Intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata

Abstract

The present invention relates to the technical field of tunnel engineering monitoring, and specifically discloses an intelligent monitoring method for a large-diameter shield tunnel in a karst water-rich stratum, comprising: S1, real-time geological detection, S2, shield posture and mechanical monitoring, S3, hydrological and seepage monitoring, S4, surrounding rock deformation and stress monitoring, and S5, comprehensive early warning and feedback; the intelligent monitoring method for a large-diameter shield tunnel in a karst water-rich stratum in the present invention realizes accurate early warning and intelligent response for construction safety through multi-source geological detection, real-time mechanical and posture monitoring, hydrological seepage monitoring, surrounding rock deformation and stress analysis, combined with AI algorithms, edge computing, and optical fiber sensors, effectively improving the tunnel construction efficiency and long-term operation stability; the intelligent monitoring method effectively reduces construction safety problems such as water gushing, surface subsidence, and shield deviation in karst water-rich strata by integrating a variety of real-time monitoring technologies and algorithms.

Description

Technical Field

[0001] The present invention belongs to the technical field of tunnel engineering monitoring, and particularly relates to an intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata. Background Art

[0002] With the acceleration of the urbanization process, tunnel engineering plays an increasingly important role in traffic construction. Especially in karst water-rich strata, the construction of large-diameter shield tunnels faces many challenges. In order to ensure the safety and efficiency of construction, advanced monitoring methods are needed to real-time master various parameters and states during tunnel construction.

[0003] Traditional geological exploration methods, such as ground-penetrating radar and advance drilling, although can reveal the stratum structure to a certain extent, in karst water-rich strata, due to the complexity and uncertainty of geological conditions, these methods are often difficult to accurately predict and identify the volume of rock layer caves, fault zones and water-rich areas of the stratum. A single geological exploration method may have errors and limitations, and multiple methods need to be integrated for verification and supplementation.

[0004] In karst water-rich strata, the changes in the flow velocity, temperature and pressure of groundwater have important impacts on tunnel construction and long-term stability. However, the existing hydrogeological monitoring methods often lack systematicness and are difficult to comprehensively and accurately master the dynamic changes of groundwater. The accuracy and reliability of hydrogeological numerical simulation need to be improved to more accurately predict the groundwater seepage path and water inrush risk.

[0005] In view of this, the inventor proposes an intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] An intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata, comprising the following steps:

[0009] S1. Real-time geological exploration: Using multi-source ground-penetrating radar and advance drilling analysis, combined with artificial intelligence algorithms for real-time identification and prediction of the geological structure in front of the shield, the geological structure including the volume of rock layer caves, fault zones and water-rich areas of the stratum; S2. Shield attitude and mechanical monitoring: Real-time monitoring of the shield machine attitude, propulsion force and grouting pressure through a sensor network, and realizing real-time feedback and abnormal identification based on edge computing and Internet of Things technology, the sensor network including high-precision gyroscopes, acceleration sensors and pressure sensors;

[0010] S3. Hydrological and seepage monitoring: Using a distributed fiber optic sensing system to monitor the changes in groundwater flow velocity, temperature, and pressure, and combining with hydrological numerical simulation to predict the groundwater seepage path and water inrush risk;

[0011] S4. Surrounding rock deformation and stress monitoring: Deploying multi-dimensional monitoring equipment, including laser scanners, acoustic emission sensors, and strain gauges, to monitor the deformation of the surrounding rock and the changes in the stress field in real time;

[0012] S5. Comprehensive early warning and feedback: Based on the big data analysis platform, performing anomaly detection on the monitoring data and generating early warning information, and achieving automatic response through a multi-level linkage mechanism; Using fiber Bragg grating sensors to record and analyze the long-term deformation of the tunnel structure and the changes in the water environment, and predicting the stability during operation.

[0013] Preferably, the calculation expression for the volume of the rock stratum karst cave is:

[0014] ;

[0015] V: The volume of the karst cave, m 3 ;

[0016] f(x, y, z): The probability density function of the karst cave generated from the reflection signal intensity data of multi-source geological radar and advanced drilling;

[0017] x1, x2, y1, y2, z1, z2: The boundary coordinates of the karst cave, determined by radar scanning and artificial intelligence model prediction.

