A long-life design method for shield tunnel structures

Abstract

The present invention provides a long-life design method for a shield tunnel structure, which relates to the technical field of shield tunnel structure design, including firstly classifying environmental conditions, hydrogeological data, etc. through a K-means clustering algorithm, extracting key parameters of a multi-field coupled environment, and based on this, performing real-time monitoring, experiments and numerical simulations, and establishing a constitutive model of the material and an adaptive model of the degradation state. Subsequently, the probability distribution and statistical characteristics of the surrounding rock and lining design parameters are obtained through Monte Carlo simulation, and finite element analysis is performed based on this to establish a numerical simulation model of the tunnel. Further, through regression analysis and hierarchical analysis, a performance evolution equation and a key indicator system for long-life design are obtained. Finally, through a performance limit state model and life cycle cost analysis, the structural design parameters of the shield tunnel are optimized, and a maintenance and replacement plan is formulated to ensure the long-life design of the tunnel. The present invention ensures the long-term reliability and economy of the structure.

Description

Technical Field

[0001] The invention relates to the technical field of shield tunnel structure design, and in particular to a long-life design method for a shield tunnel structure. Background Art

[0002] With the acceleration of urbanization, shield tunnels, as an important part of underground transportation infrastructure, are widely used in urban subways, highways and other underground projects. At present, the design method of shield tunnels mainly relies on traditional static analysis and empirical formulas, which usually involve qualitative analysis of factors such as geological conditions and environmental impacts. Although these existing technologies can provide a certain degree of safety assurance, they still have significant deficiencies when facing complex and changeable actual working conditions. For example, traditional methods are usually unable to effectively consider the dynamic impact of multi-field coupling effects on structural performance, and lack real-time tracking and accurate prediction of material degradation processes. This leads to excessive or insufficient safety assurance in tunnel design, and it is impossible to achieve efficient cost control and long-life design.

[0003] There is an urgent need for a long-life design method for shield tunnel structures that comprehensively considers the environment, material degradation and structural performance to achieve efficient cost control and long-life design. Summary of the invention

[0004] The purpose of the present invention is to provide a long life design method for a shield tunnel structure to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] The present invention provides a long-life design method for a shield tunnel structure, comprising:

[0006] Classifying the preset first information based on the K-means clustering algorithm, and analyzing the evolution trend of the first information based on the classification result to obtain the key environmental parameters of multi-field coupling evolving over time, wherein the first information includes environmental conditions of the area where the shield tunnel is located, hydrological and geological exploration data, and regional environmental historical evolution data;

[0007] Based on the multi-field coupling, the key environmental parameters that evolve over time are monitored on site, tested indoors and simulated numerically. The least squares method is used to fit the stress-strain relationship of the material to obtain the material constitutive model, and the adaptive model of the degradation state that changes over time is established by combining Bayesian updating with preset prior data.

[0008] Based on the material constitutive model and the degradation state adaptive model, the key design parameters of the surrounding rock and lining are collected and counted, and the key design parameters of the surrounding rock and lining are randomly sampled through Monte Carlo simulation to obtain the probability distribution and statistical characteristics of the key design parameters of the surrounding rock and lining;

[0009] Finite element analysis is performed based on probability distribution and statistical characteristics, and a numerical simulation model of the shield tunnel structure in a multi-field coupling environment is established;

[0010] Based on the calculation results of the numerical simulation model, regression analysis was performed to obtain the tunnel joint-lining-surrounding rock performance evolution equation and the tunnel joint-lining-surrounding rock performance degradation model;

[0011] Based on the tunnel joint-lining-surrounding rock performance evolution equation and the tunnel joint-lining-surrounding rock performance degradation model, the influence of a preset number of key performance indicators of the tunnel structure is evaluated to obtain the key indicator system for long-life design;

[0012] The performance limit state model of the shield tunnel throughout its life cycle is established based on the key indicator system and the limit state function method;

[0013] The dynamic reliability of the shield tunnel structure is calculated based on the performance limit state model, and multi-objective optimization is performed based on the calculated dynamic reliability to obtain the optimal combination of structural design parameters;

[0014] Based on the life cycle cost analysis method, a cost analysis is conducted on the optimal combination of structural design parameters to obtain a design plan for the maintenance and replacement of the shield tunnel structure, and the final long-life design plan is obtained.

[0015] The beneficial effects of the present invention are:

[0016] The present invention provides a comprehensive long-life design method for shield tunnel structures, aiming to overcome the shortcomings in the prior art. The method classifies environmental conditions, hydrological and geological exploration data, and regional environmental historical evolution data based on the K-means clustering algorithm, and analyzes their evolution trends to obtain key environmental parameters that evolve over time with multi-field coupling. Furthermore, the least squares method is used to fit the stress-strain relationship of the material, and a time-varying adaptive model of the degradation state is established in combination with Bayesian updating. On this basis, Monte Carlo simulation is used to randomly sample the key design parameters of the surrounding rock and lining to obtain their probability distribution and statistical characteristics, and finite element analysis is performed based on these data to establish a numerical simulation model in a multi-field coupling environment.

[0017] Through regression analysis, the tunnel joint-lining-surrounding rock performance evolution equation and degradation model are obtained, and the key performance indicators of the tunnel structure are evaluated using the hierarchical analysis method to form a key indicator system for long-life design. Combined with the limit state function method, the performance limit state model of the shield tunnel throughout its life cycle is established, and the dynamic reliability is calculated. Finally, based on the life cycle cost analysis method, the cost analysis of the optimal design parameter combination is carried out, and the maintenance and replacement design plan is formulated to obtain the final long-life design plan. This method provides a more accurate and optimized solution for shield tunnel design by comprehensively considering the environment, material degradation and structural performance, ensuring the long-term reliability and economy of the structure.

[0018] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a schematic flow chart of a long life design method for a shield tunnel structure described in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0023] Embodiment 1:

[0024] This embodiment provides a long-life design method for a shield tunnel structure.

[0025] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300, step S400, step S500, step S600, step S700, step S800 and step S900.

