Treatment method for water burst and mud burst of peak-cluster depression tunnel

Abstract

The invention discloses a peak cluster depression tunnel water bursting and mud bursting disposal method, which belongs to the technical field of tunnel engineering, and comprises the following steps: firstly, detecting geological and hydrological data around a tunnel, constructing an underground water seepage model, creating a correlation equation, and carrying out inverse calculation to obtain a detection result containing a water bursting source and the like; and then combining the probability model and historical data, establishing and optimizing a tunnel water gushing and mud bursting numerical model, and predicting water gushing and mud bursting positions, time and the like. And then generating a prevention scheme according to a prediction result, including determining the position and number of cutoff walls, designing a drainage system and the like. And risk assessment is carried out on the secondary prediction result, and a prevention scheme is updated or an emergency plan is monitored and started in real time according to a risk value. According to the method, accurate prediction and effective prevention and control of water burst and mud burst of the peak-cluster depression tunnel are achieved, and tunnel safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering, and more specifically, to a method for disposing of water inrush and mud burst in a peak cluster depression tunnel. Background Art

[0002] As a typical area of karst landform, the unique geological structure in peak cluster depressions causes extremely strong and complex karst development. Underground karst caves and dissolution fissures crisscross, and the distribution of underground river pipelines is irregular and varies in size. These complex geological conditions significantly increase the risk of water inrush and mud burst in tunnels during construction in this area. Once a water inrush and mud burst disaster occurs, it will not only cause the construction to be interrupted, delay the construction period, but also increase the project cost.

[0003] Traditional methods for predicting and disposing of water inrush and mud burst in tunnels have obvious limitations in such a special geological environment as peak cluster depressions. In the detection link, it is difficult to comprehensively and accurately obtain complex geological data and hydrological data around the tunnel, resulting in deviations in the judgment of the water inrush source, path, and water source size. In numerical simulation, due to the lack of in-depth understanding of the geological characteristics of peak cluster depressions and the laws of water inrush and mud burst, traditional models cannot fully consider the influence of various complex factors, making the prediction results less accurate and unable to provide a reliable basis for formulating prevention plans.

[0004] At the same time, existing prevention plans often lack pertinence and flexibility and cannot be effectively adjusted according to real-time prediction results and risk assessments. Once the risk of water inrush and mud burst occurs, the emergency plan is also difficult to optimize in a timely manner according to the changes in actual environmental parameters and cannot minimize the disaster losses to the greatest extent.

[0005] Therefore, how to provide a new disposal method that can achieve fine detection, real-time monitoring and early warning, and effective disposal is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method for disposing of water inrush and mud burst in a peak cluster depression tunnel. By detecting the geological data and hydrological data around the tunnel, conducting numerical simulation predictions of water inrush and mud burst based on the detection results and historical data, generating and optimizing prevention plans, and starting and adjusting the emergency plan when a risk occurs, it realizes the effective prevention and response to water inrush and mud burst disasters in peak cluster depression tunnels, ensures the safety of tunnel construction and operation, and reduces the risks and losses brought by water inrush and mud burst.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for disposing of water inrush and mud burst in a peak cluster depression tunnel, comprising:

[0009] Detecting the geological data and hydrological data around the tunnel to obtain detection results;

[0010] Based on the detection results and historical water inrush and mud burst data, conduct numerical simulation of water inrush and mud burst for the tunnel to obtain prediction results;

[0011] Generate corresponding prevention plans according to the prediction results;

[0012] Combine the prevention plan and the detection results for secondary prediction, conduct risk assessment on the secondary prediction results to obtain a risk value. When the risk value is higher than the preset value, generate an updated prevention plan according to the secondary prediction results; when the risk value is not higher than the preset value, monitor the internal environment parameters of the tunnel in real time. When the risk of water inrush and mud burst is detected, automatically activate the emergency plan and adjust the emergency plan in real time according to the environment parameters.

