method and related equipment for fault tunnel damage. Background Technology

[0002] Currently, numerical simulation of the entire process of surrounding rock failure and water inrush during fluid-structure interaction and tunnel construction mainly relies on hybrid methods using two or more numerical codes. These methods have shortcomings in learning, modeling, and computation, posing challenges to the subsequent vulnerability assessment of facilities and personnel in tunnel floods. Summary of the Invention

[0003] In view of this, the purpose of this disclosure is to propose a method for predicting and assessing fault tunnel damage and related equipment.

[0004] To achieve the above objectives, this disclosure provides a method for predicting and assessing fault tunnel damage, comprising: constructing a fault tunnel model; wherein the fault tunnel model includes at least one solid particle and at least one fluid particle; the parameters of the solid particle and the fluid particle are respectively assigned corresponding initial values; wherein the parameters include target parameters;

[0005] The target parameters are updated cyclically until a predetermined number of times, based on a preset time step and a preset target parameter control equation.

[0006] The impact pressure of the water body is determined based on the updated target parameters of the fluid particles.

[0007] Based on the same inventive concept, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the prediction and evaluation method as described in any of the foregoing embodiments.

[0008] Based on the same inventive concept, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute any of the prediction and evaluation methods described above.

[0009] As can be seen from the above, the present disclosure provides a method and related equipment for predicting and assessing fault tunnel damage. First, a fault tunnel model including at least one solid particle and at least one fluid particle is constructed. Then, according to a preset time step and a preset target parameter control equation, the target parameters of the particle are updated cyclically until a predetermined number of times. Finally, the impact pressure of the water body is determined based on the updated target parameters of the fluid particle, providing an effective basis for disaster prevention and early warning in tunnel construction. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in this disclosure or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1A This diagram illustrates a fault tunnel model provided in an embodiment of the present disclosure.

[0012] Figure 1B for Figure 1A Enlarged view of the image marked A;

[0013] Figure 2 This diagram illustrates a partial flowchart of a method for predicting and assessing fault tunnel damage according to an embodiment of this disclosure.

[0014] Figure 3A This diagram illustrates a nuclear interaction between solid particles according to an embodiment of the present disclosure.

[0015] Figure 3B This diagram illustrates yet another type of nuclear interaction between solid particles provided in an embodiment of the present disclosure;

[0016] Figure 4 This diagram illustrates a fault tunnel failure and water inrush process according to an embodiment of the present disclosure;

[0017] Figure 5 This diagram illustrates the velocity distribution of fault tunnel failure according to an embodiment of the present disclosure.

[0018] Figure 6 This diagram illustrates the average impact force and degree of damage in a fault tunnel according to an embodiment of the present disclosure.

[0019] Figure 7 A flowchart illustrating another method for predicting and assessing fault tunnel damage provided in this disclosure is shown.

[0020] Figure 8 This diagram illustrates the structure of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0022] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in the embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0023] To facilitate understanding of the technical solutions disclosed herein, some technical terms involved in this disclosure will be introduced below.

[0024] Smoothed Particle Hydrodynamics (SPH) is a typical meshless Lagrangian particle method. Specifically, SPH is a computational method for simulating fluid behavior. It treats the fluid as a system composed of a large number of tiny particles, each possessing properties such as mass, velocity, and density. In SPH simulations, the motion and deformation of the fluid are simulated by defining the interaction forces between particles and the influence of surrounding particles on the target particle.

[0025] Fluid-structure interaction (FSI) is a multiphysics coupling between the laws of fluid dynamics and structural mechanics. This phenomenon is characterized by the interaction between a deformable or moving structure and the surrounding or internal fluid flow; this interaction can be either stable or oscillatory. When a flowing fluid comes into contact with a solid structure, the solid is subjected to stress and strain, which cause the structure to deform.

[0026] As described in the background section, existing numerical simulations mainly rely on hybrid methods using two or more numerical codes. These methods have shortcomings in learning, modeling, and computation, posing challenges to the subsequent vulnerability assessment of facilities and personnel in tunnel floods.

[0027] In view of this, the present disclosure provides a method and related equipment for predicting and assessing fault tunnel damage. First, a fault tunnel model including at least one solid particle and at least one fluid particle is constructed. Then, according to a preset time step and a preset target parameter control equation, the target parameters of the particle are cyclically updated until a predetermined number of times. Finally, the impact pressure of the water body is determined based on the updated target parameters of the fluid particle, providing an effective basis for disaster prevention and early warning in tunnel construction.

[0028] The SPH model can be used to simulate fault tunnels. Figure 1A This diagram illustrates a fault tunnel model provided in an embodiment of the present disclosure. Figure 1A It can be seen that fault tunnels consist of rock mass (corresponding to multiple solid particles) and water body (corresponding to multiple fluid particles). The tunnel is located within the rock mass and is isolated from the water body through the rock mass. Due to the characteristics of the rock mass and the external forces borne by the rock mass and water body, the rock mass between the tunnel and the water body may undergo gradual failure and water / mud inrush process, resulting in tunnel water and mud inrush disasters, which in turn affect the safety of equipment and personnel inside the tunnel.

