Shaft surrounding rock deformation prediction method and device based on fluid-solid coupling linkage simulation

Abstract

The invention discloses a shaft surrounding rock deformation prediction method and device based on fluid-solid coupling linkage simulation, and the method comprises the steps: 1, describing the influence of rainwater permeation on surrounding rock deformation through the change of water content, constructing and forming a permeation-surrounding rock deformation model, and constructing and forming a scouring-surrounding rock deformation model, building a vertical shaft excavation model under the action of rainwater; 2, performing multi-scale linkage simulation on the permeation-surrounding rock deformation model, the scouring-surrounding rock deformation model and the vertical shaft excavation model to obtain surrounding rock deformation data; 3, taking the surrounding rock deformation data as a training set to train a TCN-LSTM neural network model, and obtaining a shaft surrounding rock deformation prediction model under the rainwater effect; and 4, acquiring real-time surrounding rock deformation data, inputting the real-time surrounding rock deformation data into the shaft surrounding rock deformation prediction model, and outputting a prediction result. The shaft surrounding rock deformation prediction precision, efficiency and application flexibility can be improved, and meanwhile the implementation complexity and the calculation amount are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of shaft construction engineering, and in particular to a method and a device for predicting deformation of shaft surrounding rocks based on fluid-solid coupling linkage simulation. Background Art

[0002] Surrounding rock deformation refers to the phenomenon that excavation, mining, etc. cause the rock mass around the engineering structure to move, deform, and redistribute stress, usually in the form of expansion, compression, shearing, bending, etc. By monitoring and analyzing surrounding rock deformation, potential geological disasters can be discovered in a timely manner, guiding the optimization of construction plans, and improving the durability and reliability of engineering structures, thereby ensuring the smooth progress of engineering projects and reducing economic losses.

[0003] During shaft excavation and construction, the deformation and stability of the surrounding rock are key indicators. In the prior art, the prediction of shaft surrounding rock deformation mostly relies on empirical formulas and simplified models. The empirical formula mainly relies on the experience and historical data of engineers. The surrounding rock deformation is predicted by establishing empirical formulas. However, such empirical formulas are usually based on specific geological conditions and engineering environments. They are only applicable to predictions of specific conditions and are not applicable to all geological environments. They have certain limitations and scope of application. When the geological conditions or construction environment change, their applicability and prediction accuracy will decrease significantly. In addition, the empirical formula cannot take into account the complex geological structure, groundwater influence, and unforeseen changes during the construction process, so the actual prediction accuracy is low.

[0004] Among the structural analysis methods, the finite element method (FEM) can handle continuous media for structural analysis, but the finite element method requires the establishment of a sophisticated numerical model, which not only has a large amount of calculation and a complex solution process, but also has poor adaptability to complex nonlinear and fracture problems. Therefore, it is mainly suitable for homogeneous and isotropic continuous media, and has poor effects on heterogeneous and anisotropic materials. For example, the deformation prediction of granular materials and block rocks belongs to non-continuous media problems. The finite element method is not suitable for handling such non-continuous media problems, let alone problems with complex geological conditions such as large deformation, fracture, and sliding. In addition, the above-mentioned prediction methods in the prior art lack the ability to process and dynamically update field monitoring data in real time, making it difficult to timely reflect the actual situation of surrounding rock deformation. Summary of the invention

[0005] The purpose of the present invention is to propose a method and device for predicting the deformation of vertical shaft surrounding rock based on fluid-solid coupling linkage simulation, by constructing a permeability-surrounding rock deformation model, a scouring-surrounding rock deformation model, and a vertical shaft excavation model under the action of rainwater for multi-scale linkage simulation, and generating a large amount of actual surrounding rock deformation data to train the TCN-LSTM neural network model, so as to efficiently obtain an accurate vertical shaft surrounding rock deformation prediction model, improve the accuracy, efficiency and applicability of vertical shaft surrounding rock deformation prediction, and reduce the complexity and amount of calculation at the same time. In order to achieve this purpose, the present invention discloses a method for predicting the deformation of vertical shaft surrounding rock based on fluid-solid coupling linkage simulation, and the steps adopted are:

[0006] Step 1: Use the change of water content to describe the influence of rainwater infiltration on surrounding rock deformation, construct a surrounding rock deformation time series model of bidirectional fluid-solid coupling under rainwater infiltration, and form a permeability-surrounding rock deformation model; based on the bidirectional fluid-solid coupling surrounding rock deformation time series model, simulate the fluid-solid coupling under the action of rainwater scouring, and construct a scouring-surrounding rock deformation model to describe the deformation of the surrounding rock over time under the action of rainwater scouring; according to the permeability-surrounding rock deformation model, the scouring-surrounding rock deformation model and the shaft excavation parameters, establish a shaft excavation model under the action of rainwater;

[0007] Step 2: Perform multi-scale linkage simulation on the infiltration-surrounding rock deformation model, the scouring-surrounding rock deformation model and the vertical shaft excavation model under the action of rainwater to obtain surrounding rock deformation data generated by actual vertical shaft excavation at different time scales;

[0008] Step 3: Using the surrounding rock deformation data obtained in step 2 as a training set to train a TCN-LSTM neural network model, a vertical shaft surrounding rock deformation prediction model under rainwater is obtained, wherein the TCN (temporal convolutional network)-LSTM (long short-term memory network) neural network model is a combination model of TCN and LSTM;

[0009] Step 4: Acquire real-time surrounding rock deformation data, input it into the vertical shaft surrounding rock deformation prediction model under the action of rainwater, and output the real-time surrounding rock deformation prediction result.

[0010] As a preferred embodiment of the present invention, in step 1, a time series model of surrounding rock deformation with bidirectional fluid-solid coupling under rainwater infiltration is constructed to form a infiltration-surrounding rock deformation model, which includes:

[0011] Step 1.1. Establish a multi-layer geological model consisting of multiple geological layers, wherein the geological layers include a soil layer, an ash layer, a surrounding rock layer, and a bedrock layer;

[0012] Step 1.2. Different geological layers in the multi-layer geological model are regarded as porous media, and the soil water retention curve model is used to calculate the water content change of each geological layer under the action of rainwater infiltration;

[0013] Step 1.3. Define the porosity, permeability and pore water pressure of each geological layer in the multi-layer geological model according to the calculated water content of each geological layer;

[0014] Step 1.4. Use the time series model between seepage and deformation to describe the deformation trend of surrounding rock to form a surrounding rock deformation time series model;

[0015] Step 1.5. Based on the surrounding rock deformation time series model, bidirectional fluid-solid coupling under seepage is realized to obtain the bidirectional fluid-solid coupled surrounding rock deformation time series model, and the bidirectional fluid-solid coupled surrounding rock deformation time series model is used as the seepage-surrounding rock deformation model.

[0016] As a preferred embodiment of the present invention, the calculation of the water content change of each geological layer under the action of rainwater infiltration using the soil water retention curve model in step 1.2 includes:

[0017] Step 1.2.1. Calculate the matrix suction of each geological layer:

[0018]

[0019] Among them, ψ i (h) is the matrix suction of the ith geological layer at depth h, ψ i,top and ψ i,bottom are the matrix suction at the top and bottom of the ith geological layer, h i,top and h i,bottom are the top depth and bottom depth of the i-th geological layer;

[0020] Step 1.2.2. Calculate the water content at any point in each geological layer using the soil water retention curve model based on the matrix suction of each geological layer:

[0021]

[0022] Among them, θ ωi (h) is the water content at any point in the i-th geological layer at depth h, θ ri is the residual water content of the i-th geological layer, θ si is the saturated water content of the i-th geological layer, α i 、n i are the fitting parameters in the soil water retention curve model,

[0023] Step 1.2.3. Calculate the change in matrix suction caused by rainwater infiltration using the Richards equation based on the calculated water content of each geological layer:

[0024]

[0025] Among them, K(θ ωi (h)) represents the water content-dependent permeability coefficient, represents the divergence operator, and t is time. As a preferred embodiment of the present invention, in step 1.5, bidirectional fluid-solid coupling under infiltration is realized by adopting the computational fluid dynamics (CFD) method and the discrete element method (DEM), wherein the porosity, permeability and pore water pressure of each geological layer defined are used as material parameters of the computational fluid dynamics method, the discrete element method is used to describe the mechanical behavior between particles in each geological layer, and bidirectional coupling is performed through the interaction between fluid pressure and solid deformation.

