Tunnel lining long-term safety evaluation method based on PSO-SA hybrid algorithm

Abstract

The present application relates to the technical field of tunnel engineering, and particularly relates to a tunnel lining long-term safety evaluation method based on a PSO-SA hybrid algorithm, comprising: determining a surrounding rock creep constitutive model and obtaining initial surrounding rock parameters through an inversion algorithm; introducing the influence of a supporting structure on surrounding rock deformation to obtain corrected surrounding rock parameters, so as to improve authenticity; based on time-varying factors, iteratively updating surrounding rock parameters and lining parameters to realize dynamic evaluation of long-term safety; inputting the iteratively updated surrounding rock parameters and lining parameters into numerical analysis software to obtain stress and deformation characteristics of the lining structure through the numerical analysis software; and evaluating the long-term safety of the tunnel lining structure according to the stress and deformation characteristics and relevant specifications, wherein the application constructs a dynamic evaluation model that truly reflects the time-varying characteristics of the tunnel structure, brings about a qualitative leap in evaluation accuracy, reliability and foresight, and provides strong technical support for the long-term safety of operating tunnels.

Description

Technical Field

[0001] This invention relates to the field of tunnel engineering technology, and specifically to a method for long-term safety assessment of tunnel lining based on the PSO-SA hybrid algorithm. Background Technology

[0002] Currently, the industry's assessment methods for tunnel structural safety are mainly divided into two categories: theoretical calculation and numerical simulation. Theoretical calculation is represented by the load-structure method, which simplifies the effect of the strata after tunnel excavation into external loads on the lining structure. It uses beam elements to simulate the support structure and elastic supports to simulate the surrounding rock constraints. Based on the principles of structural mechanics, it solves the stress state of the lining to assess safety. However, this method ignores the synergistic effect between the surrounding rock and the support structure, and has limited accuracy in characterizing the structural response under complex geological conditions. Numerical simulation, on the other hand, can achieve a refined solution of the stress and deformation characteristics of the soil and rock mass and the lining structure by constructing a constitutive model of the soil and rock mass and selecting physical and mechanical parameters. It can more realistically reflect the mechanism of the surrounding rock's effect on the support structure and is the current mainstream assessment technology.

[0003] However, existing assessment techniques have significant limitations: on the one hand, compared to the excavation stage, operating tunnels have added support structures (such as anchor bolts, shotcrete, and secondary lining), but existing methods do not fully consider the changes in the mechanical state of the surrounding rock caused by the support structures, resulting in deviations between the values ​​of the surrounding rock parameters and the actual working conditions; on the other hand, during the long-term operation of the tunnel, under the cyclical action of loads (such as train vibration) and the erosion of harsh environments (such as groundwater, temperature and humidity changes), the support structures will deteriorate, and the surrounding rock will also exhibit time-varying characteristics due to creep and stress redistribution, causing the surrounding rock parameters and lining parameters to change dynamically with the service time. Existing assessment techniques generally do not incorporate the above-mentioned time-varying factors and only rely on initial parameters for static assessment, resulting in assessment results that are out of sync with the actual safety state of the tunnel, and are unreasonable and inaccurate, making it difficult to meet the actual needs of long-term safety assurance for operating tunnels. There is an urgent need for a long-term safety assessment technique for tunnel lining that can take into account both the influence of the support structure and the time-varying characteristics of the parameters. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a long-term safety assessment method for tunnel lining based on the PSO-SA hybrid algorithm, which solves the problem that existing assessment methods ignore the time-varying characteristics of the surrounding rock and lining materials and cannot achieve long-term dynamic assessment.

[0005] To address the above problems, the technical solution of this invention is as follows: A method for long-term safety assessment of tunnel lining based on the PSO-SA hybrid algorithm, comprising:

[0006] S1. On-site sampling of tunnel lining was conducted, and samples were sent to the laboratory for long-term creep tests. A surrounding rock creep constitutive model was used to characterize the creep characteristics of the tunnel surrounding rock. Simulated annealing algorithm was introduced into particle swarm optimization algorithm to obtain surrounding rock parameters.

[0007] S2. Introduce the influence of the support structure on the deformation of the surrounding rock to obtain corrected surrounding rock parameters in order to improve the realism.

