A method for predicting river bottom settlement of shield tunneling under a river

Abstract

The application discloses a kind of river bottom settlement prediction methods of shield tunneling underpass river, belong to underpass river settlement prediction field, comprising the following steps: S1, geological parameter acquisition: before tunnel construction, obtain the geological parameters of tunnel along line and underpass river river bottom;S2, three-dimensional geological modeling: based on the geological parameters collected in step S1, three-dimensional geological model of tunnel along line and underpass river river bottom is established using three-dimensional geological modeling software;S3, construction parameter setting: according to tunnel construction scheme, determine the construction parameters of shield underpass river;S4, numerical simulation: three-dimensional geological model and construction parameters are input into finite element analysis software, the soil deformation under the condition of considering groundwater seepage is numerically simulated, and the settlement of river bottom in the construction process is predicted.The above-mentioned river bottom settlement prediction method of shield tunneling underpass river can predict the settlement of river bottom in real time and accurately, provide scientific basis for shield construction, and ensure the smooth progress of tunnel construction.

Description

Technical Field

[0001] This invention relates to the field of riverbed settlement prediction technology, and in particular to a method for predicting riverbed settlement during shield tunneling. Background Technology

[0002] With the accelerating pace of urbanization, the development and utilization of underground space has become an important direction for sustainable urban development. Tunnel engineering, as a key component of modern urban transportation and infrastructure construction, is increasingly important. However, the complexity and unique characteristics of tunnel engineering demand extremely high levels of safety and stability, especially during construction under complex geological conditions such as river crossings. In this context, the prediction and control of riverbed subsidence becomes a crucial technical challenge.

[0003] Especially when tunnel boring machines (TBMs) are crossing riverbeds, the complex geological conditions and abundant groundwater can easily cause riverbed subsidence during construction. This subsidence not only affects the stability and safety of the tunnel but can also lead to serious riverbed deformation. Therefore, accurately predicting and controlling riverbed subsidence has become a significant technical challenge in TBM tunnel construction.

[0004] Currently, commonly used settlement prediction methods mainly include geological exploration and numerical simulation. Geological exploration obtains detailed information about underground soil and rock layers through drilling, ground-penetrating radar, and other means, while numerical simulation uses computer software to simulate and analyze geological conditions and construction processes. Although these methods can provide valuable data support to some extent, they still have the following limitations:

[0005] 1. The accuracy of geological exploration and numerical simulation is often not high enough, making it difficult to accurately reflect the complexity and variability of actual geological conditions;

[0006] 2. The inability to monitor and dynamically adjust the construction process in real time may lead to significant discrepancies between the predicted results and the actual situation, thereby affecting the safety and efficiency of construction. Summary of the Invention

[0007] The purpose of this invention is to provide a method for predicting riverbed settlement during shield tunneling under rivers, thereby solving the aforementioned technical problems.

[0008] To achieve the above objectives, this invention provides a method for predicting riverbed settlement during shield tunneling under rivers, comprising the following steps:

[0009] S1. Geological parameter acquisition: Before tunnel construction, acquire geological parameters along the tunnel route and the riverbed beneath it;

[0010] S2. Three-dimensional geological modeling: Based on the geological parameters collected in step S1, a three-dimensional geological model of the tunnel route and the riverbed beneath it is established using three-dimensional geological modeling software.

[0011] S3. Construction Parameter Setting: Determine the construction parameters for the shield tunneling under the river based on the tunnel construction plan;

[0012] S4. Numerical simulation: Input the three-dimensional geological model and construction parameters into the finite element analysis software to perform numerical simulation of soil deformation under groundwater seepage conditions and predict the settlement of the riverbed during construction.

[0013] Preferably, the geological parameters mentioned in step S1 include soil and rock physical and mechanical parameters, borehole data, ground-penetrating radar images, and groundwater level distribution information. The soil and rock physical and mechanical parameters include soil and rock strength, permeability coefficient, soil and rock type, elastic modulus, density, and water content. The borehole data includes the location, depth, lithology, and thickness of the borehole. The groundwater level distribution information includes the depth and distribution of the groundwater level.

[0014] Preferably, step S2 specifically includes the following steps:

[0015] S21. Import the geotechnical physical and mechanical parameters, borehole data, geological structure information, and groundwater level information into the 3D geological modeling software;

[0016] S22. Construct a three-dimensional geological framework for the shield tunnel to pass under the strata along the river;

[0017] S221. Based on borehole data and ground-penetrating radar images, determine the location and morphology of the stratigraphic interface and obtain a two-dimensional profile of the stratigraphic interface.