[0026] As a preferred embodiment of the present invention, in the process of constructing the scouring-surrounding rock deformation model in step 1, computational fluid dynamics method and discrete element method are used to perform fluid-solid coupling simulation under the action of rainwater scouring, and the steps include:

[0027] When using the computational fluid dynamics method, the fluid domain of rainwater is defined, and the corresponding boundary conditions and initial conditions are applied, and a turbulence model is selected to simulate the scouring effect of rainfall water; when using the discrete element method, the discrete element method is used to model the solid particles, and each particle unit is regarded as an independent rigid body;

[0028] The computational fluid dynamics method and the discrete element method are exchanged for data to achieve bidirectional coupling between fluid mechanics and the motion interaction of solid particles. The fluid pressure force in the computational fluid dynamics method is transmitted to the solid particles in the J-OCTA software and the discrete element method to simulate the erosion process of soil and volcanic ash layers caused by rainwater scouring and evaluate the stress and deformation of each layer of material. The J-OCTA software is used to deal with the interface problems of fluid-solid coupling in different geological layers.

[0029] As a preferred embodiment of the present invention, in the process of constructing the scour-surrounding rock deformation model in step 1, processing the interface problem of fluid-solid coupling of different geological layers includes:

[0030] Computational fluid dynamics and discrete element methods are used to simulate the particle loss process when rainwater infiltrates and scours the soil. The multi-scale model of J-OCTA software is used to couple the movement of soil particles under rainwater scouring, and the relationship model between pore water pressure and soil deformation in fluid-solid coupling is solved to simulate the deformation and stress changes of soil under scouring and seepage conditions.

[0031] The movement of soil particles in the soil layer under load and the interaction between the fluid are described by the following formula:

[0032]

[0033] Among them, F i is the total force on soil particle i, is the collision force between particles i and j, and are the electrostatic force and van der Waals force of particles i and j, respectively, and F i 流体 is the drag and buoyancy of the fluid on particle i;

[0034] The numerical solver of J-OCTA software is used to solve the following relationship model between pore water pressure and soil deformation in fluid-solid coupling:

[0035]

[0036] Where σ′ is the effective stress, α is the Biot coefficient, p is the pore water pressure, ρ is the soil density, g is the gravitational acceleration, k is the permeability coefficient, and μ is the fluid viscosity;

[0037] The coarse-graining method in the J-OCTA software is used to describe the interaction of volcanic ash particles in the volcanic ash layer, and the built-in MPS (Moving Particle Semi-Implicit) fluid model is called to simulate the behavior of particles separating and depositing from the volcanic ash layer with rain erosion. The coarse-graining model of volcanic ash particles is established in the J-OCTA software, the fine particles of volcanic ash are processed by coarse-graining, and the multi-scale coupling function of J-OCTA is used to perform multi-scale coupling simulation of the phase field method to deal with the erosion problem of the volcanic ash layer under rain erosion. The interaction potential between volcanic ash particles is described as:

[0038]

[0039] where U(r) is the potential energy between particles, k B is the Boltzmann constant, A is the Hamaker constant, σ is the particle diameter, r is the particle spacing, T is the temperature, and n is the fitting parameter;

[0040] The phase field method is used to describe the gradual loss and migration of ash at the interface through the continuous phase change equation as follows:

[0041]

[0042] Among them, φ represents the spatial distribution of volcanic ash concentration, μ is the chemical potential, and M is the kinetic energy coefficient.

[0043] As a preferred embodiment of the present invention, in the process of constructing the scouring-surrounding rock deformation model in step 1, it also includes: by calling the MPS fluid model and the phase field fracture model in the J-OCTA software for coupling, the phase field fracture model and the fluid model are calculated by J-OCTA multi-scale coupling, so as to use the phase field fracture model to simulate the crack expansion process that may be generated in the surrounding rock layer under rain scouring according to the following formula:

[0044]

[0045] Among them, G c is the fracture energy, Φ is the crack extension, l is the crack width scale, and G is the fracture toughness of the material;

[0046] By calling the MPS fluid model in the J-OCTA software and combining it with Darcy's law, the fracture seepage in the bedrock layer is solved:

[0047]

[0048] Where q is the flow velocity, k is the permeability, μ is the fluid viscosity, is the pressure gradient.

[0049] As a preferred embodiment of the present invention, the step of training the TCN-LSTM neural network model in step 3 includes:

[0050] Step 3.1. The time series data X consisting of surrounding rock deformation data at different time scales is converted into 1 ,x 2 ,…,x t} is input to the TCN layer in the TCN-LSTM neural network model, where xt represents the displacement deformation of the surrounding rock observed at time point t;

[0051] Step 3.2. Extract the time series data X = {x 1 ,x 2 ,…,x t} features to obtain time series features;

[0052] Step 3.3. Use the output of the TCN layer as the input of the LSTM layer in the TCN-LSTM neural network model, and the LSTM layer captures the long-term dependencies of the time series data;

[0053] Step 3.4. Convert the output of the LSTM layer into the predicted deformation and calculate the difference between the predicted deformation and the actual observed value;

[0054] Step 3.5. Use the initial hyperparameters to train the TCN-LSTM neural network model according to steps 3.1 to 3.4, and use the Bayesian optimization algorithm to select the optimal hyperparameters;

[0055] Step 3.6. Use the optimal hyperparameters to retrain the TCN-LSTM neural network model according to steps 3.1 to 3.4 to obtain the final shaft surrounding rock deformation prediction model under the action of rainwater.

[0056] As a preferred embodiment of the present invention, selecting the optimal hyperparameters using the Bayesian optimization algorithm in step 3.5 includes:

[0057] Step 3.5.1. Initialize the Gaussian process model, select the kernel function of the Gaussian process and the initial hyperparameter Θ (0) ;

[0058] Step 3.5.2. Use the current hyperparameter Θ (i) Train the TCN-LSTM model, where i is the number of execution steps, and calculate the loss L on the training set (i) , update the Gaussian process model to include the new observation Θ (i) , L (i) ;

[0059] Step 3.5.3. Calculate the acquisition function EI:

[0060] EI(Θ)=φ(Θ)-μ(Θ)

[0061] Where φ and μ are the standard deviation and mean of the Gaussian process prediction, respectively;

[0062] Step 3.5.4. Select the next hyperparameter Θ according to the acquisition function EI (i+1) , repeat steps 3.5.1 to 3.5.4 until the optimal hyperparameter set Θ is found opt .

[0063] A device for predicting deformation of surrounding rocks of a vertical shaft based on linkage simulation comprises a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the above method.

[0064] Compared with the prior art, the advantages of the present invention are as follows: the present invention considers the influence of rainwater infiltration and rainwater scouring on the deformation of the shaft surrounding rock, and respectively constructs a penetration-surrounding rock deformation model considering the effect of rainwater infiltration and a scouring-surrounding rock deformation model considering the effect of rainwater scouring, and then constructs a shaft excavation model under the action of rainwater. It can fully consider the effect of rainwater infiltration and scouring on the surrounding rock deformation, effectively improve the accuracy and reliability of the shaft surrounding rock deformation prediction, and enhance the scope of application and flexibility of the prediction. At the same time, by combining the TCN-LSTM neural network model training to form a shaft surrounding rock deformation prediction model, it can also reduce the complexity of the prediction, improve the efficiency and real-time performance of the prediction, thereby realizing efficient, accurate and real-time prediction of the shaft surrounding rock deformation under the action of rainwater. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The present invention will be described in more detail below based on embodiments and with reference to the accompanying drawings, wherein:

[0066] Figure 1 It is a schematic flow chart of the method for predicting deformation of surrounding rock of a vertical shaft based on fluid-solid coupling linkage simulation in this embodiment.

[0067] Figure 2 Schematic diagram of the multi-layer geological layer model constructed in this embodiment.

[0068] Figure 3 It is a schematic diagram of the results obtained by using the coarse-graining method of J-OCTA software to characterize the evolution process of mud material under the action of rainwater in a specific embodiment.

[0069] Figure 4 Schematic diagram of the structure of the TCN-LSTM neural network used in this embodiment. DETAILED DESCRIPTION

[0070] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereby.

