A method and system for repairing cracks in the lining of an operating railway tunnel

Abstract

The present invention provides a method and a repair system for repairing cracks in railway tunnel linings to solve the problems of low repair quality and low construction efficiency in the prior art. The method accurately detects tunnel cracks through a laser scanner and an ultrasonic imager, classifies the cracks in combination with a data processing system, selects appropriate repair materials, and uses intelligent spraying, intelligent grouting and reinforcement devices for filling and repair. At the same time, real-time monitoring and reinforcement of the repair quality are realized through micro-crack sensors and a remote monitoring system to ensure the stability and long-term effectiveness after repair.

Description

Technical Field

[0001] The present invention relates to the field of tunnel repair, and particularly to a method and a system for repairing lining cracks of operating railway tunnels. Background Art

[0002] With the development of railway transportation, the maintenance and repair of railway tunnels have gradually become a key issue. Due to the long-term influence of factors such as vehicle flow and temperature difference, tunnels often show cracks and damages of varying degrees. At present, most conventional tunnel repair methods rely on manual detection and construction, and the repair materials are often traditional grouting materials and simple coatings. This method is not only time-consuming and laborious, but also difficult to ensure the consistency and durability of the repair effect. In the prior art, some methods use automated equipment, but these methods have problems such as low automation level, poor repair accuracy, and inaccurate selection of repair materials, resulting in unsatisfactory tunnel repair effects and even affecting the service life of the tunnels.

[0003] The current repair solutions also lack automated means for accurately classifying crack types and targeted repairs, and at the same time lack a system for post-construction quality monitoring and automatic reinforcement. Therefore, there is an urgent need for an efficient, accurate and sustainable repair method to ensure the quality of tunnel repair and reduce maintenance costs and cycles. Summary of the Invention

[0004] The main object of the present invention is to provide a method and a system for repairing lining cracks of operating railway tunnels, so as to at least solve the problem of low repair quality of tunnel linings in the prior art.

[0005] To achieve the above object, in the first aspect of the present invention, a method for repairing lining cracks of operating railway tunnels is provided, which specifically includes the following steps:

[0006] Step 1, Laser surface detection: Use a laser scanner to quickly detect the tunnel surface to obtain information on the width, depth and orientation of the cracks;

[0007] Step 2, Ultrasonic depth detection: Use an ultrasonic imager for depth detection to obtain depth data of the cracks, and further accurately locate the three-dimensional spatial distribution of the cracks in combination with the information obtained by the laser scanner;

[0008] Step 3, Crack classification: Analyze the crack characteristics of the information obtained through a data processing system to determine the type of the cracks. The types include shallow cracks, deep cracks and through cracks. The depth of shallow cracks is less than 10 mm, the depth of deep cracks is greater than or equal to 10 mm and less than 50 mm, and the depth of through cracks is greater than or equal to 50 mm;

[0009] Step 4. Selection of repair materials: Select matching repair materials according to the crack type. For shallow cracks, use permeable polymer materials; for deep cracks, use nano-reactive grouting materials to cure the interior of the cracks; for through cracks, use high-strength grouting materials in combination with mechanical reinforcement devices;

[0010] Step 5. Intelligent filling repair of cracks:

[0011] First step: Adopt an intelligent spraying system to pre-apply a bond enhancement layer in the crack area. The bond enhancement layer is a polymer resin coating with a bond strength of 1.5 MPa - 3 MPa and a curing time of 30 min - 120 min;

[0012] Second step: Adopt an intelligent grouting device to automatically adjust the grouting pressure and flow rate according to the crack depth;

[0013] Third step: Use a flexible sealing material for secondary covering;

[0014] Step 6. Repair quality monitoring:

[0015] First step: Install a micro crack sensor at the repaired location to monitor the crack changes in real time and upload the data to the remote monitoring system;

[0016] Second step: Use ultrasonic waves to detect the filled area. If new cracks are detected, the remote monitoring system will automatically alarm and activate the mechanical automatic reinforcement device for secondary repair.

[0017] Furthermore, in step 1, the laser scanner is a 3D lidar scanning device. The scanning accuracy of the 3D lidar scanning device is 0.05 mm - 0.1 mm, and the measurement range is 0.5 m - 30 m; the 3D lidar scanning device outputs the acquired 3D coordinate data of the cracks to the data processing system.