[0018] S222. Use inverse distance weighted interpolation to generate a 3D mesh of the formation interface;

[0019] S223. Based on the three-dimensional mesh of the stratigraphic interface, determine the boundary and thickness of the stratigraphy, establish the three-dimensional framework of the geological body, and obtain the three-dimensional geological framework.

[0020] S23. Assign geological parameters to the strata of the three-dimensional geological framework, and use Kriging interpolation to extend the parameter values ​​in the strata to the entire strata to obtain a three-dimensional geological model.

[0021] S24. Model calibration and verification;

[0022] S25. Visualization results of generating a three-dimensional geological model.

[0023] Preferably, step S221 specifically includes the following steps:

[0024] S2211. Based on borehole data, determine the stratigraphic interface depth and soil type at each borehole location, and plot each borehole data on a map to form a preliminary stratigraphic interface distribution map.

[0025] S2212. Using image processing technology, identify reflected wave signals in ground-penetrating radar images, determine the location of stratigraphic interfaces, and project the identified stratigraphic interface locations onto a map.

[0026] S22121. Image preprocessing: Gaussian filtering is used to remove noise from the ground-penetrating radar image, and then histogram equalization is used to enhance the contrast of the ground-penetrating radar image.

[0027] S22122. Use the Sobel operator to extract the edges of ground-penetrating radar images and identify reflected wave signals;

[0028] S221221, Calculate the horizontal gradient Gx and vertical gradient Gx of the ground-penetrating radar image. y :

[0029]

[0030] In the formula, R represents the radius of the convolution kernel; (x+o,y+j) represents the coordinates of a neighboring point around the current pixel (x,y); K x (o,j) represents the horizontal kernel value of the Sobel operator at position (o,j); K y (o,j) represents the kernel value of the Sobel operator in the vertical direction at position (o,j);

[0031] S221222, Calculate the gradient magnitude M(x,y) and direction θ(x,y):

[0032]

[0033] S221223. Compare the gradient magnitude of each pixel and its 8 neighboring pixels. If the gradient magnitude of a pixel is not the maximum value in its direction, set its gradient magnitude to 0 to obtain a local maximum value.

[0034] S221224. Determine the final edge points using dual-threshold detection and edge connection: Set a high threshold T. high and low threshold T low And the gradient magnitude is greater than the high threshold T high Pixels that are not selected as edge points are retained, and gradient magnitudes that fall between the high threshold T are selected. high and low threshold T low If there are pixels between and within its 8-neighborhood that retain edge points, then the pixels that are finally retained are connected to obtain the final edge.

[0035] S22123. Use Fourier transform to analyze the frequency components, analyze the waveform characteristics of the reflected wave signal, and determine the location of the formation interface:

[0036]

[0037] In the formula, F(u) represents the frequency domain signal; f(x,y) represents the line-of-sight signal; M and N represent the width and height of the ground-penetrating radar image, respectively; e -j2π(ux / M+vy / N) Represents a complex exponential function;

[0038] S22124. Use support vector machines to identify the formation interface patterns to be predicted:

[0039]

[0040] In the formula, α q Represents Lagrange multipliers; y q κ(x) represents the label of the q-th sample. q (x) represents the kernel function, and b is the bias term;

[0041] S22125. Determine the location of the stratigraphic interface: Convert the location of the identified reflected wave signal from the ground-penetrating radar image coordinates to the actual depth:

[0042]

[0043] In the formula, Z0 represents the depth of the known point; v represents the radar wave velocity; and t represents the total time.

[0044] S2213. Based on the determined location of the stratigraphic interface and the preliminary distribution map of the stratigraphic interface, draw a two-dimensional cross-sectional view of the stratigraphic interface.

[0045] Preferably, in step S222, the area to be predicted is divided into grids, the value of each grid node is calculated using the inverse distance weighted interpolation algorithm, and then the values ​​of all grid nodes are connected to form a continuous stratigraphic interface, thereby obtaining a three-dimensional grid of the stratigraphic interface. The inverse distance weighted interpolation expression is as follows:

[0046]

[0047] In the formula, Z i (x p ) represents the depth value of the stratum at grid node i; This represents the distance from a known point to grid node i; n represents the total number of grid points.

[0048] Preferably, step S223 specifically includes the following steps:

[0049] S2231. Based on the three-dimensional mesh of the stratigraphic interface, determine the boundary mesh point I of each stratigraphic layer;

[0050] S2232. Calculate the thickness of each stratum:

[0051] T I =Z I+1 -Z I

[0052] In the formula, T I Z represents the thickness of the I-th stratum; I+1 Z represents the bottom depth of the (I+1)th stratum; I Indicates the bottom depth of the I-th stratum;

[0053] S2233. Integrate stratigraphic boundary and thickness data to construct a complete three-dimensional geological framework.

