Optimization processing method for surrounding rock displacement monitoring data in cavern group excavation process

Abstract

The invention relates to an optimization processing method for surrounding rock displacement monitoring data in a cavern group excavation process, and the method comprises the steps: determining a strength parameter of a rock mass in combination with the landform information of a target region and on-site geological exploration data, and building a three-dimensional geological model in a factory site region range; calculating to obtain a corresponding three-dimensional crustal stress field; determining an approximate range of surrounding rock strength deformation parameters according to specifications and a rock mechanics manual through surrounding rock categories determined by surrounding rock classification and surrounding rock parameters obtained by indoor tests; relative deformation of different measuring sections is calculated, and optimized surrounding rock parameters are obtained through inversion of the relative deformation, obtained through monitoring of the multi-point displacement meter, of the different measuring sections; and verifying the reliability of the surrounding rock parameters obtained by inversion by comparing and actually measuring the relative deformation characteristics of the surrounding rock. The method has the advantages that surrounding rock excavation response characteristics are obtained by optimizing surrounding rock parameters, field monitoring data are corrected and analyzed and used for correcting monitoring data of the multi-point displacement meter, and the monitoring data obtained by the multi-point displacement meter are more accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field of rock mechanics and engineering, and particularly relates to an optimized processing method for surrounding rock displacement monitoring data during the excavation process of a cavern group. Background Technique

[0002] The research on the response characteristics of surrounding rock excavation is of great significance in underground engineering, which can provide a theoretical basis for the design of underground caverns, excavation construction, and support optimization. The multi-point displacement meter is a commonly used engineering monitoring instrument for measuring displacement changes at multiple positions or depths. It is widely used in civil engineering, geological engineering, water conservancy engineering and other fields, especially in monitoring the deformation of surrounding rock of underground caverns, slope stability, and foundation settlement. Its monitoring data can be used to monitor the deformation convergence of surrounding rock of underground caverns, comprehensively analyze the surrounding rock deformation data of different monitoring sections, and judge the degree and stage of surrounding rock deformation by referring to the deformation interval for deformation grading; or for excavation feedback analysis to support the stability evaluation of surrounding rock of underground caverns. Since the initial deformation amount cannot be monitored, in actual engineering, the deformation increment of the measuring point before and after excavation during layered excavation is generally used to analyze the back-analysis of surrounding rock mechanical parameters, and the stability of the surrounding rock during subsequent excavation is evaluated and predicted in combination with the in-situ stress field. Among them, the deformation increment of the measuring point often uses the absolute deformation of this point. There are differences in the determination of the fixed point during the solution of the absolute deformation, so there are doubts about the reliability and accuracy of the parameter back-analysis results.

[0003] Therefore, aiming at the disadvantages of the above monitoring data method, an optimized processing method for surrounding rock displacement monitoring data during the excavation process of a cavern group is proposed. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present application provides an optimized processing method for surrounding rock displacement monitoring data during the excavation process of a cavern group, including the following steps:

[0005] Step S1: Based on the on-site geological exploration data, determine the strength parameters of the rock mass, the coordinate data of the target area, and the elevation information of the target area to establish a three-dimensional geological model;

[0006] Step S2: Based on the in-situ stress data of the observation points in the target area obtained by on-site measurement, calculate the corresponding three-dimensional in-situ stress field through the multiple linear regression method;

[0007] Step S3: Obtain the range of surrounding rock strength and deformation parameters, where the surrounding rock strength and deformation parameters include Young's modulus, Poisson's ratio, cohesion, and internal friction angle. Each surrounding rock strength and deformation parameter is divided into t different factor levels with the same quantity, and t groups of orthogonal test groups of surrounding rock strength and deformation parameters are designed through orthogonal experiment;

[0008] Step S4: Input the three-dimensional in-situ stress field of the target area and the surrounding rock strength and deformation parameters of different groups into the numerical model, calculate the displacement response field of the surrounding rock, extract the displacements at various positions of multiple multi-point displacement gauges arranged on-site based on the displacement response field, and calculate the relative deformation of the measuring sections between different positions;