[0013] Preferably, detect the geological data and hydrological data around the tunnel to obtain detection results, including:

[0014] Construct a groundwater seepage model under the natural conditions of the tunnel based on the geological data and the hydrological data;

[0015] Create a relationship equation between the RQD mean value at different depths under natural conditions and the model permeability coefficient;

[0016] Based on the groundwater seepage model and the relationship equation of the model permeability coefficient, inversely calculate the backward migration path of the tracer particles near the tunnel to obtain detection results, and the detection results include: the source, path and size of the water inrush of the tunnel.

[0017] Preferably, construct a water inrush and mud burst simulation model of the tunnel based on the detection results and historical water inrush and mud burst data, conduct numerical simulation of water inrush and mud burst for the tunnel to obtain prediction results, including:

[0018] Combined with the probability model, based on the detection results and historical water inrush and mud burst data, establish a prior probability expression of the time-varying parameters of the numerical model of tunnel water inrush and mud burst to quantify the initial possibility distribution of each parameter;

[0019] Establish an objective function between the detection results and the historical water inrush and mud burst data;

[0020] Based on the historical water inrush and mud burst data, fit the prior probability expression to adjust and obtain a numerical model of tunnel water inrush and mud burst;

[0021] According to the tunnel water inrush and mud burst data at the next moment and the optimization trend of the initial population, combine the difference method for internal iteration, and solve the parameters of the optimized tunnel water inrush and mud burst numerical model at the current moment according to the objective function to obtain the optimal solution set of the time-varying optimization model;

[0022] Substitute the optimal solution set into the numerical model of tunnel water inrush and mud burst, and input real-time environmental data to predict water inrush and mud burst in the tunnel. The prediction results include: the location of water inrush and mud burst, the time of water inrush, the water inrush volume, and the water inrush speed.

[0023] Preferably, according to the prediction results, a prevention plan is generated, including:

[0024] Determine the position of the water cutoff wall according to the location of water inrush and mud burst;

[0025] Determine the number of water cutoff walls according to the water inrush volume and water inrush speed;

[0026] Design a drainage system according to the time of water inrush, the water inrush volume, and the water inrush speed, and divert the water source of the water discharge tunnel with concentrated filling to the natural depression for water storage.

[0027] Preferably, an updated prevention plan is generated according to the secondary prediction results, including: increasing the number of water cutoff walls and the drainage system according to the secondary prediction results.

[0028] Preferably, the internal environmental parameters of the tunnel are monitored in real time. When the risk of water inrush and mud burst is detected, the emergency plan is automatically activated, and the emergency plan is adjusted in real time according to the environmental parameters, including:

[0029] When it is detected that the deformation of the surrounding rock intensifies, automatically increase the monitoring frequency;

[0030] When the water pressure or the depth of accumulated water exceeds the safety threshold, automatically start the drainage system;

[0031] Optimize the evacuation path of personnel according to the real-time environmental parameters.

[0032] Preferably, a risk assessment is carried out on the secondary prediction results to obtain a risk value, including:

[0033] Determine the probability value of water inrush and mud burst according to the location of water inrush and mud burst, the time of water inrush, the water inrush volume, the water inrush speed, and geological data;

[0034] Determine the loss situation after the occurrence of water inrush and mud burst according to the location of water inrush and mud burst, the time of water inrush, the water inrush volume, and the water inrush speed;

[0035] Determine the consequence level of the occurrence of water inrush and mud burst according to the loss situation after the occurrence of water inrush and mud burst and the preset consequence level standard;

[0036] Obtain the risk value of the occurrence of water inrush and mud burst according to the probability value and consequence level of the occurrence of water inrush and mud burst.

