Double-engine prediction method and system for stratum deformation caused by shield tunneling

Abstract

The invention relates to the field of shield tunneling and stratum deformation prediction, and provides a double-engine prediction method and system for stratum deformation caused by shield tunneling, and the method comprises the steps: S1, determining an analytical solution model; s2, according to the geological parameters of a certain geological section, the field actual measurement deformation value of the geological section and the analytical solution model, the stratum loss rate of the geological section is obtained; s3, the step S2 is repeated, and the stratum loss rates of the multiple geological sections are obtained; s4, selecting and training a neural network; s5, taking the geological parameters of the to-be-predicted section as input, substituting the geological parameters into the neural network, and outputting the stratum loss rate of the to-be-predicted section; and S6, substituting into the analytical solution model to obtain a stratum deformation prediction value of the to-be-predicted section. The method is clear in theoretical basis, simple in application and low in sample demand quantity, and engineering application can be facilitated on the premise that the prediction precision is met.

Description

Technical Field

[0001] The present invention relates to the field of shield tunneling and stratum deformation prediction, and in particular to a dual-engine prediction method and system for stratum deformation caused by shield tunneling. Background Art

[0002] The ground deformation caused by tunnel excavation is an issue that must be paid attention to in tunnel construction. Although the shield method can minimize the disturbance of the ground caused by excavation activities, the ground deformation caused by shield excavation is still inevitable. In the analysis of ground deformation caused by tunnel excavation, the ground loss rate is a very important parameter, which is defined as the ratio of the difference between the excavation space of the tunnel and the space occupied by the actual tunnel to the tunnel excavation space. The ground loss rate is an input quantity in many ground deformation analysis methods caused by tunnel excavation. It is generally given based on engineering experience and has a certain degree of uncertainty.

[0003] In recent years, with the advancement of computer technology, artificial neural networks have been widely used in all walks of life. Many scholars have used different neural networks to conduct a lot of research and analysis on the problem of stratum deformation caused by tunnel excavation, and have achieved remarkable results. However, in the analysis of the stratum deformation caused by shield excavation, the existing neural network methods mostly use shield excavation parameters as the input layer and stratum deformation as the output layer. Although this lacks a strict theoretical basis, due to the powerful nonlinear fitting function of neural networks, a certain fitting effect can be obtained when the sample size is large enough. However, there are some defects such as unclear theory, large sample size requirements, long network training time, and insufficient credibility of results. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a dual-engine prediction method and system for stratum deformation caused by shield tunneling. It has a clear theoretical basis, is simple to apply, and requires a small amount of samples. It can be convenient for engineering applications while meeting the prediction accuracy.

[0005] The present invention adopts the following technical solution:

[0006] On the one hand, the present invention provides a dual-engine prediction method for ground deformation caused by shield tunneling, comprising:

[0007] S1. Determine an analytical solution model for stratum deformation caused by shield tunneling; the input of the analytical solution model is geological parameters and stratum loss rate of the geological section, and the output is an analytical solution for stratum deformation of the geological section;

[0008] S2, obtaining the formation loss rate of a geological section according to the geological parameters of the geological section, the field measured deformation value of the geological section, and the analytical solution model of step S1;

[0009] S3, repeating step S2 to obtain formation loss rates of multiple geological sections;

[0010] S4, selecting a neural network, taking the geological parameters of a plurality of geological sections as sample inputs of the neural network, taking the formation loss rates of the plurality of geological sections obtained in step S3 as sample expected outputs of the neural network, training the neural network, and obtaining a trained neural network;

[0011] S5, selecting a section to be predicted, taking the geological parameters of the section to be predicted as input, substituting them into the trained neural network obtained in step S4, and outputting a predicted value of the formation loss rate of the section to be predicted;

[0012] S6. Substitute the geological parameters of the section to be predicted and the predicted value of the formation loss rate of the section to be predicted obtained in step S5 into the analytical solution model to obtain the predicted value of the formation deformation of the section to be predicted.

[0013] Any possible implementation method as described above further provides an implementation method, wherein the method also includes: S0, constructing a tunnel geological section database, the database including geological parameters of multiple geological sections of the tunnel, and field measured deformation values ​​corresponding to the geological sections; the geological parameters of the geological sections are read from the database.

