Optimization method and system for shield tunnel roof thickness under simultaneous construction of subway tunnel and station

Abstract

The present invention belongs to the technical field of synchronous construction of subway tunnels and stations, and discloses a method and system for optimizing the top slab thickness of a shield tunnel during synchronous construction of a subway tunnel and station. The method comprises: establishing a three-dimensional numerical model, employing a multi-field coupling analysis using the explosive detonation JWL model, the rock mass dynamic response ALE method, and the segment damage CSCM model; extracting the vibration velocity, maximum tensile stress, and joint deformation indicators of the segments under different top slab thicknesses; performing iterative optimization of the top slab thickness; and verifying and providing feedback based on the iterative optimization results in combination with on-site monitoring data. The present invention utilizes dynamic damage thresholds and multi-index criteria to ensure structural safety. Probabilistic optimization reduces redundant design, improves optimization efficiency, and saves costs. Rapid response under complex geological conditions enhances construction reliability.

Description

Technical Field

[0001] The present invention belongs to the technical field of subway tunnel station synchronous construction, and in particular relates to a method and system for optimizing the thickness of a shield tunnel top plate during subway tunnel station synchronous construction. Background Art

[0002] With the rapid development of urban rail transit, the synchronous construction technology of subway tunnels and stations has become a key means to improve construction efficiency and shorten construction period. The shield method is widely used in tunnel construction due to its advantages such as small disturbance to the stratum and high degree of mechanization. However, during the synchronous construction of stations and tunnels, the dynamic load generated by the blasting and excavation of the upper rock mass will have a significant impact on the lower shield tunnel segment structure. If the top plate thickness is not designed properly, it is very easy to cause segment cracking, joint leakage and even structural instability. Traditional top plate thickness design mostly relies on empirical formulas or static analysis, which is difficult to accurately reflect the transient effects of blasting dynamic loads, resulting in conservative design or insufficient safety. In recent years, numerical simulation technology (such as finite element method and discrete element method) has gradually been introduced into the field of tunnel engineering, but its application in the coupled analysis of blasting dynamic response and structural damage still has limitations.

[0003] Research on tunnel structural response under blasting loads has made some progress. However, existing numerical simulation techniques fail to implement multi-field coupled analysis of explosive detonation (JWL model), rock mass dynamic response (ALE method), and segment damage (CSCM model), making it impossible to accurately capture the blasting load transmission path and structural damage evolution. Existing safety assessments often rely on single indicators (such as maximum stress or vibration velocity), ignoring the dynamic damage threshold of concrete and the coordinated control of joint deformation, resulting in one-sided safety criteria. Existing methods also fail to iteratively modify numerical models based on field monitoring data, resulting in significant deviations between design results and actual operating conditions, making them difficult to adapt to complex geological conditions. Summary of the Invention

[0004] In order to overcome the problems existing in the related art, the embodiments disclosed in the present invention provide a method and system for optimizing the thickness of the top plate of a shield tunnel under the synchronous construction of a subway tunnel and a station.

[0005] The technical solution is as follows: A method for optimizing the top slab thickness of a shield tunnel during synchronous construction of a subway tunnel and station, comprising the following steps:

[0006] S1, establish a three-dimensional numerical model based on tunnel geological parameters, blasting parameters and shield segment parameters;

[0007] S2, based on the established three-dimensional numerical model, adopts the multi-field coupling analysis of explosive detonation JWL model, rock mass dynamic response ALE method and segment damage CSCM model;

[0008] S3, based on the results of multi-field coupling analysis, extract the vibration velocity, maximum tensile stress, and joint deformation indicators of the segments under different roof thicknesses;

[0009] S4: Iteratively optimize the roof thickness based on the extracted vibration velocity, maximum tensile stress, and joint deformation indicators of the pipe segments under different roof thicknesses; verify and provide feedback based on the iterative optimization results combined with on-site monitoring data.

[0010] In step S1, a three-dimensional numerical model is established, including:

[0011] After on-site investigation and design drawings of the tunnel and station, the geological parameters, blasting parameters and segment parameters are determined;

[0012] According to the locations of the station excavation area, shield tunnel, surrounding rock mass and blast source, a geometric model of the dynamic response of drilling and blasting excavation to the segments under the conditions of synchronous construction of the tunnel and station was constructed.

