Layered rock mass tunnel construction surrounding rock deformation prediction method, device, equipment and medium

Abstract

The invention provides a stratified rock mass tunnel construction surrounding rock deformation prediction method and device, equipment and a medium. The method comprises the following steps: acquiring tunnel geological data, construction parameter data and surrounding rock deformation data in a target tunnel construction process; correlation coefficients between each kind of tunnel geological data and the surrounding rock deformation data and between each kind of construction parameter data and the surrounding rock deformation data are determined; according to the correlation coefficient, extracting influence factor data of surrounding rock deformation in the tunnel construction process from the tunnel geological data and the construction parameter data; based on the surrounding rock deformation data, the influence factor data and a preset surrounding rock deformation prediction model, obtaining a surrounding rock deformation prediction value of the target tunnel; wherein the surrounding rock deformation prediction model is obtained by training a preset time domain convolutional network according to historical surrounding rock deformation data and historical influence factor data of the target tunnel. According to the method, surrounding rock deformation in the tunnel construction process can be accurately predicted.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel deformation prediction, and in particular to a method, device, equipment and medium for predicting surrounding rock deformation during layered rock tunnel construction. Background Art

[0002] With the acceleration of infrastructure development, mountain tunnel construction projects are also increasing. In the process of mountain tunnel construction, when passing through layered surrounding rocks, shear slip, tension failure, and bending bulging are prone to occur, which leads to problems such as arch block falling and landslides during tunnel construction, affecting construction safety and progress. Therefore, accurate prediction of surrounding rock deformation during tunnel construction is very important in improving tunnel safety.

[0003] In the related technologies, empirical formulas and long short-term memory neural networks are mainly used to comprehensively predict tunnel deformation. However, the prediction accuracy of these methods is relatively poor, and it is difficult to accurately predict the surrounding rock deformation during tunnel construction. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment and medium for predicting surrounding rock deformation during layered rock tunnel construction, so as to accurately predict surrounding rock deformation during tunnel construction.

[0005] In a first aspect, an embodiment of the present application provides a method for predicting surrounding rock deformation during layered rock tunnel construction, comprising: Obtain tunnel geological data, construction parameter data and surrounding rock deformation data during the construction of the target tunnel; respectively determining the correlation coefficients between each type of tunnel geological data, each type of construction parameter data and the surrounding rock deformation data; Extracting, according to the correlation coefficient, data of factors affecting surrounding rock deformation during tunnel construction from the tunnel geological data and construction parameter data; Based on the surrounding rock deformation data, the influencing factor data and a preset surrounding rock deformation prediction model, a surrounding rock deformation prediction value of the target tunnel is obtained; wherein the surrounding rock deformation prediction model is obtained by training a preset time domain convolutional network based on the historical surrounding rock deformation data and historical influencing factor data of the target tunnel.

[0006] In a possible implementation, the influencing factor data include surrounding rock strength, cross-sectional area, bedding plane spacing, bedding plane inclination, excavation footage, construction method and tunnel burial depth.

[0007] In a possible implementation, after obtaining the predicted value of the surrounding rock deformation of the target tunnel based on the surrounding rock deformation data, the influencing factor data and a preset surrounding rock deformation prediction model, the method further includes: Obtain the ultimate displacement value of the surrounding rock deformation of the target tunnel; Determining the surrounding rock deformation degree of the target tunnel according to the limit displacement value and the surrounding rock deformation prediction value; According to the surrounding rock deformation degree, the safety level of the target tunnel deformation is determined, and the tunnel monitoring frequency and construction parameters are adjusted.

[0008] In a possible implementation, determining the surrounding rock deformation degree of the target tunnel according to the limit displacement value and the surrounding rock deformation prediction value includes: According to the expression: , determine the surrounding rock deformation of the target tunnel; In the formula, represents the surrounding rock deformation of the target tunnel, represents the predicted value of surrounding rock deformation, Indicates the limit displacement value.