[0014] According to any possible implementation described above, a further implementation is provided, in step S1, the analytical solution model of the formation deformation is expressed as:

[0015] u(x,y,z)=f(x,y,z,r,h,E s ,v s ,u w ,k0,η,σ' v ,q,E q ,v q )

[0016] Where: u(x, y, z) is the stratum deformation at the coordinate (x, y, z) in a geological section; x, y and z are the coordinate variables in the shield tunneling direction, horizontal direction and vertical direction respectively; r is the tunnel radius; h is the tunnel depth; E s and v s are the soil compression modulus and Poisson's ratio at the center of the tunnel respectively; σ' v is the effective vertical earth pressure at the tunnel center; u w is the water pressure at the center of the tunnel; k0 is the static earth pressure coefficient at the center of the tunnel; q is the grouting volume per unit length; E q and v q are the elastic modulus and Poisson’s ratio of the grouting body respectively; η is the formation loss rate.

[0017] For any possible implementation described above, a further implementation is provided, in which the specific method of step S2 is:

[0018] S21, select geological section X, read geological parameters of the geological section X, and measured deformation values ​​u at different positions i of the geological section X i , and record the coordinate positions of different positions i;

[0019] S22, given the initial formation loss rate η0 and the formation loss rate incremental step length Δη, and taking η0 as the initial formation loss rate trial value, and giving the accuracy ξ;

[0020] S23, using the geological parameters of the geological section X in step S21 and the initial formation loss rate η0 in step S22 as trial values, and substituting them into the analytical solution model, to obtain the analytical solution of the formation deformation of the geological section X under the trial value of the formation loss rate, and to obtain the deformation analytical value u at the same position as the measured formation deformation i ';

[0021] S24, calculate |u i -u i '|, determine whether it is less than the precision ξ;

[0022] S25. If the accuracy requirement is met, the current trial value of the formation loss rate is the true value of the formation loss rate corresponding to the measured deformation value at the position i on the geological section X; if the accuracy requirement is not met, the formation loss rate is increased by Δη as a new trial value of the formation loss rate, and steps S23 and S24 are repeated until the accuracy requirement is met, and the formation loss rate η corresponding to the measured deformation value at different positions i on the geological section X can be obtained. i ;

[0023] S26, the formation loss rate η corresponding to the measured deformation value at different positions i of the geological section X i The formation loss rate vector η X , calculate η based on the weight vector k of the measured position X ·k, and obtain the formation loss rate of geological section X.

[0024] Any possible implementation as described above further provides an implementation, in which in step S4, the output layer of the neural network is a node, and the number of input layers corresponds to the sample input vector; the sample input vector is composed of multiple geological parameters of the geological section.

[0025] As for any possible implementation described above, a further implementation is provided, wherein the neural network is a BP neural network or a RF neural network, and other neural networks may also be used.

[0026] Any possible implementation as described above, further provides an implementation, in step S1, the geological parameters include multiple ones of tunnel radius, tunnel burial depth, compression modulus and Poisson's ratio of soil, effective vertical earth pressure, water pressure, earth pressure coefficient, grouting volume per unit length, grouting body elastic modulus and Poisson's ratio, lining elastic modulus and Poisson's ratio, and lining thickness.

[0027] Any possible implementation as described above further provides an implementation, in which in step S0, the method for constructing a tunnel geological section database is:

[0028] S01. Establish a soil layer dictionary, where the key is the soil layer number and the corresponding value is a list of soil layer parameters. The parameters in the list are: soil layer density, deformation modulus, soil pressure coefficient, and Poisson's ratio.

[0029] S02. Divide the geological section, read the soil layer number and corresponding thickness of the section on the geological map, and establish three corresponding lists respectively: one is the soil layer number list, that is, the soil layer distribution from the surface to the bottom; the second is the soil layer thickness list, that is, the thickness of each soil layer corresponding to the elements in the soil layer number list; the third is the list of other parameters such as tunnel burial depth and groundwater depth;

[0030] S03. Establish a geological condition dictionary. Each section has three key-value pairs. The first key is mileage number + soil layer number, and the corresponding value is a list of soil layer numbers; the second key is mileage number + soil layer thickness, and the corresponding value is a list of soil layer thicknesses; the third key is mileage number + tunnel depth and groundwater, and the corresponding value is a list of tunnel depth, groundwater depth and other parameters.

