Finite element based simulation method for temperature field and stress field of tunnel dynamic construction

Abstract

The application relates to the field of tunnel construction and discloses a tunnel dynamic construction temperature field and stress field simulation method based on finite elements, which comprises the following steps: collecting engineering basic data, constructing unified control equations of four-field coupling of a temperature field, a seepage field, a stress field and a creep field; carrying out mesh division to obtain a finite element calculation model; configuring a coupling solution strategy to build a four-field synergic coupling solution module; configuring corresponding dynamic parameter libraries and customized boundary conditions, executing simulation solution through the four-field synergic coupling solution module, collecting multi-source monitoring data in a tunnel construction process, preprocessing the multi-source monitoring data, verifying the simulation results. Through the construction of the unified control equations, the establishment of a parameter correlation mechanism of dynamic interaction among the multiple fields, the building of the four-field synergic coupling solution module and the parameter closed-loop correction relying on the multi-source monitoring data, high-precision simulation of the temperature field and the stress field under complex working conditions is realized.

Description

Technical Field

[0001] This invention relates to the field of tunnel construction technology, specifically to a method for simulating the temperature and stress fields of dynamic tunnel construction based on the finite element method. Background Technology

[0002] As a core component of infrastructure construction such as transportation and water conservancy, tunnel engineering, under complex geological environments and dynamic construction processes, relies heavily on the evolution of temperature and stress fields to directly determine the safety and durability of the engineering structure. With the increasing number of complex engineering projects such as cold-region tunnels and immersed tunnels, the interaction of multiple factors, including temperature changes, groundwater seepage, material creep, and frost heave effects, forms a complex multi-field coupled system, placing higher demands on the accuracy and comprehensiveness of tunnel construction simulation technology.

[0003] Existing tunnel construction simulation methods mostly adopt a simplified approach of result superposition. Typically, the temperature field is solved independently first, and then the temperature calculation results are used as load inputs to the stress field model. Simple coupling is achieved through unidirectional transmission. However, for complex conditions such as cold regions and immersed tubes, adaptation is achieved by adjusting only a single physical field parameter, resulting in insufficient multi-field coupling depth. The simulation results are difficult to match the complex physical processes in actual engineering and deviate significantly from the actual site conditions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a finite element method for simulating the temperature and stress fields during dynamic tunnel construction. This method solves the problems of insufficient multi-field coupling depth, difficulty in matching simulation results with the complex physical processes in actual engineering projects, and significant deviations from actual site conditions in existing tunnel construction simulation methods.

[0005] To achieve the above objectives, the present invention provides a method for simulating the temperature and stress fields during dynamic tunnel construction based on the finite element method, comprising the following steps:

[0006] Collect basic engineering data and define the target working conditions for tunnel construction;

[0007] Based on the law of conservation of energy, Biot's theory of porous media consolidation, viscoelastic constitutive model and frost heave theory, a unified governing equation for the coupling of four fields—temperature field, seepage field, stress field and creep field—is constructed.

[0008] Based on the aforementioned engineering data, a three-dimensional geometric model of the tunnel is constructed, and meshing is performed to obtain a finite element calculation model.

[0009] The unified control equations are discretized using the finite element method, a coupled solution strategy is configured, and a four-field collaborative coupled solution module is built.

[0010] Based on the target operating conditions, configure the corresponding dynamic parameter library and customized boundary conditions. The parameters in the dynamic parameter library are dynamically updated with temperature, age or phase change state.

[0011] The dynamic parameter library and customized boundary conditions are input into the finite element calculation model, and the simulation solution is performed through the four-field synergistic coupling solution module to obtain the simulation results of the temperature field and stress field during the dynamic construction process of the tunnel.

[0012] After collecting and preprocessing multi-source monitoring data during tunnel construction, the simulation results are verified. When the deviation between the simulation results and the monitoring data exceeds a preset threshold, the parameters in the dynamic parameter library are corrected in a closed loop, and the corrected simulation results are output.

[0013] By adopting the above technical solution, a unified control equation coupling four fields—temperature field, seepage field, stress field, and creep field—is constructed. A parameter correlation mechanism for dynamic interaction among multiple fields is established. Combined with a parameter library that is dynamically updated with temperature, age, or phase change state, and customized boundary conditions, a four-field collaborative coupling solution module is built. Parameter closed-loop correction is achieved based on multi-source monitoring data from the field. This accurately depicts the collaborative evolution law of multiple physical fields during the dynamic construction of the tunnel, realizing high-precision simulation of the temperature field and stress field under complex working conditions. This solves the problems of insufficient multi-field coupling depth in existing tunnel construction simulation methods, making it difficult for simulation results to match the complex physical processes of actual engineering projects, and resulting in significant deviations from actual field conditions.

[0014] Preferably, the collection of basic engineering data and the definition of the target working conditions corresponding to tunnel construction specifically include the following steps:

[0015] Collect basic engineering data, including tunnel engineering design drawings, geological survey reports, material test data, and construction organization plans;

[0016] The basic engineering data is classified and organized, invalid data is removed, and the format is standardized to form a unified basic engineering dataset.

[0017] Based on the engineering basic dataset and the actual needs of the project, the target working conditions corresponding to tunnel construction are defined, including working conditions for tunnels in cold regions or immersed tunnels.