[0071] Considering that weather is an important factor in shaft construction, especially rainfall, it will have a significant impact on the deformation of the shaft surrounding rock. For example, consider the deformation of the surrounding rock under the action of rain:

[0072] (1) Rainwater penetration

[0073] When rainwater infiltrates into the surrounding rock, it increases the pore water pressure in the surrounding rock. This increased water pressure acts on the surrounding rock, causing changes in the effective stress in the surrounding rock and a decrease in the mechanical strength of the surrounding rock, which in turn causes deformation of the surrounding rock. Especially in areas where the surrounding rock joints and fissures are developed, the infiltration water flow will further widen these fissures and become channels for groundwater migration, thereby affecting the integrity and stability of the surrounding rock. In the horizontal direction, the infiltration water flow may cause the horizontal convergence of the surrounding rock; in the vertical direction, the infiltration water flow may cause the vault settlement and the floor uplift, which will affect the stability of the surrounding rock.

[0074] The effect of seepage on surrounding rock deformation is usually time-dependent. As time goes by, the seepage water will continue to act on the surrounding rock, causing the deformation and damage of the surrounding rock to gradually develop. The above time dependence shows that the deformation and damage of the surrounding rock is a gradual process that requires long-term monitoring and evaluation.

[0075] (2) Rainwater erosion

[0076] Rainwater scouring may cause the destruction of the surface soil structure and reduce the soil's ability to resist erosion. This scouring action may reduce the stability of the soil layer, form mud, and increase the risk of landslides and erosion. The porosity and water content of the soil layer will increase, affecting the mechanical properties of the soil. For the volcanic ash layer, rainwater scouring may cause the volcanic ash particles to mix with water, increase the viscosity and density of the water, affect the flow characteristics and scouring ability of the water, and form muddy substances, which may flow along the slope, causing scouring and erosion of the surrounding rock layer below, and changing the structure and porosity of the volcanic ash layer. Although the surrounding rock layer and bedrock layer may not directly form mud like the soil layer due to their relatively compact structure and high strength, long-term rainwater scouring may cause weathering of the surface of these rock layers, forming geomorphic features such as potholes, especially in places where bedrock fissures are developed, the penetration and scouring of rainwater may cause the destruction and deformation of the surrounding rock.

[0077] As mentioned above, the rainfall effect is divided into rainwater infiltration and rainwater scouring. According to different rainwater effects, a vertical shaft surrounding rock deformation prediction model is constructed, which can fully consider the influence of rainfall conditions on the deformation of the vertical shaft surrounding rock and improve the prediction accuracy.

[0078] The present invention considers the influence of rainwater infiltration and rainwater scouring on the deformation of the shaft surrounding rock, respectively constructs an infiltration-surrounding rock deformation model considering the effect of rainwater infiltration and an scouring-surrounding rock deformation model considering the effect of rainwater scouring, and then constructs a shaft excavation model under the action of rainwater, which can fully consider the effect of rainwater infiltration and scouring on the deformation of the surrounding rock, simulate the influence of rainwater infiltration and scouring on the deformation of the surrounding rock, thereby effectively improving the accuracy and reliability of the prediction of the deformation of the shaft surrounding rock, especially in the rainy season construction environment, improving the scope of prediction and flexibility, avoiding the limitations of the traditional empirical formula method, and combining TCN-LS The TM neural network model is trained to form a vertical shaft surrounding rock deformation prediction model. The model is used to realize real-time surrounding rock deformation prediction during the vertical shaft excavation process under the action of rainwater. It can also reduce the complexity of the prediction, improve the efficiency and real-time performance of the prediction. Through real-time processing and dynamic updating of data, it can timely reflect the actual situation of surrounding rock deformation, realize efficient, accurate and real-time prediction of vertical shaft surrounding rock deformation under the action of rainwater, overcome the shortcomings of traditional prediction methods in prediction accuracy, calculation efficiency and dynamic updating ability, solve the surrounding rock stability problems that may be encountered in the actual prediction project, and ensure the safety and economy of the vertical shaft construction process under rainfall conditions.

[0079] like Figure 1 As shown, the method for predicting deformation of surrounding rock of a shaft based on fluid-solid coupling linkage simulation in this embodiment includes the following steps:

[0080] Step 1: Use the change of water content to describe the influence of rainwater infiltration on surrounding rock deformation, construct a two-way fluid-solid coupling surrounding rock deformation time series model under rainwater infiltration, and form a seepage-surrounding rock deformation model; based on the two-way fluid-solid coupling surrounding rock deformation time series model, simulate the fluid-solid coupling under the action of rainwater scouring, and construct a scouring-surrounding rock deformation model to describe the deformation of the surrounding rock over time under the action of rainwater scouring; according to the seepage-surrounding rock deformation model, the scouring-surrounding rock deformation model and the shaft excavation parameters, establish a shaft excavation model under the action of rainwater.

[0081] In this embodiment, in order to predict the deformation of the surrounding rock under the action of seepage, a multi-layer geological model containing bedrock layers is first established, such as Figure 2 As shown, the change of water content is used to describe the infiltration of rainwater, and then the water content of different geological layers is analyzed. The following steps can be used to construct a time series model of surrounding rock deformation with two-way fluid-solid coupling under the action of rainwater infiltration and form a permeability-surrounding rock deformation model:

[0082] Step 1.1. Establish a multi-layer geological model consisting of multiple geological layers, including soil layer, ash layer, surrounding rock layer and bedrock layer.

[0083] It should be particularly noted that the surrounding rock layer in the multi-layer geological model refers to the rock surrounding a certain crustal material, which is different from the surrounding rock in the surrounding rock deformation prediction in this embodiment.

[0084] Step 1.2. Different geological layers in the multi-layer geological model are regarded as porous media, and the soil water retention curve model (VG model) is used to calculate the water content change of each geological layer under the action of rainwater infiltration;

[0085] Step 1.3. Define the porosity, permeability and pore water pressure of each geological layer in the multi-layer geological model according to the calculated water content of each geological layer;

[0086] Step 1.4. Use the time series model between seepage and deformation to describe the deformation trend of surrounding rock to form a surrounding rock deformation time series model;

[0087] Step 1.5. Based on the surrounding rock deformation time series model, bidirectional fluid-solid coupling under seepage is realized to obtain a bidirectional fluid-solid coupled surrounding rock deformation time series model, and the bidirectional fluid-solid coupled surrounding rock deformation time series model is used as the seepage-surrounding rock deformation model.

[0088] The soil water retention curve model (VG model) can be used not only for soil layers, but also for other porous media that need to describe the water characteristic curve, such as rock, concrete, etc. In this embodiment, different geological layers are regarded as porous media, and the VG model is used to describe the matrix suction ψ and water content θ w The relationship between.

[0089] In this embodiment, the soil water retention curve model is used in step 1.2 to calculate the change in water content of each geological layer under the action of rainwater infiltration, which can be done by the following steps:

[0090] Step 1.2.1. Define the geological layer parameters of the geological layer, including the residual water content θ ri , saturated water contentθ si and soil water retention curve model parameter α i and n i ;

[0091] Step 1.2.2. Calculate the matrix suction of each geological layer:

[0092]

[0093] Among them, ψ i (h) is the matrix suction of the ith geological layer at depth h, ψ i,top and ψ i,bottom are the matrix suction at the top and bottom of the ith geological layer, h i,top and h i,bottom are the top depth and bottom depth of the i-th geological layer.

[0094] Step 1.2.3 Calculate the water content at any point in each geological layer using the soil water retention curve model based on the matrix suction of each geological layer:

[0095]

[0096] Among them, θ ωi (h) is the water content at any point in the i-th geological layer at depth h, θ ri is the residual water content of the i-th geological layer, θ si is the saturated water content of the i-th geological layer, α i 、n i are the fitting parameters, The number of geological layers.

[0097] Step 1.2.4. Calculate the change in matrix suction caused by rainwater infiltration using the Richards equation based on the calculated water content of each geological layer:

[0098]

[0099] Among them, K(θ ωi (h)) represents the water content-dependent permeability coefficient, represents the divergence operator and t is the time.

[0100] According to the above steps, this embodiment can construct a matrix suction that is generally used to calculate any geological layer i at a depth h, so that it can not only describe the matrix suction in a single layer, but also calculate the change of matrix suction in any number of geological layers. By referring to the top and bottom variables, it can more intuitively describe the change of matrix suction with depth, especially when the thickness of the geological layer is uneven. At the same time, based on the constructed matrix suction calculation model for multiple geological layers, a water content calculation model is constructed, which can accurately calculate the change of water content of matrix suction in multiple geological layers, and then effectively obtain the change of matrix suction based on the water content of matrix suction.