[0029] Next, the parameter control equations for various particles at different stages during the water and mud inrush process in fault tunnels are illustrated by way of example. Optionally, the basic particle (corresponding to the target particle of this disclosure) interacts with its neighboring particles in its influence domain in pairs to calculate the target parameters of SPH within a single time step.

[0030] In some embodiments, the interaction between fluid particles is based on a preset search range (e.g., twice the kernel radius of the base particle), using the linklist algorithm to pair base particles with neighboring particles. The SPH simulation of water movement is based on the discretized Navier-Stokes equations. Since the presence of cement-like two phases in the fluid and its viscosity are considered, the fluid particle formula takes into account its shear stress. Specifically, as shown in equation (1):

[0031]

[0032] Where a and b are the fundamental fluid particle and its neighboring fluid particles in its influence domain, respectively; m represents the mass of the particle; N s The value represents the total number of neighboring particles of the fluid particle; x represents the particle's position, W ab For a smooth kernel function, the Cubic-spline function can be used; σ represents the Cauchy stress tensor; the Greek superscripts α and β represent the coordinate dimensions applied to the repeating index using Einstein conventions; Π ab For artificial viscosity; ρ is density; t is time; v represents the velocity component of the fluid particles, f α It is an external force, such as gravity.

[0033] In some embodiments,

[0034] Where μ is the dynamic viscosity of the fluid, which can be preset as needed, and is usually located at 1×10⁻⁶. –6 ~1×10 –3 Between; δ αβ is the Dirac function; P is the pressure term, which is calculated using an appropriate state equation, as shown in equation (3);

[0035]

[0036]

[0037] Where ρ0 is the reference density, which can be taken as 1000 kg / m³. 3 c0 is the calculated numerical speed of sound, which is a constant and is set to 10; γ is a dimensionless parameter and is set to 7.

[0038]

[0039] Where β1 and β2 are control parameters for artificial viscosity, r ab v is the position difference between two paired particles. ab The velocity difference between the two paired particles This represents the average density of the particles.

[0040] In some embodiments, the interaction between solid particles is based on a linked list search method for particle pairing, and the motion of the solid is simulated by SPH based on the discretized NS equations, as shown in equation (6):

[0041]

[0042] Where i is the index of the target particle; j is the index of the neighboring particle; N is the total number of neighboring solid particles; α and β represent the x, y, and z directions that can be taken in the Cartesian coordinate system, i.e., the Einstein summation convention, applied to repeated indexing; W ij The kernel function is a smooth kernel; σ represents the Cauchy stress tensor; δ αβ For Dirac function; Π ij The viscosity is artificial; g can be the acceleration due to gravity, for example, 9.81 m / s². 2 It should be noted that the meanings of other parameters can be found in the description of fluid particles, and will not be repeated here.

[0043] In some embodiments, the formula for calculating the Cauchy stress tensor in equation (6) can be:

[0044] σ αβ =-Pδ αβ +Sαβ (7)

[0045] In the formula, P is the isotropic pressure and S is the deviatoric stress.

[0046] Optionally,

[0047] In equation (8), ρ0 is the initial density of the target particle, ρ is the current density of the target particle, and E is the elastic modulus.

[0048] In solid mechanics, the basic model generally allows stress to be a function of strain and strain rate. For anisotropic shear stress, if a small displacement is assumed, the stress rate and strain rate are proportional. The proportionality constant is the shear modulus G = E / (2 × (1 + u)), where E is the elastic modulus and u is Poisson's ratio. Based on this, the equation for the stress rate tensor is:

[0049]

[0050] In equation (9), ε γγ =ε xx +ε yy +ε zz , ε αβ The strain rate tensor is defined as:

[0051]

[0052] To align the material information with strain, a continuity equation is derived by introducing the Jaumann rate of change:

[0053]

[0054] γ represents the x, y, and z axes in the Cartesian coordinate system, i.e., the Einstein summation convention, applied to repeated indexing; ω βγ and ω αγ These are torsion rate tensors, which are defined as:

[0055]

[0056] In some embodiments, Π ij Artificial viscosity, its calculation formula is:

[0057]

[0058] In the formula, c0 is the numerical sound velocity of the solid particle, the magnitude of which can be determined by the elastic modulus E and the initial density, for example... v ij It refers to speed; β1 and β2 are control parameters predetermined according to the specific problem, as detailed in Example 1. This represents the average particle density.

[0059] As particles interact with each other, some solid particles will break down. Typically, rocks fail in a brittle manner. All particles in these materials possess the same properties as the parent material; therefore, the initiation and propagation of particle damage are determined by the failure criteria of the rock material.