[0018] Furthermore, in step 2, the ultrasonic imager adopts phased array ultrasonic detection technology. The frequency range of the ultrasonic imager is 1 MHz - 10 MHz, and the detection depth range is 10 mm - 100 mm.

[0019] Furthermore, in step 5, the flexible sealing material is a high molecular elastomer, and the elongation rate of the high molecular elastomer is greater than or equal to 300% to adapt to the micro-deformation of the tunnel structure caused by temperature changes.

[0020] According to another aspect of the present invention, an operating railway tunnel lining crack repair system is provided. This operating railway tunnel lining crack repair system is applied to the operating railway tunnel lining crack repair method described in any one of the above, and this operating railway tunnel lining crack repair system includes: a data processing system, which analyzes the information collected by the laser surface scanner and the ultrasonic imager to judge the crack type; an intelligent spraying system, which pre-coats an adhesion enhancement layer at the crack; an intelligent grouting device, which cooperates with the intelligent spraying system to perform intelligent filling repair at the crack; a micro crack sensor, which is arranged at the repaired part to monitor the crack change in real time and upload the data to the remote monitoring system; a mechanical automatic reinforcement device, which cooperates with the micro crack sensor to monitor the repair quality at the repaired part.

[0021] Furthermore, the data processing system includes:

[0022] A first data module, to which the laser scanner and the ultrasonic imager transmit the data;

[0023] A second data module, to which the first data module inputs the received data. The second data module performs classification operations based on the crack width, depth, and orientation, and outputs the crack type;

[0024] A third data module, to which the second data module sends the crack classification result. The third data module generates a construction instruction and sends a control signal to the intelligent grouting device to perform the repair operation.

[0025] Furthermore, the intelligent spraying system includes: a spraying robot, which uses a six-axis robotic arm; a spraying pump, whose pressure range is 0.5 MPa - 3 MPa, and controls the spraying thickness to be 0.2 mm - 2 mm; a permeable polymer coating, whose viscosity is 200 - 800 cP.

[0026] Furthermore, the intelligent grouting device includes: an adjustable grouting pump, whose grouting pressure range is 0.2 MPa - 5 MPa; a sensing control system, which monitors the slurry filling situation in real time to prevent material overflow or uneven filling; a grouting material conveying system, which includes multiple independently controllable grouting pipelines to meet the repair requirements of different crack types.

[0027] Furthermore, the micro crack sensor includes a MEMS strain sensor and a wireless data transmission module. The working frequency range of the MEMS strain sensor is 10 Hz - 100 Hz, and the data collected is transmitted to the remote monitoring system through the wireless data transmission module.

[0028] Furthermore, the mechanical automatic reinforcement device includes: a robotic arm, which is provided with an adjustable nozzle, and the opening diameter range of the adjustable nozzle is 1 mm - 5 mm to adapt to the filling requirements of different crack sizes; a robot control system, which analyzes the crack monitoring data to automatically determine whether reinforcement is needed; and a secondary grouting module, which reinforces and processes the through cracks again.

[0029] Compared with the prior art, the method and system for repairing cracks in the lining of an operating railway tunnel of the present invention have the following beneficial effects: First, by the combined application of a laser scanner and an ultrasonic imager, accurate detection of tunnel cracks is achieved, greatly improving the detection efficiency and accuracy. Second, appropriate repair materials are selected according to the crack depth and type, and precise filling is carried out through an intelligent spraying system and an intelligent grouting device to ensure the continuity and stability of the repair effect. In addition, by combining flexible sealing materials and self-healing materials, the adaptability of the tunnel structure is enhanced, and it can effectively cope with environmental temperature changes and crack propagation that may occur during long-term use. Through the real-time monitoring system, the repair status is continuously tracked. If new cracks are found, the system can automatically activate the reinforcement device for secondary repair, thus ensuring that the repair effect is not affected by the external environment. This method not only improves the repair quality and construction efficiency but also greatly reduces the risks and errors of manual operations, and has strong practicability and popularization value. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The specification drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0031] Figure 1 is a flow chart of the method for repairing cracks in the lining of an operating railway tunnel;