[0054] Preferably, step S23 specifically includes the following steps:

[0055] S231. Calculate the semivariogram:

[0056]

[0057] In the formula, γ(h) represents the value of the variogram; h represents the distance between two data points; N(h) represents the number of data points at a distance of h; z(x) represents the number of data points at a distance of h; and z(x) represents the number of data points at a distance of h. i ) and z(x i +h) represents data point x i and data point x i +h geological parameter values;

[0058] S232. Establish the Kriging equation: Let the known points be (x1, x2, ..., x...). n The observation points are (z(x1), z(x2), ..., z(x)). n Then we have:

[0059]

[0060] In the formula, ω i Indicates the weighting coefficient;

[0061] S233. Solving the Kriging equation yields the estimated geological parameters of unknown points. This allows us to extend the geological parameter values ​​of known points to the entire stratum. This method can fully utilize existing geological data, improve the accuracy and reliability of geological parameter estimation, and thus enhance the precision and reliability of the three-dimensional geological model.

[0062] Preferably, step S25 specifically includes the following steps:

[0063] S251. Calculate the error between ground-penetrating radar images and borehole data based on a three-dimensional geological model, and compare the calculated error with a set threshold to check the consistency of the stratigraphic interface.

[0064] S252. To minimize the prediction error, a genetic algorithm is used to optimize the three-dimensional geological model.

[0065] Preferably, the construction parameters mentioned in step S3 include the tunnel boring machine's advance speed, excavation chamber pressure, total thrust, cutterhead rotation speed, grouting volume, and grouting pressure.

[0066] Preferably, the finite element analysis software mentioned in step S4 is ABAQUS or PLAXIS;

[0067] Darcy's Law describes the flow of groundwater: In the formula, q * The pore water seepage velocity is represented by k, the permeability coefficient is represented by h, and (x,y,z) represents the spatial coordinates.

[0068] Soil deformation is described using a viscoelastic-plastic model, where the stress-strain relationship is expressed as follows:

[0069]

[0070] In the formula, σ represents the stress tensor; D represents the elastic modulus tensor; ε represents the total strain tensor; and η represents the viscosity coefficient. ξ represents the strain rate tensor; ε represents the plastic hardening modulus; p Represents the plastic strain tensor; Represents the initial plastic strain tensor;

[0071] The rules for plastic flow are as follows:

[0072]

[0073] In the formula, λ represents the plastic strain rate tensor; λ represents the plastic multiplier; f represents the yield function.

[0074] The expression for the yield function is as follows:

[0075]

[0076] In the formula, s represents the deviatoric stress tensor, and I represents the unit tensor; g(p) represents the plastic potential function; p represents the mean stress, and

[0077] The hardening principle is as follows:

[0078]

[0079] In the formula, λ represents the rate of change of the plastic potential function; h(λ) represents the hardening function, used to describe the effect of plastic deformation on the yield surface;

[0080] Fluid-structure interaction analysis:

[0081]

[0082]

[0083] σ'=σ-uI

[0084] In the formula, n represents porosity; S represents fluid saturation. The pore water pressure represents the rate of change; k represents the permeability coefficient. σ' represents the gradient of pore water pressure; σ' represents the effective stress tensor; u represents the pore water pressure.

[0085] Therefore, the beneficial effects of the above-mentioned method for predicting riverbed settlement during shield tunneling under rivers are as follows:

[0086] 1. Before tunnel construction, the geological parameters of the tunnel route and the riverbed beneath it are obtained to ensure the accuracy and reliability of the basic data for subsequent modeling and analysis. At the same time, through detailed geological surveys, the distribution, physical and mechanical properties and hydrological conditions of the underground soil layers can be understood more accurately, thereby improving the accuracy of riverbed settlement prediction.

[0087] 2. 3D modeling can more realistically reflect the complexity of underground structures, including the distribution and interrelationships of different soil layers, providing a more accurate geometric basis for subsequent numerical simulations;

[0088] 3. Based on the tunnel construction plan, construction parameters affecting riverbed settlement were set to ensure that the input parameters of the numerical simulation were consistent with the actual construction conditions, thereby improving the reliability of the simulation results. Furthermore, based on the results of the numerical simulation, the construction plan can be optimized, the optimal construction parameters and construction sequence can be selected, the impact of construction on the surrounding environment can be controlled, and construction efficiency and economy can be improved.

[0089] 4. Through accurate settlement prediction, potential risk points can be identified in advance, and corresponding engineering measures (such as early reinforcement measures and further parameter optimization for high-risk sections) can be taken to improve the safety and reliability of the project, help reduce the disturbance to the river ecosystem, protect the water environment, and meet the requirements of sustainable development.

