Early warning method and system for water inrush during excavation of water-rich tunnel in fractured rock stratum

Abstract

The invention relates to a fractured rock stratum water-rich tunnel excavation water inrush early warning method and system. The method comprises the steps that real-time monitoring data in the tunnel excavation process are obtained; based on the real-time monitoring data, predicting by using the water flow inrush prediction model, and generating a water flow inrush amount prediction result; analyzing crack propagation, deformation and instability conditions of the surrounding rock of the tunnel under different excavation parameters through the rock mass damage evaluation model in combination with the real-time monitoring data, and generating a rock mass damage evaluation result; generating a construction optimization scheme based on the water flow inflow prediction result and the rock mass damage evaluation result; comparing the real-time monitoring data with a preset safety threshold value, and judging whether a water inrush risk or a surrounding rock instability risk exists or not; if the real-time monitoring data exceed the preset safety threshold value, a safety early warning signal is triggered, and emergency measures are taken immediately. The method has the effect of improving the safety in tunnel construction.

Description

Technical Field

[0001] The present application relates to the technical field of underground engineering construction, and particularly relates to a method and system for predicting water inrush during the excavation of a water-rich tunnel in a fractured rock formation. Background Art

[0002] Currently, during the tunnel excavation process in fractured rock formations and water-rich areas, the risk of water inrush has always been a major problem affecting construction safety. The influx of water during tunnel excavation usually causes the instability of the surrounding rock, and in severe cases, it may trigger a water inrush disaster, endangering the lives of construction workers. To effectively prevent the risk of water inrush, traditional warning methods usually rely on static water flow prediction models and simplified rock mass stability assessments. However, these methods mainly rely on historical data and fixed parameters, ignoring the dynamic changes in rock mass and water flow conditions during the construction process, resulting in the lag and insufficient accuracy of the warning.

[0003] Most existing water influx prediction models adopt simplified geological assumptions or static models, and often fail to adapt to the changes in the surrounding geological environment in real time during the actual construction process, especially in complex water-rich and fractured rock formations. Although there are some numerical simulation-based models that can provide relatively accurate predictions, they usually lack the dynamic combination with real-time monitoring data and fail to adjust the prediction results in real time during the construction process, thus unable to timely reflect the changes in the water inrush risk.

[0004] The above-mentioned existing technical solutions have the following defects: Most existing water influx prediction and rock mass failure assessment models rely on static data and cannot dynamically adjust the prediction results according to real-time monitoring data, resulting in the lag and insufficient accuracy of the warning. Therefore, there is room for improvement. Summary of the Invention

[0005] In order to improve the safety during tunnel construction, the present application provides a method and system for predicting water inrush during the excavation of a water-rich tunnel in a fractured rock formation.

[0006] The first invention object of the present application is achieved through the following technical solutions: A method for predicting water inrush during the excavation of a water-rich tunnel in a fractured rock formation, the method for predicting water inrush during the excavation of a water-rich tunnel in a fractured rock formation includes: Obtaining real-time monitoring data during the tunnel excavation process, the real-time monitoring data including the stress and strain of the surrounding rock; Based on the real-time monitoring data, using a water influx prediction model to predict the expected water influx of the tunnel, generating a water influx prediction result, and the water influx prediction model is dynamically adjusted based on the local geological characteristics and hydrological conditions of the tunnel; Through the rock mass failure assessment model, combined with the real-time monitoring data, analyze the crack propagation, deformation and instability of the surrounding rock of the tunnel under different excavation parameters, generate the rock mass failure assessment result, and dynamically adjust the parameters of the rock mass failure assessment model according to the construction parameters; Based on the water inflow prediction result and the rock mass failure assessment result, generate a construction optimization plan; Compare the real-time monitoring data with a preset safety threshold to determine whether there is a risk of water inrush or surrounding rock instability; If the real-time monitoring data exceeds the preset safety threshold, trigger a safety warning signal and immediately take emergency measures.

[0007] By adopting the above technical solutions, by obtaining the real-time monitoring data during the tunnel excavation process, including but not limited to the stress and strain of the surrounding rock, the deformation state of the surrounding rock can be reflected in real time, thereby improving the detection accuracy of the water inrush risk and the surrounding rock instability risk; by using the water inflow prediction model to predict the water inflow volume and dynamically adjusting according to the local geological characteristics and hydrological conditions of the tunnel, it can adapt to different construction environments, provide accurate water flow prediction in real time, identify the water inflow risk in advance, and enhance the construction safety; by combining the rock mass failure assessment model to analyze the crack propagation, deformation and instability of the surrounding rock, the stability of the surrounding rock can be accurately evaluated, the construction plan can be adjusted in real time, and the probability of rock mass failure can be reduced; by comparing the real-time monitoring data with the preset safety threshold to determine whether there is a risk of water inrush or surrounding rock instability, the early warning can be triggered in time and emergency measures can be taken, thereby ensuring the safety and construction progress during the construction process.

[0008] In one example of the present application, it can be further configured that: the obtaining of the real-time monitoring data during the tunnel excavation process includes: Use a position sensor to monitor the displacement data of the tunnel excavation area; Use a stress sensor to monitor the stress change data of the tunnel surrounding rock; Use a strain sensor to monitor the strain data of the tunnel surrounding rock; Use a pore water pressure sensor to monitor the pore water pressure data of the tunnel surrounding rock.

[0009] By adopting the above technical solutions, by using a position sensor to monitor the displacement data of the tunnel excavation area, a stress sensor to monitor the stress change data of the surrounding rock, a strain sensor to monitor the strain data of the surrounding rock, and a pore water pressure sensor to monitor the pore water pressure data of the surrounding rock, the key parameters of the surrounding rock during the tunnel construction process can be comprehensively obtained, providing accurate real-time data support, thereby effectively detecting potential water inrush risks and surrounding rock instability risks, and improving the accuracy and timeliness of early warning.

[0010] In one example, the present application can be further configured as follows: Before predicting the expected water inflow of the tunnel using the water inflow prediction model based on the real-time monitoring data and generating the water inflow prediction result, the method for predicting water inrush during the excavation of a water-rich tunnel in a broken rock formation further includes: Construct the water inflow prediction model based on the finite difference method groundwater flow model; Obtain the geological exploration data around the tunnel, where the geological exploration data includes the permeability of the rock formation, fault distribution, and groundwater level; Combine the geological exploration data and the real-time monitoring data to establish a local geological feature model of the tunnel; Based on the local geological feature model, adjust the initial parameters of the water inflow prediction model.

[0011] By adopting the above technical solutions, by constructing a water inflow prediction model based on the finite difference method groundwater flow model and obtaining the geological exploration data around the tunnel, including the permeability of the rock formation, fault distribution, and groundwater level, it can ensure that the water inflow prediction model accurately reflects the geological environment and hydrogeological conditions around the tunnel, thereby improving the reliability and accuracy of the inflow prediction; by combining the geological exploration data and the real-time monitoring data to establish a local geological feature model of the tunnel, it can further enhance the dynamic adjustment ability of the model and ensure accurate prediction of the water inflow volume under different construction environments.