[0032] Figure 2 is a flow chart of intelligent filling repair at the crack;

[0033] Figure 3 is a flow chart of repair quality monitoring. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0035] The following further describes the present invention with reference to the drawings:

[0036] In the figure:

[0037] As shown in the appendix Figures 1 to 3As shown in the figure, according to the first aspect of the present invention, a method for repairing cracks in the lining of an operating railway tunnel is provided. A method for repairing cracks in the lining of an operating railway tunnel specifically includes the following steps:

[0038] Step 1, Laser surface detection: Use a laser scanner to quickly detect the tunnel surface to obtain information on the width, depth, and orientation of the cracks;

[0039] Step 2, Ultrasonic depth detection: Use an ultrasonic imager for depth detection to obtain the depth data of the cracks, and combine the information obtained by the laser scanner to further accurately locate the three-dimensional spatial distribution of the cracks;

[0040] Step 3, Crack classification: Analyze the crack characteristics of the information obtained through a data processing system to judge the type of cracks. The types include shallow cracks, deep cracks, and through cracks. The depth of shallow cracks is less than 10 mm, corresponding to micro-damage on the surface of the lining, accounting for more than 70% of the total number of cracks; the depth of deep cracks is greater than or equal to 10 mm and less than 50 mm, involving local failure of the structural layer and prone to leakage expansion; the depth of through cracks is greater than or equal to 50 mm, threatening the overall bearing safety of the tunnel; by introducing a machine learning model, the classification accuracy rate is increased from 75% of manual experience judgment to 98.5%, avoiding material matching errors caused by misjudgment of types;

[0041] Step 4, Selection of repair materials: Select matching repair materials according to the type of cracks. For shallow cracks, a permeable polymer material with a viscosity of 200 - 800 cP can be used. Through capillary action, it fills micro-cracks of 0.1 mm level, and the penetration speed reaches 5 mm / s, which is 3 times higher than that of traditional epoxy resin. And the bonding strength with the concrete interface after curing is ≥3 MPa; for deep cracks, match with nano-reactive grouting materials, which generate a volume expansion rate of 6% - 8% during curing, effectively compensating for structural shrinkage deformation, and the 28-day compressive strength reaches 45 MPa, and the durability is 4 times higher than that of ordinary cement-based materials; for through cracks, a 60 MPa grade high-strength grouting material can be used in combination with mechanical reinforcement of prestressed carbon fiber plates to form a rigid-flexible composite reinforcement system, which can withstand a crack propagation rate of 0.2 mm / year, and the repair life is extended to more than 15 years;

[0042] Step 5, Intelligent filling repair of the cracks:

[0043] The first step: Use an intelligent spraying system to pre-coat a bonding enhancement layer in the crack area. The bonding enhancement layer is a polymer resin coating with a bonding strength of 1.5 MPa - 3 MPa and a curing time of 30 min - 120 min;

[0044] The second step: Use an intelligent grouting device to automatically adjust the grouting pressure and flow rate according to the crack depth;

[0045] Step 3: Cover it again with a flexible sealing material;

[0046] Step 6: Repair quality monitoring:

[0047] Step 1: Install a micro crack sensor at the repaired area to monitor the crack changes in real time and upload the data to the remote monitoring system;

[0048] Step 2: Use ultrasonic to detect the filling area. If new cracks are detected, the remote monitoring system will automatically alarm and activate the mechanical automatic reinforcement device for secondary repair.

[0049] Furthermore, a three-dimensional lidar scanning device with a scanning accuracy of 0.05 mm - 0.1 mm is used to quickly detect the tunnel surface. The device can achieve full-section coverage scanning within a measurement range of 0.5 m - 30 m and can complete the detection operation of a single-ring lining within 5 minutes. Compared with the traditional manual visual inspection with a missed inspection rate of more than 20%, this technology can accurately capture the crack profile with a width ≥ 0.1 mm through the output of three-dimensional coordinate data with millimeter-level accuracy, eliminating the misjudgment problem of crack orientation caused by visual observation and providing a high-resolution surface model for subsequent repair.

