Tunnel multi-physics field coupling simulation and support design method considering construction disturbance

Abstract

The invention relates to the field of tunnel engineering, in particular to a tunnel multi-physics field coupling simulation and support design method considering construction disturbance, which comprises the following steps of: modeling a disturbance source, collecting disturbance factor information introduced in a tunnel construction process or an external environment, establishing a disturbance source model to represent the spatial position, intensity and evolution characteristics of a disturbance source; constructing a three-dimensional disturbance propagation function; coupling the three-dimensional disturbance propagation function with a multi-physical field model, and introducing disturbance as a source item into a tunnel heat-water-force coupling model; according to the construction method customization mechanism, modeling parameters and solving strategies are adjusted according to the characteristics of different tunnel construction methods, and corresponding disturbance source model parameters, boundary conditions and constitutive relation adjustment coefficients are set according to the different construction methods. The problems that disturbance modeling is rough, a disturbance propagation mechanism is idealized, the disturbance propagation mechanism is disjointed with a multi-physical field model, and a construction method is not included in a disturbance response system are solved.

Description

Technical Field

[0001] This invention relates to the field of tunnel engineering, and specifically to a method for multi-physics field coupling simulation and support design of tunnels that takes into account construction disturbances. Background Technology

[0002] As a crucial component of linear transportation infrastructure, tunnel engineering often operates in complex and variable geological environments. With the acceleration of urbanization and the deepening of underground space development, projects such as deep-buried tunnels, tunnels in fractured surrounding rock, and tunnels crossing aquifers are becoming increasingly common. These projects pose severe challenges to the mechanical response, safety stability, and support adaptability of tunnel structures under construction disturbances.

[0003] Existing tunnel design and construction analysis methods are mainly based on static mechanics assumptions and simplified geological parameter settings, generally neglecting the disturbance effects during construction and their dynamic driving effect on the long-term evolution of the surrounding rock structure. This is especially true in the following engineering scenarios:

[0004] ①The surrounding rock is a weak rock layer with well-developed joints and sensitive stress, and construction disturbance can easily induce sudden deformation.

[0005] ② There is an intertwined and superimposed disturbance behavior from multiple sources, including blasting, mechanical propulsion, and drainage;

[0006] ③ The response of the support structure exhibits obvious time delay and phased characteristics;

[0007] ④ The deformation of the surrounding rock exhibits a long-term evolution trend characterized by nonlinearity, dilatation, or creep.

[0008] The aforementioned factors make it difficult for traditional modeling methods to fully reflect the inherent coupling relationship between disturbance, structure, and time, thereby limiting the timeliness and reliability of accurate identification of deformation mechanisms and risk prediction.

[0009] Current common methods have shortcomings in the following aspects:

[0010] The disturbance modeling is crude: most modeling methods only take the excavation outline as the disturbance source, ignoring key factors such as blasting effect, support lag, and precipitation disturbance, and cannot fully characterize the type, range and action path of the disturbance source.

[0011] Idealization of disturbance propagation mechanism: The diffusion process of disturbance intensity in space is usually simplified by homogeneous spherical diffusion, which cannot take into account directional propagation, asymmetric distribution and tensor field effects under complex geological conditions.

[0012] Disconnected from multiphysics models: The perturbation field is often calculated independently and fails to be linked with the thermal-permeability-mechanical coupling mechanism, resulting in distorted predictions of the surrounding rock deformation mechanism (such as temperature-induced deformation and pressure-induced instability).

[0013] Construction methods are not included in the disturbance response system: The disturbance forms, loading rhythms and support delay mechanisms corresponding to different construction methods (such as open excavation, cut-and-cover, shield tunneling, and blasting) are significantly different, but existing methods lack systematic parameter customization and dynamic modeling mechanisms, and cannot adapt to the actual construction site conditions where construction methods vary.

[0014] Therefore, there is an urgent need for a method for analyzing the evolution of tunnel structure behavior based on disturbance response modeling, which can achieve: ① multi-dimensional identification and modeling of construction disturbance sources; ② expression of the directionality, amplitude, and range of disturbance propagation in heterogeneous media; ③ analysis of multi-physics response caused by disturbance in conjunction with the thermo-hydraulic-mechanical coupling mechanism; ④ dynamic calling of disturbance modeling parameter sets according to different construction methods to achieve adaptive evolution simulation. Summary of the Invention

[0015] Based on the above description, this invention provides a tunnel multi-physics field coupled simulation and support design method that considers construction disturbances, in order to solve the problems of rough disturbance modeling, idealized disturbance propagation mechanism, disconnection from multi-physics field model and failure to incorporate construction methods into the disturbance response system.

[0016] On the one hand, the technical solution of the present invention to solve the above-mentioned technical problems is as follows: a tunnel disturbance response modeling method, the method includes the following steps: disturbance source modeling, collecting information on disturbance factors introduced during tunnel construction or in the external environment, and establishing a disturbance source model to characterize the spatial location, intensity and evolution characteristics of the disturbance source;

[0017] A three-dimensional perturbation propagation function is constructed, which is controlled by a second-order symmetric positive definite perturbation propagation tensor K_d, and its mathematical form is:

[0018] ;

[0019] in:

[0020] ;

[0021] Used to describe the decay and propagation of disturbances in heterogeneous media over time and space;

[0022] Multiphysics model coupling is achieved by jointly solving the tunnel thermal-hydraulic-mechanical coupling model, where the perturbation intensity field... The output of the perturbation propagation function is introduced as an additional source term into the governing equations to form a unified coupled equation set, which is solved simultaneously to simulate the perturbation response under the interaction of various physical fields.

[0023] The construction method customization mechanism adjusts the modeling parameters and solution strategies according to the characteristics of different tunnel construction methods. It sets corresponding disturbance source model parameters, boundary conditions and constitutive relation adjustment coefficients for different construction methods, and calls the corresponding parameter set during the modeling and solution process so that the model results reflect the disturbance characteristics and surrounding rock response under the selected construction method.

[0024] Based on the above technical solution, the present invention can be further improved as follows.

[0025] Furthermore, perturbation source modeling includes constructing a second-order symmetric positive definite perturbation tensor. The perturbation source is characterized in three-dimensional space in tensor form, where the diagonal elements represent the perturbation intensity in the principal direction and the off-diagonal elements represent the directional coupling effect. This perturbation tensor serves as the input parameter of the perturbation propagation function.

[0026] Furthermore, the multiphysics model coupling is achieved by jointly solving the tunnel's thermal-hydraulic-mechanical coupling model, where the disturbance intensity field... The output of the perturbation propagation function is introduced as an additional source term into the governing equations to form a unified set of coupled equations, which are solved simultaneously to simulate the perturbation response under the interaction of various physical fields.

[0027] Furthermore, the construction method customization mechanism is implemented through a construction method parameter setting strategy. For different tunnel construction methods, the corresponding disturbance source model parameters, model boundary conditions, and constitutive relation adjustment coefficients are pre-determined. During model solving, the corresponding parameter set is called according to the selected construction method to simulate, so that the model output reflects the disturbance influence characteristics and surrounding rock response under the construction method conditions.

[0028] Secondly, the technical solution of the present invention to solve the above-mentioned technical problems is as follows: a finite incremental-time-series evolution simulation method, the method being used to simulate the evolutionary influence of disturbance on the surrounding rock and support system in the above-mentioned disturbance response model, including: dividing the construction process into multiple time steps, and sequentially updating the influence area and boundary conditions of the disturbance source; activating support components such as shotcrete, anchor bolts, and steel arch frames in the corresponding time steps, and assigning initial mechanical parameters; modeling the material aging behavior using a creep model, a viscoelastic model, or a modulus growth function; outputting the stress-strain field, deformation curve, and support stress history of each time step in the entire process; the method is coupled with the above-mentioned modeling method through a disturbance intensity and time step parameter sharing mechanism to realize dynamic evolution analysis of the entire disturbance response process.

