Immersed tunnel pipe joint connector water seepage intelligent monitoring method based on optimized direct current method

Abstract

The invention relates to an intelligent immersed tunnel pipe joint water seepage monitoring method based on an optimized direct current method, and belongs to the technical field of leakage detection. The method comprises the steps that S1, an immersed tunnel connector serves as a central symmetry axis, n vertical rows of electrodes are symmetrically and evenly distributed on the two sides of the connector, and 2n electrodes are arranged in each vertical row at intervals; s2, the electrodes are divided into power supply electrodes (X, Y) and measuring electrodes (O, P), X, Y, O and P are located on the same straight line, and O and P are always located near the arrangement center of X and Y; s3, calculating the apparent resistivity of each measuring point on each measuring line, and generating a three-dimensional data matrix according to the apparent resistivity of all the measuring points; and the actual resistivity is calculated by using a three-dimensional fast inversion algorithm based on neural network design, so that the water seepage condition is judged. According to the invention, real-time and high-precision monitoring of the water seepage condition of the pipe joint connector is realized, and the safety and reliability of tunnel operation are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of leakage detection, and relates to an intelligent monitoring method for water seepage of pipe joints in immersed tube tunnels based on an optimized direct current method. Background Art

[0002] As core transportation infrastructure spanning waterways, immersed tube tunnels are widely used in projects spanning rivers and seas. Pipe joints are critical components of immersed tube tunnels, and their waterproof performance directly determines the safety and durability of the tunnel structure. In modern engineering practice, a single standard pipe joint can reach up to 180 meters in length and weigh over 80,000 tons. Precision butt-jointing technology is used to achieve millimeter-level splicing accuracy under complex hydrological conditions. However, the GINA waterstops used in pipe joints are susceptible to foundation settlement, water pressure fluctuations, and material aging during long-term operation, significantly increasing the risk of leakage.

[0003] Limitations of existing water seepage monitoring technology:

[0004] (1) Defects of manual inspection method:

[0005] Inefficiency: Traditional manual inspections require regular lane closures for visual inspections, with a single inspection taking over 48 hours, severely impacting traffic operation efficiency.

[0006] High missed detection rate: Water seepage traces in hidden areas are difficult to identify with the naked eye, resulting in a high missed detection rate;

[0007] Data lag: Unable to capture sudden leakage events, resulting in long maintenance response delays.

[0008] (2) Deficiencies in sensor monitoring technology:

[0009] 1) Pressure sensor: Affected by dynamic changes in water pressure, the false alarm rate is high. Cross-sea tunnels have experienced multiple false alarms due to tidal pressure fluctuations, increasing maintenance costs.

[0010] 2) Humidity sensor: Electrode corrosion failure in high-salt environments leads to a service life of less than 3 years (compared to the laboratory nominal 5 years), requiring frequent replacement.

[0011] 3) Fiber optic sensing system: Complex wiring and local breakage lead to monitoring blind spots and high repair costs.

[0012] (3) Disconnection between structural improvement and monitoring technology

[0013] Current technological innovations are mostly focused on optimizing the structure of the pipe joint itself (such as the steel shell reinforcement design of patent CN119956822A), while the intelligent sensing technology for joint water seepage is still stagnant at the stage of single physical quantity detection and lacks multimodal data fusion and analysis capabilities.

[0014] As immersed tube tunnels develop towards ultra-long and deep water, traditional monitoring methods can no longer meet three core requirements: 1) Real-time: a response speed of seconds is required to prevent the spread of structural damage; 2) Accuracy: the leakage positioning error must be less than 0.5 meters and the false alarm rate must be less than 1%; 3) Adaptability: stable operation for more than 10 years in extremely corrosive environments with a pH value of 2.8-10.5 and a salinity of 35‰. Summary of the Invention

[0015] In view of this, the purpose of the present invention is to provide an intelligent monitoring method for water seepage in pipe joints of immersed tube tunnels based on optimized direct current method. By increasing the number and density of electrodes, adjusting the electrode arrangement, adopting multi-mode current excitation and introducing inversion algorithm, real-time and high-precision monitoring of water seepage in pipe joints can be achieved, thereby improving the safety and reliability of tunnel operation.