[0012] In one example, the present application can be further configured as follows: The process of predicting the expected water inflow of the tunnel using the water inflow prediction model based on the real-time monitoring data and generating the water inflow prediction result includes: Utilize the surrounding rock stress, strain, displacement, and pore water pressure in the real-time monitoring data, combine with the actual working conditions during the tunnel excavation process, input them into the water inflow prediction model, and calculate the water seepage volume at different construction stages; According to the displacement and stress change conditions in the real-time monitoring data, dynamically update the model parameters in the water inflow prediction model, where the model parameters include the rock formation permeability and fracture distribution; Based on the local geological feature model and hydrogeological conditions around the tunnel, numerically simulate the flow and propagation path of water in the rock formation to generate the water inflow prediction result; Compare the water inflow prediction result with a preset water safety threshold to determine whether there is a water inflow risk. If there is a water inflow risk, generate an excessive water inflow warning signal.

[0013] By adopting the above technical solutions, by utilizing the surrounding rock stress, strain, displacement, and pore water pressure in the real-time monitoring data, combining with the actual working conditions during the tunnel excavation process, and inputting them into the water inflow prediction model for calculation, the water seepage volume can be dynamically obtained at different construction stages, ensuring the timeliness and accuracy of the water inflow prediction results; by dynamically updating the model parameters such as the rock formation permeability and fracture distribution in the water inflow prediction model according to the displacement and stress change conditions in the real-time monitoring data, the response ability of the model to the surrounding rock deformation and water seepage path can be improved, thereby more accurately predicting the water inflow volume; by conducting numerical simulations based on the local geological feature model and hydrogeological conditions around the tunnel, the flow and propagation path of water in the rock formation can be calculated, thereby accurately evaluating the water inflow volume and giving early warnings; by comparing the water inflow prediction results with the preset water safety threshold, it is possible to timely judge whether there is a risk of excessive water inflow and generate warning signals to avoid the occurrence of water inrush disasters.

[0014] In one example, the present application can be further configured as follows: through the rock mass failure assessment model, combining the real-time monitoring data, analyzing the crack propagation, deformation, and instability conditions of the surrounding rock of the tunnel under different excavation parameters, and generating the rock mass failure assessment results including: Input the real-time monitoring data into the rock mass failure assessment model to calculate the stress-strain state of the surrounding rock under different excavation progress and excavation methods; Through numerical simulation, based on the real-time monitoring data, analyze the crack propagation mode of the rock mass, identify the key areas where cracks occur and expand, and dynamically adjust the crack propagation critical value in the rock mass failure assessment model according to the construction parameters; Combining the surrounding rock deformation data in the real-time monitoring data, update the mechanical parameters of the rock mass in the rock mass failure assessment model to simulate the deformation and failure process of the rock mass under different construction conditions; Based on the calculation results of the rock mass failure model, evaluate the stability of the surrounding rock and generate the rock mass failure assessment results.

[0015] By adopting the above technical solutions, by inputting the real-time monitoring data into the rock mass failure assessment model, it is possible to calculate the stress-strain state of the surrounding rock under different excavation progress and excavation methods, so as to achieve accurate assessment of the surrounding rock; through numerical simulation, based on the analysis of the real-time monitoring data of the crack propagation mode of the rock mass, identify the key areas where cracks occur and expand, and dynamically adjust the crack propagation critical value in the rock mass failure assessment model according to the construction parameters, it is possible to accurately judge the stability of the surrounding rock and prevent the instability risk caused by crack propagation; by combining the surrounding rock deformation data in the real-time monitoring data, update the mechanical parameters of the rock mass in the rock mass failure assessment model, and simulate the deformation and failure process of the rock mass under different construction conditions, so as to improve the timeliness and accuracy of failure assessment; based on the calculation results of the rock mass failure model, evaluate the stability of the surrounding rock and generate the rock mass failure assessment results, providing a reliable decision-making basis for subsequent construction.

[0016] In one example, the present application can be further configured as: generating a construction optimization plan based on the water inflow prediction result and the rock mass failure assessment result includes: Based on the water inflow prediction result, determine the water flow risk area during the tunnel construction process, and adjust the path construction plan according to the predicted water flow rate and water flow path in the water inflow prediction result. The path construction plan includes taking grouting reinforcement and support reinforcement; Based on the rock mass failure assessment result, analyze the stability of the surrounding rock, determine the surrounding rock failure risk area, and increase the support measures according to the danger level of the surrounding rock failure risk area; Combine the water inflow prediction result and the rock mass failure assessment result to optimize the grouting plan. The grouting plan includes determining the grouting pressure, grouting point position and grouting material type, and generating an emergency response plan; Dynamically adjust the construction optimization plan according to the construction stage and the real-time monitoring data.

[0017] By adopting the above technical solutions, by determining the water flow risk area during the tunnel construction process based on the prediction result of the water flow inflow volume, it is possible to identify in advance the risk area with a large inflow volume of water, thereby optimizing the construction plan, including taking measures such as grouting reinforcement and support reinforcement, to ensure the safety of tunnel construction; by analyzing the stability of the surrounding rock based on the rock mass failure assessment result, it is possible to accurately locate the risk area of surrounding rock failure, and according to the danger level of the surrounding rock failure risk, increase appropriate support measures to ensure the stability of the surrounding rock during the construction process; by combining the prediction result of the water flow inflow volume and the rock mass failure assessment result to optimize the grouting plan, it is possible to carry out grouting reinforcement in the areas where it is most needed, improving the water flow management and rock mass stability during the construction process; by dynamically adjusting the construction optimization plan according to the construction stage and real-time monitoring data, it is possible to flexibly respond to different construction stages and on-site environmental changes, ensuring the coordination of construction safety and progress.

[0018] In one example, the present application can be further configured as follows: The setting of the preset safety threshold includes: Obtain historical monitoring data and real-time water flow data, and set a safety threshold based on the water flow inflow volume through the historical monitoring data, geological exploration data, and real-time water flow data; According to the construction progress and construction method of the tunnel excavation, determine the maximum allowable value of the surrounding rock deformation, and set a safety threshold for the surrounding rock deformation according to the maximum allowable value of the surrounding rock deformation; Based on the rock mass failure assessment model, set the critical threshold for crack propagation.

[0019] By adopting the above technical solutions, by obtaining historical monitoring data and real-time water flow data, and combining geological exploration data to set a safety threshold based on the water flow inflow volume, it is possible to accurately evaluate the risk of water flow inflow, ensure that the setting of the threshold is based on the actual situation, and improve the accuracy of water flow prediction; by setting the maximum allowable value of the surrounding rock deformation according to the construction progress and construction method of the tunnel excavation and determining the safety threshold for the surrounding rock deformation, it is possible to timely judge the deformation situation of the surrounding rock and prevent the instability of the surrounding rock; by setting the critical threshold for crack propagation based on the rock mass failure assessment model, it is possible to dynamically evaluate the failure risk of the surrounding rock and timely trigger an early warning signal to ensure the safety during the construction process.

