Tunnel multi-field coupling nonlinear deformation analysis method and system

Abstract

The invention relates to the technical field of tunnel engineering, and discloses a tunnel multi-field coupling nonlinear deformation analysis method and system.The method comprises the steps that geological environment information of a tunnel area is collected, a multi-source physical field boundary condition model is built based on collected data, and a heat-seepage-stress-time four-field coupling control model is built based on the collected data; specifying a nonlinear response model for the geological medium to truly reflect the stress-strain behavior of the geotechnical material; solving a coupling equation set, and optimizing the four-field coupling control model; introducing measured data to correct the model; the system comprises a geological data acquisition module, a boundary condition modeling module, a coupling model establishment module, a numerical solution module, a measured data correction module and a visual output and control interface module. According to the method, high-fidelity prediction and evolution analysis of the deformation behavior of the tunnel under the complex geological condition are realized based on comprehensive acquisition of the geological environment, multi-field boundary modeling, control equation construction and numerical calculation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel engineering, in particular to a tunnel multi-field coupling nonlinear deformation analysis method and system. BACKGROUND

[0002] As an important part of urban infrastructure and traffic trunk, the design and construction of tunnel engineering are often in complex geological environment, which is influenced by multiple natural factors and human disturbances. The stability of surrounding rock and the safety of structure are facing severe challenges. With the continuous expansion of the scale and the increasing depth of tunnel engineering construction, the traditional design method relying on experience and linear static analysis has gradually exposed its limitations. Especially in the case of strong nonlinear response of rock-soil medium and obvious coupling effect of multiple physical fields, it is difficult to accurately predict the deformation, damage and potential instability risk of tunnel structure.

[0003] In the prior art, numerical simulation methods such as finite element method (FEM) and finite difference method (FDM) are widely used. Although these methods can realize single-field or double-field calculation of stress field or seepage field, most of the simulation analysis relies on static boundary setting, linear material assumption or steady flow condition, and cannot fully consider the following factors:

[0004] The interaction mechanism of multiple physical fields is not accurately reflected: such as the coupling feedback effects of the change of thermal stress field caused by construction heat source, the adjustment of effective stress under the influence of pore water pressure, and the change of permeability after the damage of surrounding rock;

[0005] Geological environment information collection is disconnected from the model: most of the existing analysis methods use idealized and homogeneous geological input, and lack of model initialization and field boundary response input based on measured data;

[0006] The material nonlinear response is oversimplified: the complex behaviors of rock-soil body such as elastic-plasticity, damage softening, creep, thermal degradation are not adequately described, which leads to limited prediction accuracy of the model;

[0007] The simulation model and the measured data cannot form a feedback loop: the simulation results cannot be dynamically compared and corrected with the monitoring data during the construction period or operation period, and lack of posteriori correction mechanism, which affects the long-term applicability of the model;

[0008] The organization and multi-dimensional visualization of multi-source data are insufficient: in complex scenarios, the result analysis is limited to single physical quantity output, which is difficult to meet the demand of "time-space-physical" multi-dimensional data interaction in actual engineering.

[0009] In addition, with the development of digital design, smart construction and healthy operation of tunnel, the engineering field puts forward higher requirements for the simulation platform. Not only high-precision coupling calculation ability is required, but also fusion analysis, intelligent correction and dynamic prediction ability with field data are required.

[0010] Therefore, it is urgent to provide a tunnel multi-field coupling nonlinear deformation analysis method and system based on field data driving, with multi-physical field strong coupling capability, supporting nonlinear material response, and being able to dynamically adjust with construction measurement, so as to improve the mechanical prediction accuracy and decision support level in the whole life cycle of the tunnel. SUMMARY

[0011] In one aspect, the application provides a tunnel multi-field coupling nonlinear deformation analysis method, which realizes high-fidelity prediction and evolution analysis of the deformation behavior of the tunnel under complex geological conditions by relying on comprehensive collection of geological environment, multi-field boundary modeling, control equation construction and numerical calculation.

[0012] The technical solution of the application to solve the above technical problems is as follows: a tunnel multi-field coupling nonlinear deformation analysis method, comprising the following steps:

[0013] Collecting geological environment information of the tunnel area, including stratum structure and rock-soil distribution, underground water level and permeability characteristics, ground temperature distribution and thermal physical parameters, ground stress state and construction disturbance information, and structurally storing the above data in a unified coordinate system;

[0014] Based on the collected data, and according to the boundary conditions of heat, seepage, stress, etc., considering the time and space evolution of temperature gradient, hydraulic gradient, ground stress field, construction disturbance and other factors, a multi-source physical field boundary condition model is constructed;

[0015] Based on the collected data, the energy conservation, mass conservation and momentum balance equations are established to form a complete heat-seepage-stress-time four-field coupling control model;

[0016] A nonlinear response model is specified for the geological medium to truly reflect the stress-strain behavior of rock-soil materials;

[0017] The coupled equation set is solved, and the four-field coupling control model is optimized;

[0018] The measured data are introduced to correct the model.

[0019] The method combines the three physical fields of heat, seepage and stress, and introduces a time evolution factor to construct a heat-seepage-stress-time four-field coupling control model, which comprehensively reflects the coupling relationship between temperature gradient, hydraulic disturbance, surrounding rock stress and construction influence. By constructing a dynamic boundary condition model, the time and space evolution processes of ground temperature fluctuation, water level change, blasting disturbance, etc. can be accurately simulated, and the precision deviation caused by the traditional constant boundary assumption is overcome.

[0020] The introduction of nonlinear constitutive relations such as elastoplasticity, creep, thermal damage, etc., supports the selection of material models in different zones, more truly expresses the stress-strain response of the rock-soil medium under complex load and environment, and significantly improves the prediction ability of extreme working conditions and progressive deformation. Coupled with finite element / finite difference solution and adaptive mesh control, the stability and efficiency of the calculation can be guaranteed.

[0021] In addition, by accessing field measurement data, an error feedback mechanism between simulation and monitoring data is established, and an inversion optimization algorithm is used to dynamically correct model parameters, realizing model correction and prediction closed loop, and greatly improving the reliability and adaptability of the simulation system.

[0022] The method realizes an integrated analysis process from data-driven modeling, nonlinear response expression, multi-field co-evolution, to intelligent correction based on monitoring, and is suitable for complex tunnel engineering such as deep burial, high water pressure and high ground temperature.

[0023] Based on the above technical solutions, the present application can also be improved as follows.

[0024] Further, the geological information collection includes borehole survey, geophysical testing, water level monitoring, temperature sensing, stress testing and construction disturbance recording, and the collected data includes in-situ mechanical parameters, thermal conductivity parameters, hydrological parameters and construction event time series.

[0025] The above technical solution collects multi-dimensional environmental information including stratum structure, rock-soil properties, groundwater dynamics, ground temperature distribution, initial ground stress field and construction disturbance, and stores it in a structured manner in a unified coordinate system, achieving high completeness and high spatial resolution of model basic data; compared with traditional methods using empirical parameters or idealized hierarchical models, the present method significantly enhances the authenticity and local adaptability of model input parameters, avoiding misjudgment of high-risk sections or key structural parts.

[0026] Further, the boundary condition model is defined in the form of spatial zoning + time evolution, supporting dynamic water head boundary, construction disturbance softening boundary, temperature convection boundary and stress release boundary combination setting.

[0027] Through the above technical solution, the thermal flow, water head, ground stress and other boundary condition parameters are parameterized as time functions and spatial zoning functions, which can simulate the time sequence evolution and spatial transmission mechanism of factors such as seasonal ground temperature fluctuation, water level disturbance, excavation blasting disturbance, etc., effectively making up the technical short board of static boundary and steady calculation of existing simulation models, and providing high dynamic resolution external driving conditions for subsequent multi-field coupling analysis.