[0050] Furthermore, in Step 2, a phased array ultrasonic imager with a frequency adjustable from 1 MHz to 10 MHz is configured to cross-verify the crack depth in combination with the laser scanning data. This technology can accurately identify the hidden cracks inside the lining through the penetration depth detection ability of 10 mm - 100 mm, reducing the depth measurement error from ±5 mm of the traditional knocking method to ±0.8 mm. By fusing the laser point cloud and ultrasonic imaging data, a three-dimensional spatial distribution model of cracks is constructed, achieving a crack end positioning accuracy of ±2 mm and completely solving the problems of insufficient grouting or over-repair caused by depth misjudgment in the traditional method.

[0051] Furthermore, in Step 5, a polymer elastomer with an elongation rate ≥ 300% is used for surface sealing, which can adaptively absorb a deformation of 1.5 mm in the temperature difference environment of ±20°C, reducing the secondary cracking rate by 95% compared with the hard sealing glue.

[0052] According to another aspect of the present invention, an operating railway tunnel lining crack repair system is provided. This operating railway tunnel lining crack repair system is applied to the operating railway tunnel lining crack repair method described in any one of the above, and this operating railway tunnel lining crack repair system includes: a data processing system, which analyzes the information collected by the laser surface scanner and the ultrasonic imager to judge the crack type; an intelligent spraying system, which pre-coats an adhesion enhancement layer at the crack; an intelligent grouting device, which cooperates with the intelligent spraying system to perform intelligent filling repair on the crack; a micro crack sensor, which is arranged at the repaired place to monitor the crack change in real time and upload the data to the remote monitoring system; a mechanical automatic reinforcement device, which cooperates with the micro crack sensor to monitor the repair quality at the repaired place.

[0053] Furthermore, the data processing system includes:

[0054] The first data module, to which the laser scanner and the ultrasonic imager transmit the data. The first data module can integrate the millimeter-level precision point cloud data output by the laser scanner and the internal defect information provided by the ultrasonic imager to construct a three-dimensional digital twin model of the crack, with a data fusion error ≤ 0.1 mm;

[0055] The second data module, to which the first data module inputs the received data. The second data module performs classification operations based on the crack width, depth, and orientation and outputs the crack type; the second data module runs a crack classification algorithm based on a convolutional neural network and completes the crack type determination within 8 seconds, with an accuracy improvement of 23.5% compared to manual determination;

[0056] The third data module, to which the second data module sends the crack classification result. The third data module generates construction parameters such as pressure, flow rate, and material consumption according to the classification result, and the control instruction transmission delay < 50 ms to ensure the consistency of the repair process.

[0057] Furthermore, the intelligent spraying system includes: a spraying robot, which uses a six-axis robotic arm. The six-axis robotic arm is equipped with a laser positioning sensor to achieve spraying with a trajectory accuracy of ±0.1 mm on the surface of the curved lining, and the construction efficiency reaches 5 m² / min; a spraying pump, whose pressure range is 0.5 MPa - 3 MPa, adjusts the coating thickness through pressure feedback, and controls the volatility within the range of 0.2 mm - 2 mm to be ≤3%, avoiding material accumulation or incomplete coverage; a permeable polymer coating, whose viscosity is 200 - 800 cP, adopts a gradient viscosity formula, automatically matches the penetration rate for cracks with a width of 0.1 mm - 2 mm, and shortens the curing time from the traditional 72 hours to 120 minutes; controls the 0.5 MPa - 3 MPa spraying pump through the six-axis robotic arm to form a polymer resin transition layer with a thickness of 0.2 mm - 2 mm in the crack area, increasing the interfacial bonding strength between the new and old materials from 2.5 MPa of traditional manual brushing to 4.2 MPa, and reducing the peeling risk by 80%.

[0058] Furthermore, the intelligent grouting device includes: an adjustable grouting pump, which is equipped with a pressure-flow closed-loop control system and the grouting pressure range is 0.2 MPa - 5 MPa. When grouting a 50 mm deep crack, the slurry flow rate at the end is stable at 10 cm / s ± 5%; a sensing control system, which monitors the slurry filling situation in real time to prevent material overflow or uneven filling, and can effectively reduce the incidence of air pockets; a grouting material conveying system, which includes multiple independently controllable grouting pipelines to meet the repair requirements of different crack types, greatly improving the material utilization rate.

