Adjacent tunnel excavation disturbance vulnerability assessment method based on Bayesian update

Abstract

The invention provides an adjacent tunnel excavation disturbance vulnerability evaluation method based on Bayesian updating, which introduces a concept of system stiffness to represent uncertainty of tunnel damage caused by adjacent excavation disturbance, and brings obtained system stiffness data into an adjacent excavation disturbance model through a fitting equation. Calculating a large number of numerical values to establish a tunnel damage database; establishing an agent model between the parameters corresponding to the disturbance working condition and the tunnel damage index; collecting data for parameter characteristic statistics to obtain prior probability distribution of tunnel damage indexes, and combining engineering actual monitoring data for Bayesian updating to obtain posterior probability distribution of the tunnel damage indexes; and correcting the disturbance probability demand model by using the updated mean value and standard deviation of the tunnel damage indexes, and updating the vulnerability curve in combination with tunnel damage state threshold division and a logarithmic normal probability distribution function. The probability of different damage degrees of the tunnel structure can be made to fit the reality, and the applicability of an existing disturbance damage database is improved.

Description

Technical Field

[0001] The present invention relates to the field of evaluating the vulnerability of existing tunnels disturbed by adjacent foundation pit excavation, and particularly relates to a method for analyzing the vulnerability of shield tunnels based on Bayesian update. Background Art

[0002] With the rapid development of underground space, there is an increasing demand for commercial construction such as parking lots or underground supermarkets near the interval tunnel lines. Geotechnical engineering activities in adjacent areas, such as deep foundation pit excavation and surface loading, will inevitably change the stress state of the stratum environment, drive the movement of the soil around the adjacent tunnel, and thus cause the deformation of the surrounding building structures, accelerate the degradation of the structural service performance, and result in diseases such as cracks, water leakage, joint opening, and dislocation of tunnel segments, posing potential adverse effects or even damage to the structure, and directly threatening the safe operation of subway traffic. Therefore, in order to avoid serious damage to existing tunnels caused by adjacent excavation disturbance, carrying out tunnel disturbance vulnerability evaluation and improving the applicability of the algorithm are important issues for the whole-life cycle safe operation in the field of tunnels and underground engineering.

[0003] The analysis of the influence of adjacent disturbance on existing tunnels at home and abroad mainly includes four methods: field monitoring, model test, semi-analytical method, and numerical calculation. These methods have their own advantages, disadvantages, and applicability. Among them, field monitoring and model tests can more realistically reflect engineering conditions, but they also require a large amount of time and resources; the semi-analytical method based on elastic theory can initially provide the displacement field and stress field after disturbance, but the input parameters are quite different from the geological conditions of actual projects; the numerical method can well describe the mechanical behavior of soil and structure under adjacent disturbance by virtue of complex constitutive models, especially in complex geological conditions. However, this method is limited by hardware resources, has high requirements for modeling level, and has weak model generalization ability. In addition, the above methods are often limited to the level of deterministic analysis, and there is relatively little discussion on the uncertainty of disturbance intensity and the evaluation of the vulnerability of adjacent tunnels; the geotechnical parameter values of the disturbance vulnerability framework established based on specific strata are quite different from actual projects, especially the influence of the difference in soil compression modulus, and it is difficult to accurately evaluate the damaged degree of the tunnel after disturbance corresponding to different stratum environments. Summary of the Invention

[0004] The objective of this application is as follows: In view of the problem that the current tunnel damage and ductility assessment database is limited to specific strata, a Bayesian update and tunnel vulnerability analysis method is provided, which is of great significance for guiding the damage assessment of existing tunnels under extreme disturbances such as adjacent foundation pit excavation and the selection of post-disaster structural repair measures. The present invention uses the concept of the stiffness of the support structure system to characterize the uncertainty of disturbances and takes it into account in the tunnel vulnerability assessment, establishes a finite difference numerical model of the tunnel structure under adjacent excavation disturbances, obtains a tunnel damage database, and then establishes a vulnerability analysis model; the prior probability distribution of the tunnel damage index is calculated through the constructed response surface likelihood function, and Bayesian update is carried out in combination with actual engineering monitoring data to obtain the posterior probability distribution of the tunnel damage index, correct the disturbance probability demand model, and update the vulnerability curve in combination with the division of the tunnel damage state threshold and the lognormal probability distribution function.