[0008] S3. Based on time-varying factors, the surrounding rock parameters and lining parameters are iteratively updated to achieve long-term safety dynamic assessment;

[0009] S4. Input the iteratively updated surrounding rock parameters and lining parameters into the numerical analysis software, and obtain the stress and deformation characteristics of the lining structure through the numerical analysis software.

[0010] S5. Assess the long-term safety of tunnel lining structures based on stress and deformation characteristics and relevant specifications.

[0011] Furthermore, S1 includes:

[0012] S11. Based on tunnel engineering parameters and service environment conditions, conduct on-site sampling and perform long-term creep tests on the samples to obtain test data. ), ( ),…,( )},in, For the test time point, for The corresponding surrounding rock strain value at time 1. This represents the number of data points.

[0013] S12. Based on the deformation characteristics of the experimental data, the Burgers creep constitutive model is adopted as the surrounding rock creep constitutive model.

[0014] S13. Using the parameter R of the Burgers creep constitutive model as the design variable, a numerical model corresponding to the creep test is established using FLAC3D software. The theoretical strain data is calculated by inputting the initial values ​​of the design variables. },in, for The theoretical strain value of the surrounding rock at time t;

[0015] S14. Construct the first objective function, and use the PSO-SA hybrid algorithm in the Matlab environment to optimize the design variables. Iterate until the first objective function converges to obtain the inverted surrounding rock parameters.

[0016] Furthermore, in S12, the creep compliance expression of the Burgers creep constitutive model is:

[0017] ,

[0018] in, for Creep flexibility at any moment , The viscosity coefficient of the Burgers model. , For the shear modulus of the Burgers creep constitutive model, It is the Euler number.

[0019] Furthermore, S14 includes:

[0020] S141. Initialize algorithm parameters;

[0021] S142. Use FLAC3D software to calculate the theoretical strain data corresponding to each particle, and substitute it into the first objective function to obtain the particle fitness value.

[0022] S143. Calculate the change J of the particle's fitness value before and after the update, and determine whether the particle's position needs to be updated based on the value of J.

[0023] S144. Update the individual optimal fitness value and the population optimal fitness value. Repeat S142-S143 until the number of iterations reaches the preset value or the first objective function converges. Output the surrounding rock parameters corresponding to the population optimal particle.

[0024] Furthermore, in S14, the expression for the first objective function is:

[0025] ( , , , )= ,

[0026] in, ( , , , ) are design variables.

[0027] Furthermore, S2 includes:

[0028] S21. After the tunnel support structure construction is completed, displacement data of the surrounding rock is collected by displacement sensors deployed on site, and the on-site displacement-time data of the surrounding rock is obtained. ), ( ),…,( )}; Lining contact pressure-time data {( ), ( ),…,( )}; Lining concrete strain-time data {( ), ( ),…,( )}; Lining concrete strain-time data {( ), ( ),…,( )},in, , , They are respectively The on-site surrounding rock displacement value, lining contact pressure value, and lining concrete strain value at each moment;

[0029] S22. Using the inverted surrounding rock parameters obtained in S1 as initial values, a numerical model including the support structure is established using FLAC3D software. The surrounding rock parameters and support structure parameters are input, and the theoretical displacement data is calculated. , ),( , ),…,( , Theoretical lining contact pressure data {( , ),( , ),…,( , Theoretical lining concrete strain data {( , ),( , ),…,( , )},in, , They are respectively The theoretical displacement value of the surrounding rock, the contact pressure value of the lining, and the strain value of the lining concrete at each moment;

[0030] S23. Establish a second objective function (joint objective function), and use the PSO-SA hybrid algorithm in the Matlab environment to evaluate the design variables. The optimization process continues until the second objective function converges, yielding the corrected surrounding rock parameters.

[0031] Furthermore, in S23, the expression for the second objective function is:

[0032] ,

[0033] in, ( , , , ) is the design variable. , , These are the weighting coefficients of displacement, contact pressure, and concrete strain in the objective function, respectively.

[0034] Furthermore, in S3, the iterative update of the lining parameters includes: updating the lining parameters according to the degradation function of the lining material, wherein the time-varying elastic modulus of the lining concrete represents: ,

[0035] in, It is the elastic modulus of the lining concrete in its initial state. It is a time-dependent degradation function, defined as the percentage of the elastic modulus at time t relative to the initial elastic modulus. It was obtained by fitting the experimental data.

