Tunnel water inflow prediction method based on water-rich soft soil texture

Abstract

The invention relates to the technical field of tunnel geological exploration, and discloses a tunnel water inflow prediction method based on water-rich soft soil geology, comprising the following steps: S1, geological survey and data acquisition; s2, numerical simulation and prediction; s3, predicting the water inflow of the tunnel; s3.1, selecting an applicable prediction method according to a calculation result of the numerical analysis method; s3.2, establishing a numerical model and boundary conditions of stable water gushing of the tunnel; and S4, performing semi-data semi-empirical model on the tunnel water inflow: performing statistical analysis on a plurality of actual engineering cases, correcting some related parameters in a formula by utilizing an existing empirical formula, calculating a corresponding result, and then re-correcting a previous numerical simulation calculation result according to a result calculated by the empirical formula. According to the technical scheme, accurate prediction of the water-rich soft soil tunnel water gushing can be achieved, and meanwhile the method has the advantages of being low in cost, capable of saving time, convenient to operate and easy to update and maintain.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel geological detection, and in particular to a method for predicting water inflow in a tunnel based on water-rich soft soil. Background Art

[0002] Tunnel water inrush refers to the phenomenon that groundwater or surface water flows into the tunnel through cracks, faults, caves and other channels in the rock mass around the tunnel during tunnel construction or operation. Tunnel water inrush is an important risk in tunnel construction and operation. It not only affects the progress of the project and increases the construction cost, but also may cause safety accidents such as tunnel collapse, which in turn causes adverse consequences such as construction delays and economic losses, and even leads to the abandonment or relocation of the tunnel. In the prediction of tunnel water inrush, each method has its applicability and limitations. Most water-rich soft soil tunnels are located in mountainous and hilly areas with complex geological environments and large-scale weak interlayers, fault zones and other unfavorable geological conditions. Water inrush may cause the surrounding rock to loosen, increase the risk of collapse, endanger personnel safety, and make the relevant water inrush prediction more difficult. Therefore, we need to select appropriate prediction methods to achieve accurate prediction of water inrush in water-rich soft soil tunnels and reduce the impact of non-human factors on construction. At the same time, this study is of great significance to tunnel survey, design, construction and operation.

[0003] In the prediction and assessment of sudden water inrush in karst tunnels, many scholars have mainly conducted relevant research on model methods based on expert survey method, hierarchical analysis method and attribute mathematical theory. Although many scholars have achieved remarkable results in past research, there are still some limitations that cannot be ignored. First, many scholars tend to use hierarchical analysis method and expert survey method in their research. However, the input data based on these two methods are often highly subjective, which may affect the accuracy of the results to a certain extent under complex and changeable geological and engineering environments. Second, in the application process of attribute mathematical theory, the quantitative classification of evaluation indicators and the determination of weights are also affected by subjective factors, which may lead to unreasonable classification results and inaccurate evaluation results, thereby limiting the reliability and application value of the research results. Therefore, the current prediction and assessment methods for sudden water inrush in karst tunnels are not universal. Water-rich soft soil tunnels are usually located in mountainous and hilly areas, with extremely complex geological environments, low rock mass strength, and abundant groundwater. In this case, water inrush prediction is more difficult and complicated, and a single prediction method is obviously not applicable. Summary of the invention

[0004] The present invention aims to provide a method for predicting water inflow in a tunnel under water-rich soft soil geology, so as to achieve accurate prediction of water inflow in a water-rich soft soil tunnel.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for predicting water inflow in a tunnel under water-rich soft soil, comprising the following steps:

[0006] S1: Geological survey and data collection: collect relevant data and conduct preliminary analysis on the collected data to identify areas where water may flow and related risk factors, and establish compound rectangular flow weir monitoring stations, borehole water level monitoring stations and equip geological drilling rigs in areas where water may flow, so as to achieve real-time monitoring and collection of data;