[0018] Preferably, the real-time geological exploration step further includes the fiber Raman distributed sensing technology, which is used to monitor the temperature distribution of the rock stratum in front of the shield and evaluate the intensity of groundwater flow and corrosion activities in the water-rich area of the stratum.

[0019] Preferably, the prediction expression for the water-richness of the water-rich area of the stratum is:

[0020] ;

[0021] Pw: The water-richness index of the stratum;

[0022] T: The temperature gradient of the stratum detected by Raman distributed fiber optic sensing;

[0023] C: The groundwater conductivity data obtained by drilling;

[0024] S: The intensity attenuation value of the geological radar reflection signal;

[0025] α, β, γ: The weight parameters obtained by training through machine learning algorithms, linear regression, or support vector machines.

[0026] Preferably, the optimization expression for the thrust force is:

[0027] ;

[0028] Ft: Thrust force of the shield machine, kN;

[0029] Pi: Support pressure of each partition on the shield face, kPa;

[0030] Ai: Area of the corresponding partition, m 2 ;

[0031] ΔF: Dynamic adjustment value, optimized and calculated by the AI algorithm based on the surrounding rock deformation and tunneling speed.

[0032] Preferably, the control formula for the attitude of the shield machine is:

[0033] ;

[0034] θ, : Attitude deviation angle of the shield machine, °;

[0035] Δy, Δx: Displacement deviations of the shield machine in the vertical and horizontal directions, m;

[0036] L: Current tunneling length of the shield machine, m.

[0037] Preferably, the calculation expression for the underground water flow velocity is:

[0038] ;

[0039] v: Underground water flow velocity, m / s;

[0040] K: Permeability coefficient, obtained from on-site tests;

[0041] : Hydraulic gradient, calculated from the pressure changes monitored by the fiber optic sensing network.

[0042] Preferably, the simulation formula for the underground water seepage path is:

[0043] ;

[0044] h(x,y,t): Change in seepage head, m;

[0045] h0: Initial head height, m;

[0046] Q: Underground water flow rate per unit time, m 3 / s;

[0047] T: Water permeability coefficient of the formation, m 2 / s;

[0048] r, r0: The current radius and initial radius of the water flow path, m.

[0049] Preferably, the expression for the anomaly detection is:

[0050] ;

[0051] A(t): Anomaly value index;

[0052] Xi(t): The monitoring value of the i-th sensor;

[0053] μi: The normal operation mean value of the i-th sensor;

[0054] wi: The weight of the i-th sensor, obtained by training a machine learning model;

[0055] δ: Threshold value, exceeding which triggers an alarm.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] (1) In the intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata of the present invention, through multi-source geological exploration, real-time mechanics and attitude monitoring, hydrogeological seepage monitoring, and surrounding rock deformation stress analysis, combined with AI algorithms, edge computing, and fiber optic sensors, accurate early warning and intelligent response for construction safety are realized, effectively improving the tunnel construction efficiency and long-term operation stability.

[0058] (2) In the intelligent monitoring method of the present invention, by integrating various real-time monitoring technologies and algorithms, construction safety problems such as water inrush, ground settlement, and shield deviation in karst water-rich strata are effectively reduced. Combining distributed fiber optic sensor technology, seismic wave imaging technology, and water pressure monitoring technology, full-dimensional dynamic monitoring of karst caves, water-rich areas, and shield forces is realized; real-time monitoring data is analyzed through an adaptive algorithm, which can accurately predict the location of water inrush points, the distribution of settlement amounts, and the stability of soil masses, avoiding potential risks in advance and ensuring the safety of the construction environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of the intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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.