[0028] Further, the coupling control equation set is represented by the following mathematical model:

[0029]

[0030] wherein, is the medium density (kg / m³); is the specific heat capacity (J / kg·K); is the temperature (K); is the thermal conductivity (W / m·K); is the heat source term per unit volume (W / m³), such as construction thermal disturbance or exothermic reaction; is the pore water velocity, is the density and specific heat capacity of water; is the total stress tensor; is the displacement vector; is the gravitational acceleration.

[0031] Through the above technical solution, by establishing three major control equations of heat (energy conservation), seepage (mass conservation) and stress (momentum balance), and introducing time field coupling, a four-field joint coupling control system is formed, which can accurately depict the complex processes such as adjustment of effective stress caused by temperature change, seepage, excavation disturbance and redistribution of pore pressure, and thus truly reflect the nonlinear evolution process of tunnel surrounding rock and supporting system under the synergistic action of multiple physical fields.

[0032] Further, the nonlinear response model selects Mohr-Coulomb, Drucker-Prager, Cam-Clay, and Burgers model based on material type, and the model supports linkage with temperature field and pore pressure field variables, and parameters can be updated in real time as state variables change.

[0033] The above technical solution specifies the constitutive relationship of elastic-plastic, viscoelastic, creep, damage softening, thermal expansion, etc. for geological medium, and supports automatic selection of model type and parameters according to geological zoning, so that the model has more physical reality in the expression of the linkage relationship of stress-strain-temperature-pore pressure, and significantly improves the accuracy and reliability of structure stability and deformation prediction under complex conditions.

[0034] Further, the coupled equation set is numerically discretized based on the finite element or finite difference method, and is iteratively solved by using weak coupling or strong coupling strategy, with grid self-adaptive control and time step adjustment mechanism, to realize joint simulation of deformation field, thermal field and seepage field;

[0035] Picard or Newton-Raphson iterative strategy is used to realize strong coupling solution of multiple fields, and the residual convergence criterion is:

[0036]

[0037] and the grid is adaptively refined and the time step is shortened according to the gradient distribution of stress field and seepage field.

[0038] By the technical solution, the coupling equation solving part adopts a numerical discrete method of finite element / finite difference, supports switching of strong coupling and weak coupling strategies, combines adaptive mesh and time step control, can effectively avoid convergence failure and calculation divergence problems, and ensures that the system has good stability and scalability under large-scale, nonlinear and complex boundary conditions.

[0039] Further, the specific method for correcting the model by introducing the measured data is:

[0040] The measured data during the tunnel construction period or operation period is introduced, an error function is constructed, and a parameter inversion algorithm is used to dynamically correct the model, so that the consistency between the simulation prediction result and the measured response is improved;

[0041] The method for model error inversion by using monitoring data includes gradient descent method, genetic algorithm, particle swarm optimization, Bayesian inference or Kalman filter, forms a dynamic correction feedback loop and updates the initial value and boundary condition of the model.

[0042] By the technical solution, the method supports difference comparison between the measured data (such as displacement, pore pressure and temperature) during the construction or operation period and the simulation result, and parameter inversion and boundary function optimization are driven by the error function to form a "data-model-data" feedback mechanism, realize the transformation from "prediction modeling" to "dynamic regulation", and significantly improve the engineering adaptability and decision support capability of the model.

[0043] The method not only improves the physical authenticity and engineering accuracy of tunnel deformation analysis, but also realizes an integrated closed-loop analysis process of data-driven, nonlinear modeling, multi-field coupling and intelligent correction, has good engineering promotion and industrial application value, and is especially suitable for tunnel engineering under complex geological conditions such as deep burial, high ground temperature, high water pressure, soft and hard alternating surrounding rock and the like.

[0044] On the one hand, the application provides a tunnel multi-field coupling nonlinear deformation analysis system, which is designed by modular architecture to ensure efficient and collaborative operation in the whole process from geological data acquisition to simulation feedback.

[0045] The technical solution of the application to solve the above technical problems is as follows: a tunnel multi-field coupling nonlinear deformation analysis system, comprising:

[0046] A geological data acquisition module is used to acquire original information such as stratum structure, rock and soil distribution, underground water level, pore pressure, ground temperature, ground stress and construction disturbance of a target tunnel area, and complete data standardization and unified coordinate archiving;

[0047] A boundary condition modeling module is used to construct boundary condition models of thermal field, seepage field, stress field and disturbance field based on the collected data, and support spatial partitioning and time function expression.

[0048] a coupling model establishing module configured to establish a heat-seepage-stress-time coupling control equation set based on an energy conservation equation, a mass conservation equation, and a momentum balance equation, and integrate a material nonlinear response model;

[0049] a numerical solution module configured to perform numerical discretization and joint solution on the coupling equation set, support a finite element / finite difference method, a strong coupling or weak coupling iteration strategy, adaptive mesh control, and dynamic time step management;

[0050] a measured data correction module configured to introduce monitoring data in a construction period or an operation period to perform inversion optimization on model input parameters and boundary conditions, and form a dynamic correction feedback;

[0051] a visual output and control interface module configured to show multi-field simulation results, model error analysis charts, model evolution processes, and engineering decision suggestions, for real-time interaction and parameter adjustment by a user.

[0052] Through the above technical solutions, the geological data acquisition module can integrate multi-source information such as drilling, geophysical prospecting, hydrology, and stress, and complete data standardization and coordinate archiving, to establish a high-precision three-dimensional geological model to provide real initial conditions for simulation analysis; the boundary condition modeling module converts temperature, water head, stress, and disturbance into a time-varying and partitioned boundary expression model, supports fixed value, flux, and disturbance expansion, and truly restores the construction and environmental boundary change process; the coupling model establishing module constructs a unified model framework based on a heat-seepage-stress-time control equation set, integrates elastoplasticity, creep, and thermal damage constitutive models, and enhances the expression capability for complex nonlinear responses of rock and soil; the numerical solution module adopts a finite element or finite difference method for spatial discretization and coupling calculation, introduces a strong / weak coupling iteration, adaptive mesh, and dynamic time step strategy, and effectively improves the calculation efficiency and model stability; the measured data correction module introduces monitoring data in an operation period, optimizes model parameters and boundary functions through an error function, establishes a simulation-data feedback mechanism, and realizes dynamic adaptive updating and accurate prediction of the model; the visual and interactive module supports visual output of three-dimensional stress, seepage, temperature, and other multi-field results, and performs real-time parameter regulation and result comparison and analysis through a graphical interface, to improve engineering understanding and decision efficiency.

[0053] The system as a whole improves the precision, efficiency, and intelligent level of tunnel deformation simulation, and has good engineering promotion value and complex geological adaptability.

[0054] Further, the geological data acquisition module is in communication connection with a field monitoring system, including but not limited to a groundwater level gauge, a pore pressure gauge, a temperature sensor, a stress and strain gauge, and a construction log recording device.

[0055] The above technical scheme, by integrating drilling, geophysical prospecting, hydrology, thermal, stress testing and construction disturbance information collection means, and carrying out unified coordinate archiving and format standardization, a high-resolution, high-precision three-dimensional geological information base is constructed, which provides real and complete input conditions for multi-field modeling and nonlinear simulation, and significantly improves the model initialization accuracy.

[0056] Further, the numerical solving module adopts a general coupling matrix form to construct the coupling system stiffness matrix, and realizes the collaborative solving of four field variables through a nested iteration algorithm.

[0057] The above technical scheme adopts finite element / finite difference method to perform numerical discretization and joint iteration on the coupled equation set, and introduces adaptive mesh and dynamic time step strategies, supports strong / weak coupling switching, significantly improves the calculation efficiency and stability of the coupling solution, and adapts to the high-performance simulation needs of large-scale three-dimensional models and nonlinear fields.

[0058] Further, the measured data correction module constructs an error function and minimizes the difference between the measured value and the simulated value through an optimization algorithm, and the algorithm includes genetic algorithm, Bayesian inversion or Ensemble Kalman Filter.

[0059] The above technical scheme introduces construction or operation period field monitoring data, and establishes an error inversion mechanism and multiple optimization algorithms (such as genetic algorithm, Bayesian method), realizes dynamic adjustment and self-update of the model input and boundary, effectively solves the problem of traditional "predicted results deviating from the actual situation", and improves the long-term adaptability and decision guidance value of the simulation model.