[0036] Furthermore, in S3, the iterative update of the surrounding rock parameters includes: repeating step S2 at a fixed time period to readjust the surrounding rock parameters using a new round of field monitoring data, thereby achieving the update.

[0037] Furthermore, in S3, the update cycle of the surrounding rock parameters and the lining parameters is the same.

[0038] Compared with existing technologies, this invention has the following advantages: The use of a PSO-SA hybrid algorithm for surrounding rock parameter inversion and correction effectively overcomes the problem of traditional inversion methods easily getting trapped in local optima. The Particle Swarm Optimization (PSO) algorithm has strong global search capabilities, enabling it to quickly locate the possible optimal range of parameters; while the probabilistic jump characteristics of the Simulated Annealing (SA) algorithm endow it with the ability to escape local extrema. After the two are combined, during the inversion process, the algorithm determines whether to update particle positions by judging the change in fitness value J and based on the criterion exp(-J / θ)>γ, achieving a good balance between global exploration and local development. This ensures that the final inverted surrounding rock parameters are closer to the true mechanical properties of the rock mass, laying a solid foundation for subsequent accurate evaluation.

[0039] The introduction of the Burgers creep constitutive model accurately matches the long-term creep deformation law of the surrounding rock. Its creep compliance expression comprehensively considers instantaneous elastic deformation, delayed elastic deformation and viscous flow deformation, which can scientifically characterize the mechanical response of the surrounding rock under different time dimensions. It is more adaptable to complex geological conditions such as weak surrounding rock and deep buried tunnels, and provides more realistic theoretical support for parameter inversion, further improving the reliability of the initial surrounding rock parameters.

[0040] The PSO-SA algorithm automates the inversion process by automatically calling and exchanging data with numerical software such as FLAC3D through programmed implementation. In long-term evaluation, only new monitoring data needs to be input periodically, and the system can automatically complete the iterative update of parameters and safety calculations, which greatly improves the evaluation efficiency and makes it possible to conduct normalized and intelligent monitoring of the tunnel throughout its "full life cycle".

[0041] Every fixed period, the surrounding rock parameters are repeatedly corrected by inversion based on the PSO-SA algorithm and field displacement data. This allows the surrounding rock parameters to track changes caused by surrounding rock creep and stress redistribution. The lining parameters are corrected for time-varying parameters such as the lining elastic modulus through a degradation function. This makes the safety assessment no longer an isolated judgment at a certain point in time, but a continuous process that can dynamically reflect changes in the health status of the tunnel structure, thus achieving a true long-term safety assessment.

[0042] Not limited to specific rock types or tunnel sizes, by adjusting key indicators such as constitutive model parameters, degradation function coefficients, and update cycles, it can be adapted to different engineering scenarios such as railway tunnels, highway tunnels, and urban rail transit tunnels, as well as different geological conditions such as weak rock and hard rock, making it highly versatile. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the technical solution of the present invention;

[0044] Figure 2 This invention relates to the Burgers creep constitutive model;

[0045] Figure 3 This is a schematic diagram showing the field and theoretical data of the surrounding rock deformation according to the present invention;

[0046] Figure 4 This is a schematic diagram showing the field and theoretical data of the lining contact pressure of the present invention. Detailed Implementation

[0047] A long-term safety assessment method for tunnel lining based on PSO-SA (Particle Swarm Optimization-Simulated Annealing Hybrid Algorithm) is proposed. This method obtains initial surrounding rock parameters through inversion using a constitutive model of surrounding rock creep, further considers the correction parameters of the support structure, and iteratively updates the surrounding rock and lining parameters based on time-varying factors. Finally, numerical analysis is used to achieve a dynamic assessment of long-term safety. This method solves the problem of existing technologies neglecting the influence of the support structure and the time-varying characteristics of materials, improving the accuracy and long-term applicability of the assessment. The steps of the assessment method are as follows:

[0048] S1. On-site sampling of tunnel lining was conducted, and samples were sent to the laboratory for long-term creep tests. A surrounding rock creep constitutive model was used to characterize the creep characteristics of the tunnel surrounding rock. Simulated annealing algorithm was introduced into particle swarm optimization algorithm to obtain surrounding rock parameters.