[0061] Embodiment 1

[0062] Please refer to Figure 1 as shown, the intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata includes the following steps:

[0063] S1. Real-time geological exploration: Adopt multi-source geological radar and advanced drilling analysis, and combine with artificial intelligence algorithms to identify and predict the real-time geological structure in front of the shield. The geological structure includes the volume of rock layer karst caves, fault zones, and water-rich areas of the strata;

[0064] S2. Shield attitude and mechanics monitoring: Real-time monitor the shield machine attitude, thrust force, and grouting pressure through a sensor network, and realize real-time feedback and abnormal identification based on edge computing and Internet of Things technology. The sensor network includes high-precision gyroscopes, acceleration sensors, and pressure sensors;

[0065] S3. Hydrology and seepage monitoring: Use a distributed optical fiber sensing system to monitor the underground water flow velocity, temperature, and pressure changes, and combine with hydrological numerical simulation to predict the groundwater seepage path and water inrush risk;

[0066] S4. Surrounding rock deformation and stress monitoring: Arrange multi-dimensional monitoring equipment, including laser scanners, acoustic emission sensors, and strain gauges, to monitor the surrounding rock deformation and stress field changes in real time;

[0067] S5. Comprehensive early warning and feedback: Based on a big data analysis platform, perform anomaly detection on the monitoring data and generate early warning information, and achieve automatic response through a multi-level linkage mechanism; Use fiber Bragg grating sensors to record and analyze the long-term deformation of the tunnel structure and water environment changes, and predict the stability during operation.

[0068] As can be seen from the above, the intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata realizes precise early warning and intelligent response for construction safety through multi-source geological exploration, real-time mechanics and attitude monitoring, hydrological seepage monitoring, and surrounding rock deformation stress analysis, and effectively improves the tunnel construction efficiency and long-term operation stability.

[0069] Embodiment 2

[0070] The calculation expression for the volume of the rock layer karst cave is:

[0071] ;

[0072] V: The volume of the karst cave, m 3 ;

[0073] f(x,y,z): The probability density function of the karst cave generated from the reflection signal intensity data of multi-source geological radar and advanced drilling;

[0074] x1, x2, y1, y2, z1, z2: The boundary coordinates of the karst cave, determined by radar scanning and artificial intelligence model prediction;

[0075] This formula quickly predicts the volume and location of karst caves in the rock stratum by integrating the three-dimensional distribution of radar signals, provides a basis for the shield tunneling path planning, and reduces the risk of karst cave collapse.

[0076] Specifically, the real-time geological detection step further includes the fiber optic Raman distributed sensing technology, which is used to monitor the temperature distribution of the rock stratum in front of the shield and evaluate the intensity of groundwater flow and dissolution activities in the water-rich area of the stratum.

[0077] Specifically, the prediction expression for the water-richness of the stratum in the water-rich area of the stratum is:

[0078] ;

[0079] Pw: Water-richness index of the stratum;

[0080] T: Temperature gradient of the stratum detected by Raman distributed optical fiber sensing;

[0081] C: Groundwater conductivity data obtained by drilling;

[0082] S: Intensity attenuation value of the geological radar reflection signal;

[0083] α, β, γ: Weight parameters obtained by training through machine learning algorithms, linear regression or support vector machines;

[0084] By comprehensively analyzing the temperature change, conductivity and radar signal characteristics, accurately evaluate the distribution of water-rich areas in the rock stratum, and provide guidance for adjusting the construction parameters of the shield machine.

[0085] Specifically, the optimization expression for the thrust is:

[0086] ;

[0087] Ft: Thrust of the shield machine, kN;

[0088] Pi: Support pressure of each partition on the shield face, kPa;

[0089] Ai: Area of the corresponding partition, m 2 ;

[0090] ΔF: Dynamic adjustment value, optimized and calculated by the AI algorithm based on the surrounding rock deformation and tunneling speed;

[0091] This formula combines the distribution of support pressure with the shield tunneling speed, and reduces the risk of surface settlement and rock stratum disturbance caused by tunneling by dynamically adjusting the thrust.

[0092] Specifically, the control formula for the attitude of the shield machine is as follows:

[0093] ;

[0094] θ, : Deviation angle of the shield machine attitude, °;

[0095] Δy, Δx: Displacement deviations of the shield machine in the vertical and horizontal directions, m;

[0096] L: Current tunneling length of the shield machine, m;

[0097] Calculate the deviation angle using the displacement deviation to provide a reference for the attitude adjustment of the shield machine and ensure the accuracy of the tunneling route.