[0026] Step S100, classifying the preset first information based on the K-means clustering algorithm, and analyzing the evolution trend of the first information based on the classification result to obtain the key environmental parameters of the multi-field coupling evolving over time, wherein the first information includes the environmental conditions of the area where the shield tunnel is located, hydrological and geological exploration data, and regional environmental historical evolution data;

[0027] It can be understood that step S100 includes step S101, step S102 and step S103.

[0028] Step S101, obtaining hydrological data, geological exploration data and regional environmental historical evolution data as first information;

[0029] Step S102: clustering the first information based on a K-means clustering algorithm to obtain a plurality of clusters, each cluster representing an environment category;

[0030] Step S103, using a dynamic time warping algorithm to analyze the shape changes of the time series data of each environmental category, and taking the environmental parameter data whose shape changes within a preset time are greater than a preset value as the key environmental parameters of the multi-field coupling evolving over time.

[0031] It can be understood that this step classifies the preset first information based on the K-means clustering algorithm, and analyzes the evolution trend of the first information based on the classification results to obtain the key environmental parameters of multi-field coupling evolving over time. The first information includes the environmental conditions, hydrological and geological exploration data, and regional environmental historical evolution data of the area where the shield tunnel is located. The use of the K-means clustering algorithm can effectively group complex environmental data according to their inherent similarities, so that representative features of different environmental categories can be identified. When further time series analysis is performed on each cluster cluster, the time variation of environmental parameters is analyzed by shape matching through the dynamic time warping (DTW) algorithm to identify environmental parameters that have significant changes within the preset time. This method can not only reveal the evolution trend of environmental conditions, but also extract the key parameters that have the greatest impact on the safety of shield tunnels in a complex multi-field coupling environment. By reasonably classifying and trending environmental data, it can provide accurate input data for subsequent material performance simulation and structural analysis, thereby improving the accuracy and reliability of model prediction.

[0032] Step S200, performing on-site monitoring, indoor experiments and numerical simulations based on the key environmental parameters that evolve over time based on multi-field coupling, wherein the stress-strain relationship of the material is fitted using the least squares method to obtain a material constitutive model, and further combining Bayesian updating with preset prior data to establish an adaptive model of degradation state that changes over time;

[0033] It can be understood that step S200 includes step S201, step S202, step S203 and step S204.

[0034] Step S201: Based on the key environmental parameters of multi-field coupling evolving over time, a sensor network is deployed to perform real-time monitoring to obtain real-time monitoring data, wherein the real-time monitoring data includes stress, strain, and temperature and humidity change data of actual materials in a multi-field coupling environment;

[0035] Step S202: in a laboratory environment, perform a mechanical property test on a sample obtained from the field to obtain indoor experimental data, where the indoor experimental data is stress-strain relationship data of the material;

[0036] Step S203, simulating the behavior of the material based on the real-time monitoring data and the indoor experimental data, and calculating the simulated data and the real-time monitoring data based on the least squares method to obtain the material parameters with the smallest difference between the predicted stress and the experimental stress, thereby establishing a constitutive model of the material;

[0037] Step S204, combining the material constitutive model obtained by the least squares method with the preset prior data, and correcting the model through the Bayesian update method, wherein the posterior distribution of the model parameters is gradually adjusted through the Bayesian update method combined with new observation data to obtain an adaptive model of the degradation state that changes with time.

[0038] It can be understood that this step constructs the material constitutive model by conducting on-site monitoring, indoor experiments and numerical simulations based on the key environmental parameters that evolve over time based on multi-field coupling, and establishes an adaptive model of the degradation state that changes over time in combination with the Bayesian update method. First, by arranging a sensor network on site for real-time monitoring, the actual material stress, strain and environmental change data under the multi-field coupling environment can be collected. These real-time monitoring data will be used to reflect the response characteristics of the material in actual working conditions.

[0039] Next, the samples obtained from the field are subjected to detailed mechanical property tests in a laboratory environment to obtain the stress-strain relationship data of the material. By fitting these data using the least squares method, a constitutive model of the material can be obtained, which describes the behavior of the material under different stress and strain conditions. The advantage of the least squares method is that its optimization process is simple and computationally efficient, and it can find the best fitting curve of the stress-strain data, thereby accurately describing the mechanical properties of the material.

[0040] Then, the obtained constitutive model is combined with the preset prior data and the Bayesian update method is used to modify the model. The Bayesian update method can gradually adjust the posterior distribution of the model parameters in combination with the new observation data to form an adaptive model of the degradation state that changes over time. The core advantage of this method is that it can dynamically adjust and optimize the model in the process of continuously acquiring new data, thereby achieving accurate prediction of the material degradation process.

[0041] The comprehensive use of field monitoring, laboratory testing, least squares fitting and Bayesian updating technology can establish a high-precision material constitutive model in a complex multi-field coupling environment and dynamically track the degradation state of the material, thus providing a solid data foundation and scientific basis for subsequent engineering design and analysis. This comprehensive approach not only improves the accuracy of the model, but also enhances its adaptability and reliability in practical applications.

[0042] Step S300: collecting and counting key design parameters of surrounding rock and lining based on the material constitutive model and the degradation state adaptive model, and randomly sampling the key design parameters of surrounding rock and lining through Monte Carlo simulation to obtain probability distribution and statistical characteristics of the key design parameters of surrounding rock and lining;

[0043] It can be understood that step S300 includes step S301, step S302 and step S303.

[0044] Step S301: According to the collected data based on the material constitutive model and the degradation state adaptive model, statistical characteristics of key design parameters of surrounding rock and lining are obtained through statistical analysis, wherein the statistical analysis includes calculating the standard deviation and determining the degree of dispersion of each standard deviation;

[0045] Step S302: according to the statistical characteristics of the key design parameters of the surrounding rock and the lining, the probability distribution model of the key design parameters of the surrounding rock and the lining is obtained by comparing with the preset probability distribution model;

[0046] Step S303: Generate random samples of key design parameters of surrounding rock and lining based on Monte Carlo simulation, and calculate the mean, variance, skewness and kurtosis of the samples of key design parameters of surrounding rock and lining to obtain statistical characteristics of the key design parameters of surrounding rock and lining.

