Tunnel lining micro-crack self-repairing grouting system and method

Abstract

The application provides a tunnel lining micro-crack self-repairing grouting system and method, which comprises the following steps: S1, embedding microcapsule repair agents, grouting pipes and sensing parts when lining concrete is formed; S2, monitoring crack data around the microcapsule repair agents and the grouting pipes; S3, micro-cracks appear in the lining, the microcapsule repair agents are broken to release repair slurry to complete repair; S4, a control unit collects crack data, processes and calculates the crack data to obtain repair grouting data; S5, the grouting pipes are controlled to inject repair slurry; and S6, the hydraulic pressure in the grouting pipes is monitored, and when the hydraulic pressure in the grouting pipes reaches a preset safety threshold value or the pressure change rate is greater than a preset threshold value, the grouting is stopped. The application firstly completes preliminary repair through microcapsules, automatically starts active grouting for supplementary repair for cracks which are not repaired by the microcapsules, solves the problems that the existing microcapsule self-repairing technology has limited repair capacity and the repair is not complete, and simultaneously does not need manual intervention, and realizes full-automatic repair.

Description

Technical Field

[0001] This invention relates to the field of tunnel lining microcrack repair technology, specifically to a self-healing grouting system and method for tunnel lining microcracks. Background Technology

[0002] As the main load-bearing and protective structure of a tunnel, tunnel lining inevitably develops microcracks during long-term service due to various factors such as surrounding rock pressure, groundwater erosion, temperature changes, and train dynamic loads. These microcracks are initially small and do not significantly affect structural safety. However, if not repaired in time, groundwater and harmful ions can penetrate the concrete through these cracks, leading to steel corrosion, concrete deterioration, and eventually developing into macro-cracks, water leakage, and lining spalling. These problems seriously threaten the structural safety and service life of the tunnel, while also significantly increasing subsequent maintenance costs.

[0003] Traditional methods for repairing tunnel lining cracks mainly rely on regular manual inspections, followed by manual grouting repairs once cracks are discovered. This method has a significant time lag, as micro-cracks are difficult to detect manually and often develop into serious defects by the time they are discovered. Furthermore, manual grouting requires closing traffic, resulting in high construction costs and disruption to normal tunnel operation.

[0004] In recent years, microcapsule self-healing concrete technology has gradually attracted attention. This technology involves pre-embedding microcapsules containing a repair agent into concrete. When cracks appear in the concrete, the stress at the crack tip punctures the microcapsules, releasing the repair agent inside and filling the crack to achieve self-repair. This technology can achieve automatic repair of microcracks without human intervention.

[0005] However, the amount of repair agent inside the microcapsule is limited. For microcracks with a width exceeding 0.2 mm, the repair agent in the microcapsule is insufficient to completely fill the crack, resulting in incomplete repair and the crack will continue to develop. On the other hand, existing technologies cannot automatically evaluate the repair effect of microcapsules, nor can they automatically supplement repair when microcapsule repair is insufficient. They can only rely on manual inspection in the later stage and cannot achieve fully automated repair.

[0006] Meanwhile, most existing grouting repair techniques are manually controlled, which can easily lead to problems such as insufficient or excessive grouting. Summary of the Invention

[0007] The main objective of this invention is to provide a self-healing grouting system and method for microcracks in tunnel lining, thereby solving the problem of poor repair effect of existing microcrack repair methods.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] The self-healing grouting method for microcracks in tunnel lining includes the following steps: S1. During the molding process of the lining concrete, microcapsule repair agent, grouting pipe and sensing element are pre-embedded; S2. Real-time monitoring of crack data around the microcapsule repair agent and grouting pipe; S3. When microcracks appear in the lining, the microcapsule repair agent is ruptured by the stress at the crack tip, releasing the internal repair grout to automatically complete the repair. S4. The control unit collects crack data, and the built-in adaptive grouting algorithm model of the control unit processes the crack data to calculate the grouting data. S5. Based on the grouting data, the control unit controls the grouting pipe to inject repair grout into the crack location; S6. During the grouting process, monitor the hydraulic pressure inside the grouting pipe in real time. When the hydraulic pressure inside the pipe reaches the preset safety threshold, or when the pressure change rate is greater than the preset threshold, stop grouting.

[0010] In a preferred embodiment, the sensing element includes a fiber optic strain sensor, a pressure sensor, a temperature sensor, and a pipeline hydraulic sensor. Crack data includes strain abrupt change value ε detected by fiber optic strain sensor, gauge length L of sensor, seepage pressure P detected by pressure sensor, in-pipe hydraulic pressure A detected by pipe hydraulic sensor, and temperature K detected by temperature sensor. The grouting data includes the amount of grout and the grouting time.