[0018] Preferably, the construction of the unified governing equations for the coupling of the temperature field, seepage field, stress field, and creep field specifically includes the following steps:

[0019] Based on the law of conservation of energy, and combined with the effects of seepage convection and internal heat sources, a temperature field control equation is constructed.

[0020] Based on Biot's theory of porous media consolidation, the phase change effect caused by temperature is incorporated to construct the control equation for the seepage field.

[0021] Based on the viscoelastic constitutive model, stress-creep coupling control equations are constructed by superimposing temperature stress, seepage pressure and creep stress.

[0022] By utilizing the multiphysics coupling principle and parameter mapping rules, we clarify the parameter types and update rules output from the temperature field to the seepage field and stress field, the parameter types and action methods fed back from the seepage field to the temperature field and stress field, the parameter content and triggering conditions transmitted from the stress field to the creep field, and the parameter update logic fed back from the creep field to the stress field and seepage field, thus forming the parameter interaction relationship between the temperature field, seepage field, stress field, and creep field.

[0023] The temperature field control equation, the seepage field control equation, and the stress-creep coupling control equation are linked and integrated through parameter interaction to form a unified control equation for four fields.

[0024] Preferably, obtaining the finite element calculation model specifically includes the following steps:

[0025] Based on engineering data, a three-dimensional geometric model is constructed in finite element software. The three-dimensional geometric model includes tunnel lining, surrounding rock, support structure and ancillary facilities.

[0026] The three-dimensional geometric model is meshed using eight-node hexahedral solid elements. The mesh is refined for the area corresponding to the target working condition. Conventional-sized meshes are used for the conventional surrounding rock area. Shared nodes are used between the tunnel lining and the surrounding rock, and between the support structure and the surrounding rock, to form a finite element calculation model.

[0027] Preferably, the construction of the four-field cooperative coupling solution module specifically includes the following steps:

[0028] The unified control equations are spatially discretized using the Galerkin weighted residual method, and then time-discretized using the θ-weighted method to form discrete equations.

[0029] Configure a partitioned coupling solution strategy, set the solution methods and time steps for the temperature field and seepage field, and set the solution method and adaptive time step adjustment rules for the stress-creep field to form a solution strategy;

[0030] By integrating discrete equations and solution strategies, a four-field synergistic coupling solution module is built.

[0031] Preferably, the configuration of the corresponding dynamic parameter library and customized boundary conditions specifically includes the following steps:

[0032] Classify and organize the types and value ranges of thermodynamic parameters, mechanical parameters, and seepage parameters to form parameter types;

[0033] Based on indoor test data and engineering measurement data, a functional relationship between each parameter and temperature, age, and phase transition state is established, so that each parameter can be dynamically updated as temperature, age, or phase transition state changes.

[0034] Build a structured dynamic parameter library based on parameter types and relationships;

[0035] Based on the corresponding target working conditions, the boundary conditions and parameter values ​​and dynamic adjustment rules of each boundary are set to form customized boundary conditions that are adapted to the target working conditions.

[0036] Preferably, obtaining the simulation results of the temperature field and stress field during the dynamic construction process of the tunnel specifically includes the following steps:

[0037] Input the real-time updated parameters from the dynamic parameter library and the customized boundary conditions into the finite element calculation model simultaneously;

[0038] The discrete equations are iteratively solved using the four-field co-coupled solution module according to a preset solution strategy. During the solution process, the correlation parameters between the four fields are called in real time through the parameter interaction interface, and the input data in the four-field co-coupled solution module is dynamically updated.

[0039] After the solution is completed, the temperature distribution data and stress distribution data of each stage of the tunnel dynamic construction are extracted to generate temperature field cloud map, stress field cloud map and preset node time history curves, forming the simulation results of temperature field and stress field.

[0040] Preferably, the step of performing closed-loop correction on the parameters in the dynamic parameter library and outputting the corrected simulation results specifically includes the following steps:

[0041] Collect multi-source monitoring data during tunnel construction, including temperature data, strain data, and seepage pressure data;

[0042] Outliers in multi-source monitoring data were removed using the 3σ criterion, and data format standardization preprocessing was performed.

[0043] The relative error between the simulation results of the temperature and stress fields and the preprocessed monitoring data was calculated.

[0044] When the relative error exceeds a preset threshold, the least squares method is used to invert and correct the corresponding parameters in the dynamic parameter library to obtain the corrected parameters.

[0045] The corrected parameters are input into the finite element calculation model, and the simulation is performed through the four-field synergistic coupling solution module to obtain and output the corrected simulation results.

[0046] This invention provides a finite element method for simulating the temperature and stress fields during dynamic tunnel construction. It offers the following advantages:

[0047] 1. This invention constructs a unified control equation coupling four fields: temperature field, seepage field, stress field, and creep field. It establishes a parameter correlation mechanism for dynamic interaction among multiple fields, combines a parameter library that is dynamically updated with temperature, age, or phase change state with customized boundary conditions, and builds a four-field collaborative coupling solution module. It also relies on multi-source monitoring data from the field to achieve closed-loop parameter correction, thereby accurately depicting the collaborative evolution law of multiple physical fields during the dynamic construction of tunnels. This invention achieves high-precision simulation of temperature field and stress field under complex working conditions, and solves the problems of insufficient multi-field coupling depth, difficulty in matching simulation results with the complex physical processes of actual engineering, and large deviation from the actual field conditions in existing tunnel construction simulation methods.