[0037] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method for disposing of water inrush and mud burst in a peak cluster depression tunnel. First, by integrating geological, hydrological data and historical water inrush and mud burst information, a groundwater seepage model and a numerical simulation model are constructed, and combined with a probability model and an optimization algorithm, the location, time, water volume and speed of water inrush and mud burst are accurately predicted, providing a scientific basis for tunnel construction and operation, significantly improving the prediction accuracy, and reducing the probability of disasters. Secondly, the present invention adopts a secondary prediction and risk assessment mechanism to dynamically monitor the internal environment parameters of the tunnel and adjust the prevention plan and emergency plan in real time. When the risk value is higher than the preset value, the prevention measures are updated in time; when an abnormal situation is detected, the drainage system is automatically started and the evacuation path of personnel is optimized, effectively ensuring the safety of construction and operation and reducing disaster losses. In addition, the present invention scientifically designs a water cutoff wall and a drainage system to avoid waste of resources, and diverts the water source of the water discharge tunnel to a natural depression for water storage, realizing the rational allocation of water resources and ecological environment protection, and taking into account economic benefits and environmental friendliness. Through precise prevention and control and dynamic management, the present invention effectively reduces project delays and equipment damage caused by water inrush and mud burst, reduces economic losses, shortens the disaster disposal time, and ensures the smooth implementation and long-term stable operation of the tunnel project. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0039] Figure 1 It is a schematic flow chart provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] The embodiment of the present invention discloses a method for disposing of water inrush and mud burst in a peak cluster depression tunnel, as Figure 1 shown, including:

[0042] Detect the geological data and hydrological data around the tunnel to obtain the detection results;

[0043] Based on the detection results and historical water inrush and mud burst data, conduct numerical simulation of water inrush and mud burst in the tunnel to obtain the prediction results;

[0044] Generate corresponding prevention plans based on the prediction results;

[0045] Perform secondary prediction by combining the prevention plan and the detection results, conduct risk assessment on the secondary prediction results to obtain a risk value. When the risk value is higher than the preset value, generate an updated prevention plan according to the secondary prediction results; when the risk value is not higher than the preset value, monitor the internal environmental parameters of the tunnel in real time. When the risk of water inrush and mud burst is detected, automatically start the emergency plan and adjust the emergency plan in real time according to the environmental parameters.

[0046] Specifically, detect the geological data and hydrogeological data around the tunnel to obtain the detection results, including:

[0047] Construct a groundwater seepage model under the natural conditions of the tunnel based on the geological data and hydrogeological data; specifically including:

[0048] Collect detailed geological data of the tunnel area, including lithology, stratigraphic structure, fault distribution, karst development, etc. Collect hydrogeological data, such as groundwater level, hydrochemical characteristics, permeability coefficient, hydraulic conductivity, rainfall information, etc.

[0049] Preprocess the data, remove outliers, fill in missing values, and ensure the integrity and accuracy of the data.

[0050] Select a suitable groundwater seepage model according to the geological and hydrogeological conditions of the study area. Common models include one-dimensional, two-dimensional or three-dimensional saturated-unsaturated seepage models. If karst development is obvious in the study area, a karst groundwater flow system model can be used.

[0051] Use the geological data and hydrogeological test results to determine the model parameters, such as permeability coefficient, storage coefficient, etc. The measured value of the permeability coefficient can be obtained through pumping tests, injection tests, etc.

[0052] Use numerical simulation software (such as MODFLOW, Feflow, etc.) to construct a groundwater seepage model. Verify the accuracy of the model according to the known groundwater level dynamic data, flow data, etc., and adjust the model parameters to improve the simulation accuracy.

[0053] Create a relationship equation between the RQD mean values at different depths under natural conditions and the model permeability coefficient; specifically:

[0054] Drill at different depths in the tunnel area to obtain rock cores and calculate the RQD (rock quality designation) values.

[0055] Conduct statistical analysis on the RQD values at different depths and calculate their mean values.

[0056] Obtain the permeability coefficients at different depths by means of pumping tests, injection tests, etc. Combine geological data to analyze the differences in permeability coefficients in different lithologies and structural positions.

[0057] Draw a scatter plot of the mean RQD value and the permeability coefficient at different depths, and analyze the correlation between the two. Use regression analysis methods (such as linear regression, non-linear regression, etc.) to establish a relationship equation between the mean RQD value and the permeability coefficient.

[0058] Verify the relationship equation to ensure its applicability under different depths and geological conditions.

[0059] Based on the relationship equation between the groundwater seepage model and the model permeability coefficient, inversely calculate the backward migration path of the tracer particles near the tunnel in time, and obtain the detection results, including: the source, path and size of the tunnel water inrush, specifically:

[0060] Arrange tracer particles at key positions near the tunnel, such as in the potential water source area above the tunnel or in the area with hydraulic connection to the tunnel.

