Soft rock roadway surrounding rock three-dimensional perception critical reinforcement depth detection system and method

Abstract

The invention discloses a soft rock roadway surrounding rock stereoscopic perception critical reinforcement depth detection system and method. According to the system, a multi-element sensing unit comprises a ground penetrating radar scanning device, a deep deformation monitoring device and a surrounding rock stress monitoring device; the ground penetrating radar scanning device is used for acquiring continuous section data of surrounding rock dielectric constants; the deep deformation monitoring device is used for monitoring layered deformation in the surrounding rock; the surrounding rock stress monitoring device is used for monitoring stress dynamic change in surrounding rock; the intelligent computing center is an intelligent computing terminal, and the intelligent computing terminal is connected with the ground penetrating radar scanning device, the deep deformation monitoring device and the surrounding rock stress monitoring device. The method comprises the steps of monitoring section establishment and system installation; surrounding rock macrostructure scanning; synchronously acquiring and transmitting deformation stress data; plastic zone boundary identification; calculating energy balance; and checking long-term stability and determining critical depth. According to the system and the method, the critical reinforcement depth can be accurately obtained, and the transformation of the reinforcement depth from experience judgment to timing calculation can be realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of soft rock roadway support, and particularly relates to a soft rock roadway surrounding rock three-dimensional perception critical reinforcement depth detection system and method. BACKGROUND

[0002] With the extension of coal resource mining to the deep part, the soft rock roadway engineering is facing the challenge of "high ground pressure, large deformation and difficult support" which is becoming increasingly severe. Due to the low strength, easy weathering, softening when meeting water and significant rheological characteristics of soft rock mass, in the stress redistribution process after the excavation of the roadway, the plastic zone of the surrounding rock continuously develops, and the deformation accumulates over time. It is often difficult to effectively control the convergence of the roadway by relying on the conventional bolt-mesh-shotcrete support. The anchoring support technology combining the anchoring support and reinforcement has become a key means to govern such roadways. Among them, the reinforcement can effectively strengthen the broken rock mass and improve the integrity and self-bearing capacity of the surrounding rock, which is the core link of the support system. However, the reasonable determination of the reinforcement depth has always been a difficulty in engineering practice: if the depth is too shallow, it cannot effectively control the deformation of the deep surrounding rock, leading to support failure; if the depth is too large, it significantly increases the material cost and construction period, causing economic waste. Therefore, scientifically and reasonably determining a critical reinforcement depth which can effectively control the deformation and is economically reasonable is one of the core problems to be solved in the support design of soft rock roadways.

[0003] At present, there is still a technical disconnection between the theoretical research and engineering practice in the determination of the reinforcement depth of soft rock roadways. The existing technologies mostly focus on analyzing the range of the plastic zone of the surrounding rock through theoretical analysis or numerical simulation, and often take the boundary of the plastic zone as the main basis for the reinforcement depth. However, this method has obvious shortcomings: first, it generally regards the surrounding rock as an ideal elastic-plastic material, ignoring the strong time-dependent deformation characteristics of soft rock, i.e. rheological effect, which leads to the inability to predict the long-term deformation and energy release process of the surrounding rock during the service period; second, the traditional plastic zone theory is mostly static or quasi-static analysis, which fails to fully consider the dynamic accumulation and release mechanism of the deformation energy of the surrounding rock, resulting in the energy absorption function of the reinforcement ring being ignored in the design stage. In practice, it is more dependent on engineering experience analogy or simplified formula, lacking the fine quantitative perception of the real state of the surrounding rock. These shortcomings together lead to the current reinforcement design generally having the tendency of "one-size-fits-all" or "overly conservative", either failing to curb the continuous rheology of the roadway due to insufficient depth, leading to re-injection in the later period or even causing major safety accidents, or causing redundancy of the support structure due to excessive depth, greatly affecting the technical rationality and economy of the anchoring support.

