Mountainous city subway tunnel surface subsidence prediction method based on three-dimensional geological model

Abstract

The present application relates to a kind of mountainous city subway tunnel surface subsidence prediction method based on three-dimensional geological model, belong to tunnel technical field.The method includes the following steps: S1: establish the regional three-dimensional geological model based on spatial statistical analysis;S2: based on the three-dimensional geological model and numerical analysis method of establishment, subway tunnel construction surface subsidence prediction is carried out.The present application can effectively utilize actual drilling data to construct the three-dimensional geological model of region, and can be used in the numerical simulation of subway tunnel, carries out subway tunnel construction surface subsidence analysis and prediction, realizes the optimization of subway route selection and tunnel construction scheme considering surface subsidence influence.

Description

Technical Field

[0001] This invention belongs to the field of tunnel technology and relates to a method for predicting surface subsidence of subway tunnels in mountainous cities based on a three-dimensional geological model. Background Technology

[0002] Because subway tunnel excavation inevitably leads to ground subsidence, which can adversely affect roads, bridges, or buildings located above the tunnel, ground subsidence is a key factor to consider when selecting subway tunnel routes and developing actual tunnel construction plans in mountainous cities.

[0003] Current methods for analyzing and predicting surface settlement in subway tunnels mainly include empirical formulas, machine learning, and numerical analysis. Empirical formulas are based on research into the impact of subway tunnel construction on surface disturbance, constructing empirical formulas and using measured data to fit and correct parameters to obtain a settlement prediction formula applicable to certain conditions. However, this method only applies to given parameters, and its applicability is difficult to control when geological conditions change. Machine learning uses intelligent algorithms such as neural networks and deep learning models, combined with measured data, to predict surface settlement during subway tunnel construction. This method is highly portable, but establishing the algorithm model requires a large number of training samples, making the initial data collection work difficult. Numerical analysis uses finite element method software to simulate subway tunnel construction, considering the impact of different construction processes on surface settlement and comparing the settlement amounts of different construction techniques and excavation processes. However, this method requires prior knowledge of the soil parameters surrounding the subway tunnel, limiting its application.

[0004] Therefore, in order to fully utilize the role of numerical analysis methods in the analysis and prediction of surface settlement during subway tunnel construction, it is necessary to further explore methods for obtaining soil parameters around subway tunnels and combine them with numerical analysis methods to conduct surface settlement analysis and prediction during tunnel construction. This will provide necessary basis for subway route selection and tunnel construction scheme optimization that take into account the impact of surface settlement. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method for predicting surface settlement of subway tunnels in mountainous cities based on a three-dimensional geological model, so as to give full play to the role of numerical analysis methods in the analysis and prediction of surface settlement during subway tunnel construction, and to provide accurate and reliable basic data on surface settlement for subway tunnel route selection and optimal subway tunnel construction scheme.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for predicting surface subsidence of subway tunnels in mountainous cities based on a three-dimensional geological model, the method includes the following steps:

[0008] S1: Establish a regional three-dimensional geological model based on spatial statistical analysis;

[0009] S2: Predicting surface settlement during subway tunnel construction based on the established three-dimensional geological model and numerical analysis methods.

[0010] Optionally, S1 specifically includes:

[0011] S11: Collect actual borehole data and geological exploration information, construct a table of stratigraphic and soil physical and mechanical parameters of actual borehole points, and classify soil layers according to parameters such as lithological description and sedimentary age, classifying the same strata into the same stratigraphic type.

[0012] S12: Determine the number and distribution of virtual boreholes based on the accuracy of the regional three-dimensional geological model construction;

[0013] S13: Fit the soil layer elevation values ​​of the virtual borehole points based on the Kriging interpolation method and the actual soil layer elevation values ​​of the borehole points.