[0090] In summary, by comprehensively utilizing technologies such as geological surveys, 3D modeling, construction parameter setting, and numerical simulation, this invention can more accurately and comprehensively predict the settlement of the riverbed under the tunnel during construction, thereby improving the safety, economy, and environmental friendliness of the project.

[0091] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0092] Figure 1 This is a flowchart of a method for predicting riverbed settlement during shield tunneling under a river, according to the present invention. Detailed Implementation

[0093] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use. They are used only for the convenience of describing the 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, they should not be construed as limitations on the invention. In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," and "connect" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0094] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0095] like Figure 1 As shown, a method for predicting riverbed settlement during shield tunneling under a river includes the following steps:

[0096] S1. Geological parameter acquisition: Before tunnel construction, acquire geological parameters along the tunnel route and the riverbed beneath it;

[0097] The geological parameters mentioned in step S1 include soil and rock physical and mechanical parameters, borehole data, ground-penetrating radar images, and groundwater level distribution information. Among them, soil and rock physical and mechanical parameters include soil and rock strength, permeability coefficient, soil and rock type, elastic modulus, density, and water content. Borehole data includes the location, depth, lithology, and thickness of the borehole. Groundwater level distribution information includes the depth and distribution of the groundwater level.

[0098] S2. Three-dimensional geological modeling: Based on the geological parameters collected in step S1, a three-dimensional geological model of the tunnel route and the riverbed beneath it is established using three-dimensional geological modeling software.

[0099] Step S2 specifically includes the following steps:

[0100] S21. Import the geotechnical physical and mechanical parameters, borehole data, geological structure information, and groundwater level distribution information into the 3D geological modeling software;

[0101] S22. Construct a three-dimensional geological framework along the tunnel route and under the riverbed;

[0102] S221. Based on borehole data and ground-penetrating radar images, determine the location and morphology of the stratigraphic interface and obtain a two-dimensional profile of the stratigraphic interface.

[0103] Step S221 specifically includes the following steps:

[0104] S2211. Based on borehole data, determine the stratigraphic interface depth and soil type at each borehole location, and plot each borehole data on a map to form a preliminary stratigraphic interface distribution map.

[0105] S2212. Using image processing technology, identify reflected wave signals in ground-penetrating radar images, determine the location of stratigraphic interfaces, and project the identified stratigraphic interface locations onto a map.

[0106] S22121. Image preprocessing: Gaussian filtering is used to remove noise from the ground-penetrating radar image, and then histogram equalization is used to enhance the contrast of the ground-penetrating radar image.

[0107] S22122. Use the Sobel operator to extract the edges of ground-penetrating radar images and identify reflected wave signals;

[0108] S221221, Calculate the horizontal gradient Gx and vertical gradient Gx of the ground-penetrating radar image. y :

[0109]

[0110] In the formula, R represents the radius of the convolution kernel; (x+o,y+j) represents the coordinates of a neighboring point around the current pixel (x,y); K x (o,j) represents the horizontal kernel value of the Sobel operator at position (o,j); K y (o,j) represents the kernel value of the Sobel operator in the vertical direction at position (o,j);

[0111] S221222, Calculate the gradient magnitude M(x,y) and direction θ(x,y):

[0112]

[0113] S221223. Compare the gradient magnitude of each pixel and its 8 neighboring pixels. If the gradient magnitude of a pixel is not the maximum value in its direction, set its gradient magnitude to 0 to obtain a local maximum value.

[0114] S221224. Determine the final edge points using dual-threshold detection and edge connection: Set a high threshold T. high and low threshold T low And the gradient magnitude is greater than the high threshold T high Pixels that are not selected as edge points are retained, and gradient magnitudes that fall between the high threshold T are selected. high and low threshold T low If there are pixels between and within its 8-neighborhood that retain edge points, then the pixels that are finally retained are connected to obtain the final edge.

[0115] S22123. Use Fourier transform to analyze the frequency components, analyze the waveform characteristics of the reflected wave signal, and determine the location of the formation interface:

[0116]

[0117] In the formula, F(u) represents the frequency domain signal; f(x,y) represents the time domain signal; M and N represent the width and height of the ground-penetrating radar image, respectively; e -j2π(ux / M+vy / N) Represents a complex exponential function;

[0118] S22124. Use support vector machines to identify the formation interface patterns to be predicted:

[0119]

[0120] In the formula, α q Represents Lagrange multipliers; y q κ(x) represents the label of the q-th sample. q (x) represents the kernel function, and b is the bias term;

[0121] S22125. Determine the location of the stratigraphic interface: Convert the location of the identified reflected wave signal from the ground-penetrating radar image coordinates to the actual depth:

[0122]

[0123] In the formula, Z0 represents the depth of the known point; v represents the radar wave velocity; and t represents the total time.

