Automatic tunnel surrounding rock working condition modeling and tunnel working condition arrangement calculation method

Abstract

The invention relates to the technical field of tunnel engineering, in particular to an automatic tunnel surrounding rock working condition modeling and tunnel working condition arrangement calculation method which comprises the following steps: step 1, automatic parametric modeling; 2, automatic working condition arrangement; step 3, automatic data acquisition; and step 4, calling a Python program of the packaging module. According to the method, automatic tunnel surrounding rock working condition modeling and tunnel working condition arrangement calculation can be well carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering, and specifically, to an automated tunnel surrounding rock condition modeling and tunnel condition layout calculation method. Background Art

[0002] In the field of tunnel construction, the properties of the surrounding rock, especially the classification grade of the surrounding rock, have a decisive impact on the safety and cost-effectiveness of tunnel engineering. The classification of tunnel surrounding rock is a key step in obtaining the properties of the surrounding rock and guiding tunnel design and construction. However, traditional classification methods only perform a one-time assessment of the tunnel excavation face, that is, each excavation face corresponds to a surrounding rock category. This single classification result often does not take into account the non-uniformity and complexity of the surrounding rock in three-dimensional space when predicting the stability of the surrounding rock and designing the support structure, thus limiting the accuracy of prediction and the refinement of support design.

[0003] With the development of the refined classification technology of tunnel surrounding rock, the analysis accuracy of heterogeneous strata has been significantly improved. In the field of numerical analysis of tunnels, researchers at home and abroad have conducted a large number of studies on calculating and analyzing heterogeneous geological conditions to draw conclusions and engineering suggestions.

[0004] However, the refined classification of the surrounding rock also brings new challenges: for multiple tunnel projects, multiple working faces, and multiple cycles, the cumulative number of working conditions is extremely large, and traditional manual modeling and numerical calculation methods are difficult to handle such a large amount of working condition calculation and data collation work. Summary of the Invention

[0005] The content of the present invention is to provide an automated tunnel surrounding rock condition modeling and tunnel condition layout calculation method, which can overcome certain or some defects of the prior art.

[0006] An automated tunnel surrounding rock condition modeling and tunnel condition layout calculation method according to the present invention includes the following steps:

[0007] Step 1, automated parametric modeling;

[0008] Step 2, automated working condition layout;

[0009] Step 3, automated data collection;

[0010] Step 4, call the Python program of the encapsulated module.

[0011] Preferably, in Step 1, it specifically includes the following steps:

[0012] Step 1.1: Define the basic parameters of a three-centered circular horseshoe tunnel, including the radii of the three circles and the angles between two circles;

[0013] Step 1.2: Calculate the center and control points of the three-centered circle using trigonometric functions to define the tunnel contour;

[0014] Step 1.3: Automatically fill the tunnel contour and perform mesh generation;

[0015] Step 1.4: Save the model for subsequent working condition arrangement and calculation preparation;

[0016] Step 1.5: Package Step 1 into a single-file function a.

[0017] Preferably, in Step 2, it specifically includes the following steps:

[0018] Step 2.1: Divide and group the tunnel perimeter and the heading face according to the tunnel surrounding rock grade, and set the group ID;

[0019] Step 2.2: Write a parameter group program to set global variables and conventional physical parameters;

[0020] Step 2.3: Receive the list of surrounding rock grades, map it with the group ID, and automatically arrange the working conditions;

[0021] Step 2.4: Read the model of Step 1 without saving the result model for the next calculation;

[0022] Step 2.5: Package Step 2 into a single-file function b.

[0023] Preferably, in Step 2.2, the conventional physical parameters include elastic modulus and density.

[0024] Preferably, in Step 3, it specifically includes the following steps:

[0025] Step 3.1: Perform a balance calculation on the working conditions arranged in Step 2 to obtain the result model of the calculation;

[0026] Step 3.2: Analyze each group, call the numerical calculation judgment interface, and count the failure modes of each part;

[0027] Step 3.3: Generate list data to record the proportion of the number of blocks of different failure types;

[0028] Step 3.4: Package Step 3 into a single-file function c.

[0029] Preferably, in Step 4, it specifically includes the following steps:

[0030] Step 4.1: Use Python to import the numerical calculation library and concatenate the file functions a, b, and c;

[0031] Step 4.2: Import the Pandas library and read the working condition data table through the read_excel() interface;

[0032] Step 4.3: Use a for loop to iterate through the working condition data table, and each row of working conditions serves as a parameter set for one calculation.