[0014] In the Kriging interpolation method, let Z(x) be the regionalized variable at point x, and it is second-order stationary; Z(xi) is defined at point x. i For the region x0, i = 1, 2, ..., n; the estimator Z'(x0) used to estimate the regionalized variable Z(x0) at point x0 is:

[0015]

[0016] Equation (1) is a linear combination of n values, λ i For each known point x i Z(x) i The weighting coefficients; the Kriging interpolation method is used to ensure that the estimator is unbiased and that the estimation variance is minimized by σ. E 2 Given the given conditions, calculate the n weight coefficients:

[0017]

[0018] In equation (2), E[.] represents the expected value; under unbiased conditions, the Lagrange multiplier method is used to minimize the estimation variance:

[0019]

[0020] If μ is the Lagrange multiplier, then F is the n weight coefficients λ. i (n+1)-ary functions of μ; through F with respect to λ i The partial derivatives with respect to μ are:

[0021]

[0022] C ik Z(x) i ) and Z(x k The covariance of C) ik =E[Z i Z k ]-(E[Z]) 2 Written in matrix form:

[0023]

[0024] The coefficient matrix on the left side of equation (5) is constructed using the XY coordinates of four actual borehole points and the elevation of a certain soil layer. Then, the coefficient vector on the right side is constructed using the covariance values ​​of the virtual borehole points and the actual borehole points. The weight coefficient λ of the virtual borehole points is calculated. i Finally, the elevation value of the soil layer at the virtual borehole point is calculated using equation (1).

[0025] S14: Based on the physical parameters of each soil layer at the actual borehole points, assign values ​​to the physical parameters of each soil layer at the virtual borehole points, and construct a three-dimensional geological model of the region in conjunction with the development of the system platform.

[0026] Optionally, the header information of the soil physical and mechanical parameter information table includes borehole number, relative elevation of the top layer, relative elevation of the bottom layer, rock name, X coordinate, Y coordinate, longitude, latitude, elevation, natural unit weight, natural compressive strength, saturated compressive strength, shear strength index, and average tensile strength.

[0027] Optionally, the assignment of the physical parameters of each soil layer at the virtual borehole point is specifically performed by estimating the physical parameters of each soil layer at the actual borehole point using the Kriging interpolation method, and replacing the regional variable Z in S13 with the soil layer physical parameter values.

[0028] Optionally, S2 specifically includes:

[0029] S21: Based on the design conditions and selection of the subway tunnel, a finite element model of the subway tunnel is established using subway tunnel finite element modeling software;

[0030] S22: Consider one of the subway tunnel route selections, extract soil layer parameters from the constructed regional three-dimensional geological model, and assign values ​​to the physical properties of the soil layers along the subway tunnel route;

[0031] S23: Based on the current route selection, conduct research on the surface settlement variation patterns of subway tunnels under different excavation methods, different excavation depths, different advances, and different cross-sectional factors;

[0032] S24: Change the alignment of the subway tunnel, repeat S22 and S23, and analyze the influence of different excavation methods under the alignment of the subway tunnel.

[0033] S25: Based on the numerical simulation results of different subway tunnel alignments and construction schemes, the subway tunnel alignments and their corresponding construction schemes are ranked according to the minimum surface settlement.

[0034] The beneficial effects of this invention are: it can effectively utilize actual borehole data to construct a three-dimensional geological model of the area, and can be used in the numerical simulation of subway tunnels to conduct surface settlement analysis and prediction during subway tunnel construction, thereby realizing the optimal selection of subway routes and tunnel construction schemes that take into account the impact of surface settlement.

[0035] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0037] Figure 1 This is a flowchart of the method for establishing a regional three-dimensional geological model based on spatial statistical analysis in this invention;

[0038] Figure 2 A schematic diagram of virtual borehole points in a certain soil layer;

[0039] Figure 3 This is a flowchart of the method for predicting surface settlement during subway tunnel construction based on a three-dimensional geological model and numerical analysis method, as described in this invention. Detailed Implementation

[0040] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0041] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0042] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0043] Please see Figure 1 This paper presents a flowchart of a method for establishing a regional three-dimensional geological model based on spatial statistical analysis. Establishing a three-dimensional geological model of a city area requires first identifying the physical parameters of each soil layer. A common method is borehole drilling to obtain the physical parameters of soil layers at different depths. However, due to economic constraints, the number of boreholes in a region is always limited, and the spacing between boreholes is usually large, making it difficult to meet the requirements for establishing a regional three-dimensional geological model. Therefore, this invention proposes virtual borehole points to compensate for the excessive spacing between actual borehole points. The elevation values ​​and physical parameters of each soil layer in the virtual borehole points can be obtained by fitting the elevation values ​​and physical parameters of each soil layer in the actual borehole points using the Kriging interpolation method. Finally, the actual borehole points and virtual borehole points are merged to form the regional three-dimensional geological model.