[0060] Compared with the prior art, the technical scheme of the present application has the following beneficial technical effects:

[0061] 1. Four-field strong coupling modeling system, fully reflects the physical evolution mechanism; the present application first constructs a heat-infiltration-stress-time four-field coupling control system in tunnel engineering, systematically integrates energy conservation, mass conservation and momentum balance equations, and introduces a time field to express construction and environmental disturbance evolution, realizes dynamic modeling of the interaction between geothermal gradient, water pressure change, ground stress adjustment and surrounding rock response. Unlike existing schemes that only consider single-field or double-field (such as infiltration-stress, heat-stress) coupling, the present application can significantly improve the prediction accuracy of complex underground processes and the expression ability of multi-factor linkage processes.

[0062] 2. Nonlinear constitutive and boundary response joint modeling, suitable for complex geological conditions; The nonlinear constitutive model library (elastoplasticity, thermal damage, creep, etc.) is introduced for the complex stratum conditions such as soft and hard interlaced, multiple fissures, water-rich and high temperature, and a disturbance and environmental boundary model with time function and spatial partition characteristics is supported. Through the collaborative evolution of the material model and the boundary model, the limitations of idealization of materials and staticization of boundaries in traditional simulation tools are broken through, the expression ability of nonlinear response, disaster evolution and disturbance propagation mechanism is enhanced, and it is suitable for typical high-risk tunnel environment.

[0063] 3. Data-driven dynamic correction mechanism, realizing simulation-monitoring closed-loop linkage; The measured data correction module is integrated in the system, linkage with monitoring systems such as displacement meter, pore pressure gauge and thermometer is supported, and genetic algorithm, Bayesian inversion and other optimization strategies are adopted to dynamically update the simulation model parameters and boundary functions. The mechanism can build a closed loop chain of simulation prediction and measured feedback, so that the model has the ability of "self-adaptation", and the model can be updated, tracked and controlled, which significantly improves the model credibility and the reference value of engineering decision. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 A flowchart of a tunnel multi-field coupling nonlinear deformation analysis method provided for embodiment 1 of the present application is shown in the figure.

[0065] Figure 2 A data processing schematic diagram of a tunnel multi-field coupling nonlinear deformation analysis method provided for embodiment 1 of the present application is shown in the figure.

[0066] Figure 3 A system boundary condition modeling general flowchart of a tunnel multi-field coupling nonlinear deformation analysis method provided for embodiment 1 of the present application is shown in the figure.

[0067] Figure 4 A thermal field boundary condition modeling schematic diagram of a tunnel multi-field coupling nonlinear deformation analysis method provided for embodiment 1 of the present application is shown in the figure.

[0068] Figure 5 A stress boundary condition definition and application method of a tunnel multi-field coupling nonlinear deformation analysis method provided for embodiment 1 of the present application is shown in the figure.

[0069] Figure 6 A measured data-driven model correction flowchart of a tunnel multi-field coupling nonlinear deformation analysis method provided for embodiment 1 of the present application is shown in the figure.

[0070] Figure 7 A measured data and model alignment and processing schematic diagram of a tunnel multi-field coupling nonlinear deformation analysis method provided for embodiment 1 of the present application is shown in the figure.

[0071] Figure 8 A parameter inversion and dynamic updating flowchart of a tunnel multi-field coupling nonlinear deformation analysis method provided for the inventive embodiment 1 is shown in the figure.

[0072] Figure 9 A correction effect and reliability output schematic diagram of a tunnel multi-field coupling nonlinear deformation analysis method provided for the inventive embodiment 1 is shown in the figure.

[0073] Figure 10 A structure block diagram of a tunnel multi-field coupling nonlinear deformation analysis system provided for the inventive embodiment 2 is shown in the figure. DETAILED DESCRIPTION

[0074] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The embodiments of the present application are shown in the accompanying drawings. However, the present application can be implemented in many different forms, and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments of the present application, and are not intended to limit the present application.

[0076] Embodiment 1:

[0077] Reference Figure 1 As shown in the figure, the tunnel multi-field coupling nonlinear deformation analysis method of the present application comprises the following steps:

[0078] Collect information of stratum structure, rock and soil distribution, underground water level, temperature change and the like in a geological environment;

[0079] Based on the collected data, and according to the boundary conditions of heat, seepage, stress and the like, considering the spatio-temporal evolution of factors such as temperature gradient, hydraulic gradient, ground stress field, construction disturbance, a multi-source physical field boundary condition model is constructed;

[0080] Based on the collected data, energy conservation (thermal field), mass conservation (seepage), momentum balance (stress) equations are established, and a complete heat-seepage-stress-time four-field coupling control model is formed;

[0081] A nonlinear response model is specified for the geological medium to truly reflect the stress-strain behavior of rock and soil materials;

[0082] The coupled equation set is solved, and the four-field coupling control model is optimized;

[0083] The measured data is introduced to correct the model.

[0084] The tunnel multi-field coupling nonlinear deformation analysis method described above includes the following steps:

[0085] 1) Collect information of stratum structure, rock and soil distribution, groundwater level, temperature change, etc. in the geological environment, including:

[0086] 1.1) Stratum structure and rock and soil distribution: obtain stratum interface, layer thickness, lithology zoning, rock mass integrity, etc. through borehole exposure, geophysical testing and geological modeling;

[0087] Further, by arranging engineering drill holes at the center line and both sides of the tunnel, information such as stratum sequence, stratum interface, stratum thickness, lithology type, joint and fracture distribution and weak interlayer, etc. is revealed. The drill hole arrangement principle is based on stratum variation frequency, geological structure complexity and tunnel longitudinal section elevation variation, and the drill hole depth should meet the requirement of penetrating the tunnel bottom by 5-10m or more.

[0088] The combination of drilling and geophysical prospecting techniques is used to reveal the geological profile information of the target area. The drill holes should cover the main axis of the tunnel and its influence zone on both sides, and are preferably arranged in a plum blossom or cross shape to meet the needs of geological exploration in different directions. Geophysical prospecting methods include high-density electrical method, seismic wave reflection method and geological radar technology.

[0089] Through the above means, the following parameters are obtained:

[0090] layer thickness , stratum elevation , lithology category code , joint and fracture spacing and direction angle, rock mass integrity index .

[0091] In addition, geophysical exploration methods such as electrical method (e.g. resistivity imaging), shallow seismic reflection, micro-oscillation sounding, etc. are used to extend the stratum and identify underground structure anomalies in the un-drilled area.

[0092] The collected rock and soil samples are tested in the laboratory (including particle analysis, specific gravity, dry density, natural moisture content, liquid-plastic limit, unconfined compressive strength, triaxial shear test, etc.) to obtain physical and mechanical parameters for subsequent material constitutive model assignment.

[0093] After the rock and soil samples enter the laboratory, the following tests are performed:

[0094] moisture content , dry density , void ratio ; the results are used to assign material constitutive parameters such as elastic modulus , Poisson's ratio , internal friction angle , cohesion .

[0095] The above strata and geotechnical information are unified coded and the geological profile model and layered voxel model are constructed in the BIM platform.

[0096] 1.2) Groundwater level and permeability: Lay observation wells and pore pressure gauges, collect groundwater level dynamic curve, measure hydrogeological parameters such as permeability coefficient and porosity;

[0097] Further, in order to obtain the seepage field conditions of tunnel engineering, dynamic monitoring of underground hydrology needs to be carried out. Multiple groups of observation wells, water level gauges and pore pressure gauges are laid out to measure parameters such as groundwater level depth, water head change, hydrostatic pressure and permeability.

[0098] Water level monitoring: Use automatic recording water level gauge to collect time series data, obtain seasonal water level fluctuation, rainfall response characteristics and lag effect;

[0099] Pumping test: Implement phased pumping test in the characteristic aquifer, analyze permeability coefficient (k) and transmissibility coefficient (T) combined with recovery water level curve;

[0100] Pore pressure test: Lay pore water pressure sensors in boreholes or tunnel faces to monitor real-time effective stress change trend.