[0009] Step S5: Use the non-linear relationship between the surrounding rock strength and deformation parameters calculated in Step S4 and the corresponding relative deformations of different measuring sections to train a BP neural network to obtain a BP neural network model, and input the true relative deformations of different measuring sections monitored by the multi-point displacement gauges into the BP neural network model to inversely obtain the optimized surrounding rock strength and deformation parameters;

[0010] Step S6: Input the optimized surrounding rock deformation strength parameters and the three-dimensional in-situ stress field obtained in Step S2 into the numerical model to obtain the excavation response field of the surrounding rock through calculation;

[0011] Step S7: Based on the monitoring data of the multi-point displacement gauges obtained on-site, correct the monitoring data through the excavation response field of the surrounding rock.

[0012] The modeling process of Step S1 includes:

[0013] Step 11: Import the DEM elevation information of the target area downloaded from the geographical information data obtained by the satellite into the 3D modeling software;

[0014] Step 12: Analyze the DEM data to extract the contour line of the ground surface. Using the extracted contour line and elevation information, combined with the coordinate data of the terrain, the software generates a 3D grid;

[0015] Step 13: Stretch the generated 3D grid vertically to the corresponding elevation to form a 3D terrain entity model, and perform cutting and meshing processing on the terrain entity model to generate a 3D geological model suitable for numerical simulation analysis.

[0016] Step S2 includes:

[0017] Step 21: Construct the in-situ stress field. The construction modes include: (a) self-weight, (b) X-direction extrusion structure, (c) Y-direction extrusion structure, (d) XY horizontal shear structure, (e) vertical shear structure factors on the Z-Y plane and vertical shear structure on the Z-X plane, and use the finite difference numerical simulation software to calculate the stresses generated by the construction modes (a) to (e);

[0018] Step 22: Adopt the linear superposition principle under the elastic working state to calculate the calculated value of the initial in-situ stress value:

[0019]

[0020] Where: is the regression calculated value of j stress components at k observation points, where j ∈ [1, 6] represents the 6 stress components generated by structural patterns (a) to (e); L i (i = 1, 2, 3, … n) are regression coefficients, n = 6; σ xx 、σ yy 、σ xy 、σ xz 、σ yz are the stresses generated by the structural pattern at the k-th observation point in five directions; σ w is the stress generated by gravity at the k-th observation point; is the numerical calculated value of the j-th stress component at the k-th observation point under the i-th sub-load factor;

[0021] Step 23: Calculate the measured value of in-situ stress and the calculated value The error S 残 between them is:

[0022]

[0023] Among them, is the measured value of the j-th stress component at the k-th observation point, j ∈ [1, 6];

[0024] Step 24: Obtain the regression coefficients L and the calculated value by obtaining the minimum value of the error between the observed value i (i = 1, 2, 3, … n), n = 6;

[0025] Among them, according to the least squares principle, the system of equations that makes S 残 the minimum value is:

[0026]

[0027] By solving this matrix system of equations, all regression coefficients can be obtained.

[0028] In step S4, for a multi-point displacement meter, the displacement of the hole mouth calculated is D 0 , and the displacement of the r-th observation point of the multi-point displacement meter is D r , then the relative deformation of the 0-r measurement section of this multi-point displacement meter is D 0 -D r .

[0029] In step S5, training the BP neural network includes:

[0030] Step 51: Use the relative deformations of different measuring segments of the multi-point displacement gauge calculated in step S4 as the input data group xt, and use the surrounding rock deformations and strength parameters of different groups corresponding to it in step S4 as the output data yt. Use the input data and output data as training data, and obtain the non-linear relationship f(x) between the input data and the output data by training a BP neural network:

[0031]

[0032] In the formula, x is the expression of the input node of the neural network; y is the expression of the output node of the neural network; f(v, h 1 , …, h p , w) is the established multi-layer neural network structure, where v is the number of input layer F x nodes, h 1 is the number of units in the middle layer F 1 , h p is the number of units in the middle layer F p , w is the number of nodes in the output layer F y , h v is the number of units in the v-th middle layer; R v →R w represents the mapping from R v to R w ;