[0016] In order to achieve the above object, the present invention provides the following technical solutions:

[0017] Solution 1: An intelligent monitoring method for water seepage in immersed tube tunnel joints based on optimized direct current method, specifically comprising the following steps:

[0018] S1: Electrode layout: With the immersed tunnel joint as the central symmetry axis, n vertical rows of electrodes are symmetrically and evenly arranged on both sides of the joint. Each vertical row has 2n electrodes, n ≥ 5, and the horizontal and vertical spacing between electrodes is the same;

[0019] S2: Multi-mode current excitation and reception: The electrodes are divided into power electrodes (X, Y) and measurement electrodes (O, P), where X, Y, O, and P are located on a straight line, O and P are always located near the center of the X and Y arrangement, and so on. Each horizontal or vertical 2n electrode can be combined into a measurement line. During each measurement, the power electrode excites and receives the current I, and the two measurement electrodes measure the voltage, which is recorded as V.

[0020] S3: Signal processing and analysis: Calculate the apparent resistivity of each measuring point on each measuring line and generate a three-dimensional data matrix of the apparent resistivity of all measuring points; then use a three-dimensional fast inversion algorithm based on neural network design to calculate the actual resistivity; finally, compare the actual resistivity distribution obtained by inversion with the resistivity under normal conditions. If there is a clear low-resistivity area, it is determined that the water seepage in this area is abnormal.

[0021] Furthermore, in step S1, the electrodes are made of corrosion-resistant and highly conductive materials and are securely mounted on the tunnel wall using special fixing devices.

[0022] Furthermore, in step S3, the calculation formula of apparent resistivity is:

[0023] ρ=(π×((XY / 2) 2-(OP / 2) 2 ) / OP)×(V / I)

[0024] Where ρ represents the apparent resistivity of each measuring point, XY represents the distance between the power supply electrodes X and Y, and OP represents the distance between the power supply electrodes O and P.

[0025] Furthermore, in step S3, after each survey line is calculated, each measuring point corresponds to an apparent resistivity ρ value. A single survey line can obtain an inverted triangular two-dimensional profile, recording the apparent resistivity ρ at different positions and depths in the profile direction of the survey line. Therefore, all the results of all 2n horizontal survey lines and 2n vertical survey lines in the array are combined to generate a three-dimensional data matrix, and three-dimensional imaging can be performed.

[0026] Furthermore, in step S3, the three-dimensional fast inversion algorithm based on neural network design specifically includes:

[0027] 1) Constructing the forward model: Under the background of homogeneous concrete, gridding is used to generate apparent resistivity data d;

[0028] 2) Data scale: Generate 10 4 ~10 5 A three-dimensional apparent resistivity matrix, a three-dimensional resistivity model m, and corresponding apparent resistivity data d;

[0029] 3) Noise injection: Add Gaussian noise (signal-to-noise ratio ≥ 20 dB) to the data d to simulate actual measurement errors;

[0030] 4) Neural Network Design:

[0031] Input: 3D apparent resistivity matrix (size: 10 × 10 × 10, consisting of 10 horizontal and 10 vertical lines, with points without data generated using the mean interpolation method);

[0032] Output: three-dimensional actual resistivity distribution matrix (same size);

[0033] 5) Network Architecture: Utilizes an encoder-decoder structure based on 3D U-Net to capture spatial features, and skip connections to preserve details; introduces physical constraint layers (such as non-negative activation functions to ensure resistivity ≥ 0); and adds a residual module to enhance deep feature extraction capabilities;

[0034] 6) Hyperparameters: The loss function is weighted MSE (focusing on abnormal areas); the optimizer is Adam (initial learning rate 1e-4, dynamic decay);

[0035] 7) Training and optimization: The training strategy is to use 80% of the data for training and 20% for validation;

[0036] 8) Inversion process:

[0037] Preprocessing: normalize the measured apparent resistivity data ρ;

[0038] Obtaining actual resistivity: Input the normalized data into the trained network and directly output the three-dimensional resistivity distribution matrix. Each data is the actual resistivity ρ obtained after calculation. 实 ;

[0039] Post-processing: Apply background filtering to remove noise.

[0040] Solution 2: An intelligent monitoring system for water seepage in immersed tube tunnel pipe joints based on optimized direct current method, including electrodes, a signal processing unit, a wireless communication module and a storage module.