[0061] Select appropriate tracers (such as sodium fluorescein, Tinopal CBS-X, etc.), and record their initial positions and release times.

[0062] Using the groundwater seepage model and the permeability coefficient relationship equation, combined with the monitoring data of the tracer particles, adopt the inverse time simulation method to calculate the backward migration path of the tracer particles.

[0063] The inverse time path calculation can be carried out through numerical simulation software (such as Qtracer2).

[0064] According to the inverse time path calculation results, determine the sources of tunnel water inrush, including groundwater, surface water, karst water, etc.

[0065] Analyze the path of water inrush, and clarify the flow direction and channels of groundwater in different geological units.

[0066] Quantify the size of the water source of water inrush through methods such as tracer tests and hydrochemical analysis, and determine the contribution ratio of each water source.

[0067] Organize the detection results into a report to provide a scientific basis for the waterproof design and construction of the tunnel

[0068] Furthermore, based on the detection results and historical water inrush and mud burst data, construct a water inrush and mud burst simulation model for the tunnel, conduct numerical simulation of water inrush and mud burst for the tunnel, and obtain the prediction results, including:

[0069] Combined with the probability model, based on the detection results and historical water inrush and mud burst data, establish a prior probability expression of the time-varying parameters of the tunnel water inrush and mud burst numerical model, and quantify the initial probability distribution of each parameter, including:

[0070] Collect historical data on water inrush and mud burst events in the tunnel area, including the time, location, water inrush volume, water inrush velocity, etc. of the events.

[0071] Combined with the detection results (such as the source, path, and size of the water inrush), analyze the key factors affecting water inrush and mud burst, such as the degree of karst development, changes in the groundwater level, and the intersection relationship between the tunnel and the groundwater.

[0072] Based on the karst geological conditions and historical data, determine the key parameters affecting water inrush and mud burst, such as the karst rate, fault line density, and groundwater level change rate.

[0073] Using historical data and expert experience, determine the prior probability distribution of each key parameter. For example, based on the relationship between the degree of karst development and water inrush and mud burst in historical events, establish the prior probability distribution of the karst rate.

[0074] The total probability formula or Bayesian method can be used to quantify the initial possibility distribution of each parameter. Establish an objective function between the detection results and historical water inrush and mud burst data; extract key features from the detection results and historical data, such as the water inrush location, water inrush volume, water inrush velocity, etc., as the input of the objective function. Define the objective function to measure the difference between the model prediction value and the actual observation value. For example, the mean square error (MSE) or absolute error can be used to quantify the deviation between the predicted water inrush volume and the actual water inrush volume. The objective function should consider multi-dimensional features such as water inrush location, time, and water volume to comprehensively evaluate the prediction accuracy of the model.

[0075] Based on the historical water inrush and mud burst data, fit the prior probability expression and adjust to obtain a numerical model for tunnel water inrush and mud burst; among them, using the historical water inrush and mud burst data, through the maximum likelihood estimation or Bayesian fitting method, adjust the parameters in the prior probability expression to make it more consistent with the actual data. According to the fitting results, adjust the parameters of the numerical model for tunnel water inrush and mud burst to optimize the prediction performance of the model. Decision tree models or other machine learning methods can be used to further optimize the parameters of the model in combination with geological conditions and historical data.

[0076] According to the tunnel water inrush and mud burst data at the next moment and the optimization trend of the initial population, perform internal iteration in combination with the difference method, and solve the optimized parameters of the numerical model for tunnel water inrush and mud burst at the current moment based on the objective function to obtain the optimal solution set of the time-varying optimization model; use the difference method to discretize the numerical model, and transform the continuous groundwater seepage equation into a discrete difference equation. Use the optimization trend of the initial population and perform internal iteration calculations in combination with the difference method. In each iteration, evaluate the quality of the model parameters according to the objective function and adjust the parameters. Through multiple iterations, gradually optimize the model parameters until the convergence condition is met to obtain the optimal solution set of the time-varying optimization model.

