Python-based tunnel undercrossing pipeline settlement prediction method and device

Abstract

The invention relates to the field of urban underground space engineering calculation methods, in particular to a tunnel undercrossing pipeline settlement prediction method and device based on Python, and the method comprises the steps: building an initial tunnel settlement prediction model based on a Boussinesq theory, and calculating original theoretical settlement data; correcting the tunnel settlement prediction model; performing accuracy verification on the corrected tunnel settlement prediction model; drawing and outputting a visual graph; and calculating corrected theoretical settlement data of the pipeline at the unexcavated part. The method can guarantee the normal use of the existing pipeline during the construction period that the shield tunnel passes through the existing pipeline, predicts the risk of the existing pipeline in the subsequent construction of the shield tunnel, carries out the safety risk assessment in advance, prevents the occurrence of engineering disasters, and guarantees that the tunnel safely and smoothly passes through the existing pipeline for construction.

Description

Technical Field

[0001] The present invention relates to the field of urban underground space engineering calculation methods, and in particular to a Python-based method and device for predicting the settlement of a pipeline under a tunnel. Background Art

[0002] Urban rail transit can effectively alleviate the increasing congestion in urban areas. However, during construction, urban rail transit inevitably passes through existing structures, such as shield tunnels crossing existing railway bridges and subway tunnels. However, the impact of tunnels passing under existing structures is currently unclear, and prediction methods are relatively scarce, requiring further research.

[0003] At present, the main prediction methods for shield tunnel construction crossing existing pipelines are: (1) A method for predicting the settlement of existing pipelines during shield tunnel construction, with publication number CN116776445A, is proposed. By combining the theory of joint force between pipe and soil, the differential equation of pipeline deflection after the pipeline is subjected to additional displacement is obtained. The finite difference method is used in combination with the pipeline boundary conditions to simplify the fourth-order ordinary differential equation into a high-order stiffness matrix product form. Based on the simplified form of the equation, a MATLAB program is compiled to facilitate the calculation of the numerical solution of the pipeline deflection differential equation.

[0004] (2) A method for predicting the settlement of a pipeline under a shield tunnel, publication number CN114357575A, estimates the soil settlement caused by shield tunnel excavation, applies the estimated soil settlement to the pipeline, establishes a load balance differential equation for the pipeline, wherein the pipeline is regarded as a Timoshenko beam on a Vlasov foundation, and obtains the pipeline settlement by solving the load balance differential equation for the pipeline.

[0005] The above prediction methods have the following main disadvantages: (1) The calculation method is complex, the theoretical equations involved are difficult, and there are certain difficulties in combining them with actual engineering.

[0006] (2) MATLAB programs are relatively complex and not as concise and easy to use as Python programs.

[0007] (3) A lot of information is required, which takes a long time to apply on site and makes it impossible to make predictions in a timely manner.

[0008] In summary, the existing prediction methods are relatively cumbersome and complex in application. They require a lot of information and a large amount of calculation, which can easily affect the construction period. Therefore, a more concise and rapid prediction is needed for pipelines under tunnels, which can be quickly adapted to different working conditions in order to improve universality. Summary of the Invention

[0009] In order to overcome the above-mentioned shortcomings, the present invention aims to provide a Python-based method and device for predicting the settlement of pipelines under tunnels. This method is relatively accurate and convenient, meets certain accuracy requirements, and can quickly adapt to the needs of tunnels under different geological conditions.

[0010] According to one aspect of the present invention, a Python-based method for predicting the settlement of pipelines under tunnels is provided, comprising: constructing an initial tunnel settlement prediction model in Python based on the Boussinesq theory, inputting model parameters, and obtaining original theoretical settlement data under different tunnel-to-pipeline spacings; performing linear fitting based on the original theoretical settlement data under different tunnel-to-pipeline spacings in combination with the measured settlement data under the corresponding tunnel-to-pipeline spacings to obtain a revised tunnel settlement prediction model; performing accuracy verification on the revised tunnel settlement prediction model; drawing and outputting a visual graph of the relationship between the measured settlement and the theoretical settlement; and inputting model parameters of the pipeline at the unexcavated portion based on the revised tunnel settlement prediction model to calculate the revised theoretical settlement data of the pipeline at the unexcavated portion.

