A method, device, medium and product for evaluating influence degrees of long-term deformation of tunnel excavation by multiple parameters

Abstract

This application discloses a method, equipment, medium, and product for assessing the multi-parameter influence of long-term deformation during tunnel excavation, relating to the field of tunnel engineering. The method includes: establishing a finite element numerical model of tunnel excavation; determining multiple values ​​for each research parameter; for any set of parameter values, using the finite element numerical model to simulate the long-term deformation behavior of the soil under tunnel excavation conditions, obtaining numerical simulation results corresponding to the parameter values; determining the response index at the end of excavation and the response index over a long period based on the numerical simulation results; and calculating the excavation influence and time influence of each research parameter on the response index based on the response index at the end of excavation and the response index over a long period corresponding to each set of parameter values, to determine the importance and influence trend of each research parameter at different stages. This application can accurately identify the dominant factors at different stages of tunnel excavation, providing a scientific basis for tunnel structural design, dynamic construction control, and long-term maintenance.

Description

Technical Field

[0001] This application relates to the field of tunnel engineering, and in particular to a method, equipment, medium and product for assessing the multi-parameter influence of long-term deformation during tunnel excavation. Background Technology

[0002] After tunnel excavation, the long-term (time-dependent) deformation of the surrounding rock is one of the main causes of engineering defects, such as lining cracking, tunnel bottom heave, and differential settlement between the surface and underground structures. Existing research mainly falls into two categories: one is developing new analytical models and numerical simulation techniques; the other is applying these techniques to study the long-term mechanical response laws. However, existing technologies still have the following shortcomings: (1) Insufficient research depth: Most long-term deformation studies are concentrated on the surface or excavation face, and lack systematic revelation of the relative settlement law at different underground depths and its non-monotonic change mechanism over time (such as the settlement trough deepening first and then becoming shallower).

[0003] (2) The method of assessing the stability of the lining is unreasonable: Some studies use the strength envelope derived from elastic theory to assess the bearing capacity of the lining, which is inconsistent with the elastic-plastic limit state design principle adopted in the current concrete structure design code (such as GB 50010, Eurocode 2); at the same time, the time-varying characteristics of the internal forces of the lining are often ignored.

[0004] (3) Incomplete parameter impact analysis: Existing parameter sensitivity analysis is mostly qualitative and fails to effectively distinguish between "instantaneous excavation impact" and "long-term rheological impact", making it difficult to control key parameters in a targeted manner during design. Summary of the Invention

[0005] The purpose of this application is to provide a method, equipment, medium, and product for assessing the multi-parameter influence of long-term deformation during tunnel excavation. This method can accurately identify the dominant factors at different stages of tunnel excavation, providing a scientific basis for tunnel structural design, dynamic control during construction, and long-term maintenance.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for evaluating the multi-parameter influence of long-term deformation during tunnel excavation, including: Establish a finite element numerical model for tunnel excavation; Multiple values ​​for each research parameter were determined, resulting in multiple sets of parameter values. The research parameters include the coefficient of earth pressure at rest, peak stress ratio, compression index, rebound index, secondary consolidation coefficient, and overconsolidation ratio. For any set of parameter values, the finite element numerical model is used to simulate the long-term deformation behavior of the soil under tunnel excavation conditions, and the numerical simulation results corresponding to the parameter values ​​are obtained. Based on the numerical simulation results corresponding to the parameter values, the response indicators at the end of excavation and the long-term response indicators corresponding to the parameter values ​​are determined; the response indicators include the maximum relative settlement of the ground surface, the minimum reinforcement ratio of the lining, and the minimum failure factor of the lining. Based on the response index at the end of excavation and the response index at a long time corresponding to the values ​​of each set of parameters, the excavation influence degree and time influence degree of each research parameter on the response index are calculated to determine the importance and influence trend of each research parameter at different stages.

[0007] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for evaluating the multi-parameter influence of long-term deformation in tunnel excavation.

[0008] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for evaluating the multi-parameter influence of long-term deformation in tunnel excavation.

[0009] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-mentioned method for evaluating the multi-parameter influence of long-term deformation in tunnel excavation.