[0101] The collected hydrological data is organized in the form of two-dimensional / three-dimensional space-time matrix, which is used to construct boundary hydraulic gradient distribution map, saturation field and seepage path model.

[0102] Water level change can be expressed as:

[0103]

[0104] where, is the initial water level, is the amplitude, is the frequency, is the phase difference.

[0105] Permeability test can calculate the transmissibility coefficient and permeability coefficient , where:

[0106]

[0107] In addition, the moisture distribution information of the unsaturated zone is obtained by distributed optical fiber moisture measurement, FDR and other equipment, which is used to construct the boundary of the unsaturated-saturated seepage model.

[0108] 1.3) Ground temperature and thermal boundary parameters: Use buried temperature sensors to obtain ground temperature distribution at different depths, collect regional climate data to establish thermal field outer boundary;

[0109] Further, considering the long-term impact of the thermal field on the physical properties of the rock mass and the performance of the tunnel lining structure, temperature data along the tunnel and within its soil coverage range need to be collected. Specifically, it includes:

[0110] Ground temperature measurement: In a typical borehole or observation well, multiple point buried thermistors or optical fiber sensors are arranged to obtain the ground temperature gradient at different depths;

[0111] Surface temperature: Using surface temperature sensors combined with historical records from regional weather stations, an annual average temperature change curve is established;

[0112] Thermal parameters: The thermal conductivity, heat capacity, and thermal diffusivity of the rock-soil mass are determined through thermal conductivity experiments;

[0113] Combined with typical seasonal temperature fluctuations, a time-varying boundary temperature function is constructed to establish the boundary control conditions of the thermal field.

[0114] For the modeling requirements of the temperature field, in-situ temperature data at different depths need to be collected, and a thermal field boundary model needs to be established. Multi-point thermistors / thermocouples / distributed optical fibers can be used for temperature monitoring.

[0115] The ground temperature gradient is often fitted with a linear formula:

[0116]

[0117] where, is the surface temperature, is the ground temperature gradient ( ), is the burial depth.

[0118] Thermal conductivity , specific heat capacity , volumetric heat capacity , and other thermal physical parameters are supplemented by laboratory tests or literature.

[0119] 1.4) Ground stress field information: Obtain the initial ground stress tensor through stress testing, microseismic inversion, etc.

[0120] Further, the accurate acquisition of the initial ground stress field of the tunnel is the basis for stress-deformation analysis. In this invention, the following methods are preferred to determine the initial ground stress state:

[0121] Direct testing: Use the hydraulic fracturing method (HF), strain gauge method, borehole relaxation method, etc. to measure in the characteristic rock layer to obtain the three principal stress values and directions;

[0122] Numerical inversion: In some areas without actual measurement, combined with seismic wave velocity, microseismic event distribution, topography and stratum parameters, regional inversion analysis is used to construct the ground stress field tensor field;

[0123] Local correction: model back-calculation according to the structural stress monitoring data of the excavated section of the tunnel, fine-tuning the stress boundary value.

[0124] The obtained stress field is input into the coupling model in the form of a tensor field, and is iteratively updated synchronously with the deformation analysis process.

[0125] As preferred, the in-situ stress field information can be obtained by combining in-situ testing and inversion methods. The main parameters include three principal stresses , , and their directions, which are commonly expressed in tensor form as:

[0126]

[0127] If there are not enough test points, microseismic data and inversion algorithms (such as the least squares method) can be used to establish a continuous stress field tensor distribution model.

[0128] 1.5) Construction disturbance information: including excavation sequence, blasting radius, support scheme, etc., corresponding to the formation of the disturbance boundary influence domain.

[0129] Further, the influence of construction disturbance on the surrounding rock and physical field has time sequence and regional characteristics, therefore, construction-related parameters need to be collected in advance and modeled, including:

[0130] Excavation process and method: such as shield / blasting / open excavation, excavation footage, excavation spacing;

[0131] Blasting parameters: such as explosive charge, single shot delay, action radius;

[0132] Supporting method: initial support thickness, shotcrete material, anchor rod layout;

[0133] Disturbance zone estimation: determine the disturbance influence range (softening zone radius, initial stiffness reduction rate, etc.) according to field experience and numerical simulation;

[0134] All construction disturbance information should be recorded at stage time nodes, to facilitate the definition of the evolution process of the disturbance boundary in the coupling model.

[0135] In order to accurately simulate the disturbance of the construction process on the physical field, construction disturbance information needs to be collected in advance, including:

[0136] Blasting parameters: explosive charge , explosive radius , delay time of initiation , excavation sequence and step distance, support structure configuration, material parameters,

[0137] Disturbance radius estimation model (empirical formula or numerical regression):

[0138]

[0139] wherein, is the perturbation radius, is the tunnel diameter, , is an empirical coefficient.

[0140] The perturbation zone can be defined as a local material softening zone or a zone of rapid variation in pore pressure, which is treated as a boundary transition zone in the model.

[0141] All of the above collected information needs to be unified in the projection coordinate system, time format and physical units, and after standardized processing, a structured database is formed, including: geological stratification coding table; groundwater level time series table; geothermal field data body; stress tensor library; perturbation zone annotation layer, etc.

[0142] To realize the thermal-infiltration-stress-time coupling nonlinear analysis of the tunnel structure in complex geological environment, systematic, multi-level and multi-type information collection of the geological environment in the proposed tunnel area is needed. Through the integration of engineering geological investigation, hydrological monitoring, temperature field testing and stress measurement, a geological information data set containing spatial, temporal and physical property dimensions is constructed, which serves as the basis for model construction and parameter initialization.

[0143] 2) Construction of multi-source physical field boundary condition model

[0144] The steps of constructing the multi-source physical field boundary condition model are based on the original data obtained from the geological environment information collection, combined with the spatial location, construction stage and physical field evolution law of the tunnel, and the multi-source boundary condition models of the thermal field, seepage field, stress field and perturbation influence field are established respectively, providing time and space variation constraints for the subsequent multi-field coupling control equations.

[0145] The core of this step is to unify the boundary conditions between different physical fields through multi-source input data and dynamically drive them in the form of time series, so that the boundary state of each physical field can truly reflect the actual engineering working conditions and can be updated iteratively with time in the coupling model

[0146] Based on the collected field data, the time and space boundary conditions of each physical field are constructed, including:

[0147] 2.1) Thermal field boundary: considering the temperature difference between the surface and the rock mass, the geothermal flux density, and the construction heat source (such as the heat of the shield machine), the temperature boundary is set as a constant temperature, heat flux density or convective heat transfer boundary;

[0148] The thermal field boundary condition model is established, and for the boundary control of the thermal field, the geothermal gradient, the change of surface air temperature, the heating during construction (such as the heat source of the shield machine, the hydration heat of concrete) and other influencing factors are considered, and a time-depth bivariate function model is established.

[0149] The geothermal boundary can be expressed as a linear or nonlinear function:

[0150]

[0151] where, is the surface temperature as a function of time, is the geothermal gradient, is the construction thermal disturbance term.

[0152] According to the borehole geothermal data and historical meteorological data, the periodic boundary function (such as annual temperature cycle) is recommended:

[0153]

[0154] The boundary type can be defined as:

[0155] Constant temperature boundary (Dirichlet type);

[0156] Heat flux density boundary (Neumann type);

[0157] Convection boundary (Robin type), commonly used for ventilation tunnel or ventilation shaft boundary

[0158] 2.2) Seepage field boundary: According to the water level distribution, groundwater recharge source, set the constant water head, free drainage, closed boundary and other boundary conditions;

[0159] Seepage field boundary condition modeling:

[0160] According to the groundwater level depth, hydrological recharge / discharge conditions and underground aquifer structure characteristics, define the seepage field boundary model, common types include:

[0161] Constant water head boundary (such as surface water source, lake);

[0162] Free outflow boundary (such as lateral seepage zone);

[0163] Impervious boundary (such as aquiclude);

[0164] Pressure-driven boundary (used for surface evaporation / pressure fluctuation simulation);

[0165] Taking the top and both sides of the tunnel as an example, the water head boundary can be set as:

[0166]

[0167] The following form can be used to apply varying boundary conditions to the groundwater system:

[0168]

[0169] where, represents the normal outflow, is the permeability coefficient, is the water head.