[0011] In the preferred embodiment, S3 further includes: S31. The microcapsule repair agent ruptures, and the monitoring data of the sensor on the side of the microcapsule repair agent changes. S32. The time taken for the data monitored by the detection sensor to stabilize after the initial change, and the data when it stabilizes; S33. The control unit compares the data when it is about to stabilize with the preset data and calculates the remaining amount of grout to fill the microcracks. S34. Compare the remaining amount of grout with the preset value and record it; S35. When the amount of grout added is greater than the preset value or the accumulated amount of grout added is greater than the preset value, control the grouting pipe to add grout.

[0012] In the preferred embodiment, the grouting pipes are installed in sections according to the tunnel segment, with one grouting structure and one control unit installed in each tunnel segment; Several grouting pipes within each tunnel lining section connect to the grouting structure of that section; Several sensors inside each tunnel lining section are connected to the control unit of that section, and the monitored crack data is acquired and processed by the control unit of that section. Each tunnel section is equipped with several micro-grouting structures, which are connected to grouting pipes and used to locally fill micro-cracks within a preset range.

[0013] In the preferred embodiment, the steps for obtaining grouting data are as follows: S61. Calculate the characteristic parameters of the crack based on the crack data, including crack width w, crack depth h and crack length l; Wherein, the crack width w is calculated by the strain mutation value and the gauge length, and the calculation formula is as follows: ; S62. Calculate the total volume of the crack based on the crack characteristic parameters. ; S63. Based on the crack width, adaptively obtain the repair efficiency η of the microcapsules and calculate the filling amount of the microcapsules for automatic repair. ; S64. Based on the seepage pressure and ambient temperature, obtain the seepage correction coefficient β and the temperature correction coefficient γ, and calculate the amount of grout to be actively injected. The calculation formula is as follows: ; Wherein, α is the expansion compensation coefficient of the repair grout, with a value of 1.05 to 1.1; β is the seepage correction coefficient, which is positively correlated with the seepage pressure P, with a value of 1.0 to 1.3; and γ is the temperature correction coefficient, which is negatively correlated with the ambient temperature K, with a value of 0.95 to 1.05.

[0014] The preferred scheme also includes a step for obtaining the adaptive slurry replenishment algorithm model, as detailed below: S71. Simulate different tunnel service environments, create multiple lining concrete models with different strength grades, and pre-embed several microcapsule repair agents, capillary grouting pipes and sensing devices in each model; S72. Microcracks of different sizes are generated in the concrete model by mechanical loading equipment. After the microcapsule repair agent ruptures, different amounts of repair grout are injected into different concrete models through grouting pipes, and various parameters of the grouting process are recorded. S73. Real-time detection of microcrack data at different times and locations using sensors, including strain mutation value ε, gauge length L, seepage pressure P, hydraulic pressure A inside the pipe and temperature K at different time points; S74. After the repair grout has completely solidified, record the repair effect data at different locations, including strength recovery rate and permeability recovery rate, through CT scanning, permeability testing and mechanical property testing. S75. Import the data on grout replenishment volume, strain mutation value ε, gauge length L, seepage pressure P, in-pipe hydraulic pressure A, temperature K, crack characteristic parameters, and repair effect into the adaptive grout replenishment algorithm model. Train the model based on a BP neural network, with crack data and environmental parameters as inputs and optimal grout replenishment volume and grout replenishment time as outputs, to complete the training and verification of the model. S76. Import the trained and validated adaptive grouting algorithm model into the control unit at the tunnel site, and set up an online update mechanism to periodically feed the actual repair data from the site back to the model for iterative optimization.

[0015] The tunnel lining microcrack self-healing grouting system applies the tunnel lining microcrack self-healing grouting method, including a grouting unit, a control unit, a detection unit, and a self-healing unit; The self-healing unit is used to automatically release repair agent to complete grouting repair when micro-cracks appear in the tunnel lining; The detection unit is used to monitor crack data and environmental data within the lining in real time; The grouting unit is used to inject repair grout into the cracks in the tunnel lining to supplement the shortcomings of microcapsule self-healing and ensure the repair effect of microcracks. The control unit is connected to the grouting unit, detection unit, and self-healing unit to process monitoring data, calculate grouting parameters, and control the grouting action.

[0016] In a preferred embodiment, the self-healing unit comprises several microcapsules mixed in the concrete matrix of the lining body; The grouting unit includes several capillary grouting pipes and several first sensors pre-embedded in the lining body. The capillary grouting pipes are provided with several grout outlet holes for delivering repair grout into the lining. The first sensor includes a fiber optic strain sensor, a miniature pressure sensor, and a temperature sensor, used to monitor the cracking strain of the lining, the outlet pressure of the grouting pipe, the grout pressure at the microcapsule, the crack seepage pressure, and the temperature. The grouting unit also includes grouting equipment located inside the lining body, and a control unit is provided at the grouting equipment. The control unit is electrically connected to the first sensor and the grouting equipment. The grouting equipment is connected to the capillary grouting pipe for grouting.