[0004] In order to fundamentally overcome the above technical defects, it is urgent to provide a critical reinforcement depth dynamic determination technology based on the real state perception of the surrounding rock. SUMMARY

[0005] To address the aforementioned problems in existing technologies, this invention provides a three-dimensional sensing system and method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels. This system is simple in structure, highly intelligent, and can accurately determine the critical reinforcement depth. Based on a precise quantitative perception of the actual state of the surrounding rock on-site, it transforms the reinforcement depth from empirical judgment to quantitative calculation, effectively solving the problem of unreasonable reinforcement depths caused by neglecting the spatiotemporal evolution characteristics of the surrounding rock in traditional technologies. It provides a scientific and reliable technical means for the support design of soft rock tunnels. The method is simple to implement and has low implementation costs. It effectively establishes a complete technical process from on-site data acquisition to reinforcement decision output, ultimately forming a quantitative solution that can scientifically determine the critical reinforcement depth. It accurately obtains the critical reinforcement depth and effectively solves the problem of unreasonable reinforcement design caused by neglecting the time-dependent deformation and dynamic energy evolution of the surrounding rock in traditional methods.

[0006] To achieve the above objectives, the present invention provides a three-dimensional sensing critical reinforcement depth detection system for soft rock tunnel surrounding rock, comprising a multi-sensor unit and an intelligent computing center. The multi-sensor unit includes a ground-penetrating radar scanning device, a deep deformation monitoring device, and a surrounding rock stress monitoring device located on the same monitoring section. The ground-penetrating radar scanning device includes a radar host and an antenna, used to acquire continuous profile data of the dielectric constant of the surrounding rock. The deep deformation monitoring device includes at least three sets of multi-point displacement gauges. One set of multi-point displacement gauges is installed vertically at the center of the roadway roof, and the other two sets of multi-point displacement gauges are installed horizontally at the middle of the left and right sides of the roadway, respectively. The three measuring points of each set of multi-point displacement gauges are buried by drilling at three different depths: shallow, middle, and deep, to monitor the amount of layered deformation inside the surrounding rock. The surrounding rock stress monitoring device includes at least three sets of stress sensors. One set of stress sensors is installed vertically at the center of the roadway roof, and the other two sets of stress sensors are installed horizontally at the middle of the left and right sides of the roadway, respectively. Two stress sensors in each set are buried at two different depths, namely medium and deep, by drilling, to monitor the dynamic changes of stress inside the surrounding rock. The intelligent computing center is an intelligent computing terminal. The intelligent computing terminal is connected to the ground penetrating radar scanning device, the deep deformation monitoring device, and the surrounding rock stress monitoring device via wired or wireless means, respectively. It is used to identify the boundary of the plastic zone based on multi-source monitoring data, calculate the minimum reinforcement circle thickness based on the energy balance principle, perform a quantitative assessment of long-term stability, and output the final critical reinforcement depth.

[0007] Furthermore, to ensure the security of the intelligent computing terminal, the intelligent computing terminal is encapsulated in a protective casing.

[0008] Furthermore, to ensure the accuracy of the detection, the operating frequency range of the ground-penetrating radar scanning device is 100MHz to 500MHz; the range of the multi-point displacement gauge is 0-200mm; and the range of the stress sensor is 0-10MPa.

[0009] As a preferred embodiment, the intelligent computing terminal is a computer; the intelligent computing terminal integrates a plastic zone identification module, an energy balance calculation module, a rheological effect verification module, and a result output display module.

[0010] In this invention, a ground-penetrating radar (GPR) scanning device, a deep deformation monitoring device, and a surrounding rock stress monitoring device are respectively installed in the same monitoring section. Multiple displacement gauges and stress sensors are vertically installed at the roadway roof and horizontally installed in the middle of both sides of the roadway. This allows for the formation of a three-dimensional sensing network within the same monitoring section, enabling comprehensive perception of multi-dimensional monitoring data. The GPR scanning device obtains the dielectric constant profile of the surrounding rock through scanning, effectively identifying the boundaries of the plastic zone. The synchronous monitoring process of the multiple displacement gauges and stress sensors allows for the simultaneous acquisition of the layered deformation and dynamic stress changes within the surrounding rock. With the addition of an intelligent computing terminal, based on the fusion of multi-source monitoring data, efficient and accurate identification of the plastic zone boundaries, calculation of the minimum reinforcement layer thickness, quantitative assessment of long-term stability, and calculation and output of the final critical reinforcement depth can be performed.

[0011] The system has a simple structure and a high degree of intelligence. It can accurately obtain the critical reinforcement depth. Based on the precise quantitative perception of the actual state of the surrounding rock on site, it can realize the transformation of reinforcement depth from experience-based judgment to timed calculation. It can effectively solve the problem of unreasonable reinforcement depth caused by neglecting the spatiotemporal evolution characteristics of the surrounding rock in traditional technology, and provide a scientific and reliable technical means for soft rock tunnel support design.