[0124] S2213. Based on the determined location of the stratigraphic interface and the preliminary distribution map of the stratigraphic interface, draw a two-dimensional cross-sectional view of the stratigraphic interface.

[0125] S222. Use inverse distance weighted interpolation to generate a 3D mesh of the formation interface;

[0126] In step S222, the area to be predicted is divided into grids, and the value of each grid node is calculated using the inverse distance weighted interpolation algorithm. Then, the values ​​of all grid nodes are connected to form a continuous stratigraphic interface, thus obtaining a three-dimensional grid of the stratigraphic interface. The inverse distance weighted interpolation expression is as follows:

[0127]

[0128] In the formula, Z i (x p ) represents the depth value of the stratum at grid node i; This represents the distance from a known point to grid node i; n represents the total number of grid points.

[0129] S223. Based on the three-dimensional mesh of the stratigraphic interface, determine the boundary and thickness of the stratigraphy, establish the three-dimensional framework of the geological body, and obtain the three-dimensional geological framework.

[0130] Step S223 specifically includes the following steps:

[0131] S2231. Based on the three-dimensional mesh of the stratigraphic interface, determine the boundary mesh point I of each stratigraphic layer;

[0132] S2232. Calculate the thickness of each stratum:

[0133] T I =Z I+1 -Z I

[0134] In the formula, T I Z represents the thickness of the I-th stratum; I+1 Z represents the bottom depth of the (I+1)th stratum; I Indicates the bottom depth of the I-th stratum;

[0135] S2233. Integrate stratigraphic boundary and thickness data to construct a complete three-dimensional geological framework.

[0136] S23. Assign geological parameters to the strata of the three-dimensional geological framework, and use Kriging interpolation to extend the parameter values ​​in the strata to the entire strata to obtain a three-dimensional geological model.

[0137] Step S23 specifically includes the following steps:

[0138] S231. Calculate the semivariogram:

[0139]

[0140] In the formula, γ(h) represents the value of the variogram; h represents the distance between two data points; N(h) represents the number of data points at a distance of h; z(x) represents the number of data points at a distance of h; and z(x) represents the number of data points at a distance of h. i ) and z(x i +h) represents data point x i and data point x i +h geological parameter values;

[0141] S232. Establish the Kriging equation: Let the known points be (x1, x2, ..., x...). n The observation points are (z(x1), z(x2), ..., z(x)). n Then we have:

[0142]

[0143] In the formula, ω i Indicates the weighting coefficient;

[0144] S233. Solve the Kriging equation to obtain the estimated geological parameters of the unknown points.

[0145] S24. Model calibration and verification;

[0146] S25. Visualization results of generating a three-dimensional geological model.

[0147] In this embodiment, the visualization function of 3D geological modeling software is used to generate a 3D view of the geological body. Different geological parameters are represented by different colors and transparency. Geological profiles and geological plan views are also generated to aid in the analysis and interpretation of geological conditions.

[0148] Step S25 specifically includes the following steps:

[0149] S251. Calculate the error between ground-penetrating radar images and borehole data based on a three-dimensional geological model, and compare the calculated error with a set threshold to check the consistency of the stratigraphic interface.

[0150] S252. To minimize the prediction error, a genetic algorithm is used to optimize the three-dimensional geological model.

[0151] S3. Setting construction parameters: Based on the tunnel construction plan, set the construction parameters that affect riverbed settlement;

[0152] The construction parameters mentioned in step S3 include the tunnel boring machine's advance speed, excavation chamber pressure, total thrust, cutterhead rotation speed, grouting volume, and grouting pressure.

[0153] S4. Numerical simulation: Input the three-dimensional geological model and construction parameters into the finite element analysis software, and conduct numerical simulation considering groundwater flow and soil deformation to predict the settlement of the riverbed during construction.

[0154] The finite element analysis software mentioned in step S4 is ABAQUS or PLAXIS;

[0155] Darcy's Law describes the flow of groundwater: In the formula, q represents the seepage velocity, k represents the permeability coefficient, h represents the hydraulic head, and (x,y,z) represents the spatial coordinates;

[0156] The finite element analysis software mentioned in step S4 is ABAQUS or PLAXIS;

[0157] Darcy's Law describes the flow of groundwater: In the formula, q * The pore water seepage velocity is represented by k, the permeability coefficient is represented by h, and (x,y,z) represents the spatial coordinates.

