Safety detection method and system for large-span multi-arch tunnel

Abstract

The invention relates to the technical field of tunnel safety, and discloses a safety detection method and system for a large-span multi-arch tunnel, and the method comprises the following steps: a preventive monitoring step: carrying out the dynamic monitoring of a middle rock pillar, a double-hole junction section and a vault region of the multi-arch tunnel through a monitoring device, and collecting the deformation data and stress data of surrounding rock and a structure; a disease detection step: detecting the tunnel lining and the surrounding rock by adopting a mode of combining surface detection and hidden detection to obtain surface disease characteristics and hidden disease characteristics; and a data fusion step: carrying out fusion processing on the deformation data, the stress data, and the surface disease characteristics and the hidden disease characteristics obtained in the disease detection step. The deformation data and the stress data are obtained by dynamically monitoring the middle rock pillar, the double-hole junction section and the arch crown area, and the problem that the tunnel safety state evaluation is not comprehensive due to the fact that a single-dimension monitoring or detection mode is mostly adopted in traditional tunnel safety detection is solved.

Description

Technical Field

[0001] This invention relates to the field of tunnel safety technology, specifically to a safety inspection method and system for long-span continuous arch tunnels. Background Technology

[0002] Long-span arch tunnels are a special type of tunnel structure. Their core feature is the connection between two parallel main tunnels via a central arch, resulting in a double-tunnel, single-arch configuration. They are suitable for areas requiring ample passage space and where terrain conditions are limited, such as bustling urban areas and mountain valleys. These tunnels typically have spans far exceeding those of conventional split tunnels, effectively reducing surface impact and environmental disturbance. They offer significant advantages in alleviating traffic congestion and shortening travel distances. However, due to the complex structural stresses, construction is extremely difficult. Precise handling of key issues such as surrounding rock stability, arch load-bearing balance, and drainage is crucial. A construction technique of excavating a pilot tunnel first and then proceeding in stages is often employed to ensure project safety.

[0003] Traditional tunnel safety inspections mostly employ single-dimensional monitoring or inspection methods. Because the data from different dimensions are not integrated, the assessment of the tunnel's safety status is not comprehensive enough. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a safety inspection method and system for long-span continuous arch tunnels. It solves the problem that traditional tunnel safety inspections mostly adopt single-dimensional monitoring or inspection methods, and the assessment of tunnel safety status is not comprehensive because the data from various dimensions are not integrated.

[0005] This invention provides the following technical solution: a safety inspection method for long-span continuous arch tunnels, comprising the following steps: Preventive monitoring steps: Dynamic monitoring of the intermediate rock pillars, the junction of the two tunnels and the arch area of ​​the arch tunnel is carried out using monitoring devices to collect deformation data and stress data of the surrounding rock and structure; Disease detection steps: The tunnel lining and surrounding rock are detected by combining surface detection and concealed detection to obtain surface disease characteristics and concealed disease characteristics; Data fusion step: The deformation data and stress data obtained in the preventive monitoring step are fused with the surface disease characteristics and hidden disease characteristics obtained in the disease detection step to generate risk assessment data; Early warning procedure: Based on the risk assessment data, output an early warning signal corresponding to the risk level.

[0006] By adopting the above technical solution, dynamic monitoring of the intermediate rock pillar, the junction of the two tunnels, and the arch area is used to obtain deformation and stress data. At the same time, a combination of surface detection and concealed detection is used to obtain surface and concealed defect characteristics. Then, the above data and characteristics are fused and processed to generate risk assessment data and output corresponding early warning signals. This improves the problem that traditional tunnel safety inspections mostly use single-dimensional monitoring or detection methods. Because the data from various dimensions are not fused, the assessment of the tunnel safety status is not comprehensive enough.

[0007] Preferably, in the preventive monitoring step, dynamic monitoring includes: collecting horizontal displacement data and internal stress data of the intermediate rock column through a strain monitoring device; collecting crown settlement data and clearance convergence data of the junction of the two tunnels through a displacement monitoring device; and collecting cumulative stress data under load through a load monitoring device.