[0010] According to the specific embodiments provided in this application, this application has the following technical effects: By simultaneously incorporating multiple key parameters covering the instantaneous elasticity, plastic compression, unloading rebound, and time-dependent creep characteristics of soil, such as the static earth pressure coefficient, peak stress ratio, compression index, rebound index, secondary consolidation coefficient, and overconsolidation ratio, it effectively overcomes the prediction inaccuracies caused by traditional methods due to the selection of only one parameter or the neglect of time effects. Furthermore, it constructs a dual-phase response index system based on the excavation end time and long-term time, selecting the maximum relative settlement of the ground surface, the minimum reinforcement ratio of the lining, and the minimum failure factor of the lining as core evaluation indicators, which can comprehensively reflect the safety status of the structure's collaborative work during construction and long-term operation. In particular, by separately calculating and separating the independent contributions of each parameter to the short-term excavation response and the long-term time response, it can accurately identify the dominant control factors and their influence trends at different construction and operation stages, effectively guiding dynamic parameter adjustments during construction and maintenance decisions during operation. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is an application environment diagram of a method for evaluating the influence of long-term deformation of tunnel excavation using multiple parameters, as described in one embodiment of this application.

[0013] Figure 2 This is a flowchart illustrating a method for evaluating the influence of long-term deformation of tunnel excavation using multiple parameters, as provided in an embodiment of this application.

[0014] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] The method for evaluating the multi-parameter influence of long-term deformation in tunnel excavation provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on another server.

[0018] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 102 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0019] In one exemplary embodiment, such as Figure 2As shown, a method for evaluating the multi-parameter influence of long-term deformation during tunnel excavation is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps 201 to 205.

[0020] Step 201: Establish a finite element numerical model for tunnel excavation.

[0021] Specifically, a three-dimensional finite element model including the tunnel surrounding rock, lining, and grouting materials is established to obtain the finite element numerical model of tunnel excavation. Among them, the tunnel surrounding rock adopts an elastic-plastic-viscosity (EPV) constitutive model, and the lining and grouting materials adopt a linear elastic model.

[0022] This application employs the EPV constitutive model (Lu et al., 2020) to describe the time-dependent deformation of the tunnel surrounding rock. The EPV constitutive model introduces the mean principal stress... p and stress ratio η The loading and unloading criteria can reasonably describe the typical stress path changes caused by tunnel excavation and comprehensively reflect the properties of soil such as cohesion, dilatation, and overconsolidation ratio.

[0023] Since all subsequent analyses (settlement patterns, lining internal forces, and parameter effects) are based on the numerical simulation results of the EPV constitutive model, it is first demonstrated that the EPV constitutive model can accurately simulate the long-term deformation behavior of soil under tunnel excavation conditions.

[0024] A physical model test was selected to verify the tunnel's passage under an existing tunnel. The test strata consisted of silty clay (550 mm thick) and gravel (850 mm thick), with the existing tunnel located in the silty clay layer and the new shield tunnel located in the gravel layer.

[0025] The verification method is as follows: (1) Obtain publicly available and reliable physical model test data for tunnel excavation (the physical model test was conducted on the shield tunneling simulation test platform at Beijing University of Technology (Lin et al., 2021)). The test strata consisted of silty clay (550 mm thick) and gravel (850 mm thick). The new tunnel was located in the gravel layer, while the existing tunnel was located in the silty clay layer.

[0026] (2) Establish the corresponding numerical model: Based on the geometric dimensions, boundary conditions, material zoning and excavation steps of the above physical model test, establish a finite element model of the shield tunnel passing under the existing tunnel. The model includes the existing tunnel, the new shield tunnel, the silty clay layer and the gravel layer.

[0027] (3) Assigning material models: Assigning EPV constitutive models to the surrounding rock (silty clay and pebbles), and inputting material parameters determined through indoor tests and inversion analysis. The linings of existing tunnels and new shield tunnels are simulated using linear elastic models.

[0028] (4) Simulate the excavation process: Simulate the process of the shield tunnel passing under the existing tunnel according to the actual sequence of the physical model test.

[0029] (5) Extract comparative data: Extract time history data of key measuring points from the numerical simulation results, including existing tunnel arch settlement and surface settlement.