[0009] In a possible implementation, determining the safety level of the target tunnel deformation according to the surrounding rock deformation degree, and adjusting the tunnel monitoring frequency and construction parameters include: If the surrounding rock deformation is less than a first preset value, the safety level of the target tunnel deformation is determined to be level one, and the tunnel monitoring frequency is maintained at a first frequency; If the surrounding rock deformation is greater than or equal to a first preset value, and the surrounding rock deformation is less than a second preset value, the safety level of the target tunnel deformation is determined to be level 2, and the tunnel monitoring frequency is adjusted to a second frequency; wherein the second preset value is greater than the first preset value, and the second frequency is greater than the first frequency; If the surrounding rock deformation is greater than or equal to the second preset value, and the surrounding rock deformation is less than the third preset value, the safety level of the target tunnel deformation is determined to be level three, and the tunnel monitoring frequency is adjusted to a third frequency; wherein the third preset value is greater than the second preset value, and the third frequency is greater than the second frequency; If the surrounding rock deformation degree is greater than or equal to the third preset value, the safety level of the target tunnel deformation is determined to be level four, and the tunnel monitoring frequency is adjusted to the third frequency, and the construction parameters of the tunnel are adjusted.

[0010] In a possible implementation, after obtaining the tunnel geological data, construction parameter data and surrounding rock deformation data during the construction of the target tunnel, the method further includes: The tunnel geological data, the construction parameter data and the surrounding rock deformation data are normalized.

[0011] In a second aspect, an embodiment of the present application provides a device for predicting surrounding rock deformation during layered rock tunnel construction, comprising: An acquisition module is used to acquire tunnel geological data, construction parameter data and surrounding rock deformation data during the construction of the target tunnel; A determination module, used to respectively determine the correlation coefficient between each tunnel geological data, each construction parameter data and the surrounding rock deformation data; An extraction module, used to extract data of factors affecting surrounding rock deformation during tunnel construction from the tunnel geological data and construction parameter data according to the correlation coefficient; A prediction module is used to obtain a predicted value of surrounding rock deformation of the target tunnel based on the surrounding rock deformation data, the influencing factor data and a preset surrounding rock deformation prediction model; wherein the surrounding rock deformation prediction model is obtained by training a preset time domain convolutional network based on the historical surrounding rock deformation data and historical influencing factor data of the target tunnel.

[0012] In a possible implementation, the influencing factor data include surrounding rock strength, cross-sectional area, bedding plane spacing, bedding plane inclination, excavation footage, construction method and tunnel burial depth.

[0013] In a third aspect, an embodiment of the present application provides 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 described in the first aspect or any possible implementation method of the first aspect are implemented.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0015] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device executes the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0016] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The embodiment of the present application determines the correlation coefficient between the tunnel geological data, the construction parameter data and the surrounding rock deformation data, and extracts the influencing factor data of the surrounding rock deformation during the tunnel construction process from the tunnel geological data and the construction parameter data, so as to accurately find the relevant data of the surrounding rock deformation during the tunnel construction process, remove the data with low correlation, and retain the data with high correlation, so as to make accurate predictions in the future; and then make predictions through the surrounding rock deformation data, the influencing factor data and the preset surrounding rock deformation prediction model, so as to accurately obtain the predicted value of the surrounding rock deformation of the target tunnel, realize the accurate prediction of the surrounding rock deformation of the unexcavated section, and improve the safety of the subsequent construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flow chart for realizing the method for predicting surrounding rock deformation during layered rock tunnel construction provided by the embodiment of the present application; Figure 2 It is a schematic diagram of the structure of a device for predicting surrounding rock deformation during layered rock tunnel construction provided by an embodiment of the present application; Figure 3 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0020] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0021] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0022] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0023] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0024] In addition, the “plurality” mentioned in the embodiments of the present application should be interpreted as two or more.

[0025] The inventors found that tunnel deformation prediction usually involves comprehensive prediction of tunnel deformation, considering the overall deformation of the tunnel, and it is difficult to accurately predict surrounding rock deformation; there are also methods that directly predict using long short-term memory models, which have low prediction accuracy and are also difficult to accurately predict surrounding rock deformation. Therefore, it is necessary to consider a method for predicting surrounding rock deformation during tunnel construction.