[0017] In the preferred embodiment, the grouting equipment is connected to an external pipe, which is provided with several connecting pipes, and the capillary grouting pipe is connected to the connecting pipes; A valve is installed inside the connecting pipe; a second sensor, which is a hydraulic sensor, is also installed in the connecting pipe. Several micro-grouting devices are installed on the inner side of the lining body. The micro-grouting devices are connected to capillary grouting pipes within a range of 1-5m before and after them for small-area grouting.

[0018] In a preferred embodiment, the microcapsules are polymeric binuclear microcapsules, including small-diameter microcapsules with a particle size of 100-200 μm and large-diameter microcapsules with a particle size of 300-500 μm; The microcapsules contain a repair slurry, which is a two-component reactive slurry. The repair slurry includes epoxy resin, curing agent, penetrant, and binder. The mass ratio of epoxy resin to curing agent is 3:1-5:1, the amount of penetrant added is 5%-8% of the total mass, and the amount of binder added is 3%-5% of the total mass. The first sensor is located at the microcapsule.

[0019] This invention provides a self-healing grouting system and method for microcracks in tunnel lining. By adopting the above solution, the following beneficial effects are achieved: This technology enables graded automatic repair of microcracks in tunnel lining. First, pre-embedded microcapsules complete the initial self-repair. For cracks where the microcapsule repair is insufficient, active grouting is automatically initiated for supplementary repair. This solves the problems of limited repair capacity and incomplete repair of existing microcapsule self-repair technology. At the same time, it eliminates the need for manual intervention and achieves fully automated repair.

[0020] An adaptive grouting algorithm was proposed, which combines multiple parameters such as crack parameters, seepage pressure, and ambient temperature to adaptively calculate the amount of grouting. It can also adapt to different tunnel environments, ensuring the accuracy of the grouting amount and avoiding the problems of insufficient or excessive grouting, thus improving the repair effect.

[0021] A segmented grouting and control structure was adopted, and both main grouting equipment and micro grouting equipment were set up. Different grouting methods can be used for cracks of different sizes. Small micro-cracks can be quickly repaired by micro grouting equipment, while large cracks are repaired by main grouting equipment, which improves the repair response speed. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the structure of the present invention. Figure 1 ; Figure 2 This is a schematic diagram of the structure of the present invention. Figure 2 ; Figure 3 This is a cross-sectional structural diagram of the present invention; Figure 4 This is a schematic diagram of the structure during microcapsule repair according to the present invention; Figure 5 This is a schematic diagram of the structure of the capillary grouting pipe during repair according to the present invention; In the picture: Lining body 1, capillary grouting pipe 2, grout outlet 201, connecting pipe 3, microcapsule 4, first sensor 5, external pipe 6, connecting pipe 601, grouting equipment 7, second sensor 8, micro grouting equipment 9. Detailed Implementation

[0023] Example 1: A self-healing grouting method for microcracks in tunnel lining includes the following steps: S1. During the molding process of the lining concrete, microcapsule repair agent, capillary grouting pipe and sensing element are pre-embedded in the concrete matrix; S2. Real-time monitoring of crack data around the microcapsule repair agent and grouting pipe using the aforementioned sensing element; S3. When microcracks appear in the lining, the microcapsule repair agent is ruptured by the stress at the crack tip, releasing the internal repair grout to automatically complete the initial repair. S4. The control unit collects the crack data, processes the crack data through the built-in adaptive grouting algorithm model, and calculates the grouting data. S5. Based on the grouting data, the control unit controls the grouting pipe to inject repair grout into the crack location to complete the active grouting repair. S6. During the grouting process, monitor the hydraulic pressure inside the grouting pipe in real time. When the hydraulic pressure inside the pipe reaches the preset safety threshold, or when the rate of change of the hydraulic pressure inside the pipe is greater than the preset threshold, stop the grouting.

[0024] Furthermore, the sensing element includes an optical fiber strain sensor, a seepage pressure sensor, a temperature sensor, and a pipeline hydraulic sensor; The crack data includes: the strain mutation value ε detected by the fiber optic strain sensor, the gauge length L of the sensor, the seepage pressure P detected by the seepage pressure sensor, the internal hydraulic pressure A detected by the pipe hydraulic sensor, and the ambient temperature K detected by the temperature sensor; at the i-th point, the detected crack data corresponds to... , , , and ; The grouting data includes the amount of grout and the grouting time.

[0025] In the following calculations, point i is included to distinguish the location of microcrack repair.

[0026] Furthermore, the step following S3 includes a repair effect evaluation step, as detailed below: S31. After the microcapsule repair agent ruptures, the changes in monitoring data are monitored through the sensors on its side. S32. The time taken for the monitoring data of the detection sensor to stabilize from the initial change, and the stable monitoring data when it stabilizes; S33. The control unit compares the stable monitoring data with the preset benchmark data and calculates the actual amount of fluid replenishment for microcapsule self-repair. S34. Compare the actual amount of grout added with the preset threshold, and record the cumulative value of the grout added at that point. S35. When the actual grouting volume is greater than the preset threshold, or the accumulated grouting volume at that point is greater than the preset accumulation threshold, the active grouting process is triggered to control the grouting pipe to perform grouting.