[0013] In step S2, the explosive detonation JWL model adopts the MAT-HIGH-EXPLOSIVE-BURN material model and uses the JWL state equation to calculate the detonation pressure. The JWL state equation is as follows:

[0014] ;

[0015] Where, is the detonation pressure, are all material parameters, is the relative volume, is the internal energy per unit volume, is a constant;

[0016] The air model uses the MAT-NULL material model and uses the EOS-LINEAR-POLYNOMIAL linear polynomial equation to describe the state. The expression is:

[0017] ;

[0018] Where, is the air constant,

[0019] , is a dimensionless parameter, , is the density, is the density under standard conditions, is the internal energy of the material;

[0020] Simplified to the ideal gas model:

[0021] ;

[0022] Where, is the specific heat ratio, ;

[0023] The JWL model of explosive detonation provides the energy release law of the explosion source, and the air model simulates the propagation dynamics of the shock wave in the air. The JWL model of explosive detonation and the air model realize the interaction between energy and momentum through the fluid-structure interaction method;

[0024] The CSCM model for segment damage includes compression damage, tensile cracking, strain rate effects, and stiffness degradation. MAT_PLASTIC_KINEMATIC is used to simulate steel bars, which are connected to the segments via common nodes or CONTACT_TIE to ensure stress transfer.

[0025] The JH-2 model is used for rock mass, and the progressive failure process of the material is described by damage accumulation and strain rate effect. The ALE fluid model uses an Eulerian grid, and the structural model uses a Lagrangian grid, and the two are coupled using the CONSTRAINED_LAGRANGE_IN_SOLID method.

[0026] The stations, tunnels, rock masses and air are meshed together, and the mesh density at the flow-solid interface is increased;

[0027] Set initial geostress, non-reflecting boundary conditions, and solution controls.

[0028] In step S3, the vibration velocity, maximum tensile stress, and joint deformation indexes of the segments under different roof thicknesses are extracted, including:

[0029] Perform spectrum analysis on the vibration velocity at key locations, identify the main frequency components, and analyze whether the main vibration frequency is close to the natural frequency of the segment;

[0030] The segment damage CSCM model is used to output the principal stress contour of the segment, and LS-PrePost is used to identify the maximum tensile stress area and the stress peak under each working condition.

[0031] The normal displacement difference of the nodes on both sides of the segment joint is monitored to obtain the joint opening amount, and the tangential displacement difference of the nodes on both sides of the segment joint is monitored to obtain the joint dislocation amount.

[0032] In step S4, the top plate thickness is iteratively optimized, including:

[0033] Input parameters;

[0034] Select the uncertain parameters as random variables, including elastic modulus , internal friction angle , single hole explosive , the center of explosion distance , segment thickness ;

[0035] Extracted using Latin hypercube sampling Group samples,uniformly distributed covering the random variable space;

[0036] Construct Kriging metamodel based on sample points Enter its response , output proxy model;

[0037] The Kriging metamodel was used to calculate the Sobol' index for sensitivity analysis to obtain the contribution of each parameter to the response;

[0038] Monte Carlo sampling method was used to extract group, input the Kriging metamodel to calculate the tunnel failure probability;

[0039] Draw a curve showing the relationship between the safety roof thickness and the failure probability to determine the critical safety roof thickness; multiply the critical thickness by the safety factor to obtain the final design thickness.

[0040] Furthermore, Latin hypercube sampling method is used to extract Group samples, uniformly distributed over the space of random variables, including:

[0041] For each parameter , whose value range is divided into There are equal probability intervals, and a sample point is randomly selected in each interval, and it is ensured that the sample points of each parameter are not repeated:

[0042] ;

[0043] Where, For parameters The inverse function of the cumulative distribution function CDF is, For the The sample in The order of the parameter dimensions, is a random offset within the interval.

[0044] Furthermore, we construct a Kriging meta-model based on the sample points Enter its response , output proxy model, including:

[0045] The Kriging model assumes that the response is a Gaussian process:

[0046] ;

[0047] Where, is the global mean, is a zero-mean Gaussian process;

[0048] The covariance function is:

[0049] ;

[0050] Where, is the covariance function, is a zero-mean Gaussian process, is the Gaussian process variance, is the related function, and Input point, is a hyperparameter, optimized by maximum likelihood estimation MLE;

[0051] The related function is:

[0052] ;

[0053] Where, is the dimension of the input space, is the dimension index, For the dimensional scaling parameters, For the The first point Dimension value, For the The first point Dimension value.