[0047] It can be understood that this step extracts the key design parameters of the surrounding rock and lining from the field monitoring data and laboratory test results by combining the material constitutive model and the degradation state adaptive model. These parameters include the strength, stiffness, elastic modulus, compression strength, shear strength, etc. of the material, which are all key factors affecting the performance of the tunnel structure.

[0048] Next, these key design parameters are statistically analyzed. By calculating the mean, variance, standard deviation and other statistical characteristics of each parameter, the basic distribution characteristics under normal working conditions are determined. This step provides basic data for subsequent random sampling and probability analysis.

[0049] Subsequently, Monte Carlo simulation is used to randomly sample the key design parameters of the surrounding rock and lining. Monte Carlo simulation is a numerical calculation method based on random samples. By generating a large number of random samples, the probability distribution and statistical characteristics of complex systems can be accurately estimated. Specifically, the initial probability distribution of the parameters is set (such as normal distribution, lognormal distribution, etc.), and then a large number of random samples are generated. The probability distribution and statistical characteristics of each design parameter are calculated through these samples. The unique advantage of Monte Carlo simulation lies in its ability to handle high-dimensional and complex problems, and can provide reliable statistical predictions under uncertain conditions.

[0050] Finally, based on the results of Monte Carlo simulation, the detailed probability distribution and statistical characteristics of the key design parameters of the surrounding rock and lining are obtained, including mean, variance, skewness, kurtosis, etc. These statistical characteristics provide a detailed probabilistic basis for subsequent finite element analysis and risk assessment.

[0051] Through this process, we can systematically analyze and understand the changes in design parameters of surrounding rock and lining under different working conditions and their impact, providing accurate data support for further engineering design and risk assessment. The technical effect of this step is that the random sampling of Monte Carlo simulation fully considers the randomness and uncertainty of design parameters, thereby providing more real and comprehensive statistical information for subsequent analysis.

[0052] Step S400, performing finite element analysis based on probability distribution and statistical characteristics, and establishing a numerical simulation model of the shield tunnel structure in a multi-field coupling environment;

[0053] It can be understood that step S400 includes step S401, step S402, step S403, step S404 and step S405.

[0054] Step S401, performing data normalization processing on the probability distribution and statistical characteristics to obtain normalized parameter data;

[0055] Step S402: Based on the normalized design parameter data, a preliminary finite element model of the shield tunnel structure is established, wherein the preliminary finite element model includes the surrounding rock, lining structure and connection mode of the tunnel, and the meshing is initially based on the overall geometric shape and material properties;

[0056] Step S403, using the preliminary finite element model to perform simulation calculations to obtain the stress, strain and displacement results of the shield tunnel structure;

[0057] Step S404: Calculate the area where the error is greater than the preset threshold based on the gradient estimation method, and refine the mesh of the area where the error is greater than the preset threshold to regenerate a finer finite element mesh;

[0058] Step S405, repeat the error estimation and mesh adjustment steps until the calculation results of the model meet the preset accuracy requirements, and obtain the numerical simulation model of the shield tunnel structure in the multi-field coupling environment.

[0059] It can be understood that this step normalizes the probability distribution and statistical characteristics of the key design parameters of the surrounding rock and lining to adapt them to the input requirements of the finite element analysis. Data normalization includes scaling the range of parameters to a uniform scale, for example, converting all data to the interval of 0 to 1, which helps to improve the stability and accuracy of numerical calculations.

[0060] Next, a preliminary finite element model is constructed. Based on the standardized design parameter data, a finite element model of the shield tunnel structure is created. This model should include the surrounding rock, lining structure and its connection method of the tunnel. The meshing of the model is initially based on the overall geometry and material properties. The division of the finite element mesh has an important influence on the calculation accuracy, and it should be ensured that the mesh can accurately represent the details of the structure and complex boundary conditions.

[0061] Subsequently, the preliminary finite element model was used for numerical simulation to obtain the stress, strain, displacement and other response data of the shield tunnel structure in a multi-field coupling environment. The multi-field coupling environment here may include various factors such as temperature, humidity, and earth pressure, and these factors are considered through coupling simulation to have a comprehensive impact on the structure.

[0062] Error estimation and mesh adjustment are key steps to improve model accuracy. By performing error estimation on the simulation results, we can identify areas where the error in the calculation results is greater than a preset threshold, and use the gradient estimation method to make adjustments. This may include refining the mesh to obtain more accurate calculation results. This process usually involves repeated calculations and adjustments until the calculation results of the model meet the preset accuracy requirements.

[0063] Finally, a numerical simulation model of the shield tunnel structure in a multi-field coupling environment is obtained. This model can reflect the structural behavior under various environmental influences that may be encountered in actual engineering, and provide detailed stress, strain and displacement information. The technical effect of this step is that through systematic numerical simulation and precise error adjustment, it is ensured that the model can accurately predict the actual performance of the structure and can cope with the coupling effects of multiple environmental factors. This model will provide solid data support for subsequent performance evaluation and design optimization.

[0064] Step S500: performing regression analysis based on the calculation results of the numerical simulation model to obtain a tunnel joint-lining-surrounding rock performance evolution equation and a tunnel joint-lining-surrounding rock performance degradation model;

[0065] It can be understood that step S500 includes step S501, step S502 and step S503.

[0066] Step S501, constructing a regression model based on the calculation results of the numerical simulation model, including the mechanical properties data of the tunnel joint, lining and surrounding rock under different working conditions;

[0067] Step S502: Based on the calculation results of the numerical simulation model, a tunnel joint-lining-surrounding rock performance evolution equation is obtained by processing with a high-dimensional sparse regression method;

[0068] Step S503: performing fault tree analysis on the regression model, wherein the fault tree analysis includes defining failure events of the shield tunnel structure and their causes, and obtaining a degradation model of the tunnel structure by analyzing the probability of occurrence and mutual relationship of each failure mode.