[0011] In some optional implementations of some embodiments, based on the Boussinesq theory, an initial tunnel settlement prediction model is constructed in Python, model parameters are input, and original theoretical settlement data under different tunnel-pipeline spacings are obtained, including: based on the Boussinesq theory, programming is performed through the Numpy extension library in Python to construct an initial tunnel settlement prediction model, and model parameters are input, wherein the model parameters include the uniform load applied to the ground, the elastic modulus of the soil, the Poisson's ratio, and the spacing between the tunnel and the pipeline, and the original theoretical settlement data under different tunnel-pipeline spacings are obtained.

[0012] In some optional implementations of some embodiments, the formula of the initial tunnel settlement prediction model is: Where, is the original theoretical settlement data under different tunnel-pipeline distances, in meters; is the uniform load applied to the ground, in N / m 2 ; is Poisson's ratio, ranging from 0.2 to 0.4; is the elastic modulus of the soil, in Pa; The distance between the tunnel and the pipeline, in meters.

[0013] In some optional implementations of some embodiments, the original theoretical settlement data under different tunnel-to-pipeline spacings are combined with the measured settlement data under the corresponding tunnel-to-pipeline spacings to perform linear fitting to obtain a revised tunnel settlement prediction model, including: based on the original theoretical settlement data under different tunnel-to-pipeline spacings and the measured settlement data under the corresponding tunnel-to-pipeline spacings, linear regression optimization is performed using a linear fitting technique to obtain a revised tunnel settlement prediction model; the formula of the revised tunnel settlement prediction model is: Where, is the original theoretical settlement data under different tunnel-pipeline distances, in meters; is the correction factor; is the correction parameter; The modified theoretical settlement data under different tunnel-pipeline distances.

[0014] In some optional implementations of some embodiments, the accuracy verification of the modified tunnel settlement prediction model includes: based on the modified tunnel settlement prediction model, inputting model parameters, obtaining modified theoretical settlement data under different tunnel-to-pipeline spacings, combining the original theoretical settlement data under the corresponding tunnel-to-pipeline spacings, and calculating error indicators for judgment to verify the accuracy of the modified tunnel settlement prediction model, wherein the error indicators include RMSE, MAE, R 2 .

[0015] In some optional implementations of some embodiments, the drawing and outputting of a visual graph of the relationship between the measured settlement and the theoretical settlement includes: drawing and outputting a visual graph including a relationship curve between different types of settlement data and spacing, a fitting curve between the measured settlement and the corrected theoretical settlement, and a relationship graph between the corrected theoretical settlement and the residual.

[0016] In some optional implementations of some embodiments, the visual graphics are obtained by drawing and outputting through the Matplotlib database in Python.

[0017] According to a second aspect of the present invention, a Python-based tunnel underpass pipeline settlement prediction device is provided, comprising: a model construction module for constructing an initial tunnel settlement prediction model in Python based on the Boussinesq theory, inputting model parameters, and obtaining original theoretical settlement data under different tunnel-to-pipeline spacings; a model correction module for performing linear fitting based on the original theoretical settlement data under different tunnel-to-pipeline spacings in combination with the measured settlement data under the corresponding tunnel-to-pipeline spacings to obtain a revised tunnel settlement prediction model; a model verification module for verifying the accuracy of the revised tunnel settlement prediction model; a graphics output module for drawing and outputting a visual graph of the relationship between the measured settlement and the theoretical settlement; and a settlement prediction module for inputting model parameters of the unexcavated pipeline based on the revised tunnel settlement prediction model to calculate the revised theoretical settlement data of the unexcavated pipeline.

[0018] According to a third aspect of the present invention, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0019] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, which implements the steps of the above method when executed by a processor.