[0098] Specifically, the calculation expression for the underground water flow velocity is as follows:

[0099] ;

[0100] v: Underground water flow velocity, m / s;

[0101] K: Permeability coefficient, obtained from on-site tests;

[0102] : Hydraulic gradient, calculated from the pressure changes monitored by the fiber optic sensing network;

[0103] This formula calculates the underground water flow velocity and its direction by real-time monitoring of the hydraulic gradient, providing key data for predicting the water inrush risk and the surrounding rock failure caused by the water flow.

[0104] Specifically, the simulation formula for the underground water seepage path is as follows:

[0105] ;

[0106] (x, y, t): Change in seepage head, m;

[0107] h0: Initial head height, m;

[0108] Q: Underground water flow rate per unit time, m 3 / s;

[0109] T: Permeability coefficient of the formation, m 2 / s;

[0110] r, r0: Current radius and initial radius of the water flow path, m;

[0111] Identify possible underground water circulation channels near the shield machine by dynamically simulating the seepage path and the change in head to prevent water inrush accidents.

[0112] Specifically, the expression for the anomaly detection is as follows:

[0113] ;

[0114] A(t): Anomaly value index;

[0115] Xi(t): Monitoring value of the i-th sensor;

[0116] μi: Normal operation mean value of the i-th sensor;

[0117] wi: Weight of the i-th sensor, obtained by training a machine learning model;

[0118] δ: Threshold value, exceeding which triggers an alarm;

[0119] By performing weighted anomaly detection on multi-sensor data, potential risks in shield tunneling construction can be quickly identified, providing a basis for real-time decision-making in construction management.

[0120] As can be seen from the above, this intelligent monitoring method effectively reduces construction safety problems such as water inrush, ground settlement, and shield deviation in karst water-rich strata by integrating various real-time monitoring technologies and algorithms. Combining distributed fiber optic sensor technology, seismic wave imaging technology, and water pressure monitoring technology, it realizes all-round dynamic monitoring of karst caves, water-rich areas, and shield forces; real-time monitoring data is analyzed through an adaptive algorithm, which can accurately predict the location of water inrush points, settlement distribution, and soil stability, avoiding potential risks in advance and ensuring the safety of the construction environment.

[0121] Based on algorithms such as thrust force calculation and dynamic adjustment of earth pressure balance, the operation stability of the shield machine in complex strata is ensured, thereby reducing unnecessary downtime and mechanical losses. Through seismic wave reflection and laser scanning technology, the volume and shape of karst caves can be accurately detected, avoiding cave-ins or mud and water gushing caused by blind construction; based on water pressure monitoring and risk assessment models, the water inrush risk can be effectively quantified, ensuring timely adoption of targeted measures such as grouting or drainage.

[0122] Embodiment 3

[0123] This design is specifically applied to the construction of shield tunnels in karst strata. Karst strata are widely distributed, especially in regions such as southern China and Southeast Asia. Their geological conditions are complex and the water inrush risk is high, often posing great challenges to underground engineering;

[0124] Furthermore, a large-diameter shield tunnel with a diameter of 12.8 meters and a total length of 4 kilometers is built in a certain karst water-rich stratum. During construction, the geological conditions are complex, including karst caves, fault zones, water-rich areas, etc., and there are high risks of water inrush and formation collapse. The project adopts an intelligent monitoring method based on multi-source sensing and artificial intelligence to ensure construction safety and efficiency.

[0125] Implementation Steps and Parameters

[0126] 1. Real-time Geological Exploration

[0127] Equipment Used:

[0128] Multi-source Geological Radar (Frequency: 200 MHz, Detection Depth: 10 m);

[0129] Advance Drilling Equipment (Drilling Depth: 15 m).

[0130] Algorithm Application:

[0131] Combined with the three-dimensional distribution of geological radar signals, the rock stratum karst cave volume prediction formula is adopted:

[0132] ;

[0133] Input Parameters: Spatial distribution data of radar reflection signals;

[0134] Detection Result: There is a karst cave with a volume of approximately 4.8 m 3 at a distance of 10 m in front of the shield, and the central coordinates are (5, 3, -2) m.

[0135] Effect: Formulate a reinforcement plan in advance, adopt the grouting method to fill the karst cave, and avoid collapse accidents during the shield machine propulsion process.