[0069] It can be understood that this step constructs a regression model to describe the performance data of tunnel joints, linings and surrounding rocks under different working conditions through the calculation results of the numerical simulation model obtained. The data here usually include parameters such as stress, strain, displacement, etc. at different time points. The purpose of the regression model is to find the mathematical relationship between these parameters, so as to describe the changes in structural performance over time or other influencing factors. Among them, tunnel joints, linings and surrounding rocks form an interrelated system in the tunnel structure, which work together to maintain the structural stability of the tunnel. The connection performance of the joints, the bearing capacity of the lining and the supporting role of the surrounding rock interact with each other and play a key role in the design, construction and long-term maintenance of the tunnel structure.

[0070] Next, the high-dimensional sparse regression method is applied. In order to process the high-dimensional data obtained from numerical simulation, the high-dimensional sparse regression method is used. This method can effectively handle a large number of input variables and complex model relationships, reduce computational complexity, and retain important performance characteristics. Sparse regression introduces regularization terms to make some coefficients in the regression model zero, thereby simplifying the model and improving the accuracy of prediction.

[0071] Fault tree analysis is a further processing step of regression analysis. Through fault tree analysis, failure events and their causes of shield tunnel structures are identified and defined. Fault tree analysis method can help identify potential failure modes and influencing factors, analyze the probability of occurrence of these failure modes and their interrelationships, and further build a comprehensive degradation model. This step provides a failure prediction tool based on actual engineering data through systematic analysis.

[0072] Finally, the evolution equation and degradation model of tunnel joint-lining-surrounding rock performance are obtained. These models can be used to predict the performance changes and degradation of the structure under different working conditions, providing a scientific basis for design optimization and maintenance decisions. Specifically, the performance evolution equation can describe the changes in the mechanical properties of tunnel joints, linings and surrounding rocks in long-term use, while the degradation model provides a prediction tool for performance degradation. The technical effect of this step is to systematically extract the performance evolution law of the tunnel structure through high-dimensional data processing and fault tree analysis, and effectively predict the degradation process of the structure, thereby improving the reliability and safety of the design.

[0073] Step S600, evaluating the influence of a preset number of key performance indicators of the tunnel structure based on the tunnel joint-lining-surrounding rock performance evolution equation and the tunnel joint-lining-surrounding rock performance degradation model, and obtaining a key indicator system for long-life design;

[0074] It can be understood that step S600 includes step S601, step S602, step S603 and step S604.

[0075] Step S601, constructing a hierarchical model, wherein the long life design goal of the tunnel structure is taken as the top layer, a preset number of key performance indicators are taken as the second layer, and factors affecting the key performance indicators are taken as the third layer;

[0076] Step S602: Establish a judgment matrix of various key performance indicators and influencing factors through historical data, tunnel joint-lining-surrounding rock performance evolution equation and tunnel joint-lining-surrounding rock performance degradation model;

[0077] Step S603: Calculate the weight of each indicator using the weighted average method and determine the consistency of the matrix. If the consistency ratio is less than a preset threshold, obtain the comprehensive weight of each performance indicator.

[0078] Step S604: Allocate a weight to each performance indicator based on the comprehensive weight to obtain a key indicator system for long life design.

[0079] It can be understood that the preset number of key performance indicators in this step includes at least seven key performance indicators, including deformation of tunnel surrounding rock, overall deformation of tunnel, anti-floating of pipe segments, compression and crack resistance of lining cross section, bending resistance of circumferential joints, and shear resistance of longitudinal joints. Among them, through historical data, tunnel joint-lining-surrounding rock performance evolution equation and tunnel joint-lining-surrounding rock performance degradation model, a judgment matrix of each key performance indicator and influencing factor is established, and a judgment matrix method of multi-factor influence is adopted to quantitatively analyze the weight relationship between each performance indicator and influencing factor. The construction of the judgment matrix can analyze the contribution of key factors to performance attenuation by comparing the actual performance data changes of tunnel joints, linings and surrounding rocks, and quantify the influence of different factors. This matrix can not only reflect the direct impact of each factor, but also can be combined with a weighted scoring system to achieve the weight distribution of each performance indicator by mapping with parameters in the performance evolution equation.

[0080] It can be understood that in this step, the analytic hierarchy process (AHP) is a method for decomposing complex decision problems into multiple levels. In this step, a hierarchical model needs to be established first, in which the top layer represents the long-life design goal, the second layer includes multiple preset key performance indicators, and the third layer involves the influencing factors of these indicators. Through the hierarchical design of the hierarchy, AHP can decompose complex decision problems into sub-problems that are easier to analyze.

[0081] In AHP, experts or engineers need to make pairwise comparisons of each performance indicator and its influencing factors to form a judgment matrix. Each matrix element represents the importance ratio between two indicators. In order to ensure the rationality of the judgment matrix, a consistency test is also required. The consistency ratio of the judgment matrix must be lower than the preset threshold to ensure the reliability and accuracy of the judgment. The calculation of the consistency ratio usually relies on the eigenvalue decomposition technique to evaluate the logical consistency of the matrix. Based on the judgment matrix, the weight of each indicator is calculated by the weighted average method. The weighted average method converts the relative importance of each indicator into a weight value so that it can be reflected in the overall design. By combining the weight of each indicator, the relative influence of each indicator on the long-life design is obtained.

[0082] According to the calculation results, the weights and influences of various performance indicators are integrated to determine which indicators are most critical for achieving long-life design. This key indicator system will provide a scientific basis for subsequent design optimization. The use of the AHP method can systematically and quantitatively evaluate the impact of different performance indicators on the long-life design of tunnel structures, helping designers to identify which indicators are key factors in achieving long-life design. Through the application of the hierarchical analysis method, various factors can be comprehensively considered, the design scheme can be optimized, and the long-term performance and safety of the structure can be improved.

[0083] Step S700, establishing a performance limit state model for the entire life cycle of a shield tunnel based on a key indicator system and a limit state function method;

[0084] It can be understood that step S700 includes step S701 and step S702.