[0029] Furthermore, the activation time and activation method of the support components are preset according to the construction procedures and design parameters, and recorded in the support timing control module, which is used to automatically control the activation nodes and mechanical property assignment processes of various support components in the simulation model.

[0030] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A linkage response simulation method for support structures, used to simulate the dynamic response process of support components under disturbance in the disturbance response modeling method. The method includes: establishing a coupling model between support components such as shotcrete, anchor bolts, and steel frames and the surrounding rock based on the disturbance response model, and setting contact interface parameters; dynamically updating the material parameters of each component using a time stepping method, the parameters including elastic modulus, anchoring force, and stiffness attenuation coefficient; identifying the failure trend of key components based on load ratio, plastic zone expansion, and stiffness degradation criteria; outputting the stress, deformation, and energy distribution results of each component, and supporting linkage update of the model with monitoring data to realize full-process simulation feedback analysis of disturbance and support.

[0031] Furthermore, the failure trend identification is based on one or a combination of the following criteria: the ratio of the actual stress on the component to its ultimate bearing capacity exceeds a preset threshold; the length of the plastic zone accounts for more than a set proportion of the total length of the component; and the stiffness of the component decreases by more than 50% compared to the initial value.

[0032] The technical solutions of the present invention to solve the above-mentioned technical problems are as follows: a tunnel construction risk level prediction and feedback system based on disturbance response, the system is used to receive disturbance parameters and stress field results output by the disturbance response modeling method, and to combine monitoring data to conduct risk level assessment and support strategy feedback, including: a front-end sensing layer, used to collect surrounding rock deformation, stress, seepage pressure and environmental parameters;

[0033] The communication and edge processing layer completes protocol conversion, anomaly identification, and preprocessing of monitoring data;

[0034] The back-end decision-making layer includes: a risk assessment module that uses fuzzy comprehensive method or BP neural network model to classify the disturbance impact area into levels;

[0035] Support recommendation module: Matches support parameter adjustment strategies based on assessment results;

[0036] Visualization module: Displays the time-series evolution curves of disturbance and risk, as well as support response records;

[0037] The system constructs a feedback loop of disturbance-risk-support to achieve dynamic risk control during the construction process.

[0038] The technical solutions of the present invention to solve the above-mentioned technical problems are as follows: An integrated tunnel construction auxiliary decision-making system, the system is constructed based on the disturbance response modeling method, and is used for disturbance perception, simulation analysis and feedback decision-making during tunnel construction, including: a front-end monitoring terminal, used to collect disturbance-related data such as displacement, stress and seepage pressure of the tunnel surrounding rock and support structure;

[0039] Edge computing units are used to generate perturbation parameters, fit perturbation propagation functions, and perform preliminary data preprocessing.

[0040] The central simulation server receives disturbance parameters and invokes the modeling method described in claim 1 to perform disturbance-surrounding rock response simulation and risk analysis.

[0041] The human-computer interaction terminal is used to set construction method parameters, display simulation results, and provide support suggestions.

[0042] The backend database is used to store historical engineering data, simulation reports, and risk classification comparison information;

[0043] The system constructs a data-modeling-feedback closed loop, supporting disturbance response analysis and dynamic optimization of support strategies during construction.

[0044] Compared with the prior art, the technical solution of this application has the following beneficial technical effects:

[0045] 1. This invention constructs a disturbance source model and a disturbance tensor to characterize the distribution features of disturbances in different directions in space in detail. Combined with a three-dimensional disturbance propagation function, it simulates the attenuation and diffusion paths of disturbances in the surrounding rock medium of tunnels. This technology not only overcomes the assumptions and errors in traditional disturbance modeling regarding the uniformity and simplified distribution of disturbances, but also dynamically captures the influence range and intensity distribution of disturbances over time. Based on tensor-form input, it enables anisotropic expression of disturbance propagation, more closely reflecting actual working conditions, significantly improving the accuracy of disturbance response simulation, and laying a more reliable input foundation for subsequent support response analysis and risk assessment.

[0046] 2. By introducing the disturbance source as a dynamic source term into the thermo-hydraulic-mechanical multiphysics coupling model, a detailed simulation of the coordinated evolution of the temperature field, seepage field, and stress field of the surrounding rock structure caused by the disturbance is achieved. Compared with traditional single-field simulation, this coupling mechanism can simultaneously solve multiple field governing equations, revealing mechanisms such as seepage migration and stress redistribution caused by sudden temperature changes after blasting disturbances, significantly improving the model's adaptability to complex working conditions. Especially in scenarios such as weak surrounding rock and hydrologically sensitive areas, multiphysics linkage can effectively assess the long-range cumulative effects and indirect damage modes of disturbances, enhancing the model's risk prediction effectiveness.

[0047] 3. To address the differences in disturbance characteristics caused by different tunnel construction methods (such as open-cut, cut-and-cover, blasting, and shield tunneling), this invention establishes a method-customized mechanism. It pre-sets corresponding disturbance source parameters, boundary conditions, and constitutive adjustment coefficients, which are automatically invoked as needed during the simulation. This mechanism significantly improves the model's portability and versatility, avoiding the problem of "one model being difficult to adapt to multiple scenarios." By matching the design procedures, the simulation results can realistically reflect the specific impact of construction technology on the surrounding rock disturbance response, which is helpful for the method selection and demonstration before construction and for risk identification and strategy optimization during construction.

[0048] 4. By discretizing the tunnel construction process into multiple time steps, a time-series disturbance evolution response mechanism is established by simulating the activation of disturbance sources, the action of support components, and the evolution of material properties using finite increment simulation. Combined with the actual activation sequence during construction, material parameters and boundary conditions are dynamically updated to achieve full-process simulation from initial disturbance release to the establishment of the support system. Viscoelastic and creep models are specifically introduced to ensure that the material's aging behavior evolves synchronously with the disturbance history. This technology effectively captures the cumulative effect of disturbances and the risk of support lag, supporting the optimization of construction schemes and phased support design, and improving the scientific nature and predictability of construction control.

[0049] 5. By establishing a linkage and coupling model between support components and surrounding rock, and combining time-step parameter updates, stiffness degradation criteria, and failure trend identification algorithms, the system outputs response indicators such as stress deformation and energy evolution for various support components. This data is then linked in real-time with monitoring data, forming a dynamic closed-loop system of disturbance-response-feedback. Simultaneously, the risk assessment module integrates neural networks and fuzzy logic methods to automatically assess the disturbance impact level and generate support strategy recommendations. This system bridges the gap between disturbance modeling and intelligent decision-making, enhancing proactive risk control capabilities during tunnel construction and providing operable and visualized real-time feedback methods for on-site operations. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the overall process of a tunnel disturbance response modeling method provided in Embodiment 1 of the present invention;

[0051] Figure 2 This is a schematic diagram of the impact of the disturbance source provided in Embodiment 1 of the present invention;

[0052] Figure 3 This is a schematic diagram of the interference propagation model and tensor analysis provided in Embodiment 1 of the present invention;

[0053] Figure 4 This is a flowchart of the multiphysics coupling mechanism provided in Embodiment 1 of the present invention;

[0054] Figure 5 This is a schematic diagram of the underground tunnel disturbance response simulation provided in Embodiment 1 of the present invention;

[0055] Figure 6 This is a flowchart of the finite incremental-temporal evolution simulation method provided in Embodiment 2 of the present invention.

[0056] Figure 7 This is a schematic diagram of the timing control of construction disturbance and support activation provided in Embodiment 2 of the present invention;

[0057] Figure 8 This is a flowchart of the component failure trend identification and early warning method of the linkage response simulation method for support structures provided in Embodiment 3 of the present invention;

[0058] Figure 9 This is a schematic diagram of the overall structure of a risk level prediction and feedback system provided in Embodiment 4 of the present invention.

[0059] Figure 10 This is a flowchart of the risk assessment and feedback closed loop provided in Embodiment 4 of the present invention;

[0060] Figure 11 This is a schematic diagram of the integrated tunnel construction auxiliary decision-making system, which combines software and hardware, provided in Embodiment 5 of the present invention.