[0026] In order to improve the accuracy of surrounding rock deformation prediction during tunnel construction, in the implementation mode of the present application, the correlation between tunnel geological data, construction parameter data and surrounding rock deformation data is judged, and the influencing factor data with high correlation with the surrounding rock deformation data is extracted to reduce the interference of other data with relatively low correlation; then the surrounding rock deformation prediction model obtained by training the time domain convolutional network is used to predict the surrounding rock deformation, which can accurately obtain the predicted value of the surrounding rock deformation in the unconstructed part of the tunnel, so as to adjust the construction data and improve the safety of subsequent construction.

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0028] Figure 1 The implementation flow chart of the method for predicting surrounding rock deformation during tunnel construction in layered rock mass provided in the embodiment of the present application is described in detail as follows: Step 101, obtaining tunnel geological data, construction parameter data and surrounding rock deformation data during the construction of the target tunnel.

[0029] In this embodiment, the tunnel geological data may include surrounding rock parameters, bedding characteristics, and geometric parameters. Among them, the surrounding rock parameters may include surrounding rock strength, lateral earth pressure coefficient, natural porosity, and the depth of each soil layer. Bedding characteristics may include bedding plane spacing and bedding plane inclination. Geometric parameters may include cross-sectional area, tunnel depth, and tunnel line type.

[0030] The construction parameter data may include construction methods and tunneling parameters, etc. The construction methods may include shield diameter, shield length, and shield technology, etc. The tunneling parameters may include shield tunneling progress, tunneling footage, tunneling speed, and total thrust, etc.

[0031] Here, surrounding rock deformation data can be obtained through monitoring.

[0032] Optionally, after acquiring the tunnel geological data, construction parameter data and surrounding rock deformation data during the construction of the target tunnel, this embodiment may also perform normalization processing on the tunnel geological data, construction parameter data and surrounding rock deformation data.

[0033] In this embodiment, the tunnel geological data, construction parameter data and surrounding rock deformation data may be normalized to eliminate the influence of the eigenvalue dimensions of different samples on the prediction efficiency and accuracy.

[0034] Here, the tunnel geological data, construction parameter data and surrounding rock deformation data can be normalized to [0, 1], so that the tunnel geological data, construction parameter data and surrounding rock deformation data can be directly compared with each other, avoiding the influence of data of different magnitudes on subsequent processing.

[0035] The normalized formula can be: , where represents the normalized standard value, represents the maximum value in the normalized interval, which is 1. Represents the minimum value in the normalized interval, which is 0. represents the sample value to be normalized, Indicates the minimum value in the sample set to which the sample value to be normalized belongs. Indicates the maximum value in the sample set to which the sample value to be normalized belongs.

[0036] Step 102, respectively determine the correlation coefficients between each type of tunnel geological data, each type of construction parameter data and surrounding rock deformation data.

[0037] In this embodiment, tunnel geological data and construction parameter data include multiple types. The correlation coefficient between each type of tunnel geological data and surrounding rock deformation data, as well as the correlation coefficient between each type of construction parameter data and surrounding rock deformation data are determined respectively. The correlation coefficient between each variable in the tunnel construction process and the surrounding rock deformation data can be obtained, so as to find the main influencing factors of surrounding rock deformation.

[0038] Here, the Pearson correlation coefficient can be used.

[0039] Step 103: extracting the influencing factor data of surrounding rock deformation during tunnel construction from the tunnel geological data and the construction parameter data according to the correlation coefficient.

[0040] In this embodiment, data with a strong correlation with the surrounding rock deformation data can be extracted from the tunnel geological data and construction parameter data, that is, the main influencing factors of the surrounding rock deformation during the tunnel construction process are found, which can reduce the interference of data with low correlation in subsequent predictions, reduce the amount of calculation during prediction, and improve the accuracy of the prediction.

[0041] Here, the data of the type in which the correlation coefficient between the tunnel geological data and the construction parameter data is greater than a preset value can be used as the influencing factor data of the surrounding rock deformation.

[0042] Optionally, the influencing factor data in this embodiment include surrounding rock strength, cross-sectional area, bedding plane spacing, bedding plane inclination, excavation footage, construction method and tunnel burial depth.

[0043] Step 104, based on the surrounding rock deformation data, the influencing factor data and the preset surrounding rock deformation prediction model, obtain the surrounding rock deformation prediction value of the target tunnel; wherein the surrounding rock deformation prediction model is obtained by training a preset time domain convolutional network based on the historical surrounding rock deformation data and historical influencing factor data of the target tunnel.