[0060] In some embodiments, the Drucker-Prager yield criterion with tensile truncation can be used to determine whether particles fail under tension or shear. The Drucker-Prager yield criterion is chosen to determine the initiation and propagation of shear cracks, and its form is as follows:

[0061]

[0062] Where I1 is the first invariant of the stress tensor, I1 = σ xx +σ yy +σ zz , σ xx , σ yy and σ zz The superscripts α and β of σ are simultaneously taken as x, y, and z;

[0063] J2 is the second invariant of the stress tensor. α φ and k c This is the Drucker-Prager constant.

[0064] For example, Where c is the cohesive force of materials and φ is the internal friction angle.

[0065] To consider the assessment of tensile cracks, the Drucker-Prager criterion is improved as follows:

[0066]

[0067] In the formula R t The tensile strength is given by σ1, which is the maximum principal stress. The calculation method is as follows:

[0068] Therefore, the Drucker-Prager yield criterion with tensile truncation can determine the cracking behavior caused by tension or shear. Once the solid particles reach the failure state, they are no longer controlled by the kernel function and can be regarded as detaching from the parent material, and the material properties remain consistent with the parent material, thus providing a path for crack propagation. According to Equation (15), the fracture state of the object and the properties of the crack, whether it is tensile or shear failure, can be obtained.

[0069] It should be noted that the broken solid particles are not controlled by the kernel function. Therefore, even if the broken solid particles are located within the influence domain, they are not considered as neighboring particles and do not participate in the calculation of equation (6).

[0070] Figure 3A This diagram illustrates a nuclear interaction between solid particles according to an embodiment of the present disclosure. Figure 3B This diagram illustrates yet another type of nuclear interaction between solid particles provided in an embodiment of the present disclosure. Figure 3A Solid particle breakage (corresponding to) Figure 3A After the DPs (Devices in the DPs), cracks appear between different rock blocks. If the influence domain (usually the radius of the influence domain is the core radius) covers a large area, particles from separated rock blocks that do not interact with each other may be identified as neighboring particles, leading to calculation errors.

[0071] To reduce SPH nuclei interactions between solid particles and their counterparts, embodiments of this disclosure provide an improved particle search method. Optionally, the radius of the influence region is a smoothed kernel radius, where the ratio of the smoothed kernel radius to the initial particle spacing is h' / ΔP, and this ratio is limited to h' / ΔP ≤ 1. Figure 3B As mentioned above, by reducing the radius of the influence domain, it is possible to effectively avoid mistaking solid particles on the opposite side for neighboring particles, thereby reducing the interference of solid particles on the SPH nuclei of solid particles on the local side.

[0072] Fluid particles do not have a breakage problem. In some embodiments, the radius of the influence domain of the fluid particles is the smooth kernel radius h, and the kernel radius h / ΔP>1, which can include particles in a range greater than 2h.

[0073] It should be noted that when solid particle breakage is detected, the improved particle search method described above can be used. If no solid particle breakage is detected, a smooth nucleus radius similar to that used for fluid particles can be used.

[0074] After the solid particle breaks down, when the fluid particle enters the influence domain of the solid particle, the solid particle is treated as a deformable boundary, and velocity and displacement boundary conditions are applied to the fluid particle. At the same time, the fluid particle within the influence domain of the solid particle exerts its force f on the solid particle. i s Its main contribution comes from the pressure of the surrounding fluid particles, as shown in the following equation:

[0075]

[0076] P i It is the pressure of the solid particles, calculated by equation (8), P a It is the pressure of the fluid particles, calculated by equation (3); Π iaIt is artificial viscosity, which can be calculated by equation (5). It should be noted that the control parameters of artificial viscosity here are based on the parameters of fluid particles. It is the gradient of the smooth kernel function.

[0077] According to Newton's third law, the equal reaction force of adjacent solid particles on fluid particles can be calculated as follows:

[0078]

[0079] It should be understood that f i S =-f a F .

[0080] Furthermore, considering that the sum of the densities of fluid and solid particles are separate when they are close to each other, this modification prevents the density of solid particles from accumulating in the sum of the densities of the fluid domain, and vice versa. These improvements significantly enhance the numerical stability between the fluid and solid domains. Therefore, the final conservation equations for both the fluid and solid domains in FSI are rewritten as follows:

[0081]

[0082]

[0083] Equation (18) represents the governing equation for non-destructive solid particles, with the subscript i and U representing the total number of non-destructive solid particles in the influence domain; Equation (19) represents the governing equation for fluid particles, with the subscript a.

[0084] Finally, depending on whether the target particle in the model is a solid particle or a fluid particle, and the relative positions of the two, by selecting the appropriate equation (1), equation (6), equation (18) or equation (19), the density change rate, acceleration and velocity of each particle in the model can be calculated.

[0085] By combining a preset time step, the density change rate, acceleration, and velocity of a single time step can be integrated over time to obtain the particle density, velocity, and displacement of the particle at each time step.