[0031] Any possible implementation as described above further provides an implementation, in which, as the shield machine advances, the original predicted section is used as a sample to enter the training of the neural network as a continuous correction to the neural network.

[0032] On the other hand, the present invention also provides a dual-engine prediction system for ground deformation caused by shield tunneling, the system is used to implement the above method, and the system includes:

[0033] The analytical solution module inputs the geological parameters and stratum loss rate of the geological section, and outputs the analytical solution of stratum deformation of the geological section based on the analytical solution model of stratum deformation caused by tunneling;

[0034] The formation loss rate iterative determination module iteratively obtains the formation loss rate of a geological section according to the geological parameters of the geological section, the field measured deformation value of the geological section, and the analytical solution model;

[0035] A neural network training module selects a neural network, takes the geological parameters of multiple geological sections as sample inputs of the neural network, takes the formation loss rates of multiple geological sections obtained by the formation loss rate iterative determination module as sample expected outputs of the neural network, trains the neural network, and obtains a trained neural network;

[0036] The stratum deformation prediction module takes the geological parameters of the section to be predicted as input, substitutes them into the trained neural network, and outputs the stratum loss rate of the section to be predicted; then substitutes the geological parameters of the section to be predicted and the stratum loss rate of the section to be predicted into the analytical solution model to obtain the stratum deformation prediction value of the section to be predicted.

[0037] Any possible implementation as described above further provides an implementation, wherein the system also includes a tunnel geological section database, the database including geological parameters of multiple geological sections of the tunnel, and field measured deformation values ​​corresponding to the geological sections.

[0038] The beneficial effects of the present invention are:

[0039] The present invention takes into account the empirical nature of the formation loss rate in the analysis of formation deformation caused by shield excavation, adopts a neural network to predict the formation loss rate, and combines the analytical solution of formation deformation caused by shield tunneling to achieve dual-engine prediction of formation deformation. This prediction idea gives full play to the advantages of analytical methods and neural networks. Compared with other prediction methods, it has the advantages of clear theory, short prediction time, small sample requirement, and high credibility of results. Furthermore, this method provides a prediction idea for formation deformation caused by underground excavation, and can also be used to predict formation deformation caused by mechanical shafts, foundation pits and other underground engineering excavations. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Shown is a flowchart of a dual-engine prediction method for ground deformation caused by shield tunneling according to an embodiment of the present invention.

[0041] Figure 2 Shown is a structural diagram of the tunnel geological section database in the embodiment.

[0042] Figure 3 The figure shows a comparison curve between the actual deformation value of the ground surface just above the center of the tunnel and the prediction result of the dual engine when the typical shield project (a shield project of Shanghai Metro) in the embodiment is applied to the soft soil layer.

[0043] Figure 4 Shown is a comparison curve of the measured deformation of the ground surface directly above the center of the tunnel, the numerical simulation and the dual-engine prediction results when the typical shield project (a shield project of the Guanghua Intercity Railway) in the embodiment is applied to hard rock formations. DETAILED DESCRIPTION

[0044] The specific embodiments of the present invention will be described in detail below in conjunction with specific drawings. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated, and they can be combined with each other to achieve better technical effects.

[0045] like Figure 1 As shown, a dual-engine prediction method for ground deformation caused by shield tunneling according to an embodiment of the present invention includes:

[0046] S1. Determine an analytical solution model for stratum deformation caused by tunneling; the input of the analytical solution model is geological parameters of the geological section and stratum loss rate, and the output is an analytical solution for stratum deformation of the geological section;

[0047] S2, obtaining the formation loss rate of a geological section according to the geological parameters of the geological section, the field measured deformation value of the geological section, and the analytical solution model of step S1;

[0048] S3, repeating step S2 to obtain formation loss rates of multiple geological sections;

[0049] S4, selecting a neural network, taking the geological parameters of a plurality of geological sections as sample inputs of the neural network, taking the formation loss rates of the plurality of geological sections obtained in step S3 as sample expected outputs of the neural network, training the neural network, and obtaining a trained neural network;

[0050] S5, selecting a section to be predicted, taking the geological parameters of the section to be predicted as input, substituting them into the trained neural network obtained in step S4, and outputting a predicted value of the formation loss rate of the section to be predicted;

[0051] S6. Substitute the geological parameters of the section to be predicted and the predicted value of the formation loss rate of the section to be predicted obtained in step S5 into the analytical solution model to obtain the predicted value of the formation deformation of the section to be predicted.