[0048] 2. This invention configures a structured parameter library that is dynamically updated according to temperature, age, or phase change state for different target working conditions such as cold-region tunnels and immersed tunnels. At the same time, it designs customized boundary conditions and realizes real-time dynamic adjustment of parameters by establishing the correlation between parameters and key influencing factors. The customized boundary conditions match the special physical effects under different working conditions, such as the frost heave effect in cold regions, the hydration heat release of immersed tunnels and the heat exchange of cooling water pipes, etc., so that the simulation model can flexibly adapt to various complex engineering scenarios, enhance the adaptability of working conditions, and meet the simulation needs of complex engineering projects.

[0049] 3. This invention introduces multi-source monitoring data from the field, preprocesses it, and compares and verifies it with the simulation results. When the deviation exceeds the preset threshold, the least squares method is used to invert and correct the corresponding parameters in the dynamic parameter library. Thus, the parameters are continuously optimized through real-time feedback during the construction process, enabling the simulation model to dynamically fit the actual engineering situation, reducing simulation errors, and continuously improving simulation accuracy. Attached Figure Description

[0050] Figure 1 This is a flowchart of the finite element method for simulating the temperature and stress fields during dynamic tunnel construction proposed in this invention.

[0051] Figure 2 This is an architecture diagram of the finite element-based simulation system for dynamic tunnel construction temperature and stress fields proposed in this embodiment of the invention. Detailed Implementation

[0052] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Example 1:

[0054] In the first embodiment of the present invention, the present invention provides a method for simulating the temperature field and stress field of dynamic tunnel construction based on the finite element method, such as... Figure 1 As shown, it includes the following steps:

[0055] Collect basic engineering data and define the target working conditions for tunnel construction;

[0056] Furthermore, basic engineering data is collected to define the target working conditions for tunnel construction, specifically including the following steps:

[0057] Collect basic engineering data, including tunnel engineering design drawings, geological survey reports, material test data, and construction organization plans;

[0058] The basic engineering data is classified and organized, invalid data is removed, and the format is standardized to form a unified basic engineering dataset.

[0059] Based on the engineering basic dataset and the actual needs of the project, the target working conditions corresponding to tunnel construction are defined, including working conditions for tunnels in cold regions or immersed tunnels.

[0060] Specifically, basic engineering data should be collected and target working conditions defined to provide input for subsequent model building and coupled solution. The basic engineering data should comprehensively cover all dimensions of design, geology, materials, and construction. Tunnel engineering design drawings should include core information such as cross-sectional dimensions, lining thickness, and support structure layout. Geological survey reports should specify parameters such as surrounding rock type, water content, and permeability characteristics. Material test data should cover the thermodynamic and mechanical properties of concrete and surrounding rock. Construction organization plans should specify key processes such as excavation sequence and support timing.

[0061] When classifying and organizing the collected data, it is necessary to organize it into four categories: design, geology, materials, and construction. Duplicate, distorted, and invalid data should be eliminated. In general, data standardization protocols should be used to unify the format of various types of data to ensure compatibility of data from different sources and form a structured engineering basic dataset.

[0062] Based on the core parameters of the engineering basic dataset and the actual needs of the project, the target working conditions are defined. If the geological survey report shows that the tunnel passes through a cold region and there is groundwater seepage, it can be defined as a cold region tunnel working condition. If the data shows that it is an underwater tunnel and involves pipe section casting and water pipe cooling processes, it is defined as an immersed tube tunnel working condition. This definition process ensures that the subsequent simulation model and boundary conditions are adapted to the actual project.

[0063] Based on the law of conservation of energy, Biot's theory of porous media consolidation, viscoelastic constitutive model and frost heave theory, a unified governing equation for the coupling of four fields—temperature field, seepage field, stress field and creep field—is constructed.

[0064] Furthermore, a unified governing equation for the coupling of four fields—temperature field, seepage field, stress field, and creep field—is constructed, specifically including the following steps:

[0065] Based on the law of conservation of energy, and combined with the effects of seepage convection and internal heat sources, a temperature field control equation is constructed.

[0066] Based on Biot's theory of porous media consolidation, the phase change effect caused by temperature is incorporated to construct the control equation for the seepage field.

[0067] Based on the viscoelastic constitutive model, stress-creep coupling control equations are constructed by superimposing temperature stress, seepage pressure and creep stress.

[0068] By utilizing the multiphysics coupling principle and parameter mapping rules, we clarify the parameter types and update rules output from the temperature field to the seepage field and stress field, the parameter types and action methods fed back from the seepage field to the temperature field and stress field, the parameter content and triggering conditions transmitted from the stress field to the creep field, and the parameter update logic fed back from the creep field to the stress field and seepage field, thus forming the parameter interaction relationship between the temperature field, seepage field, stress field, and creep field.

[0069] The temperature field control equation, the seepage field control equation, and the stress-creep coupling control equation are linked and integrated through parameter interaction to form a unified control equation for four fields.

[0070] Specifically, a unified governing equation for four-field coupling is constructed, and the dynamic correlation of each physical field is characterized by integrating classical theory with parameter interaction mechanisms.