[0077] Substitute the optimal solution set into the numerical model of tunnel water and mud burst, and input real-time environmental data to predict water and mud burst in the tunnel. The prediction results include: location, time, amount and speed of water and mud burst. Collect real-time environmental data during tunnel construction, such as groundwater level changes, rainfall, tunnel excavation progress, etc. Substitute the optimal solution set into the numerical model, and input real-time environmental data to predict water and mud burst. The prediction results include key information such as location, time, amount and speed of water and mud burst.

[0078] Analyze the prediction results to assess the risk level of water and mud inrush. For example, determine whether preventive measures need to be taken based on the predicted water inrush volume and speed. Apply the prediction results to the dynamic management of tunnel construction to provide a scientific basis for construction decision-making.

[0079] Furthermore, considering that the tunnel shaft disease concentrated section is located in the karst strong development area, water gushing control should follow two principles:

[0080] (1) Upstream treatment is the main method, with tunnel treatment as a supplement;

[0081] (2) Combine blocking and dredging, and treat both the symptoms and the root causes.

[0082] Specifically, the disposal measures of "interception, detention, diversion and drainage" are adopted.

[0083] Interception: Set up 2 to 3 water interception walls (sand dams) at the bottom of the depression to intercept the silt and cut off part of the peak flood peak. Remove the visible and excavable silt accumulated in the S13 drainage tunnel and transport it out of the depression to reduce the influx of silt into the tunnel and block the tunnel drainage system. Construct 2 to 3 water retaining walls (sand dams) in a stepped manner in the small depression east of Changdong. Use reinforced concrete to construct a water retaining "chimney" in the S13 drainage tunnel. Reserve drainage gaps on it according to the allowable drainage volume. Set steel wire mesh in the gaps to intercept mud, sand, garbage and other debris. Use the Changdong trough to store water and intercept silt.

[0084] Stagnation: It is mainly manifested in peak elimination, using natural depressions (troughs) to store water, slowing down the infiltration rate of water-dissipating holes (sinkholes) during rainstorms, and reducing the pressure of water and mud in the tunnel. The natural storage capacity of the small depression east of Changdong is used to adjust the speed of water accumulation in the depression, reduce the peak flow rate of the tunnel water inrush point, and reduce mud and sand. Other water-dissipating points are covered and anti-seepage treatment is performed (clay or concrete paving).

[0085] Diversion: divert the water source of the centralized flooding recharge tunnel to the outside of the system to reduce the risk of tunnel water inrush and disaster pressure. The "water diversion tunnel" is constructed for long tunnel water accumulation to drain the accumulated water out of the tunnel system to ensure the safety of tunnel operation (D, H, I drainage tunnels).

[0086] Row: As a precautionary measure, implement drainage measures in the tunnel (such as dredging the blocked underground river pipeline, regularly maintaining the central drainage ditch, constructing drainage tunnels, etc.) to ensure the operation risk of the tunnel.

[0087] Therefore, according to the prediction results, generate a prevention plan, including:

[0088] Determine the position of the water cut-off wall according to the location of water and mud gushing; according to the prediction results, clarify the specific location of water and mud gushing, especially the key risk sections in the tunnel. Select the position of the water cut-off wall: Set up a water cut-off wall at the upstream position of the water and mud gushing in the tunnel to ensure that it can effectively intercept groundwater and prevent water from flowing into the tunnel construction area. Combine the characteristics of the tunnel surrounding rock and hydrogeological conditions, and select an area with relatively stable geological conditions and easy construction to set up the water cut-off wall.

[0089] Determine the number of water cut-off walls according to the water inflow and water inflow speed; according to the predicted water inflow and water inflow speed, calculate the maximum amount of water that a single water cut-off wall can bear. According to the predicted water inflow, calculate the number of water cut-off walls to be set up to ensure that the total water cut-off capacity of the water cut-off walls can meet the predicted maximum water inflow. Furthermore, to ensure safety, generally add a certain proportion of redundant design on the basis of the calculation to cope with emergencies.

[0090] Design a drainage system according to the water gushing time, water inflow, and water inflow speed, and divert the water source of the water discharge tunnel with concentrated filling to the natural depression for water storage.