[0044] In this embodiment, the surrounding rock deformation data and the influencing factor data are input into the surrounding rock deformation prediction model, and the surrounding rock deformation prediction value output by the surrounding rock deformation prediction model for the unconstructed area in the target tunnel can be obtained.

[0045] Among them, the surrounding rock deformation prediction model can be trained by using a temporal convolutional network (TCN). TCN consists of multiple convolutional layers, and each convolutional layer is followed by an optional residual block.

[0046] By establishing a surrounding rock deformation prediction model through TCN, the convolution layer can be used to capture the patterns and internal characteristics of the surrounding rock deformation data and influencing factor data in the time series. By using causal dilated convolution, the receptive field of the observed data can be expanded to accurately predict the surrounding rock deformation.

[0047] Here, when training the TCN model, the data set is divided into a training set and a test set. The training set is used to train the TCN model, and the test set is used to test the trained TCN model.

[0048] The time series data in the sample set is converted into input and output formats that can be processed by the TCN model, and the time window is divided. The past observation data in the training set is used as input and the future observation data is used as output to train the TCN model. At the same time, the hyperparameters of the TCN model are also adjusted during the training process, such as the number of convolution kernels in each convolution layer, the size of the convolution kernel, the moving step size of the convolution kernel in the time dimension, and the number of iterations.

[0049] In addition, when the surrounding rock deformation prediction model is obtained by training the TCN model, the prediction accuracy of the model is also evaluated. Specifically, the mean absolute percentage error (MAPE) and the goodness of fit R 2 To test the accuracy of the model.

[0050] Among them, MAPE is a measure of the error between the predicted value and the actual value, and is an important indicator for measuring the accuracy of the predicted value. The MAPE indicator appears in the form of a percentage, indicating that the average percentage of the relative error between the predicted value and the actual value ranges from [0, +∞). The smaller the value, the more accurate the prediction model; R 2 It is used to measure the discreteness of the sample. Its value range is 0~1. The better the model fitting effect, the closer its value is to 1. The specific formula is as follows: ; ; In the formula, represents the total number of samples, Indicates the surrounding rock deformation value monitored historically, It represents the average value of surrounding rock deformation values ​​monitored historically. Represents the predicted value of the trained model.

[0051] In addition, after obtaining the output result of the surrounding rock deformation prediction model, the output result can also be denormalized to obtain the true value of the output result, that is, the surrounding rock deformation prediction value.

[0052] The embodiment of the present application determines the correlation coefficient between the tunnel geological data, the construction parameter data and the surrounding rock deformation data, and extracts the influencing factor data of the surrounding rock deformation during the tunnel construction process from the tunnel geological data and the construction parameter data, so as to accurately find the relevant data of the surrounding rock deformation during the tunnel construction process, remove the data with low correlation, and retain the data with high correlation, so as to make accurate predictions in the future; and then make predictions through the surrounding rock deformation data, the influencing factor data and the preset surrounding rock deformation prediction model, so as to accurately obtain the predicted value of the surrounding rock deformation of the target tunnel, realize the accurate prediction of the surrounding rock deformation of the unexcavated section, and improve the safety of the subsequent construction.

[0053] In some embodiments, after obtaining the predicted value of the surrounding rock deformation of the target tunnel based on the surrounding rock deformation data, the influencing factor data and the preset surrounding rock deformation prediction model, the ultimate displacement value of the surrounding rock deformation of the target tunnel can also be obtained; then, based on the ultimate displacement value and the predicted value of the surrounding rock deformation, the surrounding rock deformation degree of the target tunnel is determined; finally, based on the surrounding rock deformation degree, the safety level of the deformation of the target tunnel is determined, and the tunnel monitoring frequency and construction parameters are adjusted.

[0054] In this embodiment, after the predicted value of the surrounding rock deformation of the target tunnel is predicted, in order to improve the safety of subsequent construction, a safety evaluation of the surrounding rock deformation can be performed, and the tunnel monitoring frequency and construction parameters can be adjusted to make the tunnel construction parameters suitable for the current surrounding rock deformation of the tunnel, thereby ensuring safe construction of the tunnel.