[0041] Electrode installation: During pipe joint construction, holes for electrode installation are reserved. Use specialized fixtures to install the electrodes, ensuring close contact between the electrodes and the tunnel wall, and ensuring that the electrode spacing and position accuracy meet standards. Conduct a conductivity test after installation.

[0042] Intelligent alarm and data storage: When abnormal water seepage is detected, an alarm is sent to the monitoring center through the wireless communication module, and data such as the water seepage location, time and degree of abnormality are saved in the local storage module.

[0043] Monitoring system debugging: Assemble and debug the signal processing unit and other equipment, set the excitation current parameters, as well as the standard value range and alarm threshold of the receiving signal; simulate different degrees of water seepage to test the accuracy and reliability of the system.

[0044] Daily monitoring and maintenance: The monitoring system automatically runs during tunnel operation. Regular inspections and maintenance of monitoring equipment are performed, and stored data is analyzed to identify water seepage trends.

[0045] The beneficial effects of the present invention are:

[0046] (1) High-precision water seepage monitoring capability: By adopting a dense electrode array and a multi-mode current excitation mode, combined with a three-dimensional fast inversion algorithm (3D U-Net network + physical constraint layer), the present invention can obtain more comprehensive and accurate current and potential difference signals, accurately invert the resistivity distribution of the pipe joint area, effectively capture minor water seepage, greatly improve monitoring accuracy, and effectively overcome the problem of insufficient signal resolution caused by the low electrode density of traditional DC electrical methods. Compared with the two-dimensional inversion error of conventional methods, the present invention greatly improves the resistivity inversion accuracy of the water seepage area through three-dimensional data matrix and noise modeling.

[0047] (2) Real-time warning: The present invention processes and analyzes monitoring data in real time, and immediately issues an alarm if abnormal water seepage is detected, thereby promptly identifying potential safety hazards and ensuring safe operation of the tunnel.

[0048] (3) Complete spatial coverage: This invention uses a symmetrical electrode arrangement (with the joint as the axis of symmetry) combined with a multi-dimensional measurement line to form a three-dimensional detection network. Compared with conventional unidirectional electrode layouts, the monitoring blind area is reduced and water seepage cavities can be accurately identified.

[0049] (4) Easy installation and maintenance: The present invention uses prefabricated electrode mounting holes and a dedicated fixture design to improve electrode replacement efficiency and minimize contact resistance fluctuations. Compared to embedded electrode solutions, maintenance costs are reduced.

[0050] (5) The system has good anti-interference performance: the introduction of weighted MSE loss function + adaptive background filtering algorithm reduces the false alarm rate of the system in a strong electromagnetic interference environment.

[0051] (6) High degree of intelligence: It has automatic alarm and data storage functions, which reduces manual intervention and improves monitoring and management efficiency.

[0052] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0054] Figure 1 This is a schematic diagram of the motor installation according to an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of electrode test positions for multi-mode current excitation and reception in an embodiment of the present invention;

[0056] Figure 3 Schematic diagram of the formed three-dimensional resistivity matrix. DETAILED DESCRIPTION

[0057] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the features in the following embodiments and embodiments can be combined with each other without conflict.

[0058] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0059] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0060] See also Figures 1 to 3 The embodiment of the present invention provides an intelligent monitoring method for water seepage in pipe joints of immersed tube tunnels based on an optimized direct current method, which specifically includes the following steps:

[0061] 1. Electrode layout

[0062] (1) Quantity and density: 5 vertical rows of electrodes are arranged on both sides of the immersed tube tunnel joint, with 10 electrodes in each vertical row and a spacing of 1 meter between each electrode (see Figure 1 The electrodes are made of corrosion-resistant and highly conductive materials, such as stainless steel and copper alloy, and are securely mounted on the tunnel wall using special fixtures.

[0063] (2) Arrangement: With the joint as the central symmetry axis, the electrodes are arranged symmetrically on both sides to make the current distribution more uniform, avoid monitoring blind spots, obtain resistivity signals from different angles, and more accurately reflect the resistivity changes at the joint.

[0064] 2. Multi-mode current excitation and reception

[0065] (1) Divided into power supply electrodes (X, Y) and measuring electrodes (O, P), where X, Y, O, and P are located on a straight line, and O and P are always located near the center of the arrangement of X and Y (see Figure 2 ), and so on.