[0027] Furthermore, the grouting pipes are set up in sections according to the tunnel segment, with one grouting structure and one independent control unit set up in each tunnel segment with a length of 40-60m. Several capillary grouting pipes in each tunnel lining section are connected to the grouting structure of that section; several sensors in each tunnel lining section are connected to the control unit of that section, and the crack data obtained by monitoring is independently acquired and processed by the control unit of that section. Each tunnel section is also equipped with several micro-grouting structures. Each micro-grouting structure is connected to capillary grouting pipes within a range of 1-5m before and after it, which are used for local grouting of micro-cracks within a preset range to improve the response speed of grouting.

[0028] Furthermore, the adaptive grouting algorithm model acquires grouting data through the following steps: S61. Calculate the characteristic parameters of the crack based on the crack data, including crack width w, crack depth h, and crack length l; wherein, the crack width w is calculated by the strain abrupt change value and the gauge length, and the calculation formula is: ; S62. Calculate the total volume of the crack based on the crack characteristic parameters. The calculation formula is as follows: ; S63. Based on the crack width, the repair efficiency η of the microcapsules is adaptively obtained, and the filling amount of the microcapsules for automatic repair is calculated. The calculation formula is as follows: ; Wherein, the repair efficiency η is adaptively adjusted according to the crack width: when hour, ;when hour, ;when hour, ;when hour, ; S64. Based on the seepage pressure and ambient temperature, obtain the seepage correction coefficient β and the temperature correction coefficient γ, and calculate the amount of grout to be actively injected. The calculation formula is as follows: ; Wherein, α is the expansion compensation coefficient of the repair grout, with a value of 1.05~1.1, which is used to compensate for the volume shrinkage during the curing process of the grout; β is the seepage correction factor, which is positively correlated with the seepage pressure P. It is used to compensate for grout loss caused by seepage. The rule for its value is: when... hour, ;when hour, ;when hour, ;when hour, ; γ is a temperature correction coefficient, negatively correlated with ambient temperature K, used to compensate for the effect of temperature on the fluidity and expansion properties of the slurry. Its value is determined by the following rule: when... hour, ;when hour, ;when hour, ;when hour, .

[0029] Furthermore, it also includes the training and acquisition steps of the adaptive slurry replenishment algorithm model, as follows: S71. Simulate different tunnel service environments, create multiple lining concrete models with different strength grades, covering three commonly used tunnel lining concrete strengths: C30, C35, and C40. In each model, pre-embed several microcapsule repair agents, capillary grouting pipes, and sensing elements at point i. S72. By using different mechanical loading methods such as three-point bending loading and uniaxial compression loading, microcracks of different sizes are generated in the concrete model, covering the common tunnel lining microcrack width range of 0.05mm~0.4mm. After the microcapsule repair agent at different points ruptures, different amounts of repair grout are injected into different points of different concrete models through grouting pipes, and the pressure, flow rate and other parameters of the grouting process at each point are recorded. S73. Real-time detection of microcrack data at different times and locations using sensors, including strain abrupt changes at the i-th location at different time points such as 1h, 6h, 12h, and 24h after crack formation. Gauge length seepage pressure Hydraulic pressure inside the pipe and temperature It covers the common tunnel ambient temperature range of 5℃ to 35℃, and the seepage pressure range of 0 to 0.3MPa; S74. After the repair grout has completely solidified, the filling status of each crack is obtained by industrial CT scanning, the impermeability of the repaired lining is obtained by permeability testing, the mechanical properties of the repaired lining are obtained by uniaxial compression testing, and the repair effect data at different locations, including strength recovery rate and permeability recovery rate, are recorded. S75. Calculate the amount of repair grout added and the strain mutation value at all points. Gauge length seepage pressure Hydraulic pressure inside the pipe ,temperature The crack feature parameters and repair effect data were compiled into a training dataset, which was then divided into a training set and a test set in an 8:2 ratio. An adaptive grouting algorithm model based on a BP neural network was constructed. The input layer consists of eight parameters: strain mutation value at the i-th point, gauge length, seepage pressure, hydraulic pressure inside the pipe, temperature, crack width, crack depth, and crack length. The hidden layer consists of two layers, containing 16 and 8 neurons respectively. The output layer consists of two parameters: optimal grouting amount and grouting time. During training, the gradient descent method with momentum is used for parameter updates. The core algorithm formula is as follows: (1) Forward propagation calculation: For the i-th training sample, the input vector is First, calculate the output of the hidden layer: ; ; in, It is the Sigmoid activation function. , The weights and biases of the first hidden layer. The weights and biases of the second hidden layer; Let be the crack width of the i-th sample; Let be the crack depth of the i-th sample; Let be the crack length of the i-th sample; This is the first hidden layer, and the output vector for the i-th sample has a dimension of 16×1. This is the second hidden layer, and the output vector for the i-th sample has a dimension of 8×1. Then calculate the predicted output of the output layer: ; in, , where represents the amount and time of slurry replenishment predicted by the model for the i-th sample; in, This is the model's predicted output vector for the i-th sample; The slurry replenishment amount component in the predicted output is the optimal slurry replenishment amount predicted by the model for the i-th sample. The grouting time component in the prediction output is the optimal grouting time predicted by the model for the i-th sample. This is the weight matrix of the output layer; The bias vector of the output layer; (2) Loss function calculation: Mean squared error is used as the loss function to measure the model's prediction error: ; in, Let N be the true slurry filling parameters for the i-th sample, N be the total number of training samples, and Loss be the mean squared error loss for the entire training set. This represents the slurry volume component in the actual slurry parameters, i.e., the actual slurry volume in the i-th sample test. The grouting time component is the actual grouting time in the true grouting parameters, i.e., the actual grouting time in the i-th sample test; This is the model's predicted output vector for the i-th sample, consistent with the definition in forward propagation; (3) Backpropagation parameter update: The learning rate is set to 0.01, the momentum factor to 0.9, the maximum number of iterations to 1000, and the parameter update formula is as follows: ; ; in, For learning rate, Momentum factor For all trainable parameters (weights and biases) of the network. Let be the momentum term for the t-th iteration. Training is stopped when the training loss is less than 0.001. Use a test set to validate the model and adjust the model parameters until the model's prediction accuracy reaches more than 95%. (4) Incremental learning and updating: During the online update phase, a weighted loss incremental learning method is used to fine-tune the model, and the updated loss function is: ; in, This is the weight for the old data, with a value of 0.7, used to retain old model knowledge and avoid catastrophic forgetting. The training loss of the old model, For the loss of new field data; S76. Import the trained and validated adaptive grouting algorithm model into the control unit at the tunnel site, and set up an online update mechanism. Using incremental learning, upload the monitoring data and repair effect data of the actual repair process to the model regularly. Fine-tune the model once a month and retrain it completely every six months to continuously improve the prediction accuracy of the model while avoiding model overfitting.