[0008] Preferably, in the defect detection step, the surface detection uses an image acquisition device to scan the lining surface to identify crack distribution characteristics and water seepage trace characteristics.

[0009] Preferably, in the defect detection step, the concealed detection uses an electromagnetic wave detection device to scan the interior of the lining and the surrounding rock to identify the characteristics of void areas and crack development, and uses an acoustic wave detection device to detect the density characteristics of the lining concrete.

[0010] A safety monitoring system for long-span continuous arch tunnels includes: a data acquisition module, a defect detection module, a data processing module, and an early warning module; The data acquisition module is used to collect geological foundation data, structural deformation data, and stress data of the arch tunnel; the defect detection module is used to detect defects on the tunnel lining surface and inside the surrounding rock, and output surface defect data and hidden defect data; the data processing module is connected to the data acquisition module and the defect detection module respectively, and is used to receive structural deformation data, stress data, surface defect data, and hidden defect data, and perform fusion processing to generate risk assessment results; the early warning module is connected to the data processing module and is used to output corresponding early warning information based on the risk assessment results.

[0011] Preferably, the data acquisition module includes a key area monitoring unit and an environmental parameter acquisition unit; the key area monitoring unit is deployed in the intermediate rock pillar, the junction of the two tunnels and the arch area, and is used to collect horizontal displacement data of the intermediate rock pillar, net clearance convergence data of the junction of the two tunnels and settlement data of the arch; the environmental parameter acquisition unit is used to collect temperature and humidity data and seepage flow data in the tunnel.

[0012] Preferably, the key area monitoring unit includes a fiber optic grating sensor, an automated monitoring instrument, and a stress sensor; the fiber optic grating sensor is embedded inside the intermediate rock column to collect horizontal displacement data and internal stress data of the rock column; the automated monitoring instrument is used to collect real-time data on the clearance convergence of the double-tunnel junction and the settlement data of the arch; the stress sensor is deployed on the lining surface to collect stress change data of the lining structure.

[0013] Preferably, the defect detection module includes a surface detection component, which is an image acquisition device used to scan the lining surface and output crack distribution data and water seepage imprint data.

[0014] Preferably, the defect detection module further includes a concealed detection component, which consists of a ground-penetrating radar and an ultrasonic detector. The ground-penetrating radar is used to scan the area behind the lining and inside the surrounding rock, and outputs data on the void area and the distribution of cracks. The ultrasonic detector is used to detect the density data of the lining concrete.

[0015] Preferably, the data processing module includes a data normalization unit and a coupling calculation unit; the data normalization unit is used to convert structural deformation data, stress data, surface defect data and hidden defect data into standardized values; the coupling calculation unit is used to calculate the standardized values ​​using a multi-parameter coupling algorithm and output the risk assessment results.

[0016] This invention provides a method and system for safety inspection of long-span continuous arch tunnels. It has the following beneficial effects: 1. This invention obtains deformation and stress data by dynamically monitoring the intermediate rock pillar, the junction of the two tunnels, and the arch area. At the same time, it uses a combination of surface detection and concealed detection to obtain surface and concealed defect characteristics. Then, the above data and characteristics are fused and processed to generate risk assessment data and output corresponding early warning signals. This improves the problem that traditional tunnel safety inspections mostly use single-dimensional monitoring or detection methods. Because the data from various dimensions are not fused, the assessment of the tunnel safety status is not comprehensive enough.

[0017] 2. This invention integrates deformation and stress data obtained from preventive monitoring with surface and hidden defect features obtained from defect detection to generate risk assessment data and output corresponding early warning signals. This improves upon the traditional tunnel safety inspection method, which mostly processes monitoring or detection data separately and fails to establish a correlation between the two, making it difficult to accurately determine the relationship between defects and structural stress state.