[0020] The present application provides a Python-based method and device for predicting the settlement of pipelines under tunnels, which has the following beneficial effects: (1) The present application obtains the influence of tunnel excavation on pipeline settlement based on Python program, which can combine actual engineering problems with machine learning, realize rapid prediction of the construction process, help optimize design schemes, improve engineering quality and efficiency, and is low-cost; (2) The present application considers the influence of tunnel excavation under various conditions, and has high universality, and can have good adaptability for pipelines under tunnels under different conditions; (3) The present application realizes the visualization output of the influence range, and can obtain linear visualization calculation results, which is convenient for engineering personnel to optimize the working conditions of the engineering construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The figure is a flow chart of a method for predicting settlement of a pipeline under a tunnel based on Python in an embodiment of the present invention.

[0022] Figure 2 This is a diagram showing the relationship between the shield tunnel and the existing pipeline positions in an embodiment of the present invention.

[0023] Figure 3 1 is a curve showing the relationship between different types of settlement data and spacing in an embodiment of the present invention.

[0024] Figure 4 It is the fitting curve of the measured settlement and the corrected theoretical settlement in the embodiment of the present invention.

[0025] Figure 5 Graph showing the relationship between the corrected theoretical settlement and the residual in an embodiment of the present invention.

[0026] Figure 6 Schematic diagram of a Python-based tunnel underpass settlement prediction device in an embodiment of the present invention.

[0027] Figure 7 Schematic diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0029] Example 1 Reference Attachment Figure 1 , the present application provides a Python-based method for predicting the settlement of pipelines under tunnels, which specifically includes the following steps.

[0030] S1: Based on the Boussinesq theory, an initial tunnel settlement prediction model was constructed in Python. The model parameters were input to obtain the original theoretical settlement data under different tunnel-pipeline spacings.

[0031] In some embodiments, based on the Boussinesq theory, an initial tunnel settlement prediction model is constructed in Python, model parameters are input, and original theoretical settlement data under different tunnel-to-pipeline spacings are obtained, including: based on the Boussinesq theory, programming is performed through the Numpy extension library in Python to construct an initial tunnel settlement prediction model, and model parameters are input, wherein the model parameters include the uniform load applied to the ground, the elastic modulus of the soil, the Poisson's ratio, and the spacing between the tunnel and the pipeline, and the original theoretical settlement data under different tunnel-to-pipeline spacings are obtained.

[0032] In some embodiments, the Numpy extension library is imported into Python and programming is performed using the Numpy extension library, where Numpy is an extension library of the Python language that supports a large number of dimensional arrays and matrix operations. In addition, it also provides a large number of mathematical function libraries for array operations.

[0033] In some embodiments, the present application is a tunnel settlement prediction model constructed based on the Boussinesq theory, which takes into account the distance between the tunnel and the pipeline. Figure 2 Based on Python programming, after running the code, you only need to set the corresponding model parameters one by one. The model parameters specifically include: the uniform load applied to the ground, the elastic modulus of the soil, Poisson's ratio, and the distance between the tunnel and the pipeline. The corresponding original theoretical settlement data can be calculated. After repeated calculations, the original theoretical settlement data under different tunnel-pipeline distances can be obtained.

[0034] In some embodiments, the present application uses Python programming to simplify the calculation process of the Boussinesq theory for calculating the theoretical settlement of the pipeline and improve the calculation speed. At the same time, based on the corresponding machine learning library in Python, a tunnel settlement prediction model is constructed to obtain visual results. Compared with other related technologies, it can be quickly applied to pipeline settlement prediction under different geological conditions, and the prediction method is simple and efficient.

[0035] In some embodiments, the formula of the initial tunnel settlement prediction model is: Where, is the original theoretical settlement data under different tunnel-pipeline distances, in meters, representing the vertical displacement of the ground or structure under load; is the uniform load applied to the ground, in N / m 2 (Pascal, Pa), in Boussinesq theory, the influence of uniformly distributed load on pipeline is usually considered; Poisson's ratio, with a value range of 0.2 to 0.4, is the ratio of the transverse strain to the longitudinal strain when the material is stretched or compressed. The Poisson's ratio is generally between 0 and 0.5, with 0.5 indicating that the material is incompressible. The Poisson's ratio of actual soil is usually between 0.2 and 0.4; is the elastic modulus of the soil, in Pa, which is used to describe the stiffness of the soil under load and represents the strain caused by unit stress; The distance between the tunnel and the pipeline, in meters.