[0005] To achieve the above objective, the present invention adopts the following technical solutions:

[0006] A vulnerability assessment method for adjacent tunnel excavation disturbances based on Bayesian update, which is a tunnel vulnerability evaluation update algorithm, including:

[0007] Step S1: Conduct literature research and data statistics to obtain the distribution model of the normalized maximum deflection deformation of the diaphragm wall, and inversely calculate the system stiffness through the fitted relationship equation between the diaphragm wall deflection deformation and the system stiffness;

[0008] Step S2: Establish a refined numerical model for adjacent tunnel foundation pit excavation, consider the uncertainty of the disturbance intensity, and obtain a database of tunnel damage indicators through a large number of numerical calculations;

[0009] Step S3: Through numerical calculation orthogonal experimental design, use a quadratic response surface function containing cross terms to establish a surrogate model between important input parameters and tunnel damage indicators;

[0010] Step S4: Given the relevant design and on-site monitoring data of a certain project, combine the surrogate model in Step S3 to obtain the likelihood function under specific working conditions, and use Bayesian estimation to obtain the posterior probability distribution of the tunnel damage index under adjacent excavation disturbances;

[0011] Step S5: According to the corrected disturbance probability demand model, combine the division of the tunnel damage state threshold and the lognormal probability distribution model to complete the update of the tunnel vulnerability curve under adjacent excavation disturbances.

[0012] More preferably, the Step S1 includes:

[0013] Through research on existing literature, collect the excavation depth H and the maximum deflection deformation u in the global foundation pit excavation cases hmThe monitoring data, after normalization, is fitted with a lognormal probability distribution model for u hm / H distribution.

[0014] The relative position relationship between the foundation pit and the adjacent tunnel is defined as the disturbance intensity index, including the excavation depth H of the foundation pit, the buried depth H t of the tunnel and the horizontal distance L between the foundation pit and the tunnel t , and the system stiffness EI / γ w h 4 is used to characterize the performance level of the excavation support system and indirectly adjust the displacement and deformation of the adjacent tunnel, where EI represents the stiffness of the diaphragm wall, γ w represents the unit weight of water, and h represents the vertical spacing of the supports. Based on different working condition combinations and a large number of numerical calculations, the fitting equation of the diaphragm wall u hm / H and the system stiffness EI / γ w h 4 is obtained, and its form is:

[0015]

[0016] where p 1 , p 2 , p 3 represent fitting coefficients.

[0017] Twenty representative u hm / H are collected by Latin hypercube sampling, and the EI / γ w h 4 is calculated by substituting them into the fitting equation and regarded as the source of disturbance uncertainty.

[0018] More preferably, the step S2 includes:

[0019] Determine the formation, tunnel, excavation size and mechanical parameters of the foundation pit, establish a numerical analysis model for the excavation disturbance of the adjacent tunnel, conduct a large number of finite difference calculations considering the system stiffness calculated in step S1, obtain the displacement and deformation responses of the tunnel under different disturbance intensity indexes, and establish a tunnel damage database.

[0020] Under the action of adjacent excavation disturbance, the damage form of the tunnel is mainly lateral displacement and deformation. Therefore, the damage state division of the interval tunnel based on the horizontal displacement is shown in the following table:

[0021]

[0022] More preferably, the step S3 includes:

[0023] Based on the adjacent tunnel excavation disturbance model established in step S2, an L25(5×5) orthogonal experimental design is carried out. Among them, 3 variables are the indexes of adjacent excavation disturbance intensity, 1 variable represents the stiffness of the diaphragm wall, 1 represents the soil compression modulus, and for each adjacent tunnel excavation disturbance condition, four different excavation depths H are set, with a total of 25 test conditions and 100 test points.