[0020] The second above-mentioned invention object of the present application is achieved through the following technical solutions: A water inrush early warning system for the excavation of a water-rich tunnel in a broken rock stratum, the water inrush early warning system for the excavation of a water-rich tunnel in a broken rock stratum includes: A monitoring module for obtaining real-time monitoring data during the tunnel excavation process, the real-time monitoring data including the stress and strain of the surrounding rock; A water flow prediction module, which is used to predict the expected water flow influx of the tunnel based on the real-time monitoring data by using a water flow influx prediction model, and generate a water flow influx prediction result. The water flow influx prediction model is dynamically adjusted based on the local geological characteristics and hydrological conditions of the tunnel; A damage assessment module, which is used to analyze the crack propagation, deformation and instability of the surrounding rock of the tunnel under different excavation parameters through a rock mass damage assessment model in combination with the real-time monitoring data, generate a rock mass damage assessment result, and dynamically adjust the parameters of the rock mass damage assessment model according to the construction parameters; A scheme optimization module, which is used to generate a construction optimization scheme based on the water flow influx prediction result and the rock mass damage assessment result; A safety comparison module, which is used to compare the real-time monitoring data with a preset safety threshold to determine whether there is a risk of water inrush or surrounding rock instability; An early warning module, which is used to trigger a safety warning signal and immediately take emergency measures if the real-time monitoring data exceeds the preset safety threshold.

[0021] By adopting the above technical solutions, by obtaining the real-time monitoring data during the tunnel excavation process, including but not limited to the stress and strain of the surrounding rock, the deformation state of the surrounding rock can be reflected in real time, thereby improving the detection accuracy of the water inrush risk and the surrounding rock instability risk; by using the water flow influx prediction model to predict the water flow influx and dynamically adjusting according to the local geological characteristics and hydrological conditions of the tunnel, it can adapt to different construction environments, provide accurate water flow predictions in real time, identify the water flow influx risk in advance, and enhance construction safety; by analyzing the crack propagation, deformation and instability of the surrounding rock in combination with the rock mass damage assessment model, the stability of the surrounding rock can be accurately evaluated, the construction plan can be adjusted in real time, and the probability of rock mass damage can be reduced; by comparing the real-time monitoring data with the preset safety threshold to determine whether there is a risk of water inrush or surrounding rock instability, an early warning can be triggered in time and emergency measures can be taken, thereby ensuring the safety and construction progress during the construction process.

[0022] In summary, the present application includes the following beneficial technical effects: 1. By obtaining the real-time monitoring data during the tunnel excavation process, including but not limited to the stress and strain of the surrounding rock, the deformation state of the surrounding rock can be reflected in real time, thereby improving the detection accuracy of the water inrush risk and the surrounding rock instability risk; by using the water flow influx prediction model to predict the water flow influx and dynamically adjusting according to the local geological characteristics and hydrological conditions of the tunnel, it can adapt to different construction environments, provide accurate water flow predictions in real time, identify the water flow influx risk in advance, and enhance construction safety; 2. By analyzing the crack propagation, deformation and instability of surrounding rock through the combined rock mass failure assessment model, the stability of surrounding rock can be accurately evaluated, the construction plan can be adjusted in real time, and the probability of rock mass failure can be reduced; by comparing the real-time monitoring data with the preset safety threshold to judge whether there is a risk of water inrush or surrounding rock instability, early warning can be triggered in time and emergency measures can be taken, thus ensuring the safety and construction progress during the construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flowchart of a water inrush early warning method for the excavation of a water-rich tunnel in a broken rock stratum according to an embodiment of the present application; Figure 2 is a flowchart for implementing step S10 in a water inrush early warning method for the excavation of a water-rich tunnel in a broken rock stratum according to an embodiment of the present application; Figure 3 is a flowchart for implementing a water inrush early warning method for the excavation of a water-rich tunnel in a broken rock stratum according to an embodiment of the present application; Figure 4 is a flowchart for implementing step S20 in a water inrush early warning method for the excavation of a water-rich tunnel in a broken rock stratum according to an embodiment of the present application; Figure 5 is a flowchart for implementing step S30 in a water inrush early warning method for the excavation of a water-rich tunnel in a broken rock stratum according to an embodiment of the present application; Figure 6 is a flowchart for implementing step S40 in a water inrush early warning method for the excavation of a water-rich tunnel in a broken rock stratum according to an embodiment of the present application; Figure 7 is a flowchart for implementing a water inrush early warning method for the excavation of a water-rich tunnel in a broken rock stratum according to an embodiment of the present application; Figure 8 is a schematic block diagram of the principle of a water inrush early warning system for the excavation of a water-rich tunnel in a broken rock stratum according to an embodiment of the present application; DETAILED DESCRIPTION OF THE EMBODIMENTS The present application will be further described in detail below with reference to the accompanying drawings.

[0024] In one embodiment, as Figure 1 shown, the present application discloses a water inrush early warning method for the excavation of a water-rich tunnel in a broken rock stratum, which specifically includes the following steps: S10: Obtain real-time monitoring data during the excavation of the tunnel. The real-time monitoring data includes the stress and strain of the surrounding rock.

[0025] Specifically, by deploying different types of sensors in the tunnel excavation area, key parameters of the surrounding rock are obtained in real time. Stress sensors can be used to monitor stress changes in the tunnel surrounding rock, especially stress concentration or release when the surrounding rock is subjected to different external forces during the excavation process; strain sensors are used to detect the deformation of the surrounding rock, especially during construction, due to excavation or changes in the surrounding geological environment; position sensors obtain displacement data of the surrounding rock by monitoring position changes at different points in the tunnel, which is used to determine whether abnormal displacement or inclination of the surrounding rock has occurred, indicating possible rock mass instability; pore water pressure sensors are used to obtain changes in the pore water pressure in the surrounding rock, and these changes may indicate the rise and fall of the groundwater level or the penetration of water flow. By integrating these real-time monitoring data, the state of the tunnel surrounding rock can be comprehensively grasped, supporting subsequent prediction and assessment work.

[0026] S20: Based on the real-time monitoring data, use the water inflow prediction model to predict the expected water inflow of the tunnel, generate the water inflow prediction result, and the water inflow prediction model is dynamically adjusted based on the local geological characteristics and hydrological conditions of the tunnel.

[0027] Specifically, after obtaining data such as stress, strain, displacement, and water pressure of the surrounding rock in real time, these data are input into the water inflow prediction model. The model dynamically adjusts according to these data combined with the local geological characteristics of the tunnel (such as the permeability of the rock formation, fracture distribution, groundwater level, etc.) and hydrological conditions (such as precipitation, groundwater flow, etc.), and can update parameters such as the permeability and fracture expansion around the tunnel in real time, optimizing the prediction result of the inflow water volume. For example, when the tunnel excavation progress changes or the surrounding rock formation changes, the model can immediately correct the parameters according to the new monitoring data, thereby improving the prediction accuracy of the water inflow volume. If the predicted water inflow volume exceeds a certain threshold, the risk of excessive water inflow can be detected in time, helping construction personnel to take preventive measures in advance.

[0028] S30: Through the rock mass failure assessment model, combined with the real-time monitoring data, analyze the crack propagation, deformation and instability of the tunnel surrounding rock under different excavation parameters, generate the rock mass failure assessment result, and dynamically adjust the parameters of the rock mass failure assessment model according to the construction parameters.

[0029] Specifically, information such as surrounding rock stress, strain, and displacement in real-time monitoring data is input into the rock mass failure assessment model. By analyzing the stress-strain state of the surrounding rock under different excavation progress, depths, and construction methods, the model can evaluate the stability of the surrounding rock and identify areas where cracks may expand. For example, monitoring real-time stress changes and surrounding rock displacement can help assess whether cracks have occurred or expanded, predict potential instability patterns in the future, and ensure that the assessment results reflect the current construction situation in real-time. When the monitoring data changes, the model will dynamically adjust the parameters for failure assessment according to the new data, such as the mechanical properties of the rock mass (compressive strength, elastic modulus, etc.) and the critical value of crack expansion, so as to more accurately predict the stability of the surrounding rock and reduce the risk of accidents caused by instability.