[0033] Select the root mean square error as the error function, and adjust the weights and biases of the middle layer backward according to the error RMSE of the output layer until the error converges. The error function is the square root of the cumulative average of the squares of the differences between all true values and predicted values, and the calculation formula is:

[0034]

[0035] The correction of the monitoring data through the excavation response field of the surrounding rock includes:

[0036] Obtain the numerically simulated absolute displacement Ci of each observation point through the excavation response field of the surrounding rock; take the observation point with the smallest absolute value of the numerically simulated absolute displacement Ci as the displacement reference point imin;

[0037] Obtained from the monitoring data of the multi-point displacement gauge on site, calculate the cumulative relative deformation Aimin from the orifice to the displacement reference point imin, and calculate the absolute deformation B' = Cimin + Aimin of the orifice relative to the displacement reference point imin;

[0038] The absolute displacement of the corrected observation point is calculated as A' = B' - Ai based on the relative displacement of the original monitoring point relative to the orifice; Ai is the relative displacement reading of each observation point relative to the orifice.

[0039] The beneficial effects of the present invention are as follows: 1. By combining the measured data of the relative deformations of different measuring sections of multiple multi-point displacement gauges, optimized surrounding rock parameters based on the actual surrounding rock excavation response characteristics are obtained. 2. Reliable surrounding rock excavation response characteristics are obtained through the optimized surrounding rock parameters, and on this basis, the on-site monitoring data is corrected and analyzed. 3. The surrounding rock excavation response characteristics obtained by the above method make full use of the on-site excavation surrounding rock response monitoring data and can be used to correct the monitoring data of the multi-point displacement gauge, making the monitoring data obtained by the multi-point displacement gauge more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The established three-dimensional geological model diagram;

[0041] Figure 2 The monitoring deformation analysis diagram of the multi-point displacement gauge;

[0042] Figure 3 The surrounding rock deformation distribution diagram of the cavern excavation;

[0043] Figure 4 The displacement vector diagram of the cavern excavation. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In order to make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0045] Embodiment 1

[0046] An optimized processing method for the surrounding rock displacement monitoring data during the excavation process of a cavern group, comprising the following steps:

[0047] Step 1: According to the on-site geological exploration data, determine the strength parameters of the rock mass, the coordinate data of the target area, and the elevation information of the target area to establish a three-dimensional geological model.

[0048] Modeling: Import the DEM elevation information of the target area downloaded from the geographical information data obtained by the satellite into 3D modeling software. In the modeling software, analyze the DEM data to extract the contour lines (contour lines) of the ground surface. Using the extracted contour lines and elevation information, combined with the coordinate data of the terrain, the software generates a 3D grid, stretches the generated 3D grid vertically to the corresponding elevation, thereby forming a 3D terrain solid model, and performs cutting and meshing processing on the terrain solid model, and finally generates a 3D geological model suitable for numerical simulation analysis. The entire process gradually transforms from a 2D graph to a 3D solid, and through detailed grid meshing and manifold grid combination, it ensures the accuracy and usability of the model in numerical simulation. The established 3D geological model is as shown in Figure 1 。

[0049] Determine the strength parameters of the rock mass: The bedrock exposed in this area is mostly granite. For the dikes and altered zones exposed in the strata, due to their complex and discrete distribution, only the differences in the strength parameters of the rock mass are considered from the surrounding rock category in the 3D geological model of numerical calculation. The strength parameters of the rock mass are obtained through laboratory tests, as shown in Table 1 below: In Table 1, II, III1, III2, IV, and V represent Class II surrounding rock, Class III1 surrounding rock, Class III2 surrounding rock, Class IV surrounding rock, and Class V surrounding rock respectively, which are rock masses of different quality grades divided according to the specifications.

[0050] Table 1 Granite surrounding rock parameters

[0051]

[0052] Step 2: Based on the in-situ stress data of the target area obtained from on-site measurements, calculate the corresponding in-situ stress field through the multiple linear regression method.