[0101] In this embodiment, after making corrections and setting initial conditions according to actual conditions, the water content changes of each geological layer under the action of rainwater infiltration can be obtained based on the above process. At the same time, the VG model can be used to construct a water content change curve, and the inflow and discharge per unit time can be further obtained.

[0102] Changes in water content have a significant impact on surrounding rock deformation. For example, the effects of increased water content include: reducing surrounding rock strength, increasing pore water pressure, intensifying seepage, changing stress state, causing displacement, and coupling with groundwater. These effects will lead to reduced surrounding rock stability and increased deformation and damage risks (especially in weak, broken or layered rock masses). That is, water content affects seepage, and then affects surrounding rock deformation. This embodiment uses water content to describe the effect of rainwater seepage on surrounding rock deformation, which can effectively quantify the effect of rainwater seepage on surrounding rock deformation.

[0103] Discrete element method (DEM) can handle complex nonlinear and fracture problems, has high flexibility, and is suitable for non-continuous media. In this embodiment, the rock mass is regarded as composed of a large number of particles or blocks, and the deformation is predicted by simulating the interaction between these particles, so that deformation prediction of granular materials and block rocks can be achieved, thereby dealing with problems of complex geological conditions such as large deformation, fracture, and sliding. In this embodiment, in step 1.5, bidirectional fluid and solid coupling under infiltration is realized by using computational fluid dynamics (CFD) and discrete element method (DEM), wherein the porosity, permeability and pore water pressure of each geological layer defined are used as material parameters of the computational fluid dynamics method, and the discrete element method is used to describe the mechanical behavior between particles in each geological layer, and bidirectional coupling is performed through the interaction between fluid pressure and solid deformation.

[0104] In coupling, changes in water content will affect the boundary conditions of water flow and the interaction between materials, thereby changing the stress transfer and force between fluid and solid. This embodiment constructs a fluid-solid coupling and surrounding rock deformation model based on the influence of water content on surrounding rock deformation. The fluid-solid coupling process will cause the stress state of the surrounding rock to change, thereby changing the seepage path and pore water pressure. This embodiment updates the surrounding rock deformation state at each time step through iterative calculation and feeds back to the fluid simulation to achieve fluid-solid coupling.

[0105] The VG model can help estimate the change in pore water pressure under different water content states, and the pore water pressure in turn affects the effective stress distribution of the surrounding rock. In this embodiment, the change in water content is combined with fluid pressure and surrounding rock deformation, and the stress and strain state of the rock mass is adjusted by changing the pore water pressure of the geological layer. The water content of each layer obtained by the VG model can define the porosity, permeability and pore water pressure of each layer, input the CFD method as the material parameter, and then use DEM to describe the mechanical behavior between particles in each geological layer. Through the interaction between fluid pressure and solid deformation, two-way coupling is achieved. Since the change in water content is related to time, the deformation trend of the surrounding rock can be described by the time series model between seepage and deformation, and then the surrounding rock deformation time series can be constructed.

[0106] According to the above process, a bidirectional fluid-solid coupling surrounding rock deformation time series model under seepage can be constructed using water content to represent the seepage effect, that is, a seepage-surrounding rock deformation model can be obtained.

[0107] Based on the above-mentioned multi-layer geological model constructed in rainwater infiltration, taking into account that the geological layer is a three-dimensional entity composed of rock stacking and deposition in reality, and considering the scouring effect on the scouring surface of the geological layer, this embodiment uses the end face of the above-mentioned multi-layer geological model constructed (i.e., the end face used to characterize the wall of the vertical shaft) as the scouring surface of rainwater scouring.

[0108] When constructing the scouring-surrounding rock deformation model considering the scouring effect of rainwater, computational fluid dynamics (CFD) and discrete element method (DEM) are used to simulate the fluid-solid coupling under the action of rainwater scouring. At the same time, in order to achieve more accurate and realistic coupling, the interface problem of the fluid-solid coupling model is processed based on J-OCTA software, thereby providing more accurate input for the prediction of surrounding rock deformation.

[0109] In this embodiment, the scour-surrounding rock deformation model can be constructed by the following steps:

[0110] When using computational fluid dynamics, define the fluid domain of rainwater, apply corresponding boundary conditions and initial conditions, and select a turbulence model to simulate the flushing effect of rainfall. When using discrete element method, use discrete element method to model solid particles, treat each particle unit as an independent rigid body, and follow Newton's laws of motion and contact mechanics model to interact with each other.

[0111] The computational fluid dynamics method and the discrete element method are exchanged for data to couple the fluid mechanics with the motion interaction of solid particles in a two-way manner. The fluid pressure force in the computational fluid dynamics method is transmitted to the solid particles in the J-OCTA software and the discrete element method. In turn, the solid deformation affects the flow field to simulate the erosion process of soil and volcanic ash layers caused by rainwater scouring and evaluate the stress and deformation of each layer of material. The J-OCTA software is used to deal with the interface problems of fluid-solid coupling in different geological layers.

[0112] When simulating fluid-solid coupling phenomena in different geological layers under the action of rainwater scouring, the J-OCTA software can simulate interface problems across scales from micro to macro. The simulation of solid materials at the interface is very accurate and can handle problems such as fracture, deformation, and stress transfer of complex materials. It is suitable for handling fluid-solid interactions at different scales.

[0113] Under the action of scouring, the fluid affects the deformation and stability of the geological layer through the penetration and particle deposition of the geological layer. In this embodiment, the J-OCTA software is used to analyze the interface problems of fluid-solid coupling in different geological layers, focusing on the mechanical interaction and deformation characteristics of the fluid and solid (different geological layers) at the interface. The J-OCTA software method can accurately simulate the interface problems in fluid-solid coupling, especially the problems of fracture, deformation, stress transfer, etc. of complex materials, which can improve the accuracy and applicability of the model in dealing with fluid-solid coupling problems.

[0114] In this embodiment, the interface problem of fluid-solid coupling of different geological layers in constructing the scour-surrounding rock deformation model includes:

[0115] 1. Soil layer

[0116] This embodiment adopts computational fluid dynamics method and discrete element method (i.e., DEM+CFD method) to simulate the particle loss process when rainwater penetrates and scours the soil in the soil layer, and characterizes the movement behavior of soil particles under rainwater scouring by using the multi-scale model of J-OCTA software. The relationship model between pore water pressure and soil deformation in fluid-solid coupling is solved by finite element method to simulate the deformation and stress changes of soil under scouring and seepage conditions.

[0117] For example, the movement of soil particles in a soil layer under load and the interaction between the fluid can be described using the following equation:

[0118]

[0119] Among them, F i is the total force on soil particle i, is the collision force between particles i and j, and are the electrostatic force and van der Waals force of particles i and j, respectively, and F i 流体 is the drag force and buoyancy of the fluid on particle i. The combined effect of the above forces determines the movement behavior of soil particles under rain erosion, which can be coupled and simulated by the multi-scale model of J-OCTA software.

[0120] When using general coupling methods to deal with interface problems, usually only the collision between particles and the effect of the fluid on the particles are considered. In this embodiment, electrostatic force and van der Waals force are also considered when using J-OCTA software for coupling calculations. A multi-scale method is used to consider the forces acting on the particles, so that more accurate results can be obtained.

[0121] In this embodiment, when establishing a multi-scale model of the soil layer in the J-OCTA software, the geometric shape, size distribution and macroscopic structure of the soil particles and the soil layer are first defined; then the corresponding physical and chemical properties are defined for the soil particles and the fluid, including the electrostatic force, van der Waals force, interaction force between particles and the permeability and porosity of the soil, and the boundary conditions of the simulation are set according to the actual situation. The multi-scale coupling function of the J-OCTA software is used to couple the movement of the soil particles with the fluid flow, so as to combine the kinetic equations at the particle level with the continuous medium equations of the fluid flow.

[0122] Rainwater infiltration into the soil will cause changes in pore water pressure, affecting the stress distribution of the soil layer. For example, the relationship between pore water pressure and soil deformation in fluid-solid interaction can be described using Biot's equation based on the finite element method (FEM):

[0123]

[0124] where σ′ is the effective stress, α is the Biot coefficient, p is the pore water pressure, ρ is the soil density, g is the gravitational acceleration, k is the permeability coefficient, and μ is the fluid viscosity.