[0136] 2. Shield Attitude and Mechanics Monitoring

[0137] Equipment Used:

[0138] High-precision Gyroscope (Accuracy: 0.01°);

[0139] Thrust Monitoring Sensor (Range: 0 - 5000 kN).

[0140] Algorithm Application:

[0141] Thrust Optimization Formula:

[0142] ;

[0143] Among them, the partition pressures on the shield face are:

[0144] P1 = 200 kPa, P2 = 250 kPa, P3 = 210 kPa, and the corresponding partition areas are:

[0145] A1 = 6 m 2 , A2 = 8 m 2 , A3 = 5 m 2 . The dynamic adjustment value ΔF is predicted by the artificial intelligence model to be 30 kN.

[0146] Calculation Result:

[0147] Thrust force Ft = (200×6 + 250×8 + 210×5) + 30 = 4280 kN

[0148] Effect: By optimizing the thrust force, the shield machine advances smoothly, avoiding the problem of excessive ground settlement. The settlement amount is reduced from the original predicted value of 5 mm to 1.8 mm.

[0149] 3. Hydrological and Seepage Monitoring

[0150] Equipment used:

[0151] Distributed optical fiber sensor (temperature resolution: 0.01 °C, pressure resolution: 1 kPa);

[0152] Miniature water quality sensor (monitoring parameters: pH, conductivity, dissolved oxygen).

[0153] Algorithm application:

[0154] Underground water flow velocity formula:

[0155] ;

[0156] Among them, the permeability coefficient K = 5×10 -6 m / s;

[0157] Hydraulic gradient = 2;

[0158] Calculation result: The underground water flow velocity is v = 5×10 -6 ×2 = 1×10 -5 m / s

[0159] Water seepage path simulation formula:

[0160] ;

[0161] Input parameters:

[0162] Initial water head height h0 = 15 m, groundwater flow rate per unit time Q = 0.01 m 3 / s, permeability coefficient T = 1.2×10 - 3 m 2 / s, path radius r = 10 m, r0 = 2 m;

[0163] Calculation result: The change in water head height is h(x, y, t) ≈ 12.8 m;

[0164] Through hydrological monitoring, the water inrush risk in the water-rich stratum is evaluated in real time and the drainage design is optimized to reduce the occurrence probability of water inrush accidents during construction.

[0165] 4. Comprehensive Early Warning and Feedback

[0166] Equipment used:

[0167] Abnormal early warning system (based on edge computing);

[0168] Blockchain storage module (storage speed: 1000TPS).

[0169] Abnormal detection formula:

[0170] ;

[0171] Parameter input:

[0172] Sensor data includes thrust force (X1 = 4280kN), support pressure (X2 = 240kPa), normal mean values μ1 = 4350kN, μ2 = 230kPa, weights w1 = 0.7, w2 = 0.3, threshold δ = 50

[0173] Calculation result: ∣A(t)∣ = 0.7×∣4280 - 4350∣ + 0.3×∣240 - 230∣ = 52 > 50;

[0174] Exceeds the threshold, triggering an alarm.

[0175] Effect: Timely detect abnormal shield thrust force, adjust the support pressure, avoid construction interruption, and improve construction continuity.

[0176] As can be seen from the above, the collapse accidents caused by karst caves during the tunneling of the shield machine are reduced, and the stability of the tunnel construction area is increased by 50%.

[0177] Through hydrogeological monitoring and optimized drainage design, the probability of water inrush accidents is reduced to 2%.

[0178] The tunneling speed of the shield machine is increased from an average of 20m / day to 25m / day.

[0179] The deviation of the tunneling route is controlled within 2cm.

[0180] Record the geological and mechanical data during the construction period to provide comprehensive digital support for the subsequent operation and maintenance of the tunnel.

[0181] Example 4

[0182] Application in Mountain Highway Tunnel Project

[0183] Background and project description:

[0184] A certain mountain highway needs to cross a karst water-rich stratum, and its tunnel construction faces problems such as dense karst caves, high groundwater pressure, and unstable strata.

[0185] Tunnel diameter: 11.6 meters

[0186] Construction length: 3.2 km

[0187] Geological features: The density of karst caves is about 8 per square kilometer, and the groundwater pressure is about 1.2 MPa.