[0024] A large number of sample points are obtained through numerical calculation. The data is fitted by a quadratic response surface containing cross terms to realize the input of key formation and disturbance parameters, and the damage index of the adjacent tunnel under specific conditions of the tunnel is obtained. The form of the surrogate model is:

[0025]

[0026] In the formula, Y represents the horizontal displacement of the tunnel (mm), and X i respectively represent the i-th influencing factor, ε is the error term, and β 0 , β i , β ii , β ij respectively represent the constant term coefficient, the linear term coefficient, the quadratic term coefficient, and the cross term coefficient.

[0027] More preferably, step S4 includes:

[0028] According to the literature research, the statistical characteristics of the parameters are analyzed to determine the mean and standard deviation of the foundation pit excavation depth H, the tunnel burial depth H t , the soil compression modulus E s , the stiffness of the diaphragm wall EI, and the horizontal distance L between the foundation pit and the tunnel t . And 10,000 groups of samples are sampled by Monte Carlo, and the prior probability distribution f(θ) of the tunnel damage index is calculated by using the surrogate model established in step S3.

[0029] For the existing design parameters related to the adjacent foundation pit excavation of a certain railway line, the target value is unknown. Using the H, H t , E s , EI, L t of this project to update Y, then the likelihood function can be obtained from the surrogate model and these design parameters. Based on the basic idea of Bayesian update, the posterior probability distribution f(θ|y) of the existing tunnel damage index under adjacent excavation disturbance is expressed as:

[0030]

[0031] In the formula, L(θ|y) is the likelihood function. It can be predicted that the predicted value of the tunnel damage index will always fall within the 95% confidence intervals of the two distributions and be closer to the mean of the posterior probability distribution of the tunnel damage index. The variability of the updated target value is reduced.

[0032] More preferably, step S5 includes:

[0033] Based on the tunnel response data obtained from the numerical calculation in step S2, i.e., the tunnel horizontal displacement determined in step S3, a database of a large number of excavation disturbance intensity indexes EDI and tunnel damage indexes DI is obtained. Through linear regression analysis, a probabilistic demand model under the excavation disturbance of a foundation pit adjacent to the tunnel is obtained:

[0034] ln(DI) = a + bln(EDI)

[0035] In the formula, the parameters a and b represent fitting coefficients.

[0036] Determine the definition of the damage state and the threshold division of the tunnel with the horizontal displacement as the index through the specification. The relevant thresholds can be obtained from the "Technical Specification for the Safety Protection of Urban Rail Transit Structures (CJJ / T 202–2013)", and the lognormal distribution function is used to describe the vulnerability curve of the tunnel:

[0037]

[0038] In the formula, P(·) is the probability of exceeding a certain damage state ds, EDI is the disturbance intensity index, Φ is the cumulative probability function of the standard normal density, and EDI dsi is the threshold of the disturbance intensity index corresponding to the i-th damage state dsi, and β is the logarithmic standard deviation, which expresses the variability of the vulnerability curve.

[0039] The posterior distribution of the tunnel damage index obtained through Bayesian updating is in the DI-EDI database. For the corresponding disturbance conditions that are similar while the formation information, i.e., the soil compression modulus, is somewhat different, then the form of the corrected probabilistic demand model is:

[0040] ln(DI) = a + bln(EDI) + Δ

[0041] In the formula, Δ represents the correction coefficient. On this basis, combined with the tunnel damage state and the threshold definition standard and the lognormal distribution function for calculating the tunnel vulnerability curve, the update of the tunnel vulnerability evaluation index obtained based on specific geotechnical parameters can be realized.