[0036] S2: Based on the original theoretical settlement data at different tunnel-pipeline spacings, combined with the measured settlement data at the corresponding tunnel-pipeline spacings, a linear fit is performed to obtain the revised tunnel settlement prediction model.

[0037] In some embodiments, a revised tunnel settlement prediction model is obtained by performing linear fitting based on the original theoretical settlement data at different tunnel-to-pipeline spacings and the measured settlement data at the corresponding tunnel-to-pipeline spacings, including: performing linear regression optimization using a linear fitting technique based on the original theoretical settlement data at different tunnel-to-pipeline spacings and the measured settlement data at the corresponding tunnel-to-pipeline spacings to obtain a revised tunnel settlement prediction model; the formula of the revised tunnel settlement prediction model is: Where, is the original theoretical settlement data under different tunnel-pipeline distances, in meters; is the correction factor; is the correction parameter; The modified theoretical settlement data under different tunnel-pipeline distances.

[0038] In some embodiments, based on the original theoretical settlement data under different tunnel-pipeline spacings and the measured settlement data under corresponding tunnel-pipeline spacings, the corresponding database in Python is used for processing and predictive analysis, that is, the relationship between the measured settlement and the theoretical settlement is obtained, wherein the corresponding database in Python includes: Pandas database, Matplotlib database and Sklearn database in Python. The Pandas database and Sklearn database are mainly used to calculate and process the original theoretical settlement data of different pipeline-tunnel spacings and the measured settlement data of the corresponding pipeline-tunnel spacing to obtain the relationship between the measured settlement and the theoretical settlement. The Matplotlib database is used to draw and output a visual graph of the relationship between the measured settlement and the theoretical settlement.

[0039] S3: Verify the accuracy of the revised tunnel settlement prediction model.

[0040] In some embodiments, the accuracy of the modified tunnel settlement prediction model is verified, including: based on the modified tunnel settlement prediction model, inputting model parameters, obtaining modified theoretical settlement data under different tunnel-to-pipeline spacings, combining the original theoretical settlement data under the corresponding tunnel-to-pipeline spacings, and calculating error indicators for judgment to verify the accuracy of the modified tunnel settlement prediction model, the error indicators including RMSE, MAE, R 2 .

[0041] In some embodiments, the smaller the RMSE and MAE, the better, indicating that the error index is smaller, R 2 The closer it is to 1, the more accurate the model is.

[0042] S4: Draw and output a visual graph of the relationship between measured settlement and theoretical settlement.

[0043] In some embodiments, drawing and outputting a visual graph of the relationship between the measured settlement and the theoretical settlement includes: drawing and outputting a visual graph including a relationship curve between different types of settlement data and spacing, a fitting curve between the measured settlement and the modified theoretical settlement, and a relationship graph between the modified theoretical settlement and the residual, which can be referred to in the attached drawings. Figure 3 , Attachment Figure 4 , Attachment Figure 5 .

[0044] S5: Based on the revised tunnel settlement prediction model, the model parameters of the pipeline in the unexcavated area are input to calculate the revised theoretical settlement data of the pipeline in the unexcavated area.

[0045] In some embodiments, with respect to the deformation of the pipeline at the unexcavated portion, relevant parameters are input to perform pipeline settlement prediction, thereby obtaining corrected theoretical settlement data of the pipeline at the unexcavated portion.

[0046] In some embodiments, for example, when the distance between the tunnel and the pipeline is 5m, 8m, 10m, 13m, 15m, 16m, and 19m, the calculated original theoretical settlement data corresponds to 4.5mm, 2.812mm, 2.25mm, 1.73mm, 1.5mm, 1.4mm, and 1.18mm. The original theoretical settlement data can be calculated based on the initial tunnel settlement prediction model. In the field conditions, the measured settlement data under the corresponding distance between the tunnel and the pipeline are 5.72mm, 4.23mm, 3.58mm, 3.02mm, 2.71mm, 2.43mm, and 1.98mm. The deformation settlement prediction of the pipeline 25m above the unexcavated tunnel is shown in Table 1. The relevant model parameters are imported into the code and calculated. The results are as follows: Slope : 1.08, intercept : 1.00; root mean square error RMSE: 0.17 mm, mean absolute error MAE: 0.16 mm, determination coefficient R²: 0.98; the original theoretical settlement data at a spacing of 25 m is: 0.50 mm, and the predicted revised theoretical settlement data is: 1.54 mm.