[0055] Here, the limit displacement value can be solved by exponential regression. By fitting the existing surrounding rock deformation data, we can get the following The expression of represents the deformation of surrounding rock, and represents the fitting parameters, represents time. When the variable time approaches infinity, the deformation of the surrounding rock approaches a constant value. , and this value is a maximum value, so this value can be used as the ultimate displacement value of tunnel deformation.

[0056] Optionally, the surrounding rock deformation of the target tunnel is determined according to the limit displacement value and the predicted value of surrounding rock deformation, which can be based on the expression: , determine the surrounding rock deformation of the target tunnel; where, represents the surrounding rock deformation of the target tunnel, represents the predicted value of surrounding rock deformation, Indicates the limit displacement value.

[0057] In this embodiment, when the deformation of the surrounding rock reaches a certain value, deformation failure will occur, and this value is the limit displacement value. The smaller the deformation value of the surrounding rock of the tunnel is relative to the limit displacement value, the larger the deformation margin of the surrounding rock of the tunnel is, and thus the higher the stability is.

[0058] Therefore, the surrounding rock deformation of the target tunnel can be determined by the ratio of the predicted surrounding rock deformation value to the ultimate displacement value, and a safety judgment can be made on the predicted surrounding rock deformation.

[0059] Optionally, in this embodiment, there may be four safety levels, namely level one, level two, level three and level four, and as the number of levels increases, the degree of deformation of the surrounding rock of the tunnel increases.

[0060] In this embodiment, the safety level of the target tunnel deformation is determined according to the surrounding rock deformation degree, and the tunnel monitoring frequency and construction parameters are adjusted, which may be: If the surrounding rock deformation is less than the first preset value, the safety level of the target tunnel deformation is determined to be level 1, and the tunnel monitoring frequency is maintained at the first frequency. Here, the surrounding rock deformation is within the normal range, and monitoring and inspection can be performed at the normal frequency.

[0061] If the surrounding rock deformation is greater than or equal to the first preset value and the surrounding rock deformation is less than the second preset value, the safety level of the target tunnel deformation is determined to be level two, and the tunnel monitoring frequency is adjusted to the second frequency; wherein the second preset value is greater than the first preset value and the second frequency is greater than the first frequency.

[0062] Here, the deformation of the surrounding rock increases, but there is still a large deformation margin, and it is necessary to track the deformation of the surrounding rock, and the monitoring of the tunnel can be increased. For example, monitoring can be carried out at a normal frequency, and the patrol frequency can be appropriately increased.

[0063] If the surrounding rock deformation is greater than or equal to the second preset value, and the surrounding rock deformation is less than the third preset value, the safety level of the target tunnel deformation is determined to be level three, and the tunnel monitoring frequency is adjusted to the third frequency; wherein the third preset value is greater than the second preset value, and the third frequency is greater than the second frequency.

[0064] Here, the surrounding rock deformation has further increased, and there are safety risks. This is a preliminary warning state, and the monitoring of the tunnel can be further increased. For example, the frequency of monitoring and inspection can be increased.

[0065] If the surrounding rock deformation is greater than or equal to the third preset value, the safety level of the target tunnel deformation is determined to be level four, and the tunnel monitoring frequency is adjusted to the third frequency, and the construction parameters of the tunnel are adjusted.

[0066] Here, the predicted value of surrounding rock deformation approaches the limit displacement value, which means there is a serious safety hazard. It is in an alarm state, and the monitoring and inspection frequencies can be increased. If necessary, reinforcement measures should be taken and the construction parameters of the tunnel should be adjusted to ensure construction safety. In addition, the risk of surrounding rock deformation can be sent to relevant construction personnel, and an alarm can be sounded to remind them.

[0067] Exemplarily, the first preset value may be 0.55, the second preset value may be 0.7, and the third preset value may be 0.85.