[0125] J-OCTA software can more comprehensively consider the forces acting on particles from a multi-scale perspective. In this embodiment, by using the numerical solver of J-OCTA software to solve equations (4), (5), and (6), more accurate results can be obtained than with traditional coupling methods. J-OCTA can create phase separation structure data as voxel grids of STL data, LS-DYNA, etc., to provide support for finite elements. Therefore, J-OCTA can directly call finite element software such as LS-DYNA for simulation, thereby solving (5) and (6) through the solver. In this way, combining J-OCTA can help quickly and accurately analyze and predict the mechanical behavior of materials in fluid-solid coupling problems.

[0126] This embodiment introduces J-OCTA to provide a more comprehensive description of the forces acting on particles and uses J-OCTA to perform multi-scale simulations, so that when performing coupled simulations, J-OCTA can be used to handle simulations at the nano, micro, and even millimeter levels, while the finite element method can only be used to handle macro-scale structural analysis. Therefore, through this coupling method, multi-scale simulations from micro to macro can be achieved, providing a more comprehensive understanding of material design and performance prediction, thereby improving the accuracy of predictions.

[0127] This embodiment uses J-OCTA software to solve the above equations through the finite element method, which can effectively simulate the deformation and stress changes of soil under scouring and seepage conditions.

[0128] 2. Volcanic ash layer

[0129] The volcanic ash layer is prone to erosion and particle migration under rain erosion due to its fine and loose particles. This example uses J-OCTA software to combine the coarse-graining method and the finite element method to process the dynamic behavior of the volcanic ash layer under rain erosion while retaining the key physical properties of the material.

[0130] The fine particles of volcanic ash are processed by a coarse-grained model to reduce the amount of calculation. This embodiment uses a coarse-grained method to describe the interaction of volcanic ash particles in the volcanic ash layer, and uses a fluid model to simulate the behavior of particles separating and depositing from the volcanic ash layer with rain erosion. At the same time, the erosion problem of the volcanic ash layer under rain erosion is processed by the Phase Field Method.

[0131] In this embodiment, the interaction between volcanic ash particles in the volcanic ash layer is described by using J-OCTA software combined with a coarse-granulation method. A coarse-granulation model of volcanic ash particles is established in the J-OCTA software. The fine particles of volcanic ash are processed by coarse-granulation, and the interaction between particles can be analyzed at the molecular level, which can not only reduce the amount of calculation, but also retain the key physical properties between particles.

[0132] For example, the interaction potential between volcanic ash particles is described as:

[0133]

[0134] where U(r) is the potential energy between particles, k B is the Boltzmann constant, A is the Hamaker constant, σ is the particle diameter, r is the particle spacing, T is the temperature, and n is the fitting parameter (the initial value can be 1.1). This potential function can simulate the adsorption, separation and aggregation processes of particles.

[0135] In J-OCTA, the fluid model includes parameters such as velocity, pressure, density and viscosity of the fluid, and can be a fluid model based on fluid dynamics. In addition, J-OCTA supports the automatic calculation of coarse-grained potential energy, and can map the coarse-grained model to a molecular dynamics (MD) or finite element (FEM) model, which facilitates the construction of the fluid model. This embodiment directly calls the fluid model in J-OCTA to combine the fluid model to simulate the behavior of particles separating and depositing from the volcanic ash layer with rain erosion. In multi-scale simulations, the fluid model can not only be combined with the coarse-grained method model, but also directly call the finite element software for more complex fluid-solid coupling simulations.

[0136] In a specific embodiment of the present invention, the coarse-grained method of J-OCTA software is used to characterize the mud material evolution process under the action of rainwater, and the results obtained are as follows: Figure 3 As shown in (a) to (d) in the figure, the key characteristics of various materials will be preserved during the evolution process. As an input parameter of fluid-solid coupling, the fluid is not a fixed substance in fluid-solid coupling, but a variable fluid that changes in real time with the coupling effect, which can be accurately characterized by the coarse-grained method.

[0137] When the phase field method is used to deal with the erosion problem of volcanic ash layers under rain erosion, the gradual loss and migration of volcanic ash at the interface can be described by continuous phase change equations, for example:

[0138]

[0139] Among them, φ is the phase field variable, which represents the spatial distribution of volcanic ash concentration, μ is the chemical potential, and M is the kinetic energy coefficient. This equation can describe the dynamic changes of particles at the interface, especially the erosion process of volcanic ash under the action of rain.

[0140] This embodiment performs multi-scale coupling simulation on the phase field method and utilizes the multi-scale coupling function of J-OCTA to combine the coarse-grained model with the fluid model as well as the finite element model and the phase field method, integrates the physical processes at different scales into a unified simulation framework, solves the interface problem through the solver of J-OCTA, and analyzes the erosion and particle migration behavior of the volcanic ash layer under rain erosion.

[0141] 3. Surrounding rock layer

[0142] The surrounding rock layer may generate crack expansion under the erosion of rainwater, which can be simulated by the phase field fracture model. In this embodiment, the phase field fracture model is used to simulate the crack expansion process of the surrounding rock layer under the erosion of rainwater according to the following formula, which can be expressed as:

[0143]

[0144] Among them, G c is the fracture energy, Φ is the phase field variable, which indicates the expansion of the crack, l is the crack width scale, and G is the fracture toughness of the material. Through this model, the crack expansion process at the interface between different geological layers under the action of water flow scouring and stress can be simulated, especially the destructive behavior of the material at the interface can be simulated.

[0145] In this embodiment, a geometric model of the surrounding rock layer (including size, shape, material properties, etc.) is established in J-OCTA, the fluid model is called to couple with the phase-field fracture model, the phase-field fracture model and the fluid model are calculated by multi-scale coupling through J-OCTA, boundary conditions and loads are set according to actual conditions, and a numerical solver is used to solve the coupled simulation.

[0146] 4. Bedrock

[0147] In this embodiment, the fracture seepage in the bedrock layer is described by Darcy's law combined with the finite element method, for example, it can be expressed as:

[0148]

[0149] Among them, q is the seepage velocity, k is the permeability, μ is the fluid viscosity, and ▽p is the pressure gradient. This equation can be combined with the stress distribution of the bedrock to simulate the stress concentration and deformation of the bedrock layer under the action of fracture water pressure.

[0150] In this embodiment, the MPS fluid model is directly called in J-OCTA and combined with Darcy's law to solve the seepage state of rainwater in the cracks.

[0151] This embodiment processes the interface problem of fluid-solid coupling of different geological layers in the above manner, and can capture the impact of water pressure changes on the mechanical behavior of each stratum in real time, so as to more accurately analyze the stress redistribution and deformation of soil and rock strata, and at the same time can evaluate the impact of moisture on stratum stability, such as softening of soil layers, crushing of surrounding rocks, and stress transmission of bedrock, thereby helping to predict potential landslides, collapses or fracture risks. Analyzing the four geological layers in the above manner can more accurately simulate the interaction between permeability, water pressure and stratum deformation, especially at complex interfaces with higher processing accuracy, so that more accurate surrounding rock deformation time series data input can be provided for the model.

[0152] This embodiment uses J-OCTA software to accurately simulate material properties in interface problems, including adhesion, strength, permeability, friction coefficient and elastic-plastic behavior at the interface. Combined with multiple scale models such as molecular dynamics, coarse-grained model, finite element method and phase field method, it can analyze the mechanical response and physical properties of different geological layers or materials at the interface, such as porosity, pore water pressure, and deformation and stress transfer of the interface, and can effectively solve fluid-solid coupling problems, especially fluid-solid coupling problems in stress and deformation prediction in the interface area.

[0153] According to the above process, a fluid-solid coupling surrounding rock deformation model can be constructed to deal with interface problems based on multiple scale methods under rainfall scouring conditions, forming a scouring-surrounding rock deformation time series model. After multi-scale coupling, the deformation of the surrounding rock over time under scouring conditions can be obtained.

[0154] Step 2: Perform multi-scale linkage simulation on the infiltration-surrounding rock deformation model, the scouring-surrounding rock deformation model and the shaft excavation model to obtain the surrounding rock deformation data generated by the actual shaft excavation at different time scales.