[0085] Step S701: define a limit state function of each indicator according to the indicators in the key indicator system, wherein the limit state function represents the limit state of the structure;

[0086] Step S702: Integrate the limit state functions of all key performance indicators to form a performance limit state model for the entire life cycle.

[0087] It can be understood that the limit state function in this step is a mathematical function that describes whether the structure meets the design requirements. For example, for the bearing capacity of the structure, the limit state function can be expressed as the difference between the actual bearing capacity of the structure and the designed bearing capacity. The form of the limit state function needs to reflect the design requirements and allowable performance limits of each key indicator.

[0088] The limit state functions of all key indicators are combined to form a performance limit state model for the entire life cycle. This model should take into account the performance requirements of different life cycle stages, including the construction stage, operation stage, and maintenance stage. The full life cycle model needs to consider the impact of all possible environmental factors and degradation processes on structural performance. The weighted average method or other multi-objective optimization methods can be used to combine the various limit state functions to ensure that the model can fully reflect the comprehensive performance of the structure.

[0089] After the preliminary model is established, the model needs to be calibrated through historical data, tunnel joint-lining-surrounding rock performance evolution equation, tunnel joint-lining-surrounding rock performance degradation model and field monitoring data. By comparing the limit state predicted by the model with the actual observation data, the model parameters are adjusted to improve the accuracy and reliability of the model. This process can use optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, to minimize the error between the model prediction value and the actual value. By combining the limit state function method with the key indicator system, a model that comprehensively reflects the performance of the shield tunnel structure can be established. This model can not only evaluate the performance in the current state, but also predict the performance of the structure throughout its life cycle, thereby helping designers identify potential risk factors and optimize them in the design stage to improve the overall safety and reliability of the structure.

[0090] Among them, the limit state based on surrounding rock strain is calculated as follows:

[0091] ;

[0092] in, represents the limit state value of surrounding rock strain, is the tunnel surrounding rock strain; is its critical strain; is the tunnel radius; is the total radial inward deformation of the cave wall; is the deformation modulus; is Poisson's ratio; is the initial geostress field; For support resistance; is the radius of the plastic zone; is the support resistance at the elastic-plastic interface;

[0093] Among them, the compressive limit state of the positive section built based on the current concrete structure design specifications is calculated as follows:

[0094] ;

[0095] In the formula, It represents the compressive limit state value of the positive section of the concrete structure. is the bending moment value of the positive section of the lining structure, is the thickness of the tube segment, is the bending limit value of the lining positive section; is the stress equivalent coefficient of the rectangular stress diagram; is the design value of concrete axial compressive strength; is the cross-section width; is the height of the compression zone; is the effective height of the section, i.e. the distance from the combined force point of the tensile reinforcement to the edge of the compression zone of the section; is the design value of compressive strength of the compression steel bar; is the cross-sectional area of ​​the compressive reinforcement; It is the distance from the combined force point of the compression reinforcement to the compression edge of the section; is the axial force on the lining section; is the additional eccentricity; It is the distance from the combined force point of the tensile reinforcement to the tensile edge;

[0096] Among them, the positive section crack resistance limit state based on the current concrete structure design specifications is calculated as follows:

[0097] ;

[0098] in, It represents the ultimate limit state value of the positive section crack resistance of the concrete structure. is the maximum crack width limit; is the calculated value of the maximum crack width of the lining section; is the member force characteristic coefficient, and is taken as 1.9 when checking the eccentric compression of reinforced concrete members; is the strain non-uniformity coefficient of the longitudinal tensile reinforcement between cracks, When , take 0.2, when When , take 1.0; is the stress of the longitudinal reinforcement in the tension zone of reinforced concrete members; is the elastic modulus of the steel bar; is the distance from the outer edge of the outermost longitudinal tensile reinforcement to the edge of the tensile zone. When , take it as 20, when When , take 65; is the equivalent diameter of the longitudinal reinforcement in the tension zone; is the longitudinal tensile reinforcement ratio, when When , take 0.01;

[0099] Among them, the limit state based on the overall deformation of the shield tunnel is calculated as follows:

[0100] ;

[0101] in, Represents the limit state value of the overall deformation of the shield tunnel, is the diameter convergence limit; is the calculated value of horizontal diameter / vertical diameter convergence; is the horizontal diameter deformation value of the lining ring; is the vertical diameter deformation value of the lining ring; is the vertical water and soil pressure; is the lateral water and soil pressure at the top of the tunnel; is the lateral water and soil pressure at the bottom of the tunnel; The deadweight of the lining; is the calculated radius of the tunnel; is the lining section stiffness reduction factor; is the elastic modulus of the lining; is the moment of inertia of the lining section; is the foundation bed coefficient;

[0102] Among them, the limit state based on the anti-floating property of the segment is calculated as follows:

[0103] ;

[0104] in, Indicates the limit state value of the segment's anti-floating property, The safety factor is the load against uplift divided by the buoyancy. When the value is greater than 1, the tunnel will not uplift. is the thickness of the covering soil; is the thickness of soil cover to the groundwater level; The deadweight per unit area of ​​the segment, reflected in the internal load middle; For ground overload; is the outer radius of the tunnel; is the bulk density of soil; is the buoyant density of soil; is the weight of water;

[0105] Among them, the limit state based on the bending resistance of the annular joint is calculated as follows:

[0106] ;

[0107] in, represents the limit state value of the segment's anti-floating property, represents the ultimate compressive strain of concrete, is the integral symbol, represents the integral variable between the effective geometric dimensions of the joint surface, is the ultimate bending bearing capacity of the annular joint; Calculate bending moments for joint surfaces; Calculate axial forces for joint surfaces; is the segment width; is the thickness of the segment; is the segment size of the joint surface geometry; is the number of effective geometric dimensions of the joint surface; is the integral variable between the effective geometric dimensions of the joint surface; is the concrete stress, is the height of the compression zone on the joint surface Function of is the number of bolts in the circumferential joint; is the bolt stress; is the bolt diameter; is the angle between the bolt and the joint surface; It is the distance from the joint bolt action point to the outer edge of the segment joint surface;

[0108] Among them, the limit state based on the shear resistance of the longitudinal joint is calculated as follows:

[0109] ;

[0110] in, represents the limit state value of the shear resistance of the longitudinal joint, is the design shear capacity of the joint bolts; Calculate shear forces for structures; is the ultimate shear bearing capacity of the bolt; is the standard value of the shear yield strength of the bolt; is the effective cross-sectional area of ​​the bolt shear surface; is the bolt material partial factor; is the partial factor of the longitudinal joint member; is the ultimate shear bearing capacity of concrete; is the standard value of tensile strength of concrete; is the projection area of ​​the failure surface corresponding to a single joint; is the concrete material partial factor.