[0061] Figure 12 This is a flowchart illustrating the closed-loop operation of system software and hardware collaboration provided in Embodiment 5 of the present invention. Detailed Implementation

[0062] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0063] 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 this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0064] To avoid ambiguity in terminology, this application defines the key terms involved as follows:

[0065] 1. (1) Perturbation tensor ( ): Defined as a second-order symmetric positive definite tensor, used to characterize the intensity of the perturbation source's effect in each direction in three-dimensional space. Its diagonal elements represent the intensity of the principal direction, and its off-diagonal elements represent the directional coupling effect. Mathematical form:

[0066] ;

[0067] (2) Perturbation propagation tensor ( : is defined as a second-order symmetric positive definite tensor used to characterize the propagation ability of a disturbance in different directions. Its eigenvalues ​​correspond to the propagation radius, and its eigenvectors correspond to the main propagation direction.

[0068] (3) Disturbance propagation function: its mathematical form is ,

[0069] in: This is used to describe the decay of disturbances over time and space. , This is the time-series weighting function.

[0070] (4) Construction method customization mechanism: refers to the mechanism through functions The parameter sets of different construction methods (open excavation, blasting, NATM, shield tunneling) are automatically mapped to the disturbance propagation function and multiphysics coupling equations to achieve model adaptation.

[0071] Example 1: A tunnel disturbance response modeling method. In a preferred embodiment of the present invention, a disturbance response modeling method is provided that is applicable to multi-field coupling analysis and construction process disturbance prediction in tunnel engineering. It is particularly suitable for response modeling and evolution analysis of tunnel surrounding rock structures under construction disturbance under complex geological environments such as weak surrounding rock, deep burial, fractured zone, strong unloading area or aquifer.

[0072] In this embodiment, the disturbance response modeling method includes the following steps:

[0073] S101 Disturbance source modeling: collect information on disturbance factors introduced during tunnel construction or in the external environment, and establish disturbance source models to characterize the spatial location, intensity, and evolution characteristics of disturbance sources.

[0074] ;

[0075] in, This represents the intensity field of the disturbance source. For the disturbance amplitude, Let be the perturbation function. For the parameter set.

[0076] Disturbance types: excavation, blasting, dewatering, support lag, etc., and each type of disturbance can be selected by function (exponential, sine, impulse).

[0077] S102 constructs a three-dimensional disturbance propagation function, establishes a functional relationship for the propagation of disturbance from the disturbance source to the tunnel surrounding rock medium in three-dimensional space based on mechanical theory, and describes the attenuation and propagation law of disturbance at various locations in space over time, and calculates the disturbance response at each location;

[0078] Directional mathematical model:

[0079] ;

[0080] in, For the intensity of disturbance propagation, The attenuation coefficient is... For the direction weight function, This is the time-series evolution function.

[0081] Define boundaries: For transmission distance, The direction angle is denoted by a parameter that can be determined based on geological measurements.

[0082] Construct a three-dimensional perturbation propagation function, wherein the propagation function is composed of a second-order symmetric positive definite perturbation propagation tensor. Control, its mathematical form is ,

[0083] in: It is used to describe the decay and propagation of disturbances in heterogeneous media over time and space.

[0084] S103 couples the three-dimensional disturbance propagation function with the multiphysics model, introduces the disturbance as a source term into the tunnel thermal-hydraulic-mechanical (THM) coupled model, and simulates the coupled response of the temperature field, seepage field and stress field caused by the disturbance by jointly solving the heat conduction, fluid seepage and rock mechanics control equations;

[0085] Multiphysics model coupling is achieved by jointly solving the tunnel thermal-hydraulic-mechanical coupling model, where the perturbation intensity field... The output of the perturbation propagation function is introduced as an additional source term into the governing equations to form a unified set of coupled equations, which are solved simultaneously to simulate the perturbation response under the interaction of various physical fields.

[0086] The S104 construction method customization mechanism adjusts modeling parameters and solution strategies according to the characteristics of different tunnel construction methods. It pre-sets corresponding disturbance source model parameters, boundary conditions, and constitutive relation adjustment coefficients for different construction methods such as open excavation, cut-and-cover excavation, shield tunneling, and blasting. During the modeling and solution process, it calls the corresponding parameter set so that the model results reflect the disturbance characteristics and surrounding rock response under the selected construction method.

[0087] ① The mathematical logic of the construction method customization mechanism

[0088] The construction method customization mechanism refers to the dynamic invocation of disturbance source model parameters, boundary conditions, and constitutive relation adjustment coefficients through function mapping relationships during tunnel construction simulation, based on the characteristics of different construction methods. Its mathematical form is:

[0089] ;

[0090] in:

[0091] The Disturbance Parameter Set (DPS) represents a method-specific parameter set, including the type of disturbance source, disturbance intensity, range of action, boundary conditions, and constitutive adjustment coefficients.

[0092] This indicates the construction method code (e.g., open-cut method = 01, blasting method = 02, NATM = 03, shield tunneling method = 04).

[0093] This indicates the type of disturbance source and the triggering method (such as excavation disturbance, blasting disturbance, support lag disturbance, precipitation disturbance, etc.).

[0094] This represents the set of boundary conditions, including free boundaries, constrained boundaries, and support delay time.

[0095] ② Parameter mapping table

[0096] To enhance operability, this application establishes the following parameter mapping table for common construction methods:

[0097]

[0098] ③ Calling logic

[0099] During the simulation process, the system first identifies the construction method code. In conjunction with the current type of disturbance source With boundary conditions Through function Automatically generate the corresponding parameter set .

[0100] This parameter set is directly used as input to the disturbance propagation function and the multiphysics coupling equation, so that the modeling results can truly reflect the disturbance characteristics and surrounding rock response under different construction conditions.

[0101] More specifically, S101 disturbance source modeling; firstly, based on the tunnel construction process, disturbance sources are identified and classified. These disturbance sources include, but are not limited to:

[0102] Excavation profile disturbance source: Stress release caused by the free surface formed during tunnel excavation;

[0103] Blasting disturbance zone: The short-term, intense dynamic load disturbance generated during construction using blasting methods;

[0104] Delayed support zone: The area of ​​accumulated disturbance caused by prolonged exposure of the surrounding rock and delayed initial support;

[0105] Drainage disturbance zone: The area of ​​disturbance caused by changes in pore water pressure due to precipitation, seepage, etc.

[0106] The aforementioned disturbance sources are abstracted as initial disturbance conditions in the model, and a disturbance intensity field is constructed. As the basis for perturbation propagation modeling.

[0107] In the disturbance source modeling part of the disturbance response modeling method, a standardized modeling process for disturbance sources in tunnel construction is established by using multi-source information fusion and disturbance classification and partitioning to ensure that the input of the subsequent disturbance propagation function has a physical basis and spatial consistency.

[0108] The disturbance source modeling includes the following steps:

[0109] 1) Disturbance source type identification and spatial calibration

[0110] (1) Disturbance type identification: Based on tunnel design drawings, construction process plans, and on-site monitoring data, identify disturbance sources that may cause changes in stress field or structural response during construction. Disturbance sources include, but are not limited to, the following types:

[0111] Excavation disturbance source: The release of the original stress field caused by excavation operations, which is common in the free surface formation area;

[0112] Blasting disturbance source: High-frequency instantaneous dynamic load caused by blast waves during excavation using blasting methods;

[0113] Delayed support disturbance source: When support is not implemented in a timely manner, the disturbance caused by the continuous loosening and instability of the surrounding rock accumulates.

[0114] Sources of drainage disturbance: Dewatering wells, seepage pressure reduction measures, etc., alter the groundwater flow field and indirectly affect the stress of the surrounding rock.