[0042] The Bayesian estimation algorithm can update the probability distribution of soil parameters. The present invention applies it to the stability research and risk assessment of slopes, tunnels, foundation pits, etc. Further, based on this method, an updated algorithm for tunnel damage assessment under seismic and extreme disturbance conditions is given, overcoming the complexity of disturbance conditions and forming a systematic framework for tunnel disturbance vulnerability and toughness assessment. Specifically, the present invention obtains a surrogate model of tunnel damage indicators and physical and mechanical parameters under adjacent excavation disturbance through numerical calculation orthogonal experiments, establishes a probability mapping relationship between tunnel damage indicators and engineering design parameters based on Bayesian update, and combines the tunnel vulnerability assessment process under adjacent excavation disturbance to obtain the tunnel vulnerability curve under specific strata and engineering requirement parameters, which has strong practical significance and also provides guidance for quantifying the degree of tunnel damage and taking recoverable measures.

[0043] From the above technical solutions, the benefits of the present invention are as follows:

[0044] The present invention proposes to use the concept of system stiffness to characterize the disturbance uncertainty caused by adjacent excavation, and the distribution form of system stiffness is obtained by back-calculating based on a large number of actual monitoring data of diaphragm wall deflection deformation. Considering the tunnel vulnerability analysis model, it has important guiding significance for the selection of repair measures and toughness assessment after the disturbance of the interval tunnel.

[0045] Based on the Bayesian update principle, the tunnel vulnerability assessment model under adjacent excavation disturbance is corrected by constructing a surrogate model and actual engineering design parameters, so that the tunnel disturbance damage database originally constructed for specific strata can be applied to other engineering activities, improving the applicability of the tunnel vulnerability assessment algorithm. Description of the Drawings

[0046] Figure 1 It is the general flow chart of the tunnel vulnerability assessment method under adjacent excavation disturbance based on Bayesian update of the present invention;

[0047] Figure 2 It is the distribution map of the measured maximum deflection of the diaphragm wall in the global foundation pit excavation cases investigated and statistically analyzed by the present invention;

[0048] Figure 3 It is the relationship equation between the maximum deflection of the diaphragm wall and the system stiffness fitted in this implementation case;

[0049] Figure 4 It is the schematic diagram of the numerical model of adjacent tunnel excavation disturbance in the implementation case;

[0050] Figure 5 It is the surrogate model between the key input parameters and the horizontal displacement of the tunnel in the implementation case;

[0051] Figure 6The prior probability distribution and the posterior probability distribution after Bayesian update of the tunnel damage index in the implementation case;

[0052] Figure 7 The tunnel disturbance probability demand models before and after the update in the implementation case;

[0053] Figure 8 The tunnel disturbance vulnerability curves before and after the update in the implementation case. Detailed implementation manners

[0054] The present invention proposes a vulnerability assessment method for adjacent tunnel excavation disturbance based on Bayesian update, introduces the concept of system stiffness to characterize the uncertainty of tunnel damage caused by adjacent excavation disturbance, brings the obtained system stiffness data into the adjacent excavation disturbance model through a fitting equation, and conducts a large number of numerical calculations to establish a tunnel damage database; establishes a surrogate model between the corresponding parameters of the disturbance working condition and the tunnel damage index through numerical calculation orthogonal experimental design; conducts parameter characteristic statistics by collecting data based on literature research, calculates the prior probability distribution of the tunnel damage index through the constructed response surface likelihood function, and conducts Bayesian update by combining the actual engineering monitoring data to obtain the posterior probability distribution of the tunnel damage index; uses the mean and standard deviation of the updated tunnel damage index to correct the disturbance probability demand model, and combines the tunnel damage state threshold division and the lognormal probability distribution function to update the vulnerability curve. The present invention overcomes the limitations of the tunnel damage evaluation under adjacent excavation disturbance constructed based on specific strata. By correcting the tunnel damage index through Bayesian update, the probability of different damage degrees of the tunnel structure is more in line with the actual situation, and the applicability of the existing disturbance damage database is improved.

[0055] In order to make the above objects, technical solutions and advantages of the present invention clearer and easier to understand, the present invention will be further explained and illustrated below with reference to the accompanying drawings and specific embodiments.