[0111] Step S800, calculating the dynamic reliability of the shield tunnel structure based on the performance limit state model, and performing multi-objective optimization based on the calculated dynamic reliability to obtain an optimal combination of structural design parameters;

[0112] It can be understood that step S800 includes step S801, step S802 and step S803.

[0113] Step S801, generating random samples based on the probability distribution of key design parameters of surrounding rock and lining, and calculating the limit state function of each random sample based on the performance limit state model;

[0114] Step S802: estimating the reliability of the limit state function of each random sample based on a preset reliability estimation formula;

[0115] It can be understood that the estimation formula of reliability in this step is as follows:

[0116] ;

[0117] in, Represents reliability, represents the total number of samples, is the indicator function, when The value is 1 when , otherwise it is 0.

[0118] Step S803: Obtain the optimal design parameter combination based on a preset multi-objective optimization algorithm.

[0119] It can be understood that the constraints of the multi-objective optimization algorithm in this step are as follows:

[0120] ;

[0121] ;

[0122] ;

[0123] in, The representative long life design must meet the requirements of minimum construction and operation costs; The generation needs to meet the requirements of long life design index and maximum reliability; is a weight function for comprehensively considering the importance of reliability of various performances of shield tunnels; It is a reliability index for each performance of long life design; To consider the limit state of load and resistance evolution; It is the allowable reliability within the working life of the shield tunnel structure.

[0124] Step S900: Perform a cost analysis based on the optimal combination of structural design parameters based on the life cycle cost analysis method to obtain a design plan for the maintenance and replacement of the shield tunnel structure, and obtain a final long-life design plan.

[0125] It can be understood that step S900 includes step S901 and step S902.

[0126] Step S901, determining all cost elements of shield tunnel structure maintenance based on the optimal structural design parameter combination, wherein the cost elements include operation and maintenance costs;

[0127] Step S902: Perform cost calculation based on all cost elements, and reselect the cost design scheme with the lowest operation and maintenance cost and construction cost.