[0086] In some embodiments, a frog-leapfrog algorithm is used for time integration, recursively obtaining the position, velocity, and density of each base particle at each step. An exemplary integration formula is shown below:

[0087]

[0088] In the formula: t and t0 are the calculation time and the initial time, respectively; Δt is the calculation time step; ρ is the particle density; v is the particle velocity; and x is the particle position coordinate. It should be noted that the density, velocity, and position coordinates corresponding to t0 can be values ​​obtained from the previous calculation or values ​​assigned by the model, while the density, velocity, and position coordinates corresponding to t are obtained from the current calculation. It should be noted that Equation (20) is used as an example for explaining solids. Fluid particles can be calculated with reference to Equation (20), and will not be repeated here.

[0089] The frog-leap algorithm is used to solve for the changes in stress and velocity displacement of a material through time integration, which is computationally efficient.

[0090] The density, velocity, and position of the particles are calculated iteratively until a predetermined number of calculations are reached. The results of these multiple calculations can demonstrate the process of tunnel fault failure.

[0091] Impact pressure is commonly used to assess the extent of damage to structures during floods. Impact force primarily consists of two parts: dynamic overpressure and hydrostatic pressure. The average impact pressure P acting on a structure or building is calculated by adding the dynamic overpressure and the hydrostatic pressure. t as follows:

[0092]

[0093] In equation (21), the first term is the average hydrostatic pressure component, and the second term is the dynamic overpressure component. H is the average height of the water body inside the tunnel; ρ a This is the average density of the fluid particles, which can be taken as 1000 kg / m³. 3 v a This represents the average velocity of the water body. After each update of the target parameters, the evaluation impact pressure is calculated using equation (21).

[0094] Table 1 lists the categories of damage caused to buildings by tunnel floods. Non-reinforced concrete frame buildings are more susceptible to flooding than reinforced concrete frame buildings.

[0095] Table 1 Classification of Damage Caused to Buildings by Tunnel Floods

[0096]

[0097] Based on the impact pressure and Table 1, the damage to fault tunnels in the event of water or mud inrush can be assessed, providing effective guidance for construction personnel to take preventative measures in advance.

[0098] Figure 2 This diagram illustrates a partial flowchart of a method for predicting and assessing fault tunnel damage according to an embodiment of this disclosure. The following will be discussed in conjunction with... Figure 2The present disclosure provides a detailed description of the method for predicting and evaluating fault tunnel damage, along with the aforementioned control equations applicable to different particles and different stages.

[0099] It should be noted that different fault tunnels typically exhibit different failure processes, and the characteristic parameters of a fault tunnel directly determine these processes. Here, characteristic parameters are information used to characterize the properties of a substance or phenomenon. Specifically for fault tunnels, characteristic parameters can include geometric parameters and the physical and mechanical parameters of matrix particles (including solid and fluid particles). Geometric parameters describe the shape and size of the fault tunnel, such as length, width, height, and tilt angle, as well as the positional distribution of solids and fluids. Physical and mechanical parameters describe the physical and mechanical characteristics of particles, such as density, elastic modulus, Poisson's ratio, cohesion, and angle of internal friction.

[0100] Therefore, in the method for predicting and evaluating the fault tunnel failure process provided in this embodiment, the characteristic data of the fault tunnel are first obtained before constructing the fault tunnel model.

[0101] Next, an initial model is constructed based on the geometric parameters of the fault tunnel. This initial model has a specific height, length, tunnel location, and fluid region location. Furthermore, the initial model region can include both solid and water particles, with preset parameters such as the initial spacing between matrix particles and the radius of the smooth core. Figure 1A This diagram illustrates a fault tunnel model provided in an embodiment of the present disclosure. Figure 1A It can be seen that the tunnel face is 9m high, the water-bearing fault is 15m thick, and the dip angle is 81°. Figure 1B for Figure 1A Enlarged view marked A in the middle. Figure 1B As can be seen, the initial model includes multiple solid particles located to the left and multiple fluid particles located to the right. Optionally, the initial model can be an SPH model.

[0102] Then, corresponding physical and mechanical parameters are assigned to the solid particles and the fluid particles, and corresponding boundary conditions are also assigned. It should be noted that the boundary conditions can be implemented by virtual particles applying boundary repulsive forces, and this embodiment of the present disclosure does not limit this.

[0103] For example, based on the obtained physical and mechanical parameters of the particles, such as elastic modulus, Poisson's ratio, cohesion, and internal friction angle, corresponding particles are assigned values; then, gravitational acceleration (g = 9.81 m / s²) is applied. 2 This serves as the driving force for water inrush and rock movement. A vertical in-situ stress load equivalent to 200m of overburden is applied at the upper boundary of the tunnel, while the lower boundary remains fixed (see [link]). Figure 1AThe lower triangle). The lateral boundary is fixed in the horizontal direction (see below). Figure 1A (Circles on the left and right sides). In the initial equilibrium calculation stage, the stress balance is achieved using the infinite shear and tensile strengths of the rock mass to avoid rock damage. When the expected in-situ stress reaches 5 MPa, the actual tensile and shear strengths are calculated, with the calculation time set to 0 seconds.