[0066] (2) Each of the 10 horizontal measuring points or the 10 vertical measuring points can be combined into Figure 2 A measuring line is shown.

[0067] (3) During each measurement, the power supply electrode excites and receives the current I, and the two measuring electrodes measure the voltage, which is recorded as V.

[0068] 3. Signal processing and analysis

[0069] (1) Calculation formula of apparent resistivity ρ:

[0070] ρ=(π×((XY / 2) 2 -(OP / 2) 2 ) / OP)×(V / I)

[0071] (2) Calculation steps:

[0072] 1) Data acquisition: record V and I at different XY spacings;

[0073] 2) Calculate the apparent resistivity: Substitute the apparent resistivity calculation formula to obtain ρ for each measuring point.

[0074] (3) Data Matrix

[0075] refer to Figure 3 After calculation for each survey line, each red dot represents a ρ value obtained. A single survey line generates an inverted triangular 2D profile, recording the apparent resistivity ρ at different locations and depths along the profile. Therefore, combining the results from all 10 horizontal and 10 vertical survey lines in the array generates a 3D data matrix, enabling 3D imaging.

[0076] (4) Three-dimensional fast inversion algorithm based on neural network design

[0077] 1) Construct the forward model: Under the background of homogeneous concrete, gridding is used to generate apparent resistivity data d.

[0078] 2) Data scale: Generate 10 4 ~10 5 Group (three-dimensional resistivity model m, corresponding apparent resistivity data d).

[0079] 3) Noise injection: Add Gaussian noise (SNR ≥ 20 dB) to the data d to simulate actual measurement error.

[0080] 4) Neural Network Design

[0081] Input: 3D apparent resistivity matrix (size: 10 × 10 × 10, consisting of 10 horizontal and 10 vertical lines, with points without data generated using the mean interpolation method).

[0082] Output: 3D true resistivity distribution matrix (same size).

[0083] 5) Network Architecture: 3D U-Net: Utilizes an encoder-decoder structure to capture spatial features, with skip connections preserving details. Physical constraints are introduced (e.g., non-negative activation functions to ensure resistivity ≥ 0). A residual module is added to enhance deep feature extraction capabilities.

[0084] 6) Hyperparameters: The loss function is weighted MSE (focusing on abnormal areas); the optimizer is Adam (initial learning rate 1e-4, dynamic decay).

[0085] 7) Training and optimization: The training strategy is to use 80% of the data for training and 20% for validation.

[0086] 8) Inversion process

[0087] Preprocessing: Normalize the measured apparent resistivity data ρ.

[0088] Obtaining actual resistivity: Input the data into the trained network and directly output the three-dimensional resistivity matrix. Each data is the actual resistivity ρ obtained after calculation. 实 .

[0089] Post-processing: Apply background filtering to remove noise.

[0090] (5) Determine water seepage: According to the actual resistivity ρ obtained by inversion 实 Distribution, compared with the resistivity under normal circumstances, if there is an obvious low resistivity area, it is determined that there may be water seepage in the area.

[0091] 4. Intelligent alarm and data storage

[0092] When abnormal water seepage is detected, an alarm is sent to the monitoring center through the wireless communication module, and data containing information such as the water seepage location, time, and degree of abnormality is stored locally.

[0093] 5. Electrode installation

[0094] During pipe joint construction, holes are reserved for electrode installation. Special fixtures are used to install the electrodes, ensuring close contact between the electrodes and the tunnel wall, and ensuring that the electrode spacing and position accuracy meet the standards. Conductivity testing is performed after installation.

[0095] 6. Monitoring system debugging

[0096] Assemble and debug the signal processing unit and other equipment, set the excitation current parameters, the standard value range of the received signal, and the alarm threshold. Simulate different levels of water seepage to test the accuracy and reliability of the system.