[0170] 2.3) Stress boundary: According to the relationship between regional stress field and tunnel space, set the far-field stress tensor boundary or boundary constraint;

[0171] Stress field boundary condition modeling:

[0172] The boundary conditions of the stress field are defined according to the field measurement data or regional stress inversion results, and the main forms include:

[0173] Far-field stress boundary: Tensor form input principal stress direction and size

[0174]

[0175] where, , , respectively represent the maximum horizontal stress, the minimum horizontal stress and the vertical principal stress.

[0176] Surface load boundary: such as traffic load, stacking load, blasting wave, etc., the input is:

[0177]

[0178] Fixed displacement boundary: Set zero displacement constraint at the edge of the model to avoid rigid body drift.

[0179] For the influence of earthquakes, stress disturbance items with time history can be applied to the boundary to realize dynamic load simulation

[0180] 2.4) Disturbance evolution model: Model the disturbance effect in the construction process as local boundary stress release, stiffness weakening, pore pressure change and other time-varying effects.

[0181] The construction process will cause local unloading of strata, opening of fractures, relaxation of surrounding rock and redistribution of pore pressure, and a time-varying disturbance boundary needs to be constructed.

[0182] The disturbance model can be composed of the following control parameters:

[0183] Excavation time node

[0184] Disturbance bandwidth

[0185] Material stiffness weakening coefficient

[0186] Pore pressure release ratio

[0187] The elastic modulus time function is defined in the perturbation zone:

[0188]

[0189] where is the indicator function within the time window.

[0190] At the same time, the piecewise function of the pore pressure instantaneous drop with the disturbance is set:

[0191]

[0192] By modeling the above boundary conditions as time functions or spatial partition inputs, dynamic boundary control of each physical field is achieved.

[0193] 2.5) Unified management of boundary conditions and data structure

[0194] The above thermal, seepage, stress, and disturbance boundary data are structured in the following unified format for easy access by the modeling platform:

[0195]

[0196] 3) Based on the geological environment information collected in step one and the multi-source boundary condition model constructed in step two, further establish the thermal-seepage-stress-time coupling control equation set for simulating the multi-physical response of the tunnel structure and the surrounding rock-soil mass. This equation set considers the heat transfer behavior, seepage action, mechanical response of the rock-soil medium, and the interaction between these physical fields, forming a complete multi-field coupling calculation model.

[0197] 3.1) Thermal field control equation

[0198] The thermal field control is based on the law of conservation of energy, considering heat conduction, heat source terms, and heat accumulation changing with time in the rock-soil mass. Under the condition of no convection, the general form is:

[0199]

[0200] where:

[0201] is the medium density (kg / m³);

[0202] is the specific heat capacity (J / kg·K);

[0203] is the temperature (K);

[0204] is the thermal conductivity (W / m·K);

[0205] The heat source term (W / m³) in unit volume, such as construction thermal disturbance or exothermic reaction.

[0206] For systems with fluid seepage, the convection term should be added to form the modified heat transport equation:

[0207]

[0208] where, is the pore water velocity, is the density and specific heat capacity of water.

[0209] 3.2) Seepage field control equation

[0210] Based on the principle of mass conservation in saturated porous media, the seepage field control equation is expressed as:

[0211]

[0212] where,

[0213] is the porosity;

[0214] is the density of water;

[0215] is the Darcy velocity (based on Darcy's law);

[0216] is the total water head, is the permeability coefficient, is the dynamic viscosity;

[0217] is the unit volume inflow / outflow source term.

[0218] When considering the change of permeability under stress action, it can be extended to a stress-permeability coupling model:

[0219]

[0220] where, is the effective stress, is the stress sensitivity coefficient.

[0221] 3.3) Stress field control equation (momentum conservation)

[0222] The mechanical response of tunnel surrounding rock is established according to the momentum conservation law:

[0223]

[0224] where,

[0225] is the total stress tensor;

[0226] is the displacement vector;

[0227] is the gravitational acceleration;

[0228] is the material density.

[0229] In static problems, the inertia term can be neglected, and the equation is simplified as:

[0230]

[0231] Meanwhile, the stress-strain relationship is given by the constitutive model, and the commonly used elastic-plastic relationship is:

[0232]

[0233] where, is the elastic stiffness matrix, is the plastic strain tensor.

[0234] Effective stress satisfies the Terzaghi principle:

[0235]

[0236] where, is the pore water pressure, is the Biot coefficient.

[0237] 3.4) Multi-field coupling mechanism and unified control model

[0238] The control equations of the above thermal field, seepage field, and stress field show strong coupling characteristics in the solving process, mainly including the following physical linkage mechanisms:

[0239] Thermal → seepage: temperature affects fluid viscosity , permeability , changing the seepage distribution;

[0240] Seepage → stress: pore water pressure participates in the calculation of effective stress, affecting the stability of surrounding rock;

[0241] Stress → seepage: deformation of surrounding rock leads to changes in porosity, thereby affecting permeability;

[0242] Thermal → stress: temperature gradient generates thermal expansion stress or damage stress;

[0243] Construction disturbance → multi-field coupling system response: causes local heat source, water pressure disturbance, stiffness weakening, etc.

[0244] The final control model can be summarized as the following partial differential equations:

[0245]

[0246] The equation set constitutes the thermal-infiltration-stress-time four-field coupling nonlinear control core framework proposed by the present application, and provides an equation basis for subsequent solver design and numerical simulation

[0247] 4) Assign a nonlinear response model to the geological medium

[0248] In a preferred embodiment of the present application, in order to truly reflect the complex mechanical behavior of the tunnel surrounding rock and the adjacent rock-soil medium under the coupling action of thermal-infiltration-stress-time multi-physical fields, a nonlinear response model suitable for the constitutive characteristics of the medium needs to be assigned to the medium. The model should consider elastic-plastic behavior, stress path dependence, temperature influence, pore pressure influence, strain rate dependence, and long-term creep or creep effect, so as to realize dynamic description of the whole process from elasticity, plasticity, failure to recovery of the material state.

[0249] The nonlinear response model should have the following functional characteristics:

[0250] (1) It can support different types of strata (rock, clay, sand, silty soil, etc.);

[0251] (2) It can adapt to different action conditions (high temperature, strong infiltration, high stress area);

[0252] (3) It can be linked with thermal-infiltration coupling parameters to reflect changes in material properties;

[0253] (4) It can support zoned assignment, stress path loading, and loading-unloading path reversible simulation.

[0254] 4.1) Elastic-plastic constitutive model:

[0255] For most tunnel rock-soil media, a classical or improved elastic-plastic model can be used to simulate its stress-strain response characteristics, and common models include:

[0256] (1) Mohr-Coulomb model: suitable for various types of medium-strength rock layers, soft rock and clay, and its yield surface is expressed as:

[0257]

[0258] wherein, is the shear stress, is the normal stress, is the cohesion, is the internal friction angle.

[0259] (2) Drucker-Prager model: suitable for sandy soil or isotropic material, the yield function is:

[0260]

[0261] where, is the stress invariant, is the second deviatoric stress invariant, is the material parameter.

[0262] (3) Cam-Clay model (suitable for soft soil):

[0263]

[0264] where, is the deviatoric stress, is the mean stress, is the slope of the critical state line, is the pre-consolidation pressure.

[0265] 4.2) Stress-permeability coupling model:

[0266] To simulate the influence of pore water pressure on the strength and deformation of rock and soil, the concept of effective stress is introduced in the nonlinear model:

[0267]

[0268] where, is the Biot coefficient, is the pore pressure. For saturated-unsaturated media, the influence of matrix suction on strength also needs to be introduced:

[0269]

[0270] where, is the degree of saturation, is the strength increment correction term.