[0030] (6) Comparison with actual measurements: The simulation results above are compared with the actual measurement results in the physical model test to verify the accuracy of the EPV constitutive model for the long-term deformation behavior of soil under the simulated tunnel excavation condition.

[0031] Step 202: Determine multiple values ​​for each research parameter to obtain multiple sets of parameter values. Among them, the research parameters include the coefficient of earth pressure at rest, peak stress ratio, compression index, rebound index, secondary consolidation coefficient, and overconsolidation ratio, with baseline values, baseline value +20%, and baseline value -20% set respectively.

[0032] Specifically, a baseline value and a percentage value for the fluctuation of the baseline value are selected for each research parameter, and multiple values ​​for each research parameter are combined to obtain multiple sets of parameter values.

[0033] Step 203: For any set of parameter values, use the finite element numerical model to simulate the long-term deformation behavior of the soil under tunnel excavation conditions, and obtain the numerical simulation results corresponding to the parameter values.

[0034] Specifically, tunnel excavation is simplified to a single-step excavation, focusing on the long-term effects caused by soil rheology after excavation. A three-step simulation method is adopted: (1) Ground stress balance: Gravity and horizontal stress are applied to the surrounding rock of the tunnel to make the resultant deformation of the soil approach zero. (2) Tunnel excavation and lining installation: This is achieved using unit birth and death technology. (3) Surrounding rock and soil rheological analysis: The analysis step duration is set to simulate the creep of the surrounding rock within a specific time (such as 1 day, 1 month, 1 year, 10 years, 100 years) after excavation.

[0035] Step 204: Based on the numerical simulation results corresponding to the parameter values, determine the response indicators at the excavation completion time and the response indicators over a long period (e.g., 100 years) corresponding to the parameter values. The response indicators include the maximum relative settlement of the ground surface, the minimum reinforcement ratio of the lining, and the minimum failure factor of the lining.

[0036] In a specific application example, step 204 includes the following steps (1) to (6).

[0037] (1) Based on the numerical simulation results, determine the evolution curve of the relative settlement trough at different depths over time and the bending moment, axial force and shear force of the lining at different times.

[0038] Specifically, the formula for calculating relative settlement is: ;in, For relative settlement, t This represents the time elapsed since the tunnel was excavated. x The horizontal distance between the point of interest and the vertical axis of the tunnel. Let Z be the settlement at depth Z, where depth Z (Z≥0) is a fixed parameter. Different values ​​are used for analysis. The settlement at the boundary of the finite element model is taken as 60m.

[0039] Simulations were used to obtain the evolution curves of relative subsidence troughs over time at different geological depths Z (e.g., Z=0m surface, Z=11.25m, Z=22.50m). Based on these curves, the depth and width of the shallow (surface) subsidence trough increased continuously over time before stabilizing; the middle layer (Z=11.25m) subsidence trough first increased and then decreased, exhibiting a non-monotonic change.

[0040] The depth and width of the relative settling trough exhibit non-monotonic time-varying characteristics, attributed to the coupling of three key mechanical effects: creep, buoyancy, and convergence. ① Creep effect: determined by the secondary consolidation coefficient in the EPV constitutive model. ψ Characterization. This effect causes the depth and width of the settlement trough to increase over time, with the change being greater in shallow soil than in deep soil. ② Buoyancy effect: Caused by the difference between the unit weight of the tunnel structure and the unit weight of the excavated soil. This effect causes the depth and width of the settlement trough to decrease over time, exhibiting an effect opposite to creep. ③ Convergence effect: Caused by the elastic modulus of the lining. E Control. As time increases, the surrounding earth pressure gradually reduces the cross-section of the lining. The convergence of the lining can be regarded as an increase in ground volume loss, leading to an increase in the depth and width of the settlement trough, with the change in deeper soil being greater than that in shallower soil. The dominant effects differ at different stages, and the coupling of these three factors determines the final evolution pattern of relative settlement.

[0041] Further extraction of time-varying internal forces: extracting the bending moment of the lining at different times from the numerical simulation results. M axial force N and shear force V Determine the location of the critical section (for bending moment). M and axial force N The critical section is located at the crown, waist, or bottom of the arch; for shear forces... V and axial force NThe critical section is located at the arch shoulder or arch foot.