[0068] The embodiment of the present application determines the correlation coefficient between tunnel geological data, construction parameter data and surrounding rock deformation data, extracts the influencing factor data of surrounding rock deformation during tunnel construction from the tunnel geological data and construction parameter data, and can accurately find the relevant data of surrounding rock deformation during tunnel construction, remove the data with low correlation, and retain the data with high correlation, so as to make accurate predictions later; then, through the surrounding rock deformation data, influencing factor data and the preset surrounding rock deformation prediction model, the prediction value of surrounding rock deformation of the target tunnel can be accurately obtained, and the surrounding rock deformation of the unexcavated section can be accurately predicted, thereby improving the safety of subsequent construction. Among them, the surrounding rock deformation prediction model is established through the time domain convolutional network, and the convolution layer can be used to capture the pattern and internal characteristics of the surrounding rock deformation data and the influencing factor data in the time series, and the causal dilation convolution can be used to expand the receptive field of the observed data, so as to achieve accurate prediction of surrounding rock deformation. In addition, the surrounding rock deformation degree of the target tunnel is determined by the predicted value of surrounding rock deformation and the ultimate displacement value, so that the surrounding rock deformation degree of the target tunnel can be accurately judged and the surrounding rock deformation situation of the target tunnel can be obtained; by determining the safety level of deformation of the target tunnel and adjusting the tunnel monitoring frequency and construction parameters, the monitoring of the target tunnel can be strengthened and the potential risks of the tunnel can be obtained, so that corresponding measures can be taken in time to improve the safety of tunnel construction.

[0069] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0070] The following is an embodiment of the device of the present application. For details not described in detail, please refer to the corresponding method embodiment described above.

[0071] Figure 2 The schematic diagram of the structure of the surrounding rock deformation prediction device for layered rock tunnel construction provided by the embodiment of the present application is shown. For the convenience of explanation, only the part related to the embodiment of the present application is shown, which is described in detail as follows: like Figure 2 As shown, the surrounding rock deformation prediction device 20 for layered rock tunnel construction includes: An acquisition module 21 is used to acquire tunnel geological data, construction parameter data and surrounding rock deformation data during the construction of the target tunnel; A determination module 22, for respectively determining the correlation coefficients between each type of tunnel geological data, each type of construction parameter data and surrounding rock deformation data; An extraction module 23 is used to extract the influencing factor data of surrounding rock deformation during tunnel construction from the tunnel geological data and the construction parameter data according to the correlation coefficient; The prediction module 24 is used to obtain the predicted value of the surrounding rock deformation of the target tunnel based on the surrounding rock deformation data, the influencing factor data and the preset surrounding rock deformation prediction model; wherein the surrounding rock deformation prediction model is obtained by training a preset time domain convolutional network based on the historical surrounding rock deformation data and the historical influencing factor data of the target tunnel.

[0072] In a possible implementation, the influencing factor data include surrounding rock strength, cross-sectional area, bedding plane spacing, bedding plane inclination, excavation footage, construction method and tunnel burial depth.

[0073] In a possible implementation, the device 30 for predicting surrounding rock deformation during layered rock tunnel construction further includes an evaluation module for: Obtain the ultimate displacement value of the surrounding rock deformation of the target tunnel; Determine the surrounding rock deformation of the target tunnel based on the limit displacement value and the predicted value of surrounding rock deformation; According to the deformation degree of the surrounding rock, the safety level of deformation of the target tunnel is determined, and the tunnel monitoring frequency and construction parameters are adjusted.

[0074] In a possible implementation, the evaluation module is specifically used to: According to the expression: , determine the surrounding rock deformation of the target tunnel; In the formula, represents the surrounding rock deformation of the target tunnel, represents the predicted value of surrounding rock deformation, Indicates the limit displacement value.

[0075] In a possible implementation, the evaluation module is specifically used to: If the surrounding rock deformation is less than the first preset value, the safety level of the target tunnel deformation is determined to be level one, and the tunnel monitoring frequency is maintained at the first frequency; If the surrounding rock deformation is greater than or equal to the first preset value, and the surrounding rock deformation is less than the second preset value, the safety level of the target tunnel deformation is determined to be level 2, and the tunnel monitoring frequency is adjusted to the second frequency; wherein the second preset value is greater than the first preset value, and the second frequency is greater than the first frequency; If the surrounding rock deformation is greater than or equal to the second preset value, and the surrounding rock deformation is less than the third preset value, the safety level of the target tunnel deformation is determined to be level three, and the tunnel monitoring frequency is adjusted to the third frequency; wherein the third preset value is greater than the second preset value, and the third frequency is greater than the second frequency; If the surrounding rock deformation is greater than or equal to the third preset value, the safety level of the target tunnel deformation is determined to be level four, and the tunnel monitoring frequency is adjusted to the third frequency, and the construction parameters of the tunnel are adjusted.