[0128] It can be understood that this step uses the life cycle cost analysis (LCCA) method to conduct an economic evaluation of the optimized structural design parameter combination to develop a maintenance and replacement design plan for the shield tunnel structure and finally determine the long-life design plan. This process comprehensively considers the cost investment in the entire life cycle of the tunnel, including the initial construction cost, regular maintenance costs, emergency maintenance expenses, and replacement costs when necessary. The core of LCCA is to quantify the costs of each stage and adjust the time value. The accuracy of the cost estimate can be ensured by discounting the initial construction cost and future maintenance and replacement costs. The discount rate used here needs to be set according to the inflation rate, discount rate and the economic environment of tunnel operation to ensure that each future expenditure has a reasonable estimate based on the present value. This step not only ensures the long-life operation of the tunnel, but also achieves the best economic performance, that is, the total cost is minimized throughout the life cycle. The formulation process of this plan ensures that the rationality of economic investment is balanced with the safety and reliability of the tunnel structure, thereby achieving dual optimization of technology and economy.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0130] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A design method for a shield tunnel structure, characterized in that: include: Classifying the preset first information based on the K-means clustering algorithm, and analyzing the evolution trend of the first information based on the classification result to obtain the key environmental parameters of multi-field coupling evolving over time, wherein the first information includes environmental conditions of the area where the shield tunnel is located, hydrological and geological exploration data, and regional environmental historical evolution data; Based on the multi-field coupling, the key environmental parameters that evolve over time are monitored on site, tested indoors and simulated numerically. The least squares method is used to fit the stress-strain relationship of the material to obtain the material constitutive model, and the adaptive model of the degradation state that changes over time is established by combining Bayesian updating with preset prior data. Based on the material constitutive model and the degradation state adaptive model, the key design parameters of the surrounding rock and lining are collected and counted, and the key design parameters of the surrounding rock and lining are randomly sampled through Monte Carlo simulation to obtain the probability distribution and statistical characteristics of the key design parameters of the surrounding rock and lining; Finite element analysis is performed based on probability distribution and statistical characteristics, and a numerical simulation model of the shield tunnel structure in a multi-field coupling environment is established; Based on the calculation results of the numerical simulation model, regression analysis was performed to obtain the tunnel joint-lining-surrounding rock performance evolution equation and degradation model; Based on the tunnel joint-lining-surrounding rock performance evolution equation, the hierarchical analysis method is used to evaluate the influence of a preset number of important performance indicators of the tunnel structure, and the key indicator system for long-life design is obtained; The performance limit state model of the shield tunnel throughout its life cycle is established based on the key indicator system and the limit state function method; The dynamic reliability of the shield tunnel structure is calculated based on the performance limit state model, and multi-objective optimization is performed based on the calculated dynamic reliability to obtain the optimal combination of structural design parameters; Based on the life cycle cost analysis method, the cost analysis is carried out according to the optimal combination of structural design parameters, and the design scheme for the maintenance and replacement of the shield tunnel structure is obtained, and the final long-life design scheme is obtained; Among them, the limit state based on surrounding rock strain is calculated as follows: Where, Z1 represents the limit state value of surrounding rock strain, ε is the tunnel surrounding rock strain; cr is its critical strain; R is the tunnel radius; is the total radial inward deformation of the cave wall; E is the deformation modulus; v is the Poisson's ratio; P0 is the initial ground stress field; p s is the support resistance; R pl is the radius of the plastic zone; p eq is the support resistance at the elastic-plastic interface; Among them, the compressive limit state of the positive section built based on the current concrete structure design specifications is calculated as follows: Where Z2 represents the compressive limit state value of the positive section of the concrete structure, M is the bending moment value of the positive section of the lining structure, h is the thickness of the segment, and M u is the bending limit of the lining cross section; α1 is the stress equivalent coefficient of the rectangular stress diagram; f c is the design value of the concrete axial compressive strength; b is the section width; x is the height of the compression zone; h0 is the effective height of the section, that is, the distance from the combined force point of the tensile reinforcement to the edge of the compression zone of the section; f' y is the design value of compressive strength of the compressive reinforcement; A' s is the cross-sectional area of ​​the compressive reinforcement; a' s is the distance from the combined force point of the compression steel bars to the compression edge of the section; N is the axial force on the lining section; e a is the additional eccentricity; a s It is the distance from the combined force point of the tensile reinforcement to the tensile edge; Among them, the positive section crack resistance limit state based on the current concrete structure design specifications is calculated as follows: Where Z3 represents the positive section crack resistance limit state value of the concrete structure, w lim is the maximum crack width limit; w max is the calculated value of the maximum crack width of the lining section; α cr is the stress characteristic coefficient of the component, and is taken as 1.9 when checking the eccentric compression of reinforced concrete components; ψ is the strain non-uniformity coefficient of the longitudinal tensile reinforcement between cracks, and is taken as 0.2 when ψ<0.2, and is taken as 1.0 when ψ>1.0; σ s E is the stress of the longitudinal reinforcement in the tension zone of reinforced concrete components; s is the elastic modulus of the steel bar; c s is the distance from the outer edge of the outermost longitudinal tensile reinforcement to the edge of the tensile zone. s When <20, take 20. s >65, take 65; d eq is the equivalent diameter of the longitudinal reinforcement in the tension zone; ρ te is the longitudinal tensile reinforcement ratio, when ρ te When <0.01, take 0.01; Among them, the limit state based on the overall deformation of the shield tunnel is calculated as follows: Among them, Z4 represents the limit state value of the overall deformation of the shield tunnel, δ0 is the diameter convergence limit; δ is the calculated value of the horizontal diameter / vertical diameter convergence; δ H is the horizontal diameter deformation value of the lining ring; δ V is the vertical diameter deformation value of the lining ring; P V is the vertical water and soil pressure; P H1 is the lateral water and soil pressure at the top of the tunnel; P H2 is the lateral water and soil pressure at the bottom of the tunnel; g is the deadweight of the lining; R c is the calculated radius of the tunnel; η is the lining section stiffness reduction factor; E is the lining elastic modulus; I is the lining section inertia moment; k is the foundation bed coefficient; Among them, the limit state based on the anti-floating property of the segment is calculated as follows: Among them, Z5 represents the limit state value of the anti-floating of the segment, F s is the safety factor of the load to resist buoyancy divided by the buoyancy. When its value is greater than 1, the tunnel will not float. H is the thickness of the cover soil. W is the thickness of the covering soil to the groundwater level; g is the deadweight per unit area of ​​the segment, which is reflected in the internal load P i P0 is the ground overload; R is the outer radius of the tunnel; γ is the bulk density of the soil; γ' is the floating bulk density of the soil; γ w is the weight of water; Among them, the limit state based on the bending resistance of the annular joint is calculated as follows: Where Z6 represents the limit state value of the anti-floating property of the segment, ε cu represents the ultimate compressive strain of concrete, d is the integral symbol, p represents, M ujf M is the ultimate bending bearing capacity of the annular joint; jf Calculate the bending moment for the joint surface; N jf is the axial force calculated for the joint surface; b is the width of the segment; h is the thickness of the segment; d i is the geometric segment size of the joint surface; m is the number of effective geometric dimensions of the joint surface; p is the integral variable between the effective geometric dimensions of the joint surface; σ is the concrete stress, which is a function of the height x of the compression zone of the joint surface; n bolt is the number of bolts in the circumferential joint; σ bolt is the bolt stress; d bolt is the bolt diameter; θ is the angle between the bolt and the joint surface; l bolt It is the distance from the joint bolt action point to the outer edge of the segment joint surface; Among them, the limit state based on the shear resistance of the longitudinal joint is calculated as follows: Among them, Z7 represents the limit state value of longitudinal joint shear resistance, Q u is the design shear bearing capacity of the joint bolts; Q is the calculated shear force of the structure; Q bu is the ultimate shear bearing capacity of the bolt; f vyk A is the standard value of the shear yield strength of the bolt; b is the effective cross-sectional area of ​​the bolt shear surface; m is the bolt material partial factor; γ b is the partial factor of the longitudinal joint member; Q cu is the ultimate shear bearing capacity of concrete; f tk A is the standard value of tensile strength of concrete; ci is the projection area of ​​the failure surface corresponding to a single joint; γ c is the concrete material partial factor.

2. The design method of a shield tunnel structure according to claim 1, characterized in that ,Based on the K-means clustering algorithm, the preset first information is classified, and the evolution trend of the first information is analyzed based on the classification results, and the key environmental parameters of the multi-field coupling evolving over time are obtained, including: Obtain hydrological data, geological exploration data and regional environmental historical evolution data as the first information; Clustering the first information based on a K-means clustering algorithm to obtain a plurality of clusters, each cluster representing an environment category; The shape changes of the time series data of each environmental category are analyzed using a dynamic time warping algorithm, and the environmental parameter data whose shape changes are greater than a preset value within a preset time are taken as the key environmental parameters of the multi-field coupling evolving over time.