[0042] (2) Based on the bending moment and axial force of the lining at different times, determine the curve of the minimum reinforcement ratio required to avoid failure of the normal section as a function of time.

[0043] Establish a strength envelope based on elastoplastic theory (according to current standards such as GB 50010-2010), and determine the normal section bearing capacity: construct the bending moment-axial force ( M - N Interaction diagram. Considering the grades of concrete (e.g., C35) and steel reinforcement (e.g., HRB500), parameters such as additional eccentricity and relative limit compression zone height are introduced to determine the reinforcement ratio of different tensile steel bars. ρ The safety boundary is determined to obtain the minimum reinforcement ratio required to avoid failure of the normal section.

[0044] (3) Calculate the shear bearing capacity of the lining at different times based on the axial force of the lining at different times, and determine the curve of the minimum failure factor of the lining changing with time based on the axial force and shear bearing capacity of the lining at different times.

[0045] Specifically, based on the strength envelope of the elastoplastic theory (according to current standards such as GB 50010-2010), the shear capacity of the inclined section is determined by constructing the axial force-shear force ratio. N - V Interaction diagram. The shear capacity is calculated using the following formula: V ]: ; in, V conc Contribute to concrete V stir Contribute to the stirrups, V thru It contributes to axial force (axial pressure can improve shear resistance). For the shear span ratio, f t For the tensile strength of concrete, b The width of the lining section is usually taken as 1m. h 0 represents the effective height of the cross-section. , h The height of the lining section. The distance from the compression reinforcement to the boundary of the compression zone of the section. f yv For the tensile strength of the stirrups, A sv The area of ​​the stirrups is... s The spacing between the stirrups. The contribution of axial force to shear force, when axial force N Less than 0.3 fc bh hour, ,otherwise .

[0046] Define failure factor γ = V / [ V ], γ=0 indicates that the failure is far away, γ=1 indicates failure, and the larger the value, the more dangerous it is.

[0047] (at different times) M , N Points are drawn on M - N The graph shows how the required minimum reinforcement ratio changes over time; based on the values ​​at different times... N , V ) Point calculation of failure factor γ Thus, the failure factor is obtained. γ = V / [ V The trend of change over time.

[0048] (4) Determine the maximum relative settlement of the ground surface at the end of excavation based on the evolution curves of the relative settlement trough at different depths over time. and the maximum relative subsidence of the Earth's surface over a long period of time .

[0049] (5) Determine the minimum reinforcement ratio of the lining at the end of the excavation based on the curve of the minimum reinforcement ratio changing with time. and the minimum reinforcement ratio of the lining over a long period of time .

[0050] (6) Determine the minimum failure factor of the lining at the end of excavation based on the curve of the minimum failure factor of the lining versus time. and the minimum failure factor of the lining over a long period of time .

[0051] Step 205: Based on the response index at the end of excavation and the response index at a long time corresponding to the values ​​of each set of parameters, calculate the excavation influence degree and time influence degree of each research parameter on the response index, so as to determine the importance and influence trend (positive correlation / negative correlation) of each research parameter at different stages (short-term vs. long-term).

[0052] This application defines two evaluation factors to quantitatively evaluate the impact of soil parameters, in order to decouple the contributions of "instantaneous excavation" and "long-term time effects." The excavation impact degree is used to evaluate the research parameters. At the moment of excavation ( t =0) affects the outcome; time influence is used to study parameters. exist tThe degree to which time affects the outcome.

[0053] In a specific application example, step 205 includes the following steps (1) to (3).

[0054] (1) For any research parameter, calculate the excavation influence degree and time influence degree of the research parameter on the maximum relative settlement of the surface at the end of excavation and the maximum relative settlement of the surface at a long time corresponding to different values ​​of the research parameter.

[0055] Specifically, the research parameters are calculated using the following formula. The degree of excavation impact on the maximum relative settlement of the ground surface: ; in, For the research parameters, To investigate the influence of parameter α on the maximum relative settlement of the land surface, , and For research parameters Maximum relative settlement of the ground surface at the end of excavation for different values Indicates research parameters The maximum relative settlement of the ground surface at the end of excavation corresponding to the benchmark value. Indicates research parameters The maximum relative settlement of the ground surface at the end of excavation, corresponding to a baseline value +20%, is taken. Indicates research parameters The maximum relative settlement of the ground surface at the end of excavation when the baseline value is taken as -20%.