[0158] Soil deformation is described using a viscoelastic-plastic model, where the stress-strain relationship is expressed as follows:

[0159]

[0160] In the formula, σ represents the stress tensor; D represents the elastic modulus tensor; ε represents the total strain tensor; and η represents the viscosity coefficient. ξ represents the strain rate tensor; ε represents the plastic hardening modulus; p Represents the plastic strain tensor; Represents the initial plastic strain tensor;

[0161] The rules for plastic flow are as follows:

[0162]

[0163] In the formula, λ represents the plastic strain rate tensor; λ represents the plastic multiplier; f represents the yield function.

[0164] The expression for the yield function is as follows:

[0165]

[0166] In the formula, s represents the deviatoric stress tensor, and I represents the unit tensor; g(p) represents the plastic potential function; p represents the mean stress, and

[0167] The hardening principle is as follows:

[0168]

[0169] In the formula, λ represents the rate of change of the plastic potential function; h(λ) represents the hardening function, used to describe the effect of plastic deformation on the yield surface;

[0170] Fluid-structure interaction analysis:

[0171]

[0172] σ'=σ-uI

[0173] In the formula, n represents porosity; S represents fluid saturation. The pore water pressure represents the rate of change; k represents the permeability coefficient. σ' represents the gradient of pore water pressure; σ' represents the effective stress tensor; u represents the pore water pressure.

[0174] Verification Example

[0175] In this verification example, shield tunneling was conducted over the Fen River. The geological conditions are as follows: The section passing under the Fen River traverses strata consisting of 2-5-2 medium sand (yellowish-brown, saturated, medium-dense, relatively uniform particles, poor gradation; exposed in all boreholes except for specific ones, layer thickness 1.1–14.5 m, average thickness 4.88 m) and 2-2-12 clayey silt (yellowish-brown, medium-dense, containing mica flakes, low dry strength, low toughness; exposed in all boreholes within the exploration area, layer thickness 0.8 m). 1.0-12.0m thick, with an average thickness of 4.24m), 2-1-22 silty clay (yellowish-brown, plastic, medium dry strength, medium toughness, locally containing lumpy silt. Exposed by boreholes within the exploration area, layer thickness 1.0-6.4m, average thickness 3.22m), 2-4-3 fine sand (grayish-brown, saturated, dense, relatively uniform particles, poor gradation. Exposed only by specific boreholes within the exploration area, layer thickness 3.7-7.6m, average thickness 5.65m).

[0176] The hydrological conditions are as follows: 1. Groundwater types: Loose pore water: mainly found in shallow clay, silt, and sand layers. The water volume is relatively small, mainly recharged by vertical infiltration of atmospheric precipitation, and discharged mostly through evaporation. Water level changes are greatly affected by seasonal and climatic conditions. Confined water: found in silt and sand layers at or below the second terrestrial stratum, exhibiting confined characteristics. 2. Geological environment of the proposed section: The proposed section needs to pass under the Fen River, and the survey period coincides with the dry season. The upper reaches of the Fen River have been dammed for maintenance, with only a small amount of water remaining in the middle of the riverbed. 3. Measured Water Level Data: During the exploration, the stable groundwater level in the boreholes was measured. The results showed that the stable water level depth ranged from 0.00 to 7.20 meters, with corresponding stable water level elevations of 779.57 to 782.18 meters. The Fen River passes under this section for approximately 320 meters, with a relatively straight channel and flat riverbed, requiring no navigation. The average water depth is approximately 1.0 to 1.5 meters year-round. 4. Hydrological Characteristics and Influencing Factors: The pore water in the site is mainly replenished by atmospheric precipitation, and water level changes are closely related to seasonal and climatic conditions. Especially during the dry season, when river flow decreases, the groundwater level is significantly affected.