[0049] S2. Introduce the influence of the support structure on the deformation of the surrounding rock to obtain corrected surrounding rock parameters in order to improve the realism.

[0050] S3. Based on time-varying factors, the surrounding rock parameters and lining parameters are iteratively updated to achieve long-term safety dynamic assessment;

[0051] S4. Input the iteratively updated surrounding rock parameters and lining parameters into the numerical analysis software, and obtain the stress and deformation characteristics of the lining structure through the numerical analysis software.

[0052] S5. Assess the long-term safety of tunnel lining structures based on stress and deformation characteristics and relevant specifications.

[0053] Specifically, a long-term safety assessment method for tunnel lining based on the PSO-SA hybrid algorithm is presented, using a deep-buried railway tunnel in weak surrounding rock as an application scenario. The tunnel has a depth of 280m and a cross-sectional dimension of 13m × 7.8m (width × height). The service environment experiences seasonal temperature and humidity changes and slight groundwater erosion. The support structure adopts a combination of "anchors + shotcrete + secondary lining" (anchor length 4m, spacing 1.5m; shotcrete thickness 12cm; secondary lining concrete strength grade C35). The method includes the following steps:

[0054] S1. On-site sampling of tunnel lining was conducted, and samples were sent to the laboratory for long-term creep tests. A constitutive model of surrounding rock creep was used to characterize the creep characteristics of the tunnel surrounding rock. The simulated annealing algorithm was introduced into the particle swarm optimization algorithm to obtain the surrounding rock parameters, as detailed below:

[0055] Based on tunnel engineering parameters (burial depth, cross-sectional dimensions, excavation method) and service environment (groundwater distribution, surrounding rock lithology of silty sandstone), four representative sampling points were selected at the tunnel face and both sidewalls. Four sets of cylindrical surrounding rock samples with a diameter of 50 mm and a length of 100 mm were obtained using a diamond core drill. During the sampling process, the samples were protected from vibration and compression to maintain the integrity of their original structure. After sampling, the samples were immediately wrapped in sealed bags, and the sampling location, depth and date were marked. The samples were then sent to the laboratory for pretreatment (natural air drying for 24 hours to remove surface impurities).

[0056] The long-term creep testing machine for rocks, model RLJW-3000, was used for the test. The test conditions were as follows: axial pressure was 2.5 times the natural weight of the surrounding rock (7.2 MPa); ambient temperature was controlled at 20±1℃; relative humidity was maintained at 65±3%; and the test duration was 200 days. Data acquisition frequency: strain data were collected every 30 minutes for the first 10 days, every hour from days 10 to 30, and every 2 hours after 30 days, recording data at different time points. The surrounding rock strain values ​​corresponding to (i=1,2,…,n,n=9600) To form a set of experimental data {( ), ( ),…,( )}.

[0057] Analysis of the experimental data revealed that the surrounding rock strain exhibits a three-stage characteristic over time: instantaneous elastic deformation, delayed elastic deformation, and stable creep. This aligns with the mechanical response law of the Burgers creep constitutive model. Therefore, this model is selected to characterize the creep properties of the surrounding rock, and its creep compliance expression is as follows: ,

[0058] in, Let be the creep compliance at time t; , The shear modulus (in kPa) is for the Maxwell model and the Kelvin model, respectively. , The viscosity coefficients (in kPa·d) for the Maxwell and Kelvin models are respectively; e is the Euler number (value 2.71828). The Burgers creep constitutive model accurately fits the creep law of weak surrounding rocks and comprehensively covers instantaneous elasticity, delayed elasticity and viscous flow deformation. Compared with the traditional simplified model, the mechanical characterization accuracy is improved by more than 20%.

[0059] Determine design variables ( , , , Based on the range of mechanical parameters of weak surrounding rock, the initial value range is set as follows: A three-dimensional numerical model (50mm×50mm×100mm) corresponding to the indoor test was established using FLAC3D software. The mesh was generated using tetrahedral elements (6000 meshes). The Burgers creep constitutive model was then embedded, and the initial values ​​of the design variables were substituted into the model to calculate the theoretical strain data. }, for Given the theoretical strain value of the surrounding rock at time t, establish an objective function with the optimization objective of minimizing the sum of squares of the differences between the experimental strain and the theoretical strain. Establish the first objective function as follows:

[0060] .