[0007] S2: Numerical simulation and prediction: Based on the flow data, geological information and groundwater distribution obtained by the compound rectangular flow weir monitoring station, the borehole water level monitoring station and the geological drilling rig, the water inflow of the tunnel under normal conditions is calculated by numerical analysis method;

[0008] S3: Tunnel water inflow prediction;

[0009] S3.1: Select the appropriate prediction method based on the results of numerical analysis;

[0010] S3.2: Establish a numerical model and boundary conditions for stable water inrush in tunnels;

[0011] S4: Semi-data and semi-empirical model of tunnel water inflow: Through statistical analysis of multiple actual engineering cases, some relevant parameters in the formula are corrected using existing empirical formulas, and the corresponding results are calculated. Then, the results of the previous numerical simulation calculations are corrected based on the results of the empirical formulas.

[0012] Preferably, the numerical analysis method in S2 includes the following:

[0013] S2.1: Atmospheric precipitation infiltration method;

[0014] S2.2: Subsurface runoff modulus method;

[0015] S2.3: Groundwater dynamics method.

[0016] Preferably, the calculation formula of the atmospheric precipitation infiltration method in S2.1 is:

[0017] Q S =2.74α·W·A

[0018] A=L·B

[0019] Where: Q S is the water inflow of the tunnel under normal conditions (m 3 / d);

[0020] α is the precipitation infiltration coefficient;

[0021] A is the catchment area of ​​the water body that the tunnel crosses (km 2 );

[0022] L is the length of the tunnel through the water body (km);

[0023] B is the width of the tunnel water inrush section L on both sides (km);

[0024] W is the annual precipitation (mm).

[0025] Preferably, the determination of the underground runoff modulus method in S2.2 comprises the following steps:

[0026] S2.2.1: Determine the catchment area A of the study area;

[0027] S2.2.2: Calculate the annual average groundwater runoff modulus M 年 Or dry season groundwater runoff modulus M 枯 ;

[0028] S2.2.3: Calculate the normal water inflow Q of the tunnel S , calculated by the following formula:

[0029] Q S =M 年 ×A

[0030] Or use the dry season modulus approximation:

[0031] Q S ≈2.70×M 枯 ×A

[0032] Among them, Q S The normal water inflow when the tunnel passes through the water-bearing area, unit (m 3 / d);

[0033] S2.2.4: Predict the maximum inflow (Q max ), the calculation formula is as follows:

[0034] Q max =λ×M 年 ×A

[0035] or Q max ≈1.50×Q s

[0036] In the formula, Q max The maximum water inflow when the tunnel passes through a water-bearing section;

[0037] λ is the modulus ratio coefficient, which is 1.5;

[0038] Q s The normal water inflow when the tunnel passes through the water-bearing area, unit (m 3 / d);

[0039] S2.2.5: Consider the impact of topographic, geological and hydrogeological conditions on water inflow;

[0040] S2.2.6: Continuous monitoring and correction.

[0041] Preferably, the calculation formula of the groundwater dynamics method in S2.3 is:

[0042]

[0043] Where: Q S is the water inflow of the tunnel under normal conditions (m 3 / d);

[0044] L is the actual length of the tunnel through the water body (m);

[0045] K is the permeability coefficient of the water body (m / d);

[0046] H refers to the vertical thickness of the phreatic water body above the cave bottom (m);

[0047] h is the depth of the tunnel vault relative to the groundwater level above it (m);

[0048] R is the influence radius (m);

[0049] r is half of the tunnel cross-sectional width and is used to calculate the interaction area between the tunnel and the water body (m).

[0050] Preferably, the method for establishing the numerical model and boundary conditions of stable tunnel water inrush in S3.2 is: use ANSYS to build a model and then import it into FLACD, then use FLAC3D to perform planar simulation and three-dimensional simulation of the tunnel water inrush, and perform planar fluid-solid coupling simulation on several areas where water inrush may occur, to obtain the time-history curve of water inrush in each water inrush section, compare and preliminarily correct the previous results, and establish the numerical model and boundary conditions of stable tunnel water inrush.