[0177] The tunnel boring machine (TBM) was used to excavate under the Fen River. During construction, the earth chamber pressure was set at 1.25-1.30 bar, the total thrust at 15000-18000 kN, the torque during tunneling at 1400-2900 kN, the advance speed at 0-50 mm / min, the cutterhead rotation speed at 1.0-1.5 rpm, and the synchronous grouting volume at 7.5 m³. 3 / ring, secondary grouting is performed 5-10 rings after the shield tail has disengaged, with a grouting pressure of 0.3-0.5 MPa and a secondary grouting depth of 0.5 m. 3 / ring-1.5m 3 / ring. High-quality cement mortar is used as the synchronous grouting material, and the grouting volume is matched with the tunneling speed. The grouting volume is 150%–250% of the theoretical building void, which helps reduce ground loss and control ground settlement. In this embodiment, the tunnel boring machine's advance speed is set at 15m–30m per day; the tunneling chamber pressure is 1.5bar–2.5bar for soft soil layers; and the grouting volume is 6.5m of synchronous grouting. 3 / ring-7.5m 3 The maximum grouting pressure was 1 MPa. During this process, real-time monitoring of the riverbed settlement was conducted, yielding a maximum settlement value of 25.3 mm. The maximum settlement value obtained using this invention was 25.0 mm, which is close to the measured settlement, with the error within an acceptable range. This demonstrates the effectiveness and accuracy of the invention.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. 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 still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for predicting riverbed settlement during shield tunneling under a river, characterized in that: Includes the following steps: S1. Geological parameter acquisition: Before tunnel construction, acquire geological parameters along the tunnel route and the riverbed beneath it; S2. Three-dimensional geological modeling: Based on the geological parameters collected in step S1, a three-dimensional geological model of the tunnel route and the riverbed beneath it is established using three-dimensional geological modeling software. Step S2 specifically includes the following steps: S21. Import the geotechnical physical and mechanical parameters, borehole data, geological structure information, and groundwater level distribution information into the 3D geological modeling software; S22. Construct a three-dimensional geological framework for the shield tunnel to pass under the strata along the river; S221. Based on borehole data and ground-penetrating radar images, determine the location and morphology of the stratigraphic interface and obtain a two-dimensional profile of the stratigraphic interface. Step S221 specifically includes the following steps: S2211. Based on borehole data, determine the stratigraphic interface depth and soil type at each borehole location, and plot each borehole data on a map to form a preliminary stratigraphic interface distribution map. S2212. Using image processing technology, identify reflected wave signals in ground-penetrating radar images, determine the location of stratigraphic interfaces, and project the identified stratigraphic interface locations onto a map. S22121. Image preprocessing: Gaussian filtering is used to remove noise from the ground-penetrating radar image, and then histogram equalization is used to enhance the contrast of the ground-penetrating radar image. S22122. Use the Sobel operator to extract the edges of ground-penetrating radar images and identify reflected wave signals; S221221, Calculate the horizontal gradient Gx and vertical gradient Gx of the ground-penetrating radar image. y : In the formula, R represents the radius of the convolution kernel; (x+o,y+j) represents the coordinates of a neighboring point around the current pixel (x,y); K x (o,j) represents the horizontal kernel value of the Sobel operator at position (o,j); K y (o,j) represents the kernel value of the Sobel operator in the vertical direction at position (o,j); S221222, Calculate the gradient magnitude M(x,y) and direction θ(x,y): S221223. Compare the gradient magnitude of each pixel and its 8 neighboring pixels. If the gradient magnitude of a pixel is not the maximum value in its direction, set its gradient magnitude to 0 to obtain a local maximum value. S221224. Determine the final edge points using dual-threshold detection and edge connection: Set a high threshold T. high and low threshold T low And the gradient magnitude is greater than the high threshold T high Pixels that are not selected as edge points are retained, and gradient magnitudes that fall between the high threshold T are selected. high and low threshold T low If there are pixels between and within its 8-neighborhood that retain edge points, then the pixels that are finally retained are connected to obtain the final edge. S22123. Use Fourier transform to analyze the frequency components, analyze the waveform characteristics of the reflected wave signal, and determine the location of the formation interface: In the formula, F(u) represents the frequency domain signal; f(x,y) represents the time domain signal; M and N represent the width and height of the ground-penetrating radar image, respectively; e -j2π(ux / M+vy / N) Represents a complex exponential function; S22124. Use support vector machines to identify the formation interface patterns to be predicted: In the formula, α q Represents Lagrange multipliers; y q κ(x) represents the label of the q-th sample. q (x) represents the kernel function, and b is the bias term; S22125. Determine the location of the stratigraphic interface: Convert the location of the identified reflected wave signal from the ground-penetrating radar image coordinates to the actual depth: In the formula, Z0 represents the depth of the known point; v represents the radar wave velocity; and t represents the total time. S2213. Based on the determined location of the stratigraphic interface and the preliminary distribution map of the stratigraphic interface, draw a two-dimensional cross-sectional view of the stratigraphic interface. S222. Use inverse distance weighted interpolation to generate a 3D mesh of the formation interface; In step S222, the area to be predicted is divided into grids, and the value of each grid node is calculated using the inverse distance weighted interpolation algorithm. Then, the values ​​of all grid nodes are connected to form a continuous stratigraphic interface, thus obtaining a three-dimensional grid of the stratigraphic interface. The inverse distance weighted interpolation expression is as follows: In the formula, Z i (x p ) represents the depth value of the stratum at grid node i; This represents the distance from a known point to grid node i; n represents the total number of grid points. S223. Based on the three-dimensional mesh of the stratigraphic interface, determine the boundary and thickness of the stratigraphy, establish the three-dimensional framework of the geological body, and obtain the three-dimensional geological framework. S23. Assign geological parameters to the strata of the three-dimensional geological framework, and use Kriging interpolation to extend the parameter values ​​in the strata to the entire strata to obtain a three-dimensional geological model. S24. Model calibration and verification; S25. Visualization results of generating a three-dimensional geological model; S3. Construction Parameter Setting: Determine the construction parameters for the shield tunneling under the river based on the tunnel construction plan; S4. Numerical simulation: Input the three-dimensional geological model and construction parameters into the finite element analysis software to perform numerical simulation of soil deformation under groundwater seepage conditions and predict the settlement of the riverbed during construction.