[0104] Next, using the fault tunnel model and the aforementioned parameter control equations and integral equations, the rupture process of the fault tunnel can be simulated. It should be noted that at any given time step, the target parameter values ​​for each particle, such as velocity, position, and density, are calculated separately.

[0105] It should be noted that, Figure 2 The steps for constructing a fault tunnel model are not shown. Figure 2 The process of the k-th calculation is shown below. Taking the k-th iteration as an example, the calculation steps for the target particle are explained as follows. Here, k is an integer greater than or equal to 1 and less than or equal to the preset number of calculations. It should be understood that, except for solid particles marked as damaged, all other solid particles and all fluid particles are sequentially used as target particles.

[0106] First, it is determined whether the target particle is a solid particle or a fluid particle 201. It should be noted that labels can be set for solid and fluid particles in the model, which facilitates particle differentiation. Alternatively, the model can set up a set of solid particles and a set of fluid particles, with particles in the solid particle set being solid particles and particles in the fluid particle set being fluid particles. This disclosure does not specifically limit the method used to determine solid and fluid particles.

[0107] If the target particle is a solid particle, its damage state is further determined 202. If the target particle is damaged, it is marked 204. It should be noted that the marked damaged target particle can be output to indicate that the fault tunnel has begun to break. If the target particle is not damaged, the neighboring particles of the target particle are determined, and it is determined whether the neighboring particles include fluid particles. It should be noted that the radius of the influence domain can be different during the process of determining neighboring particles. For example, before a damaged particle is found, the radius of the influence domain can be the radius of the smooth core; after a damaged particle is found, the radius of the influence domain can be smaller than the radius of the smooth core to avoid core interference between different rock blocks. It should be understood that the method of determining neighboring particles is as described above, and this disclosure does not limit it. Of course, those skilled in the art can also choose a suitable search method to determine neighboring particles. If the neighboring particles do not include fluid particles, step 2052 is executed; if the neighboring particles include fluid particles, step 2051 is executed.

[0108] If the target particle is a fluid particle, determine whether the target particle is located within the influence domain of at least one solid particle 203. In some embodiments, during each cycle, multiple solid particles are first sequentially identified as target particles, and then multiple fluid particles are sequentially identified as target particles, thereby facilitating the determination of whether the fluid particle is located within the influence domain of the solid particle. If it is located within the influence domain of the solid particle, proceed to step 2061; otherwise, proceed to step 2062.

[0109] Based on the rate of change of the target parameter obtained in steps 2051, 2052, 2061, and 2062, the target parameter value is obtained by integrating according to equation (20) and the target parameter 207 is updated.

[0110] It should be noted that the target parameters of each particle are updated after all particles have been calculated.

[0111] The calculation is repeated multiple times until the preset number of iterations is reached, at which point the calculation can be stopped.

[0112] In some embodiments, the average impact pressure can be determined using equation (21) based on the updated target parameters; the damage level can be determined according to the average impact pressure and the preset damage level correspondence table.

[0113] It should be understood that the calculation of the average impact pressure can begin after the position of the fluid particles overlaps with the preset position of the tunnel. Here, it can be calculated with each update, or it can be calculated once every predetermined number of times, such as 5 times. This disclosure does not limit this.

[0114] This study simulates the progressive failure of rock, water / mud inrush processes, and the resulting tunnel water and mud inrush disasters using a single architecture. Specifically, based on the SPH architecture, a DP strength criterion with tensile truncation and particle transformation are employed to simulate the entire process of crack initiation and propagation, complete failure, hydraulic fracturing, and water and mud inrush under in-situ stress and hydraulic action. Furthermore, an improved particle search method is introduced to avoid kernel function interactions between individual rock blocks. This approach can simulate the behavior of rock and fluids and their interactions during catastrophic disasters, and is widely applicable to surface and subsurface problems involving FSI (Fluid-Induced Seismic Inrush).

[0115] Simultaneously, an algorithm capable of evaluating the vulnerability of both RC and non-RC structures was introduced. Based on this method, strong fluid-structure interaction between tunnel floods and rock walls can be achieved within a unified framework, and the results can be used for disaster prevention and evaluation during tunnel construction.

[0116] To make the technical solution of this disclosure clearer and easier to understand, the method for predicting and evaluating fault tunnel damage provided by the embodiments of this disclosure is described below with reference to the accompanying drawings.