[0059] Furthermore, the micro-crack sensor includes a MEMS strain sensor and a wireless data transmission module. The MEMS strain sensor uses high-frequency sampling at 10 Hz - 100 Hz to capture instantaneous deformations at the 0.005 mm level caused by train vibrations, improving the monitoring effect; and transmits the collected data to the remote monitoring system through the wireless data transmission module. The wireless data transmission module can use a LoRa wireless transmission module to achieve stable communication at a distance of 500 m in the complex electromagnetic environment of the tunnel, and the data packet loss rate < 0.01%.

[0060] Furthermore, the mechanical automatic reinforcement device includes: a robotic arm, which is provided with an adjustable nozzle. The opening diameter range of the adjustable nozzle is 1 mm - 5 mm to adapt to the filling requirements of different crack sizes, so as to achieve grouting with a positioning accuracy of ±0.2 mm in a narrow space; a robot control system, which analyzes the crack monitoring data to automatically determine whether reinforcement is needed, accurately and quickly, avoiding human errors; a secondary grouting module, which can use nano-modified grouting materials to form an interpenetrating network structure on the existing repair layer, and the interfacial bonding strength is increased by 40%.

[0061] Example 1

[0062] During the maintenance of operating railway tunnels, a three-dimensional lidar scanning device is first used to comprehensively scan the tunnel surface to quickly detect the width, depth, and orientation of cracks. The laser scanner can provide high-precision scanning data with an accuracy of 0.05 mm - 0.1 mm, ensuring the accuracy and meticulousness of crack information. Based on this data and combined with the geometric shape of the tunnel, the system can generate the precise three-dimensional spatial distribution of cracks, helping subsequent repair work to carry out targeted operations. This method can avoid the errors and time consumption caused by traditional manual measurements and improve the efficiency of repair operations.

[0063] Furthermore, a phased array ultrasonic imager is used for depth detection. The frequency range of this device is 1 MHz - 10 MHz, and it can detect crack depths up to 100 mm. Compared with traditional crack depth detection techniques, the ultrasonic imager can provide more accurate depth information, avoiding the limitations of solely relying on surface crack detection. Combining the surface information obtained by laser scanning, the depth data can further accurately locate the crack position, thereby improving the repair accuracy.

[0064] After the data processing system aggregates the scanning and imaging data, it automatically determines the crack type through a preset crack classification model. According to the crack depth and nature, the system will classify cracks into shallow cracks with a depth less than 10 mm, deep cracks with a depth of 10 mm to 50 mm, and through cracks with a depth greater than 50 mm. This automatic classification step not only reduces the errors caused by manual intervention but also improves the classification accuracy, ensuring the targeted selection of repair materials.

[0065] According to the crack type, different repair materials are selected. For shallow cracks, an epoxy-based penetrant is used. This is a permeable polymer material that can penetrate the crack surface and form a strong bonding layer, effectively preventing water penetration and increasing the durability of the repair layer. For deep cracks, a nano-sodium silicate grouting material is adopted. This material can penetrate deep into the cracks and undergo a curing reaction therein, forming a solid repair layer, enhancing the structural strength, and preventing the cracks from continuing to expand. For through cracks, a high-strength cement-based grouting material is used, combined with a mechanical reinforcement device for double repair. The mechanical reinforcement device can ensure the uniform filling of the deep cracks with slurry by precisely controlling the pressure and flow rate, and provide real-time monitoring during the repair process to prevent material overflow or uneven filling.

[0066] During the repair process, a spraying robot with a six-axis robotic arm is used for intelligent spraying to evenly coat the bonding enhancement layer in the crack area. The working pressure of the spraying pump is controlled within the range of 0.5 MPa - 3 MPa, and the spraying thickness is controlled within 0.2 mm - 2 mm to ensure uniform coverage of the material. The efficiency and precision of this operation cannot be achieved in traditional manual spraying, greatly shortening the construction period and improving the repair quality.

[0067] Subsequently, an intelligent grouting device is used for automated grouting. The grouting pump can automatically adjust the pressure and flow rate according to the depth of the crack, enabling the grout to evenly fill the crack and solidify. The sensing and control system in this process monitors the grout filling situation in real time, avoiding over-grouting or uneven filling, and further ensuring the repair quality.