[0052] In a specific embodiment, the method also includes: S0, constructing a tunnel geological section database, the database including geological parameters of multiple geological sections of the tunnel, and field measured deformation values ​​corresponding to the geological sections; the geological parameters of the geological sections are read from the database.

[0053] In a specific embodiment, the analytical solution model of formation deformation can adopt any one of the existing technologies, for example:

[0054] Reference [1] Zhao Jiale, Li Binwei, Fu Yonggui, Liu Yang. Parameter determination of analytical method for ground deformation caused by tunnel excavation [J]. Railway Construction, 2023, 63(2): 99-102. and reference [2] Zhao Jiale. Three-dimensional analysis and prediction of ground deformation caused by shield tunneling [D]. University of Science and Technology Beijing, 2024, provide a specific analytical solution model.

[0055] In a specific embodiment, in step S1, the analytical solution model of the formation deformation is expressed as:

[0056] u(x,y,z)=f(x,y,z,r,h,E s ,v s ,u w ,k0,η,σ' v ,q,E q ,v q )

[0057] Where: u(x, y, z) is the stratum deformation at the coordinate (x, y, z) in a geological section; x, y and z are the coordinate variables in the shield tunneling direction, horizontal direction and vertical direction respectively; r is the tunnel radius; h is the tunnel depth; E s and v s are the soil compression modulus and Poisson's ratio at the center of the tunnel respectively; σ' v is the effective vertical earth pressure at the tunnel center; u w is the water pressure at the center of the tunnel; k0 is the static earth pressure coefficient at the center of the tunnel; q is the grouting volume per unit length; E q and v q are the elastic modulus and Poisson’s ratio of the grouting body respectively; η is the formation loss rate.

[0058] In a specific embodiment, the specific method of step S2 is:

[0059] S21, select geological section X, read geological parameters of the geological section X, and measured deformation values ​​u at different positions i of the geological section X i , and record the coordinate positions of different positions i;

[0060] S22, given the initial formation loss rate η0 and the formation loss rate incremental step length Δη, and taking η0 as the initial formation loss rate trial value, and giving the accuracy ξ;

[0061] S23, using the geological parameters of the geological section X in step S21 and the initial formation loss rate η0 in step S22 as trial values, and substituting them into the analytical solution model, to obtain the analytical solution of the formation deformation of the geological section X under the trial value of the formation loss rate, and to obtain the deformation analytical value u at the same position as the measured formation deformation i ';

[0062] S24, calculate |u i -u i '|, determine whether it is less than the precision ξ;

[0063] S25. If the accuracy requirement is met, the current trial value of the formation loss rate is the true value of the formation loss rate corresponding to the measured deformation value at the position i on the geological section X; if the accuracy requirement is not met, the formation loss rate is increased by Δη as a new trial value of the formation loss rate, and steps S23 and S24 are repeated until the accuracy requirement is met, and the formation loss rate η corresponding to the measured deformation value at different positions i on the geological section X can be obtained. i ;

[0064] S26, the formation loss rate η corresponding to the measured deformation value at different positions i of the geological section X i The formation loss rate vector η X , calculate η based on the weight vector k of the measured position X ·k, and obtain the formation loss rate of geological section X.

[0065] In a specific embodiment, in step S4, the output layer of the neural network is a node, and the number of input layers corresponds to the sample input vector; the sample input vector is composed of multiple geological parameters of the geological section.

[0066] Generally, the sample input vector should include: tunnel radius, tunnel burial depth, soil compression modulus and Poisson's ratio, effective vertical earth pressure, water pressure, earth pressure coefficient, grouting volume per unit length, grouting elastic modulus and Poisson's ratio, lining elastic modulus and Poisson's ratio, lining thickness, etc.