2. The method for predicting riverbed settlement during shield tunneling under a river as described in claim 1, characterized in that: The geological parameters mentioned in step S1 include soil and rock physical and mechanical parameters, borehole data, ground-penetrating radar images, and groundwater level distribution information. Among them, soil and rock physical and mechanical parameters include soil and rock strength, permeability coefficient, soil and rock type, elastic modulus, density, and water content. Borehole data includes the location, depth, lithology, and thickness of the borehole. Groundwater level distribution information includes the depth and distribution of the groundwater level.

3. The method for predicting riverbed settlement during shield tunneling under a river, as described in claim 2, is characterized in that: Step S223 specifically includes the following steps: S2231. Based on the three-dimensional mesh of the stratigraphic interface, determine the boundary mesh point I of each stratigraphic layer; S2232. Calculate the thickness of each stratum: T I =Z I+1 -Z I In the formula, T I Z represents the thickness of the I-th stratum; I+1 Z represents the bottom depth of the (I+1)th stratum; I Indicates the bottom depth of the I-th stratum; S2233. Integrate stratigraphic boundary and thickness data to construct a complete three-dimensional geological framework.

4. The method for predicting riverbed settlement during shield tunneling under a river, as described in claim 3, is characterized in that: Step S23 specifically includes the following steps: S231. Calculate the semivariogram: In the formula, γ(h) represents the value of the variogram; h represents the distance between two data points; N(h) represents the number of data points at a distance of h; z(x) represents the number of data points at a distance of h; and z(x) represents the number of data points at a distance of h. i ) and z(x i +h) represents data point x i and data point x i +h geological parameter values; S232. Establish the Kriging equation: Let the known points be (x1, x2, ..., x...). n The observation points are (z(x1), z(x2), ..., z(x)). n Then we have: In the formula, ω i Indicates the weighting coefficient; S233. Solve the Kriging equation to obtain the estimated geological parameters of the unknown points.

5. The method for predicting riverbed settlement during shield tunneling under a river, as described in claim 4, is characterized in that: Step S25 specifically includes the following steps: S251. Calculate the error between ground-penetrating radar images and borehole data based on a three-dimensional geological model, and compare the calculated error with a set threshold to check the consistency of the stratigraphic interface. S252. To minimize the prediction error, a genetic algorithm is used to optimize the three-dimensional geological model.

6. The method for predicting riverbed settlement during shield tunneling under a river, as described in claim 5, is characterized in that: The construction parameters mentioned in step S3 include the tunnel boring machine's advance speed, excavation chamber pressure, total thrust, cutterhead rotation speed, grouting volume, and grouting pressure.

7. The method for predicting riverbed settlement during shield tunneling under a river as described in claim 6, characterized in that: The finite element analysis software mentioned in step S4 is ABAQUS or PLAXIS; Darcy's Law describes the flow of groundwater: In the formula, q * The pore water seepage velocity is represented by k, the permeability coefficient is represented by h, and (x,y,z) represents the spatial coordinates. Soil deformation is described using a viscoelastic-plastic model, where the stress-strain relationship is expressed as follows: In the formula, σ represents the stress tensor; D represents the elastic modulus tensor; ε represents the total strain tensor; η represents the viscosity coefficient; ξ represents the strain rate tensor; ξ represents the plastic hardening modulus. ε p Represents the plastic strain tensor; Represents the initial plastic strain tensor; The rules for plastic flow are as follows: In the formula, λ represents the plastic strain rate tensor; λ represents the plastic multiplier; f represents the yield function. The expression for the yield function is as follows: In the formula, s represents the deviatoric stress tensor, and I represents the unit tensor; g(p) represents the plastic potential function; p represents the mean stress, and The hardening principle is as follows: In the formula, λ represents the rate of change of the plastic potential function; h(λ) represents the hardening function, used to describe the effect of plastic deformation on the yield surface; Fluid-structure interaction analysis: σ′=σ-uI In the formula, n represents porosity; S represents fluid saturation. The pore water pressure represents the rate of change; k represents the permeability coefficient. The gradient representing pore water pressure; σ ′ represents the effective stress tensor; u represents the pore water pressure.

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