[0047] Table 1 Measured, theoretical, and corrected settlement results for different tunnel-pipeline distances

[0048] Example 2 This embodiment provides a Python-based tunnel underpass settlement prediction device 200 based on the above embodiment 1. Figure 6, used to implement the steps of a Python-based tunnel underpass settlement prediction method described in the above embodiment 1, the device mainly includes: a model construction module 201, a model correction module 202, a model verification module 203, a graphics output module 204 and a settlement prediction module 205.

[0049] In some embodiments, the model building module 201 is used to build an initial tunnel settlement prediction model in Python based on the Boussinesq theory, input model parameters, and obtain original theoretical settlement data under different tunnel-to-pipeline spacings.

[0050] In some embodiments, the model correction module 202 is used to perform linear fitting based on the original theoretical settlement data under different tunnel-to-pipeline spacings and the measured settlement data under corresponding tunnel-to-pipeline spacings to obtain a corrected tunnel settlement prediction model.

[0051] In some embodiments, the model verification module 203 is used to verify the accuracy of the revised tunnel settlement prediction model.

[0052] In some embodiments, the graphic output module 204 is used to draw and output a visual graphic of the relationship between the measured settlement and the theoretical settlement.

[0053] In some embodiments, the settlement prediction module 205 is used to input the model parameters of the unexcavated pipeline based on the revised tunnel settlement prediction model and calculate the revised theoretical settlement data of the unexcavated pipeline.

[0054] Example 3 This embodiment further provides an electronic device based on the above embodiment 1. Figure 7 , Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0055] like Figure 7 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage device 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for the operation of the electronic device. Processing device 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0056] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, etc., an output device 307 including, for example, a liquid crystal display (LCD), a speaker, etc., a storage device 308 including, for example, a magnetic tape, a hard disk, etc., and a communication device 309. The communication device 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Figure 7 The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 7 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0057] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0058] Example 4 Based on the above-mentioned embodiment 1, this embodiment further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0059] It should be noted that in some embodiments of the present disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. Furthermore, in some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0060] In this embodiment, the client and server may communicate using any currently known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0061] The computer-readable medium may be included in the apparatus or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs. When executed by the electronic device, the electronic device: obtains training data and converts the training data into initial data; determines an initial rule base based on the initial data and optimizes the parameters of the initial rule base to obtain a target rule base; calculates activation weights for the rules in the target rule base according to a preset activation weight calculation formula; and determines abnormal information based on the test data and the activation weights.

[0062] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0063] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0064] The units described in some embodiments of the present disclosure may be implemented in software or hardware. The units described may also be provided in a processor. For example, they may be described as follows: a processor includes a data acquisition unit, a rule determination unit, a weight calculation unit, and an anomaly determination unit. The names of these units do not, in some cases, constitute limitations on the units themselves. For example, the data acquisition unit may also be described as a "unit for acquiring training data."

[0065] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0066] Obviously, those skilled in the art will appreciate that the various steps of the present invention described above can be performed in different ways than the present invention, and that simulation methods and experimental equipment include but are not limited to those described above. The various steps of the present invention described above can, in some cases, be performed in a different order than that shown here, and the steps shown or described above can be performed separately. Therefore, the present invention is not limited to any particular combination of hardware and software.

[0067] The above content is a further detailed description of the present invention in conjunction with specific embodiments, and the specific implementation of the present invention cannot be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A Python-based method for predicting the settlement of pipelines under tunnels, characterized by: include: Based on the Boussinesq theory, an initial tunnel settlement prediction model was constructed in Python. The model parameters were input to obtain the original theoretical settlement data under different tunnel-pipeline spacings. Based on the original theoretical settlement data at different tunnel-pipeline distances, combined with the measured settlement data at the corresponding tunnel-pipeline distances, a linear fit was performed to obtain the revised tunnel settlement prediction model. Verify the accuracy of the revised tunnel settlement prediction model; Draw and output a visual graph of the relationship between measured settlement and theoretical settlement; Based on the revised tunnel settlement prediction model, the model parameters of the unexcavated pipeline are input to calculate the revised theoretical settlement data of the unexcavated pipeline.