[0018] 3. This invention improves upon the traditional tunnel safety inspection method by dynamically monitoring the intermediate rock pillar, the junction of the two tunnels, and the arch area, and by combining the characteristic data obtained from the defect detection with the data for fusion processing and outputting early warnings. This method addresses the problem that traditional tunnel safety inspections mostly rely on periodic inspections, which cannot capture deformation and stress changes in key areas in real time, resulting in untimely early warnings of sudden risks. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of a safety inspection method for long-span arch tunnels proposed in this invention. Figure 2 This is a schematic diagram of the system architecture of a safety detection system for long-span arch tunnels proposed in this invention; Figure 3 This is a schematic diagram of the module architecture of the data acquisition module in a safety detection system for long-span arch tunnels proposed in this invention; Figure 4 This is a schematic diagram of the module architecture of the key area monitoring unit in a safety detection system for long-span arch tunnels proposed in this invention; Figure 5 This is a schematic diagram of the module architecture of the defect detection module in a safety detection system for long-span continuous arch tunnels proposed in this invention; Figure 6 This is a schematic diagram of the module architecture of the data processing module in a safety detection system for long-span continuous arch tunnels proposed in this invention. Detailed Implementation

[0020] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see the appendix Figure 1 In the first embodiment of the present invention, the present invention provides a safety inspection method for long-span continuous arch tunnels, comprising the following steps: Preventive monitoring steps: Dynamic monitoring of the intermediate rock pillars, the junction of the two tunnels and the arch area of ​​the arch tunnel is carried out using monitoring devices to collect deformation data and stress data of the surrounding rock and structure; Disease detection steps: The tunnel lining and surrounding rock are detected by combining surface detection and concealed detection to obtain surface disease characteristics and concealed disease characteristics; Data fusion step: The deformation data and stress data obtained from the preventive monitoring step are fused with the surface disease characteristics and hidden disease characteristics obtained from the disease detection step to generate risk assessment data; Early warning procedure: Based on risk assessment data, output early warning signals corresponding to the risk level.

[0022] Specifically, monitoring devices are used to dynamically monitor the intermediate rock pillars, the junction of the two tunnels, and the arch crown area of ​​the continuous arch tunnel to collect deformation and stress data of the surrounding rock and structure. Simultaneously, a combination of surface and concealed detection methods is employed to inspect the tunnel lining and surrounding rock to obtain surface and concealed defect characteristics. The deformation and stress data are then fused with these surface and concealed defect characteristics to generate risk assessment data. Finally, based on the risk assessment data, early warning signals corresponding to the risk level are output. This system enables dynamic monitoring, multi-type defect detection, and multi-dimensional assessment of key areas in large-span continuous arch tunnels. Data fusion analysis and risk warning are used to comprehensively monitor and assess the safety status of tunnels. Dynamic monitoring of intermediate rock pillars, the junction of two tunnels, and the arch area is used to obtain deformation and stress data. At the same time, surface detection and hidden detection are combined to obtain surface and hidden defect characteristics. The above data and characteristics are then fused and processed to generate risk assessment data and output corresponding warning signals. This improves the problem that traditional tunnel safety detection mostly uses single-dimensional monitoring or detection methods. Because the data from various dimensions are not fused, the assessment of the tunnel safety status is not comprehensive enough.

[0023] In the data fusion step, a multi-parameter coupling algorithm is used for the fusion processing. The formula for the multi-parameter coupling algorithm is as follows: R = ω1×A + ω2×B + ω3×C + ω4×D Where R is the risk assessment data, A is the normalized value of deformation data, B is the normalized value of stress data, C is the quantitative value of surface disease characteristics, D is the quantitative value of hidden disease characteristics, and ω1, ω2, ω3, and ω4 are weighting coefficients, and ω1+ω2+ω3+ω4=1.