[0117] Example 1

[0118] Based on the actual geological conditions and tunnel construction conditions, a model of the Zhongjiashan Tunnel, 70m high and 70m long, was established, such as... Figure 1A As shown. The tunnel face height is 9m, the water-bearing fault thickness is 15m, and the dip angle is 81°. The density of water is assumed to be ρ = 1000 kg / m³. 3 The initial distance between rock and water particles, ΔP, is 0.25 m, and the smooth length of the particles, h, is 0.25 m. The SPH parameters of the tunnel model are shown in Table 2. Gravitational acceleration (g = 9.81 m / s²) is applied. 2 This serves as the driving force for water inrush and rock movement. A vertical in-situ stress load equivalent to a 200m overburden layer is applied to the upper boundary of the tunnel, while the lower boundary is fixed. The transverse boundary is fixed in the horizontal direction. In the initial equilibrium calculation stage, the stress balance is performed using the infinite shear and tensile strengths of the rock mass to avoid rock damage. When the expected in-situ stress reaches 5MPa, the actual tensile and shear strengths are calculated, with the calculation time set to 0s. The dynamic viscosity is set to 1×10⁻⁶. –6 The time step is 5×10 -5 A total of 120,000 steps were recorded, covering the entire inrush process. Water flow velocity, water level, and the number of rock fissures were recorded and analyzed to quantitatively assess the risk of sudden water inrush.

[0119] Table 2 SPH parameters of the tunnel model

[0120]

[0121]

[0122] The entire model was divided into 60,250 rock particles and 18,150 water particles within the fault, with the average thickness of the tunnel face set at 1m. Different material parameters were assigned to the rock particles, and corresponding boundary conditions were given to the fault tunnel. The rock mass was set to have infinite strength, and stress balance was initially achieved within the tunnel.

[0123] The fracture state of rock particles is determined by equation (15). If the particles meet the failure criterion, the rock particles have reached failure, indicating that fracture has begun. The rate of change of target parameters for model particles in a single time step is calculated based on equations (1), (6), (18), and (19).

[0124] By performing time integration (20) on the target parameters within a single time step, the particle density, velocity, and displacement of all particles at each time step are obtained.

[0125] Determine if the calculation step has reached the set calculation step. If the maximum calculation step has not been reached, repeat the calculation and update. If the maximum calculation step has been reached, i.e., tunnel failure or water / mud inrush, terminate the calculation.

[0126] Through the above steps, the tunnel damage and water / mud inrush process were obtained, such as... Figure 4 As shown. Figure 4 This diagram illustrates a fault tunnel failure and water inrush process according to an embodiment of the present disclosure. Figure 4 The rectangular dashed box shows a magnified view of the FSI, which reveals the location of the water inrush and the number of fractures. Figure 5 This diagram illustrates the velocity distribution of fault tunnel failure according to an embodiment of the present disclosure.

[0127] Based on on-site measurements, the degree of damage to a set of different fluid dynamic viscosity coefficients is discussed, and a damage assessment is conducted (e.g., 1×10⁻⁶). –6 / 5×10 –6 / 1×10 –5 / 5×10 –5 / 1×10 –4 / 5×10 –4 / 1×10 –3 ),like Figure 6 As shown. Figure 6 This diagram illustrates the average impact force and degree of damage in a fault tunnel failure according to an embodiment of this disclosure. Figure 6 It can be seen that the impact force changes over time, as does the degree of damage during water and mud inrushes in fault tunnels.

[0128] Therefore, it can be seen that the technical solution of this disclosure can accurately capture the entire process of rock damage, water inrush, and flooding.

[0129] Figure 7 This diagram illustrates a flowchart of another method for predicting and assessing fault tunnel damage provided in an embodiment of this disclosure. Figure 7 As shown, the prediction and evaluation methods include:

[0130] Step S701: Construct a fault tunnel model (e.g.) Figure 1A (as shown); wherein, the fault tunnel model includes at least one solid particle and at least one fluid particle; the parameters of the solid particle and the fluid particle are respectively assigned corresponding initial values; wherein, the parameters include target parameters; here, the target parameters may be velocity, density and position coordinates;

[0131] Step S703: Update the target parameter cyclically until a predetermined number of times according to the preset time step and the preset target parameter control equation; here, the number of cycles can be preset, and this disclosure does not limit it;

[0132] Step S705: Determine the impact pressure of the water body based on the updated target parameters of the fluid particles; it should be noted that the calculation rate of the impact pressure can be set as needed, for example, the impact pressure is calculated once every 5 updates.

[0133] Step S707: Determine the damage level according to the impact pressure and the preset damage level correspondence table.

[0134] Based on this methodology, strong fluid-structure interaction between tunnel floods and rock walls can be achieved within a unified framework, and the results can be used for disaster prevention and evaluation during tunnel construction.

[0135] In some embodiments, step S701, constructing the fault tunnel model, includes:

[0136] An initial model is constructed based on the first feature data of the fault tunnel; wherein the first model includes solid particles and fluid particles; for example, the solid particles may be rock particles and the fluid particles may be water particles.

[0137] Based on the second characteristic data of the fault tunnel, the parameters of the solid particles and the fluid particles are assigned corresponding initial values.

[0138] It should be noted that the steps for constructing a fault tunnel model can be referred to above and will not be repeated here.