[0076] In a possible implementation, the acquisition module 21 is further configured to: The tunnel geological data, construction parameter data and surrounding rock deformation data are normalized.

[0077] The embodiment of the present application determines the correlation coefficient between tunnel geological data, construction parameter data and surrounding rock deformation data, extracts the influencing factor data of surrounding rock deformation during tunnel construction from the tunnel geological data and construction parameter data, and can accurately find the relevant data of surrounding rock deformation during tunnel construction, remove the data with low correlation, and retain the data with high correlation, so as to make accurate predictions later; then, through the surrounding rock deformation data, influencing factor data and the preset surrounding rock deformation prediction model, the prediction value of surrounding rock deformation of the target tunnel can be accurately obtained, and the surrounding rock deformation of the unexcavated section can be accurately predicted, thereby improving the safety of subsequent construction. Among them, the surrounding rock deformation prediction model is established through the time domain convolutional network, and the convolution layer can be used to capture the pattern and internal characteristics of the surrounding rock deformation data and the influencing factor data in the time series, and the causal dilation convolution can be used to expand the receptive field of the observed data, so as to achieve accurate prediction of surrounding rock deformation. In addition, the surrounding rock deformation degree of the target tunnel is determined by the predicted value of surrounding rock deformation and the ultimate displacement value, so that the surrounding rock deformation degree of the target tunnel can be accurately judged and the surrounding rock deformation situation of the target tunnel can be obtained; by determining the safety level of deformation of the target tunnel and adjusting the tunnel monitoring frequency and construction parameters, the monitoring of the target tunnel can be strengthened and the potential risks of the tunnel can be obtained, so that corresponding measures can be taken in time to improve the safety of tunnel construction.

[0078] Figure 3 Schematic diagram of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 30 of this embodiment includes: a processor 31, a memory 32, and a computer program 33 stored in the memory 32 and executable on the processor 31. When the processor 31 executes the computer program 33, the steps in the above-mentioned embodiments of the method for predicting surrounding rock deformation during tunnel construction in layered rock mass are implemented, for example Figure 1 Alternatively, when the processor 31 executes the computer program 33, the functions of each module in the above-mentioned device embodiments are implemented, for example Figure 2The functions of modules 21 to 24 are shown.

[0079] Exemplarily, the computer program 33 may be divided into one or more modules / units, one or more modules / units are stored in the memory 32 and executed by the processor 31 to complete the present application. The one or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program 33 in the electronic device 30. For example, the computer program 33 may be divided into Figure 2 Modules 21 to 24 are shown.

[0080] The electronic device 30 may include, but is not limited to, a processor 31 and a memory 32. Those skilled in the art will appreciate that Figure 3 It is only an example of the electronic device 30 and does not constitute a limitation of the electronic device 30. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0081] The processor 31 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0082] The memory 32 may be an internal storage unit of the electronic device 30, such as a hard disk or memory of the electronic device 30. The memory 32 may also be an external storage device of the electronic device 30, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 30. Further, the memory 32 may also include both an internal storage unit of the electronic device 30 and an external storage device. The memory 32 is used to store computer programs and other programs and data required by the electronic device. The memory 32 may also be used to temporarily store data that has been output or is to be output.

[0083] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0084] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0085] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0086] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0087] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0089] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0090] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for predicting surrounding rock deformation during tunnel construction in layered rock mass, characterized in that: include: Obtain tunnel geological data, construction parameter data and surrounding rock deformation data during the construction of the target tunnel; respectively determining the correlation coefficients between each type of tunnel geological data, each type of construction parameter data and the surrounding rock deformation data; Extracting, according to the correlation coefficient, data of factors affecting surrounding rock deformation during tunnel construction from the tunnel geological data and construction parameter data; Based on the surrounding rock deformation data, the influencing factor data and a preset surrounding rock deformation prediction model, a surrounding rock deformation prediction value of the target tunnel is obtained; wherein the surrounding rock deformation prediction model is obtained by training a preset time domain convolutional network based on the historical surrounding rock deformation data and historical influencing factor data of the target tunnel.