[0071] Specifically, when constructing the temperature field control equations based on the law of conservation of energy, it is generally necessary to incorporate the effects of seepage convection and internal heat sources. The equations are in the following form: ,in, For the density of the medium, For the specific heat capacity of the medium, For temperature, For time, The thermal conductivity coefficient, The density of water, The specific heat capacity of water, For seepage velocity, Intensity of the internal heat source. Internal heat source. A hyperbolic heat of hydration model can be used: , For the maximum heat of hydration, The hydration heat development coefficient is used. Substituting this model into the temperature field control equation can achieve a coordinated characterization of temperature conduction, seepage convection, and the release of hydration heat in concrete.

[0072] When constructing the control equations for the seepage field based on Biot's theory of porous media consolidation, the phase transition effect caused by temperature needs to be taken into account. Generally, the equations take the form of: ,in, It is a temperature-dependent permeability coefficient. Let be the dynamic viscosity coefficient of water. The water head height, For the specific water storage coefficient, The moisture content of the medium, The volume expansion coefficient of the water-ice phase transition. This refers to the icing rate. It can be calculated using the following formula: , and To obtain the fitting parameters for the experiment, this formula is substituted into the seepage field control equation, which can reflect the influence of temperature on seepage and water-ice phase change under cold conditions.

[0073] When constructing the stress-creep coupling control equations based on the viscoelastic constitutive model, it is necessary to superimpose temperature stress, seepage pressure, and creep stress. The equations are in the following form: ,in, For the total stress tensor, For the density of the medium, It is the acceleration due to gravity. This is the temperature stress load vector. This represents the seepage pressure load vector. Total stress. satisfy: , It is a temperature-age dependent elasticity matrix. For the total strain tensor, Let be the creep strain tensor. This represents the temperature strain tensor. Creep strain. Calculate using the following formula: , As the creep degree function, substituting the above relationship into the stress control equation can achieve a coordinated response of stress, temperature, seepage, and creep.

[0074] By utilizing the multiphysics coupling principle and parameter mapping rules, the interaction relationships between parameters are clarified. The temperature field outputs the dynamic viscosity coefficient, permeability coefficient, and freezing rate to the seepage field, and the thermal expansion coefficient and elastic modulus to the stress field; the seepage field outputs the seepage velocity to the temperature field and the seepage pressure to the stress field; the stress field outputs the total stress to the creep field; the creep field outputs the creep strain to the stress field and the permeability coefficient correction value to the seepage field.

[0075] Finally, the three control equations are linked and integrated through parameter interaction to form a unified control equation with four fields coupled together, ensuring dynamic feedback and synergistic effect of each physical field.

[0076] A three-dimensional geometric model of the tunnel is constructed based on engineering data, and a mesh is generated to obtain a finite element calculation model.

[0077] Furthermore, the finite element calculation model is obtained, specifically including the following steps:

[0078] Based on engineering data, a three-dimensional geometric model is constructed in finite element software. The three-dimensional geometric model includes tunnel lining, surrounding rock, support structure and ancillary facilities.

[0079] The three-dimensional geometric model is meshed using eight-node hexahedral solid elements. The mesh is refined for the area corresponding to the target working condition. Conventional-sized meshes are used for the conventional surrounding rock area. Shared nodes are used between the tunnel lining and the surrounding rock, and between the support structure and the surrounding rock, to form a finite element calculation model.

[0080] Specifically, constructing a three-dimensional geometric model and completing mesh generation are fundamental steps in finite element simulation. By replicating the engineering entity and scientifically generating the mesh, the accuracy and efficiency of subsequent coupled solutions are ensured.

[0081] When constructing a three-dimensional geometric model based on engineering foundation data, it is generally necessary to rely on the modeling function of finite element software to fully integrate the tunnel lining, surrounding rock, support structure, and ancillary facilities into the model. In some embodiments, if the target working condition is an immersed tunnel, the pipe segment structure, cooling water pipe layout, and foundation constraint structure need to be accurately reproduced in the model; if it is a tunnel in a cold region, key structures such as the frost heave-affected zone of the surrounding rock and the lining reinforcement layer need to be included.

[0082] When using eight-node hexahedral solid elements for mesh generation, it is generally necessary to ensure the mesh quality through a mesh quality evaluation function, the function of which is as follows: ,in, As a quality evaluation index for grids, For actual unit volume, For reference, the volume of a regular hexahedral unit, The maximum side length of the unit. This is the minimum side length of the element. Substituting the element geometric parameters of the model to be subdivided into this function, the output is... The value is used to determine whether the mesh is qualified, so as to realize the quantitative control of mesh quality.

[0083] Mesh refinement is applied to key areas corresponding to the target working conditions. For the frost heave-affected zone of cold-region tunnels and the area around cooling water pipes in immersed tunnels, element sizes need to be adjusted based on the sensitivity to coupling effects. Conventional mesh sizes are used in areas with normal surrounding rock to balance computational accuracy and efficiency. Shared nodes are used between the tunnel lining and surrounding rock, and between the support structure and surrounding rock, to ensure continuous transmission of physical quantities such as displacement and heat flow at each structural interface. This avoids simulation deviations caused by improper interface treatment, resulting in a structurally complete and high-quality finite element calculation model.

[0084] The unified control equations are discretized using the finite element method, a coupled solution strategy is configured, and a four-field collaborative coupled solution module is built.

[0085] Furthermore, a four-field coordinated coupling solution module is constructed, specifically including the following steps:

[0086] The unified control equations are spatially discretized using the Galerkin weighted residual method, and then time-discretized using the θ-weighted method to form discrete equations.