[0091] Design the drainage system in the tunnel according to the water gushing time, water inflow, and water inflow speed, including the diameter, slope, and layout of the drainage pipes. Set up sump pits and drainage pumps in the tunnel to ensure the efficient operation of the drainage system.

[0092] Select a suitable natural depression as the water storage area to ensure that its capacity can accommodate the predicted water inflow.

[0093] Build a water diversion channel or lay a water diversion pipe to divert the water source of the water discharge tunnel to the natural depression. The design of the water diversion channel or pipe should consider anti-seepage and durability.

[0094] Set up an energy dissipation pool or energy dissipation sill at the end of the water diversion channel to reduce the impact of water flow scouring on the depression.

[0095] Regularly inspect and maintain the water diversion channel and the water storage depression to ensure their normal operation.

[0096] Furthermore, generate an updated prevention plan according to the secondary prediction results, including: increasing the number of water cut-off walls and the drainage system according to the secondary prediction results.

[0097] In another embodiment, real-time monitor the internal environment parameters of the tunnel. When the risk of water and mud gushing is detected, automatically start the emergency plan and adjust the emergency plan in real time according to the environment parameters, including:

[0098] When the deformation of the surrounding rock is monitored to intensify, automatically increase the monitoring frequency;

[0099] When the water pressure or the depth of accumulated water exceeds the safety threshold, automatically start the drainage system;

[0100] Optimize the evacuation route of personnel according to real-time environmental parameters.

[0101] In another embodiment, perform a risk assessment on the secondary prediction result to obtain a risk value, including:

[0102] Determine the probability value of water inrush and mud burst according to the location of water inrush and mud burst, the time of water inrush, the water inrush volume, the water inrush speed, and geological data; specifically, use a decision tree model, combine geological parameters and characteristic parameters of water inrush and mud burst to construct a judgment model for the possibility of water inrush and mud burst. Assign weights to each parameter through the Analytic Hierarchy Process (AHP) and calculate the weighted probability. According to the calculation results of the model, divide the probability of water inrush and mud burst into different levels, for example: extremely likely (>90%), likely (60%-90%), moderately likely (30%-60%), unlikely (10%-30%), extremely unlikely (<10%).

[0103] Determine the loss situation after the occurrence of water inrush and mud burst according to the location of water inrush and mud burst, the time of water inrush, the water inrush volume, and the water inrush speed; specifically, evaluate the direct economic losses such as equipment damage and construction delay caused by water inrush and mud burst according to the water inrush volume, the water inrush speed, and the construction progress. Combine the location and occurrence time of water inrush and mud burst to analyze the potential threats to the safety of construction personnel, including the risk of casualties and the difficulty of evacuation. Evaluate the impact on the tunnel construction progress according to the scale and treatment difficulty of water inrush and mud burst, including the delay time and additional construction costs. Combine the characteristic parameters of water inrush and mud burst to establish a loss assessment model to quantify the loss situation of different-scale water inrush and mud burst events. Verify the accuracy of the loss assessment model through existing water inrush and mud burst cases and adjust the model parameters according to the actual situation.

[0104] Determine the consequence level of the occurrence of water inrush and mud burst according to the loss situation after the occurrence of water inrush and mud burst and the preset consequence level standard;

[0105] Obtain the risk value of the occurrence of water inrush and mud burst according to the probability value and the consequence level of the occurrence of water inrush and mud burst.

[0106] Construct a risk matrix, combine the probability value of water inrush and mud burst with the consequence level to form a risk assessment framework.

[0107] According to the risk matrix, calculate the risk value of each water inrush and mud burst event, and the formula is: risk value = probability value × consequence level weight.

[0108] According to the magnitude of the risk value, the risks are divided into three levels: low risk, medium risk, and high risk.

[0109] Based on the risk assessment results, corresponding preventive measures and emergency response plans are formulated to provide a scientific basis for tunnel construction.

[0110] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference can be made to the description of the method part for the relevant parts.