[0056] The research parameters are calculated using the following formula. The time-dependent effect on the maximum relative settlement of the land surface: ; in, For research parameters exist t The degree of influence of time on the maximum relative settlement of the land surface. , and For research parameters Different values ​​after excavation t The maximum relative subsidence of the Earth's surface at a given time. Indicates research parameters The benchmark value is taken after excavation. t The maximum relative subsidence of the Earth's surface at a given time. Indicates research parameters When the baseline value is taken +20%, after excavation tThe maximum relative subsidence of the Earth's surface at a given time. Indicates research parameters When the baseline value is -20%, after excavation t The maximum relative subsidence of the Earth's surface at a given time.

[0057] (2) Based on the minimum reinforcement ratio of the lining at the end of the excavation and the minimum reinforcement ratio of the lining at a long time corresponding to different values ​​of the research parameters, calculate the excavation influence and time influence of the research parameters on the minimum reinforcement ratio of the lining.

[0058] Specifically, the research parameters are calculated using the following formula. The impact of excavation on the minimum reinforcement ratio of the lining: ; in, To investigate the influence of parameter α on the excavation of the minimum reinforcement ratio of the lining, , and For research parameters Minimum reinforcement ratio of the lining at the end of excavation for different values. Indicates research parameters The minimum reinforcement ratio of the lining at the end of the excavation corresponding to the benchmark value. Indicates research parameters The minimum reinforcement ratio of the lining at the end of excavation, corresponding to a base value +20%. Indicates research parameters The minimum reinforcement ratio of the lining at the end of the excavation when the benchmark value is taken as -20%.

[0059] The research parameters are calculated using the following formula. The time effect of minimum reinforcement ratio of lining: ; in, For research parameters The time-dependent effect of the minimum reinforcement ratio of the lining. , and For research parameters Different values ​​after excavation t Minimum reinforcement ratio of the lining at any given time. Indicates research parameters The benchmark value is taken after excavation. t Minimum reinforcement ratio of the lining at any given time. Indicates research parameters When the baseline value is taken +20%, after excavation t Minimum reinforcement ratio of the lining at any given time. Indicates research parameters When the baseline value is -20%, after excavation t Minimum reinforcement ratio of the lining at any given time.

[0060] (3) Based on the minimum failure factor of the lining at the end of the excavation and the minimum failure factor of the lining at a long time corresponding to different values ​​of the research parameters, calculate the excavation influence and time influence of the research parameters on the minimum failure factor of the lining.

[0061] Specifically, the research parameters are calculated using the following formula. The impact of excavation on the minimum failure factor of the lining: ; in, To investigate the excavation influence of parameter α on the minimum failure factor of the lining, , and For research parameters Minimum failure factor of lining at the end of excavation for different values Indicates research parameters The minimum failure factor of the lining at the end of the excavation corresponding to the benchmark value. Indicates research parameters The minimum failure factor of the lining at the end of excavation, corresponding to a base value +20%, is taken. Indicates research parameters The minimum failure factor of the lining at the end of the excavation when the baseline value is taken as -20%.

[0062] The research parameters are calculated using the following formula. The time-dependent effect of the minimum failure factor of the lining: ; in, For research parameters The time-dependent effect of the minimum failure factor of the lining. , and For research parameters Different values ​​after excavation t The minimum failure factor of the lining at any given time. Indicates research parameters The benchmark value is taken after excavation. t The minimum failure factor of the lining at any given time. Indicates research parameters When the baseline value is taken +20%, after excavation t The minimum failure factor of the lining at any given time. Indicates research parameters When the baseline value is -20%, after excavation tThe minimum failure factor of the lining at any given time.