[0024] Specifically, in the multi-parameter coupling algorithm formula R=ω1×A+ω2×B+ω3×C+ω4×D, the input data includes the normalized deformation data value A, the normalized stress data value B, the quantified surface defect characteristic value C, and the quantified hidden defect characteristic value D. Here, A is the ratio of the actual deformation (e.g., the horizontal displacement of the intermediate rock column or the settlement of the arch) collected in the preventive monitoring step to the maximum allowable deformation determined based on tunnel design parameters; B is the ratio of the real-time stress monitoring value of the lining or intermediate rock column collected in the preventive monitoring step to the design compressive strength value of the corresponding structural material, i.e., the material's ultimate stress value; and C is the ratio of the deformation data collected in the defect detection step to the stress data collected in the preventive monitoring step. The values ​​obtained by quantifying the crack distribution characteristics and water seepage imprint characteristics identified by the image acquisition device in the step are as follows: D is the value obtained by quantifying the void area characteristics, crack development characteristics identified by the electromagnetic wave detection device and the lining concrete density characteristics detected by the acoustic wave detection device in the defect detection step; ω1, ω2, ω3, and ω4 are weighting coefficients set according to the degree of influence of each parameter on tunnel safety, and their sum is 1. The output of this formula is the risk assessment data R. This risk assessment data is used in the early warning step to determine the corresponding risk level based on its value and output the early warning signal corresponding to the risk level.

[0025] The normalized value A of deformation data is calculated as follows: A = actual deformation / maximum allowable deformation, where the actual deformation is the horizontal displacement of the intermediate rock column or the settlement of the arch, and the maximum allowable deformation is a limit determined based on the tunnel design parameters; the normalized value B of stress data is calculated as follows: B = actual stress value / material ultimate stress value, where the actual stress value is the real-time stress monitoring value of the lining or intermediate rock column, and the material ultimate stress value is the design compressive strength value of the corresponding structural material.

[0026] Specifically, the normalized value A of deformation data is calculated as the ratio of actual deformation to maximum allowable deformation, where actual deformation is the horizontal displacement of the intermediate rock column or the settlement of the arch, and maximum allowable deformation is a limit determined based on tunnel design parameters; the normalized value B of stress data is calculated as the ratio of actual stress to the ultimate stress of the material, where actual stress is the real-time stress monitoring value of the lining or intermediate rock column, and ultimate stress is the design compressive strength value of the corresponding structural material. This can convert different types of deformation and stress data into directly comparable standardized values, giving deformation and stress monitoring data from different parts such as the intermediate rock column, arch, and lining a unified quantitative scale, and providing standardized input data for the calculation of multi-parameter coupling algorithms.

[0027] In the preventive monitoring steps, dynamic monitoring includes: collecting horizontal displacement data and internal stress data of the intermediate rock column through strain monitoring devices; collecting crown settlement data and clearance convergence data of the junction of the two tunnels through displacement monitoring devices; and collecting cumulative stress data under load through load monitoring devices.

[0028] Specifically, by using strain monitoring devices to collect horizontal displacement and internal stress data of the intermediate rock pillar, displacement monitoring devices to collect crown settlement and clearance convergence data of the junction of the two tunnels, and load monitoring devices to collect cumulative stress data under load, dynamic information on horizontal displacement, internal stress, crown settlement, clearance convergence, and long-term load cumulative stress can be obtained for key areas such as the intermediate rock pillar and the junction of the two tunnels in the continuous arch tunnel. This enables targeted monitoring of these areas and provides multi-type monitoring data from different key locations for subsequent risk assessment.

[0029] In the disease detection process, surface detection uses an image acquisition device to scan the lining surface to identify crack distribution characteristics and water seepage traces.

[0030] Specifically, the lining surface is scanned by an image acquisition device to identify crack distribution characteristics and water seepage marks. The image acquisition device can obtain visual information of the lining surface, thereby identifying the distribution of cracks and the characteristics of water seepage marks, providing lining surface defect data for the defect detection step, and serving as one of the input data for subsequent data fusion processing.