[0068] After the repair is completed, a polyurethane sealant is used for secondary covering. This flexible sealing material has an elongation rate of more than 300%, can adapt to the small deformations generated by the tunnel structure under temperature changes, and effectively avoids the re-expansion of cracks in the later stage.

[0069] To ensure the repair quality, MEMS strain sensors and micro crack sensors are installed. These sensors can monitor the changes in cracks in real time and upload the data to the remote monitoring system through a wireless data transmission module. If new cracks or problems occur in the repaired area are detected, the system will automatically alarm and activate the mechanical automatic reinforcement device for secondary repair.

[0070] This embodiment effectively solves the problems existing in traditional repair methods, such as incomplete repair, excessive manual intervention, and long repair cycle, through precise crack detection, automated repair technology, and an intelligent monitoring system, greatly improving the efficiency and quality of tunnel repair, and ensuring the long-term stability and safety of the repair effect.

[0071] Embodiment 2

[0072] In this embodiment, first, a 3D lidar scanner is used to detect tunnel cracks and obtain the geometric information of the cracks, including data such as the width, depth, and orientation of the cracks. These data are used for subsequent determination of crack types and selection of repair materials. Next, a phased array ultrasonic imager is used to measure the crack depth, and the frequency range of the ultrasonic waves is 1 MHz - 10 MHz to ensure accurate detection of the crack depth. Through the data processing system, combining the data obtained from laser scanning and ultrasonic imaging, the system classifies the cracks. For smaller cracks, polyurethane sealant is used for repair, while for larger cracks, especially dynamic cracks, such as cracks affected by temperature changes, thermoplastic polyurethane elastomer (TPU) is used. It has an elongation rate of over 300% and can adapt to the minute deformation of the cracks to prevent re-cracking. Using thermoplastic polyurethane elastomer (TPU) as the repair material can effectively solve common problems in tunnel crack repair, such as poor sealing and low durability. TPU has excellent elasticity and wear resistance, can adapt to the minute deformation of the tunnel structure due to temperature changes, and prevent cracks from re-cracking. The construction of TPU is simple and the curing speed is fast, which can effectively shorten the repair time and improve the construction efficiency. In addition, during the repair of through-cracks, a mechanical reinforcement device is combined, and a high-strength cement-based grouting material is used for double repair. This cement-based grouting material can effectively fill the through-cracks, enhance the stability of the repair layer, and avoid possible future crack propagation.

[0073] During the repair process, the intelligent spraying system sprays a reinforcement layer in the crack area to ensure uniform distribution and increase the adhesion and repair effect of the material. The grouting pump uses adjustable pressure and flow rate to ensure that the slurry evenly fills the cracks. Subsequently, real-time monitoring is carried out through miniature crack sensors and MEMS strain sensors, and the data is uploaded to the remote monitoring system through a wireless data transmission module. If it is found that the cracks change further, the system will activate the automatic reinforcement mechanism for secondary repair. This embodiment highlights the use of thermoplastic polyurethane elastomer (TPU), which can adapt to the micro-deformation of the tunnel structure due to temperature changes and is especially suitable for the repair of dynamic cracks. By combining flexible repair materials and mechanical reinforcement, this embodiment can enhance the adaptability and stability of the repair area while maintaining long-term sealing, and is especially suitable for the repair of tunnel cracks affected by temperature changes.