[0054] Furthermore, the Kriging metamodel was used to calculate the Sobol' index for sensitivity analysis to obtain the contribution of each parameter to the response, including:

[0055] Total variance decomposition:

[0056] ;

[0057] Where, For the The main effect variance of the parameter, For the and The interaction effect variance of the parameters, and is the index of the input parameter, is the variance of the higher-order interaction effects of all parameters;

[0058] The total effect Sobol' index is:

[0059] ;

[0060] Where, To exclude parameters The variance after .

[0061] Furthermore, Monte Carlo sampling method is used to extract Group, input Kriging meta-model to calculate tunnel failure probability, including:

[0062] Define the failure function:

[0063] ;

[0064] Where, is the allowable stress, is the maximum stress;

[0065] Then the failure probability is:

[0066] ;

[0067] Where, is the number of Monte Carlo samples, for 1 if yes, 0 otherwise.

[0068] Another object of the present invention is to provide a system for optimizing the top slab thickness of a shield tunnel under synchronous construction of a subway tunnel and a station. The system implements the method for optimizing the top slab thickness of a shield tunnel under synchronous construction of a subway tunnel and a station. The system comprises:

[0069] A 3D numerical model building module is used to build a 3D numerical model based on tunnel geological parameters, blasting parameters, and shield segment parameters;

[0070] The multi-field coupling analysis module is used for multi-field coupling analysis based on the established three-dimensional numerical model, using the explosive detonation JWL model, the rock mass dynamic response ALE method and the segment damage CSCM model;

[0071] The index extraction module is used to extract the vibration velocity, maximum tensile stress, and joint deformation indicators of the segments under different roof thicknesses based on the results of multi-field coupling analysis;

[0072] The roof thickness iterative optimization module is used to iteratively optimize the roof thickness based on the extracted vibration velocity, maximum tensile stress, and joint deformation indicators of the pipe segments under different roof thicknesses; verification and feedback are carried out based on the iterative optimization results combined with on-site monitoring data.

[0073] In combination with all the above technical solutions, the beneficial effects of the present invention are as follows:

[0074] First, this invention introduces the ALE-Lagrange fluid-solid coupling technology for the first time in the design of shield tunnel roof thickness under the conditions of synchronous tunnel and station construction, accurately capturing the propagation path of the explosion shock wave in the rock mass and the dynamic response of the tunnel segments; outputting dynamic damage evolution data through the CSCM model, combined with joint deformation monitoring, forms a multi-dimensional safety assessment system; identifying key parameters through Sobol's index sensitivity analysis, achieving probabilistic design of roof thickness, and avoiding over-reliance on safety factors; combining on-site monitoring data feedback with parameter sensitivity analysis to improve model modification efficiency and shorten the design verification cycle.

[0075] Second, this optimization method achieves dynamic optimization of shield tunnel roof thickness through multi-field coupling simulation, uncertainty quantification analysis, and real-time feedback of field data. This directly promotes the safety, economy, and efficiency upgrades of subway tunnel and station synchronous construction technology. Its core value is reflected in:

[0076] Safety risks are controllable: dynamic damage threshold and multi-index criteria ensure structural safety.

[0077] Efficient resource allocation: Probabilistic optimization reduces redundant design, improves optimization efficiency, and saves costs.

[0078] Enhanced engineering adaptability: rapid response under complex geological conditions and improved construction reliability.

[0079] This technology can be widely used in similar engineering scenarios (such as mining tunnels) and has significant promotion value.

[0080] Third, by constructing a dynamic response analysis framework and multi-parameter uncertainty quantification method, this invention significantly improves the accuracy and safety of shield tunnel roof thickness design. This effectively reduces construction risks, material waste, and subsequent maintenance costs, thereby improving the economic efficiency and construction efficiency of subway projects. This technological achievement can be directly applied to urban rail transit construction and has broad market prospects and industrial potential.

[0081] The present invention combines the JWL detonation model, CSCM damage model, ALE fluid-structure coupling and Kriging metamodel to construct a full-process technical system covering dynamic response, failure probability analysis and optimization design, filling the gap in this field in structural safety assessment and thickness optimization under complex working conditions.