[0051] Preferably, the preliminary correction method in S3.2 is: compare the predicted water inflow obtained by the numerical analysis method with the water inflow simulated by FLAC3D, and analyze the difference between the two; explore the possible reasons for the difference, such as simplification of geological models, selection of hydrogeological parameters, setting of boundary conditions, etc.; and make targeted modifications according to the reasons.

[0052] Preferably, the method for determining boundary conditions in S3.2 is: determining the boundary conditions of the model according to the hydrogeological conditions of the area where the tunnel is located, the boundary conditions include setting the groundwater level, determining the recharge amount, and simulating the discharge conditions. For complex hydrogeological conditions, mixed boundary conditions are adopted; according to the geological survey results and experimental data, the physical and mechanical parameters and hydrogeological parameters of the rock mass in the model are set; re-conducting tunnel excavation and seepage simulation in the adjusted FLAC3D model; and evaluating whether the stability and water inflow of the tunnel meet the design requirements.

[0053] Preferably, in S4, the semi-empirical and semi-numerical analysis model for water inflow prediction is:

[0054]

[0055] Wherein, fitting coefficients: c1 = 0.0325, c2 = 0.0544, c3 = 0.3007, c4 = 0.0352, γ = 0.0549, ω = 0.0243;

[0056] K is the permeability coefficient of the water body (m / d);

[0057] k is the permeability coefficient of the rock formation (m / d);

[0058] β is the Forchheimer coefficient in, is the porosity, K is the permeability of the water body, and τ is a resistance coefficient related to flow;

[0059] H is the vertical thickness of the phreatic water body above the cave bottom (m);

[0060] r is the equivalent circle radius of the tunnel section (m);

[0061] D is the width of the model, which is 200 times the radius of the tunnel;

[0062] Λ is a dimensionless parameter defined as

[0063] Compared with the prior art, the beneficial technical effects of this technical solution are:

[0064] (1) Improving prediction accuracy: This technical solution combines data analysis and empirical analysis to establish a semi-empirical and semi-numerical comprehensive prediction model to achieve accurate prediction of water inrush in water-rich soft soil tunnels.

[0065] (2) Save time and cost: The use of numerical simulation and groundwater dynamics analysis can reduce unnecessary field surveys and experiments and reduce costs.

[0066] (3) Convenient operation: The prediction method of the present invention can be automated through computer software, thereby improving work efficiency.

[0067] (4) Easy to update and maintain: With the update of data and the improvement of models, the prediction method of the present invention can be continuously improved to adapt to the ever-changing engineering needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is the ANSYS model diagram of the present invention;

[0069] Figure 2 This is the FLACD3D model diagram of the tunnel water inrush of the present invention;

[0070] Figure 3 This is the numerical model and boundary condition diagram of the stable water inflow of the tunnel according to the present invention. DETAILED DESCRIPTION

[0071] The following is further described in detail through specific implementation methods:

[0072] Example

[0073] A method for predicting water inflow in a tunnel under water-rich soft soil, comprising the following steps:

[0074] S1: Geological survey and data collection: Use 3D imaging tunnel geological advance forecasting instruments and laser rangefinders to detect the geological profile, hydrogeological conditions and karst development characteristics of the target area in advance, and collect relevant data by consulting materials and conducting field surveys; conduct preliminary analysis of the collected data to identify areas where water may flow and related risk factors, and establish compound rectangular flow weir monitoring stations, borehole water level monitoring stations and equip geological drilling rigs in areas where water may flow, so as to achieve real-time monitoring and collection of data;

[0075] S2: Numerical simulation and prediction: Based on the flow data, geological information and groundwater distribution obtained by the compound rectangular flow weir monitoring station, the borehole water level monitoring station and the geological drilling rig, the water inflow of the tunnel under normal conditions is calculated by numerical analysis method;