2. A Python-based method for predicting settlement of pipelines under tunnels according to claim 1, characterized in that: Based on the Boussinesq theory, an initial tunnel settlement prediction model was constructed in Python. Model parameters were input to obtain the original theoretical settlement data under different tunnel-pipeline spacings, including: Based on the Boussinesq theory, an initial tunnel settlement prediction model was constructed using the Numpy extension library in Python. The model parameters, including the uniform load applied to the ground, the elastic modulus of the soil, the Poisson's ratio, and the spacing between the tunnel and the pipeline, were input to obtain the original theoretical settlement data under different tunnel-pipeline spacings.

3. The Python-based method for predicting settlement of pipelines under tunnels according to claim 2 is characterized in that: The formula of the initial tunnel settlement prediction model is: ； Where, is the original theoretical settlement data under different tunnel-pipeline distances, in meters; is the uniform load applied to the ground, in N / m 2 ; is Poisson's ratio, ranging from 0.2 to 0.4; is the elastic modulus of the soil, in Pa; The distance between the tunnel and the pipeline, in meters.

4. The Python-based method for predicting settlement of pipelines under tunnels according to claim 1 is characterized in that: The original theoretical settlement data under different tunnel-to-pipeline spacings are combined with the measured settlement data under the corresponding tunnel-to-pipeline spacings to perform linear fitting to obtain a revised tunnel settlement prediction model, including: Based on the original theoretical settlement data under different tunnel-pipeline spacings and the measured settlement data under the corresponding tunnel-pipeline spacings, linear regression optimization is performed using linear fitting technology to obtain a revised tunnel settlement prediction model; the formula of the revised tunnel settlement prediction model is: ； Where, is the original theoretical settlement data under different tunnel-pipeline distances, in meters; is the correction factor; is the correction parameter; The modified theoretical settlement data under different tunnel-pipeline distances.

5. The Python-based method for predicting settlement of pipelines under tunnels according to claim 1 is characterized in that: The accuracy verification of the revised tunnel settlement prediction model includes: Based on the modified tunnel settlement prediction model, the model parameters are input to obtain the modified theoretical settlement data under different tunnel-pipeline spacings. The original theoretical settlement data under the corresponding tunnel-pipeline spacings are combined with the error index to make judgments and verify the accuracy of the modified tunnel settlement prediction model. The error indexes include RMSE, MAE, R 2 .

6. The Python-based method for predicting settlement of pipelines under tunnels according to claim 1 is characterized in that: The method of drawing and outputting a visual graph of the relationship between the measured settlement and the theoretical settlement includes: Draw and output visual graphics including curves showing the relationship between different types of settlement data and spacing, fitting curves between measured settlement and modified theoretical settlement, and a relationship diagram between modified theoretical settlement and residual.

7. The Python-based method for predicting settlement of pipelines under tunnels according to claim 6 is characterized in that: The visual graphics are drawn and outputted using the Matplotlib database in Python.

8. A Python-based tunnel underpass settlement prediction device, characterized in that: include: The model building module is used to build an initial tunnel settlement prediction model in Python based on the Boussinesq theory, input model parameters, and obtain the original theoretical settlement data under different tunnel-pipeline spacings; The model correction module is used to perform linear fitting based on the original theoretical settlement data under different tunnel-pipeline spacings and the measured settlement data under the corresponding tunnel-pipeline spacings to obtain a corrected tunnel settlement prediction model; Model verification module, used to verify the accuracy of the revised tunnel settlement prediction model; Graphics output module, used to draw and output visual graphics of the relationship between measured settlement and theoretical settlement; The settlement prediction module is used to input the model parameters of the unexcavated pipeline based on the revised tunnel settlement prediction model and calculate the revised theoretical settlement data of the unexcavated pipeline.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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