[0155] The shaft excavation model in this embodiment is a surrounding rock deformation time series model constructed by discrete element method. The aforementioned infiltration-surrounding rock deformation model and scouring-surrounding rock deformation model are obtained by multi-scale coupling through J-OCTA software. J-OCTA can create phase separation structure data as voxel grids of STL data, LS-DYNA, etc., to provide support for finite elements, and finite element software can be directly called in J-OCTA. At the same time, J-OCTA has the ability to couple with discrete element method and can be applied with discrete element software EDEM, etc. Based on this, this embodiment uses J-OCTA multi-scale coupling software to realize linkage simulation of the infiltration-surrounding rock deformation model, scouring-surrounding rock deformation model and shaft excavation model, and exchange data with each other.

[0156] In this embodiment, the constructed infiltration-surrounding rock deformation time series model and scouring-surrounding rock deformation time series model are combined with the on-site construction conditions of the shaft and the surrounding rock grade to determine the necessary parameters for shaft excavation, and establish a shaft excavation model under the action of rainwater. The shaft excavation model under the action of rainwater is the model obtained by linking the infiltration-surrounding rock deformation model, the scouring-surrounding rock deformation model and the shaft excavation model through J-OCTA.

[0157] Then, the central axis of the shaft is taken as the cutting surface of the model, and the displacement deformation of the surrounding rock in the direction of the shaft section under the action of rainwater is considered. The constructed infiltration-surrounding rock deformation time series model and scour-surrounding rock deformation time series model are used to perform linkage simulation with the shaft excavation model. After multiple simulations using the multi-scale numerical simulation method, a large amount of surrounding rock deformation data generated by the actual shaft excavation under the action of rainwater is obtained.

[0158] The above-mentioned multi-scale numerical simulation method is the coupling method pointed out by J-OCTA, that is, the coupling method of coarse-grained model, fluid model, phase field fracture model, finite element and discrete element.

[0159] This embodiment can accurately capture the influence of fluid flow on formation deformation, especially the transition and interaction at the interface, through the linked fluid-solid coupling simulation; the multi-scale simulation method can also more comprehensively capture the complexity of surrounding rock deformation under different geological conditions, provide more accurate simulation results, and flexibly handle physical processes at different scales from micro to macro, thereby enhancing the applicability and accuracy of the model.

[0160] Step 3: Use the surrounding rock deformation data obtained in step 2 as a training set to train the TCN-LSTM neural network model to obtain a vertical shaft surrounding rock deformation prediction model under the action of rainwater. The TCN-LSTM neural network model is a combination model of TCN and LSTM.

[0161] In this embodiment, the surrounding rock deformation data within a set time range obtained by a multi-scale numerical simulation method is used as a learning sample of the algorithm to train a pre-established TCN-LSTM neural network model. After learning and training, a vertical shaft surrounding rock deformation prediction model under the action of rainwater is constructed. The prediction range is set according to the accuracy of the vertical shaft surrounding rock deformation prediction model, and the surrounding rock deformation prediction value can be automatically output to determine the deformation value of the specified section of the vertical shaft at each position under rainwater conditions.

[0162] In this embodiment, a mathematical model of the surrounding rock deformation time series curve is established through the TCN-LSTM neural network, that is, the relationship between the excavation time t and the surrounding rock deformation y is established, so as to realize the prediction of the surrounding rock deformation of the vertical shaft under the action of rainwater based on the TCN-LSTM neural network. The TCN can identify the local patterns and trends in the time series, and the LSTM network can process data at each time step to capture the dynamic characteristics in the time series. A large amount of surrounding rock deformation data obtained by the multi-scale simulation method is input, and the surrounding rock deformation data includes the displacement of the surrounding rock at different time points. The displacement series has time dependence, and the time series data can also be converted into features suitable for input, including the time point itself or time-related features, such as historical deformation, deformation rate, deformation acceleration, etc.

[0163] This embodiment uses the combined model TCN-LSTM of a temporal convolutional network (TCN) and a long short-term memory network (LSTM) to train a shaft surrounding rock deformation prediction model under the action of rainwater. It can combine the TCN's ability to recognize local patterns in time series data and the LSTM's ability to capture long-term dependencies, thereby effectively improving the model's prediction accuracy for surrounding rock deformation time series data.

[0164] In order to eliminate the influence of different dimensions and obtain better network training results, multiple groups of training samples are normalized to convert the sample values ​​into the (0,1) interval. The normalization process is as follows:

[0165]

[0166] The processed data is divided into a training set and a test set. The training set is used to build the model, and the test set is used to verify the predictive ability of the model.

[0167] In this embodiment, the TCN part effectively processes time series data through causal convolution operations, and the LSTM part can memorize important historical information, combining TCN and LSTM to form a TCN-LSTM neural network model. Figure 4As shown, in the TCN-LSTM neural network used in this embodiment, the first layer is the input layer, which is used to receive time series data; the second layer TCN part is used to extract features from the input time series data, and TCN uses convolution operations to identify local patterns and trends in the time series; different x represents different dimensions of the input data or data at different time steps, and the feature dimension refers to the number of features after TCN processing, which is then sent to the LSTM network. The third layer LSTM network is composed of multiple LSTM units, each of which contains a forget gate, an input gate, and an output gate, where the forget gate determines which information will be forgotten or retained from the cell state; the input gate controls the writing of new information, including which part of the cell state is updated, and the output gate determines the output of the LSTM unit, that is, the final prediction or state. The fourth layer output layer is the final result of the network. The fully connected layer converts the output of the LSTM into the final prediction result, and can map high-dimensional features to the target dimension.

[0168] In this embodiment, the following steps may be used to train the TCN-LSTM neural network model:

[0169] Step 3.1. The time series data X consisting of surrounding rock deformation data at different time scales is converted into 1 ,x 2 ,…,x t} is input to the TCN layer in the TCN-LSTM neural network model, where xt represents the displacement deformation of the surrounding rock observed at time point t;

[0170] Step 3.2. Extract the time series data X = {x 1 ,x 2 ,…,x t} features to obtain time series features.

[0171] Step 3.3. Use the output of the TCN layer as the input of the LSTM layer in the TCN-LSTM neural network model, and the LSTM layer captures the long-term dependencies of the time series data.

[0172] In the TCN layer, for a single time point t, the output of each causal convolutional layer l is:

[0173]

[0174] in, is the output of the lth layer causal convolution at time point t, is the weight of the l-th convolution kernel at time delay τ, b (l) is the bias term of the lth layer, f (l) is the activation function of the lth layer.

[0175] In this embodiment, when the TCN layer includes multiple convolutional layers, the output Z of each layer t (l) It will be used as the input of the next layer, and the outputs of all layers can be combined into the final feature vector:

[0176]

[0177] Where M is the total number of TCN convolutional layers, Z t is the feature vector output by the TCN layer at time point t.

[0178] For the entire time series, the TCN layer generates a feature vector Z for each time point t t , forming a feature sequence:

[0179] Z={Z 1 ,Z 2 ,…Z T} (13)

[0180] Where T is the length of the time series.

[0181] The output of the TCN layer is used as the input of the LSTM layer, and the LSTM layer is used to capture the long-term dependencies of time series data, where the forget gate can be expressed as:

[0182] f t =σ(W f ·[Z t-1 ,W h ·h t-1 ]+b f ) (14)

[0183] Among them, W f is the weight matrix of the forget gate, W h is the weight associated with the hidden state at the previous time step, h t-1 is the hidden state of the previous time step, b f is the bias term, and σ is the sigmoid activation function.

[0184] The input gate can be expressed as:

[0185] i t =σ(W i ·[Z t ,W h ·h t-1 ]+b i ) (15)

[0186]

[0187] Among them, i t is the activation value of the input gate, C tis the candidate cell state, W i and W C is the weight matrix of the input gate and candidate cell state, b i and b C is the corresponding bias term.

[0188] The unit status can be updated as follows:

[0189]

[0190] Among them, C t is the cell state at the current time step, C t-1 is the cell state at the previous time step.

[0191] The output gate can be expressed as:

[0192] o t =σ(W o ·[Z t ,W h ,h t-1 ]+b o ) (18)

[0193] Among them, t is the activation value of the output gate, W o is the weight matrix of the output gate, b o is the bias term.