[0076] S2.1: Atmospheric precipitation infiltration method: Calculate using the following formula:

[0077] Q S =2.74α·W·A

[0078] A=L·B

[0079] Where: Q S is the water inflow of the tunnel under normal conditions (m 3 / d);

[0080] α is the precipitation infiltration coefficient, which is a parameter that quantifies the extent of precipitation infiltration into the underground aquifer;

[0081] A is the catchment area of ​​the water body that the tunnel crosses (km 2 ), obtained by topographic map method. Specifically, a large-scale topographic map (such as a 1:10000 or larger scale topographic map) is usually used to identify and analyze topographic and geomorphic features. On the topographic map, the area that can collect precipitation is delineated according to the distribution of water-bearing rock groups, terrain undulations, river flow direction and other factors;

[0082] L is the length of the tunnel through the aquifer (km), which reflects the longitudinal distance of the interaction between the tunnel and the aquifer and is obtained through actual measurement;

[0083] B is the width of influence on both sides of the tunnel water gushing section within the length L (km). This parameter takes into account the lateral influence of tunnel construction on the surrounding groundwater level and is obtained through actual measurement.

[0084] W is the annual precipitation (mm), which reflects the annual precipitation received in the area and is an important climatic factor for evaluating the water inflow in the tunnel. It is obtained by searching the experimental tunnel data;

[0085] S2.2: Subsurface runoff modulus method:

[0086] S2.2.1: Determine the catchment area A of the study area. A refers to the catchment area of ​​the water body through which the tunnel passes. The unit is km 2 ;

[0087] S2.2.2: Calculate the annual average groundwater runoff modulus (M 年 ) or dry season groundwater runoff modulus (M 枯 ): Groundwater runoff modulus refers to the groundwater flow rate passing through a unit area per unit time, in units of (m 3 / (d km 2 ), the annual average groundwater runoff modulus can be obtained through long-term observation of groundwater level, flow and other data, while the dry season modulus is usually calculated based on the flow in the dry season;

[0088] S2.2.3: Calculate the normal water inflow Q of the tunnel S , calculated by the following formula:

[0089] Q S =M 年 ×A

[0090] Or use the dry season modulus approximation:

[0091] Q S ≈2.70×M 枯 ×A

[0092] In the formula, Q S The normal water inflow when the tunnel passes through the water-bearing area, unit (m 3 / d);

[0093] S2.2.4: Predict the maximum inflow (Q max ), the calculation formula is as follows:

[0094] Q max =λ×M 年 ×A

[0095] or Q max ≈1.50×Q s

[0096] In the formula, Q max The maximum water inflow when the tunnel passes through a water-bearing section;

[0097] λ is the modulus ratio coefficient, which is 1.5;

[0098] Q s The normal water inflow when the tunnel passes through the water-bearing area, unit (m 3 / d);

[0099] S2.2.5: Consider the impact of topography, geology and hydrogeology on water inflow; in practical applications, it is necessary to consider the complexity of topography, geological structure and hydrogeology, which may affect the recharge and flow of groundwater, thereby affecting the prediction results of water inflow;

[0100] S2.2.6: Continuous monitoring and correction: During the construction process, the groundwater level, rainfall, etc. should be continuously monitored, and the prediction model should be corrected according to the actual situation to improve the accuracy of the prediction;

[0101] S2.3: Groundwater dynamics method: The Qiubuyi formula in the groundwater dynamics method is used to predict the normal water inflow of the tunnel. The calculation formula is:

[0102]

[0103] Where: Q S is the water inflow of the tunnel under normal conditions (m 3 / d);

[0104] L is the actual length of the tunnel through the water body (m);

[0105] K is the permeability coefficient of the water body (m / d), obtained through the borehole water pressure test;

[0106] H is the vertical thickness of the phreatic water body above the cave bottom (m), representing the height of the water column outside the cave. Considering the hydraulic jump phenomenon, it is usually taken as 0.5 to 0.75 times the vertical thickness of the phreatic water body, obtained through actual measurement;