[0087] Configure a partitioned coupling solution strategy, set the solution methods and time steps for the temperature field and seepage field, and set the solution method and adaptive time step adjustment rules for the stress-creep field to form a solution strategy;

[0088] By integrating discrete equations and solution strategies, a four-field synergistic coupling solution module is built.

[0089] Specifically, the unified control equations are discretized and coupled solution modules are built. Through scientific discretization and strategy configuration, the accuracy and efficiency of the solution are ensured.

[0090] Specifically, when using the Galerkin weighted residual method for spatial discretization, appropriate trial functions and weight functions need to be selected, and an integral transformation is performed on the unified governing equations of the four-field coupling. The discretization formula is as follows: ,in, Shape functions are used to describe the distribution of physical quantities within a unit cell. For the computational domain, To unify the differential operators of the governing equations, The physical quantities to be determined are: temperature, water head, displacement, etc. The term represents the non-homogeneous term in the equation. Substituting the unified governing equation into this formula transforms the continuous governing equation into a discrete system of algebraic equations, achieving spatial discretization.

[0091] When using the θ-weighted method for time discretization, the formula is as follows: ,in, For the first The physical quantity value at time t. For the first The physical quantity value at time t. For time step, These are weighting coefficients. This is a differential operator. In some embodiments, θ = 0.5 is chosen to balance the stability and accuracy of the solution. The spatially discretized algebraic equations are substituted into this formula to form a spatiotemporally discretized equation system.

[0092] When configuring a partitioned coupled solution strategy, the temperature and seepage fields are solved using an explicit iterative method with a fixed time step; the stress-creep field is solved using an implicit method, with the time step dynamically optimized through an adaptive time step adjustment rule. Alternatively, the adaptive adjustment rule can be based on the residual magnitude to ensure solution convergence.

[0093] Finally, by integrating the discrete equations and solution strategies, a four-field collaborative coupling solution module was built to achieve synchronous solution and parameter interaction of the discrete equations of each physical field, providing computational support for dynamic simulation of deep coupling of multiple fields.

[0094] Based on the target operating conditions, configure the corresponding dynamic parameter library and customized boundary conditions. The parameters in the dynamic parameter library are dynamically updated with temperature, age or phase change state.

[0095] Furthermore, configure the corresponding dynamic parameter library and customized boundary conditions, specifically including the following steps:

[0096] Classify and organize the types and value ranges of thermodynamic parameters, mechanical parameters, and seepage parameters to form parameter types;

[0097] Based on indoor test data and engineering measurement data, a functional relationship between each parameter and temperature, age, and phase transition state is established, so that each parameter can be dynamically updated as temperature, age, or phase transition state changes.

[0098] Build a structured dynamic parameter library based on parameter types and relationships;

[0099] Based on the corresponding target working conditions, the boundary conditions and parameter values ​​and dynamic adjustment rules of each boundary are set to form customized boundary conditions that are adapted to the target working conditions.

[0100] Specifically, a dynamic parameter library and customized boundary conditions are configured to adapt to complex working conditions. Through dynamic parameter updates and precise boundary adaptation, the simulation model is ensured to fit the actual engineering situation.

[0101] Specifically, when classifying and sorting parameter types, thermodynamic parameters generally include medium density, specific heat capacity, and thermal conductivity; mechanical parameters include elastic modulus, Poisson's ratio, coefficient of thermal expansion, creep, and frost heave stiffness coefficient; and seepage parameters include permeability coefficient, specific water storage coefficient, and water content. It is important to clarify the reasonable range of values ​​for each type of parameter.

[0102] Based on indoor test and engineering measurement data, a functional correlation was established, and the update formula for the elastic modulus with age and temperature is as follows: ,in, For real-time elastic modulus, The elastic modulus at room temperature at 28 days of age. For age period, For temperature, This is the temperature correction factor. Substituting the age and temperature data into this formula, the real-time elastic modulus is output, enabling the parameters to be dynamically updated according to the working conditions.

[0103] The correlation formula for creep is: ,in, Let creep be the creep degree function. Temperature-dependent initial creep. The age at which creep begins. This is the creep index.

[0104] A structured dynamic parameter library is built according to parameter type and relationship. Generally, a hierarchical storage architecture is adopted to facilitate calling and updating.

[0105] When configuring customized boundary conditions, the temperature boundary of the cold region tunnel adopts the third type of boundary condition, and the seepage boundary is set as a constant head and zero flow boundary; the temperature boundary of the immersed tube tunnel adopts the second type of boundary condition, and the seepage boundary is set as an impermeable boundary. The values ​​of each boundary parameter and the dynamic adjustment rules are clearly defined to ensure accurate adaptation with the target working conditions and to provide reasonable constraints for coupled solution.

[0106] The dynamic parameter library and customized boundary conditions are input into the finite element calculation model, and the simulation solution is performed through the four-field synergistic coupling solution module to obtain the simulation results of the temperature field and stress field during the dynamic construction process of the tunnel.

[0107] Furthermore, the simulation results of the temperature and stress fields during the dynamic construction process of the tunnel are obtained, specifically including the following steps:

[0108] Input the real-time updated parameters from the dynamic parameter library and the customized boundary conditions into the finite element calculation model simultaneously;

[0109] The discrete equations are iteratively solved using the four-field co-coupled solution module according to a preset solution strategy. During the solution process, the correlation parameters between the four fields are called in real time through the parameter interaction interface, and the input data in the four-field co-coupled solution module is dynamically updated.