[0030] S40: Generate a construction optimization plan based on the prediction result of water inflow and the assessment result of rock mass failure.

[0031] Specifically, combining the prediction result of water inflow with the assessment result of rock mass failure, the construction optimization plan will consider the risks in both aspects of water inflow and surrounding rock stability. For example, when a large amount of water inflow is predicted, the system may recommend increasing grouting or support measures to strengthen the water flow control in the tunnel; at the same time, if the assessment result of rock mass failure shows that there is a risk of crack expansion or deformation in the surrounding rock, the optimization plan will require adjusting the excavation progress or taking reinforcement measures. By dynamically evaluating the risks of water inflow volume and surrounding rock failure, the generated construction optimization plan can reasonably adjust the construction method, select appropriate grouting materials, determine reinforcement measures and construction methods, so as to ensure construction safety and reduce potential risks such as water inrush or surrounding rock instability.

[0032] S50: Compare the real-time monitoring data with the preset safety thresholds to determine whether there is a risk of water inrush or surrounding rock instability.

[0033] Specifically, compare the real-time monitoring data with the preset safety thresholds, such as setting the threshold of water inflow volume and the maximum allowable value of surrounding rock deformation. If the water inflow volume in the real-time monitoring data exceeds the set safety threshold, or the surrounding rock displacement and strain data exceed the allowable range, the system will determine that there may be a risk of water inrush or surrounding rock instability and trigger the corresponding warning mechanism. For example, if the water flow rate is greater than the set maximum safety value, or the displacement of the surrounding rock exceeds the predetermined limit, the warning system will automatically generate a risk warning message to remind the construction personnel to pay attention to these potential dangerous areas and make preparations for emergency response in advance.

[0034] S60: If the real-time monitoring data exceeds the preset safety threshold, trigger a safety warning signal and immediately take emergency measures.

[0035] Specifically, once the real-time monitoring data exceeds the preset safety threshold, the system will immediately trigger a warning signal and take actions according to the predetermined emergency response process. For example, when the water inflow volume exceeds the safety threshold, the system will recommend that the construction workers suspend excavation and initiate a grouting reinforcement program or other water flow control measures; if the surrounding rock deformation exceeds the limit, the system will immediately issue a risk warning and recommend strengthening the support structure, or even suspending construction to prevent further instability of the surrounding rock. The implementation of these emergency measures can effectively reduce the safety risks brought by water inrush or surrounding rock instability, and ensure the safety of construction workers and construction equipment.

[0036] By adopting the above technical solutions, by obtaining the real-time monitoring data during the tunnel excavation process, including but not limited to the stress and strain of the surrounding rock, the deformation state of the surrounding rock can be reflected in real time, thereby improving the detection accuracy of water inrush risk and surrounding rock instability risk; by using the water inflow prediction model to predict the water inflow volume and dynamically adjusting according to the local geological characteristics and hydrological conditions of the tunnel, it can adapt to different construction environments, provide accurate water flow prediction in real time, identify the water inflow risk in advance, and enhance construction safety; by combining the rock mass failure assessment model to analyze the crack propagation, deformation and instability of the surrounding rock, the stability of the surrounding rock can be accurately evaluated, the construction plan can be adjusted in real time, and the probability of rock mass failure can be reduced; by comparing the real-time monitoring data with the preset safety threshold to judge whether there is a risk of water inrush or surrounding rock instability, a warning can be triggered in time and emergency measures can be taken, thereby ensuring the safety and construction progress during the construction process.

[0037] In one embodiment, as Figure 2 shown, in step S10, that is, obtaining the real-time monitoring data during the tunnel excavation process, specifically includes: S11: Using position sensors to monitor the displacement data of the tunnel excavation area.

[0038] Specifically, the position sensors are arranged at key positions of the tunnel, especially in areas where tunnel excavation may cause deformation or instability of the surrounding rock. The sensors monitor the displacement of the surrounding rock in real time. By sensing minute displacement changes, the position sensors can capture the displacement trend and speed of the surrounding rock in the tunnel. For example, when the tunnel is excavated to a certain depth, the surrounding rock may expand or settle, and the sensors will record the displacement data of the surrounding rock in real time. These data can reflect the stability of the tunnel wall or roof and help analyze whether there is a risk of potential displacement or uneven settlement. If the displacement data exceeds the set safety threshold, the system can issue a warning in time so that reinforcement measures can be taken to avoid the occurrence of instability accidents.

[0039] S12: Using stress sensors to monitor the stress change data of the tunnel surrounding rock.

[0040] Specifically, stress sensors are installed at different positions in the surrounding rock of the tunnel, especially in areas where stress concentration may occur. By real-time monitoring of the stress changes in the surrounding rock, the external force changes on the surrounding rock during the construction process can be captured. Stress data helps analyze whether the surrounding rock is under excessive pressure or experiencing stress concentration. Especially during tunnel excavation, as the excavation depth and the surrounding environment change, the stress distribution may change unevenly. Stress sensors can detect these changes, thereby identifying the risks that may lead to crack propagation or surrounding rock failure. For example, if the stress value in a certain area increases abnormally, it may indicate the failure point of the rock mass. Through the detected stress changes, the system can timely evaluate the stability of the surrounding rock and trigger an early warning or adjust the construction plan to avoid further damage.

[0041] S13: Monitor the strain data of the surrounding rock of the tunnel using strain sensors.

[0042] Specifically, strain sensors are used to monitor the deformation of the surrounding rock during tunnel excavation, especially in key areas where the surrounding rock undergoes strain or deformation. These sensors can detect the linear or non-linear deformation of the surrounding rock in real time. Especially in areas where the strain value increases during excavation, it may indicate the risk of excessive deformation or instability of the surrounding rock. By obtaining strain data in real time, it can be observed whether the surrounding rock has experienced expansion or contraction, or whether local tensile or compressive deformation has occurred. These strain data are crucial for analyzing the failure critical point of the surrounding rock. For example, if the strain value of the surrounding rock continues to increase, it may mean that the surrounding rock is approaching an unstable state. The system can dynamically adjust the support strength or change the excavation progress based on these data to prevent instability or collapse.

[0043] S14: Monitor the pore water pressure data of the surrounding rock of the tunnel using pore water pressure sensors.

[0044] Specifically, pore water pressure sensors are installed in the surrounding rock to monitor the impact of groundwater flow on the surrounding rock in real time, especially in water-rich layers or fractured rock formations. The change of water flow has an important impact on the stability of the surrounding rock. As the tunnel is excavated, the pore water pressure of the surrounding rock will change. The seepage of water flow and the change of the groundwater level will cause the rise and fall of the pore water pressure. Monitoring these pressure data can reflect the flow path of groundwater and the change of the water level. For example, when the groundwater pressure suddenly increases, it may mean that the water flow is seeping into the tunnel, which will affect the stability of the surrounding rock. Through real-time data feedback, appropriate protective measures such as grouting or support reinforcement can be taken when too much water flows in to ensure the stability of the tunnel and construction safety.