[0053] Considering the tectonic action in the target area, the following tectonic modes for the formation of the in-situ stress field are considered in the 3D numerical calculation conditions: (a) self-weight, (b) X-direction compression tectonics, (c) Y-direction compression tectonics, (d) XY horizontal shear tectonics, (e) vertical shear tectonics on the Z-Y plane and vertical shear tectonics on the Z-X plane.

[0054] Use the finite difference numerical simulation software to calculate the stresses generated by the tectonic modes (a) to (e), and then use equations (1) to (2) to solve for the 3D initial in-situ stress of the observation points in the target area.

[0055] According to the linear superposition principle under the elastic working state, the calculation formula for the 3D initial in-situ stress value of the observation point is:

[0056]

[0057] Where: is the regression calculated value of j stress components at k observation points, where j ∈ [1, 6] represents the 6 stress components generated by structural patterns (a) to (e); L i (i = 1, 2, 3, … n) are the regression coefficients, n = 6; σ xx , σ yy , σ xy , σ xz , σ yz are the stresses generated by the structural pattern at the k-th observation point in five directions; σ w is the stress generated by gravity at the k-th observation point; is the numerical calculated value of the j-th stress component at the k-th observation point under the i-th sub-load factor;

[0058] Measured in-situ stress value and the calculated value The error S 残 is:

[0059]

[0060] Among them, is the measured value of the j-th stress component at the k-th observation point, j ∈ [1, 6]; the number of stress observation points is m.

[0061] By obtaining the observation value and the calculated value The error S 残 When it is the minimum value, the obtained regression coefficients L i (i = 1, 2, 3, … n), n = 6;

[0062] Among them, according to the least squares principle, the equation for making S 残 the minimum value is:

[0063]

[0064] By solving this matrix equation system, all regression coefficients can be obtained.

[0065] Step 3: Determine the approximate range of the surrounding rock strength and deformation parameters, as shown in Table 2:

[0066] Among them, the approximate range of the surrounding rock strength and deformation parameters is determined from the rock mechanics parameter manual and laboratory tests by considering the seismic conditions of the lithology. The cohesion and internal friction angle are obtained from laboratory tests.

[0067] Table 2 Range of surrounding rock strength and deformation parameters

[0068] Parameter Young's modulus E / GPa Poisson's ratio μ Cohesion c / MPa Internal friction angle / ° Range 2-15 0.23-0.30 8-16 20-35

[0069] Step 4: Each surrounding rock strength and deformation parameter is divided into five different factor levels, and 25 groups of surrounding rock strength and deformation parameter orthogonal test groups are designed through orthogonal experiments as shown in Table 3;

[0070] Table 3 Surrounding rock strength and deformation parameters designed by orthogonal experiment

[0071] Test number Young's modulus E (GPa) Poisson's ratio μ Cohesion c (MPa) Internal friction angle (°) 1 2 0.23 8 20 2 2 0.2475 10 23.75 3 2 0.265 12 27.75 4 2 0.2825 14 31.25 5 2 0.30 16 35.00 6 5.25 0.23 10 27.75 7 5.25 0.2475 12 31.25 8 5.25 0.265 14 35.00 9 5.25 0.2825 16 20 10 5.25 0.30 8 23.75 11 8.50 0.23 12 35.00 12 8.50 0.2475 14 20 13 8.50 0.265 16 23.75 14 8.50 0.2825 8 27.75 15 8.50 0.30 10 31.25 16 11.75 0.23 14 23.75 17 11.75 0.2475 16 27.75 18 11.75 0.265 8 31.25 19 11.75 0.2825 10 35.00

[0072] Continued Table 3

[0073]

[0074] Based on the three-dimensional in-situ stress field in the target area, the corresponding surrounding rock parameters are input into the numerical model, and the displacement response field of the surrounding rock is calculated using Flac3D. In Flac3D, the built-in program is used to assign the surrounding rock parameters to the target area, and then the rock mass is excavated using the program to balance the calculation to obtain the displacement response field of the surrounding rock. The numerical model in this step is different from the stress field in Step 2 and belongs to a new model formed after inputting the in-situ stress field data and surrounding rock strength and deformation parameters in the target area. The output result is the displacement response field. Based on the displacement response field, the displacements at various positions of multiple multi-point displacement gauges arranged on-site are extracted, and the relative deformation of the measurement sections between different positions is calculated. For example, Table 4 shows the numerical simulation relative deformation of multiple measurement sections of different multi-point displacement gauges calculated according to a set of surrounding rock strength and deformation parameters in the in-situ stress field of the target area:

[0075] Table 4 Relative deformation of multiple measurement sections of multi-point displacement gauges calculated from 6 groups of surrounding rock strength and deformation parameters

[0076]

[0077] As Figure 2 shown, a multi-point displacement gauge has four measuring points, and the distance from each measuring point to the hole mouth is a measurement section. A total of four measurement sections for a multi-point displacement gauge.

[0078] Based on the displacement response field, the displacements at various positions of multiple multi-point displacement gauges arranged on-site are extracted. Assuming that the displacement at the hole mouth calculated for a multi-point displacement gauge is D 0 , and the displacement of the r-th measuring point of the multi-point displacement gauge is D r , then the relative deformation of the 0-r measurement section for this multi-point displacement gauge is D 0 -D r of, and thus the relative deformation of the measurement sections between different positions can be calculated.

[0079] Step 5: Take the surrounding rock strength and deformation parameters of different groups as the output data yt, where yt is the data in Table 3; take the corresponding relative deformations of different measurement sections calculated therefrom as the input data group xt, and xt is the data in Table 4; take the input data and their corresponding output data as training data, and obtain the non-linear relationship f(x) between the input data and the output data through BP neural network training:

[0080]

[0081] The BP neural network has a simple structure, including an input layer, an intermediate layer, and an output layer. The number of training data sets in this embodiment is 25.

[0082] In the formula, x is the expression of the input node of the neural network; y is the expression of the output node of the neural network; f(v, h 1 , …, h p , w) is the established multi-layer neural network structure, where v is the number of nodes in the input layer F x (assuming that there are 6 monitoring data in a set of multi-point displacements of the input data, then v = 6), h 1 is the number of units in the intermediate layer F 1 , h p is the number of units in the intermediate layer F p , w is the number of nodes in the output layer F y (assuming that there are 4 data in a set of surrounding rock strength and deformation parameters of the output data, then w = 4), h v is the number of units in the v-th intermediate layer; R v → R w represents the mapping from R v to R w .

[0083] Select the root mean square error as the error function, and adjust the weights and biases of the intermediate layer backward according to the error RMSE of the output layer until the error converges. The error function is the square root of the cumulative average of the squares of the differences between all true values and predicted values. The calculation formula is:

[0084]

[0085] In this embodiment, the number of intermediate layers is taken as 2, and the number of layer units is 46 and 53.

[0086] Training the BP neural network obtains the non-linear mapping between the model inversion parameters (modulus, cohesion, etc.) and the relative deformations at different positions of different multi-point displacement gauges measured inside the model, that is, the trained BP neural network model.

[0087] Since it is impossible to use the measured model as training data, the goal of this embodiment is to obtain a reliable numerical simulation model. Therefore, the surrogate numerical simulation model in Table 4 is first used to obtain the training data, and then the model parameters (surrounding rock strength and deformation parameters) are inversely obtained through the real monitoring data according to Step 6. The ultimate goal is to serve for obtaining a reliable numerical model.

[0088] Step 6: After obtaining the monitoring data of the multi-point displacement meter on site, use the trained BP neural network model. Take the real relative deformation data of different measuring sections monitored by the multi-point displacement meter actually obtained on site as the input data, and inversely obtain the optimized surrounding rock strength and deformation parameters. The optimized surrounding rock strength and deformation parameters obtained by inputting the monitoring data of the multi-point displacement meter in this step into the BP neural network model are shown in Table 5:

[0089] Table 5 Range of surrounding rock strength and deformation parameters

[0090]