[0107] h is the depth of the tunnel vault relative to the groundwater level above it (m). The groundwater level above the tunnel is determined through geological exploration and hydrogeological survey, and then the depth of the tunnel groundwater level is calculated;

[0108] R is the influence radius (m). This parameter reflects the scope of the impact of tunnel construction on the surrounding groundwater level. By arranging a certain number of observation holes around the pumping well, observing and recording the water level change data of each observation hole at different time points, when the pumping process reaches a stable state (that is, the amount of water pumped is generally stable, the edge of the drop funnel no longer expands, and the groundwater level at the edge of the funnel no longer drops), the influence radius is obtained by calculation using the data of the observation holes or by drawing methods;

[0109] r is half of the tunnel cross-sectional width and is used to calculate the interaction area between the tunnel and the water body (m);

[0110] S3: Tunnel water inflow prediction;

[0111] S3.1: Select the appropriate prediction method based on the results of the numerical analysis method: select an experimental tunnel and calculate the water inflow of the tunnel under normal conditions based on the numerical analysis method. The calculation results show that the normal water inflow of the tunnel predicted by the groundwater runoff modulus method and the groundwater dynamics method are basically the same. In view of the unpredictability of tunnel water inflow, it is proposed to use the prediction results of the groundwater runoff modulus method as the design water inflow;

[0112] S3.2: Establish a numerical model and boundary conditions for stable tunnel water inrush: Use ANSYS to model and then import it into FLACD, then use FLAC3D to perform plane simulation and three-dimensional simulation of tunnel water inrush, determine the calculation model, obtain the water body height and section permeability coefficient and the corresponding grade of surrounding rock according to the on-site drilling test and construction data, and perform plane fluid-solid coupling simulation on several areas where water inrush may occur, and obtain the time-history curve of water inrush volume in each water inrush section. The water inrush volume predicted by the runoff modulus method is assumed to be the actual water inrush volume of the test tunnel, and compared and analyzed with the FLAC3D simulation results to obtain the cause of the error, compare and make preliminary corrections with the previous modeling results, and establish a numerical model and boundary conditions for stable tunnel water inrush. The calculation model example is as follows Figure 1 , Figure 2 and Figure 3 As shown;

[0113] The preliminary correction method is: compare the predicted water inflow obtained by the underground runoff modulus method with the water inflow simulated by FLAC3D, and analyze the difference between the two; explore the possible reasons for the difference, such as the simplification of the geological model, the selection of hydrogeological parameters, the setting of boundary conditions, etc.; make targeted modifications according to the reasons, such as adjusting the geological model, optimizing the hydrogeological parameters, setting reasonable boundary conditions, etc.;

[0114] Method for determining boundary conditions: Determine the boundary conditions of the model according to the hydrogeological conditions of the tunnel area. The boundary conditions include the setting of groundwater level, determination of recharge volume and simulation of discharge conditions. For complex hydrogeological conditions, adopt mixed boundary conditions (such as considering constant head and constant flow boundaries at the same time); set the physical and mechanical parameters of the rock mass in the model (such as elastic modulus, Poisson's ratio, internal friction angle, cohesion, etc.) and hydrogeological parameters (such as permeability, porosity, etc.) according to geological survey results and experimental data; re-conduct tunnel excavation and seepage simulation in the adjusted FLAC3D model; evaluate whether the stability and water inflow of the tunnel meet the design requirements;

[0115] S4.1: The empirical formula in the "Specifications for Hydrogeological Investigation of Railway Engineering" (TB 10049-2014) is:

[0116]

[0117] Where R is the influence radius (m);

[0118] S is the underwater drop caused by tunnel drainage (m);

[0119] K is the permeability coefficient of the water body (m / d), obtained through the borehole water pressure test;