[0063] In summary, this application significantly improves the scientific rigor, comprehensiveness, and engineering applicability of long-term deformation assessment for tunnel excavation. By simultaneously incorporating multiple key parameters covering the instantaneous elasticity, plastic compression, unloading rebound, and time-dependent creep characteristics of soil, such as the static earth pressure coefficient, peak stress ratio, compression index, rebound index, secondary consolidation coefficient, and overconsolidation ratio, it effectively overcomes the prediction inaccuracies caused by traditional methods due to single parameter selection or neglect of time effects. This method innovatively constructs a dual-phase response index system based on the excavation completion time and long-term timeframes, selecting maximum relative settlement of the ground surface, minimum reinforcement ratio of the lining, and minimum failure factor of the lining as core evaluation indicators. This comprehensively reflects the safety status of the structure's collaborative operation during both instantaneous construction disturbances and long-term operation. In particular, by separately calculating and separating the independent contributions of each parameter to the short-term excavation response and long-term time response, it accurately identifies the dominant control factors and their influence trends at different construction and operation stages, effectively guiding dynamic parameter adjustments during construction and maintenance decisions during operation. This phased, multi-factor coupled quantitative evaluation strategy provides solid data support for optimizing lining structure design and rationally allocating steel reinforcement, and preventing long-term instability. While ensuring the safety of the structure throughout its entire life cycle, it can effectively balance engineering safety and economy, reduce material waste caused by over-design or service risks caused by under-design, and provide efficient and reliable support for the leap from experience-based decision-making to refined numerical decision-making in tunnel engineering.

[0064] Based on the same inventive concept, this application also provides a system for implementing the methods described above. The solution provided by this system is similar to the solution described in the methods above; therefore, specific limitations in one or more system embodiments provided below can be found in the limitations of the methods described above, and will not be repeated here.

[0065] In one exemplary embodiment, a multi-parameter influence assessment system for long-term deformation during tunnel excavation is provided, comprising the following functional modules: The model building module is used to create a finite element numerical model of tunnel excavation. The parameter value module is used to determine multiple values ​​for each research parameter, resulting in multiple sets of parameter values. Among them, the research parameters include the coefficient of earth pressure at rest, peak stress ratio, compression index, rebound index, secondary consolidation coefficient, and overconsolidation ratio. The numerical simulation module is used to simulate the long-term deformation behavior of soil under tunnel excavation conditions using the finite element numerical model for any set of parameter values, and to obtain the numerical simulation results corresponding to the parameter values. The index determination module is used to determine the response index at the end of excavation and the response index at a long time corresponding to the parameter values ​​based on the numerical simulation results corresponding to the parameter values. The response index includes the maximum relative settlement of the ground surface, the minimum reinforcement ratio of the lining, and the minimum failure factor of the lining. The impact analysis module is used to calculate the excavation impact and time impact of each research parameter on the response index based on the response index at the end of excavation and the response index at a long time corresponding to the value of each set of parameters, so as to determine the importance and impact trend of each research parameter at different stages.

[0066] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores finite element numerical models of tunnel excavation. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for evaluating the multi-parameter influence of long-term deformation in tunnel excavation.

[0067] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 3 The diagram shows more or fewer components, or combinations of certain components, or different component arrangements.

[0068] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0069] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0070] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0071] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0072] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0073] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for evaluating the multi-parameter influence of long-term deformation during tunnel excavation, characterized in that, include: Establish a finite element numerical model for tunnel excavation; Multiple values ​​for each research parameter were determined, resulting in multiple sets of parameter values. The research parameters include the coefficient of earth pressure at rest, peak stress ratio, compression index, rebound index, secondary consolidation coefficient, and overconsolidation ratio. For any set of parameter values, the finite element numerical model is used to simulate the long-term deformation behavior of the soil under tunnel excavation conditions, and the numerical simulation results corresponding to the parameter values ​​are obtained. Based on the numerical simulation results corresponding to the parameter values, the response indicators at the end of excavation and the long-term response indicators corresponding to the parameter values ​​are determined; the response indicators include the maximum relative settlement of the ground surface, the minimum reinforcement ratio of the lining, and the minimum failure factor of the lining. Based on the response index at the end of excavation and the response index at a long time corresponding to the values ​​of each set of parameters, the excavation influence degree and time influence degree of each research parameter on the response index are calculated to determine the importance and influence trend of each research parameter at different stages.