[0067] However, in practical applications, the sample input vector can be adjusted according to the actual situation. For example, when this method is applied to a specific project, the tunnel radius, lining elastic modulus and Poisson's ratio, and lining thickness remain unchanged and do not need to be input. Due to the great differences in the geological conditions and construction parameters of different shield tunnels, when there are few parameters in the input vector and the training sample size is not large, the training samples of different projects should not be mixed.

[0068] It should be noted that the geological parameters in the sample input vector do not need to completely correspond to the geological parameters required in the analytical solution model. The selection of geological parameters in the input vector is determined by experiments or experience according to specific circumstances.

[0069] In a specific embodiment, in step S1, the geological parameters include multiple ones of tunnel radius, tunnel burial depth, compression modulus and Poisson's ratio of soil, effective vertical earth pressure, water pressure, earth pressure coefficient, grouting volume per unit length, grouting elastic modulus and Poisson's ratio, lining elastic modulus and Poisson's ratio, and lining thickness.

[0070] In a specific embodiment, in step S0, the method for constructing a tunnel geological section database is:

[0071] S01. Establish a soil layer dictionary, where the key is the soil layer number and the corresponding value is a list of soil layer parameters. The parameters in the list are: soil layer density, deformation modulus, soil pressure coefficient, and Poisson's ratio.

[0072] S02. Divide the geological section, read the soil layer number and corresponding thickness of the section on the geological map, and establish three corresponding lists respectively: one is the soil layer number list, that is, the soil layer distribution from the surface to the bottom; the second is the soil layer thickness list, that is, the thickness of each soil layer corresponding to the elements in the soil layer number list; the third is the list of other parameters such as tunnel burial depth and groundwater depth;

[0073] S03. Establish a geological condition dictionary. Each section has three key-value pairs. The first key is mileage number + soil layer number, and the corresponding value is a list of soil layer numbers; the second key is mileage number + soil layer thickness, and the corresponding value is a list of soil layer thicknesses; the third key is mileage number + tunnel depth and groundwater, and the corresponding value is a list of tunnel depth, groundwater depth and other parameters.

[0074] In a specific embodiment, a simplified database of tunnel geological sections is established by using a python list corresponding method. The structure of the tunnel geological section database is as follows: Figure 2 shown.

[0075] Using this method to build a geological section database can greatly simplify the parameter calculation process and is the basis for programming and automation of tedious steps such as neural network sample acquisition and model correction.

[0076] In a specific embodiment, as the shield machine advances, the original predicted section is used as a sample and enters the training of the neural network as a continuous correction to the neural network.

[0077] The embodiment of the present invention provides a dual-engine prediction system for ground deformation caused by shield tunneling, which is used to implement the above method. The system includes:

[0078] The analytical solution module inputs the geological parameters and stratum loss rate of the geological section, and outputs the analytical solution of stratum deformation of the geological section based on the analytical solution model of stratum deformation caused by tunneling;

[0079] The formation loss rate iterative determination module iteratively obtains the formation loss rate of a geological section according to the geological parameters of the geological section, the field measured deformation value of the geological section, and the analytical solution model;

[0080] A neural network training module selects a neural network, takes the geological parameters of multiple geological sections as sample inputs of the neural network, takes the formation loss rates of multiple geological sections obtained by the formation loss rate iterative determination module as sample expected outputs of the neural network, trains the neural network, and obtains a trained neural network;

[0081] The stratum deformation prediction module takes the geological parameters of the section to be predicted as input, substitutes them into the trained neural network, and outputs the stratum loss rate of the section to be predicted; then substitutes the geological parameters of the section to be predicted and the stratum loss rate of the section to be predicted into the analytical solution model to obtain the stratum deformation prediction value of the section to be predicted.

[0082] In a specific embodiment, the system further includes a tunnel geological section database, which includes geological parameters of multiple geological sections of the tunnel and field measured deformation values ​​corresponding to the geological sections.