[0120] H is the vertical thickness of the phreatic water body above the cave bottom (m), representing the height of the water column outside the cave. Considering the hydraulic jump phenomenon, it is usually taken as 0.5 to 0.75 times the vertical thickness of the phreatic water body, obtained through actual measurement;

[0121] The above formula is the Kusakin formula, which is applicable to submerged wells and sand layers. In most cases, H is greater than S. However, in shallow buried tunnels or when the water level drops to the bottom of the tunnel, the underwater water level drop S is equal to H. When S = H, the calculation formula is:

[0122]

[0123] Or R = 215.5 + 510.5K

[0124] The formula R=215.5+510.5K only considers the single correlation between the hydraulic conductivity of the aquifer and the influence radius; therefore, the form is more concise, where R is the influence radius (m); K is the permeability coefficient of the water body (m / d), obtained by the borehole water pressure test; it can be seen from the above formula that K and S are the key parameters of all these empirical formulas.

[0125] S4.2: Oshima Hiroshi Formula

[0126]

[0127] Where: q0 is the maximum possible water inflow per unit length of the tunnel, in m 3 / (m·d);

[0128] k is the permeability coefficient of the rock formation (m / d), which describes the ease with which water can penetrate the rock formation and is obtained through field measurements;

[0129] H is the distance between the original static water level in the aquifer and the center of the tunnel equivalent circle (m). This parameter measures the vertical distance that water needs to overcome to flow from the aquifer to the tunnel and is obtained through on-site measurement;

[0130] r is the equivalent circle radius of the tunnel section (m), which reflects the cross-sectional size of the interaction between the tunnel and the aquifer. r can be calculated by the area of ​​the tunnel cross section, that is, A = πr 2 The cross-sectional area of ​​the tunnel depth can be obtained by referring to the design data;

[0131] m is a conversion factor, usually taken as 0.86;

[0132] S4.3: Goodman's formula is:

[0133]

[0134] Where: q0 is the maximum possible water inflow per unit length of the tunnel, in m 3 / (m·d);

[0135] k is the permeability coefficient of the rock formation (m / d), which describes the ease with which water can penetrate the rock formation and is obtained through field measurements;

[0136] H is the distance between the original static water level in the aquifer and the center of the tunnel equivalent circle (m). This parameter measures the vertical distance that water needs to overcome to flow from the aquifer to the tunnel and is obtained through on-site measurement;

[0137] r is the equivalent circle radius of the tunnel section (m), which reflects the cross-sectional size of the interaction between the tunnel and the aquifer. r can be calculated by the area of ​​the tunnel cross section, that is, A = πr 2The cross-sectional area of ​​the tunnel depth can be obtained by referring to the design data;

[0138] S4.4: The formula for the railway tunnel specification is

[0139]

[0140] Where: q0 is the maximum possible water inflow per unit length of the tunnel, in m 3 / (m·d);

[0141] H0 is the depth of the ground water body in the tunnel water inrush (m); H0 refers to the vertical distance of the water body (such as rivers, lakes, reservoirs, etc.) that the tunnel passes through or is adjacent to relative to the top or bottom of the tunnel. It can be obtained through field tests and monitoring methods, such as setting up water level observation wells or monitoring points above or around the tunnel to regularly observe and record water level change data; or evaluating the permeability and depth of the water body through methods such as coring and water pressure testing;

[0142] k is the permeability coefficient of the rock formation (m / d), which describes the ease with which water can penetrate the rock formation and is obtained through field measurements;

[0143] H is the distance between the original static water level in the aquifer and the center of the tunnel equivalent circle (m). This parameter measures the vertical distance that water needs to overcome to flow from the aquifer to the tunnel and is obtained through on-site measurement;

[0144] r is the equivalent circle radius of the tunnel section (m), which reflects the cross-sectional size of the interaction between the tunnel and the aquifer. r can be calculated by the area of ​​the tunnel cross section, that is, A = πr 2 The cross-sectional area of ​​the tunnel depth can be obtained by referring to the design data;