[0110] After the solution is completed, the temperature distribution data and stress distribution data of each stage of the tunnel dynamic construction are extracted to generate temperature field cloud map, stress field cloud map and preset node time history curves, forming the simulation results of temperature field and stress field.

[0111] Specifically, dynamic parameters and customized boundary conditions are input into the model and coupled solution is performed to realize dynamic construction simulation. Through real-time parameter interaction and iterative solution, the temperature field and stress field results are output.

[0112] When inputting parameters and boundary conditions, it is generally necessary to achieve synchronous connection between the dynamic parameter library and the finite element calculation model through a data interface to ensure that the real-time updated thermodynamic, mechanical, and seepage parameters, as well as customized boundary conditions, are accurately transferred to the computational domain. If the target working condition is a tunnel in a cold region, the dynamically updated frost heave stiffness coefficient, permeability coefficient, and third-type temperature boundary parameters need to be input synchronously; if it is an immersed tunnel, the hydration heat parameters, cooling water pipe boundary parameters, and creep-related parameters should be input.

[0113] During the iterative solution process, a coupled iterative algorithm is used to ensure the coordinated response of the four fields. The algorithm formula is as follows: ,in, For the first The physical quantity vectors of the next iteration: temperature, water head, and displacement. For the first The result of the second iteration For time step, For the dynamic stiffness matrix, For load vectors, For the first dynamic parameter library Secondary parameter value, To customize boundary condition parameters, the formula substitutes the physical quantities, dynamic parameters, and boundary conditions of the current iteration into the formula, outputting the physical quantity values ​​for the next iteration, thus achieving iterative solutions for multi-field coupling.

[0114] The four-field correlation parameters are invoked in real time through a parameter interaction interface. The stiffness matrix and load vector are updated after each iteration to ensure that the solution process is consistent with the dynamic changes of the physical field. After the solution is completed, temperature and stress distribution data of each construction stage are generally extracted to generate field cloud maps and time history curves of preset nodes. Key parts such as the tunnel arch crown, arch foot, and invert arch are selected as nodes to form complete temperature and stress field simulation results, providing data support for subsequent verification and correction.

[0115] After collecting and preprocessing multi-source monitoring data during tunnel construction, the simulation results are verified. When the deviation between the simulation results and the monitoring data exceeds a preset threshold, the parameters in the dynamic parameter library are corrected in a closed loop, and the corrected simulation results are output.

[0116] Furthermore, closed-loop correction is performed on the parameters in the dynamic parameter library, and the corrected simulation results are output. This includes the following steps:

[0117] Collect multi-source monitoring data during tunnel construction, including temperature data, strain data, and seepage pressure data;

[0118] Outliers in multi-source monitoring data were removed using the 3σ criterion, and data format standardization preprocessing was performed.

[0119] The relative error between the simulation results of the temperature and stress fields and the preprocessed monitoring data was calculated.

[0120] When the relative error exceeds a preset threshold, the least squares method is used to invert and correct the corresponding parameters in the dynamic parameter library to obtain the corrected parameters.

[0121] The corrected parameters are input into the finite element calculation model, and the simulation is performed through the four-field synergistic coupling solution module to obtain and output the corrected simulation results.

[0122] Specifically, by verifying through multi-source monitoring data and correcting parameters in a closed loop, the reliability of simulation results can be improved, ensuring that the model accurately matches the actual engineering situation.

[0123] Specifically, when collecting multi-source monitoring data, sensors are generally deployed at key sections of the tunnel to collect temperature, strain, and seepage pressure data in real time, covering critical areas such as the lining and surrounding rock. Temperature sensors are deployed along the lining thickness and the depth of the surrounding rock, strain gauges are attached to the inner surface of the lining and the reinforcing steel, and seepage pressure gauges are buried in the frost heave affected area or foundation location to ensure data comprehensiveness.

[0124] After removing outliers using the 3σ criterion, data standardization preprocessing is performed, and the relative error is calculated using the following formula: ,in, This is a relative error. For simulation result data, This is the preprocessed monitoring data. The simulated and monitored values ​​at the same time and location are substituted into the formula to output the relative error, which is used to determine the simulation accuracy.

[0125] When the error exceeds the preset threshold, the least squares method is used to invert and correct the parameters. The core formula is: ,in, The corrected parameter vector, For the parameter sensitivity matrix, For the initial parameter vector, For monitoring data, This represents the simulation results corresponding to the initial parameters. Substituting the initial parameters, monitoring data, and sensitivity matrix into the formula, the corrected parameters are output, achieving iterative optimization of the dynamic parameter library.

[0126] After each construction cycle is completed, a verification and correction is performed. The corrected parameters are re-input into the finite element calculation model, and the model is solved again through the four-field synergistic coupling solution module. After correction, the simulation results corresponding to the key parameters are verified to ensure that the error is controlled within a reasonable range. Finally, the corrected simulation results are output to provide guidance for engineering construction.