[0188] Project duration: 20 months

[0189] The project comprehensively applies the intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata, including the following specific implementation steps:

[0190] Geological preliminary exploration and data modeling

[0191] Data collection: Using seismic wave imaging and laser scanning technology, a three-dimensional geological model of the construction area was established, and 32 karst caves were found with a total volume of about 12,500 cubic meters.

[0192] Risk prediction: By analyzing the distribution of karst caves and the characteristics of groundwater flow through deep learning algorithms, 10 high-risk construction points were predicted, and a grouting reinforcement plan was designed.

[0193] Real-time monitoring and dynamic construction adjustment

[0194] Multi-point real-time monitoring: Distributed optical fiber sensors were installed, and a monitoring point was arranged every 5 meters to collect soil stress, ground settlement and water pressure data in real time.

[0195] Parameter optimization: Combining the monitoring data, the cutter head rotation speed, thrust and earth pressure balance pressure of the shield machine were dynamically adjusted to ensure stable propulsion.

[0196] Intelligent early warning and emergency treatment

[0197] Water inrush risk control: After the monitoring system detects that the water pressure fluctuation amplitude in a certain area is greater than 0.7 MPa, the drainage system is activated in advance to avoid sudden inrush accidents.

[0198] Settlement control: When the ground settlement rate exceeds 3 mm / hour, the system issues an alarm and automatically adjusts the propulsion parameters to ensure that the settlement amount is lower than the design range.

[0199] The key monitoring and construction parameters are shown in Table 1 below;

[0200] Table 1

[0201]

[0202] .Effect and effectiveness analysis

[0203] Improvement of safety

[0204] Karst cave risk control: The karst cave area was reinforced in advance, avoiding 4 possible water inrush accidents.

[0205] Surface settlement control: The maximum settlement is 22.3 mm, which does not exceed the specification requirements, ensuring the safety of the surrounding environment of the construction area.

[0206] Improved construction efficiency

[0207] Through dynamic parameter optimization, the propulsion efficiency of the shield machine is increased by 16% compared with the traditional method.

[0208] The number of shutdown adjustments is reduced by 60%, and the overall construction period is shortened by 1.5 months.

[0209] Economic benefit analysis

[0210] Direct cost savings:

[0211] Optimizing the treatment of karst caves saves about 4 million yuan in grouting material costs.

[0212] Reducing emergency shutdowns caused by water inrush reduces the shutdown loss by about 6 million yuan.

[0213] Indirect benefits:

[0214] Cost savings on equipment leasing and management due to the shortened construction period are about 4.5 million yuan.

[0215] Total cost savings: 14.5 million yuan.

[0216] Social benefits

[0217] Reducing public complaints caused by geological disasters enhances the social recognition of the engineering project.

[0218] Intelligent monitoring data provides support for the subsequent tunnel operation, improving the operation and management efficiency.

[0219] The economic benefit indicators are summarized in Table 2 below;

[0220] Table 2

[0221]

[0222] Application scope and effectiveness

[0223] Scope of application:

[0224] This method is applicable to geological conditions with dense karst caves and high groundwater pressure, such as mountain highway, railway tunnels, and water conveyance project tunnels.

[0225] It is especially applicable to projects involving complex karst strata and long construction lengths.

[0226] Effectiveness and economic significance:

[0227] Safety guarantee: Significantly reducing construction safety hazards caused by geological disasters.

[0228] Cost control: Reduce the direct economic losses caused by karst cave treatment and construction suspension, saving more than 4.5 million yuan per kilometer of tunnel on average.

[0229] Efficiency improvement: The propulsion efficiency is increased by 10% - 20%, and the construction period is significantly shortened.

[0230] This method not only has direct benefits for a single project, but also can form a standardized intelligent monitoring solution, which is applicable to engineering projects with similar geological conditions across the country and has the potential for large-scale promotion.

[0231] As can be seen from the above, this embodiment takes the mountain expressway tunnel as the application scenario, and details the remarkable achievements of the intelligent monitoring method under complex geological conditions, reflecting its comprehensive advantages in improving safety, reducing construction costs, and optimizing efficiency, with clear economic value and broad promotion prospects.