[0115] (2) Spatial location and geometric range calibration: Using the tunnel's 3D BIM model, construction section information, and sensor deployment data, the center coordinates, effective range, and shape boundaries of each type of disturbance source are calibrated. Preferably, for each disturbance source... It is expressed as a perturbation source tuple containing the following properties: ;

[0116] in: Geometric center of the disturbance source Disturbance radius Disturbance type encoding (e.g., excavation / blasting / drainage). : Disturbance duration or cumulative intensity parameter.

[0117] 2) Initial field modeling of disturbance intensity: Each disturbance source is transformed into an initial disturbance field. A local activation subdomain is defined as the region within which the disturbance intensity is assigned in a number of spatial units surrounding the center point of the disturbance source. Preferably, a spatial weighting function is used to assign the disturbance intensity, and this function has the following form: ;

[0118] in, As a source of disturbance The maximum disturbance intensity, For attenuation functions based on Euclidean distance, the following two forms are commonly used:

[0119] Exponential functions: ;

[0120] Gaussian function: ;

[0121] in, , It can be adaptively set according to the stratum type, surrounding rock integrity, disturbance duration, etc.

[0122] 3) Construction of the perturbation source tensor: For perturbation sources with significant anisotropic propagation characteristics (such as blasting perturbations propagating along joint surfaces), a perturbation propagation tensor is further introduced. This tensor is used to describe the inconsistency in the propagation intensity of the disturbance in various spatial directions. The expression for the initial field of the disturbance is modified as follows:

[0123] ;

[0124] in It is a positive definite symmetric matrix, and its principal direction is consistent with the propagation bias of the disturbance source.

[0125] 4) Disturbance source synthesis and model input: Combine all disturbance sources The initial disturbance fields are superimposed to form the initial disturbance field of the entire tunnel section or segment: ;

[0126] The initial perturbation intensity field serves as the initial boundary condition for the three-dimensional perturbation propagation function, and in subsequent steps, it works in conjunction with the thermal-permeability-mechanical multi-field coupling model to drive the temporal evolution simulation process of the surrounding rock response.

[0127] More specifically, the S102 disturbance propagation function was constructed to accurately simulate the diffusion and attenuation process of disturbance behavior in space during tunnel construction. A disturbance propagation function model for three-dimensional heterogeneous media was constructed, which aims to reflect the spatial distribution characteristics of the intensity change of disturbance as it propagates outward from the disturbance source.

[0128] The construction of the perturbation propagation function consists of the following three steps:

[0129] 1) Determination of the disturbance attenuation law: Disturbance behavior usually exhibits a significant spatial decreasing characteristic, that is, the disturbance intensity is greatest at the center of the disturbance source and decreases rapidly with increasing spatial distance. Based on a large amount of measured data from tunnel engineering and theoretical analysis, it was found that this attenuation process can approximately satisfy an exponential or Gaussian decreasing law.

[0130] Preferably, this invention employs a radial attenuation model centered on the disturbance source, that is, using the geometric distance of the disturbance propagation path as the main control variable to construct a mathematical expression for the decreasing disturbance intensity. One preferred expression is as follows:

[0131] ;

[0132] in: For spatial points The intensity of the disturbance at that location; The initial intensity at the center of the disturbance source; This is the disturbance attenuation coefficient; This is the distance vector for the perturbation propagation.

[0133] This model boasts advantages such as computational stability and clear physical meaning, making it suitable for various geological conditions and construction methods. In practical applications, users can flexibly adjust the disturbance propagation behavior under different scenarios by setting parameters such as initial disturbance intensity, attenuation coefficient, and propagation radius. Parameter settings can be differentiated based on engineering geological characteristics such as stratum type (e.g., sandstone, mudstone), structural integrity (e.g., joint density, soft-hard interlocking), and surrounding rock classification (e.g., Class III, Class IV).

[0134] 2) Modeling of propagation directionality: For some heterogeneous or structurally controlled surrounding rock formations, disturbance propagation exhibits a clear directionality. For example, in layered rock masses or surrounding rocks with well-developed joints and fissures, disturbances propagate faster and attenuate more slowly along the direction of structural planes, while propagation in the vertical direction is restricted, exhibiting anisotropic diffusion characteristics.

[0135] To accurately characterize this directionality, this invention introduces a disturbance propagation direction control mechanism. Specifically, by setting a direction weighting factor, the propagation radius of the disturbance in different directions is adjusted, transforming the disturbance isosurface from a sphere to an ellipsoid or offset surface. For example, the disturbance influence radius along the main diffusion direction... and vertical direction The proportional relationship can satisfy: ;

[0136] This directional control mechanism is particularly suitable for the following scenarios: fractured zones with faults or joints; multi-layered soft and hard strata or dip structures; and tunnel sections with clear advance drainage or unloading guidance paths.

[0137] By modeling directional disturbances, we can not only improve the accuracy of disturbance field simulation, but also provide a more accurate prediction basis for subsequent support design.

[0138] 3) Further introduction of the perturbation propagation tensor mechanism: To fully express the ability of perturbations to propagate in any direction in three-dimensional space, this invention preferably introduces the "perturbation propagation tensor" mechanism. This tensor is a third-order symmetric positive definite matrix, which essentially represents the diffusion ability coefficients of perturbations in the principal and secondary directions, and is applicable to highly anisotropic geological environments.

[0139] Introducing the perturbation propagation tensor Then, the disturbance intensity function can be extended to:

[0140] ;

[0141] in: It is a position vector; The diffusion rate control tensor of the disturbance in each spatial direction is represented by its eigenvalues, which determine the main diffusion direction and amplitude. If the main direction of the tensor is consistent with the geological structure surface or the direction of the maximum principal stress, the "disturbance-tectonic" co-propagation model can be realized.

[0142] Through tensor mechanisms, this invention can simulate the following complex perturbation propagation scenarios: ① rapid diffusion along oblique joint surfaces; ② perturbation restricted perpendicular to structural surfaces; ③ aberrant development of perturbation under complex principal stress rotation structures within geological bodies.

[0143] This mechanism is applicable to typical complex working conditions such as blasting disturbance, strong local unloading, and heterogeneous surrounding rock structures, and has good model versatility and system portability.

[0144] 4) Output format and interface design of the disturbance propagation function: The final output of the disturbance propagation function is a disturbance intensity distribution field covering the three-dimensional region of the target tunnel segment. Its output format supports: ① mesh volume distribution (Voxel format); ② continuous function expression (for post-processor call); ③ visualization of three-dimensional isosurface or isointensity line plot.

[0145] In addition, the disturbance propagation results have an open interface standard, which can be seamlessly connected to the following functional modules: ① Surrounding rock plastic deformation analysis module; ② Thermal-permeability-force (THM) coupling calculation module; ③ Support structure dynamic response module; ④ Risk level evolution assessment and early warning platform.

[0146] Through the construction and parameterization mechanism of this disturbance propagation function, this invention not only realizes the ability to spatially model disturbance behavior under complex geological backgrounds, but also provides basic support for response analysis, support optimization design and engineering safety evaluation throughout the tunnel construction process.

[0147] More specifically, the S103 disturbance field and multi-physics coupling mechanism constructs a disturbance response field that not only exists as an independent result of the stress redistribution analysis of the tunnel surrounding rock, but can also be used as an input to be coupled with the multi-physics simulation module to form a thermal-permeability-mechanical integrated response prediction model, which is suitable for tunnel engineering scenarios with high ground temperature, complex groundwater activity, or weak and fractured surrounding rock.

[0148] The preferred multiphysics model is the thermo-hydro-mechanical (THM) coupling model, whose core modeling aspect lies in identifying the driving and feedback mechanisms of perturbations on different physical fields, including but not limited to the following three aspects:

[0149] (1) Coupling with the temperature field; In tunnel construction in some high geothermal areas or near heat sources, disturbance behavior will significantly affect the thermal conductivity and heat conduction path of the rock mass. On the one hand, disturbance will change the integrity of the internal structure of the rock mass, resulting in changes in thermal conductivity; on the other hand, local disturbance may lead to the redistribution of the temperature gradient, thereby inducing thermal expansion and contraction effects.