[0045] In one embodiment, as Figure 3As shown, before step S20, that is, before predicting the expected water inflow of the tunnel using the water inflow prediction model based on real-time monitoring data and generating the water inflow prediction result, this method for early warning of water inrush during the excavation of a water-rich tunnel in fractured rock formations further includes: S201: Construct a water inflow prediction model based on the finite difference groundwater flow model.

[0046] Specifically, the finite difference method (FDM) is used to numerically simulate groundwater flow to construct a water inflow prediction model for the tunnel. The finite difference method discretizes the geological body around the tunnel, transforms the complex equation of groundwater flow into a computable numerical model, and simulates the water infiltration and propagation through a series of time steps. This method can more accurately describe the flow characteristics and infiltration paths of water in different rock formations. Especially in complex geological environments, it can simulate the flow of water in faults, fractures or other irregular pores. For example, through this model, it can be predicted whether the water will quickly infiltrate into the tunnel along a certain fracture, so as to make predictions and response strategies for water inflow in advance.

[0047] S202: Obtain the geological exploration data around the tunnel, and the geological exploration data includes the permeability of the rock formation, fault distribution and groundwater level.

[0048] Specifically, through geological exploration work, detailed data of the rock formation around the tunnel are obtained, including the permeability of the rock formation, the distribution of faults and the groundwater level, etc. The permeability data can describe the water infiltration ability of the rock formation, the fault distribution data helps to identify potential channels for water flow, and the groundwater level reflects the flow trend of groundwater and possible water sources. These data provide the basis for the possible water inflow in the tunnel excavation area. By combining the exploration data with real-time monitoring data, the behavior of water flow under different geological conditions can be further understood, so as to provide more accurate model inputs for subsequent water inflow prediction and avoid the risk of water inflow caused by unknown geological conditions.

[0049] S203: Combine the geological exploration data and real-time monitoring data to establish a local geological feature model of the tunnel.

[0050] Specifically, by combining the geological exploration data around the tunnel with real-time monitoring data, a local geological feature model of the tunnel can be created, which can reflect the geological complexity and hydrogeological characteristics of the tunnel area. For example, by combining the permeability data of the rock formation, the groundwater level change data and the stress and strain data of the surrounding rock, it can be analyzed which areas may have high permeability or the risk of concentrated water infiltration. Through this model, it is possible to evaluate how water flows in the rock formation around the tunnel under different geological conditions, and adjust the water inflow prediction in real time to ensure that the geological features can be flexibly adjusted according to the excavation progress and improve the accuracy of water flow prediction.

[0051] S204: Adjust the initial parameters of the water inflow prediction model based on the local geological feature model.

[0052] Specifically, based on the data in the local geological feature model, the system can adjust the initial parameters in the water inflow prediction model, such as rock formation permeability, fracture distribution, and groundwater level. The establishment of the local geological feature model combines the geological features around the tunnel and the changes in real-time monitoring data, and can accurately reflect the flow path and seepage capacity of water in different areas. During the excavation process, as the geological environment changes, the model will dynamically adjust the model parameters according to the new real-time monitoring data. For example, when it is detected that the permeability of a certain rock formation changes, the system will automatically update the permeability parameter in the water inflow prediction model, thereby improving the accuracy and real-time nature of the prediction and helping the construction personnel to timely master the water seepage situation.

[0053] In one embodiment, as Figure 4 shown, in step S20, that is, based on the real-time monitoring data, use the water inflow prediction model to predict the expected water inflow of the tunnel and generate a water inflow prediction result, which specifically includes: S21: Use the surrounding rock stress, strain, displacement, and pore water pressure in the real-time monitoring data, combine with the actual working conditions during the tunnel excavation process, input them into the water inflow prediction model, and calculate the water seepage volume at different construction stages.

[0054] Specifically, input the data such as surrounding rock stress, strain, displacement, and pore water pressure in the real-time monitoring data into the water inflow prediction model, and combine with the actual working condition data during the tunnel excavation process, such as excavation depth, excavation progress, and construction method, to calculate the water seepage volume at different construction stages. As the excavation process progresses, the stress and strain states of the surrounding rock will change, and the real-time monitoring data can provide dynamic inputs to reflect the changes in surrounding rock deformation, pressure, and water flow. The water inflow prediction model can calculate the water seepage volume at different construction stages based on these data, thereby helping the construction team to timely master the water flow changes and formulate corresponding prevention and control measures.

[0055] S22: Dynamically update the model parameters in the water inflow prediction model according to the displacement and stress change conditions in the real-time monitoring data. The model parameters include rock formation permeability and fracture distribution.

[0056] Specifically, the displacement and stress changes in the real-time monitoring data can provide a basis for real-time adjustment of the water inflow prediction model. Model parameters, such as rock formation permeability and fracture distribution, will be dynamically updated as the geological and hydrological conditions change during the excavation process. For example, when the excavation of the tunnel causes significant changes in the displacement or stress of the surrounding rock, these changes will affect the seepage path and flow velocity of the water. The system will adjust the permeability parameters of the rock formation and the fracture distribution parameters in real time according to the displacement and stress data, so as to make the water inflow prediction more accurate and reliable, and ensure the timeliness and accuracy of the prediction results.

[0057] S23: Based on the local geological feature model and hydrological conditions around the tunnel, calculate the flow and propagation path of water in the rock formation through numerical simulation to generate the prediction result of water inflow volume.

[0058] Specifically, by combining the local geological feature model and hydrological conditions around the tunnel, numerical simulation calculations are carried out to simulate the flow path and propagation of water in the rock formation. Through simulation, it can be predicted how the water seeps along different fractures or pores, considering the permeability of different rock formations and the influence of groundwater flow. The system can generate the prediction result of water inflow volume based on this data. For example, numerical simulation can show whether the water will quickly penetrate into the tunnel through some fractured rock formations, so as to calculate the flow velocity and total volume of the water, ensuring that the prediction result can reflect the actual situation in the complex hydrogeological environment in real time.

[0059] S24: Compare the water inflow prediction result with the preset water safety threshold to judge whether there is a risk of water inflow. If there is a risk of water inflow, an early warning signal for excessive water inflow will be generated.

[0060] Specifically, the water inflow prediction result is compared with the preset safety threshold. If the predicted water flow exceeds the set water safety threshold, the system will immediately judge that there is a risk of water inflow and generate an early warning signal for excessive water inflow, notifying the construction personnel to take preventive measures in time, such as increasing support, adjusting the excavation progress or adopting grouting reinforcement, etc. This can identify the water inrush risk in advance, avoid the occurrence of water inrush disasters, and ensure the safety of tunnel construction.

[0061] In one embodiment, as Figure 5 shown, in step S30, that is, through the rock mass failure assessment model, combined with real-time monitoring data, analyze the crack propagation, deformation and instability of the surrounding rock of the tunnel under different excavation parameters to generate the rock mass failure assessment result, specifically including: S31: Input the real-time monitoring data into the rock mass failure assessment model to calculate the stress-strain state of the surrounding rock under different excavation progress and excavation methods.

[0062] Specifically, information such as surrounding rock stress, strain, and displacement in the real-time monitoring data is input into the rock mass failure assessment model. According to different excavation progress and methods, the model can calculate the stress-strain state of the surrounding rock under these construction conditions. Especially during the excavation process, when the construction depth and method change, the stress and strain states of the surrounding rock will change accordingly, and the model can reflect these changes in real time and predict the possible failure areas of the surrounding rock. By inputting this data, the model can comprehensively analyze the surrounding rock and determine the stability of the surrounding rock and the possible crack propagation directions under different excavation conditions, timely identify potential risk areas, and ensure construction safety.