[0082] Fourth, traditional design methods struggle to accurately predict the dynamic damage effects of blasting vibration on shield segments during simultaneous construction, and they fail to account for the combined impact of multi-parameter uncertainty on roof safety. This invention, through dynamic simulation and probabilistic analysis, achieves the first quantitative correlation modeling between roof thickness and failure probability. This addresses the long-standing core challenge of quantifying the coupled relationship between dynamic disturbance, structural response, and safety threshold, providing reliable technical support for shield tunnel design under complex geological conditions.

[0083] Fifth, the present invention combines the JWL detonation model, the CSCM damage model, the ALE fluid-structure coupling and the Kriging element model to establish a new technical path of "dynamic-probability-optimization", which provides a certain reference for the design of the shield tunnel roof thickness under the conditions of synchronous construction of the tunnel and station. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure;

[0085] Figure 1 This is a flow chart of a method for optimizing the top plate thickness of a shield tunnel during synchronous construction of a subway tunnel and station provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0086] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0087] The innovation of the present invention lies in: by constructing a dynamic response model that integrates the JWL detonation model, the CSCM damage model and the ALE fluid-structure coupling, and combining it with the uncertainty quantification method of the Latin hypercube-Kriging element model, the present invention can achieve accurate prediction of the failure probability of the roof under the coupling effect of multiple parameters (such as elastic modulus, explosion center distance, segment thickness, etc.); based on sensitivity analysis and failure probability curves, a multi-objective optimization design method for the roof thickness is proposed, which takes into account both structural safety and construction economy, and effectively solves the complexity and uncertainty problems of the shield tunnel roof thickness design under synchronous construction disturbance.

[0088] Example 1, as Figure 1 As shown, the method for optimizing the top plate thickness of a shield tunnel during the synchronous construction of a subway tunnel and a station provided by an embodiment of the present invention includes the following steps:

[0089] S1, establish a three-dimensional numerical model based on tunnel geological parameters, blasting parameters and shield segment parameters;

[0090] S2, based on the established three-dimensional numerical model, adopts the multi-field coupling analysis of explosive detonation JWL model, rock mass dynamic response ALE method and segment damage CSCM model;

[0091] S3, based on the results of multi-field coupling analysis, extract the vibration velocity, maximum tensile stress, and joint deformation indicators of the segments under different roof thicknesses;

[0092] S4: Iteratively optimize the roof thickness based on the extracted vibration velocity, maximum tensile stress, and joint deformation indicators of the pipe segments under different roof thicknesses; verify and provide feedback based on the iterative optimization results combined with on-site monitoring data.

[0093] For example, in step S1, the specific steps of establishing a three-dimensional numerical model are as follows:

[0094] After on-site investigation and design drawings of the tunnel and station, the geological parameters, blasting parameters and segment parameters are determined;

[0095] According to the locations of the station excavation area, shield tunnel, surrounding rock mass and blast source, a geometric model of the dynamic response of drilling and blasting excavation to the segments under the conditions of synchronous construction of the tunnel and station was constructed.

[0096] For example, in step S2, the specific steps of the multi-field coupling analysis using the explosive detonation JWL model, the rock mass dynamic response ALE method, and the segment damage CSCM model are as follows:

[0097] During the simultaneous construction of subway tunnels and stations, optimizing the shield tunnel roof thickness requires comprehensive consideration of the dynamic loads from explosive blasting, the dynamic response of the rock mass, and the damage evolution of the segment structure. The rock mass dynamic response ALE method utilizes the fluid-structure interaction model (ALE). By combining the JWL model of explosive detonation, the ALE model, and the CSCM model of segment damage, it enables multi-physics coupling analysis and accurately predicts rock-segment interactions under blasting impact.