[0150] This invention simulates the cumulative effect of disturbance response in the temperature field by introducing it as an additional energy transfer source term into the heat conduction model. This coupling mechanism is of great significance for predicting the performance degradation of temperature-sensitive materials (such as shotcrete and anchoring agents) under disturbance conditions.

[0151] (2) Coupling with the seepage field; the disturbance also affects the pore structure in the soil and rock medium, which in turn causes significant changes in pore water pressure, seepage path and water content distribution. Especially in the case of aquifers that are connected or fault fracture zones, the activation of the disturbance source will lead to seepage reconstruction, which is manifested as a sharp increase in local seepage flow and intensified seepage pressure fluctuations.

[0152] This method treats the disturbance field as a dynamic boundary or source variable in the seepage model, and corrects parameters such as permeability coefficient and porosity by the disturbance intensity, thereby accurately simulating the groundwater dynamic process induced by the disturbance. This has important reference value for predicting tunnel seepage and providing early warning of water hazard risks.

[0153] (3) Coupling with stress-strain field; at the structural mechanics level, disturbance behavior directly causes the redistribution of stress field disturbance, which in turn stimulates the development of local plastic zones, shear slip zones or tensile cracks. Especially in areas with weak surrounding rock or obvious structural plane control, disturbance often leads to the migration of stress concentration location and the change of principal stress direction, inducing nonlinear deformation or even instability and failure.

[0154] This invention effectively expresses the driving effect of disturbance on deformation response by embedding the disturbance intensity field as an additional body force term or constraint loading term into the mechanical equilibrium equation. If the surrounding rock material exhibits obvious plastic or nonlinear characteristics, this invention further introduces a constitutive relation evolution mechanism coupled with the disturbance, that is, adjusting the strength parameters in the rock mass yield criterion, such as shear strength, friction angle, and cohesion, through the disturbance intensity.

[0155] Taking dilatation-shear behavior as an example, under the action of a perturbation field, the originally stable surrounding rock will be induced to undergo shear slip or local expansion, manifested as phenomena such as yield surface movement and plastic zone expansion. Through a perturbation-driven constitutive relation update mechanism, this nonlinear mechanical response process can be dynamically simulated, exhibiting high real-world adaptability and predictive accuracy.

[0156] (4) Comprehensive integration of coupling mechanisms; The disturbance field coupling mechanism described in this invention supports unidirectional driving coupling or bidirectional feedback coupling with the above-mentioned multiple physical field models. In the unidirectional driving coupling mode, the output of the disturbance response model is used as the input variable of the temperature, seepage or stress model; in the bidirectional coupling mode, the variables between the physical fields interact to form a comprehensive control system that solves the problem simultaneously, reflecting a more realistic evolution path of the tunnel surrounding rock.

[0157] Furthermore, this invention allows for the setting of coupling weights and interaction frequencies between different physical fields according to engineering needs, thereby achieving a balance between modeling accuracy and computational efficiency. This module possesses good versatility and can be adapted to existing commercial software platforms or self-developed numerical simulation systems.

[0158] More specifically, the S104 construction method customization mechanism, in order to improve the applicability and accuracy of disturbance response modeling methods under different construction process conditions, sets up a disturbance model customization mechanism based on construction method classification. That is, by establishing a mapping relationship between the construction method and the modeling parameter set of disturbance source, the disturbance propagation, boundary setting and constitutive response mechanism are adjusted in a construction method-specific manner, thereby ensuring that the modeling results truly reflect the disturbance behavior and surrounding rock response characteristics in the actual construction process.

[0159] The customized construction method mechanism includes the following four sub-modules:

[0160] 1) Classification and Standard Coding of Construction Methods; Preferably, this invention standardizes and codes common tunnel construction methods for system invocation and model recognition. The classification includes, but is not limited to:

[0161]

[0162] Construction method information can be manually entered by the user during the model initialization stage, or it can be automatically obtained by the BIM system through the recognition of engineering design schemes.

[0163] 2) Disturbance source model parameter set configuration; For each construction method, a set of disturbance source model parameter sets (DPS) is preset, including but not limited to the following:

[0164] (1) Types of disturbance sources and activation methods: ① Open-cut method: unidirectional disturbance source, representing the impact of top-down layered excavation; ② Blasting method: periodic strong impact disturbance source, requiring superposition of dynamic load amplitude and frequency factor; ③ NATM method: continuous disturbance source, support delay time Key parameters; ④ TBM method: The disturbance source has a ring-shaped propagation mode, and the disturbance center moves with time.

[0165] (2) The disturbance range (radius of influence) is set according to the excavation size, construction rhythm and construction equipment characteristics. For example: ① The disturbance range of open excavation is usually 1.5 times the excavation width; ② The disturbance range of blasting is calculated based on the amount of explosive and the rock strength.

[0166] (3) Disturbance attenuation factor and propagation function type: ① NATM is suitable for Gaussian function; ② Explosion disturbance is preferred for exponential fast decay model; ③ TBM disturbance is suitable for ellipsoidal tensor control model.

[0167] 3) Boundary conditions and structural feedback mechanism settings: Different construction methods have fundamentally different ways of setting model boundary conditions, and the following parameters need to be customized:

[0168] Types of free and constrained boundaries: In open-cut tunneling, the surface is a free boundary, while in shield tunneling, the outer edge of the tunnel segment is usually the constrained boundary;

[0169] Support delay time: Support delay time in cut-and-cover method It is a key control factor that has a decisive influence on the evolution of perturbations. Its influence can be expressed by adjusting the perturbation relaxation function: ;

[0170] Simulation step size for time-series construction: For blasting methods, the model needs to be set with a smaller time step to capture the dynamic response of impact disturbances; while for shield tunneling methods, the simulation progress is controlled by the number of advance rings.

[0171] 4) Constitutive relation adjustment mechanism: Considering the differences in surrounding rock loading methods, disturbance triggering mechanisms, and structural feedback paths under different construction methods, this invention sets up a constitutive parameter adjustment mechanism based on construction method driving, specifically including:

[0172] Adjustments to shear dilatation coefficient and elastic modulus are necessary, such as when the surrounding rock is in a long-term creep state during shield tunneling, requiring the introduction of a time-dependent softening function.

[0173] The failure criterion switching mechanism applies to the dilatation-shear yield function (such as the Drucker-Prager model) in the NATM scenario, while the blasting method applies to the strain rate-dependent damage model.

[0174] Support structure-surrounding rock coupling stiffness correction: Due to the difference in support stiffness between open-cut and cut-and-cover methods, coupling stiffness parameters need to be embedded in the constitutive model. To simulate the ability of support to limit disturbance propagation under different construction methods.

[0175] 5) Model Invocation Strategy and Dynamic Adaptation: During the modeling and solving process, this invention utilizes a dynamic parameter retrieval mechanism to automatically invoke the corresponding parameter set and boundary condition configuration based on the currently set construction method code, achieving an integrated modeling process encompassing "construction method—disturbance—structural response." This strategy ensures the following objectives:

[0176] Modeling flexibility: Supports automatic switching of modeling rules for mixed sections with multiple construction methods (such as shield tunneling-cut-and-cover transition sections);

[0177] Results Reliability: Improve the accuracy of disturbance field simulation and enhance the consistency between structural response and actual field conditions;

[0178] Computational efficiency: Avoids repeated solutions and manual parameter adjustments, improving the level of automation in modeling.

[0179] Through the above-mentioned construction method customization mechanism, this invention realizes the scalability, adaptability and practicality of the disturbance response modeling method in complex engineering scenarios, and has good application prospects. It is particularly suitable for tunnel engineering projects with sudden changes in geological conditions or frequent changes in construction methods.

[0180] In summary, this invention achieves accurate expression and engineering adaptability modeling of tunnel disturbances in complex geological environments through disturbance source modeling, disturbance propagation function construction, multi-physics coupling modeling, and construction parameterized adjustment, and has broad engineering application value.