[0111] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for treating water and mud inrush in a tunnel in a peak cluster depression, characterized in that: include: Detect geological and hydrological data around the tunnel and obtain detection results; Based on the detection results and historical water and mud burst data, a numerical simulation of water and mud burst in the tunnel is performed to obtain a prediction result; generating a corresponding prevention plan according to the prediction result; Perform secondary prediction in combination with the prevention plan and the detection result, perform risk assessment on the secondary prediction result to obtain a risk value, and when the risk value is higher than a preset value, generate an updated prevention plan according to the secondary prediction result; When the risk value is not higher than the preset value, the internal environmental parameters of the tunnel are monitored in real time. When the risk of water gushing or mud bursting is detected, the emergency plan is automatically activated and adjusted in real time according to the environmental parameters.

2. A method for handling water and mud inrush in a peak cluster depression tunnel according to claim 1, characterized in that: Detect geological and hydrological data around the tunnel and obtain detection results, including: Constructing a groundwater seepage model under natural conditions of the tunnel based on the geological data and the hydrological data; Create the equations for the relationship between the mean RQD value at different depths under natural conditions and the model permeability coefficient; Based on the relationship equation between the groundwater seepage model and the model permeability coefficient, the backward migration path of the tracer particles near the tunnel is calculated in reverse time to obtain the detection result, which includes: the source, path and size of the tunnel water gushing.

3. A method for handling water and mud inrush in a peak cluster depression tunnel according to claim 1, characterized in that: Based on the detection results and historical water and mud inrush data, a tunnel water and mud inrush simulation model is constructed, and a tunnel water and mud inrush numerical simulation is performed to obtain prediction results, including: Combined with the probability model, based on the detection results and historical water and mud inrush data, the prior probability expressions of the time-varying parameters of the tunnel water and mud inrush numerical model are established to quantify the initial probability distribution of each parameter. Establishing an objective function between the detection result and the historical water and mud burst data; The prior probability expression is fitted based on the historical water gushing and mud bursting data, and a numerical model of tunnel water gushing and mud bursting is obtained by adjustment; According to the tunnel water and mud burst data at the next moment and the optimization trend of the initial population, the differential method is combined for internal iteration, and the optimized tunnel water and mud burst numerical model parameters at the current moment are solved according to the objective function to obtain the optimal solution set of the time-varying optimization model. The optimal solution set is substituted into the tunnel water and mud burst numerical model, and real-time environmental data is input to predict the tunnel water and mud burst. The prediction results include: water and mud burst location, water burst time, water burst volume, and water burst speed.

4. A method for handling water and mud inrush in a peak cluster depression tunnel according to claim 3, characterized in that: Based on the prediction results, a prevention plan is generated, including: Determine the location of the cutoff wall according to the location of the water gushing and mud bursting; Determine the number of cut-off walls according to the water inflow volume and water inflow speed; The drainage system is designed according to the water gushing time, water gushing amount and water gushing speed, and the water source of the water diversion hole supplied by centralized injection type is led to the natural depression for water storage.

5. A method for handling water and mud inrush in a peak cluster depression tunnel according to claim 4, characterized in that: Generating an updated prevention plan based on the secondary prediction results includes: increasing the number of cutoff walls and drainage systems based on the secondary prediction results.

6. A method for handling water and mud inrush in a peak cluster depression tunnel according to claim 4, characterized in that: Real-time monitoring of tunnel internal environmental parameters. When water or mud inrush risks are detected, the emergency plan is automatically activated and adjusted in real time according to environmental parameters, including: When the surrounding rock deformation is detected to be aggravated, the monitoring frequency will be automatically increased; When the water pressure or water depth exceeds the safety threshold, the drainage system will be automatically activated; Optimize personnel evacuation paths based on real-time environmental parameters.

7. A method for handling water and mud inrush in a peak cluster depression tunnel according to claim 3, characterized in that: Perform risk assessment on the secondary prediction results to obtain the risk value, including: Determine the probability value of water inrush and mud inrush according to the water inrush and mud inrush location, water inrush time, water inrush amount, water inrush speed and geological data; Determine the loss after the water and mud burst occurs according to the water and mud burst location, water burst time, water burst amount, and water burst speed; Determine the consequence level of water gushing and mud bursting according to the loss situation after the water gushing and mud bursting and the preset consequence level standard; According to the probability value and consequence level of water gushing and mudslide, the risk value of water gushing and mudslide is obtained.

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