[0061] A PSO-SA hybrid algorithm program was developed in the Matlab R2022b environment. This algorithm combines the global search capability of PSO with the local optimization advantages of SA, effectively avoiding the problem of single algorithms getting trapped in local optima. This reduces the inversion error of surrounding rock parameters by 25%, and the inversion results show a consistency of over 90% with the actual mechanical properties of the surrounding rock, laying a high-precision data foundation for subsequent evaluation. Algorithm parameter settings: particle swarm size is 60, initial particle positions are randomly generated within the design variable range, and particle flight velocity range is [-8×10⁻⁶]. 3 8×10 3 kPa (shear modulus), [-4×10 6 4×10 6 [kPa·d (viscosity coefficient); initial temperature θ=120, temperature decay coefficient λ=0.93, maximum number of iterations is 600, objective function convergence threshold is 5×10] -7 .

[0062] Algorithm execution flow:

[0063] 1. Call the interface program between FLAC3D and Matlab, input the design variable values ​​corresponding to each particle into the numerical model, calculate the theoretical strain data, and substitute them into the objective function to obtain the particle fitness value;

[0064] 2. Calculate the change in fitness value J before and after the particle update (J = fitness value after update - fitness value before update): If J < 0, update the particle position directly; if J ≥ 0, calculate exp(-J / θ), generate a random number γ in the interval [0,1], and if exp(-J / θ) < γ, update the particle position; otherwise, keep the original position.

[0065] 3. Update the individual optimal fitness (the best historical value of each particle) and the swarm optimal fitness (the best historical value of all particles), according to θ. k+1 =λ·θ k Update temperature;

[0066] 4. Repeat the above steps until the number of iterations reaches 600 or the objective function value is less than 5 × 10⁻⁶. -7 The output shows the design variable values ​​corresponding to the optimal particles in the swarm, i.e., the inverted surrounding rock parameters: Gm = 1.9 × 10⁻⁶. 4 kPa, Gk=1.5×10 4 kPa, ηm = 2.8 × 10 8kPa·d,η k =1.3×10 8 kPa·d.

[0067] S2. Introduce the influence of the support structure on the surrounding rock deformation to obtain corrected surrounding rock parameters, thereby improving the realism. Compare the field data and theoretical data of the surrounding rock deformation, such as... Figure 3 As shown, the field data and theoretical data of the lining contact pressure are compared, such as... Figure 4 As shown, the details are as follows:

[0068] After the tunnel support structure was constructed, five sets of multi-point displacement gauges were installed at representative tunnel sections (15m, 40m, and 65m from the tunnel face). Each section had six monitoring points (crown, left and right arch shoulders, left and right arch waists, and the middle of the sidewalls). The displacement gauges had a measurement accuracy of 0.005mm. The data acquisition instrument used an automatic acquisition mode, with a monitoring cycle set to 180 days. Data was collected twice daily for the first 45 days and once daily thereafter, recording data at different time points. The surrounding rock displacement values ​​corresponding to (i=1,2,…,n,n=225) The field displacement-time data set was obtained by sorting the data. ), ( ),…,( Record different time points. (i=1,2,…,n,n=225) Corresponding lining contact pressure values The field displacement-time data set was obtained by sorting the data. ), ( ),…,( Record different time points. The strain values ​​of the lining concrete corresponding to (i=1,2,…,n,n=225) The field displacement-time data set was obtained by sorting the data. ), ( ),…,( )}.

[0069] Using the surrounding rock parameters obtained from S1 inversion as initial values, a three-dimensional numerical model of the tunnel including the support structure was established using FLAC3D software. The model dimensions were 70m × 50m × 45m (length × width × height). The tunnel cross-section was modeled according to the actual dimensions. The support structure was simulated using cable elements (elastic modulus 206 GPa, cross-sectional area 0.001257 m²). 2 The shotcrete and secondary lining were constructed using zone elements (elastic modulus of shotcrete 32 GPa, elastic modulus of secondary lining 34.5 GPa), and the total number of meshes in the model was 108,000 (tetrahedral elements).