[0083] Compared with the existing methods, this method gives full play to the advantages and disadvantages of neural networks and analytical theories, and has the advantages of clear theoretical basis, short prediction time, simple application, and small sample requirements. When applied to a typical shield project in soft soil (a shield project of Shanghai Metro), the comparison curve between the measured deformation value of the surface just above the center of the tunnel and the prediction result of the dual engine is shown in the figure below. Figure 3 As shown in the figure; when applied to a typical shield project in hard rock formations (a shield project of the Guanghua Intercity Railway), the measured deformation of the ground surface just above the tunnel center, the numerical simulation and the dual-engine prediction results are compared. Figure 4 The application results of a shield tunneling project in Guanghua Intercity Railway show that when the number of samples is only 12 groups, a simple single hidden layer BP neural network can achieve Figure 4 The prediction effect shown takes about 3.0 seconds; while conventional neural network prediction methods often require thousands of sets of samples, and the numerical simulation method takes about 6 hours just for the solution process, which verifies that this method has good engineering applicability.

[0084] The invention is applicable to the prediction of stratum deformation caused by shield excavation, and can also be used for the prediction of stratum deformation caused by mechanical shaft, foundation pit and other underground engineering excavation.

[0085] Although several embodiments of the present invention have been given herein, those skilled in the art should understand that the embodiments of the present invention may be modified without departing from the spirit of the present invention. The above embodiments are merely exemplary and should not be used as a limitation on the scope of the present invention.

Claims

1. A dual-engine prediction method for ground deformation caused by shield tunneling, characterized in that: The method comprises: S1. Determine an analytical solution model for stratum deformation caused by shield tunneling; the input of the analytical solution model is geological parameters and stratum loss rate of the geological section, and the output is an analytical solution for stratum deformation of the geological section; S2, obtaining the formation loss rate of a geological section according to the geological parameters of the geological section, the field measured deformation value of the geological section, and the analytical solution model of step S1; S3, repeating step S2 to obtain formation loss rates of multiple geological sections; S4, selecting a neural network, taking the geological parameters of a plurality of geological sections as sample inputs of the neural network, taking the formation loss rates of the plurality of geological sections obtained in step S3 as sample expected outputs of the neural network, training the neural network, and obtaining a trained neural network; S5, selecting a section to be predicted, taking the geological parameters of the section to be predicted as input, substituting them into the trained neural network obtained in step S4, and outputting the formation loss rate of the section to be predicted; S6. Substitute the geological parameters of the section to be predicted and the formation loss rate of the section to be predicted obtained in step S5 into the analytical solution model to obtain a predicted value of the formation deformation of the section to be predicted.

2. The dual-engine prediction method for ground deformation caused by shield tunneling as claimed in claim 1, characterized in that: The method also includes: S0, constructing a tunnel geological section database, the database including geological parameters of multiple geological sections of the tunnel and field measured deformation values ​​corresponding to the geological sections; the geological parameters of the geological sections are read from the database.

3. The dual-engine prediction method for ground deformation caused by shield tunneling as claimed in claim 1, characterized in that: In step S1, the analytical solution model of the formation deformation is expressed as: u(x,y,z)=f(x,y,z,r,h,E s ,v s ,u w ,k0,η,σ' v ,q,E q ,v q ) Where: u(x, y, z) is the stratum deformation at the coordinate (x, y, z) in a geological section; x, y and z are the coordinate variables in the shield tunneling direction, horizontal direction and vertical direction respectively; r is the tunnel radius; h is the tunnel depth; E s and v s are the soil compression modulus and Poisson's ratio at the center of the tunnel respectively; σ' v is the effective vertical earth pressure at the tunnel center; u w is the water pressure at the center of the tunnel; k0 is the static earth pressure coefficient at the center of the tunnel; q is the grouting volume per unit length; E q and v q are the elastic modulus and Poisson’s ratio of the grouting body respectively; η is the formation loss rate.