[0074] Embodiment Three

[0075] In this embodiment, a three-dimensional lidar scanner is first used to detect tunnel cracks and obtain the geometric information of the cracks, including data such as the width, depth, and orientation of the cracks. These data are used for subsequent determination of crack types and selection of repair materials. Next, a phased array ultrasonic imager is used to measure the crack depth to ensure accurate detection of the crack depth. The frequency range of the ultrasonic waves is 1 MHz - 10 MHz. Through the data processing system, combining the data obtained from laser scanning and ultrasonic imaging, the system classifies the cracks. For smaller cracks, epoxy resin grouting materials are used for repair; for larger cracks, especially dynamic cracks affected by temperature changes, silicone elastomers are selected. It has excellent weather resistance and ultraviolet resistance and can adapt to the small deformations generated by the tunnel due to temperature changes, effectively preventing the recurrence of cracks. The silicone elastomer has stronger temperature resistance and shear resistance, effectively improving the long-term stability of the repair. Especially in high-temperature and long-term humid environments, it can ensure the integrity of the tunnel structure. Compared with traditional repair materials, the silicone elastomer shows better elasticity and adaptability in the tunnel environment with large temperature changes. Through the application of this material, the common problem of crack recurrence can be effectively avoided. For the repair of through cracks, cement-based high-strength grouting materials are used for double repair. Such materials have the characteristics of high strength, good durability, and suitability for high-pressure grouting. During the repair process, an intelligent spraying system is used to spray a strengthening layer in the crack area to ensure good adhesion between the repair layer and the substrate and enhance the repair effect of the material. The grouting pump uses adjustable pressure and flow rate to ensure uniform filling of the cracks with the slurry. Subsequently, real-time monitoring is carried out through micro-crack sensors and MEMS strain sensors, and the data is uploaded to the remote monitoring system through a wireless data transmission module. If it is found that the cracks change further, the system will activate the automatic reinforcement mechanism for secondary repair.

[0076] Embodiment 4

[0077] In this embodiment, first, a 3D lidar scanner is used to detect tunnel cracks and obtain the geometric information of the cracks, including data such as the width, depth, and orientation of the cracks. These data are used for subsequent determination of crack types and selection of repair materials. Then, a phased array ultrasonic imager is used to measure the crack depth to ensure accurate detection of the crack depth, and the ultrasonic frequency range is 1 MHz - 10 MHz. Through the data processing system, combining the data obtained from laser scanning and ultrasonic imaging, the system classifies the cracks. For small cracks, polyurethane resin is used for repair; for large cracks, especially dynamic cracks affected by temperature changes, acrylate elastomer is selected. Using acrylate elastomer as a repair material can effectively adapt to the micro-deformation of the tunnel structure caused by temperature changes and is particularly suitable for the repair of dynamic cracks. The chemical stability and high and low temperature resistance of this material enable it to maintain excellent repair effects in various harsh environments. Compared with traditional repair materials, acrylate elastomer can provide higher temperature adaptability, reduce repair failures caused by temperature difference changes, and is particularly suitable for tunnel environments with large temperature differences. For the repair of through cracks, high-strength grouting materials are used for filling to provide strong adhesion and durability. During the repair process, an intelligent spraying system is used to spray a strengthening layer in the crack area to ensure the repair effect in the crack area and good adhesion of the material. The grouting pump automatically adjusts the grouting pressure and flow rate according to the different crack depths to ensure uniform filling of the cracks with the slurry. Subsequently, real-time monitoring is carried out through micro crack sensors and MEMS strain sensors, and the data is transmitted to the remote monitoring system through a wireless data transmission module. If it is found that the cracks change further, the system will activate the automatic reinforcement mechanism for secondary repair.

[0078] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for repairing cracks in the lining of an operating railway tunnel, characterized in that, It includes the following steps: Step 1, Laser surface detection: Use a laser scanner to quickly detect the tunnel surface and obtain information on the width, depth, and orientation of cracks; Step 2, Ultrasonic depth detection: Use an ultrasonic imager for depth detection to obtain the depth data of cracks, and further accurately locate the three-dimensional spatial distribution of cracks in combination with the information obtained by the laser scanner; Step 3, Crack classification: Analyze the crack characteristics of the information obtained through a data processing system to judge the type of cracks. The types include shallow cracks, deep cracks, and through cracks. The depth of the shallow cracks is less than 10mm, the depth of the deep cracks is greater than or equal to 10mm and less than 50mm, and the depth of the through cracks is greater than or equal to 50mm; Step 4, Selection of repair materials: Select matching repair materials according to the crack type. For the shallow cracks, use permeable polymer materials; for the deep cracks, use nano-reactive grouting materials to cure the inside of the cracks; for the through cracks, use high-strength grouting materials and combine with mechanical reinforcement devices; Step 5, Intelligent filling and repair of cracks: The first step: Adopt an intelligent spraying system to pre-apply a bond enhancement layer in the crack area. The bond enhancement layer is a polymer resin coating with a bond strength of 1.5MPa - 3MPa and a curing time of 30min - 120min; The second step: Adopt an intelligent grouting device to automatically adjust the grouting pressure and flow rate according to the crack depth; The third step: Use a flexible sealing material for secondary covering; Step 6; Repair quality monitoring: The first step: Install a micro crack sensor at the repaired area to monitor the crack changes in real time and upload the data to the remote monitoring system; The second step: Use ultrasonic detection for the filling area. If new cracks are detected, the remote monitoring system will automatically alarm and activate the mechanical automatic reinforcement device for secondary repair.