[0070] Input the initial surrounding rock parameters and support structure parameters, select the Burgers creep constitutive model, and run numerical software to calculate the theoretical displacement data of the surrounding rock at different time points {( , ),( , ),…,( , )};Theoretical data on lining contact pressure{( , ),( , ),…,( , )};Theoretical data on strain of lining concrete {( , ),( , ),…,( , )}, , for The theoretical displacement value of the surrounding rock, the theoretical value of the lining contact pressure, and the theoretical value of the lining concrete strain at any given time.

[0071] A joint objective function is established, with the optimization objective being to minimize the sum of squares of the differences between the monitored displacement and the theoretical displacement. A second objective function is then constructed as follows:

[0072] ,

[0073] Among them, design variables ( , , , ), consistent with the design variables of S1, , , These are the weighting coefficients for displacement, contact pressure, and concrete strain in the objective function, respectively, and are set based on engineering experience.

[0074] The PSO-SA hybrid algorithm was used for parameter optimization. The algorithm parameters were adjusted as follows: particle swarm size of 45, initial particle positions centered on the inversion parameters with a fluctuation range of ±18%; initial temperature θ=90°C, temperature decay coefficient λ=0.94, maximum number of iterations of 450, and convergence threshold of 3×10⁻⁶. -6 Repeat the algorithm execution process of S1 to finally obtain the corrected surrounding rock parameters: G m =2.1×10 4 kPa, G k =1.7×10 4 kPa, η m =3.1×108 kPa·d,η k =1.5×10 8 kPa·d.

[0075] S3. Based on time-varying factors, the surrounding rock parameters and lining parameters are iteratively updated to achieve long-term safety dynamic assessment, as detailed below:

[0076] Based on the tunnel surrounding rock type (weak surrounding rock), service environment (slight groundwater erosion) and engineering maintenance experience, the parameter iteration update cycle is set to a=4 years, that is, the surrounding rock parameters and lining parameters are updated once every 4 years to ensure that the parameters can respond in a timely manner to the time-varying characteristics under long-term operation.

[0077] Deterioration test of lining concrete: Four groups of C35 concrete test blocks (3 in each group) with the same mix proportion as the tunnel lining were selected. Accelerated deterioration test was carried out under simulated service environment (temperature 20±2℃, relative humidity 75%, intermittent immersion in 3% sodium chloride solution) for 180 days. The elastic modulus and compressive strength of the test blocks were tested at 30 days, 60 days, 90 days, 120 days, 150 days and 180 days respectively.

[0078] Deterioration function establishment: The experimental data were processed using a nonlinear fitting method to obtain the deterioration function of the elastic modulus of the lining concrete. Where t is the service time in days, the expression for the time-varying elastic modulus of the lining is: ,in, (Initial elastic modulus).

[0079] Other parameter updates: Based on the time-varying law of elastic modulus, the time-varying expression of compressive strength is obtained. , =40MPa is the initial compressive strength, and Poisson's ratio is expressed in the time-varying formula. This is the initial Poisson's ratio.

[0080] Periodic update calculation: Within each 4-year cycle, the lining parameters corresponding to that cycle are calculated based on the time-varying expression above. For example, the lining parameters for the first update cycle t=1460d are:

[0081] ,

[0082] ,

[0083] .

[0084] Data collection: Within each 4-year update cycle, data is collected continuously for 45 days (data collection frequency is once per day). After removing outliers (data deviating from the mean by ±3 standard deviations), the displacement-time data set for that cycle is obtained. Lining contact pressure-time data group {( ), ( ),…,( )}; Lining concrete strain-time data group {( ), ( ),…,( )}.

[0085] Parameter Correction and Update: Using the updated surrounding rock parameters from the previous cycle as initial values, the parameter correction method from step two (with the same objective function, algorithm parameters, and numerical simulation settings) is employed to optimize the surrounding rock parameters for the current cycle using the PSO-SA hybrid algorithm. For example, in the first update cycle, using the corrected surrounding rock parameters as initial values ​​and combining them with newly acquired displacement data, the updated surrounding rock parameters are finally obtained: , .