4. The dual-engine prediction method for ground deformation caused by shield tunneling as claimed in claim 1, characterized in that: The specific method of step S2 is: S21, select geological section X, read geological parameters of the geological section X, and measured deformation values ​​u at different positions i of the geological section X i , and record the coordinate positions of different positions i; S22, given the initial formation loss rate η0 and the formation loss rate incremental step length Δη, and taking η0 as the initial formation loss rate trial value, and giving the accuracy ξ; S23, using the geological parameters of the geological section X in step S21 and the initial formation loss rate η0 in step S22 as trial values, and substituting them into the analytical solution model, to obtain the analytical solution of the formation deformation of the geological section X under the trial value of the formation loss rate, and to obtain the deformation analytical value u at the same position as the measured formation deformation i '; S24, calculate |u i -u i '|, determine whether it is less than the precision ξ; S25. If the accuracy requirement is met, the current trial value of the formation loss rate is the true value of the formation loss rate corresponding to the measured deformation value at the position i on the geological section X; if the accuracy requirement is not met, the formation loss rate is increased by Δη as a new trial value of the formation loss rate, and steps S23 and S24 are repeated until the accuracy requirement is met, and the formation loss rate η corresponding to the measured deformation value at different positions i on the geological section X can be obtained. i ; S26, the formation loss rate η corresponding to the measured deformation value at different positions i of the geological section X i The formation loss rate vector η X , calculate η based on the weight vector k of the measured position X ·k, and obtain the formation loss rate of geological section X.

5. The dual-engine prediction method for ground deformation caused by shield tunneling as claimed in claim 1, characterized in that: In step S4, the output layer of the neural network is a node, and the number of input layers corresponds to the sample input vector; the sample input vector is composed of multiple geological parameters of the geological section.

6. The dual-engine prediction method for ground deformation caused by shield tunneling as claimed in claim 1, characterized in that: In step S1, the geological parameters include multiple ones of tunnel radius, tunnel burial depth, compression modulus and Poisson's ratio of soil, effective vertical earth pressure, water pressure, earth pressure coefficient, grouting volume per unit length, grouting body elastic modulus and Poisson's ratio, lining elastic modulus and Poisson's ratio, and lining thickness.

7. The dual-engine prediction method for ground deformation caused by shield tunneling as claimed in claim 2, characterized in that: In step S0, the method for constructing a tunnel geological section database is: S01. Establish a soil layer dictionary, where the key is the soil layer number and the corresponding value is a list of soil layer parameters. The parameters in the list are: soil layer density, deformation modulus, soil pressure coefficient, and Poisson's ratio. S02. Divide the geological section, read the soil layer number and corresponding thickness of the section on the geological map, and establish three corresponding lists respectively: one is the soil layer number list, that is, the soil layer distribution from the surface to the bottom; the second is the soil layer thickness list, that is, the thickness of each soil layer corresponding to the elements in the soil layer number list; the third is the list of other parameters such as tunnel burial depth and groundwater depth; S03. Establish a geological condition dictionary. Each section has three key-value pairs. The first key is mileage number + soil layer number, and the corresponding value is a list of soil layer numbers; the second key is mileage number + soil layer thickness, and the corresponding value is a list of soil layer thicknesses; the third key is mileage number + tunnel depth and groundwater, and the corresponding value is a list of tunnel depth, groundwater depth and other parameters.

8. The dual-engine prediction method for ground deformation caused by shield tunneling as claimed in claim 1, characterized in that: As the shield machine advances, the original predicted section is used as a sample and entered into the training of the neural network, and the neural network is continuously corrected.

9. A dual-engine prediction system for ground deformation caused by shield tunneling, characterized in that: The system is used to implement the method according to any one of claims 1 to 8, and the system includes: The analytical solution module inputs the geological parameters and stratum loss rate of the geological section, and outputs the analytical solution of stratum deformation of the geological section based on the analytical solution model of stratum deformation caused by tunneling; The formation loss rate iterative determination module iteratively obtains the formation loss rate of a geological section according to the geological parameters of the geological section, the field measured deformation value of the geological section, and the analytical solution model; A neural network training module selects a neural network, takes the geological parameters of multiple geological sections as sample inputs of the neural network, takes the formation loss rates of multiple geological sections obtained by the formation loss rate iterative determination module as sample expected outputs of the neural network, trains the neural network, and obtains a trained neural network; The stratum deformation prediction module takes the geological parameters of the section to be predicted as input, substitutes them into the trained neural network, and outputs the stratum loss rate of the section to be predicted; then substitutes the geological parameters of the section to be predicted and the stratum loss rate of the section to be predicted into the analytical solution model to obtain the stratum deformation prediction value of the section to be predicted.

10. The dual-engine prediction system for ground deformation caused by shield tunneling as claimed in claim 9, characterized in that: The system also includes a tunnel geological section database, which includes geological parameters of multiple geological sections of the tunnel and field measured deformation values ​​corresponding to the geological sections.

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