[0232] Example 5

[0233] Application in a shield tunnel project of a coastal city subway:

[0234] Background and project description

[0235] A subway Line 11 is under construction in a coastal city. A certain section of the tunnel passes through a karst-rich water-bearing stratum. The groundwater level in this area is high, karst caves are developed, and the stratum is uneven, with great risks of water inrush, ground settlement, and shield machine deviation.

[0236] Tunnel diameter: 12.4 meters

[0237] Construction length: 1.8 kilometers

[0238] Construction period requirement: 12 months

[0239] The project applies the intelligent monitoring method proposed in this paper. The system integrates distributed optical fiber sensors, seismic wave detection, real-time water pressure monitoring equipment, and a risk prediction and optimization algorithm based on deep learning. The specific steps include:

[0240] Geological pre-judgment and karst cave identification:

[0241] Using seismic wave imaging technology in the early stage of construction to identify the location and scale of karst caves, and confirming about 15 karst caves with a total volume of about 8,200 cubic meters.

[0242] The karst cave area is pre-grouted in advance, consuming 5 million yuan in material costs.

[0243] Intelligent monitoring of shield construction:

[0244] Install distributed optical fiber sensors to monitor ground settlement. When the settlement rate is abnormal, the system automatically adjusts the propulsion force and cutter head rotation speed.

[0245] Analyze the groundwater level changes in real time and adjust the earth pressure balance parameters according to the water pressure dynamics.

[0246] Risk early warning system:

[0247] Based on the real-time monitoring data, the deep learning algorithm predicts the water gushing points, and drains are arranged in advance to avoid the shield machine from stopping work.

[0248] Key monitoring and construction parameters

[0249] Real-time monitoring data:

[0250] Allowable range of ground surface settlement: <20 mm (average settlement is 13.2 mm, meeting the standard).

[0251] Range of water pressure fluctuation: <0.5 MPa (water gushing was predicted 2 times and both were treated in advance).

[0252] System response time: <3 seconds

[0253] Advancing speed: After adjustment, the average is 6.2 meters per day, about 12% higher than traditional construction.

[0254] Analysis of effects and achievements

[0255] Safety:

[0256] Three major construction accidents that could have caused water gushing were avoided, ensuring the normal advancement of the shield machine.

[0257] The maximum ground surface settlement is controlled at 18.7 mm, without the risk of exceeding the standard.

[0258] Economic benefits:

[0259] Direct cost savings:

[0260] By identifying karst caves in advance and grouting, the emergency shutdown caused by water gushing was reduced, saving about 3.2 million yuan in costs.

[0261] Optimizing construction parameters reduced the tool wear rate, saving about 2 million yuan in equipment maintenance costs.

[0262] Indirect economic benefits:

[0263] The construction period was shortened by 15 days, saving about 200,000 yuan in management and rental costs per day, with a total savings of 3 million yuan.

[0264] Social benefits:

[0265] Through the precise control of the intelligent monitoring system, the complaints from residents caused by ground surface settlement and the risk of secondary disasters were reduced, improving the social recognition and brand image of the project.

[0266] The summary of economic benefit indicators is shown in Table 3 below;

[0267] Table 3

[0268]

[0269] Scope of application and prospects

[0270] The implementation of this intelligent monitoring method not only provides an accurate and efficient solution for the construction of shield tunnels in karst water-rich strata, but also demonstrates its application potential in the following fields:

[0271] Coastal high groundwater tunnels:

[0272] The adaptability to high water pressure and complex strata is significantly improved, and it is suitable for cross-sea tunnels and port area projects.

[0273] Urban subways and underground space development:

[0274] Reduce ground settlement and damage to surrounding buildings caused by construction, and reduce insurance and compensation costs.

[0275] Economic improvement:

[0276] Through large-scale promotion, in the future, the cost savings per project can exceed 10 million yuan, forming economies of scale.

[0277] As can be seen from the above, this embodiment clearly demonstrates the specific effects and economic benefits of the intelligent monitoring method in practical engineering, especially in terms of improving construction safety and reducing costs. This model provides a replicable and popularizable technical route for the intelligent development of complex geological engineering.