[0271] 4.3) Temperature-mechanical coupling model:

[0272] In the thermal-stress coupling scenario, thermal expansion strain, thermal damage and material property degradation caused by temperature change should be considered, expressed as follows:

[0273] Thermal strain term:

[0274]

[0275] where, is the thermal expansion coefficient, is the reference temperature;

[0276] Temperature-dependent constitutive parameters:

[0277]

[0278] wherein, is the temperature softening coefficient.

[0279] 4.4) Creep and creep model:

[0280] For deep buried tunnels, high water pressure environment or long-term load area, the rock or soil may produce significant time-dependent deformation, and the creep model needs to be introduced, which is commonly expressed as:

[0281] Power function creep model:

[0282]

[0283] Burgers model:

[0284]

[0285] Such models can be used to simulate soft soil settlement, long-term crushing of surrounding rock and other phenomena.

[0286] 4.5) Material partitioning and model selection logic:

[0287] According to the results of step one of geological information collection, a geological material-model mapping table is established:

[0288]

[0289] Each model is bound to a set of constitutive parameters and has a state variable updating mechanism, supporting load-unload path simulation and nonlinear residual strain accumulation.

[0290] The above response model constitutes the constitutive core module of the coupling system of the present application, providing a nonlinear basis for the response of the heat-infiltration-stress-time field at the material level. The model is extensible and can support modular enhancement when more fields (such as air pressure, chemical corrosion) are introduced in the future.

[0291] 5) Solve the coupled equations and optimize the four-field coupling control model

[0292] In a preferred embodiment of the present application, for the heat-infiltration-stress-time four-field coupling control equation set established in step three, combined with the nonlinear constitutive relationship of geological materials set in step four, a numerical discrete model is further constructed, and joint calculation is realized through a coupling solving algorithm to obtain the dynamic response of the tunnel structure and its surrounding rock under the action of complex multi-physical fields.

[0293] To ensure the calculation accuracy and solving efficiency, the model structure needs to be optimized for multiple rounds by combining the grid self-adaptive strategy, time step control, iteration acceleration method and parameter sensitivity analysis mechanism, so as to realize the stable convergence of the coupled model and the improvement of the result reliability.

[0294] 5.1) Numerical Discretization Method

[0295] The coupled control equation set is a set of nonlinear, strongly coupled, time-varying partial differential equations, which is difficult to solve analytically and needs to be approximated by numerical discretization method. The present application preferably uses finite element method (FEM) for spatial discretization of the control region, and finite difference method (FDM) or theta method (such as Crank-Nicolson) for time discretization. After spatial discretization, the following general algebraic expression can be obtained:

[0296]

[0297] Wherein:

[0298] is the system stiffness matrix, containing multi-physical field parameters;

[0299] is the state variable (displacement , temperature , water head );

[0300] is the time-varying external load or source term.

[0301] The block or general coupled matrix form is used to organize different physical fields:

[0302]

[0303] 5.2) Coupling Solution Strategy:

[0304] To improve the stability and efficiency of multi-field joint solution, the following coupling strategies can be selected according to the physical strong coupling degree and boundary evolution characteristics of the actual problem:

[0305] Weak coupling strategy (sequential loose coupling): each physical field is solved independently in turn, and the boundary is updated gradually, which is suitable for problems with weak coupling relationship and stable boundary condition change.

[0306] Strong coupling strategy (full quantity simultaneous solution): each field is combined to form a large nonlinear equation set for unified solution, which is suitable for situations with high accuracy requirements such as critical state and instability failure prediction.

[0307] Nested iteration method (Picard or Newton-Raphson): multiple rounds of sub-problem solving are iterated in each time step until the residual criterion is met .

[0308] Multi-scale method (FEM-MPM, FEM-DEM coupling): Different solving mechanisms are used for different regions or fields, and are coupled through the interface.

[0309] 5.3) Adaptive mesh and time step control:

[0310] Considering that the calculation error is large in the high strain area, crack development area or thermal-flow gradient mutation area in the nonlinear model, the application introduces an adaptive mesh division strategy, which adjusts the grid density in real time according to the following conditions:

[0311] Stress gradient threshold:

[0312] Pore pressure rapid change:

[0313] Thermal gradient concentration area:

[0314] The grid density change cooperates with the adaptive adjustment of the time step, and follows the stability condition (such as the CFL condition):

[0315]

[0316] Wherein is the thermal diffusion coefficient.

[0317] 5.4) Model parameter optimization and sensitivity analysis

[0318] In order to improve the accuracy and robustness of the model prediction, a parameter optimization and sensitivity analysis mechanism is introduced. The model correction and simplification are completed through the following process:

[0319] (1) Parameter sensitivity sorting: Sobol method, Morris method, etc. are used to sort the variance contribution of input parameters, and high sensitivity parameters are determined;

[0320] (2) Local optimization adjustment: small range perturbation is carried out on the high sensitivity parameters, and the residual function is minimized:

[0321]

[0322] (3) Model structure simplification: remove low sensitivity input variables or merge regional grids to reduce computational complexity.

[0323] 5.5) Convergence control and result verification

[0324] All numerical solving processes need to set convergence criteria, including:

[0325] Variable residual criterion: ,

[0326] Energy balance criterion: ,

[0327] Stability criterion: Jacobi matrix spectral radius ,

[0328] After the calculation is completed, the prediction accuracy of the model is evaluated by comparing the field monitoring data (such as tunnel settlement, lining stress, pore pressure change, temperature response, etc.), and the local boundary parameters are calibrated to realize the whole process precision control.

[0329] In summary, through multi-physical field collaborative solution, grid self-adaption and parameter iteration optimization, the present application can realize high-precision and stable coupling simulation of tunnel structure under complex geological conditions, and provide a solid foundation for subsequent measurement correction and intelligent early warning.

[0330] 6) Introducing measured data to correct the model:

[0331] In a preferred embodiment of the present application, to further improve the engineering adaptability and prediction accuracy of the heat-infiltration-stress-time multi-field coupling model, after the preliminary numerical solution is completed, field measured data need to be introduced to invert and correct the model parameters, boundary conditions and calculation results, and dynamically update them. This step not only narrows the deviation between the simulation results and the observed values, but also provides data support for subsequent risk warning and decision assistance.

[0332] The measured data can come from the multi-source monitoring system laid during the tunnel construction period and the operation period, including but not limited to:

[0333] Surrounding rock deformation monitoring: inclinometer, laser scanner, convergence meter, etc.

[0334] Lining stress monitoring: strain gauge, rebar meter, fiber Bragg grating sensor

[0335] Pore water pressure and seepage pressure monitoring: vibrating wire pore pressure gauge, multi-point water level gauge

[0336] Ground temperature monitoring: geothermal cable, thermistor array

[0337] Ground stress change: borehole relaxation method, core stress test

[0338] Construction event record: excavation footage, support node, explosive parameter, etc.

[0339] 6.1) Data alignment and standardization:

[0340] To realize the effective docking of the model and the monitoring data, the following preprocessing steps are needed:

[0341] (1) Spatial alignment: unify the field data and model coordinate system, elevation datum (e.g. CGCS2000), and map them to the simulation grid nodes through interpolation;

[0342] (2) Time synchronization: match the data sequence to the simulation time step (Δt) according to the monitoring timestamp;

[0343] (3) Unit conversion and normalization: convert the field data to SI units and perform range standardization to facilitate multivariate fitting;

[0344] (4) Abnormal data removal: remove sensor faults or abnormal data through moving average, Z-score or IQR method.

[0345] 6.2) Model error evaluation and residual construction:

[0346] Compare the simulation output results with the measured values to construct the residual vector and form the error function:

[0347]

[0348] The comprehensive error objective function is:

[0349]

[0350] Where:

[0351] is the set of model parameters to be adjusted;

[0352] is the weight coefficient of different monitoring indicators (e.g. high priority weight for settlement);

[0353] is the total number of observation data points.