[0091] Step 7: Input the optimized surrounding rock deformation strength parameters obtained by inversion in Step 6 and the three-dimensional in-situ stress field of the target area obtained in Step 2 into the numerical model Flac3D to obtain the excavation response field of the surrounding rock. Excavate the rock mass on the basis of the optimized surrounding rock strength and deformation parameters in Step 6 to obtain the excavation response characteristics of the surrounding rock. By comparing the measured excavation response characteristics of the surrounding rock with the numerically simulated excavation response characteristics of the surrounding rock, as shown in Table 6 below, the relaxation depth of the surrounding rock on the upstream side of Tiaoheng 0+161.9, and the relative deformations of Multi-point Displacement Meter M4tys3-1 and Multi-point Displacement Meter M4tys5-2 before and after the excavation of the fourth layer are compared. The results are shown in Table 6. Among them, the relaxation depth can also be called the failure depth; Tiaoheng 0+161.9 refers to the coordinate position of a certain target area. The numerically simulated excavation response characteristics of the surrounding rock calculated from the optimized surrounding rock strength and deformation parameters are in good agreement with the on-site measured excavation response characteristics of the surrounding rock.

[0092] Table 6 Comparison between the numerical simulation values calculated from the inversely optimized surrounding rock strength and deformation parameters and the on-site measured values

[0093] Position Monitoring category Numerical simulation value Field measured value Adjustment horizontal 0 + 161.9 upstream side Surrounding rock relaxation depth 1m 1.2m <![CDATA[M 4 tys3-1 Multi-point displacement meter]]> Relative deformation of the fourth layer excavation 0.375 mm 0.40 mm <![CDATA[M 4 tys5-2 Multi-point displacement meter]]> Relative deformation of the fourth layer excavation 0.25 0.2

[0094] Step 8: On the basis of the monitoring data of the multi-point displacement meter obtained on site, correct the monitoring data through the numerically simulated excavation response characteristics of the surrounding rock. The following takes the multi-point displacement meters numbered M 4 ZB3-2-1 ~M 4 ZB3-2-4 as an example to illustrate the specific correction steps:

[0095] According to the analysis of the monitoring data, it is found that the displacement change is large at a depth of 17 m of a certain multi-point displacement meter after excavation (the total length of this multi-point displacement meter is 34 m), as shown in Table 7; According to the monitoring data, it can be directly seen that, for exampleFigure 2 As shown: (1) The first point M from the orifice to a depth of 2 m 4 ZB3-2-1 , with a deformation of approximately 11 mm; (2) The deformation within the range of 2 - 5 m in the surrounding rock is 7 - 11 = -4 mm, and the deformation within the range of 5 - 17 m in the surrounding rock is 2 - 7 = -5 mm; (3) The deformation within the range of 17 - 34 m in the surrounding rock is 20 - 2 mm = 18 mm.

[0096] Table 7 Monitoring data of multi - point displacement meters

[0097] Measuring point number Accumulated relative deformation <![CDATA[M 4 ZB3-2-1 > 11.10 <![CDATA[M 4 ZB3-2-2 > 7.02 <![CDATA[M 4 ZB3-2-3 > 2.33 <![CDATA[M 4 ZB3-2-4 > 20.53

[0098] First, analyze the rationality of the monitoring data as follows:

[0099] As Figure 3 shown, according to the deformation nephogram at the current excavation stage of the main transformer chamber, combined with the positions of the displacement observation points, it can be seen that the deepest point at 34 m of the observation point is close to the side wall of the tail gate. The displacement of each observation point from the orifice to 34 m shows a trend of first decreasing and then increasing deformation. Further, according to the displacement vector diagram, as Figure 4 shown, it can be analyzed that the displacement vector direction from the orifice to 17 m of the observation point is towards the main transformer chamber, and the displacement vector direction from 17 m to 34 m is towards the tail gate chamber. Currently, there are differences between the third - layer pre - splitting and the calculation stage in the tail gate chamber, but the law of the deformation vector direction is the same.

[0100] Therefore, the deformation measured at the 4th observation point is 20 mm, which may be related to the deformation of the last point of the multi - point displacement meter towards the main transformer chamber. So this deformation may be relatively large. Considering that the deformation at the orifice reaches 11 mm, it is also reasonable that the measured deformation reaches 20 mm.