[0030] Example 2: like Figure 1-5 As shown, the present invention also provides a tunnel lining microcrack self-repairing grouting system, which is applied to the above-mentioned tunnel lining microcrack self-repairing grouting method, including a grouting unit, a control unit, a detection unit and a self-repairing unit; The self-healing unit is used to automatically release repair agent to complete the initial grouting repair when microcracks appear in the tunnel lining; The detection unit is used to monitor crack data and environmental data within the lining in real time. The grouting unit is used to inject repair grout into the cracks in the tunnel lining to supplement the shortcomings of microcapsule self-healing and ensure the repair effect of microcracks. The control unit is connected to the grouting unit, the detection unit, and the self-healing unit respectively, and is used to process monitoring data, calculate grouting parameters, and control the grouting action.

[0031] Furthermore, the self-healing unit includes several polymeric binuclear microcapsules 4 mixed in the concrete matrix of the lining body 1; The grouting unit includes several capillary grouting pipes 2 and several first sensors 5 pre-embedded in the lining body 1. The capillary grouting pipes 2 are provided with several grout outlet holes 201 with a diameter of 0.1-0.2mm on the pipe wall for conveying repair grout into the lining. The first sensor 5 includes an optical fiber strain sensor, a micro seepage pressure sensor and a temperature sensor, used to monitor the cracking strain of the lining, the outlet pressure of the grouting pipe, the grout pressure at the microcapsule 4, the crack seepage pressure and the ambient temperature. The grouting unit also includes a main grouting device 7 located inside the lining body 1. The main grouting device 7 is equipped with the control unit, which is electrically connected to the first sensor 5 and the main grouting device 7 respectively. The main grouting device 7 is connected to each capillary grouting pipe 2 to provide grouting power.

[0032] Furthermore, the main grouting equipment 7 is connected to an external pipe 6, and the external pipe 6 is branched with several connecting pipes 601, and the end of each capillary grouting pipe 2 is connected to the corresponding connecting pipe 601. The connecting pipe 601 is equipped with an electromagnetic valve for independently controlling the opening and closing of the corresponding capillary grouting pipe 2 to achieve precise positioning grouting; the connecting pipe 601 is also equipped with a second sensor 8, which is a pipeline hydraulic sensor for monitoring the hydraulic pressure inside the pipe and providing data for pressure control during the grouting process. The inner side of the lining body 1 is also provided with several micro grouting devices 9. Each micro grouting device 9 is connected to a capillary grouting pipe 2 within a range of 1-5m before and after it, which is used for local grouting in a small area. There is no need to start the main grouting equipment, which improves the response speed of grouting and reduces energy consumption.