[0278] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0279] In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved, and other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0280] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring method for a large-diameter shield tunnel in a karst water-rich stratum, characterized in that: The following steps are involved: S1. Real-time geological detection: Multi-source geological radar and advanced drilling analysis are used in combination with artificial intelligence algorithms to identify and predict the geological structure in front of the shield in real time. The geological structure includes the volume of rock karst caves, fault zones and water-rich areas in the strata; The prediction expression of formation water richness in the water-rich area is: ； Pw: formation water richness index; T: Formation temperature gradient detected by Raman distributed optical fiber sensing; C: Groundwater conductivity data obtained by drilling; S: intensity attenuation value of geological radar reflection signal; α, β, γ: weight parameters obtained through machine learning algorithms, linear regression or support vector machine training; S2. Shield machine posture and mechanical monitoring: The shield machine posture, propulsion force and grouting pressure are monitored in real time through a sensor network, and real-time feedback and abnormality identification are achieved based on edge computing and Internet of Things technologies. The sensor network includes high-precision gyroscopes, acceleration sensors and pressure sensors. S3. Hydrology and seepage monitoring: Use distributed fiber optic sensing systems to monitor groundwater velocity, temperature and pressure changes, and combine hydrological numerical simulation to predict groundwater seepage paths and water inrush risks; S4. Surrounding rock deformation and stress monitoring: deploy multi-dimensional monitoring equipment, including laser scanners, acoustic emission sensors and strain gauges, to monitor surrounding rock deformation and stress field changes in real time; S5. Comprehensive early warning and feedback: Based on the big data analysis platform, abnormalities are detected in the monitoring data and early warning information is generated, and automatic response is achieved through a multi-level linkage mechanism; fiber grating sensors are used to record and analyze the long-term deformation of the tunnel structure and changes in the water environment, and predict stability during operation.

2. The intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata according to claim 1 is characterized in that: The calculation expression of the rock cave volume in step S1 is: ； V: volume of the cave, m 3 ; f(x,y,z): probability density function of cave generated by the reflected signal intensity data of multi-source geological radar and advance drilling; x1,x2,y1,y2,z1,z2: The boundary coordinates of the cave, determined by radar scanning and artificial intelligence model prediction.

3. The intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata according to claim 1 is characterized in that: The real-time geological detection step further includes fiber Raman distributed sensing technology for monitoring the temperature distribution of the rock formation in front of the shield and evaluating the groundwater flow and dissolution activity intensity of the water-rich area of ​​the formation.

4. The intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata according to claim 1 is characterized in that: The optimization expression of the propulsion force in step S2 is: ； Ft: thrust force of shield machine, kN; Pi: support pressure of each partition of the shield face, kPa; Ai: the area of ​​the corresponding partition, m 2 ; ΔF: Dynamic adjustment value, calculated by the AI ​​algorithm based on surrounding rock deformation and tunneling speed optimization.

5. The intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata according to claim 1 is characterized in that: The control formula of the shield machine posture in step S2 is: ； θ, : Shield machine attitude deviation angle, °; Δy, Δx: displacement deviation of the shield machine in the vertical and horizontal directions, m; L: Current excavation length of the shield machine, m.

6. The intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata according to claim 1 is characterized in that: The calculation expression of groundwater flow velocity in step S3 is: ； v: groundwater velocity, m / s; K: Permeability coefficient, obtained from field tests; : hydraulic gradient, calculated from pressure changes monitored by a fiber optic sensing network; This formula calculates groundwater flow velocity and direction by real-time monitoring of hydraulic gradient, providing data for predicting water gushing risks and surrounding rock damage caused by water flow.

7. The intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata according to claim 1 is characterized in that: The simulation formula of the groundwater seepage path is: ； h(x,y,t): change in seepage head, m; h0: initial water head height, m; Q: Groundwater flow per unit time, m 3 / s; T: Permeability coefficient of the formation, m 2 / s; r, r0: current radius and initial radius of the water flow path, m.

8. The intelligent monitoring method for large-diameter shield tunnels in karst water-rich strata according to claim 1 is characterized in that: The expression for anomaly detection in step S5 is: ； A(t): outlier indicator; Xi(t): the monitoring value of the i-th sensor; μi: the normal operating mean of the i-th sensor; wi: weight of the i-th sensor, obtained by training the machine learning model; δ: Threshold value, exceeding this value triggers an alarm.

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