[0101] Next, start the correction based on the simulation data: The absolute displacement C of each observation point obtained through numerical simulation. Taking the point with the smallest absolute value of the absolute displacement C calculated by numerical simulation as the displacement reference point, it can be obtained that the absolute displacement calculated by numerical simulation at a depth of 5 m is C = 7.5 mm. As the displacement reference point, the relative deformation from the orifice at a depth of 5 m is A2 = 7.02 mm;

[0102] Absolute displacement of the orifice = absolute displacement of the point a to be corrected + relative displacement from the point a to be corrected to the orifice section. Therefore, the absolute deformation at the orifice after correction is B’ = C + A2 = 14.52 mm. Then, the absolute displacement of the observation point after correction can be calculated from the relative displacement of the original observation point relative to the orifice as A’ = B’ - Ai, where Ai is the relative displacement reading of each observation point relative to the orifice. By analogy, the absolute displacements of the remaining observation points after correction are as shown in Table 8 below. It can be seen that the absolute displacements of the observation points after correction are obviously closer to the absolute displacements calculated by numerical simulation. From the above results, it can be seen that there is a certain error when taking the absolute displacements of the original observation points at different base points for the absolute displacement of the multi-point displacement meter. Therefore, it is considered that the numerical simulation parameter inversion work carried out using the relative displacements of multiple observation points is more accurate than using single-point or multi-point displacements.

[0103] The absolute deformation at the orifice after correction is B’, the absolute displacement of the observation point after correction is A’, and the original monitored relative displacement Ai of the orifice is the actual monitored value.

[0104] The absolute displacement of the original observation point is the absolute displacement of the observation point calculated according to the original method with the deepest measurement point considered as the fixed point. For example, if the orifice = 20.53, then at 2 m it is 20.53 - 11.10.

[0105] The absolute displacement of the observation point after correction is that first, the absolute displacement at the orifice is corrected to 14.52 mm. Then, the absolute displacement at the observation point = absolute displacement at the orifice - relative displacement of the original monitored observation point relative to the orifice. For example, at 2 m it is 14.52 - 11.10 = 3.42;

[0106] The absolute displacement calculated by numerical simulation is the absolute displacement directly obtained through numerical simulation in step 7;

[0107] Table 8 Displacements of measurement points obtained by numerical simulation

[0108]

[0109] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An optimization processing method for surrounding rock displacement monitoring data during cavern group excavation, characterized in that: The steps include: Step S1, based on the on-site geological survey data, determine the strength parameters of the rock mass and the coordinate data and elevation information of the target area to establish a three-dimensional geological model; Step S2: Based on the on-site measurement, the geostress data of the observation points in the target area are obtained, and the corresponding three-dimensional geostress field is calculated by the multivariate linear regression method; Step S3, obtaining the range of surrounding rock strength deformation parameters, the surrounding rock strength deformation parameters include Young's modulus, Poisson's ratio, cohesion, and internal friction angle, each surrounding rock strength deformation parameter is divided into t different factor levels of the same number, and t groups of surrounding rock strength deformation parameter orthogonal test groups are designed through orthogonal experiments; Step S4, inputting the three-dimensional geostress field of the target area and the strength deformation parameters of the surrounding rock of different groups into the numerical model, calculating the displacement response field of the surrounding rock, extracting the displacements of multiple multi-point displacement meters arranged on site at various positions on the basis of the displacement response field, and calculating the relative deformations of the measuring sections between different positions; Step S5, using the nonlinear relationship between the surrounding rock strength deformation parameters calculated in step S4 and the corresponding relative deformations of different measuring sections to train the BP neural network to obtain a BP neural network model, and using the real relative deformations of different measuring sections monitored by a multi-point displacement meter to input into the BP neural network model, and inverting to obtain the optimal surrounding rock strength deformation parameters; Step S6, inputting the optimal surrounding rock deformation strength parameters and the three-dimensional geostress field obtained in step S2 into a numerical model to obtain the excavation response field of the surrounding rock through calculation; Step S7: Based on the multi-point displacement meter monitoring data obtained on site, the monitoring data is corrected by using the excavation response field of the surrounding rock.