[0354] 6.3) Parameter inversion and model correction strategy:

[0355] According to the error function , use one or more of the following inversion algorithms to optimize the model input, so that the simulation output gradually approaches the measured data:

[0356] (1) Gradient descent method (GD): quickly adjust continuous parameters (such as permeability, elastic modulus);

[0357] (2) Genetic algorithm (GA) / particle swarm optimization (PSO): for non-continuous parameters or multi-peak problems;

[0358] (3) Bayesian updating method: consider parameters as distributed variables, combine prior distribution and observation likelihood for posterior update;

[0359] (4) Kalman filter / EnKF: fuse real-time monitoring data in dynamic time steps to correct the state.

[0360] post-inversion parameters The feedback is incorporated into the control equation set, and the boundary, initial value or constitutive parameters of the thermal-infiltration-stress field are revalued to generate a new round of corrected simulation results.

[0361] 6.4) Multi-round iterative correction and model convergence criterion:

[0362] The above inversion-simulation process can form the following iterative process: initial simulation → comparison with monitoring values → adjustment of parameters → update of model input → re-simulation → iteration 1-5 until the error convergence condition is met;

[0363] The convergence criterion can be set as:

[0364]

[0365] wherein, is the preset error tolerance.

[0366] 6.5) Output correction results and reliability evaluation:

[0367] After correction, the output includes:

[0368] Model parameter inversion report (in list or chart form);

[0369] Comparison chart of simulation results before and after correction and monitoring values;

[0370] Uncertainty bandwidth (such as confidence interval, deformation prediction range);

[0371] Model reliability evaluation index (such as goodness of fit R², RMSE, etc.).

[0372] Through the above model dynamic correction mechanism, the present application can realize the transition from "physical law dominance" to "data-driven correction", build a digital simulation tunnel model highly consistent with the actual working conditions, and significantly improve the reliability, practicality and engineering adaptability of the analysis and prediction results.

[0373] Example 2:

[0374] Referring to FIG. x, a tunnel multi-field coupled nonlinear deformation analysis system, characterized in that it comprises:

[0375] 1) A geological data acquisition module for obtaining original information such as stratum structure, rock and soil distribution, underground water level, pore pressure, ground temperature, ground stress and construction disturbance of the target tunnel area, and completing data standardization and unified coordinate archiving;

[0376] As preferred, the geological data acquisition module is communicatively connected with the field monitoring system, including but not limited to: groundwater level meter, pore pressure meter, temperature sensor, stress-strain meter and construction log recording equipment.

[0377] As preferred, the geological data acquisition module is a front-end input module of the system, responsible for obtaining the original information of the geological environment of the tunnel area, and completing the standardization and structured processing to form a unified input data set. Its specific functions include:

[0378] Drilling and lithology information acquisition subunit: obtain drilling profile, rock thickness, lithology code, rock mass integrity (RQD), etc., data formats support Excel, JSON, Shapefile, etc.;

[0379] Hydrogeological monitoring interface: connect water level meter, pore pressure meter, realize automatic collection and archiving of groundwater dynamic data;

[0380] Temperature field acquisition interface: interface with thermistor, cable thermocouple, optical fiber temperature sensor, etc., record ground temperature changes in time series;

[0381] Geostress acquisition sub-module: support import of stress tensor data measured by hydraulic fracturing method and borehole relaxation method, or access regional geostress database;

[0382] Construction disturbance information synchronization unit: real-time read construction footage, blasting parameters, support technology, etc. Information, and model to establish disturbance timing;

[0383] Data preprocessing engine: realize coordinate conversion, data denoising, unit unification, missing value completion, etc. Processing, finally stored in the form of structured multi-dimensional data table.

[0384] 2) Boundary condition modeling module, for building boundary condition models of thermal field, seepage field, stress field and disturbance field based on collected data, and supporting spatial partitioning and time function expression;

[0385] As preferred, the boundary condition modeling module analyzes and abstracts the collected data, establishes the boundary condition expression model of the thermal field, seepage field, stress field and disturbance field, the boundary can be set according to spatial partitioning, and the external condition evolution is expressed by time function (such as sine, step, linear growth function), and the boundary type supports fixed value boundary, normal flux boundary, free boundary, disturbance expansion boundary, etc.

[0386] As preferred, the original geological and environmental data are converted into boundary control conditions of thermal, seepage, stress, etc. Field, supporting static and dynamic input methods, including:

[0387] Boundary type identification unit: automatically determine the boundary type (such as Dirichlet type constant boundary, Neumann type flux boundary, Robin type convection boundary) according to the characteristics of the physical field;

[0388] Spatial partition modeling tool: support users to define different boundary areas and bind attributes in the graphical interface, such as tunnel periphery, ground surface, floor, etc.;

[0389] Time function assignment sub-module: support sine function (simulate seasonality), step function (simulate blasting disturbance), exponential decay function (simulate pore pressure release), etc.;

[0390] Composite boundary expression interface: allow multiple physical fields to share boundary areas (such as the same area having both heat flow and pore pressure change boundaries);

[0391] Boundary variability support mechanism: allow users to define spatial gradient or temporal disturbance of boundary parameters, such as linear water head decline, thermal flow periodic modulation, etc.

[0392] 3) Coupling model establishment module, for establishing heat-infiltration-stress-time coupling control equation set based on energy conservation equation, mass conservation equation and momentum balance equation, and integrating material nonlinear response model;

[0393] As preferred, the coupling model establishment module is used to establish a heat-infiltration-stress-time four-field control equation set, and introduce elastic-plastic constitutive relation, thermal stress coupling model, pore pressure-strength coupling model and creep model, adapt to different material response characteristics under various geological partitions. The model library supports automatic selection of constitutive models according to lithology classification, and constructs coupling terms and source terms between control variables.

[0394] As preferred, the coupling model establishment module constructs the core coupling control framework of the system, combines multiple physical field control equations and constitutive models to form a complete mathematical solving model. Its functions include:

[0395] Control equation modeling subsystem: built-in energy conservation, mass conservation, momentum balance equation, which can be automatically selected according to regional or material differences;

[0396] Constitutive model library: support 30+ constitutive models including Mohr-Coulomb, Drucker-Prager, Modified Cam-Clay, Burgers, viscoelasticity, etc.

[0397] Model automatic selection mechanism: automatically divide appropriate material models and parameters according to lithology label, groundwater state, temperature range;

[0398] Inter-field coupling term generator: automatically identify and construct temperature-stress, pore pressure-effective stress, thermal expansion-displacement, seepage-porosity, etc. coupling terms;

[0399] Control variable mapping table: clearly define the relationship between temperature T, displacement u, water head h, pore pressure u_p, etc. control variables and grid nodes.

[0400] 4) Numerical solution module, for numerical discretization and joint solution of coupled equations, supporting finite element / finite difference method, strong coupling or weak coupling iteration strategy, adaptive grid control and dynamic time step management;

[0401] As preferred, the numerical solution module completes the numerical discretization and joint solution of the control equation, adopts finite element method (FEM) or finite difference method (FDM) for spatial and temporal discretization, handles nonlinear and coupling terms with nested iteration strategy (such as Picard method, Newton method), supports weak coupling and strong coupling mode switching, and embeds grid adaptive control and dynamic time step automatic adjustment function, ensuring convergence and solving efficiency.

[0402] As preferred, the numerical solution module is responsible for converting the coupled control model into a numerical calculation task, completing spatial / time discretization, matrix assembly and solving process, and the main functions include:

[0403] Spatial discretization processor: supports 2D / 3D finite element and finite difference method, grid types include quadrilateral, hexahedron, free grid, etc.;

[0404] Time discretization controller: supports explicit, semi-implicit and Crank-Nicolson time marching format;

[0405] Iteration algorithm manager: built-in Picard, Newton-Raphson, GCR, etc. nonlinear solver, can automatically select the optimal method;

[0406] Adaptive grid module: automatically refine the grid according to the gradient, residual and crack evolution;

[0407] Time step length controller: dynamically adjusts according to the Courant condition, convergence rate and user-defined limit ;

[0408] Solution stability analysis subunit: real-time monitor Jacobi matrix spectral radius, energy balance error and other indicators during iteration process to prevent numerical divergence.