[0031] The concealed detection method uses an electromagnetic wave detection device to scan the interior of the lining and the surrounding rock to identify the characteristics of void areas and crack development. An acoustic wave detection device is used to detect the density characteristics of the lining concrete.

[0032] Specifically, by using acoustic detection devices to detect the density characteristics of the lining concrete, information on surface defects and hidden defects of the tunnel can be obtained from the lining surface to the interior and surrounding rock. This enables multi-dimensional defect detection of the tunnel lining and surrounding rock, providing comprehensive defect characteristic data for subsequent risk assessment.

[0033] Please see the appendix Figure 2 A safety detection system for long-span continuous arch tunnels includes: a data acquisition module, a defect detection module, a data processing module, and an early warning module; The data acquisition module is used to collect geological foundation data, structural deformation data, and stress data of the arch tunnel; the defect detection module is used to detect defects on the surface of the tunnel lining and inside the surrounding rock, and output surface defect data and hidden defect data; the data processing module is connected to the data acquisition module and the defect detection module respectively, and is used to receive structural deformation data, stress data, surface defect data, and hidden defect data, and perform fusion processing to generate risk assessment results; the early warning module is connected to the data processing module and is used to output corresponding early warning information based on the risk assessment results.

[0034] Specifically, in a safety monitoring system for long-span arch tunnels, a data acquisition module collects geological foundation data, structural deformation data, and stress data of the arch tunnel. A defect detection module detects defects on the tunnel lining surface and inside the surrounding rock and outputs surface defect data and hidden defect data. A data processing module is connected to both the data acquisition module and the defect detection module to receive structural deformation data, stress data, surface defect data, and hidden defect data, and performs fusion processing to generate risk assessment results. An early warning module is connected to the data processing module to output corresponding early warning information based on the risk assessment results. Through the coordinated operation of these modules, comprehensive data collection and detection of the geological foundation, structural deformation, stress, and defects of the arch tunnel can be achieved. The system then uses data fusion processing to generate risk assessment results and finally outputs corresponding early warning information, thus completing the systematic detection and early warning of the tunnel's safety status.

[0035] Please see the appendix Figure 3 The data acquisition module includes a key area monitoring unit and an environmental parameter acquisition unit. The key area monitoring unit is deployed in the intermediate rock pillar, the junction of the two tunnels, and the arch area to collect horizontal displacement data of the intermediate rock pillar, net clearance convergence data of the junction of the two tunnels, and settlement data of the arch. The environmental parameter acquisition unit is used to collect temperature and humidity data and seepage flow data in the tunnel.

[0036] Specifically, the key area monitoring unit in the data acquisition module is deployed in the intermediate rock pillar, the junction of the two tunnels, and the arch area to collect horizontal displacement data of the intermediate rock pillar, net clearance convergence data of the junction of the two tunnels, and settlement data of the arch. The environmental parameter acquisition unit collects temperature and humidity data and seepage flow data in the tunnel. It can specifically obtain deformation data of key structural areas of the arch tunnel and environmental parameter data in the tunnel, providing the data processing module with multi-type basic data from key structural parts and environment.

[0037] Please see the appendix Figure 4 The key area monitoring unit includes fiber optic grating sensors, automated monitoring instruments, and stress sensors. The fiber optic grating sensors are embedded inside the intermediate rock column to collect horizontal displacement data and internal stress data of the rock column. The automated monitoring instruments are used to collect real-time data on the clearance convergence of the two tunnels and the settlement data of the arch. The stress sensors are deployed on the lining surface to collect stress change data of the lining structure.

[0038] Specifically, the fiber optic grating sensor in the key area monitoring unit is embedded inside the intermediate rock column to collect horizontal displacement data and internal stress data of the intermediate rock column. The automated monitoring instrument collects the net clearance convergence data of the junction of the two tunnels and the settlement data of the arch in real time. The stress sensor is deployed on the lining surface to collect stress change data of the lining structure. It can collect targeted data in key parts such as the intermediate rock column, the junction of the two tunnels, the arch and the lining surface through different types of sensors, and obtain specific monitoring data such as displacement and stress from these key areas, so as to provide detailed key area monitoring information for the data acquisition module.