[0181] Example 2: Finite Increment-Time Series Evolution Simulation Method

[0182] This embodiment provides a finite incremental-time evolution simulation method, which is suitable for simulating the coupled response evolution of surrounding rock and support structure during tunnel construction. It has functions such as construction time sequence mapping, support component activation, time-varying effect simulation, and full-process information output.

[0183] The finite incremental time-series evolution simulation method includes the following steps:

[0184] S201 Time Increment Division: The entire tunnel construction process is divided into several discrete construction time steps. These time steps are time increment units that are either equally spaced or dynamically set according to the construction sequence. Each time step corresponds to the advancement of a set of construction activities, including face excavation, support installation, grouting reinforcement, or changes in boundary conditions. This division provides a discrete foundation for subsequent simulation of surrounding rock conditions and support response.

[0185] S202 Construction Disturbance and Boundary Condition Update; Within each construction time step, determine the area to be excavated based on the current construction progress, and perform the following operations:

[0186] Activate or delete structural units within the excavation area to simulate the formation of unstable boundaries in the surrounding rock;

[0187] Apply a perturbation function or release the stress boundary at the newly formed free surface to characterize the local adjustment of the initial geostress field by the excavation disturbance;

[0188] If there is blasting or mechanical disturbance, dynamic load disturbance parameters can be introduced to correct the disturbance intensity.

[0189] S203 support structure activation modeling; based on construction sequence information, gradually activate support components such as shotcrete, anchor bolts, and steel arch frames, specifically including:

[0190] Shotcrete is activated as a shell element or solid element at a set time step and given an initial modulus, which then evolves over time.

[0191] The anchor bolt components are introduced into the model at the installation time step to construct their member-surrounding rock coupling behavior;

[0192] The steel structural components are modeled as linear elastic beam elements and activated at a set time step to form a closed support loop with adjacent support elements.

[0193] The activation time, activation method, and initial mechanical parameters of each support component are automatically matched with the design information and recorded in the support sequence control module.

[0194] S204 Time-Related Effect Simulation: Considering the physical behavior of the material over time after construction, the following mechanical model is established using time-varying parameters introduced in the constitutive relation:

[0195] For creep behavior, a power-law creep model or a Kelvin-Voigt viscoelastic model is used to express the characteristics of strain growth over time.

[0196] For relaxation behavior, an exponentially decaying relaxation model is adopted to express the law of stress decay with time under constant strain conditions;

[0197] An early-age modulus growth function is introduced for shotcrete materials to realize the time evolution process of support stiffness.

[0198] Within each time step, the system updates the material stiffness, stress, and deformation response based on the current time, thereby achieving continuous time-dependent simulation of the support-surrounding rock system.

[0199] S205 Simulation Output and Result Visualization; After completing the simulation at each time step, the system outputs the following:

[0200] Stress and strain distribution diagrams of surrounding rock and support components at each time step;

[0201] Deformation cumulative curves at typical measuring points;

[0202] The stress history and instability index of support components;

[0203] Material parameter sensitivity and risk evolution trend.

[0204] This method enables high-fidelity simulation of the entire process of construction disturbance response, and is particularly suitable for time-series coupling analysis scenarios with support lag, weak surrounding rock, or construction intervals.

[0205] Example 3: Simulation Method for Linked Response of Support Structure; This embodiment of the invention provides a simulation method for the linked response of support structure, applicable to the mechanical interaction analysis between the support system and the surrounding rock during tunnel construction. This method, based on a multi-component linkage modeling mechanism, a dynamic evolution model of support parameters, and component failure trend identification technology, constructs a digital simulation method capable of reflecting the entire process response of the support system. Its technical solution includes the following steps:

[0206] S301 Support Structure-Surrounding Rock Coupled Modeling: The support system is modeled in units according to different component types, including shotcrete, anchor bolts, steel frames and connecting components.

[0207] Specifically: ① The shotcrete layer is modeled using solid or shell elements, with initial elastic modulus, thickness, and curing growth characteristics set; ② The anchor system is divided into free and anchored sections, and the element response behavior is established based on the "elastic-plastic-damage" constitutive model; ③ The steel frame or steel grid is represented by beam elements, and its node connection stiffness and allowable rotation yield are set as controllable parameters; ④ The interface between the support components and the surrounding rock is modeled using contact elements, with parameters such as friction coefficient, interface bond strength, and normal stiffness set to form a support-surrounding rock linkage system.

[0208] The dynamic updating mechanism of the performance parameters of S302 components is implemented by using a time-stepping method for simulation. Within each construction time step, the material parameters and mechanical response state of the support structure are dynamically updated.

[0209] Among them: the elastic modulus of sprayed concrete Update using the following function:

[0210] ;

[0211] in These are the initial and final elastic moduli, respectively. For material maintenance growth factors, For time steps.

[0212] The evolution of anchor bolt anchoring force with relative displacement of surrounding rock is expressed as:

[0213] ;

[0214] in The initial anchoring force, This represents the relative displacement between the two ends of the anchor bolt. For component damage variables, These are the efficiency enhancement coefficient and the degradation coefficient, respectively.

[0215] The stiffness degradation of a component is calculated based on cumulative strain, and the expression is:

[0216] ;

[0217] in For initial stiffness, To accumulate plastic strain, This is the stiffness attenuation sensitivity coefficient.

[0218] The failure trend identification mechanism of S303 key components, in each time step, the system judges the failure trend of the support components based on the following three types of indicators: (1) Load ratio criterion: if the component is actually subjected to force Its ultimate bearing capacity (1) If the ratio exceeds 0.85, it is judged as an overload trend; (2) Plastic zone expansion criterion: When the length of the yield zone of the component exceeds 40% of the total length of the component, it is judged as a possible overall failure; (3) Stiffness degradation criterion: If the current stiffness is reduced by more than 50% compared with the initial stiffness, it is judged as a serious performance degradation.

[0219] The above criteria can be used in combination. When any two or more indicators are met at the same time, the system will automatically mark the component as entering a "high-risk warning state".

[0220] The S304 response result visualization and feedback mechanism includes: ① The simulation system provides three types of visualization outputs to assist in engineering judgment: ② Stress-strain distribution map: displays the stress and strain contour lines inside the component; ③ Displacement path animation: shows the deformation behavior of the support system in the time evolution; ④ Strain energy density map: used to analyze high energy consumption areas and potential failure paths.

[0221] In addition, the system supports the import of external monitoring data (such as anchor bolt force gauges, fiber optic strain gauges, convergence meters, etc.), which can be used to invert model parameters, realize the monitoring-simulation coupled update mechanism, and build a "simulation-monitoring-feedback closed-loop system".

[0222] In summary, the linkage response simulation method for support structures provided by this invention has the following beneficial effects:

[0223] (1) Support the linkage response modeling of multiple types of support components, considering the interaction and time-varying characteristics between components;

[0224] (2) Adopt a dynamic parameter evolution mechanism to improve the accuracy and engineering adaptability of simulation results;

[0225] (3) It can identify potential failure trends of key components and has a structural health early warning function;

[0226] (4) Provide full-process response visualization support to facilitate engineering technicians to carry out construction adjustments and structural evaluations.

[0227] This method can be widely applied to safety assessment scenarios of geotechnical structures such as tunnel construction, underground engineering, and slope protection, and has good engineering practicality and promotion value.

[0228] Example 4: A Risk Level Prediction and Feedback System; This invention provides a risk level prediction and feedback system suitable for dynamic assessment of surrounding rock stability and optimization of support response during tunnel construction. The system integrates multi-source sensor data acquisition, edge computing, and intelligent model evaluation, combined with a pre-set support strategy database, forming a feedback loop with "risk assessment—strategy push—evolution tracking" as its core logic. Through hardware and software collaboration, it achieves fully automated and intelligent processing from on-site data acquisition and risk level judgment to support recommendation output.