[0409] 5) Real-time data correction module, for introducing monitoring data during construction or operation period, optimizing model input parameters and boundary conditions, forming dynamic correction feedback;

[0410] As preferred, the measured data correction module is used to introduce monitoring data for model dynamic correction, by constructing a simulation-measured error function, using genetic algorithm, particle swarm optimization, Bayesian inversion or Ensemble Kalman Filter parameter optimization method, automatically adjusting the constitutive parameters, initial value and boundary function form, realizing online updating of model prediction ability.

[0411] As preferred, the measured data correction module compares the actual monitoring data with the simulation results, and realizes model dynamic correction through error function driven parameter optimization. The core functions include:

[0412] Data access interface: supports real-time import of field monitoring data in.csv, database, Internet of Things API and other ways;

[0413] Error function constructor: supports residual sum of squares, weighted root mean square error (RMSE), Bayesian negative log-likelihood function, etc.

[0414] Inversion algorithm engine: supports GA, PSO, Bayesian inversion, EnKF, etc. Multiple iterations can minimize the error objective function.

[0415] Parameter update strategy subunit: local correction of elastic modulus, permeability coefficient, thermal conductivity and other parameters;

[0416] Correction version manager: records the parameter version, error value and fitting effect after each optimization, supports rollback and comparison.

[0417] 6) Visual output and control interface module, used to show multi-field simulation results, model error analysis charts, model evolution process and engineering decision suggestions for user real-time interaction and parameter adjustment.

[0418] As preferred, the visual output and control interface module is used for graphical output and interactive operation of coupled calculation results, including three-dimensional stress contour map, seepage vector diagram, thermal field isosurface, settlement monitoring comparison curve, etc. Users can real-time control and simulate scene switching of model parameters, boundary functions, material properties, etc. through graphical interface.

[0419] As preferred, the visual output and control interface module is a user interaction interface and output display platform, which supports real-time viewing of coupled calculation results and dynamic control parameters. The main functions include:

[0420] Multi-physical field visualization engine: supports 3D temperature isosurface, stress contour map, seepage vector stream line, settlement surface map, etc.

[0421] Cross-section and time history analysis tool: supports viewing T / u / p / h process curves changing with time on any section;

[0422] Contrast analysis submodule: superimpose the simulation results and the measured data and generate deviation map;

[0423] Model interaction adjustment interface: adjust boundary conditions, constitutive parameters, disturbance time sequence, etc. through slider, drop-down menu and other interactive controls;

[0424] Output export function: support exporting simulation results in image, Excel, PDF, BIM plug-in format, etc.

[0425] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for tunnel multi-field coupled nonlinear deformation analysis, characterized in that, The method comprises the following steps: Collecting geological environment information of a tunnel area, including stratum structure and rock-soil distribution, underground water level and permeation characteristics, ground temperature distribution and thermal physical parameters, ground stress state and construction disturbance information, and structurally storing the above data in a unified coordinate system; Based on the collected data, and according to boundary conditions of heat, seepage flow, stress and other factors, considering the time-space evolution of temperature gradient, hydraulic gradient, ground stress field, construction disturbance and other factors, a multi-source physical field boundary condition model is constructed; Based on the collected data, an energy conservation equation, a mass conservation equation and a momentum balance equation are established, and a complete heat-seepage stress-time four-field coupling control model is formed; A nonlinear response model is specified for the geological medium to truly reflect the stress-strain behavior of rock-soil materials; The coupling equation set is solved, and the four-field coupling control model is optimized; Measured data is introduced to correct the model.

2. The method of claim 1, wherein, The geological information collection includes borehole survey, geophysical testing, water level monitoring, temperature sensing, stress testing and construction disturbance recording, and the collected data includes in-situ mechanical parameters, thermal conduction parameters, hydrological parameters and construction event time series.

3. The method of claim 1, wherein, The boundary condition model is defined in the form of spatial zoning + time evolution, and supports dynamic water head boundary, construction disturbance softening boundary, temperature convection boundary and stress release boundary combination setting.

4. The method of claim 1, wherein, The coupling control equation set is represented by the following mathematical model: wherein, is the medium density (kg / m³); is the specific heat capacity (J / kg·K); is the temperature (K); is the thermal conductivity (W / m·K); is the heat source term per unit volume (W / m³), such as construction heat disturbance or exothermic reactions; is the pore water velocity, is the density and specific heat capacity of water; is the total stress tensor; is the displacement vector; is the gravitational acceleration.

5. The method of claim 1, wherein, The nonlinear response model selects Mohr-Coulomb, Drucker-Prager, Cam-Clay and Burgers model based on material type, and the model supports linkage with temperature field and pore pressure field variables, and parameters can be updated in real time as state variables change.

6. The method according to any one of claims 1 to 5, characterized in that, Based on the finite element or finite difference method, the coupling equation set is numerically discretized, and the weak coupling or strong coupling strategy is used for iterative solution, and the grid self-adaptive control and time step adjustment mechanism are used to realize the joint simulation of the deformation field, the thermal field and the seepage field; The Picard or Newton-Raphson iteration strategy is used to realize the strong coupling solution of multiple fields, and the residual convergence criterion is: And according to the stress field and seepage field gradient distribution, the grid is adaptively encrypted and the time step is shortened.

7. The method according to any one of claims 1 to 5, characterized in that, The specific method of introducing measured data to correct the model is: Measured data during tunnel construction or operation is introduced to construct an error function and dynamically correct the model by using a parameter inversion algorithm, so as to improve the consistency between simulation prediction results and measured responses; The methods for model error inversion using monitoring data include gradient descent method, genetic algorithm, particle swarm optimization, Bayesian inference or Kalman filter, a dynamic correction feedback loop is formed to update the initial value of the model and the boundary conditions.

8. A tunnel multi-field coupled nonlinear deformation analysis system, characterized by, It comprises: A geological data collection module is used to obtain original information of stratum structure, rock-soil distribution, underground water level, pore pressure, ground temperature, ground stress and construction disturbance of a target tunnel area, and complete data standardization and unified coordinate archiving; A boundary condition modeling module is used to construct boundary condition models of thermal field, seepage field, stress field and disturbance field based on collected data, and support spatial zoning and time function expression; The coupling model establishing module is configured to establish heat-infiltration-stress-time coupling control equation groups based on energy conservation equation, mass conservation equation and momentum balance equation, and integrate material nonlinear response model; The numerical solving module is configured to perform numerical discretization and joint solving on the coupling equation groups, support finite element / finite difference method, strong coupling or weak coupling iteration strategy, adaptive grid control and dynamic time step management; The measured data correction module is configured to introduce monitoring data in construction period or operation period to perform inversion optimization on model input parameters and boundary conditions, and form dynamic correction feedback; The visual output and control interface module is configured to show multi-field simulation results, model error analysis charts, model evolution process and engineering decision suggestions for user real-time interaction and parameter adjustment.

9. The system of claim 8, wherein, The geological data acquisition module is in communication connection with a field monitoring system, including but not limited to: underground water level gauge, pore pressure gauge, temperature sensor, stress and strain gauge and construction log recording equipment.

10. The system of claim 8, wherein, The coupling model establishing module supports material partition modeling, the nonlinear response model includes elastic-plastic model, temperature coupling model, infiltration softening model and creep model, and supports automatic selection according to geological unit.

11. The system of claim 8, wherein, The numerical solving module adopts general coupling matrix form to construct coupling system stiffness matrix, and realizes collaborative solving of four field variables through nested iteration algorithm.

12. The system of claim 8, wherein, The measured data correction module constructs error function and minimizes the difference between measured value and simulation value through optimization algorithm, the algorithm includes genetic algorithm, Bayesian inversion or Ensemble Kalman Filter.

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