[0033] Furthermore, the polymeric dual-core microcapsule 4 comprises small-diameter microcapsules with a particle size of 100-200 μm and large-diameter microcapsules with a particle size of 300-500 μm, with a mass ratio of 2:1 to 3:1; the small-diameter microcapsules are used to fill tiny gaps and improve the repair effect of small cracks, while the large-diameter microcapsules are used to store more repair agents and improve the repair capacity of larger cracks; The microcapsule 4 has a dual-core structure with an alkali-resistant polyurethane wall material and two independent core cavities inside, which encapsulate the two components of the two-component reactive repair grout to prevent premature reaction between the two components. The repair grout includes epoxy resin, curing agent, penetrant, and binder. The mass ratio of epoxy resin to curing agent is 3:1 to 5:1. The amount of penetrant added is 5%-8% of the total mass of the repair grout to improve the penetration ability of the grout. The amount of binder added is 3%-5% of the total mass of the repair grout to improve the bonding strength between the grout and the concrete matrix. Each microcapsule 4 is provided with the first sensor 5 on the side of its arrangement area to monitor the repair status of the area and detect the rupture and repair process of the microcapsule in a timely manner.

[0034] Example 3: This embodiment uses the lining repair of a highway tunnel as an example to provide a detailed description of the method and system of the present invention.

[0035] The tunnel is a two-way four-lane tunnel with C35 concrete lining and a lining thickness of 45cm. The method and system of this invention are used for construction and repair.

[0036] Before construction, the required dual-core microcapsules were first prepared using the complex coagulation method. The specific steps are as follows: (1) Preparation of wall material prepolymer solution: Mix gelatin and gum arabic at a mass ratio of 1:1 to prepare an aqueous solution with a mass fraction of 2%, and adjust the pH to 4.0 with acetic acid to obtain the wall material prepolymer solution; (2) Preparation of core material emulsion: Epoxy resin and curing agent are added to emulsifier and emulsified by high-speed stirring to prepare oil-in-water emulsion, wherein the particle size of epoxy resin emulsion is controlled at 100-200μm and the particle size of curing agent emulsion is controlled at 300-500μm. (3) Crosslinking and curing: The core material emulsion is added to the wall material prepolymer liquid and stirred at a high speed of 3000r / min to form a stable emulsion system. Then, the temperature is lowered to 5℃ and a glutaraldehyde solution with a mass fraction of 25% is added for crosslinking and curing. After reacting for 2 hours, the mixture is filtered and dried to obtain dual-core microcapsules. This ensures that the two core cavities inside the microcapsules are respectively encapsulated with epoxy resin and curing agent, thus avoiding premature reaction between the two components.

[0037] Then, during the lining concrete forming process, the microcapsule repair agent, capillary grouting tubes, and sensing elements are pre-embedded into the concrete matrix: (1) Microcapsule addition: In the final stage of concrete mixing, the prepared microcapsules are added to the concrete at a dosage of 3% of the concrete mass. The mixture is stirred at low speed for 2 minutes to ensure that the microcapsules are evenly dispersed and to avoid premature rupture of the microcapsules due to high-speed mixing. (2) Pre-embedding of capillary grouting pipes: PE capillary grouting pipes with an outer diameter of 8mm are used. The pipe wall is provided with grout outlet holes 201 with a diameter of 0.15mm. Before pre-embedding, soluble wax is used to temporarily seal the grout outlet holes to prevent cement slurry from entering the pipe and causing blockage during the concrete forming process. The grouting pipes are arranged in a quincunx pattern with a spacing of 20cm to ensure that cracks in any position in the lining can be covered by grouting. After the arrangement is completed, high-pressure water is used to wash away the soluble wax to ensure that the grout outlet holes are unobstructed. (3) Pre-embedded sensing element: A first sensor 5 is set on the side of each grouting pipe, including a fiber optic strain sensor, a micro seepage pressure sensor and a temperature sensor. The gauge length of the fiber optic strain sensor is 5cm, which is used to monitor the surrounding crack data. The wiring of the sensor is arranged along the grouting pipe, led out to the inner side of the lining, and connected to the control unit. (4) Initial calibration: After the concrete is poured, all sensors are initially calibrated and the initial strain, pressure and temperature data are collected as reference data to eliminate the influence of the initial stress of the concrete on the monitoring data and ensure the accuracy of subsequent monitoring.

[0038] Segmented system layout: The tunnel is divided into 50m sections. Each section is equipped with a main grouting device 7 and an independent control unit. The main grouting device is located at the foot of the inner wall of the lining. The main grouting device is connected to an external pipe 6. A connecting pipe 601 is branched on the external pipe and connected to each capillary grouting pipe 2 of the section. An electromagnetic valve is installed in each connecting pipe, and a second sensor 8 is installed on the connecting pipe to monitor the hydraulic pressure inside the pipe. Meanwhile, a micro grouting device 9 is set up every 5m. Each micro grouting device is connected to capillary grouting pipes within a range of 1-5m before and after it for local grouting in small areas.