[0098] The JWL model of explosive detonation adopts the MAT-HIGH-EXPLOSIVE-BURN material model and uses the JWL state equation to calculate the detonation pressure. The JWL state equation is as follows:

[0099] ;

[0100] Where, is the detonation pressure, are all material parameters, is the relative volume, is the internal energy per unit volume, is a constant;

[0101] The air model uses the MAT-NULL material model and uses the EOS-LINEAR-POLYNOMIAL linear polynomial equation to describe the state:

[0102] ;

[0103] Where, is the air constant,

[0104] , is a dimensionless parameter, , is the density, is the density under standard conditions, is the internal energy of the material;

[0105] The shield tunnel segments use the MAT_CSCM_CONCRETE model, a concrete constitutive model based on the Continuous Surface Cap Model (CSCM). This model is specifically designed to simulate the complex mechanical behavior of concrete under dynamic loading, including compression damage, tensile cracking, and strain rate effects. Rebar is simulated using MAT_PLASTIC_KINEMATIC and connected to the segments via common nodes or CONTACT_TIE to ensure stress transfer.

[0106] Tensile damage evolution:

[0107] ;

[0108] Where, is the tensile damage variable, is the tensile strain, is the tensile damage threshold, All are damage parameters;

[0109] Compression damage evolution:

[0110] ;

[0111] Where, is the tensile damage variable, is the tensile strain, is the tensile damage threshold, is the damage parameter;

[0112] Strain rate effect correction:

[0113] ;

[0114] Where, is the strain rate correction factor, is the reference strain rate, is the strain rate parameter.

[0115] The core idea of the JH-2 model used for rock mass is to describe the progressive failure process of the material through damage accumulation and strain rate effect. It is particularly suitable for simulating the crushing behavior of brittle rock masses such as granite under dynamic loads.

[0116] The ALE fluid model (explosion products, air) uses the Euler grid, and the structural model (segments, rock mass) uses the Lagrangian grid, and the two use the CONSTRAINED_LAGRANGE_IN_SOLID coupling mode;

[0117] The stations, tunnels, rock masses and air are meshed together, and the mesh density at the flow-solid interface is increased;

[0118] Set initial geostress, non-reflecting boundary conditions, and solution controls;

[0119] For example, in step S3, the vibration velocity, maximum tensile stress, joint deformation and other indicators of the segments under different top plate thicknesses are extracted. The specific steps are as follows:

[0120] Perform spectrum analysis on the vibration velocity at key locations (such as the crown, haunch, and foot) to identify the main frequency component and analyze whether the main vibration frequency is close to the natural frequency of the segment to avoid resonance risks;

[0121] Monitoring points were arranged at vulnerable locations such as the arch crown, arch haunch, and arch foot. *DATABASE_NODOUT was used to define the node velocity output and output vibration velocity data. Time domain velocity data was extracted through LS-PrePost, and then converted into the frequency domain through MATLAB fast Fourier transform to identify the dominant frequency.

[0122] The segment damage CSCM model is used to output the principal stress contour of the segment, and LS-PrePost is used to identify the maximum tensile stress area and the stress peak under each working condition.

[0123] Monitor the difference in normal displacement of nodes on either side of the segment joint to obtain joint opening, and monitor the difference in tangential displacement of nodes on either side of the segment joint to obtain joint slippage. (First, define the contact surfaces on both sides of the joint in the model using CONTACT_SURFACE_TO_SURFACE. Then, configure output controls in the input file to record the displacement data of the nodes on both sides of the joint. Finally, use the LS-PrePost post-processing tool to extract the node displacement data and calculate the difference.)

[0124] For example, in step S4, the top plate thickness is optimized iteratively, and the specific steps are as follows:

[0125] Input parameters;

[0126] The uncertain parameters are selected as random variables (elastic modulus , internal friction angle , single hole explosive , the center of explosion distance , segment thickness );

[0127] Extracted using Latin hypercube sampling Group samples, uniformly distributed covering the random variable space ( ) The sampling process is as follows,

[0128] For each parameter (like etc.), its value range is divided into 𝑛 equal probability intervals. A sample point is randomly selected in each interval, and it is ensured that the sample points of each parameter are not repeated:

[0129] ;

[0130] Where, For parameters The inverse function of the cumulative distribution function CDF is, For the The sample in The order of the parameter dimensions, is a random offset within the interval.