[0097] 7. Daily monitoring and maintenance

[0098] The monitoring system automatically operates during tunnel operation. Regular inspections and maintenance of monitoring equipment are performed, and stored data is analyzed to identify water seepage trends.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent monitoring method for water seepage in immersed tube tunnel joints based on optimized direct current method, characterized in that: The method specifically comprises the following steps: S1: Electrode layout: With the immersed tunnel joint as the central symmetry axis, n vertical rows of electrodes are symmetrically and evenly arranged on both sides of the joint. Each vertical row has 2n electrodes, n ≥ 5, and the horizontal and vertical spacing between electrodes is the same; S2: Multi-mode current excitation and reception: The electrodes are divided into power electrodes (X, Y) and measurement electrodes (O, P), where X, Y, O, and P are located on a straight line, O and P are always located near the center of the X and Y arrangement, and so on. Each horizontal or vertical 2n electrode can be combined into a measurement line. During each measurement, the power electrode excites and receives the current I, and the two measurement electrodes measure the voltage, which is recorded as V. S3: Signal processing and analysis: Calculate the apparent resistivity of each measuring point on each measuring line and generate a three-dimensional data matrix of the apparent resistivity of all measuring points; then use a three-dimensional fast inversion algorithm based on neural network design to calculate the actual resistivity; finally, compare the actual resistivity distribution obtained by inversion with the resistivity under normal conditions. If there is a clear low-resistivity area, it is determined that the water seepage in this area is abnormal.

2. The method for intelligent monitoring of water seepage in immersed tube tunnel joints according to claim 1 is characterized in that: In step S1, the electrodes are firmly mounted on the tunnel wall by a special fixing device.

3. The intelligent monitoring method for water seepage in immersed tube tunnel joints according to claim 1 is characterized in that: In step S3, the calculation formula of apparent resistivity is: ρ=(π×((XY / 2) 2 -(OP / 2) 2 ) / OP)×(V / I) Where ρ represents the apparent resistivity of each measuring point, XY represents the distance between the power supply electrodes X and Y, and OP represents the distance between the power supply electrodes O and P.

4. The method for intelligent monitoring of water seepage in pipe joints of immersed tube tunnels according to claim 1, characterized in that: In step S3, after each survey line is calculated, each measuring point corresponds to an apparent resistivity ρ value. A single survey line can obtain an inverted triangular two-dimensional profile, recording the apparent resistivity ρ at different positions and depths along the profile direction of the survey line. Therefore, all the results of all 2n horizontal survey lines and 2n vertical survey lines in the array are combined to generate a three-dimensional data matrix, and three-dimensional imaging is performed.

5. The intelligent monitoring method for water seepage in immersed tube tunnel joints according to claim 1 is characterized in that: In step S3, the three-dimensional fast inversion algorithm based on neural network design specifically includes: 1) Constructing the forward model: Under the background of homogeneous concrete, gridding is used to generate apparent resistivity data d; 2) Data scale: Generate 10 4 ~10 5 A three-dimensional apparent resistivity matrix, a three-dimensional resistivity model m, and corresponding apparent resistivity data d; 3) Noise injection: Add Gaussian noise to the data d to simulate actual measurement errors; 4) Neural Network Design: Input: 3D apparent resistivity matrix; Output: three-dimensional actual resistivity distribution matrix; 5) Network Architecture: Utilizes an encoder-decoder structure based on 3D U-Net to capture spatial features, and skip connections to preserve details; introduces a physical constraint layer; and adds a residual module to enhance deep feature extraction capabilities. 6) Hyperparameters: The loss function is weighted MSE; the optimizer is Adam; 7) Training and optimization: The training strategy is to use 80% of the data for training and 20% for validation; 8) Inversion process: Preprocessing: normalize the measured apparent resistivity data ρ; Obtaining actual resistivity: Input the normalized data into the trained network and directly output the three-dimensional resistivity distribution matrix. Each data is the actual resistivity ρ obtained after calculation. 实 ; Post-processing: Apply background filtering to remove noise.

6. The intelligent monitoring method for water seepage in pipe joints of an immersed tube tunnel according to any one of claims 1 to 5, characterized in that: The monitoring system suitable for the method includes electrodes, a signal processing unit, a wireless communication module and a storage module.

7. The method for intelligent monitoring of water seepage in immersed tube tunnel joints according to claim 6, characterized in that: When abnormal water seepage is detected, an alarm is sent to the monitoring center through the wireless communication module, and the water seepage location, time and abnormality level are saved in the local storage module.

8. The intelligent monitoring method for water seepage in immersed tube tunnel joints according to claim 6 is characterized in that: During the debugging phase of the monitoring system, the signal processing unit is assembled and debugged, the excitation current parameters, as well as the standard value range and alarm threshold of the receiving signal are set; different degrees of water seepage are simulated to test the accuracy and reliability of the system.

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