[0039] Please see the appendix Figure 5 The disease detection module includes a surface detection component, which is an image acquisition device used to scan the lining surface and output crack distribution data and water seepage imprint data.

[0040] Specifically, the surface detection component of the defect detection module is an image acquisition device. This image acquisition device scans the lining surface and outputs crack distribution data and water seepage imprint data. It can scan the lining surface through the image acquisition device to obtain the crack distribution and water seepage imprint information of the lining surface and form corresponding data, providing defect data of the lining surface to the defect detection module as one of the input data for the data processing module to perform fusion processing.

[0041] Please see the appendix Figure 5 The disease detection module also includes a concealed detection component, which consists of a ground-penetrating radar and an ultrasonic detector. The ground-penetrating radar is used to scan the area behind the lining and inside the surrounding rock, and outputs data on the void area and the distribution of cracks. The ultrasonic detector is used to detect the density data of the lining concrete.

[0042] Specifically, the defect detection module also includes a concealed detection component consisting of ground-penetrating radar and an ultrasonic detector. The ground-penetrating radar scans the area behind the lining and inside the surrounding rock and outputs data on void areas and crack distribution. The ultrasonic detector detects the density data of the lining concrete. It can obtain relevant data on void areas and crack distribution by scanning the area behind the lining and inside the surrounding rock with ground-penetrating radar, and obtain its density data by detecting the lining concrete with ultrasonic detector. This provides the defect detection module with information on hidden defects inside the lining and surrounding rock, and serves as one of the input data for the data processing module to perform fusion processing.

[0043] Please see the appendix Figure 6The data processing module includes a data normalization unit and a coupled calculation unit. The data normalization unit converts structural deformation data, stress data, surface defect data, and hidden defect data into standardized values. The coupled calculation unit uses a multi-parameter coupled algorithm to calculate the standardized values ​​and outputs the risk assessment results. The formula for the multi-parameter coupled algorithm is: R = ω1×A + ω2×B + ω3×C + ω4×D Where R is the risk assessment result, A is the standardized value of structural deformation data, B is the standardized value of stress data, C is the standardized value of surface defects data, D is the standardized value of hidden defects data, and ω1, ω2, ω3, and ω4 are weighting coefficients, and ω1+ω2+ω3+ω4=1.

[0044] Specifically, the data normalization unit in the data processing module converts structural deformation data, stress data, surface defect data, and hidden defect data into standardized values. The coupled calculation unit uses a multi-parameter coupling algorithm to calculate the standardized values ​​to output the risk assessment result. The formula of the multi-parameter coupling algorithm is R=ω1×A+ω2×B+ω3×C+ω4×D, where R is the risk assessment result, A is the standardized value of structural deformation data, B is the standardized value of stress data, C is the standardized value of surface defect data, D is the standardized value of hidden defect data, and ω1, ω2, ω3, and ω4 are weighting coefficients, and ω1+ω2+ω3+ω4=1. This normalization method converts different types of data into standardized values ​​that can be calculated uniformly. Then, the multi-parameter coupling algorithm integrates and calculates these standardized values ​​to obtain a comprehensive risk assessment result, providing a quantitative assessment basis for the early warning module.

[0045] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A safety inspection method for long-span continuous arch tunnels, characterized in that, Includes the following steps: Preventive monitoring steps: Dynamic monitoring of the intermediate rock pillars, the junction of the two tunnels and the arch area of ​​the arch tunnel is carried out using monitoring devices to collect deformation data and stress data of the surrounding rock and structure; Disease detection steps: The tunnel lining and surrounding rock are detected by combining surface detection and concealed detection to obtain surface disease characteristics and concealed disease characteristics; Data fusion step: The deformation data and stress data obtained in the preventive monitoring step are fused with the surface disease characteristics and hidden disease characteristics obtained in the disease detection step to generate risk assessment data; Early warning procedure: Based on the risk assessment data, output an early warning signal corresponding to the risk level.