[0139] In some embodiments, reference Figure 2 Step S703, which involves iteratively updating the target parameter according to a preset time step and a preset target parameter control equation until a predetermined number of iterations, includes:

[0140] The target particle is selected from either an undamaged solid particle or the fluid particle; wherein the damage state of the solid particle is determined based on the Drucker-Prager yield criterion with tensile truncation.

[0141] Based on the parameters and the preset influence field radius, the neighboring particles of the target particle are determined;

[0142] Based on the particle types of the target particle and its neighboring particles, determine the corresponding target parameter control equations;

[0143] The rate of change of the target parameters of the target particle is calculated based on the parameters of the target particle, the neighboring particles, and the target parameter control equation.

[0144] The target parameters of the target particle are calculated and updated based on the preset time step, the parameters, and the rate of change of the target parameters.

[0145] Based on the SPH architecture, the DP strength criterion with tensile truncation and particle transformation are used to simulate the entire process of crack initiation and propagation, complete failure, hydraulic fracturing, and water and mud inrush under in-situ stress and hydraulic action. This method has a wide range of applications and requires less computation.

[0146] In some embodiments, the preset influence zone radius includes the rock mass influence zone radius and the water body influence zone radius; wherein...

[0147] The ratio of the radius of the rock mass influence zone to the initial distance between solid particles is not greater than 1; the ratio of the radius of the water body influence zone to the initial distance between fluid particles is greater than 1.

[0148] This approach avoids the kernel functions of individual rock blocks from interfering with each other.

[0149] In some embodiments, the step of cyclically updating the target parameter according to a preset time step and a preset target parameter control equation until a predetermined number of times further includes:

[0150] Calculate the maximum principal stress of the solid particle and the first and second invariants of the stress tensor;

[0151] Based on the parameters of the solid particles, the maximum principal stress, the first invariant, and the second invariant, the damage state of the solid particles is determined using the Drucker-Prager yield criterion with tensile truncation.

[0152] In response to determining that any of the solid particles has been damaged, the corresponding solid particle is marked and not treated as a neighboring particle.

[0153] The Drucker-Prager yield criterion with tensile truncation is used to determine the damage state of solid particles, which facilitates the detection of rock mass fractures and can determine whether the fracture is tensile or shear fracture.

[0154] In some embodiments, determining the corresponding target parameter control equation based on the particle types of the target particle and the neighboring particles includes:

[0155] In response to the determination that the target particle and each of the neighboring particles are non-destructive solid particles, the target parameter control equation is the first control equation, for example, equation (6);

[0156] In response to determining that the target particle is a non-destructive solid particle and at least one of the neighboring particles is a fluid particle, the target parameter control equation is a second control equation; for example, (18);

[0157] In response to the determination that the target particle and each of the neighboring particles are fluid particles, the target parameter control equation is the third control equation, for example, equation (1);

[0158] In response to determining that the target particle is a fluid particle and a neighboring particle of at least one non-destructive solid particle, the target parameter control equation is a fourth control equation, for example, equation (19); wherein,

[0159] The first, second, third, and fourth governing equations are constructed based on the discretized Navier-Stokes equations of smooth particle hydrodynamics.

[0160] In some embodiments, before the step of calculating and updating the target parameters of the target particle based on a preset time step, the parameter, and the target parameter change rate, the target parameter change rate of the non-destructive solid particle is calculated first, and then the target parameter change rate of the fluid particle is calculated.

[0161] This approach makes it easier to determine whether fluid particles have entered the influence domain of solid particles, thus improving computational efficiency.

[0162] In some embodiments, the target parameters include position, density, and velocity; step S705, determining the impact pressure of the water body based on the updated target parameters of the fluid particles, specifically includes:

[0163] The average hydrostatic pressure component is determined based on the updated position and density of each fluid particle;

[0164] The dynamic overpressure component is determined based on the updated density and velocity of each fluid particle;

[0165] The impact pressure of the water body is determined based on the average hydrostatic pressure component and the dynamic overpressure component, referring to formula (21).

[0166] Calculating impact pressure helps assess the damage level of a tunnel and provides effective guidance for tunnel construction.

[0167] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0168] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0169] Based on the same inventive concept, corresponding to any of the above embodiments, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the prediction and evaluation method described in any of the above embodiments.

[0170] Figure 8 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0171] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0172] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0173] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0174] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0175] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0176] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0177] The electronic devices described above are used to implement the corresponding prediction and evaluation methods in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0178] Based on the same inventive concept, corresponding to any of the above embodiments, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the prediction and evaluation method as described in any of the above embodiments.

[0179] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0180] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the prediction and evaluation method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0181] Based on the same inventive concept, corresponding to the prediction and evaluation method described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to perform the color correction method. Corresponding to the execution entity for each step in each embodiment of the color correction method, the processor executing the corresponding step can belong to the corresponding execution entity.

[0182] The computer program products of the above embodiments are used to cause the computer and / or the processor to execute the prediction and evaluation method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0183] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this disclosure as described above, which are not provided in detail for the sake of brevity.