[0056] As Figures 1 - 7 shown, the present invention provides a vulnerability assessment method for adjacent tunnel excavation disturbance based on Bayesian update, and its specific implementation process is as Figure 1 shown, including the following steps:

[0057] Step S1: Literature research and data statistics to obtain the distribution model of the normalized maximum deflection deformation of the diaphragm wall, and inversely calculate the system stiffness through the relationship equation between the diaphragm wall deflection deformation and the system stiffness obtained by fitting.

[0058] Step S1 includes:

[0059] Through the research of existing literature, collect the monitoring data of the excavation depth H and the maximum deflection deformation u hm in the global foundation pit excavation cases, and use the lognormal probability distribution model to fit u after normalizationhm The distribution of / H is as Figure 2 shown.

[0060] The relative position relationship between the foundation pit and the adjacent tunnel is defined as the disturbance intensity index, including the excavation depth H of the foundation pit, the buried depth H of the tunnel t and the horizontal spacing L between the foundation pit and the tunnel t , and the system stiffness EI / γ w h 4 is used to characterize the performance level of the excavation support system and indirectly regulate the displacement and deformation of the adjacent tunnel, where EI represents the stiffness of the diaphragm wall, γ w represents the unit weight of water, and h represents the vertical spacing of the supports.

[0061] For the foundation pit excavation near the interval tunnel in a certain city, the buried depth of the tunnel is 15m, the excavation depth of the foundation pit is 16m, the width of the foundation pit is 20m, the support system adopts a combination of multiple horizontal supports and diaphragm walls. The liner unit is used to simulate the diaphragm wall and the tunnel segments, the beam unit is used to simulate the internal supports during the foundation pit excavation, the concrete strength of the tunnel segments is C50, and the hardened soil HS model is used to simulate the soil. The specific parameter values are shown in the following table:

[0062]

[0063] Based on different working condition combinations and a large number of numerical calculations, the fitting equation of the diaphragm wall u hm / H and the system stiffness EI / γ w h 4 is obtained, and the form is:

[0064]

[0065] In the formula, p 1 , p 2 , p 3 represent the corresponding fitting coefficients. After calculation, the three coefficients are 5.516, -2.159, and -0.148 respectively, as Figure 3 shown.

[0066] Twenty representative u hm / H are collected by Latin hypercube sampling, and EI / γ w h 4 is calculated by substituting them into the above-obtained fitting equation, and it is regarded as the source of disturbance uncertainty.

[0067] Step S2: Establish a refined numerical model for the foundation pit excavation of the adjacent tunnel, consider the uncertainty of the disturbance intensity, and obtain a database of tunnel damage indicators through a large number of numerical calculations.

[0068] Determine the formation, tunnel, and foundation pit excavation dimensions and mechanical parameters, and establish a numerical analysis model for the excavation disturbance of adjacent tunnels. As Figure 4 shown, considering the system stiffness calculated in step S1, perform a large number of finite difference calculations to obtain the displacement and deformation responses of the tunnel under different disturbance intensity indices, and establish a tunnel damage database.

[0069] Step S3: Through numerical calculation and orthogonal experimental design, adopt a quadratic response surface function with cross terms to establish a surrogate model between the important input parameters and the tunnel damage index.

[0070] Based on the adjacent tunnel excavation disturbance model established in step S2, conduct an L25(5×5) orthogonal experimental design, where 3 variables are the indices of the adjacent excavation disturbance intensity, 1 variable represents the stiffness of the diaphragm wall, 1 represents the soil compression modulus, and for each adjacent tunnel excavation disturbance condition, set four different excavation depths H, with a total of 25 test conditions and 100 test points.