2. The method according to claim 1, characterized in that Step S1: The modeling process includes: Step 11: Import the DEM elevation information of the target area downloaded from the geographic information data obtained by the satellite into the 3D modeling software; Step 12: Analyze the DEM data to extract the contour lines of the surface. Using the extracted contour lines and elevation information, combined with the coordinate data of the terrain, the software generates a three-dimensional grid; Step 13: Stretch the generated three-dimensional grid to the corresponding elevation in the vertical direction to form a three-dimensional terrain entity model, and cut and grid the terrain entity model to generate a three-dimensional geological model suitable for numerical simulation analysis.

3. The method according to claim 1, characterized in that Step S2 includes: Step 21: constructing the geostress field, the structural modes include: (a) deadweight, (b) X-direction compression structure, (c) Y-direction compression structure, (d) XY horizontal shear structure, (e) vertical shear structural factors on the ZY plane and vertical shear structure on the ZX plane, and using finite difference numerical simulation software to calculate the stress generated by structural modes (a) to (e); Step 22: Calculate the initial ground stress value using the linear superposition principle under elastic working conditions: in: is the regression calculated value of j stress components based on k observation points, j∈[1,6] represents the six stress components generated by structural modes (a) to (e); L i (i=1,2,3,…n) is the regression coefficient, n=6; σ xx , σ yy , σ xy , σ xz , σ yz is the stress generated by the structural model on the kth observation point in five directions; σ w is the stress caused by gravity at the kth observation point; is the numerical calculation value of the jth stress component at the kth observation point under the i-th partial load factor; Step 23: Calculate the measured value of ground stress and calculated values The error between 残 : in, is the measured value of the jth stress component at the kth observation point, j∈[1,6]; the number of stress observation points is m; Step 24: Obtain observations and calculated values The regression coefficient L obtained by the minimum error between i (i=1,2,3,…n), n=6; Among them, according to the least squares principle, S 残 The minimum equation is: By solving this matrix equation system, all regression coefficients can be obtained.

4. The method according to claim 1, characterized in that: In step S4, the displacement of the orifice calculated by a multi-point displacement meter is D0, and the displacement of the rth observation point of the multi-point displacement meter is D r , then the relative deformation of the 0th to rth measuring section of the multi-point displacement meter is D0-D r .

5. The method according to claim 1, characterized in that Training the BP neural network in step S5 includes: The relative deformation of different measuring sections of the multi-point displacement meter calculated in step S4 is used as the input data group xt, and the deformation and strength parameters of the surrounding rock of different groups corresponding to step S4 are used as the output data yt. The input data and output data are used as training data, and the nonlinear relationship f(x) between the input data and the output data is obtained by training the BP neural network: Where x is the input node expression of the neural network; y is the output node expression of the neural network; f(v, h1,…, h p , w) is the established multi-layer neural network structure, where v is the input layer F x The number of nodes, h1 is the number of units in the middle layer F1, and h p For the middle layer F p The number of units in the output layer F y The number of nodes, h v is the number of units in the middle layer of the vth layer; R v →R w Represents R v to R w The mapping of The root mean square error is selected as the error function. According to the error RMSE of the output layer, the weights and biases of the intermediate layer are adjusted in reverse until the error converges. The error function is the root mean square of the cumulative average of the squares of the differences between all true values ​​and predicted values. The calculation formula is:

6. The method according to claim 1, characterized in that The monitoring data corrected by the excavation response field of the surrounding rock includes: The absolute displacement Ci is calculated by numerical simulation of each observation point obtained through the excavation response field of the surrounding rock; the observation point Cimin where the absolute value of the absolute displacement Ci calculated by numerical simulation is the minimum is taken as the displacement reference point imin; According to the multi-point displacement meter monitoring data obtained on site, the cumulative relative deformation Aimin from the orifice to the displacement reference point imin is obtained, and the absolute deformation B'=Cimin+Aimin of the orifice relative to the displacement reference point imin is calculated; The absolute displacement of the original monitoring point relative to the orifice is calculated to be A'=B'-Ai after correction; Ai is the relative displacement reading of each observation point relative to the orifice.

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