[0044] S11: Collect actual borehole data and geological exploration information, construct a table of stratigraphic and soil physical and mechanical parameters of actual borehole points (Table 1), and classify soil layers according to parameters such as lithological description and sedimentary age, classifying the same strata into the same stratigraphic type.

[0045] Table 1. Information on Physical and Mechanical Parameters of Soil

[0046]

[0047] S12: Determine the number and distribution of virtual boreholes based on the accuracy of the regional three-dimensional geological model construction;

[0048] S13: Based on the Kriging interpolation method and the elevation values ​​of each soil layer at the actual borehole points, the elevation values ​​of each soil layer at the virtual borehole points are fitted.

[0049] For the Kriging interpolation method, let Z(x) be the regionalized variable at point x, and it is second-order stationary. Z(xi) (i = 1, 2, ..., n) is defined at point x. i The above. Now we want to estimate the regionalized variable Z(x0) at point x0, and the estimator Z'(x0) used is:

[0050]

[0051] Equation (1) is a linear combination of n values. The advantage of the Kriging interpolation method is that it can ensure that the estimator is unbiased and minimize the estimation variance σ. E 2 Given the given conditions, calculate the n weight coefficients.

[0052]

[0053] Under unbiased conditions, minimizing the estimated variance is a problem of finding conditional extrema, which requires the use of the Lagrange multiplier method.

[0054]

[0055] Here, μ is the Lagrange multiplier, and F is the n weight coefficients λ. i An (n+1)-ary function of μ. Through F with respect to λ i The partial derivatives with respect to μ are:

[0056]

[0057] C ik Z(x) i ) and Z(x k The covariance of C) ik =E[Z i Z k ]-(E[Z]) 2 Further written in matrix form:

[0058]

[0059] The coefficient matrix on the left side of equation (5) is constructed using the XY coordinates of four actual borehole points and the elevation of a certain soil layer. Then, the coefficient vector on the right side is constructed using the covariance values ​​of the virtual borehole points and the actual borehole points. The weight coefficient λ of the virtual borehole points is calculated. i Finally, the elevation of the soil layer at the virtual borehole point is calculated using equation (1).

[0060] As shown in the table below Figure 2By interpolating the data of the four actual borehole points, the elevation of the virtual borehole point 1 (160.5, -90) can be calculated to be -17.05m.

[0061] ZC1 0 0 -15.7 mudstone ZC2 321 -176 -6.7 mudstone ZC3 309 0 -16.0 mudstone ZC4 0 -180 -29.8 mudstone

[0062] S14: Based on the physical parameters of each soil layer at the actual borehole points, assign values ​​to the physical parameters of each soil layer at the virtual borehole points (the Kriging interpolation method can be used to estimate the values ​​based on the physical parameters of each soil layer at the actual borehole points, i.e., replace the regional variable Z in S13 with the physical parameter values ​​of the soil layers). Combined with the development of the system platform, construct a three-dimensional geological model of the region.

[0063] like Figure 3 The diagram shows a flowchart of a method for predicting surface settlement during subway tunnel construction based on a three-dimensional geological model and numerical analysis. This method provides accurate and reliable surface settlement prediction data for subway tunnel alignment and construction scheme optimization.

[0064] S21: Based on the preliminary design conditions and preliminary selection of the subway tunnel, establish the finite element model of the subway tunnel using general or professional subway tunnel finite element modeling software;

[0065] S22: Considering the first option for subway tunnel alignment, extract soil layer parameters from the constructed regional three-dimensional geological model and assign values ​​to the physical properties of the soil layers along the subway tunnel.

[0066] S23: Based on the current route selection, investigate the surface settlement variation patterns under different excavation methods, excavation depths, advances, and cross-sections of the subway tunnel;

[0067] S24: Change the alignment of the subway tunnel, repeat S22 and S23, and analyze the influence of different excavation methods and other factors under the alignment of the subway tunnel.