[0184] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0185] This disclosure is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for predicting and evaluating a fault tunnel breakage, characterized by, The method comprises the following steps: constructing a fault tunnel model, wherein the fault tunnel model comprises at least one solid particle and at least one fluid particle, parameters of the solid particle and the fluid particle are respectively assigned corresponding initial values, and the parameters comprise a target parameter; performing cyclic updating on the target parameter according to a preset time step and a preset target parameter control equation until a predetermined number of times; determining an impact pressure of a water body according to an updated target parameter of the fluid particle; wherein the cyclic updating on the target parameter according to the preset time step and the preset target parameter control equation until the predetermined number of times comprises: selecting any one of the non-broken solid particle and the fluid particle as a target particle, wherein a broken state of the solid particle is determined based on a Drucker-Prager yield criterion with a tensile cut-off; determining proximate particles of the target particle based on the parameters and a preset influence domain radius; determining a corresponding target parameter control equation according to a particle type to which the target particle and the proximate particles belong; calculating a target parameter change rate of the target particle according to the parameters of the target particle and the proximate particles and the target parameter control equation; calculating the target parameter of the target particle and updating the target parameter based on a preset time step, the parameters and the target parameter change rate; the determining of the corresponding target parameter control equation according to the particle type to which the target particle and the proximate particles belong comprises: in response to determining that the target particle and each of the proximate particles are non-broken solid particles, the target parameter control equation is a first control equation; the first control equation is shown in formula (6): Equation (6); In response to determining that the target particle is a non-broken solid particle and at least one of the neighboring particles is a fluid particle, the target parameter control equation is a second control equation; the second control equation is shown as equation (18): Equation (18); In response to determining that the target particle and each of the neighboring particles are fluid particles, the target parameter control equation is a third control equation; the third control equation is as shown in equation (1): Equation (1); in response to determining that the target particle is a fluid particle and is a proximate particle of at least one non-broken solid particle, the target parameter control equation is a fourth control equation; the fourth control equation is shown in formula (19): Equation (19); where is the serial number of the target particle; j is the serial number of the neighboring particle; a and b are the base fluid particle and its neighboring fluid particle in the influence domain, respectively; N is the total number of neighboring solid particles; U represents the total number of non-broken solid particles in the influence domain; α and β represent the coordinate dimension applied to the repeated index with Einstein's convention; W is the smoothing kernel function; represents the Cauchy stress tensor; Π is the artificial viscosity; g is the gravitational acceleration; m represents the mass of the particle; x represents the position of the particle; ρ is the density; t is the time; v represents the velocity component of the fluid particle; N s represents the total number of neighboring particles of the fluid particle; is the external force; wherein, ; - ; is the pressure of the solid particles, is the pressure of the fluid particles; is the gradient of the smooth kernel function; the first control equation, the second control equation, the third control equation and the fourth control equation are respectively constructed based on a discretized N-S equation of a smoothed particle hydrodynamics.

2. The method of claim 1, wherein, The constructing of the fault tunnel model comprises: obtaining and constructing an initial model according to first characteristic data of the fault tunnel, wherein the initial model comprises solid particles and fluid particles; obtaining and assigning corresponding initial values to parameters of the solid particles and the fluid particles according to second characteristic data of the fault tunnel.

3. The method of claim 1, wherein, The preset influence domain radius comprises a rock mass influence domain radius and a water body influence domain radius; wherein, a ratio of the rock mass influence domain radius to an initial distance between the solid particles is not greater than 1, and a ratio of the water body influence domain radius to an initial distance between the fluid particles is greater than 1.

4. The method of claim 1, wherein The cyclic updating on the target parameter according to the preset time step and the preset target parameter control equation until the predetermined number of times further comprises: calculating a maximum principal stress, a first invariant and a second invariant of a stress tensor of the solid particle; According to the parameters of the solid particles, the maximum principal stress, the first invariant and the second invariant, a damage state of the solid particles is determined by using a Drucker-Prager yield criterion with tension cut-off; In response to determining that any of the solid particles is damaged, the corresponding solid particle is marked and is not taken as a neighboring particle.

5. The method of claim 1, wherein Before the step of calculating the target parameters of the target particles based on the preset time step, the parameters and the target parameter change rate and updating, a target parameter change rate of the non-damaged solid particles is calculated, and then a target parameter change rate of the fluid particles is calculated.

6. The method of claim 1, wherein The target parameters include positions, densities and velocities; and the step of determining an impact pressure of the water body according to the updated target parameters of the fluid particles specifically includes: According to the updated positions and densities of each of the fluid particles, an average hydrostatic pressure component is determined; According to the updated densities and velocities of each of the fluid particles, a dynamic overpressure component is determined; According to the average hydrostatic pressure component and the dynamic overpressure component, the impact pressure of the water body is determined.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable by the processor, wherein, The processor realizes the prediction evaluation method according to any one of claims 1 to 6 when executing the computer program.

8. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium stores computer instructions for causing a computer to execute the prediction evaluation method according to any one of claims 1 to 6.

Images