[0194] The hidden state at the current time step can be expressed as:

[0195] h t =o t *tanh(C t ) (19)

[0196] Among them, h t is the hidden state at the current time step and tanh is the activation function.

[0197] In this embodiment, the TCN layer generates a feature vector Z for each time point t. t , forming a feature sequence Z as the input of LSTM, which is not input layer by layer, but describing the data as a feature sequence as input.

[0198] Step 3.4. Convert the output of the LSTM layer into the predicted deformation and calculate the difference between the predicted deformation and the actual observation.

[0199] When converting the output of the LSTM layer into a predicted deformation, a fully connected layer can be used to predict the deformation at the current time step, for example:

[0200]

[0201] in, is the predicted deformation at the current time step, W is the weight matrix, b is the bias term, and f is the nonlinear activation function.

[0202] Define the loss function to measure the difference between the actual observed value y and the predicted value For example, the mean square error (MSE) can be used as the loss function:

[0203]

[0204] Where N is the number of samples; y t is the actual deformation at the tth time step; is the deformation predicted by the model at the tth time step.

[0205] Step 3.5. Use the initial hyperparameters to train the TCN-LSTM neural network model according to steps 3.1 to 3.4, and use the Bayesian optimization algorithm to select the optimal hyperparameters;

[0206] Step 3.6. Use the optimal hyperparameters to retrain the TCN-LSTM neural network model according to steps 3.1 to 3.4 to obtain the final shaft surrounding rock deformation prediction model under the action of rainwater.

[0207] In this embodiment, the Bayesian optimization algorithm can be used in step 3.5 to select the optimal hyperparameters by the following steps:

[0208] Step 3.5.1. Initialize the Gaussian process model, select the kernel function of the Gaussian process and the initial hyperparameter Θ (0) .

[0209] Define the hyperparameter space Θ, including the learning rate, parameters of TCN and LSTM layers, etc. Use Bayesian optimization to select the optimal hyperparameters, initialize the Gaussian process model, and select the kernel function of the Gaussian process. The present invention selects the radial basis function (RBF):

[0210]

[0211] Where x and x′ are two points in the input space; ||xx′|| represents the Euclidean distance between the two points; l is the length parameter of the sum function; k(x,x′) is the output of the kernel function, which represents the similarity between the two output points.

[0212] Step 3.5.2. Use the current hyperparameter Θ (i) Train the TCN-LSTM model, where i is the number of execution steps, and calculate the loss L on the training set (i) , update the Gaussian process model to include the new observation Θ (i) , L(i) ;

[0213] Step 3.5.3. Select Expected Improvement (EI) as the acquisition function and calculate the acquisition function EI:

[0214] EI(Θ)=φ(Θ)-μ(Θ) (22)

[0215] Where φ and μ are the standard deviation and mean of the Gaussian process prediction, respectively;

[0216] Step 3.5.4. Select the next hyperparameter Θ according to the acquisition function EI (i+1) , repeat steps 3.5.1 to 3.5.4 until the optimal hyperparameter set Θ is found opt .

[0217] This embodiment adopts the Bayesian optimization algorithm to select the optimal hyperparameters, uses the radial basis function (RBF) as the kernel function of the Gaussian process, and constructs a performance probability model of the hyperparameters to guide the search process, which can avoid invalid attempts and find the optimal hyperparameter combination within a smaller number of iterations. Moreover, since the surrounding rock deformation data changes over time, by utilizing the adaptive learning ability of the Bayesian optimization algorithm, the model can also quickly adapt to new data inputs, significantly enhancing the prediction ability for new samples and ensuring the continuous optimization and accuracy of model predictions.

[0218] Use the optimal hyperparameter Θ opt Train the TCN-LSTM model:

[0219] Θ * =argmin Θ L (23)

[0220] Where L is the loss function, which is the mean square error.

[0221] Finally, the test set is used to evaluate the model performance and calculate the prediction error. new Make a prediction, that is:

[0222]

[0223] The test set data usually includes deformation data at continuous time points to evaluate the model's ability to capture time series changes, and adjust the model parameters through continuously updated training data to adapt to changes in deformation data over time. The prediction model of this embodiment is trained and verified through a large amount of surrounding rock deformation data to ensure its effectiveness and reliability in actual engineering. At the same time, the model parameters are adjusted through continuously updated training data, and the model can adapt to changes in deformation data over time. The model obtained after the above processing obtains the predicted data of surrounding rock deformation through denormalization, and finally obtains the trained rainwater-based vertical shaft surrounding rock prediction model based on the TCN-LSTM neural network, which can be used to predict new surrounding rock deformation data under rainfall conditions.

[0224] Step 4: Obtain real-time surrounding rock deformation data, input it into the shaft surrounding rock deformation prediction model under the action of rainwater, and output the real-time surrounding rock deformation prediction result.

[0225] This embodiment constructs a seepage-surrounding rock deformation time series model and a scouring-surrounding rock deformation time series model according to the different effects of rainwater, and then performs linkage simulation with shaft excavation, which can accurately capture the impact of fluid flow on stratum deformation, effectively generate a large amount of surrounding rock deformation time series data generated by actual shaft excavation, and then use the time series data to train the TCN-LSTM neural network model. By combining the local pattern recognition ability of the time convolution network and the long-term dependency capture ability and local pattern of the long short-term memory network, the prediction accuracy of the shaft surrounding rock deformation time series data can be effectively improved. At the same time, it can also achieve fast learning, strong adaptability, hyperparameter intelligent optimization and real-time data processing, effectively improve the prediction efficiency and adaptability of the model, while reducing the computing cost, and enhancing the practicality and reliability of the model in actual engineering.

[0226] Although the present invention has been described with reference to preferred embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there are no structural conflicts. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for predicting deformation of surrounding rock of a shaft based on fluid-solid coupling simulation, characterized in that the steps include: Step 1: Use the change of water content to describe the influence of rainwater infiltration on surrounding rock deformation, construct a surrounding rock deformation time series model of bidirectional fluid-solid coupling under rainwater infiltration, and form a permeability-surrounding rock deformation model; based on the bidirectional fluid-solid coupling surrounding rock deformation time series model, simulate the fluid-solid coupling under the action of rainwater scouring, and construct a scouring-surrounding rock deformation model to describe the deformation of the surrounding rock over time under the action of rainwater scouring; according to the permeability-surrounding rock deformation model, the scouring-surrounding rock deformation model and the shaft excavation parameters, establish a shaft excavation model under the action of rainwater; Step 2: Perform multi-scale linkage simulation on the infiltration-surrounding rock deformation model, the scouring-surrounding rock deformation model and the vertical shaft excavation model under the action of rainwater to obtain surrounding rock deformation data generated by actual vertical shaft excavation at different time scales; Step 3: Use the surrounding rock deformation data obtained in step 2 as a training set to train a TCN-LSTM neural network model to obtain a vertical shaft surrounding rock deformation prediction model under the action of rainwater, wherein the TCN-LSTM neural network model is a combination model of TCN and LSTM; Step 4: Acquire real-time surrounding rock deformation data, input it into the vertical shaft surrounding rock deformation prediction model under the action of rainwater, and output the real-time surrounding rock deformation prediction result.

2. The method for predicting deformation of surrounding rock of a shaft based on fluid-solid coupling simulation according to claim 1 is characterized in that: In step 1, a time series model of surrounding rock deformation with bidirectional fluid-solid coupling under rainwater infiltration is constructed to form a permeability-surrounding rock deformation model including: Step 1.

1. Establish a multi-layer geological model consisting of multiple geological layers, wherein the geological layers include a soil layer, a volcanic ash layer, a surrounding rock layer, and a bedrock layer; Step 1.

2. Different geological layers in the multi-layer geological model are regarded as porous media, and the soil water retention curve model is used to calculate the water content change of each geological layer under the action of rainwater infiltration; Step 1.

3. Define the porosity, permeability and pore water pressure of each geological layer in the multi-layer geological model according to the calculated water content of each geological layer; Step 1.

4. Use the time series model between seepage and deformation to describe the deformation trend of surrounding rock to form a surrounding rock deformation time series model; Step 1.

5. Based on the surrounding rock deformation time series model, bidirectional fluid-solid coupling under seepage is realized to obtain the bidirectional fluid-solid coupled surrounding rock deformation time series model, and the bidirectional fluid-solid coupled surrounding rock deformation time series model is used as the seepage-surrounding rock deformation model.