3. The design method of a shield tunnel structure according to claim 1, characterized in that , based on the multi-field coupling of key environmental parameters evolving over time, field monitoring, indoor experiments and numerical simulations are carried out. The least squares method is used to fit the stress-strain relationship of the material to obtain the material constitutive model, and further combined with Bayesian updating and preset prior data to establish an adaptive model of degradation state that changes over time, including: Based on the key environmental parameters that evolve over time due to multi-field coupling, a sensor network is deployed for real-time monitoring to obtain real-time monitoring data, which includes stress, strain, and temperature and humidity change data of actual materials in a multi-field coupling environment; In a laboratory environment, a mechanical property test is performed on samples obtained from the field to obtain indoor experimental data, which are stress-strain relationship data of the material; The material behavior is simulated based on real-time monitoring data and indoor experimental data, and the simulated data and real-time monitoring data are calculated based on the least squares method to obtain the material parameters with the smallest difference between the predicted stress and the experimental stress, thereby establishing the constitutive model of the material; The material constitutive model obtained by the least squares method is combined with the preset prior data, and the model is modified by the Bayesian updating method. The posterior distribution of the model parameters is gradually adjusted by combining the new observation data through the Bayesian updating method to obtain an adaptive model of the degradation state that changes with time.

4. The design method of a shield tunnel structure according to claim 1, characterized in that ,Based on the material constitutive model and the degradation state adaptive model, the key design parameters of the surrounding rock and lining are collected and counted, and the key design parameters of the surrounding rock and lining are randomly sampled through Monte Carlo simulation to obtain the probability distribution and statistical characteristics of the key design parameters of the surrounding rock and lining, including: According to the collected data based on the material constitutive model and the degradation state adaptive model, the statistical characteristics of the key design parameters of the surrounding rock and lining are obtained through statistical analysis, wherein the statistical analysis includes calculating its standard deviation and determining the degree of dispersion of each standard deviation; According to the statistical characteristics of the key design parameters of the surrounding rock and lining, the probability distribution model of the key design parameters of the surrounding rock and lining is obtained by comparing with the preset probability distribution model; Based on Monte Carlo simulation, random samples of key design parameters of surrounding rock and lining are generated, and the mean, variance, skewness and kurtosis of the samples of key design parameters of surrounding rock and lining are calculated to obtain the statistical characteristics of the key design parameters of surrounding rock and lining.

5. The design method of a shield tunnel structure according to claim 1, characterized in that ,Based on probability distribution and statistical characteristics, finite element analysis is performed to establish a numerical simulation model of shield tunnel structure in a multi-field coupling environment, including: Performing data standardization processing on the probability distribution and statistical characteristics to obtain normalized parameter data; Based on the normalized design parameter data, a preliminary finite element model of the shield tunnel structure is established, wherein the preliminary finite element model includes the surrounding rock, lining structure and connection mode of the tunnel, and the mesh division is initially based on the overall geometric shape and material properties; Using the preliminary finite element model to perform simulation calculations, the stress, strain and displacement results of the shield tunnel structure are calculated; The area where the error is greater than the preset threshold is calculated based on the gradient estimation method, and the mesh of the area where the error is greater than the preset threshold is refined to regenerate a finer finite element mesh; The error estimation and mesh adjustment steps are repeated until the calculation results of the model meet the preset accuracy requirements, and the numerical simulation model of the shield tunnel structure in a multi-field coupling environment is obtained.

6. The design method of a shield tunnel structure according to claim 1, characterized in that ,Based on the calculation results of the numerical simulation model, a regression analysis is performed to obtain the tunnel joint-lining-surrounding rock performance evolution equation and degradation model, including: A regression model is constructed based on the calculation results of the numerical simulation model, including the mechanical properties data of the tunnel joints, lining and surrounding rock under different working conditions; Based on the calculation results of the numerical simulation model, the performance evolution equation of the tunnel joint-lining-surrounding rock performance is obtained through processing by high-dimensional sparse regression method; The regression model of the tunnel joint-lining-surrounding rock performance is subjected to fault tree analysis, wherein the fault tree analysis includes defining failure events of the shield tunnel structure and their causes, and obtaining a degradation model of the tunnel structure by analyzing the probability of occurrence and mutual relationship of each failure mode.

7. The design method of a shield tunnel structure according to claim 1, characterized in that ,Based on the tunnel joint-lining-surrounding rock performance evolution equation, the hierarchical analysis method is used to evaluate the influence of a preset number of important performance indicators of the tunnel structure, and the key indicator system for long-life design is obtained, including: The long life design goal of the tunnel structure is taken as the top layer, a preset number of key performance indicators are taken as the second layer, and the factors affecting the key performance indicators are taken as the third layer; Establish a judgment matrix of key performance indicators and influencing factors through historical data; The weight of each indicator is calculated using the weighted average method, and the consistency of the matrix is ​​judged. If the consistency ratio is less than the preset threshold, the comprehensive weight of each performance indicator is obtained; The weight of each performance index is allocated based on the comprehensive weight to obtain a key index system for long life design.

8. The design method of a shield tunnel structure according to claim 1, characterized in that ,Based on the key indicator system and limit state function method, a performance limit state model of the shield tunnel throughout its life cycle is established, including: According to the indicators in the key indicator system, a limit state function of each indicator is defined, wherein the limit state function represents the limit state of the structure; The limit state functions of all key performance indicators are integrated to form a performance limit state model for the entire life cycle.

9. The design method of a shield tunnel structure according to claim 1, characterized in that ,The dynamic reliability of the shield tunnel structure is calculated based on the performance limit state model, and multi-objective optimization is performed based on the calculated dynamic reliability to obtain the optimal structural design parameter combination, including: Generate random samples based on the probability distribution of key design parameters of surrounding rock and lining, and calculate the limit state function of each random sample based on the performance limit state model; Estimate the reliability of the limit state function of each random sample based on a preset reliability estimation formula; The optimal design parameter combination is obtained based on the preset multi-objective optimization algorithm.

10. The design method of a shield tunnel structure according to claim 1, characterized in that Based on the life cycle cost analysis method, the cost analysis is carried out according to the optimal structural design parameter combination, and the design scheme for the maintenance and replacement of the shield tunnel structure is obtained, and the final long-life design scheme is obtained, including: Determine all cost elements related to shield tunnel structure maintenance based on the optimal structural design parameter combination, wherein the cost elements include operation and maintenance costs; Cost calculation is performed based on all cost factors, and the cost design plan with the lowest operation and maintenance cost and construction cost is reselected.

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