[0071] A large number of sample points are obtained through numerical calculation, and the data is fitted with a quadratic response surface with cross terms to realize the input of key formation and disturbance parameters, and obtain the tunnel damage index of the adjacent tunnel under specific conditions. The form of the surrogate model is:

[0072]

[0073] In the formula, Y represents the horizontal displacement of the tunnel (mm), and X i respectively represent the i-th influencing factor, ε is the error term, and β 0 、β i 、β ii 、β ij respectively represent the constant term coefficient, the linear term coefficient, the quadratic term coefficient, and the cross term coefficient. As Figure 5 shown, the finite difference calculation results and the constructed response surface function have a good fitting effect, and R 2 = 0.985. The specific coefficient values are shown in the following table:

[0074]

[0075] Step S4: Given the relevant design and on-site monitoring data of a certain project, combine the surrogate model in step S3 to obtain the likelihood function under specific conditions, and use Bayesian estimation to obtain the posterior probability distribution of the tunnel damage index under adjacent excavation disturbance.

[0076] According to the parameter statistical characteristic analysis through literature research, determine the foundation pit excavation depth H, the tunnel burial depth H t 、the soil compression modulus E s 、the diaphragm wall stiffness EI, and the horizontal distance L between the foundation pit and the tunnel tThe mean and standard deviation of [], and 10,000 groups of samples are sampled by Monte Carlo. The prior probability distribution f(θ) of the tunnel damage index is calculated using the surrogate model established in step S3.

[0077] Given the design parameters related to the excavation of a foundation pit adjacent to a certain railway line, with the target values unknown, the H, H t , E s , EI, L t of this project are used to update Y. The specific parameters are shown in the following table:

[0078]

[0079] Then the likelihood function can be obtained from the surrogate model and these design parameters. Based on the basic idea of Bayesian update, the posterior probability distribution f(θ|y) of the existing tunnel damage index under adjacent excavation disturbance is expressed as:

[0080]

[0081] where L(θ|y) is the likelihood function. As Figure 6 shown, the predicted values of the tunnel damage index always fall within the 95% confidence intervals of the two distributions and are closer to the mean of the posterior probability distribution of the tunnel damage index. The variability of the updated target value is reduced.

[0082] Step S5: According to the modified disturbance probability demand model, combined with the tunnel damage state threshold division and the lognormal probability distribution model, complete the update of the tunnel vulnerability curve under adjacent excavation disturbance.

[0083] Based on the tunnel response data obtained from the numerical calculation in step S2, i.e., the tunnel horizontal displacement determined in step S3, a database of a large number of excavation disturbance intensity indicators EDI and tunnel damage indicators DI is obtained. Through linear regression analysis, the probability demand model under the excavation disturbance of the foundation pit adjacent to the tunnel is obtained:

[0084] ln(DI) = a + bln(EDI)

[0085] where the parameters a and b represent the fitting coefficients, and the results are -0.649 and 4.631 respectively.

[0086] Through the specification, the definition and threshold division of the tunnel damage state with the horizontal displacement as the index are determined. The relevant thresholds can be obtained from the "Technical Code for the Safety Protection of Urban Rail Transit Structures CJJ / T 202–2013", and the lognormal distribution function is used to describe the tunnel vulnerability curve:

[0087]

[0088] where \(P(\cdot)\) is the probability of exceeding a certain damage state \(ds\), \(EDI\) is the disturbance intensity index, \(\varPhi\) is the cumulative probability function of the standard normal density, and \(EDI_{ dsi}\) is the threshold of the disturbance intensity index corresponding to the \(i\)-th damage state \(dsi\). \(\beta\) is the logarithmic standard deviation, which expresses the variability of the vulnerability curve. After calculation, \(\beta = 0.42\). dsi The posterior distribution of the tunnel damage index obtained by Bayesian update is in the DI - EDI database. For disturbance conditions that are similar but the formation information, i.e., the soil compression modulus, is different, the form of the corrected disturbance probability demand model is:

[0089] \(\ln(DI)=a + b\ln(EDI)+\Delta\)

[0090] where \(\Delta\) represents the correction coefficient. The disturbance probability demand models before and after the update are shown in Figure 7 . The calculated result of \(\Delta\) is 0.121. On this basis, by combining the tunnel damage state and the threshold definition standard and calculating the log - normal distribution function of the tunnel vulnerability curve, the update of the tunnel vulnerability evaluation index obtained based on specific geotechnical parameters can be realized. The tunnel disturbance vulnerability curves before and after the update are shown in Figure 8 .