[0131] Construct Kriging metamodel based on sample points Enter its response , output proxy model, including:

[0132] The Kriging model assumes that the response is a Gaussian process:

[0133] ;

[0134] Where, is the global mean, is a zero-mean Gaussian process;

[0135] The covariance function is:

[0136] ;

[0137] Where, is the covariance function, is a zero-mean Gaussian process, is the Gaussian process variance, is the related function, and Input point, is a hyperparameter, optimized by maximum likelihood estimation MLE;

[0138] The related function is:

[0139] ;

[0140] Where, is the dimension of the input space, is the dimension index, For the dimensional scaling parameters, For the The first point Dimension value, For the The first point Dimension value.

[0141] The Kriging metamodel is used to calculate the Sobol' index for sensitivity analysis to obtain the contribution of each parameter to the response. The process is as follows:

[0142] Total variance decomposition:

[0143] ;

[0144] Where, For the The main effect variance of the parameter, For the and the interaction effect variance of 𝑗 parameters, and is the index of the input parameter, is the variance of the higher-order interaction effects of all parameters;

[0145] The total effect Sobol' index is:

[0146] ;

[0147] Where, To exclude parameters The variance after .

[0148] The Monte Carlo sampling method is used to extract m groups and input them into the Kriging meta-model to calculate the tunnel failure probability. The process is as follows:

[0149] Define the failure function:

[0150] ;

[0151] Where, is the allowable stress, is the maximum stress;

[0152] Then the failure probability is:

[0153] ;

[0154] Where, is the number of Monte Carlo samples, for 1 if yes, 0 otherwise.

[0155] Draw the relationship curve between the safety roof thickness and the failure probability to determine the critical safety roof thickness;

[0156] Taking into account construction uncertainty, the critical thickness is multiplied by the safety factor to obtain the final design thickness.

[0157] Exemplarily, in step S4, verification and feedback in combination with on-site monitoring data include:

[0158] Piezoelectric accelerometers are installed on the crown and waist of the pipe segment to measure the vibration velocity. (The sensors are installed correctly and the acceleration signals are collected in conjunction with the data acquisition system. The acceleration signals are converted into velocity signals through integration.)

[0159] ;

[0160] Where, For speed, is the acceleration.

[0161] Vibrating wire strain gauges are installed on the inner side of the arch crown and the inner side of the arch waist of the pipe segment to measure the actual strain and convert it into stress; (the initial frequency of the strain gauge is recorded under no load state , read the real-time frequency through the strain gauge reader , calculate the strain :

[0162] ;

[0163] Where, For strain, is the initial frequency, is the calibration coefficient;

[0164] According to Hooke's law, the stress ;

[0165] Linear displacement meters (LVDTs) and wire displacement meters were installed at the circumferential and longitudinal joints of the segments to measure the joint opening and joint offset. (LVDTs were installed in the vertical direction of the circumferential and longitudinal joints. The initial voltage of the LVDTs was recorded under no-load conditions. The voltage signals of the LVDTs were read in real time by the data acquisition system and converted into displacement values.)

[0166] ;

[0167] Where, is the displacement, is the real-time voltage, is the initial voltage, For sensitivity.

[0168] Install a wire displacement meter in the horizontal direction of the circumferential joint and the longitudinal joint. Record the initial length of the wire under no-load conditions. Use the data acquisition system to read the change in wire length and calculate the offset:

[0169] ;

[0170] Where, To stagger the amount, For real-time wire pulling amount, is the initial wire pulling amount.

[0171] If the error between the measured data and the simulation results is large, the contribution of each parameter to the response value calculated based on the Sobol' index is used to optimize the parameters and recalculate them.

[0172] In Example 2, a shield tunnel roof thickness optimization system for synchronous construction of a subway tunnel and station provided by an embodiment of the present invention includes:

[0173] A three-dimensional numerical model building module is used to build a three-dimensional numerical model based on tunnel geological parameters, blasting parameters, and shield segment parameters. The tunnel is constructed simultaneously with the tunnel station.

[0174] The multi-field coupling analysis module is used for multi-field coupling analysis based on the established three-dimensional numerical model, using the explosive detonation JWL model, the rock mass dynamic response ALE method and the segment damage CSCM model;

[0175] The index extraction module is used to extract the vibration velocity, maximum tensile stress, and joint deformation indicators of the segments under different roof thicknesses based on the results of multi-field coupling analysis;

[0176] The roof thickness iterative optimization module is used to iteratively optimize the roof thickness based on the extracted vibration velocity, maximum tensile stress, and joint deformation indicators of the pipe segments under different roof thicknesses; verification and feedback are carried out based on the iterative optimization results combined with on-site monitoring data.