[0063] S32: Through numerical simulation, based on the real-time monitoring data, analyze the crack propagation mode of the rock mass, identify the key areas where cracks occur and expand, and dynamically adjust the crack propagation critical value in the rock mass failure assessment model according to the construction parameters.

[0064] Specifically, the numerical simulation method is used to analyze the crack propagation mode of the rock mass based on the real-time monitoring data. By inputting the stress, strain, and displacement data of the surrounding rock, the process of crack propagation in the rock mass is simulated. The model can identify the key areas where cracks occur and expand, especially in high-stress areas and weak areas, where the risk of crack propagation is relatively high. The stress and strain changes in the real-time monitoring data provide a basis for dynamic adjustment of the model. According to the construction parameters, such as excavation speed, excavation depth, and support method, the critical value of crack propagation can be dynamically adjusted to ensure that the model can accurately predict the risk of crack propagation during construction, timely adjust the construction plan, and avoid the instability of the surrounding rock caused by crack propagation.

[0065] S33: Combine the surrounding rock deformation data in the real-time monitoring data to update the mechanical parameters of the rock mass in the rock mass failure assessment model and simulate the deformation and failure process of the rock mass under different construction conditions.

[0066] Specifically, the surrounding rock deformation data in the real-time monitoring data is input into the rock mass failure assessment model to update the mechanical parameters of the rock mass in real time, especially the elastic modulus, compressive strength, and fracture distribution of the rock mass. When the surrounding rock deforms, the mechanical parameters will change, and the model will adjust according to these changes to more accurately simulate the deformation and failure process of the rock mass under different construction conditions. For example, when the tunnel excavation enters a new rock stratum or the construction progress accelerates, the deformation characteristics of the surrounding rock may change significantly. By updating the mechanical parameters in the model, the response of the rock mass under these changing conditions can be simulated, the stability of the surrounding rock can be evaluated in a timely manner, and the construction plan can be optimized to prevent failures.

[0067] S34: Based on the calculation results of the rock mass failure model, evaluate the stability of the surrounding rock and generate the rock mass failure assessment results.

[0068] Specifically, the rock mass failure model evaluates the stability of the surrounding rock by calculating stress, strain, and deformation data, combined with construction parameters and real-time monitoring data, identifies possible failure areas, determines the failure risk of the surrounding rock under different construction conditions, and generates a rock mass failure assessment report based on these calculation results. The report details the stability of the surrounding rock, the risk of crack propagation, and the areas where failure may occur. Through these results, construction personnel can understand the stability status of the surrounding rock in real time and take timely support and reinforcement measures to ensure construction safety.

[0069] In one embodiment, as Figure 6 shown, in step S40, based on the prediction result of water inflow and the rock mass failure assessment result, a construction optimization plan is generated, which specifically includes: S41: Based on the prediction result of water inflow, determine the water flow risk areas during the tunnel construction process, and adjust the path construction plan according to the predicted water flow rate and water flow path in the water inflow prediction result. The path construction plan includes grouting reinforcement and support reinforcement.

[0070] Specifically, by analyzing the prediction result of water inflow, it is possible to determine which areas in the tunnel construction process have a high water flow risk, especially those key areas where the predicted water flow rate is large or the water flow path may pass through the tunnel. According to these risk predictions, the construction plan can be dynamically adjusted. For example, the water seepage channels can be sealed through grouting reinforcement technology, or the surrounding rock can be reinforced through support reinforcement measures to prevent the water flow from infiltrating and causing instability of the surrounding rock. The grouting reinforcement points and grouting pressure can be optimized in real time according to the changes in the water flow path and risk areas to ensure the safety of the construction area.

[0071] S42: Based on the rock mass failure assessment result, analyze the stability of the surrounding rock, determine the surrounding rock failure risk areas, and increase the support measures according to the danger level of the surrounding rock failure risk areas.

[0072] Specifically, through the rock mass failure assessment result, the stability of the surrounding rock at different construction stages can be accurately analyzed, and the areas where failure risks may exist can be identified, especially those areas with large crack propagation and surrounding rock deformation. According to the danger level of the surrounding rock failure risk areas, the construction plan can be dynamically adjusted. For example, the support strength can be increased or stronger support measures can be taken in areas with a higher danger level, such as increasing supports, grouting reinforcement, etc. Through this risk-level-based reinforcement measure, the stability of the surrounding rock can be significantly improved, preventing accidents during the construction process.

[0073] S43: Combine the prediction result of water inflow and the rock mass failure assessment result to optimize the grouting plan. The grouting plan includes determining the grouting pressure, grouting point location, and grouting material type, and generating an emergency response plan.

[0074] Specifically, by combining the prediction of water inflow and the evaluation results of rock mass failure, the grouting plan can be optimized. First, determine the water penetration path and inflow volume through the water inflow prediction results. Combining with the evaluation results of rock mass failure, select appropriate grouting pressure and grouting point location to ensure that the grouting can cover all high-risk areas and prevent water inflow. At the same time, according to the stability evaluation of the rock mass, select appropriate grouting materials (such as low-permeability and high-strength materials) to maximize the reinforcement effect. On this basis, an emergency response plan can also be generated. If the water inflow volume exceeds the predetermined safety threshold, immediately initiate grouting and support reinforcement measures to prevent the instability of the surrounding rock or water inrush phenomenon.

[0075] S44: Dynamically adjust the construction optimization plan according to the construction stage and real-time monitoring data.

[0076] Specifically, the construction optimization plan can be dynamically adjusted according to different construction stages and real-time monitoring data. For example, during the excavation process, if the monitoring data shows that the deformation or stress change of the surrounding rock is large, the system can automatically adjust the construction progress or suspend the excavation and increase the support strength. If the water inflow volume exceeds the safety threshold, the plan can be immediately adjusted to increase water control measures, such as adjusting the grouting point or increasing the grouting pressure. By dynamically adjusting the construction plan, it can ensure that the risks during the construction process are timely controlled and avoid potential safety hazards caused by changes in construction conditions.

[0077] In one embodiment, as Figure 7 shown, in step S50, that is, the setting of the preset safety threshold, specifically includes: S51: Obtain historical monitoring data and real-time water flow data, and set a safety threshold based on the water inflow volume through the historical monitoring data, geological exploration data, and real-time water flow data.

[0078] Specifically, by obtaining the historical monitoring data, geological exploration data, and real-time water flow data of the tunnel construction area, and combining the historical trend of water inflow and the current water flow condition, a reasonable safety threshold for water inflow volume can be set. For example, analyze the variation law of water inflow volume according to historical data, and combine with the current hydrological conditions to set a safety limit for water inflow. If the water flow exceeds this threshold, the system will trigger a warning signal and initiate corresponding emergency response measures.

[0079] S52: Determine the maximum allowable value of the surrounding rock deformation according to the construction progress and construction method of the tunnel excavation, and set a safety threshold for the surrounding rock deformation according to the maximum allowable value of the surrounding rock deformation.