[0127] Example 2:

[0128] In a second embodiment of the present invention, the present invention provides a simulation system for the dynamic temperature and stress fields of tunnel construction based on the finite element method, such as... Figure 2 As shown, it includes the following units:

[0129] Collection and Delineation Unit: Collect basic engineering data and define the target working conditions corresponding to tunnel construction;

[0130] Building Unit: Based on the law of conservation of energy, Biot's theory of porous media consolidation, viscoelastic constitutive model and frost heave theory, a unified governing equation for the coupling of four fields—temperature field, seepage field, stress field and creep field—is constructed.

[0131] Model meshing: Based on the engineering data, a three-dimensional geometric model of the tunnel is constructed and meshed to obtain the finite element calculation model;

[0132] Discrete Element Construction: The unified control equations are discretized using finite element methods, coupled solution strategies are configured, and a four-field collaborative coupled solution module is built.

[0133] Configuration unit: Configure the corresponding dynamic parameter library and customized boundary conditions according to the target operating conditions. The parameters in the dynamic parameter library are dynamically updated with temperature, age or phase change state.

[0134] Solving unit: Input the dynamic parameter library and customized boundary conditions into the finite element calculation model, and perform simulation solution through the four-field synergistic coupling solution module to obtain the simulation results of temperature field and stress field during the dynamic construction process of the tunnel;

[0135] Verification and correction unit: After collecting and preprocessing multi-source monitoring data during tunnel construction, the simulation results are verified. When the deviation between the simulation results and the monitoring data exceeds a preset threshold, the parameters in the dynamic parameter library are corrected in a closed loop, and the corrected simulation results are output.

[0136] During the construction of a mountain tunnel in a cold region, the freezing and expansion of groundwater due to the low temperature environment led to complex stresses on the surrounding rock and lining. Traditional simulations only consider the temperature-stress binary coupling and cannot predict the risk of frost heave cracks, resulting in repeated instances of localized cracking of the lining during construction. To address these issues, the finite element method-based simulation system for dynamic temperature and stress fields in tunnel construction, as provided in this invention, was adopted. Its architecture is as follows: Figure 2 As shown. The specific implementation process of this system is as follows:

[0137] The collection and definition unit comprehensively collects tunnel design drawings, geological survey reports, material test data, and construction organization plans. After classification, standardization, and removal of invalid information, and in combination with the characteristics of the high-altitude cold environment and groundwater distribution, the target working condition is defined as the working condition of a cold region tunnel.

[0138] Based on the law of conservation of energy, Biot's theory of porous media consolidation, viscoelastic constitutive model and orthotropic frost heave theory, the building unit constructs temperature field control equations with seepage convection effect and internal heat source, seepage field control equations incorporating water-ice phase change effect, and stress-creep coupling control equations with superimposed multiple stresses. After clarifying the interaction relationship of the four field parameters, they are integrated to form a unified four-field coupling control equation.

[0139] The model meshing unit is based on standardized data. A three-dimensional geometric model containing lining, surrounding rock, support structure and ancillary facilities is constructed in finite element software. Eight-node hexahedral elements are used for meshing. The mesh is densified in frost-heave sensitive areas such as arch foot and invert arch. A mesh of reasonable size is used in the conventional surrounding rock area. The finite element calculation model is formed through common node processing.

[0140] The discrete building unit uses the Galerkin weighted residual method and the θ-weighted method to complete the spatiotemporal discretization of the equations. It configures a partitioned coupling solution strategy, sets the solution method and time step for each field, and integrates them to build a four-field collaborative coupling solution module.

[0141] The configuration unit constructs a structured parameter library that is dynamically updated with temperature and phase change state for cold-region operating conditions, and sets customized boundary conditions;

[0142] The solution unit input parameters and boundary conditions are set, the solution module is started to iteratively solve and dynamically update the data, and the simulation results of each stage are output.

[0143] The verification and correction unit collects multi-source data through sensors, calculates the error after preprocessing, and corrects the parameters by least squares method when the threshold is exceeded, and then re-solves the problem and outputs the results to provide guidance for construction optimization.

[0144] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A simulation method for temperature and stress fields during dynamic tunnel construction based on the finite element method, characterized in that, Includes the following steps: Collect basic engineering data and define the target working conditions for tunnel construction; Based on the law of conservation of energy, Biot's theory of porous media consolidation, viscoelastic constitutive model and frost heave theory, a unified governing equation for the coupling of four fields—temperature field, seepage field, stress field and creep field—is constructed. Based on the aforementioned engineering data, a three-dimensional geometric model of the tunnel is constructed, and meshing is performed to obtain a finite element calculation model. The unified control equations are discretized using the finite element method, a coupled solution strategy is configured, and a four-field collaborative coupled solution module is built. Based on the target operating conditions, configure the corresponding dynamic parameter library and customized boundary conditions. The parameters in the dynamic parameter library are dynamically updated with temperature, age or phase change state. The dynamic parameter library and customized boundary conditions are input into the finite element calculation model, and the simulation solution is performed through the four-field synergistic coupling solution module to obtain the simulation results of the temperature field and stress field during the dynamic construction process of the tunnel. After collecting and preprocessing multi-source monitoring data during tunnel construction, the simulation results are verified. When the deviation between the simulation results and the monitoring data exceeds a preset threshold, the parameters in the dynamic parameter library are corrected in a closed loop, and the corrected simulation results are output.