[0033] Step 4.4: After each round of calculation, use to_excel() of Pandas to write the automatically statistical results into the output.xlsx table.

[0034] The beneficial effects of the present invention are as follows:

[0035] 1) Improve calculation efficiency:

[0036] Through automated parametric modeling, working condition arrangement, and data collection, the present invention can significantly reduce the manual operation time. For example, traditional methods may take hours or even days to complete the modeling and analysis of a tunnel section, while this system can complete the same task within minutes.

[0037] 2) Reduce human errors:

[0038] The automated process reduces the possibility of human input errors and improves the calculation accuracy. In tunnel engineering, even a tiny error may lead to serious consequences, and the high accuracy of the present invention is crucial for ensuring project safety.

[0039] 3) Enhance design accuracy:

[0040] Through refined surrounding rock classification and automated working condition arrangement, the present invention can more accurately predict the stability of the surrounding rock, thereby optimizing the support structure design and reducing the risk of over-design or under-design.

[0041] 4) Reduce project costs:

[0042] By improving efficiency and reducing errors, the present invention helps to reduce project costs. The automated process reduces the dependence on professional engineers, thereby reducing labor costs.

[0043] 5) Enhance project safety:

[0044] Accurate prediction of surrounding rock stability and optimized support structure design contribute to improving the safety of tunnel engineering and reducing the risk of engineering accidents.

[0045] 6) Facilitate information-based management:

[0046] The present invention can be docked with modern information systems, facilitating data storage, sharing, and further analysis, and contributing to the informatization and intelligentization of project management. Description of the Drawings

[0047] Figure 1It is a flowchart of a method for modeling the working conditions of the surrounding rock of an automated tunnel and calculating the layout of tunnel working conditions in the embodiment. Detailed implementation manners

[0048] To further understand the content of the present invention, the present invention will be described in detail in combination with the accompanying drawings and embodiments. It should be understood that the embodiments are only for explaining the present invention rather than limiting it.

[0049] Embodiment

[0050] As Figure 1 shown, this embodiment provides a method for modeling the working conditions of the surrounding rock of an automated tunnel and calculating the layout of tunnel working conditions, which includes the following steps:

[0051] Step 1: Automated parametric modeling;

[0052] Step 1.1: Define the basic parameters of the three-centered circular horseshoe tunnel, including the radii of the three circles and the angles between two circles, optimize the input parameters to the simplest and fewest, and give the given simplest parameters;

[0053] Step 1.2: Use trigonometric functions to calculate the centers and control points of the three-centered circle for defining the tunnel contour;

[0054] Step 1.3: Automatically fill the tunnel contour and perform mesh division to ensure the quality of the mesh and the accuracy of the calculation;

[0055] Step 1.4: Save the model for subsequent working condition layout and calculation;

[0056] Step 1.5: Package Step 1 into a single file function a.

[0057] Step 2: Automatic working condition layout;

[0058] Step 2.1: According to the surrounding rock grade of the tunnel (such as II~V2), divide and group the tunnel perimeter and the heading face, and set the group ID (such as A1~A18, B1~B12);

[0059] Step 2.2: Write a parameter group program, set global variables and conventional physical parameters; the conventional physical parameters include elastic modulus and density;

[0060] Step 2.3: Receive the list of surrounding rock grades, such as "II", "IV1", and map them to the group ID to automatically arrange the working conditions;

[0061] Step 2.4: Read the model in Step 1 without saving the result model for the next calculation;

[0062] Step 2.5: Package Step 2 into a single file function b.

[0063] Taking the tunnel surrounding rock grades II to V2 as an example, the tunnel perimeter and the heading face are divided into blocks and groups. The inner circle is set as A1 to A18, and the outer circle is set as B1 to B12 as groups for easy calling. A parameter group program is written in advance to set global variables and conventional physical parameters such as the elastic modulus and density of each of the surrounding rock grades II to V2. When encapsulating this program, it will receive an input, that is, a list of surrounding rock grades given in the block order, such as ["II", "IV1", etc.]. Then an automatic layout program is written to map this list to the previously set group IDs (such as A1 to A18), and then assign the corresponding level parameters to the corresponding groups to achieve an automated working condition layout. This process reads the model in step 1 and does not save the result model.