2. The method for predicting surrounding rock deformation during layered rock tunnel construction according to claim 1, characterized in that: The influencing factor data include surrounding rock strength, cross-sectional area, bedding plane spacing, bedding plane inclination, excavation progress, construction method and tunnel burial depth.

3. The method for predicting surrounding rock deformation during layered rock tunnel construction according to claim 1, characterized in that: After obtaining the predicted value of the surrounding rock deformation of the target tunnel based on the surrounding rock deformation data, the influencing factor data and the preset surrounding rock deformation prediction model, the method further includes: Obtain the ultimate displacement value of the surrounding rock deformation of the target tunnel; Determining the surrounding rock deformation degree of the target tunnel according to the limit displacement value and the surrounding rock deformation prediction value; According to the surrounding rock deformation degree, the safety level of the target tunnel deformation is determined, and the tunnel monitoring frequency and construction parameters are adjusted.

4. The method for predicting surrounding rock deformation during layered rock tunnel construction according to claim 3, characterized in that: Determining the surrounding rock deformation degree of the target tunnel according to the limit displacement value and the surrounding rock deformation prediction value includes: According to the expression: , determine the surrounding rock deformation of the target tunnel; In the formula, represents the surrounding rock deformation of the target tunnel, represents the predicted value of surrounding rock deformation, Indicates the limit displacement value.

5. The method for predicting surrounding rock deformation during layered rock tunnel construction according to claim 3, characterized in that: According to the surrounding rock deformation degree, the safety level of the target tunnel deformation is determined, and the tunnel monitoring frequency and construction parameters are adjusted, including: If the surrounding rock deformation is less than a first preset value, the safety level of the target tunnel deformation is determined to be level one, and the tunnel monitoring frequency is maintained at a first frequency; If the surrounding rock deformation is greater than or equal to a first preset value, and the surrounding rock deformation is less than a second preset value, the safety level of the target tunnel deformation is determined to be level 2, and the tunnel monitoring frequency is adjusted to a second frequency; wherein the second preset value is greater than the first preset value, and the second frequency is greater than the first frequency; If the surrounding rock deformation is greater than or equal to the second preset value, and the surrounding rock deformation is less than the third preset value, the safety level of the target tunnel deformation is determined to be level three, and the tunnel monitoring frequency is adjusted to a third frequency; wherein the third preset value is greater than the second preset value, and the third frequency is greater than the second frequency; If the surrounding rock deformation degree is greater than or equal to the third preset value, the safety level of the target tunnel deformation is determined to be level four, and the tunnel monitoring frequency is adjusted to the third frequency, and the construction parameters of the tunnel are adjusted.

6. The method for predicting surrounding rock deformation during layered rock tunnel construction according to any one of claims 1 to 5, characterized in that: After obtaining the tunnel geological data, construction parameter data and surrounding rock deformation data during the construction of the target tunnel, it also includes: The tunnel geological data, the construction parameter data and the surrounding rock deformation data are normalized.

7. A device for predicting surrounding rock deformation during layered rock tunnel construction, characterized in that: include: An acquisition module is used to acquire tunnel geological data, construction parameter data and surrounding rock deformation data during the construction of the target tunnel; A determination module, used to respectively determine the correlation coefficient between each tunnel geological data, each construction parameter data and the surrounding rock deformation data; An extraction module, used to extract data of factors affecting surrounding rock deformation during tunnel construction from the tunnel geological data and construction parameter data according to the correlation coefficient; A prediction module is used to obtain a predicted value of surrounding rock deformation of the target tunnel based on the surrounding rock deformation data, the influencing factor data and a preset surrounding rock deformation prediction model; wherein the surrounding rock deformation prediction model is obtained by training a preset time domain convolutional network based on the historical surrounding rock deformation data and historical influencing factor data of the target tunnel.

8. The device for predicting surrounding rock deformation during layered rock tunnel construction according to claim 7, characterized in that: The influencing factor data include surrounding rock strength, cross-sectional area, bedding plane spacing, bedding plane inclination, excavation progress, construction method and tunnel burial depth.

9. An electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 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 6 are implemented.

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