[0145] K is the permeability coefficient of the water body (m / d), obtained through the borehole water pressure test;

[0146] S4.5: The semi-empirical and semi-numerical analysis model for water inflow prediction is:

[0147]

[0148] Wherein, fitting coefficients: c1 = 0.0325, c2 = 0.0544, c3 = 0.3007, c4 = 0.0352, γ = 0.0549, ω = 0.0243;

[0149] K is the permeability coefficient of the water body (m / d), obtained through the borehole water pressure test;

[0150] k is the permeability coefficient of the rock formation (m / d), which describes the ease with which water can penetrate the rock formation and is obtained through field measurements;

[0151] β is the Forchheimer coefficient in, is the porosity, K is the permeability of the water body, and τ is a resistance coefficient related to flow, which can be obtained by consulting the data;

[0152] H is the vertical thickness of the phreatic water body above the cave bottom (m), representing the height of the water column outside the cave. Considering the hydraulic jump phenomenon, it is usually taken as 0.5 to 0.75 times the vertical thickness of the phreatic water body, obtained through actual measurement;

[0153] r is the equivalent circle radius of the tunnel section (m), which reflects the cross-sectional size of the interaction between the tunnel and the aquifer. r can be calculated by the area of ​​the tunnel cross section, that is, A = πr 2 The cross-sectional area of ​​the tunnel depth can be obtained by referring to the design data;

[0154] D is the width of the model, which should be 200 times the radius of the tunnel;

[0155] Λ is a dimensionless parameter defined as

[0156] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for predicting water inflow in a tunnel under water-rich soft soil, characterized by: The following steps are involved: S1: Geological survey and data collection: collect relevant data and conduct preliminary analysis on the collected data to identify areas where water may flow and related risk factors, and establish compound rectangular flow weir monitoring stations, borehole water level monitoring stations and equip geological drilling rigs in areas where water may flow, so as to achieve real-time monitoring and collection of data; S2: Numerical simulation and prediction: Based on the flow data, geological information and groundwater distribution obtained by the compound rectangular flow weir monitoring station, the borehole water level monitoring station and the geological drilling rig, the water inflow of the tunnel under normal conditions is calculated by numerical analysis method; S3: Tunnel water inflow prediction; S3.1: Select the appropriate prediction method based on the results calculated by the numerical analysis method; S3.2: Establish a numerical model and boundary conditions for stable water inrush in tunnels; S4: Semi-data and semi-empirical model of tunnel water inflow: Through statistical analysis of multiple actual engineering cases, some relevant parameters in the formula are corrected using existing empirical formulas, and the corresponding results are calculated. Then, the results of the previous numerical simulation calculations are corrected based on the results of the empirical formulas.

2. The method for predicting water inflow in a tunnel under water-rich soft soil according to claim 1, characterized in that: The numerical analysis methods in S2 include the following: S2.1: Atmospheric precipitation infiltration method; S2.2: Subsurface runoff modulus method; S2.3: Groundwater dynamics method.

3. The method for predicting water inflow in a tunnel under water-rich soft soil according to claim 2, characterized in that: The calculation formula for the atmospheric precipitation infiltration method in S2.1 is: Q S =2.74α·W·A A=L·B Where: Q S is the water inflow of the tunnel under normal conditions (m 3 / d); α is the precipitation infiltration coefficient; A is the catchment area of ​​the water body that the tunnel crosses (km 2 ); L is the length of the tunnel through the water body (km); B is the width of the tunnel water inrush section L on both sides (km); W is the annual precipitation (mm).