[0012] This invention also provides a method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels using a three-dimensional sensing system, comprising the following steps: Step 1: Establishment of monitoring sections and installation of the system; In the soft rock section behind the tunnel excavation face, a ring-shaped monitoring section is established; a ground-penetrating radar scanning device, a deep deformation monitoring device, and a surrounding rock stress monitoring device are installed in the ring-shaped monitoring section; a communication connection is established between the intelligent computing terminal and the deep deformation monitoring device, the surrounding rock stress monitoring device, and the radar host using cables to form a three-dimensional sensing network. Step 2: Macroscopic structural scanning of the surrounding rock; The operator holds the antenna close to the rock surface and continuously scans the roadway forward and backward at a set distance along the central axis of the roadway roof and the waistline of both sides, with the ring monitoring section as the center. The continuous profile data of the dielectric constant of the surrounding rock is obtained by the ground penetrating radar scanning device and then sent to the intelligent computing terminal. Step 3: Synchronous acquisition and transmission of deformation stress data; The deep deformation monitoring device is used to monitor the layered deformation inside the surrounding rock and send the deformation data to the intelligent computing terminal; the surrounding rock stress monitoring device is used to monitor the dynamic changes of stress inside the surrounding rock and send the stress data to the intelligent computing terminal. Step 4: Identification of the plastic zone boundary; The intelligent computing terminal analyzes and processes continuous profile data through a plastic zone identification module, identifies significant abrupt change inflection points, determines the interface between the fractured and intact zones of the surrounding rock, and outputs the depth D of the plastic zone. p ; Step 5: Energy balance calculation; The intelligent computing terminal uses an energy balance calculation module based on the plastic zone depth D p and the average value of surrounding rock stress data Calculate the minimum stiffening ring thickness ; Step Six: Long-term stability verification and determination of critical depth; S61: Parameter fitting; The intelligent computing terminal extracts the surrounding rock deformation-time series data from the surrounding rock deformation data through the rheological effect verification module, and uses the Burgers rheological model for fitting, and inversely calculates the elastic modulus and viscosity coefficient of the Burgers rheological model as a set of model parameters. S62: Long-term load prediction; Using model parameters as input data and the roadway service life as the duration, the Burgers rheological model is used to predict the radial pressure acting on the outer edge of the pre-set reinforcement ring at the end of the service life. ; S63: Safety assessment; calculate the long-term safety factor of the reinforcing ring. The final critical reinforcement depth d is determined; when K≥1.3, d=d min When 1.0 ≤ K < 1.3, take d = d min ×1.1; When K < 1.0, take d = d min ×1.2; S64: Result Output; The intelligent computing terminal outputs and displays the final critical reinforcement depth d through the result output display module.

[0013] Furthermore, in order to accurately obtain multi-dimensional monitoring data of key nodes, the process of installing deep deformation monitoring devices and surrounding rock stress monitoring devices in the annular monitoring section in step one is as follows: In the ring monitoring section, a hole is drilled at the center of the roadway roof, and a set of multi-point displacement gauges containing three measuring points (shallow, middle, and deep) are vertically installed, along with a set of stress sensors containing two stress sensors (medium and deep). At the same time, in the middle of the left and right sides of the roadway, a set of multi-point displacement gauges containing three measuring points (shallow, middle, and deep) are horizontally installed, along with a set of stress sensors containing two stress sensors (medium and deep).

[0014] Furthermore, in order to accurately quantify and evaluate the minimum reinforcement ring thickness, in step five, the minimum reinforcement ring thickness is calculated according to formula (1). ; (1); In the formula, , These are two different empirical coefficients, which were calibrated in advance through indoor experiments.

[0015] Furthermore, in order to accurately quantify the long-term stability, in step S63 of step six, the long-term safety factor of the reinforcing ring is calculated according to formula (2). ; (2); In the formula, The long-term strength of the reinforced body is obtained through indoor creep testing.

[0016] Furthermore, in order to provide effective reminders through timely warnings in case of abnormalities, in step S61 of step six, the least squares method is used to fit the parameters. When the fitting degree is lower than 90%, the intelligent computing terminal controls the alarm module connected to it to perform an abnormality warning action.

[0017] Furthermore, to achieve automated parameter updates and ensure monitoring accuracy during long-term service, after continuous monitoring for more than 30 days, the intelligent computing terminal uses new monitoring data to update the parameters. , Perform a calibration update.