[0229] The overall structure of this system can be divided into three layers: the front-end perception layer, the middle communication layer, and the back-end decision-making layer. The functional structure of each layer is as follows:

[0230] 1) Front-end sensing layer; The front-end sensing layer is deployed at key locations on the tunnel site and consists of various engineering monitoring sensors used to acquire multi-source data such as surrounding rock deformation, water pressure stress, and environmental indicators. Specifically, it includes:

[0231] Displacement monitoring unit: Using laser displacement gauges, total station automatic targets, or vibrating wire displacement gauges, it is installed at the tunnel arch, arch waist, and sidewalls to collect deformation data in real time.

[0232] Stress monitoring unit: Multi-point rock mass stress gauges or steel wire strain gauges are embedded in the surface of the surrounding rock borehole and support structure to monitor the initial stress field of the surrounding rock and the stress response of the support.

[0233] Seepage pressure monitoring unit: Pore water pressure sensors or piezometers are deployed in areas such as aquifers and faults to collect stress changes caused by hydraulic disturbances and seepage in real time.

[0234] Environmental parameter acquisition unit: Equipped with a thermometer, hygrometer, gas concentration sensor and geothermal probe to assist in assessing the impact of the surrounding rock's thermal and humid environment on stability.

[0235] The data collected by the aforementioned monitoring equipment is centrally accessed through local acquisition terminals and transmitted to edge processing nodes via standard communication interfaces (such as RS485 and Modbus RTU).

[0236] 2) Intermediate Communication Layer; The intermediate communication layer realizes data conversion, transmission, and relay processing between sensing devices and backend systems, mainly including:

[0237] Edge computing gateway: Deploys industrial-grade embedded computing units (such as ECU-1050), integrating communication protocol conversion, data filtering, and preliminary preprocessing functions. Supports local anomaly identification and processing based on moving average or Z-score methods.

[0238] Wireless communication system: Depending on the tunnel environment, LoRa self-organizing network, 4G / 5G public network, or industrial WiFi can be selected to achieve stable data transmission. Communication frequency and network redundancy strategy can be configured according to project needs.

[0239] Power supply module: It adopts a combination of solar panels and lithium batteries or a UPS backup power system to ensure continuous operation in areas without mains power, and supports low-power wake-up and timed sleep mechanisms.

[0240] 3) Back-end decision-making layer; The back-end decision-making layer is responsible for risk level assessment, support recommendation generation, and risk trend visualization, mainly including:

[0241] 3.1) The data preprocessing module performs the following processing on the received raw monitoring data: ① outlier identification and removal (using IQR or Z-score criteria); ② missing value completion (linear interpolation or moving average); ③ normalization standardization (such as Z-standardization); ④ construction of sliding time window feature sequences for neural network input construction.

[0242] 3.2) Risk Assessment Module; Users can choose from the following two models to determine the risk level:

[0243] (1) Fuzzy comprehensive evaluation model: Based on the monitoring indicators, set the fuzzy membership function, construct the fuzzy evaluation matrix and weight vector, obtain the membership degree result through fuzzy comprehensive operation, and output the risk level from level I to level IV according to the maximum membership principle.

[0244] (2) Backpropagation (BP) neural network model: A multi-layer feedforward neural network is constructed, which takes the current monitoring data as input and outputs the probability distribution of risk levels. The level corresponding to the highest probability is selected as the evaluation result. The network can be trained offline based on historical labeled samples and deployed on the cloud or local server for online inference.

[0245] 3.3) Support suggestion generation module; Based on the risk assessment results, the system automatically matches the support suggestion library and outputs customized parameter adjustment schemes. The suggestions include, but are not limited to: ① adjusting the thickness of shotcrete; ② adding anchor bolts or optimizing the spacing; ③ increasing the rigidity of the steel frame support; ④ initiating emergency closure measures and suspending excavation.

[0246] The recommended output should be in a structured format (such as JSON or XML) and should support integration with the construction scheduling platform to achieve automatic work assignment and execution record closure for support adjustment.

[0247] 3.4) Risk Evolution Visualization Module: This module plots the risk level results corresponding to each time step as a curve graph, and supports: superimposing risk level trends at multiple measurement points; automatic annotation of risk level abrupt changes; on-map marking of support adjustment measures; and construction procedure superimposition function, which facilitates the judgment of the response relationship between construction disturbance and risk.

[0248] The chart supports SVG / PNG export or real-time display on the monitoring platform via a web interface.

[0249] 4) System operation process; the system executes automatically according to the following process:

[0250] (1) The front-end perception layer collects data and transmits it to the edge nodes;

[0251] (2) Edge nodes perform preliminary data processing and cache forwarding;

[0252] (3) The backend system performs data consolidation and risk model reasoning;

[0253] (4) Output the risk level and call the suggestion module to generate adjustment strategies;

[0254] (5) The visualization module updates trend charts in real time and supports display on multiple terminals;

[0255] (6) The system can be set up with a push mechanism to notify the on-duty personnel of Level III and above risks through SMS, APP notification, LED warning screen and other means, so as to achieve rapid response.

[0256] 5) Application Example: During the construction of a high-stress soft rock tunnel, the system was deployed at the tunnel entrance to collect data on surrounding rock displacement, initial support stress, and seepage pressure. After entering the fractured zone, the crown settlement rate increased significantly, and the system determined it to be a Level III risk level, automatically recommending support measures such as "increasing the shotcrete thickness to 25cm and increasing the anchor bolt spacing to 0.8m." The construction unit completed the adjustments within 6 hours, and subsequent monitoring data showed that the displacement rate decreased, the risk level dropped to Level II, and construction safety was effectively guaranteed.

[0257] Example 5: An integrated hardware and software-based tunnel construction auxiliary decision-making system

[0258] This embodiment discloses an integrated hardware and software-based tunnel construction auxiliary decision-making system, applicable to information collection, disturbance modeling, dynamic evolution simulation, structural response analysis, and risk level feedback and suggestions throughout the entire tunnel construction process under complex geological conditions. The system integrates a physical perception layer, an edge computing layer, a model analysis layer, a human-computer interaction layer, and a data management layer, forming a closed-loop system of "data-driven - model simulation - feedback decision-making - interactive optimization".

[0259] 1) System hardware structure and deployment scheme. The hardware part of the system mainly includes the following components:

[0260] (1) Monitoring terminal subsystem (deployed at the construction site): multi-channel displacement sensor, stress gauge, pore pressure gauge, piezometer; blasting vibration monitor; industrial camera or laser scanner (for contour acquisition); all sensors are connected to the data gateway (Modbus or RS485) through an industrial data acquisition unit.

[0261] (2) Edge computing unit (deployed on-site or at the construction site): industrial-grade computing workstation or embedded host; configured with data acquisition card and GPU module (for neural network inference); integrates preliminary data filtering, missing data compensation, real-time disturbance parameter generation and data caching functions; ARM industrial computer, ≥4 cores 2.0GHz, memory ≥8GB, Linux system

[0262] (3) Central simulation and decision server (deployed in the design institute or control center): a high-performance simulation server that runs a finite element / explicit dynamics platform; equipped with simulation calculation modules, including disturbance response models, time-series evolution solvers, and support component coupling modules; receives disturbance parameters and monitoring data uploaded from the edge, and completes simulation task scheduling and distributed computing management; GPU parallel computing nodes (such as RTX A4000) with storage ≥2TB.

[0263] (4) Human-computer interaction terminal (such as engineer's laptop or operation console): graphical interface system, which allows users to set construction method parameters, time step, support structure type, etc.; supports visual browsing of simulation results and risk level; provides parameter input window, suggested output report module, task backtracking and comparison analysis function.

[0264] (5) Backend database and cloud platform: SQL database or NoSQL document library, used to store: historical project data; support template library and risk level comparison library; intermediate files and final reports for each simulation run;

[0265] It supports remote access, hierarchical access control, data backup, and encrypted export.

[0266] (6) Communication protocol: MQTT or Modbus RTU, data format JSON / CSV, including timestamp, sensor ID, and physical quantity value.

[0267] (7) Visualization interface: Based on WebSocket, it supports the display of 3D BIM models in IFC format.