2. The method for repairing cracks in the lining of an operating railway tunnel according to claim 1, characterized in that, In step 1, the laser scanner is a three-dimensional lidar scanning device. The scanning accuracy of the three-dimensional lidar scanning device is 0.05mm - 0.1mm, and the measurement range is 0.5m - 30m; the three-dimensional lidar scanning device outputs the obtained three-dimensional coordinate data of the cracks to the data processing system.

3. The method for repairing cracks in the lining of an operating railway tunnel according to claim 1, characterized in that, In step 2, the ultrasonic imager adopts phased array ultrasonic detection technology. The frequency range of the ultrasonic imager is 1MHz - 10MHz, and the detection depth range is 10mm - 100mm.

4. The method for repairing cracks in the lining of an operating railway tunnel according to claim 1, characterized in that, In step 5, the flexible sealing material is a polymer elastomer, and the elongation rate of the polymer elastomer is greater than or equal to 300% to adapt to the micro-deformation of the tunnel structure due to temperature changes.

5. An operating railway tunnel lining crack repair system, characterized in that, The operating railway tunnel lining crack repair system is applied to the operating railway tunnel lining crack repair method according to any one of claims 1 to 4. The operating railway tunnel lining crack repair system includes: A data processing system that analyzes the information collected by the laser scanner and the ultrasonic imager to judge the crack type; An intelligent spraying system that pre-applies a bonding enhancement layer to the crack; Intelligent grouting device, which cooperates with the intelligent spraying system to perform intelligent filling and repair on cracks; Micro crack sensor, which is arranged at the repaired location to monitor crack changes in real time and upload data to the remote monitoring system; Mechanical automatic reinforcement device, which cooperates with the micro crack sensor to monitor the repair quality of the repaired location.

6. The operating railway tunnel lining crack repair system according to claim 5, wherein, The data processing system includes: First data module, to which the laser scanner and the ultrasonic imager transmit data; Second data module, the first data module inputs the received data into the second data module, and the second data module performs classification operations based on crack width, depth, and orientation and outputs the crack type; Third data module, the second data module sends the crack classification result to the third data module, and the third data module generates a construction instruction and sends a control signal to the intelligent grouting device to execute the repair operation.

7. The operation railway tunnel lining crack repair system according to claim 5, characterized in that The intelligent spraying system includes: a spraying robot with a six-axis robotic arm; a spraying pump with a pressure range of 0.5 MPa - 3 MPa and a controlled spraying thickness of 0.2 mm - 2 mm; a permeable polymer coating with a viscosity of 200 - 800 cP.

8. The operation railway tunnel lining crack repair system according to claim 5, characterized in that, The intelligent grouting device includes: an adjustable grouting pump with a grouting pressure range of 0.2 MPa - 5 MPa; a sensing and control system that monitors the slurry filling situation in real time to prevent material overflow or uneven filling; a grouting material conveying system that includes multiple independently controllable grouting pipelines to meet the repair requirements of different crack types.

9. The operation railway tunnel lining crack repair system according to claim 5, characterized in that, The micro crack sensor includes a MEMS strain sensor and a wireless data transmission module. The working frequency range of the MEMS strain sensor is 10 Hz - 100 Hz, and the data collected is transmitted to the remote monitoring system through the wireless data transmission module.

10. The operation railway tunnel lining crack repair system according to claim 5, characterized in that, The mechanical automatic reinforcement device includes: a robotic arm with an adjustable nozzle, the opening diameter range of the adjustable nozzle is 1 mm - 5 mm to adapt to the filling requirements of different crack sizes; a robot control system that analyzes the crack monitoring data to automatically determine whether reinforcement is needed; a secondary grouting module that reinforces the through crack again.

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