[0064] Step 3: Automated data collection;

[0065] Step 3.1: Perform a balance calculation on the working conditions arranged in step 2 to obtain the result model of the calculation;

[0066] Step 3.2: Analyze each group, call the numerical calculation judgment interface, and count the failure modes of each part;

[0067] Step 3.3: Generate list data and record the proportion of the number of blocks of different failure types;

[0068] Step 3.4: Package step 3 into a single file function c.

[0069] Step 4: Call the Python program of the encapsulated module;

[0070] Step 4.1: Use Python to import the numerical calculation library and concatenate the file functions a, b, and c;

[0071] Step 4.2: Import the Pandas library as an Excel processing interface, read the working condition data table through the read_excel() interface, organize the data into the parameter formats required by file functions a and b, and declare them before calling the program function;

[0072] Step 4.3: Use a for loop to iterate through the working condition data table, and each row of working conditions serves as a parameter set for one calculation;

[0073] Step 4.4: After each round of calculation, use Pandas' to_excel() to write the automatic statistical results into the output.xlsx table. When the statistics are completed, the program will automatically stop, and you can view the output.xlsx table to obtain the automated statistical information.

[0074] This embodiment is based on existing numerical calculation libraries and the Python programming language. These tools are widely used in engineering calculations and data processing and have high reliability and flexibility.

[0075] The automated process of this embodiment reduces human errors, improves computational efficiency and accuracy, helps optimize engineering designs, reduce costs, and enhance engineering safety.

[0076] The above has schematically described the present invention and its implementation manners. This description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. Therefore, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, creatively design structural manners and embodiments similar to the technical solution, they shall fall within the protection scope of the present invention.

Claims

1. Automated tunnel surrounding rock condition modeling and tunnel condition layout calculation method, characterized by: The following steps are involved: Step 1: Automated parametric modeling; Step 2: Automatic working condition arrangement; Step 3: Automated data collection; Step 4: Call the Python program of the encapsulation module.

2. The method for automatic tunnel surrounding rock condition modeling and tunnel condition layout calculation according to claim 1 is characterized by: In step 1, the following steps are specifically included: Step 1.1: Define the basic parameters of the three-center circle horseshoe tunnel, including the radius of the three circles and the angle of the two circles; Step 1.2: Use trigonometric functions to calculate the centers and control points of the three-center circles to define the tunnel contour; Step 1.3: Automatically fill the tunnel outline and perform meshing; Step 1.4: Save the model to prepare for subsequent working condition layout and calculation; Step 1.5: Encapsulate step 1 into a single file function a.

3. The method for automatic tunnel surrounding rock condition modeling and tunnel condition layout calculation according to claim 2 is characterized by: In step 2, the following steps are specifically included: Step 2.1: Divide the tunnel perimeter and face into blocks according to the tunnel surrounding rock grade and set the group ID; Step 2.2: Write a parameter group program to set global variables and general physical parameters; Step 2.3: Receive the surrounding rock level list, map it with the group ID, and automatically arrange the working conditions; Step 2.4: Read the model of step 1, but do not save the result model in order to carry out the next step of calculation; Step 2.5: Encapsulate step 2 into a single file function b.

4. The method for automatic tunnel surrounding rock condition modeling and tunnel condition layout calculation according to claim 3 is characterized by: In step 2.2, conventional physical parameters include elastic modulus and density.

5. The method for automatic tunnel surrounding rock condition modeling and tunnel condition layout calculation according to claim 4 is characterized by: In step 3, the following steps are specifically included: Step 3.1: Perform a balance calculation on the working conditions arranged in step 2 to obtain a calculation result model; Step 3.2: Analyze each group, call the numerical calculation judgment interface, and count the failure modes of each part; Step 3.3: Generate list data and record the percentage of blocks with different types of damage; Step 3.4: Encapsulate step 3 into a single file function c.

6. The method for automatic tunnel surrounding rock condition modeling and tunnel condition layout calculation according to claim 5 is characterized by: Step 4 specifically includes the following steps: Step 4.1: Use Python to import the numerical computing library and concatenate file functions a, b, and c; Step 4.2: Import the Pandas library and read the working condition data table through the read_excel() interface; Step 4.3: Use a for loop to traverse the working condition data table, with each row of working condition as a parameter set for one calculation; Step 4.4: After each round of calculation, use Pandas’ to_excel() to write the automatic statistical results to the output.xlsx table.

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