[0091] Figure 7 Based on the large amount of actual monitoring data of the diaphragm wall deflection deformation, the distribution form of the system stiffness is inversely calculated in the present invention. Considering the tunnel vulnerability analysis model, it has important guiding significance for the selection of repair measures and toughness evaluation after the disturbance of the interval tunnel. Based on the Bayesian update principle, the tunnel vulnerability evaluation model under adjacent excavation disturbance is corrected by constructing a surrogate model and actual engineering design parameters, so that the tunnel disturbance damage database originally constructed for a specific formation can be applied to other engineering activities, improving the applicability of the tunnel vulnerability evaluation algorithm. Figure 8 as shown

[0092] ​​

Claims

1. A vulnerability assessment method for adjacent tunnel excavation disturbance based on Bayesian updating, characterized in that: The numerical vulnerability analysis and Bayesian estimation algorithm are used, including the following steps: Step S1: Literature research and data statistics are performed to obtain the distribution model of the normalized maximum deflection deformation of the ground-connected wall. The system stiffness is calculated by back-calculating the relationship equation between the deflection deformation of the ground-connected wall and the system stiffness, and then the process proceeds to step S2; Step S2: Establish a refined numerical model of the adjacent tunnel foundation pit excavation, consider the uncertainty of the disturbance intensity, and obtain a database of tunnel damage indicators through a large number of numerical calculations; Step S3: by numerical calculation of orthogonal experimental design, a quadratic response surface function with cross terms is used to establish a proxy model between important input parameters and tunnel damage indicators; Step S4: Given a certain project-related design and field monitoring data, the likelihood function under specific working conditions is obtained by combining the proxy model in step S3, and the posterior probability distribution of the tunnel damage index under adjacent excavation disturbance is estimated using Bayesian estimation; Step S5: According to the modified disturbance probability demand model, combined with the tunnel damage state threshold division and the log-normal probability distribution model, the tunnel vulnerability curve under the adjacent excavation disturbance is updated.

2. The vulnerability assessment method for adjacent tunnel excavation disturbance based on Bayesian updating according to claim 1 is characterized in that: The step S1 comprises: Through the investigation of existing literature, the data on excavation depth H and maximum deflection u in global foundation pit excavation cases are collected. hm The monitoring data is normalized and the log-normal probability distribution model is used to fit u hm The distribution of / H is consistent with the KS test; The relative position relationship between the foundation pit and the adjacent tunnel is defined as the disturbance intensity index, including the foundation pit excavation depth H, the tunnel burial depth H t Horizontal distance from foundation tunnel L t , system stiffness EI / γ w h 4 It is used to characterize the performance level of the excavation support system and indirectly adjust the displacement deformation of the adjacent tunnel, where EI represents the stiffness of the continuous wall, γ w represents the weight of water, h represents the vertical spacing of the support, and the continuous wall u is obtained based on the combined design of different working conditions and a large number of numerical calculations. hm / H and system stiffness EI / γ w h 4 The fitting equation is in the form of: Where p1, p2, p3 represent the fitting coefficients; Latin hypercube sampling was used to collect 20 representative u hm / H, and then put it into the fitting equation to calculate EI / γ w h 4 , which is considered as a source of disturbance uncertainty.

3. The vulnerability assessment method for adjacent tunnel excavation disturbance based on Bayesian updating according to claim 2 is characterized in that: The step S2 comprises: Determine the dimensions and mechanical parameters of the stratum, tunnel, and foundation pit excavation, establish a numerical analysis model for the disturbance caused by excavation of adjacent tunnels, perform a large number of finite difference calculations considering the system stiffness calculated in step S1, obtain the displacement and deformation responses of the tunnel under different disturbance intensity indicators, and establish a tunnel damage database.