[0177] This invention has been successfully applied to a subway under construction in Qingdao. By integrating the JWL detonation model, the CSCM damage model, and the ALE fluid-structure coupling, it can accurately simulate the dynamic damage effects of blasting vibration on shield segments during synchronous construction. Using the Latin hypercube-Kriging element model, it systematically analyzes the coupling effects of parameters such as elastic modulus, blast center distance, and segment thickness, significantly reducing the risk of structural failure due to parameter randomness and improving design reliability. Based on failure probability curves and sensitivity analysis, it achieves multi-objective optimization of roof thickness, avoiding over-design or under-design and shortening the design period by 5%-10%.

[0178] The above description is only a preferred specific implementation method of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for optimizing the top slab thickness of a shield tunnel during simultaneous construction of a subway tunnel station, characterized in that: The method comprises the following steps: S1, establish a three-dimensional numerical model based on tunnel geological parameters, blasting parameters and shield segment parameters; S2, based on the established three-dimensional numerical model, adopts the multi-field coupling analysis of explosive detonation JWL model, rock mass dynamic response ALE method and segment damage CSCM model; S3, based on the results of multi-field coupling analysis, extract the vibration velocity, maximum tensile stress, and joint deformation indicators of the segments under different roof thicknesses; S4: Iteratively optimize the roof thickness based on the extracted vibration velocity, maximum tensile stress, and joint deformation indicators of the segments with different roof thicknesses. Verify and provide feedback based on the iterative optimization results combined with on-site monitoring data. In step S3, the vibration velocity, maximum tensile stress, and joint deformation indexes of the segments under different roof thicknesses are extracted, including: Perform spectrum analysis on the vibration velocity at key locations to identify the main frequency component and analyze whether the main vibration frequency is close to the natural frequency of the segment; The segment damage CSCM model is used to output the principal stress contour of the segment, and LS-PrePost is used to identify the maximum tensile stress area and the stress peak under each working condition. The normal displacement difference of the nodes on both sides of the monitoring segment joint is used to obtain the joint opening amount, and the tangential displacement difference of the nodes on both sides of the monitoring segment joint is used to obtain the joint slippage amount; In step S4, the top plate thickness is iteratively optimized, including: Input parameters; Select the uncertain parameters as random variables, including elastic modulus , internal friction angle , single hole explosive , the center of explosion distance , segment thickness ; Extracted using Latin hypercube sampling Group samples,uniformly distributed covering the random variable space; Construct Kriging metamodel based on sample points Enter its response , output proxy model; The Kriging metamodel was used to calculate the Sobol' index for sensitivity analysis to obtain the contribution of each parameter to the response; Monte Carlo sampling method was used to extract group, input the Kriging metamodel to calculate the tunnel failure probability; Draw a curve showing the relationship between the safety roof thickness and the failure probability to determine the critical safety roof thickness; multiply the critical thickness by the safety factor to obtain the final design thickness.

2. The method for optimizing the top slab thickness of a shield tunnel during the simultaneous construction of a subway tunnel and a station according to claim 1 is characterized in that: In step S1, a three-dimensional numerical model is established, including: After on-site investigation and design drawings of the tunnel and station, the geological parameters, blasting parameters and segment parameters are determined; According to the locations of the station excavation area, shield tunnel, surrounding rock mass and blast source, a geometric model of the dynamic response of drilling and blasting excavation to the segments under the conditions of synchronous construction of the tunnel and station was constructed.