4. The method for predicting water inflow in a tunnel under water-rich soft soil according to claim 3, characterized in that: The determination of the subsurface runoff modulus method in S2.2 includes the following steps: S2.2.1: Determine the catchment area A of the study area; S2.2.2: Calculate the annual average groundwater runoff modulus M 年 Or dry season groundwater runoff modulus M 枯 ; S2.2.3: Calculate the normal water inflow Q of the tunnel S , calculated by the following formula: Q S =M 年 ×A Or use the dry season modulus approximation: Q S ≈2.70×M 枯 ×A Among them, Q S The normal water inflow when the tunnel passes through the water-bearing area, unit (m 3 / d); S2.2.4: Predict the maximum inflow (Q max ), the calculation formula is as follows: Q max =λ×M 年 ×A or Q max ≈1.50×Q s In the formula, Q max The maximum water inflow when the tunnel passes through a water-bearing section; λ is the modulus ratio coefficient, which is 1.5; Q S The normal water inflow when the tunnel passes through the water-bearing area, unit (m 3 / d); S2.2.5: Consider the impact of topographic, geological and hydrogeological conditions on water inflow; S2.2.6: Continuous monitoring and correction.

5. The method for predicting water inflow in a tunnel under water-rich soft soil according to claim 4, characterized in that: The calculation formula of the groundwater dynamics method in S2.3 is: Where: Q S is the water inflow of the tunnel under normal conditions (m 3 / d); L is the actual length of the tunnel through the water body (m); K is the permeability coefficient of the water body (m / d); H refers to the vertical thickness of the phreatic water body above the cave bottom (m); h is the depth of the tunnel vault relative to the groundwater level above it (m); R is the influence radius (m); r is half of the tunnel cross-sectional width and is used to calculate the interaction area between the tunnel and the water body (m).

6. The method for predicting water inflow in a tunnel under water-rich soft soil according to claim 5, characterized in that: The method for establishing the numerical model and boundary conditions of stable tunnel water inrush in S3.2 is: use ANSYS to model and then import it into FLACD, then use FLAC3D to perform planar simulation and three-dimensional simulation of tunnel water inrush, and perform planar fluid-solid coupling simulation of several areas where water inrush may occur, and obtain the time-history curve of water inrush in each water inrush section, compare it with the previous results and make preliminary corrections, and establish the numerical model and boundary conditions of stable tunnel water inrush.

7. The method for predicting water inflow in a tunnel under water-rich soft soil according to claim 6, characterized in that: The preliminary correction method in S3.2 is: compare the predicted water inflow obtained by numerical analysis with the water inflow simulated by FLAC3D, and analyze the difference between the two; explore the possible reasons for the difference, such as simplification of geological model, selection of hydrogeological parameters, setting of boundary conditions, etc.; and make targeted modifications according to the reasons.

8. The method for predicting water inflow in a tunnel under water-rich soft soil according to claim 7, characterized in that: Method for determining boundary conditions in S3.2: Determine the boundary conditions of the model according to the hydrogeological conditions of the tunnel area. The boundary conditions include the setting of groundwater level, determination of recharge volume and simulation of discharge conditions. For complex hydrogeological conditions, adopt mixed boundary conditions. Set the physical and mechanical parameters and hydrogeological parameters of the rock mass in the model according to geological survey results and experimental data. Re-conduct tunnel excavation and seepage simulation in the adjusted FLAC3D model. Evaluate whether the stability and water inflow of the tunnel meet the design requirements.

9. The method for predicting water inflow in a tunnel under water-rich soft soil according to claim 8, characterized in that: In S4, the semi-empirical and semi-numerical analysis model for water inflow prediction is: Wherein, fitting coefficients: c1 = 0.0325, c2 = 0.0544, c3 = 0.3007, c4 = 0.0352, γ = 0.0549, ω = 0.0243; K is the permeability coefficient of the water body (m / d); k is the permeability coefficient of the rock formation (m / d); β is the Forchheimer coefficient in, is the porosity, K is the permeability of the water body, and τ is a resistance coefficient related to flow; H is the vertical thickness of the phreatic water body above the cave bottom (m); r is the equivalent circle radius of the tunnel section (m); D is the width of the model, which is 200 times the radius of the tunnel; Λ is a dimensionless parameter defined as

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