4. The vulnerability assessment method for adjacent tunnel excavation disturbance based on Bayesian updating according to claim 2 is characterized in that: The step S3 comprises: Based on the adjacent tunnel excavation disturbance model established in step S2, an L25 (5×5) orthogonal test design is performed, in which three variables are indicators of the adjacent excavation disturbance intensity, one variable represents the stiffness of the ground-connected wall, and one represents the soil compression modulus. For each adjacent tunnel excavation disturbance condition, four different excavation depths H are set, with a total of 25 test conditions and 100 test points. Numerical calculations are performed to obtain a large number of sample points. The quadratic response surface with cross terms is used to fit the data to input key formation and disturbance parameters, and the damage index of the adjacent tunnel under specific working conditions is obtained. The proxy model is in the form of: Where Y represents the horizontal displacement of the tunnel (mm), X i They represent the i-th influencing factor, ε is the error term, β0, β i , β ii , β ij They represent the constant term coefficient, linear term coefficient, quadratic term coefficient, and cross term coefficient respectively. k represents the number of orthogonal experimental design indicators, and here k is equal to 5.

5. The vulnerability assessment method for adjacent tunnel excavation disturbance based on Bayesian updating according to claim 3 is characterized in that: The step S4 comprises: According to the literature research, the parameter statistical characteristics analysis is carried out to determine the foundation pit excavation depth H and tunnel burial depth H t , soil compression modulus E s , ground-connected wall stiffness EI, horizontal distance between foundation pit and tunnel L t The mean and standard deviation of the tunnel damage index are calculated by using Monte Carlo sampling of 10,000 samples and the proxy model established in step S3; The design parameters of the foundation pit excavation near a certain subway line are unknown. The H and H t 、E s , EI, L t By updating Y, the likelihood function can be obtained from the surrogate model and these design parameters. Based on the basic idea of ​​Bayesian updating, the posterior probability distribution f(θ|y) of the damage index of the existing tunnel under the adjacent excavation disturbance is expressed as: Where L(θ|y) is the likelihood function. It can be foreseen that the predicted value of the tunnel damage index always falls within the 95% confidence interval of the two distributions and is closer to the mean of the posterior probability distribution of the tunnel damage index. The variability of the updated target value is reduced.

6. The vulnerability assessment method for adjacent tunnel excavation disturbance based on Bayesian updating according to claim 4 is characterized in that: The step S5 comprises: Based on the tunnel response data numerically calculated in step S2, i.e., the tunnel horizontal displacement determined in step S3, a large number of databases of excavation disturbance intensity indexes EDI and tunnel damage indexes DI are obtained. Through linear regression analysis, the probability demand model under the excavation disturbance of the tunnel adjacent to the foundation pit is obtained: ln(DI)=a+bln(EDI) The parameters a and b in the formula represent the fitting coefficients; The damage state definition and threshold division of tunnels with horizontal displacement as an indicator are determined by the specification. The threshold is obtained in the "CJJ / T202-2013 Technical Specification for Safety Protection of Urban Rail Transit Structures", and the log-normal distribution function is used to describe the tunnel vulnerability curve: Where P(·) is the probability of exceeding a certain damage state ds, ds i Corresponding to the i-th damage state, EDI is the disturbance intensity index, Φ is the standard normal density cumulative probability function, and EDI dsi is the cause of the i-th damage state ds i The corresponding threshold of the disturbance intensity index, β is the logarithmic standard deviation, which expresses the variability of the vulnerability curve; The posterior distribution of the tunnel damage index obtained by Bayesian updating is in the DI-EDI database. The corresponding disturbance conditions are similar but the formation information, i.e., the soil compression modulus, is different. Then the form of the modified disturbance probability demand model is: ln(DI)=a+bln(EDI)+Δ Where Δ represents the correction coefficient. On this basis, the tunnel damage state and threshold definition standard are combined with the log-normal distribution function of the tunnel vulnerability curve to update the tunnel vulnerability evaluation index obtained based on specific geotechnical parameters.

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