[0080] Specifically, according to the excavation progress and construction method of the tunnel, the construction team can set the maximum allowable value of the surrounding rock deformation. This maximum allowable value is dynamically adjusted based on factors such as the excavation depth, construction speed, and construction method. By real-time monitoring of these data and combining with the changes in the geological environment, the deformation safety threshold of the surrounding rock can be flexibly set. For example, in the initial stage of excavation, the deformation of the surrounding rock is small and the safety threshold is high; while when excavating to a deep layer or facing a complex geological structure, the deformation of the surrounding rock is large and the safety threshold needs to be appropriately reduced in order to respond in a timely manner to the changes in the surrounding rock deformation.

[0081] S53: Based on the rock mass failure assessment model, set the critical threshold for crack propagation.

[0082] Specifically, based on the calculation results of the rock mass failure assessment model and combined with the real-time monitoring data, the critical threshold for crack propagation can be determined. When the stress or strain of the surrounding rock reaches a certain level, cracks begin to occur and propagate, and the system can set the critical value for crack propagation according to the actual conditions of the surrounding rock. This threshold is dynamically adjusted according to the excavation progress and the stress changes of the surrounding rock to ensure that when the crack propagates to the critical threshold, the construction personnel can take reinforcement measures or adjust the construction method in a timely manner, thereby avoiding the failure or instability of the surrounding rock.

[0083] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0084] In one embodiment, a water inrush early warning system for the excavation of a water-rich tunnel in a fractured rock formation is provided, and this water inrush early warning system for the excavation of a water-rich tunnel in a fractured rock formation corresponds one-to-one with the water inrush early warning method for the excavation of a water-rich tunnel in a fractured rock formation in the above embodiment. As Figure 8 shown, this water inrush early warning system for the excavation of a water-rich tunnel in a fractured rock formation includes a monitoring module, a water flow prediction module, a failure assessment module, a scheme optimization module, a safety comparison module, and an early warning module. The detailed description of each functional module is as follows: The monitoring module is used to obtain the real-time monitoring data during the tunnel excavation process, and the real-time monitoring data includes the stress and strain of the surrounding rock; The water flow prediction module is used to predict the expected water flow inflow of the tunnel based on the real-time monitoring data using a water flow inflow prediction model, and generate a water flow inflow prediction result. The water flow inflow prediction model is dynamically adjusted based on the local geological characteristics and hydrological conditions of the tunnel; The failure assessment module is used to analyze the crack propagation, deformation, and instability conditions of the surrounding rock of the tunnel under different excavation parameters through a rock mass failure assessment model, generate a rock mass failure assessment result, and dynamically adjust the parameters of the rock mass failure assessment model according to the construction parameters; A scheme optimization module, which is used to generate a construction optimization scheme based on the prediction result of water inflow and the evaluation result of rock mass failure; A safety comparison module, which is used to compare the real-time monitoring data with the preset safety threshold to judge whether there is a risk of water inrush or surrounding rock instability; An early warning module, which is used to trigger a safety warning signal and immediately take emergency measures if the real-time monitoring data exceeds the preset safety threshold.

[0085] Optionally, the monitoring module includes: A displacement monitoring sub-module, which is used to monitor the displacement data of the tunnel excavation area by using a position sensor; A stress monitoring sub-module, which is used to monitor the stress change data of the tunnel surrounding rock by using a stress sensor; A strain detection sub-module, which is used to monitor the strain data of the tunnel surrounding rock by using a strain sensor; A pressure sub-module, which is used to monitor the pore water pressure data of the tunnel surrounding rock by using a pore water pressure sensor.

[0086] Optionally, the water inrush early warning system for the excavation of a water-rich tunnel in a fractured rock stratum further includes: A model construction module, which is used to construct a water inflow prediction model based on the finite difference method groundwater flow model; A data acquisition module, which is used to acquire the geological exploration data around the tunnel, and the geological exploration data includes the permeability of the rock stratum, the fault distribution and the groundwater level; A local feature model establishment module, which is used to establish a local geological feature model of the tunnel by combining the geological exploration data and the real-time monitoring data; A model adjustment module, which is used to adjust the initial parameters of the water inflow prediction model based on the local geological feature model.

[0087] Optionally, the water flow prediction module includes: A seepage calculation sub-module, which is used to input the surrounding rock stress, strain, displacement and pore water pressure in the real-time monitoring data, and combine with the actual working conditions during the tunnel excavation process, and input them into the water inflow prediction model to calculate the water seepage volume at different construction stages; An updated parameter sub-module, which is used to dynamically update the model parameters in the water inflow prediction model according to the displacement and stress change conditions in the real-time monitoring data, and the model parameters include the rock stratum permeability and the fracture distribution; A prediction result generation sub-module, which is used to generate a water inflow prediction result by numerically simulating the flow and propagation path of water in the rock stratum based on the local geological feature model and the hydrogeological conditions around the tunnel; The water flow comparison sub-module is used to compare the water flow influx prediction result with a preset water flow safety threshold to determine whether there is a risk of water flow influx. If there is a risk of water flow influx, an excessive water flow influx warning signal is generated.

[0088] Optionally, the damage assessment module includes: The calculation status sub-module is used to input the real-time monitoring data into the rock mass damage assessment model and calculate the stress-strain state of the surrounding rock under different excavation progress and excavation methods; The identification area sub-module is used to analyze the crack propagation mode of the rock mass based on the real-time monitoring data through numerical simulation, identify the key areas where cracks occur and expand, and dynamically adjust the crack propagation critical value in the rock mass damage assessment model according to the construction parameters; The simulation sub-module is used to update the mechanical parameters of the rock mass in the rock mass damage assessment model by combining the surrounding rock deformation data in the real-time monitoring data, and simulate the deformation and failure process of the rock mass under different construction conditions; The generation of assessment result sub-module is used to evaluate the stability of the surrounding rock based on the calculation results of the rock mass damage model and generate the rock mass damage assessment result.

[0089] Optionally, the scheme optimization module includes: The determination of path sub-module is used to determine the water flow risk area during the tunnel construction process based on the water flow influx prediction result, and adjust the path construction scheme according to the predicted water flow rate and water flow path in the water flow influx prediction result. The path construction scheme includes taking grouting reinforcement and support reinforcement; The determination of area sub-module is used to analyze the stability of the surrounding rock based on the rock mass damage assessment result, determine the surrounding rock damage risk area, and increase the support measures according to the danger level of the surrounding rock damage risk area; The optimization of grouting sub-module is used to optimize the grouting scheme by combining the water flow influx prediction result and the rock mass damage assessment result. The grouting scheme includes determining the grouting pressure, grouting point location and grouting material type, and generating an emergency response scheme; The scheme adjustment sub-module is used to dynamically adjust the construction optimization scheme according to the construction stage and real-time monitoring data.

[0090] Optionally, the safety comparison module includes: The determination of water flow threshold sub-module is used to obtain the historical monitoring data and real-time water flow data, and set the safety threshold based on the water flow influx through the historical monitoring data, geological exploration data and real-time water flow data; The determination of deformation threshold sub-module is used to determine the maximum allowable value of the surrounding rock deformation according to the construction progress and construction method of the tunnel excavation, and set the safety threshold of the surrounding rock deformation according to the maximum allowable value of the surrounding rock deformation; A set expansion threshold sub-module is used to set the critical threshold for crack expansion based on the rock mass failure assessment model.