[0039] Deployment of the adaptive slurry replenishment algorithm model: The trained adaptive grouting algorithm model was imported into the control unit. The model has been trained using experimental data from 100 concrete models, achieving a prediction accuracy of 96.2%. The model supports online incremental updates and can be continuously optimized based on on-site data.

[0040] Repair process: At a specific point in a section of the lining, the detection unit detected a sudden change in strain value. Gauge length seepage pressure ,temperature .

[0041] The control unit first calculates the characteristic parameters of the crack: Crack width ; The crack depth was obtained based on other monitored data. Crack length ; Total volume of cracks .

[0042] Then, based on the crack width To obtain the repair efficiency of microcapsules Calculate the self-repairing filling volume of microcapsules .

[0043] Then, based on the seepage pressure The seepage correction coefficient is obtained. According to temperature The temperature correction coefficient is obtained. Expansion compensation coefficient .

[0044] Calculate the amount of active grouting: .

[0045] Then, the control unit determines that the amount of repair from the microcapsules is insufficient to completely fill the crack, triggering the active grouting process. It controls the micro-grouting device 9 corresponding to the point to inject 1.60 cm³ of repair grout through the corresponding capillary grouting tube 2.

[0046] During the grouting process, the hydraulic pressure inside the pipe was monitored in real time. The preset safety threshold was 0.5 MPa, and the pressure change rate threshold was 0.1 MPa / min. During the grouting process, the hydraulic pressure inside the pipe gradually increased. When the grouting was completed, the hydraulic pressure inside the pipe reached 0.45 MPa, which did not exceed the safety threshold, and the pressure change rate was 0.08 MPa / min, which did not exceed the threshold. Grouting was then stopped after completion.

[0047] After the repair was completed, the test showed that the strength recovery rate of the crack reached 92% and the permeability recovery rate reached 95%, indicating a good repair effect.

[0048] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A self-healing grouting method for microcracks in tunnel lining, characterized by: Includes the following steps: S1. During the molding process of the lining concrete, microcapsule repair agent, grouting pipe and sensing element are pre-embedded; S2. Real-time monitoring of crack data around the microcapsule repair agent and grouting pipe; S3. When microcracks appear in the lining, the microcapsule repair agent is ruptured by the stress at the crack tip, releasing the internal repair grout to automatically complete the repair. S4. The control unit collects crack data, and the built-in adaptive grouting algorithm model of the control unit processes the crack data to calculate the grouting data. S5. Based on the grouting data, the control unit controls the grouting pipe to inject repair grout into the crack location; S6. During the grouting process, monitor the hydraulic pressure inside the grouting pipe in real time. When the hydraulic pressure inside the pipe reaches the preset safety threshold, or when the pressure change rate is greater than the preset threshold, stop grouting.

2. The self-healing grouting method for microcracks in tunnel lining according to claim 1, characterized in that: The sensing elements include fiber optic strain sensors, pressure sensors, temperature sensors, and pipeline hydraulic sensors; Crack data includes strain abrupt change value ε detected by fiber optic strain sensor, gauge length L of sensor, seepage pressure P detected by pressure sensor, in-pipe hydraulic pressure A detected by pipe hydraulic sensor, and temperature K detected by temperature sensor. The grouting data includes the amount of grout and the grouting time.

3. The self-healing grouting method for microcracks in tunnel lining according to claim 1, characterized in that: S3 also includes: S31. The microcapsule repair agent ruptures, and the monitoring data of the sensor on the side of the microcapsule repair agent changes. S32. The time taken for the data monitored by the detection sensor to stabilize after the initial change, and the data when it stabilizes; S33. The control unit compares the data when it is about to stabilize with the preset data and calculates the remaining amount of grout to fill the microcracks. S34. Compare the remaining amount of grout with the preset value and record it; S35. When the amount of grout added is greater than the preset value or the accumulated amount of grout added is greater than the preset value, control the grouting pipe to add grout.

4. The self-healing grouting method for microcracks in tunnel lining according to claim 1, characterized in that: Grouting pipes are installed in sections according to the tunnel segment, with one grouting structure and one control unit installed in each tunnel segment. Several grouting pipes within each tunnel lining section connect to the grouting structure of that section; Several sensors inside each tunnel lining section are connected to the control unit of that section, and the monitored crack data is acquired and processed by the control unit of that section. Each tunnel section is equipped with several micro-grouting structures, which are connected to grouting pipes and used to locally fill micro-cracks within a preset range.

5. The self-healing grouting method for microcracks in tunnel lining according to claim 1, characterized in that: The steps to obtain grouting data are as follows: S61. Calculate the characteristic parameters of the crack based on the crack data, including crack width w, crack depth h and crack length l; Wherein, the crack width w is calculated by the strain mutation value and the gauge length, and the calculation formula is as follows: ; S62. Calculate the total volume of the crack based on the crack characteristic parameters. ; S63. Based on the crack width, adaptively obtain the repair efficiency η of the microcapsules and calculate the filling amount of the microcapsules for automatic repair. ; S64. Based on the seepage pressure and ambient temperature, obtain the seepage correction coefficient β and the temperature correction coefficient γ, and calculate the amount of grout to be actively injected. The calculation formula is as follows: ； Wherein, α is the expansion compensation coefficient of the repair grout, with a value of 1.05 to 1.1; β is the seepage correction coefficient, which is positively correlated with the seepage pressure P, with a value of 1.0 to 1.3; and γ is the temperature correction coefficient, which is negatively correlated with the ambient temperature K, with a value of 0.95 to 1.