2. The method for safety inspection of a long-span continuous arch tunnel according to claim 1, characterized in that, In the preventive monitoring steps, dynamic monitoring includes: collecting horizontal displacement data and internal stress data of the intermediate rock column through a strain monitoring device; collecting crown settlement data and clearance convergence data of the junction of the two tunnels through a displacement monitoring device; and collecting cumulative stress data under load through a load monitoring device.

3. The method for safety inspection of a long-span continuous arch tunnel according to claim 1, characterized in that, In the disease detection step, the surface detection uses an image acquisition device to scan the lining surface to identify crack distribution characteristics and water seepage trace characteristics.

4. The safety inspection method for a long-span continuous arch tunnel according to claim 1, characterized in that, In the disease detection steps, the concealed detection uses an electromagnetic wave detection device to scan the interior of the lining and the surrounding rock to identify the characteristics of void areas and crack development. An acoustic wave detection device is used to detect the density characteristics of the lining concrete.

5. A safety detection system for long-span continuous arch tunnels, characterized in that, include: Data acquisition module, disease detection module, data processing module, and early warning module; The data acquisition module is used to collect geological foundation data, structural deformation data, and stress data of the arch tunnel; the defect detection module is used to detect defects on the surface of the tunnel lining and inside the surrounding rock, and output surface defect data and hidden defect data; the data processing module is connected to the data acquisition module and the defect detection module respectively, and is used to receive structural deformation data, stress data, surface defect data, and hidden defect data, and perform fusion processing to generate risk assessment results. The early warning module is connected to the data processing module and is used to output corresponding early warning information based on the risk assessment results.

6. The safety detection system for a long-span continuous arch tunnel according to claim 5, characterized in that, The data acquisition module includes a key area monitoring unit and an environmental parameter acquisition unit. The key area monitoring unit is deployed in the intermediate rock column, the junction of the two tunnels, and the arch area to collect horizontal displacement data of the intermediate rock column, net clearance convergence data of the junction of the two tunnels, and settlement data of the arch. The environmental parameter acquisition unit is used to collect temperature and humidity data and seepage flow data inside the tunnel.

7. A safety detection system for a long-span continuous arch tunnel according to claim 6, characterized in that, The key area monitoring unit includes a fiber optic grating sensor, an automated monitoring instrument, and a stress sensor. The fiber optic grating sensor is embedded inside the intermediate rock column to collect horizontal displacement data and internal stress data of the rock column. The automated monitoring instrument is used to collect real-time data on the clearance convergence of the two tunnel junction and the settlement data of the arch. The stress sensor is deployed on the lining surface to collect stress change data of the lining structure.

8. A safety detection system for a long-span continuous arch tunnel according to claim 5, characterized in that, The disease detection module includes a surface detection component, which is an image acquisition device used to scan the lining surface and output crack distribution data and water seepage imprint data.

9. A safety detection system for a long-span continuous arch tunnel according to claim 5, characterized in that, The disease detection module also includes a concealed detection component, which consists of a ground-penetrating radar and an ultrasonic detector. The ground-penetrating radar is used to scan the back of the lining and the interior of the surrounding rock, and outputs data on the void area and the distribution of cracks. The ultrasonic detector is used to detect the density data of the lining concrete.

10. A safety detection system for a long-span continuous arch tunnel according to claim 5, characterized in that, The data processing module includes a data normalization unit and a coupled calculation unit; the data normalization unit is used to convert structural deformation data, stress data, surface defect data and hidden defect data into standardized values. The coupled calculation unit is used to calculate standardized values ​​using a multi-parameter coupled algorithm and output risk assessment results.

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