[0086] S4. Input the iteratively updated surrounding rock parameters and lining parameters into the numerical analysis software, and obtain the stress-deformation characteristics of the lining structure through the numerical analysis software, as follows:

[0087] Update the surrounding rock parameters for the current period (e.g., period 1). etc.) and lining parameters ( Input the tunnel numerical model into the FLAC3D software, keep the model mesh division, support structure parameters, boundary conditions and other settings unchanged, run the numerical software, simulate the stress and deformation process of the tunnel lining structure in this period, and output key mechanical indicators: axial force, bending moment and shear force distribution cloud map of the lining structure, maximum principal stress and minimum principal stress value of the lining, contact pressure distribution between the surrounding rock and the lining, and surrounding rock displacement convergence curve.

[0088] Relying on the mature FLAC3D numerical analysis software, the updated precise parameters are transformed into quantitative structural mechanics indicators. Compared with traditional theoretical calculations, it can more realistically and meticulously reflect the stress and deformation state of the lining structure. The numerical simulation process is repeatable and verifiable. If the parameters are adjusted or the model is optimized, it can be quickly recalculated, improving the flexibility and efficiency of the evaluation work.

[0089] S5. Evaluate the long-term safety of tunnel lining structures based on stress-deformation characteristics and relevant specifications:

[0090] Safety assessment was conducted according to the "Railway Tunnel Design Code" (TB10003-2016), with specific standards as follows:

[0091] The maximum principal stress of the lining is ≤ the allowable stress of C35 concrete (11.9MPa).

[0092] The maximum displacement of the surrounding rock is less than or equal to the allowable displacement value specified in the code (18 mm).

[0093] The safety factor (allowable stress / calculated stress) of the lining structure is ≥1.15.

[0094] Based on the numerical simulation results of S4, the evaluation results for the first update cycle are as follows: the maximum principal stress of the lining is 8.7 MPa, the maximum displacement of the surrounding rock is 13.2 mm, and the safety factor is 1.37. All indicators meet the requirements of the specifications, and the tunnel lining structure is determined to be in a safe state during this cycle.

[0095] Subsequent periodic assessment: Repeat the above "parameter update - numerical simulation - safety assessment" process every 4 years. If the following situation occurs in a certain period:

[0096] Warning status: If the safety factor is between 1.05 and 1.15, or the indicator is close to the allowable limit (difference ≤ 8%), the monitoring frequency will be increased to once every 3 days to strengthen the tracking of structural status;

[0097] Hazardous conditions: If the safety factor is less than 1.05 or the index exceeds the allowable limit, targeted reinforcement measures such as grouting to reinforce the surrounding rock and pasting carbon fiber cloth on the inner side of the lining should be taken immediately.

[0098] A three-level assessment system of "safety-early warning-hazard" has been established, which can not only determine the current structural status, but also provide early warning of potential safety hazards, providing a scientific basis for tunnel operation and maintenance. The long-term dynamic assessment model has realized the transformation from "one-time assessment" to "full life cycle monitoring", effectively reducing the risk of safety accidents and economic losses caused by inaccurate assessment.

[0099] This application solves the global optimization problem of complex parameter inversion through the innovative application of the PSO-SA hybrid algorithm; through the three-step parameter modeling strategy of "initial inversion - support correction - long-term update", it constructs a dynamic evaluation model that can truly reflect the time-varying characteristics of the tunnel structure. The combined effect of these technical means brings about a qualitative leap in evaluation accuracy, reliability and foresight, providing a strong technical guarantee for the long-term safety of operating tunnels, and has extremely high practical value and promotion prospects.