3. The method for optimizing the top slab thickness of a shield tunnel during the simultaneous construction of a subway tunnel and a station according to claim 1 is characterized in that: In step S2, the explosive detonation JWL model adopts the MAT-HIGH-EXPLOSIVE-BURN material model and uses the JWL state equation to calculate the detonation pressure. The JWL state equation is as follows: ； Where, is the detonation pressure, are all material parameters, is the relative volume, is the internal energy per unit volume, is a constant; The air model uses the MAT-NULL material model and uses the EOS-LINEAR-POLYNOMIAL linear polynomial equation to describe the state. The expression is: ； Where, is the air constant, , is a dimensionless parameter, , is the density, is the density under standard conditions, is the internal energy of the material; Simplified to the ideal gas model: ； Where, is the specific heat ratio, ; The JWL model of explosive detonation provides the energy release law of the explosion source, and the air model simulates the propagation dynamics of the shock wave in the air. The JWL model of explosive detonation and the air model realize the interaction between energy and momentum through the fluid-structure interaction method; The CSCM model for segment damage includes compression damage, tensile cracking, strain rate effects, and stiffness degradation. MAT_PLASTIC_KINEMATIC is used to simulate steel bars, which are connected to the segments via common nodes or CONTACT_TIE to ensure stress transfer. The JH-2 model is used for rock mass, and the progressive failure process of the material is described by damage accumulation and strain rate effect. The ALE fluid model uses an Eulerian grid, and the structural model uses a Lagrangian grid, and the two are coupled using the CONSTRAINED_LAGRANGE_IN_SOLID method. The stations, tunnels, rock masses and air are meshed together, and the mesh density at the flow-solid interface is increased; Set initial geostress, non-reflecting boundary conditions, and solution controls.

4. The method for optimizing the top slab thickness of a shield tunnel during the simultaneous construction of a subway tunnel and a station according to claim 1 is characterized in that: Extracted using Latin hypercube sampling Group samples, uniformly distributed over the space of random variables, including: For each parameter , whose value range is divided into There are equal probability intervals, and a sample point is randomly selected in each interval, and it is ensured that the sample points of each parameter are not repeated: ； Where, For parameters The inverse function of the cumulative distribution function CDF is, For the The sample in The order of the parameter dimensions, is a random offset within the interval.

5. The method for optimizing the top slab thickness of a shield tunnel during the simultaneous construction of a subway tunnel and a station according to claim 1 is characterized in that: Construct Kriging metamodel based on sample points Enter its response , output proxy model, including: The Kriging model assumes that the response is a Gaussian process: ； Where, is the global mean, is a zero-mean Gaussian process; The covariance function is: ； Where, is the covariance function, is a zero-mean Gaussian process, is the Gaussian process variance, is the related function, and is the input point, is a hyperparameter, optimized by maximum likelihood estimation MLE; The related function is: ； Where, is the dimension of the input space, is the dimension index, For the dimensional scaling parameters, For the The first point Dimension value, For the The first point Dimension value.

6. The method for optimizing the top slab thickness of a shield tunnel during the simultaneous construction of a subway tunnel and a station according to claim 1 is characterized in that: The Kriging metamodel was used to calculate the Sobol' index for sensitivity analysis to obtain the contribution of each parameter to the response, including: Total variance decomposition: ； Where, For the The main effect variance of the parameter, For the and The interaction effect variance of the parameters, and is the index of the input parameter, is the variance of the higher-order interaction effects of all parameters; The total effect Sobol' index is: ； Where, To exclude parameters The variance after .

7. The method for optimizing the top slab thickness of a shield tunnel during the simultaneous construction of a subway tunnel and a station according to claim 1, characterized in that: Monte Carlo sampling method was used to extract Group, input Kriging meta-model to calculate tunnel failure probability, including: Define the failure function: ； Where, is the allowable stress, is the maximum stress; Then the failure probability is: ； Where, is the number of Monte Carlo samples, 1 if yes, 0 otherwise.

8. A shield tunnel roof thickness optimization system for synchronous construction of subway tunnels and stations, characterized in that: The system implements the method for optimizing the top slab thickness of a shield tunnel during the simultaneous construction of a subway tunnel and a station as described in any one of claims 1 to 7, and the system comprises: A 3D numerical model building module is used to build a 3D numerical model based on tunnel geological parameters, blasting parameters, and shield segment parameters; The multi-field coupling analysis module is used for multi-field coupling analysis based on the established three-dimensional numerical model, using the explosive detonation JWL model, the rock mass dynamic response ALE method and the segment damage CSCM model; The index extraction module is used to extract the vibration velocity, maximum tensile stress, and joint deformation indicators of the segments under different roof thicknesses based on the results of multi-field coupling analysis; The roof thickness iterative optimization module is used to iteratively optimize the roof thickness based on the extracted vibration velocity, maximum tensile stress, and joint deformation indicators of the pipe segments under different roof thicknesses; verification and feedback are carried out based on the iterative optimization results combined with on-site monitoring data.

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