[0091] For the specific limitations of a water inrush warning system for the excavation of water-rich tunnels in fractured rock strata, reference can be made to the limitations of a water inrush warning method for the excavation of water-rich tunnels in fractured rock strata in the above text, which will not be elaborated here. Each module in the above water inrush warning system for the excavation of water-rich tunnels in fractured rock strata can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0092] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0093] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A water inrush early warning method for the excavation of a water-rich tunnel in fractured rock strata, characterized in that, The method for predicting water inrush during the excavation of a water-rich tunnel in fractured rock formations includes: Obtaining real-time monitoring data during the tunnel excavation process, where the real-time monitoring data includes the stress and strain of the surrounding rock; Based on the real-time monitoring data, using a water inflow prediction model to predict the expected water inflow of the tunnel, generating a water inflow prediction result, and the water inflow prediction model is dynamically adjusted based on the local geological characteristics and hydrogeological conditions of the tunnel; Through a rock mass failure assessment model, combined with the real-time monitoring data, analyzing the crack propagation, deformation and instability of the surrounding rock of the tunnel under different excavation parameters, generating a rock mass failure assessment result, and dynamically adjusting the parameters of the rock mass failure assessment model according to the construction parameters; Based on the water inflow prediction result and the rock mass failure assessment result, generating a construction optimization plan; Comparing the real-time monitoring data with a preset safety threshold to determine whether there is a risk of water inrush or instability of the surrounding rock; If the real-time monitoring data exceeds the preset safety threshold, triggering a safety warning signal and immediately taking emergency measures.

2. The water inrush early warning method for the excavation of a water-rich tunnel in fractured rock strata according to claim 1, characterized in that, The obtaining of the real-time monitoring data during the tunnel excavation process includes: Using a position sensor to monitor the displacement data of the tunnel excavation area; Using a stress sensor to monitor the stress change data of the tunnel surrounding rock; Using a strain sensor to monitor the strain data of the tunnel surrounding rock; Using a pore water pressure sensor to monitor the pore water pressure data of the tunnel surrounding rock.

3. A water inrush early warning method for the excavation of a water-rich tunnel in a fractured rock formation according to claim 1, characterized in that, Before using the water inflow prediction model based on the real-time monitoring data to predict the expected water inflow of the tunnel and generating a water inflow prediction result, the method for predicting water inrush during the excavation of a water-rich tunnel in fractured rock formations further includes: Constructing the water inflow prediction model based on a finite difference groundwater flow model; Obtaining geological exploration data around the tunnel, where the geological exploration data includes the permeability of the rock formation, fault distribution and groundwater level; Combining the geological exploration data and the real-time monitoring data to establish a local geological feature model of the tunnel; Based on the local geological feature model, adjusting the initial parameters of the water inflow prediction model.

4. A water inrush early warning method for the excavation of a water-rich tunnel in a fractured rock stratum according to claim 1, characterized in that, The using the water inflow prediction model based on the real-time monitoring data to predict the expected water inflow of the tunnel and generating a water inflow prediction result includes: Using the stress, strain, displacement and pore water pressure of the surrounding rock in the real-time monitoring data, combined with the actual working conditions during the tunnel excavation process, inputting them into the water inflow prediction model, and calculating the water seepage volume at different construction stages; According to the displacement and stress change conditions in the real-time monitoring data, dynamically updating the model parameters in the water inflow prediction model, where the model parameters include rock formation permeability and fracture distribution; Based on the local geological feature model and hydrogeological conditions around the tunnel, numerically simulating the flow and propagation path of water in the rock formation to generate the water inflow prediction result; Comparing the water inflow prediction result with a preset water safety threshold to determine whether there is a water inflow risk. If there is a water inflow risk, generating an excessive water inflow warning signal.

5. A water inrush early warning method for the excavation of a water-rich tunnel in fractured rock strata according to claim 1, characterized in that, Through the rock mass failure assessment model, combining the real-time monitoring data, analyze the crack propagation, deformation and instability of the surrounding rock of the tunnel under different excavation parameters, and the generated rock mass failure assessment results include: Input the real-time monitoring data into the rock mass failure assessment model to calculate the stress-strain state of the surrounding rock under different excavation progress and excavation methods; Through numerical simulation, based on the real-time monitoring data, analyze the crack propagation mode of the rock mass, identify the key areas where cracks occur and expand, and dynamically adjust the crack propagation critical value in the rock mass failure assessment model according to the construction parameters; Combined with the surrounding rock deformation data in the real-time monitoring data, update the mechanical parameters of the rock mass in the rock mass failure assessment model, and simulate the deformation and failure process of the rock mass under different construction conditions; Based on the calculation results of the rock mass failure model, evaluate the stability of the surrounding rock and generate the rock mass failure assessment results.

6. The water inrush early warning method for the excavation of a water-rich tunnel in fractured rock strata according to claim 1, characterized in that, Based on the predicted water inflow volume result and the rock mass failure assessment result, the generated construction optimization plan includes: Based on the predicted water inflow volume result, determine the water flow risk areas during the tunnel construction process, and adjust the path construction plan according to the predicted water flow volume and water flow path in the predicted water inflow volume result. The path construction plan includes adopting grouting reinforcement and support reinforcement; Based on the rock mass failure assessment result, analyze the stability of the surrounding rock, determine the surrounding rock failure risk areas, and increase the support measures according to the danger level of the surrounding rock failure risk areas; Combined with the predicted water inflow volume result and the rock mass failure assessment result, optimize the grouting plan. The grouting plan includes determining the grouting pressure, grouting point location and grouting material type, and generating an emergency response plan; According to the construction stage and the real-time monitoring data, dynamically adjust the construction optimization plan.

7. A water inrush early warning method for excavation of a water-rich tunnel in fractured rock formations according to claim 1, characterized in that, The setting of the preset safety threshold includes: Obtain historical monitoring data and real-time water flow data, and set a safety threshold based on the water inflow volume through the historical monitoring data, geological exploration data and real-time water flow data; According to the construction progress and construction method of the tunnel excavation, determine the maximum allowable value of the surrounding rock deformation, and set a safety threshold for the surrounding rock deformation according to the maximum allowable value of the surrounding rock deformation; Based on the rock mass failure assessment model, set a critical threshold for crack propagation.

8. A water inrush early warning system for the excavation of a water-rich tunnel in fractured rock strata, characterized in that, The water inrush early warning system for the excavation of a water-rich tunnel in a broken rock stratum includes: A monitoring module for obtaining real-time monitoring data during the tunnel excavation process. The real-time monitoring data includes the stress and strain of the surrounding rock; A water flow prediction module for predicting the expected water inflow volume of the tunnel based on the real-time monitoring data using a water inflow prediction model, generating a water inflow volume prediction result. The water inflow prediction model is dynamically adjusted based on the local geological characteristics and hydrological conditions of the tunnel; A failure assessment module for analyzing the crack propagation, deformation and instability of the surrounding rock of the tunnel under different excavation parameters through a rock mass failure assessment model, combining the real-time monitoring data, generating a rock mass failure assessment result, and dynamically adjusting the parameters of the rock mass failure assessment model according to the construction parameters; A scheme optimization module, which is used to generate a construction optimization scheme based on the predicted result of the water inflow and the evaluation result of the rock mass failure; A safety comparison module, which is used to compare the real-time monitoring data with a preset safety threshold to determine whether there is a risk of water inrush or surrounding rock instability; An early warning module, which is used to trigger a safety warning signal and immediately take emergency measures if the real-time monitoring data exceeds the preset safety threshold.

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