3. The method for predicting deformation of surrounding rock of a shaft based on fluid-solid coupling simulation according to claim 2 is characterized in that: The soil water retention curve model used in step 1.2 to calculate the water content change of each geological layer under the action of rainwater infiltration includes: Step 1.2.

1. Calculate the matrix suction of each geological layer: Among them, ψ i (h) is the matrix suction of the ith geological layer at depth h, ψ i,top and ψ i,bottom are the matrix suction at the top and bottom of the ith geological layer, h i,top and h i,bottom are the top depth and bottom depth of the i-th geological layer; Step 1.2.

2. Calculate the water content at any point in each geological layer using the soil water retention curve model based on the matrix suction of each geological layer: Among them, θ ωi (h) is the water content at any point in the i-th geological layer at depth h, θ ri is the residual water content of the i-th geological layer, θ si is the saturated water content of the i-th geological layer, α i 、n i are the fitting parameters in the soil water retention curve model, Step 1.2.

3. Calculate the change in matrix suction caused by rainwater infiltration using the Richards equation based on the calculated water content of each geological layer: Among them, K(θ ωi (h)) represents the water content-dependent permeability coefficient, represents the divergence operator and t is the time.

4. The method for predicting deformation of surrounding rock of a shaft based on fluid-solid coupling simulation according to claim 2 is characterized in that: In step 1.5, bidirectional fluid-solid coupling under infiltration is achieved by using computational fluid dynamics and discrete element method, wherein the porosity, permeability and pore water pressure of each geological layer are defined as material parameters of the computational fluid dynamics method, and the discrete element method is used to describe the mechanical behavior between particles in each geological layer, and bidirectional coupling is performed through the interaction between fluid pressure and solid deformation.

5. The method for predicting deformation of surrounding rock of a vertical shaft based on fluid-solid coupling simulation according to claim 1 is characterized in that: In the process of constructing the scouring-surrounding rock deformation model in step 1, computational fluid dynamics method and discrete element method are used to perform fluid-solid coupling simulation under the action of rainwater scouring, and the steps include: When using the computational fluid dynamics method, the fluid domain of rainwater is defined, and the corresponding boundary conditions and initial conditions are applied, and a turbulence model is selected to simulate the scouring effect of rainfall water; when using the discrete element method, the discrete element method is used to model the solid particles, and each particle unit is regarded as an independent rigid body; The computational fluid dynamics method and the discrete element method are exchanged for data to achieve bidirectional coupling between fluid mechanics and the motion interaction of solid particles. The fluid pressure force in the computational fluid dynamics method is transmitted to the solid particles in the J-OCTA software and the discrete element method to simulate the erosion process of soil and volcanic ash layers caused by rainwater scouring and evaluate the stress and deformation of each layer of material. The J-OCTA software is used to deal with the interface problems of fluid-solid coupling in different geological layers.

6. The method for predicting deformation of surrounding rock of a vertical shaft based on fluid-solid coupling simulation according to claim 5 is characterized in that: In the process of constructing the scour-surrounding rock deformation model in step 1, the interface problem of fluid-solid coupling of different geological layers is processed, including: Computational fluid dynamics and discrete element methods are used to simulate the particle loss process when rainwater infiltrates and scours the soil. The multi-scale model of J-OCTA software is used to couple the movement of soil particles under rainwater scouring, and the relationship model between pore water pressure and soil deformation in fluid-solid coupling is solved to simulate the deformation and stress changes of soil under scouring and seepage conditions. The movement of soil particles in the soil layer under load and the interaction between the fluid are described by the following formula: where F i is the total force on soil particle i, is the collision force between particles i and j, and are the electrostatic force and van der Waals force of particles i and j, respectively, and F i 流体 is the drag and buoyancy of the fluid on particle i; The numerical solver of J-OCTA software is used to solve the following relationship model between pore water pressure and soil deformation in fluid-solid coupling: Where σ′ is the effective stress, α is the Biot coefficient, p is the pore water pressure, ρ is the soil density, g is the gravitational acceleration, k is the permeability coefficient, and μ is the fluid viscosity; The coarse-graining method in the J-OCTA software is used to describe the interaction of volcanic ash particles in the volcanic ash layer, and the built-in MPS fluid model is called to simulate the separation and deposition of particles from the volcanic ash layer with rain erosion. The coarse-graining model of volcanic ash particles is established in the J-OCTA software, the fine particles of volcanic ash are processed by coarse-graining, and the multi-scale coupling function of J-OCTA is used to perform multi-scale coupling simulation of the phase field method to deal with the erosion problem of the volcanic ash layer under rain erosion. The interaction potential between volcanic ash particles is described as: where U(r) is the potential energy between particles, k B is the Boltzmann constant, A is the Hamaker constant, σ is the particle diameter, r is the particle spacing, T is the temperature, and n is the fitting parameter; The phase field method is used to describe the gradual loss and migration of ash at the interface through the continuous phase change equation as follows: Among them, φ represents the spatial distribution of volcanic ash concentration, μ is the chemical potential, and M is the kinetic energy coefficient.

7. The method for predicting deformation of surrounding rock of a vertical shaft based on fluid-solid coupling simulation according to claim 6 is characterized in that: In the process of constructing the scouring-surrounding rock deformation model in step 1, the process also includes: in J-OCTA software, the fluid model is called to couple with the phase field fracture model, and the phase field fracture model and the fluid model are calculated by J-OCTA multi-scale coupling, so as to use the phase field fracture model to simulate the crack expansion process that may be generated in the surrounding rock layer under rain scouring according to the following formula: Among them, G c is the fracture energy, Φ is the crack extension, l is the crack width scale, and G is the fracture toughness of the material; By calling the MPS fluid model in the J-OCTA software and combining it with Darcy's law, the fracture seepage in the bedrock layer is solved: Where q is the flow velocity, k is the permeability, μ is the fluid viscosity, is the pressure gradient.

8. The method for predicting deformation of surrounding rock of a vertical shaft based on fluid-solid coupling simulation according to any one of claims 1 to 7, characterized in that: The steps for training the TCN-LSTM neural network model in step 3 include: Step 3.

1. The time series data X = {x1, x2, …, x t } is input to the TCN layer in the TCN-LSTM neural network model, where xt represents the displacement deformation of the surrounding rock observed at time point t; Step 3.

2. Extract the time series data X = {x1, x2, …, x t } features to obtain time series features; Step 3.

3. Use the output of the TCN layer as the input of the LSTM layer in the TCN-LSTM neural network model, and the LSTM layer captures the long-term dependencies of the time series data; Step 3.

4. Convert the output of the LSTM layer into the predicted deformation and calculate the difference between the predicted deformation and the actual observed value; Step 3.

5. Use the initial hyperparameters to train the TCN-LSTM neural network model according to steps 3.1 to 3.4, and use the Bayesian optimization algorithm to select the optimal hyperparameters; Step 3.

6. Use the optimal hyperparameters to retrain the TCN-LSTM neural network model according to steps 3.1 to 3.4 to obtain the final shaft surrounding rock deformation prediction model under the action of rainwater.

9. The method for predicting deformation of surrounding rock of a vertical shaft based on fluid-solid coupling simulation according to claim 8 is characterized in that: The Bayesian optimization algorithm is used in step 3.5 to select the optimal hyperparameters, including: Step 3.5.

1. Initialize the Gaussian process model, select the kernel function of the Gaussian process and the initial hyperparameter Θ (0) ; Step 3.5.

2. Use the current hyperparameter Θ (i) Train the TCN-LSTM model, where i is the number of execution steps, and calculate the loss L on the training set (i) , update the Gaussian process model to include the new observation Θ (i) , L (i) ; Step 3.5.

3. Calculate the acquisition function EI: EI(Θ)=φ(Θ)-μ(Θ) Where φ and μ are the standard deviation and mean of the Gaussian process prediction, respectively; Step 3.5.

4. Select the next hyperparameter Θ according to the acquisition function EI (i+1) , repeat steps 3.5.1 to 3.5.4 until the optimal hyperparameter set Θ is found opt .

10. A device for predicting deformation of surrounding rock of a vertical shaft based on fluid-solid coupling linkage simulation, comprising a processor and a memory, wherein the memory is used to store a computer program, characterized in that: The processor is configured to execute the computer program to perform the method according to any one of claims 1 to 9.

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