[0100] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for long-term safety assessment of tunnel lining based on the PSO-SA hybrid algorithm, characterized in that, include: S1. On-site sampling of tunnel lining was conducted, and samples were sent to the laboratory for long-term creep tests. A surrounding rock creep constitutive model was used to characterize the creep characteristics of the tunnel surrounding rock. Simulated annealing algorithm was introduced into particle swarm optimization algorithm to obtain surrounding rock parameters. S2. Introduce the influence of the support structure on the deformation of the surrounding rock to obtain corrected surrounding rock parameters in order to improve the realism. S3. Based on time-varying factors, the surrounding rock parameters and lining parameters are iteratively updated to achieve long-term safety dynamic assessment; S4. Input the iteratively updated surrounding rock parameters and lining parameters into the numerical analysis software, and obtain the stress and deformation characteristics of the lining structure through the numerical analysis software. S5. Assess the long-term safety of tunnel lining structures based on stress and deformation characteristics and relevant specifications. S1 includes: S11. Based on tunnel engineering parameters and service environment conditions, conduct on-site sampling and perform long-term creep tests on the samples to obtain test data. ), ( ),…,( )},in, For the test time point, for The corresponding surrounding rock strain value at time 1. This represents the number of data points. S12. Based on the deformation characteristics of the experimental data, the Burgers creep constitutive model is adopted as the surrounding rock creep constitutive model. S13. Using the parameter R of the Burgers creep constitutive model as the design variable, a numerical model corresponding to the creep test is established using FLAC3D software. The theoretical strain data is calculated by inputting the initial values ​​of the design variables. },in, for The theoretical strain value of the surrounding rock at time t; S14. Construct the first objective function, and use the PSO-SA hybrid algorithm in the Matlab environment to optimize the design variables. Iterate until the first objective function converges to obtain the inverted surrounding rock parameters. In S12, the creep compliance expression of the Burgers creep constitutive model is: ， in, for Creep flexibility at any given moment , The viscosity coefficient of the Burgers model. , For the shear modulus of the Burgers creep constitutive model, It is the Euler number; S14 includes: S141. Initialize algorithm parameters; S142. Use FLAC3D software to calculate the theoretical strain data corresponding to each particle, and substitute it into the first objective function to obtain the particle fitness value. S143. Calculate the change J of the particle's fitness value before and after the update, and determine whether the particle's position needs to be updated based on the value of J. S144. Update the individual optimal fitness value and the population optimal fitness value. Repeat S142-S143 until the number of iterations reaches the preset value or the first objective function converges. Output the surrounding rock parameters corresponding to the population optimal particle. In S14, the expression for the first objective function is: （ ， ， ， ）= ， in, ( , , , ) are design variables; S2 includes: S21. After the tunnel support structure construction is completed, displacement data of the surrounding rock is collected by displacement sensors deployed on site, and the on-site surrounding rock displacement-time data is obtained. ), ( ),…,( )}; Lining contact pressure-time data {( ), ( ),…,( )}; Lining concrete strain-time data {( ), ( ),…,( )},in, , , They are respectively The on-site surrounding rock displacement value, lining contact pressure value, and lining concrete strain value at each moment; S22. Using the inverted surrounding rock parameters obtained in S1 as initial values, a numerical model including the support structure is established using FLAC3D software. The surrounding rock parameters and support structure parameters are input, and the theoretical surrounding rock displacement data is calculated {( , ),( , ),…,( , Theoretical lining contact pressure data {( , ),( , ),…,( , Theoretical lining concrete strain data {( , ),( , ),…,( , )},in, , They are respectively The theoretical displacement value of the surrounding rock, the contact pressure value of the lining, and the strain value of the lining concrete at each moment; S23. Establish a joint objective function, with the optimization objective being to minimize the sum of squares of the differences between the monitored displacement and the theoretical displacement. Construct a second objective function and use the PSO-SA hybrid algorithm in the Matlab environment to optimize the design variables. Optimize and iterate until the second objective function converges to obtain the corrected surrounding rock parameters; In S23, the expression for the second objective function is: ， in, ( , , , ) is the design variable. , , These are the weighting coefficients of displacement, contact pressure, and concrete strain in the objective function, respectively.

2. The evaluation method according to claim 1, characterized in that: In S3, the iterative update of the lining parameters includes: updating the lining parameters according to the degradation function of the lining material, wherein the time-varying elastic modulus of the lining concrete represents: , in, It is the elastic modulus of the lining concrete in its initial state. It is a time-dependent degradation function, defined as the percentage of the elastic modulus at time t relative to the initial elastic modulus. It was obtained by fitting the experimental data.

3. The evaluation method according to claim 2, characterized in that: In S3, the iterative update of the surrounding rock parameters includes: repeating step S2 at a fixed time period to readjust the surrounding rock parameters using a new round of field monitoring data, thereby achieving the update.

4. The evaluation method according to claim 3, characterized in that: In S3, the update cycle of the surrounding rock parameters and the lining parameters is the same.

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