05.

6. The self-healing grouting method for microcracks in tunnel lining according to claim 1, characterized in that: It also includes the steps for obtaining the adaptive slurry replenishment algorithm model, as follows: S71. Simulate different tunnel service environments, create multiple lining concrete models with different strength grades, and pre-embed several microcapsule repair agents, capillary grouting pipes and sensing devices in each model; S72. Microcracks of different sizes are generated in the concrete model by mechanical loading equipment. After the microcapsule repair agent ruptures, different amounts of repair grout are injected into different concrete models through grouting pipes, and various parameters of the grouting process are recorded. S73. Real-time detection of microcrack data at different times and locations using sensors, including strain mutation value ε, gauge length L, seepage pressure P, hydraulic pressure A inside the pipe and temperature K at different time points; S74. After the repair grout has completely solidified, record the repair effect data at different locations, including strength recovery rate and permeability recovery rate, through CT scanning, permeability testing and mechanical property testing. S75. Import the data on grout replenishment volume, strain mutation value ε, gauge length L, seepage pressure P, in-pipe hydraulic pressure A, temperature K, crack characteristic parameters, and repair effect into the adaptive grout replenishment algorithm model. Train the model based on a BP neural network, with crack data and environmental parameters as inputs and optimal grout replenishment volume and grout replenishment time as outputs, to complete the training and verification of the model. S76. Import the trained and validated adaptive grouting algorithm model into the control unit at the tunnel site, and set up an online update mechanism to periodically feed the actual repair data from the site back to the model for iterative optimization.

7. A tunnel lining microcrack self-healing grouting system, applied to the tunnel lining microcrack self-healing grouting method according to any one of claims 1-6, characterized in that: It includes a grouting unit, a control unit, a detection unit, and a self-healing unit; The self-healing unit is used to automatically release repair agent to complete grouting repair when micro-cracks appear in the tunnel lining; The detection unit is used to monitor crack data and environmental data within the lining in real time; The grouting unit is used to inject repair grout into the cracks in the tunnel lining to supplement the shortcomings of microcapsule self-healing and ensure the repair effect of microcracks. The control unit is connected to the grouting unit, detection unit, and self-healing unit to process monitoring data, calculate grouting parameters, and control the grouting action.

8. The tunnel lining microcrack self-healing grouting system according to claim 7, characterized in that: The self-healing unit includes several microcapsules (4) mixed in the concrete matrix of the lining body (1). The grouting unit includes several capillary grouting pipes (2) and several first sensors (5) pre-embedded in the lining body (1). Several grout outlet holes (201) are provided on the capillary grouting pipes (2) for conveying repair grout into the lining. The first sensor (5) includes an optical fiber strain sensor, a micro pressure sensor and a temperature sensor, used to monitor the cracking strain of the lining, the outlet pressure of the grouting pipe, the grout pressure at the microcapsule (4), the crack seepage pressure and temperature; The grouting unit also includes a grouting device (7) located inside the lining body (1). A control unit is provided at the grouting device (7), and the control unit is electrically connected to the first sensor (5) and the grouting device (7). The grouting equipment (7) is connected to the capillary grouting pipe (2) for grouting.

9. The tunnel lining microcrack self-healing grouting system according to claim 8, characterized in that: The grouting equipment (7) is connected to an external pipe (6), and the external pipe (6) is provided with several connecting pipes (601). The capillary grouting pipe (2) is connected to the connecting pipes (601). A valve is provided inside the connecting pipe (601); a second sensor (8) is provided in the connecting pipe (601), and the second sensor (8) is a hydraulic sensor; The inner side of the lining body (1) is provided with several micro grouting devices (9). The micro grouting devices (9) are connected to capillary grouting pipes (2) within a range of 1-5m before and after them for small-scale grouting.

10. The tunnel lining microcrack self-healing grouting system according to claim 8, characterized in that: Microcapsules (4) are high molecular weight binuclear microcapsules, including small-diameter microcapsules with a particle size of 100-200 μm and large-diameter microcapsules with a particle size of 300-500 μm; The microcapsule (4) contains a repair slurry, which is a two-component reactive slurry. The repair slurry includes epoxy resin, curing agent, penetrant and binder. The mass ratio of epoxy resin to curing agent is 3:1-5:

1. The amount of penetrant added is 5%-8% of the total mass. The amount of binder added is 3%-5% of the total mass. The microcapsule (4) is equipped with a first sensor (5).

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