[0268] 2) Software and hardware collaborative operation process

[0269] (1) Data acquisition and transmission stage: The monitoring terminal uploads the raw data of the construction site to the edge computing unit; the edge module generates disturbance increment parameters according to the set time step, including disturbance source identification, disturbance intensity calculation and propagation function fitting.

[0270] (2) Model simulation and evaluation stage: After receiving the data, the central server starts the simulation model according to the construction sequence; simulates the activation of excavation contour disturbance, support structure loading, surrounding rock response and structural failure risk; the system automatically compares the predicted data with the actual monitoring data, calls the neural network to correct the deviation; outputs the current risk level and generates support parameter optimization suggestions.

[0271] (3) Results interaction and feedback stage: The human-computer interaction terminal displays simulation images, curves and suggestion reports in real time; users can modify parameters and restart the simulation; all running data is automatically stored in the database and can be retrieved and analyzed later.

[0272] 3) Key integrated functions and technical points, hardware-model decoupling integration: distributed deployment of sensor acquisition, disturbance modeling, and structural response analysis to improve system flexibility;

[0273] Distributed computing architecture: Supports a collaborative architecture that combines on-site preliminary processing with in-depth central computing;

[0274] Three-dimensional disturbance parameter digital acquisition mechanism: sensor data automatically generates disturbance field tensor or spatial function;

[0275] Model-data dynamic coupling mechanism: model parameters are corrected through error inversion and neural network;

[0276] Feedback closed-loop mechanism: The output results correct the support design parameters in reverse, supporting real-time adjustments during construction.

[0277] 4) Technical effects: Compared with pure software simulation tools, the integrated system combined with hardware has the following advantages: (1) It can realize the automatic acquisition of on-site disturbances and reduce manual intervention; (2) It supports multi-dimensional real-time data to participate in simulation and improve the credibility of the model; (3) It is conducive to dynamic adjustment of construction and improves the safety and economy of the project; (4) It supports independent operation of modules or overall linkage and adapts to multiple project scenarios.

[0278] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of modeling response of a tunnel to perturbation, the method comprising: The method comprises the following steps: Disturbance source modeling, collecting information of disturbance factors introduced in the tunnel construction process or external environment, establishing a disturbance source model to represent the spatial position, intensity and evolution characteristics of the disturbance source; Constructing a three-dimensional disturbance propagation function, which is controlled by a second-order symmetric positive definite disturbance propagation tensor K_d, and its mathematical form is: ； Wherein: ； for describing the attenuation and propagation of disturbance in heterogeneous medium with time and space; The multi-physical field model coupling is realized by jointly solving a tunnel thermal-water-force coupling model, wherein a disturbance intensity field is output by a disturbance propagation function, introduced as an additional source term into a control equation set, forms a unified coupling equation set and simultaneously solved to simulate the disturbance response under the interaction of each physical field; Method customization mechanism, according to the characteristics of different tunnel construction methods, adjust the modeling parameters and solving strategy, set the corresponding disturbance source model parameters, boundary conditions and constitutive relationship adjustment coefficient for different construction methods, and call the corresponding parameter set in the modeling and solving process, so that the model result reflects the disturbance characteristics and surrounding rock response under the selected construction method.

2. The method of claim 1, wherein: The modeling of the disturbance source includes constructing a second-order symmetric positive definite disturbance tensor The disturbance source is characterized in a tensor form in the directions of three-dimensional space, wherein diagonal elements represent main direction disturbance intensity, and non-diagonal elements represent direction coupling effect. The disturbance tensor is used as an input parameter of a disturbance propagation function.

3. The method of claim 1, wherein: The multi-physical field model coupling is realized by jointly solving a tunnel thermal-water-force coupling model, wherein a disturbance intensity field The disturbance propagation function is output as an additional source term introduced into the control equation set, a unified coupling equation set is formed and simultaneously solved to simulate the disturbance response under the interaction of each physical field.

4. The method of claim 1, wherein: The method customization mechanism is realized through the construction method parameter setting strategy, and the corresponding disturbance source model parameters, model boundary conditions and constitutive relationship adjustment coefficients are determined in advance for different tunnel construction methods; When solving the model, call the corresponding parameter set for simulation according to the selected construction method, so that the model output reflects the disturbance influence characteristics and surrounding rock response under the condition of this method.

5. A finite-incremental-time-evolution simulation method, characterized by, The method is used to simulate the evolution influence of disturbance on surrounding rock and supporting system in the disturbance response model of claim 1, comprising: Divide the construction process into multiple time steps, and update the disturbance source influence area and boundary conditions in turn; Activate the supporting components such as shotcrete, anchor rod, steel arch and so on at the corresponding time step, and assign initial mechanical parameters; Use creep model, viscoelastic model or modulus growth function to model the time-dependent behavior of materials; Output the stress and strain field, deformation curve and supporting force history at each time step in the whole process; The method and the modeling method of claim 1 are coupled through the disturbance intensity and time step parameter sharing mechanism to realize the dynamic evolution analysis of the whole process response of disturbance.

6. The method of claim 5, wherein, The activation time and activation mode of the supporting components are preset according to the construction process and design parameters, and are recorded in the supporting time sequence control module, which is used to automatically control the activation node and mechanical property assignment process of various supporting components in the simulation model.

7. A method of simulating a response of a support structure linkage, characterized by, The method is used to simulate the dynamic response process of supporting components under the action of disturbance in the disturbance response modeling method of claim 1, comprising: On the basis of the disturbance response model, the coupling model of supporting components such as shotcrete, anchor rod, steel frame and surrounding rock is established, and the contact interface parameters are set; Use time stepping method to dynamically update the material parameters of each component, including elastic modulus, anchoring force and stiffness decay coefficient; Identify the failure trend of key components based on load ratio, plastic zone expansion and stiffness degradation criterion; Output the stress, deformation and energy distribution results of each component, and support the model updating with monitoring data to realize the whole process simulation feedback analysis of disturbance-supporting.

8. The method of claim 7, wherein, The failure trend identification is based on one or a combination of the following criteria: The ratio of actual stress of the component to its ultimate bearing capacity exceeds the preset threshold; The length of plastic zone accounts for more than a certain proportion of the total length of the component; The stiffness of the component decays more than 50% compared with the initial value.

9. A system for tunnel construction risk level prediction and feedback based on disturbance response, characterized in that, The system is used for receiving the disturbance parameters and stress field results output by the disturbance response modeling method of claim 1, and combining the monitoring data to perform risk level evaluation and support strategy feedback, including: a front-end perception layer for collecting surrounding rock deformation, stress, seepage pressure and environmental parameters; a communication and edge processing layer for completing protocol conversion, abnormality identification and preprocessing of monitoring data; a back-end decision layer including: a risk evaluation module for performing grade division on the disturbance influence area by using a fuzzy comprehensive method or a BP neural network model; a support suggestion module for matching support parameter adjustment strategies based on the evaluation results; a visualization module for displaying disturbance-risk time evolution curves and support response records; The system constructs a feedback closed loop of disturbance-risk-support, and realizes dynamic risk control of the construction process.

10. An integrated tunnel construction assistance decision system, characterized in that, The system is constructed based on the disturbance response modeling method of claim 1, and is used for disturbance perception, simulation analysis and feedback decision in the tunnel construction process, including: a front-end monitoring terminal for collecting displacement, stress, seepage pressure and other disturbance-related data of the tunnel surrounding rock and support structure; an edge computing unit for generating disturbance parameters, fitting disturbance propagation functions and performing preliminary data preprocessing; a central simulation server for receiving disturbance parameters and calling the modeling method of claim 1 to perform disturbance-surrounding rock response simulation and risk analysis; a human-computer interaction terminal for setting construction method parameters, displaying simulation results and support suggestions; a back-end database for storing historical engineering data, simulation reports and risk grading reference information; The system constructs a data-modeling-feedback closed loop, and supports disturbance response analysis and dynamic optimization of support strategies in the construction process.

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