[0068] S25: Based on the numerical simulation results of different subway tunnel alignments and construction schemes, the subway tunnel alignments and their corresponding construction schemes are ranked according to the minimum surface settlement. Furthermore, the final subway tunnel alignment and its construction scheme are determined based on the economic evaluation, social benefit evaluation, and passenger capacity of different subway tunnel alignments.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for predicting surface subsidence of subway tunnels in mountainous cities based on a three-dimensional geological model, characterized by: The method includes the following steps: S1: Establish a regional three-dimensional geological model based on spatial statistical analysis; specifically: S11: Collect actual borehole data and geological exploration information, construct a table of stratigraphic and soil physical and mechanical parameters of actual borehole points, and classify soil layers according to lithological description and sedimentary age parameters, classifying the same strata into the same stratigraphic type; S12: Determine the number and distribution of virtual boreholes based on the accuracy of the regional three-dimensional geological model construction; S13: Fit the soil layer elevation values ​​of the virtual borehole points based on the Kriging interpolation method and the actual soil layer elevation values ​​of the borehole points. In the Kriging interpolation method, let Z ( x ) is a point x The regionalized variable is second-order stationary; It is defined at a point On, i =1,2,..., n Point Regionalized variables at the location The estimation is performed, and the estimator used is... for: (1) Equation (1) is n A linear combination of values, For each known point x i Place Z ( x i The weighting coefficients; the Kriging interpolation method is used to ensure that the estimator is unbiased and that the estimated variance is within a certain range. Find the minimum value. n Weight coefficients : (2) In equation (2), E [.] represents the expected value; under unbiased conditions, the Lagrange multiplier method is used to minimize the estimation variance: (3) µ If it is a Lagrange multiplier, then F yes n Weight coefficients λ i and µ of( n +1) Metafunction; through F right λ i and µ The partial derivatives are: (4) C ik for Z ( x i )and Z ( x k The covariance of ) C ik = E [ Z i Z k ]-( E [ Z ]) 2 Written in matrix form: (5) The coefficient matrix on the left side of equation (5) is constructed using the XY coordinates and soil elevation values ​​of four actual borehole points. Then, the coefficient vector on the right side is constructed using the covariance values ​​of the virtual borehole points and the actual borehole points. The weight coefficients of the virtual borehole points are then calculated. λ i Finally, the elevation value of the soil layer at the virtual borehole point is calculated using equation (1). S14: Based on the physical parameters of each soil layer at the actual borehole points, assign values ​​to the physical parameters of each soil layer at the virtual borehole points, and construct a three-dimensional geological model of the region in conjunction with the development of the system platform; S2: Prediction of surface settlement during subway tunnel construction based on the established three-dimensional geological model and numerical analysis methods; specifically: S21: Based on the design conditions and selection of the subway tunnel, a finite element model of the subway tunnel is established using subway tunnel finite element modeling software; S22: Consider one of the subway tunnel route selections, extract soil layer parameters from the constructed regional three-dimensional geological model, and assign values ​​to the physical properties of the soil layers along the subway tunnel route; S23: Based on the current route selection, conduct research on the surface settlement variation patterns of subway tunnels under different excavation methods, different excavation depths, different advances, and different cross-sectional factors; S24: Change the alignment of the subway tunnel, repeat S22 and S23, and analyze the influence of different excavation methods under the alignment of the subway tunnel. S25: Based on the numerical simulation results of different subway tunnel alignments and construction schemes, the subway tunnel alignments and their corresponding construction schemes are ranked according to the minimum surface settlement.

2. The method for predicting surface subsidence of subway tunnels in mountainous cities based on a three-dimensional geological model according to claim 1, characterized in that: The header information of the soil physical and mechanical parameters table includes borehole number, relative elevation of the top layer, relative elevation of the bottom layer, rock name, X coordinate, Y coordinate, longitude, latitude, elevation, natural unit weight, natural compressive strength, saturated compressive strength, shear strength index, and average tensile strength.

3. The method for predicting surface subsidence of subway tunnels in mountainous cities based on a three-dimensional geological model according to claim 2, characterized in that: The assignment of the physical parameters of each soil layer at the virtual borehole point is specifically carried out by estimating the physical parameters of each soil layer at the actual borehole point using the Kriging interpolation method, and replacing the regional variable Z in S13 with the physical parameter values ​​of the soil layer.

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