2. The method for evaluating the multi-parameter influence of long-term deformation during tunnel excavation according to claim 1, characterized in that, Establish a finite element numerical model for tunnel excavation, including: A three-dimensional finite element model including the tunnel surrounding rock, lining and grouting materials was established to obtain the finite element numerical model of tunnel excavation; among them, the tunnel surrounding rock adopted an elastoplastic-viscous constitutive model, and the lining and grouting materials adopted a linear elastic model.

3. The method for evaluating the multi-parameter influence of long-term deformation during tunnel excavation according to claim 1, characterized in that, Multiple values ​​are determined for each research parameter, resulting in multiple sets of parameter values, including: Each research parameter has a baseline value and a percentage value for fluctuation above and below the baseline value. Multiple values ​​for each research parameter are then combined to obtain multiple sets of parameter values.

4. The method for evaluating the influence of long-term deformation of tunnel excavation using multiple parameters according to claim 1, characterized in that, Based on the numerical simulation results corresponding to the parameter values, determine the response index at the excavation completion time and the long-term response index corresponding to the parameter values, including: Based on the numerical simulation results, the evolution curves of the relative settlement trough at different depths over time, as well as the bending moment, axial force, and shear force of the lining at different times, were determined. Based on the bending moment and axial force of the lining at different times, determine the curve of the minimum reinforcement ratio required to avoid failure of the normal section as a function of time; The shear bearing capacity of the lining at different times is calculated based on the axial force of the lining at different times, and the curve of the minimum failure factor of the lining changing with time is determined based on the axial force and shear bearing capacity of the lining at different times. Based on the evolution curves of the relative settlement trough at different depths over time, the maximum relative settlement of the ground surface at the end of excavation and the maximum relative settlement of the ground surface over a long period of time were determined. Based on the curve of minimum reinforcement ratio changing with time, the minimum reinforcement ratio of the lining at the end of excavation and the minimum reinforcement ratio of the lining at long-term time are determined. Based on the curve of the minimum failure factor of the lining changing over time, the minimum failure factor of the lining at the end of excavation and the minimum failure factor of the lining at long-term time are determined.

5. The method for evaluating the multi-parameter influence of long-term deformation during tunnel excavation according to claim 1, characterized in that, Based on the response index at the end of excavation and the response index at a long-term time corresponding to each set of parameter values, the excavation influence and time influence of each research parameter on the response index are calculated, including: For any research parameter, based on the maximum relative settlement of the ground surface at the end of excavation and the maximum relative settlement of the ground surface at a long time corresponding to different values ​​of the research parameter, calculate the excavation influence degree and time influence degree of the research parameter on the maximum relative settlement of the ground surface. Based on the minimum reinforcement ratio of the lining at the end of excavation and the minimum reinforcement ratio of the lining at a long time corresponding to different values ​​of the research parameters, calculate the excavation influence and time influence of the research parameters on the minimum reinforcement ratio of the lining. Based on the minimum failure factor of the lining at the end of excavation and the minimum failure factor of the lining at a long time corresponding to different values ​​of the research parameters, the excavation influence and time influence of the research parameters on the minimum failure factor of the lining are calculated.

6. The method for evaluating the influence of long-term deformation of tunnel excavation using multiple parameters according to claim 5, characterized in that, The research parameters are calculated using the following formula. The degree of excavation impact on the maximum relative settlement of the ground surface: ； in, For the research parameters, To investigate the influence of parameter α on the maximum relative settlement of the land surface, , and For research parameters The maximum relative settlement of the ground surface at the end of excavation corresponding to different values.

7. The method for evaluating the influence of long-term deformation of tunnel excavation using multiple parameters according to claim 5, characterized in that, The research parameters are calculated using the following formula. The time-dependent effect on the maximum relative settlement of the land surface: ； in, For the research parameters, For research parameters exist t The degree of influence of time on the maximum relative settlement of the land surface. and For research parameters Maximum relative settlement of the ground surface at the end of excavation for different values , and For research parameters Different values ​​after excavation t The maximum relative subsidence of the Earth's surface at a given time.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for evaluating the multi-parameter influence of long-term deformation in tunnel excavation as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for evaluating the multi-parameter influence of long-term deformation in tunnel excavation as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for evaluating the multi-parameter influence of long-term deformation in tunnel excavation as described in any one of claims 1-7.

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