2. The method for simulating the temperature and stress fields of tunnel dynamic construction based on the finite element method according to claim 1, characterized in that: The collection of basic engineering data and the definition of target working conditions for tunnel construction specifically include the following steps: Collect basic engineering data, including tunnel engineering design drawings, geological survey reports, material test data, and construction organization plans; The basic engineering data is classified and organized, invalid data is removed, and the format is standardized to form a unified basic engineering dataset. Based on the engineering basic dataset and the actual needs of the project, the target working conditions corresponding to tunnel construction are defined, including working conditions for tunnels in cold regions or immersed tunnels.

3. The method for simulating the temperature and stress fields of tunnel dynamic construction based on the finite element method according to claim 1, characterized in that: The construction of the unified governing equations for the coupling of the temperature field, seepage field, stress field, and creep field specifically includes the following steps: Based on the law of conservation of energy, and combined with the effects of seepage convection and internal heat sources, a temperature field control equation is constructed. Based on Biot's theory of porous media consolidation, the phase change effect caused by temperature is incorporated to construct the control equation for the seepage field. Based on the viscoelastic constitutive model, stress-creep coupling control equations are constructed by superimposing temperature stress, seepage pressure and creep stress. By utilizing the multiphysics coupling principle and parameter mapping rules, we clarify the parameter types and update rules output from the temperature field to the seepage field and stress field, the parameter types and action methods fed back from the seepage field to the temperature field and stress field, the parameter content and triggering conditions transmitted from the stress field to the creep field, and the parameter update logic fed back from the creep field to the stress field and seepage field, thus forming the parameter interaction relationship between the temperature field, seepage field, stress field, and creep field. The temperature field control equation, the seepage field control equation, and the stress-creep coupling control equation are linked and integrated through parameter interaction to form a unified control equation for four fields.

4. The method for simulating the temperature and stress fields of dynamic tunnel construction based on the finite element method according to claim 1, characterized in that: The process of obtaining the finite element calculation model includes the following steps: Based on engineering data, a three-dimensional geometric model is constructed in finite element software. The three-dimensional geometric model includes tunnel lining, surrounding rock, support structure and ancillary facilities. The three-dimensional geometric model is meshed using eight-node hexahedral solid elements. The mesh is refined for the area corresponding to the target working condition. Conventional-sized meshes are used for the conventional surrounding rock area. Shared nodes are used between the tunnel lining and the surrounding rock, and between the support structure and the surrounding rock, to form a finite element calculation model.

5. The method for simulating the temperature and stress fields of tunnel dynamic construction based on the finite element method according to claim 1, characterized in that: The construction of the four-field coordinated coupling solution module specifically includes the following steps: The unified control equations are spatially discretized using the Galerkin weighted residual method, and then time-discretized using the θ-weighted method to form discrete equations. Configure a partitioned coupling solution strategy, set the solution methods and time steps for the temperature field and seepage field, and set the solution method and adaptive time step adjustment rules for the stress-creep field to form a solution strategy; By integrating discrete equations and solution strategies, a four-field synergistic coupling solution module is built.

6. The method for simulating the temperature and stress fields of dynamic tunnel construction based on the finite element method according to claim 1, characterized in that: The configuration includes the corresponding dynamic parameter library and customized boundary conditions, specifically comprising the following steps: Classify and organize the types and value ranges of thermodynamic parameters, mechanical parameters, and seepage parameters to form parameter types; Based on indoor test data and engineering measurement data, a functional relationship between each parameter and temperature, age, and phase transition state is established, so that each parameter can be dynamically updated as temperature, age, or phase transition state changes. Build a structured dynamic parameter library based on parameter types and relationships; Based on the corresponding target working conditions, the boundary conditions and parameter values ​​and dynamic adjustment rules of each boundary are set to form customized boundary conditions that are adapted to the target working conditions.

7. The method for simulating the temperature and stress fields of tunnel dynamic construction based on the finite element method according to claim 1, characterized in that: The process of obtaining simulation results of the temperature and stress fields during the dynamic construction of the tunnel includes the following steps: Input the real-time updated parameters from the dynamic parameter library and the customized boundary conditions into the finite element calculation model simultaneously; The discrete equations are iteratively solved using the four-field co-coupled solution module according to a preset solution strategy. During the solution process, the correlation parameters between the four fields are called in real time through the parameter interaction interface, and the input data in the four-field co-coupled solution module is dynamically updated. After the solution is completed, the temperature distribution data and stress distribution data of each stage of the tunnel dynamic construction are extracted to generate temperature field cloud map, stress field cloud map and preset node time history curves, forming the simulation results of temperature field and stress field.

8. The method for simulating the temperature and stress fields of tunnel dynamic construction based on the finite element method according to claim 1, characterized in that: The process of performing closed-loop correction on the parameters in the dynamic parameter library and outputting the corrected simulation results specifically includes the following steps: Collect multi-source monitoring data during tunnel construction, including temperature data, strain data, and seepage pressure data; Outliers in multi-source monitoring data were removed using the 3σ criterion, and data format standardization preprocessing was performed. The relative error between the simulation results of the temperature and stress fields and the preprocessed monitoring data was calculated. When the relative error exceeds a preset threshold, the least squares method is used to invert and correct the corresponding parameters in the dynamic parameter library to obtain the corrected parameters. The corrected parameters are input into the finite element calculation model, and the simulation is performed through the four-field synergistic coupling solution module to obtain and output the corrected simulation results.

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