[0018] Compared with the prior art, the present invention has the following advantages: 1) By integrating ground-penetrating radar scanning devices, deep deformation monitoring devices, and surrounding rock stress monitoring devices, a three-dimensional sensing network covering the roof and sides of the roadway was constructed, which can comprehensively acquire measured data on the structure, deformation, and stress state of the surrounding rock, providing a reliable on-site basis for reinforcement design.

[0019] 2) A calculation method combining the principle of energy balance and rheological characteristics analysis was adopted to achieve a quantitative assessment of the minimum reinforcement thickness and long-term stability, making the process of determining the reinforcement depth more scientific and reasonable, and effectively avoiding the subjective experience biases common in traditional methods.

[0020] 3) By establishing clear grading criteria, the reinforcement depth recommendation can be dynamically adjusted according to the actual state of the surrounding rock, which not only ensures the reliability of the support but also prevents the overuse of materials, and has good economic practicality.

[0021] 4) A complete technical solution has been formed, from data acquisition, transmission and processing to result output. The operation process is clear and highly practical, providing on-site engineers with an easy-to-implement method for determining reinforcement depth.

[0022] This method is simple to implement and has low implementation costs. It effectively establishes a complete technical process from field data collection to reinforcement decision output, and finally forms a quantitative solution that can scientifically determine the critical reinforcement depth. It can accurately obtain the critical reinforcement depth and effectively solve the problem of unreasonable reinforcement design caused by neglecting the time deformation and energy dynamic evolution of the surrounding rock in traditional methods. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the detection system in this invention; Figure 2 This is a flowchart of the detection method in this invention. Detailed Implementation

[0024] The invention will now be further described with reference to the accompanying drawings.

[0025] like Figure 1 As shown, the present invention provides a three-dimensional sensing critical reinforcement depth detection system for soft rock tunnel surrounding rock, including a multi-sensor unit and an intelligent computing center; The multi-sensor unit includes a ground-penetrating radar scanning device, a deep deformation monitoring device, and a surrounding rock stress monitoring device located on the same monitoring section. The ground-penetrating radar scanning device includes a radar host and an antenna, used to acquire continuous profile data of the dielectric constant of the surrounding rock. The deep deformation monitoring device includes at least three sets of multi-point displacement gauges. One set of multi-point displacement gauges is installed vertically at the center of the roadway roof, and the other two sets are installed horizontally at the middle of the left and right sides of the roadway, respectively. The three measuring points of each set of multi-point displacement gauges are buried at three different depths (shallow, middle, and deep) by drilling, and are used to monitor the stratified deformation of the surrounding rock. Preferably, the shallow burial depth ranges from 0 to 1 m, the middle burial depth ranges from 1 to 2.5 m, and the deep burial depth ranges from 2.5 to 4 m. The surrounding rock stress monitoring device includes at least three sets of stress sensors. One set of stress sensors is installed vertically at the center of the roadway roof, and the other two sets of stress sensors are installed horizontally at the middle of the left and right sides of the roadway, respectively. Two stress sensors in each set are buried at two different depths, namely medium and deep, by drilling, to monitor the dynamic changes of stress inside the surrounding rock. Preferably, the burial depth at the middle is 1.5m, and the burial depth at the deep is 3.0m. The intelligent computing center is an intelligent computing terminal. The intelligent computing terminal is connected to the ground penetrating radar scanning device, the deep deformation monitoring device, and the surrounding rock stress monitoring device via wired or wireless means, respectively. It is used to identify the boundary of the plastic zone based on multi-source monitoring data, calculate the minimum reinforcement circle thickness based on the energy balance principle, perform a quantitative assessment of long-term stability, and output the final critical reinforcement depth.

[0026] To ensure the security of the intelligent computing terminal, it is encapsulated in a protective enclosure. Preferably, the protective enclosure can be installed in a safe location near the monitoring section or in a stable area of ​​the roadway sidewall.

[0027] To ensure detection accuracy, the operating frequency range of the ground-penetrating radar scanning device is 100MHz to 500MHz; the range of the multi-point displacement gauge is 0-200mm; and the range of the stress sensor is 0-10MPa.

[0028] As a preferred embodiment, the intelligent computing terminal is a computer; the intelligent computing terminal integrates a plastic zone identification module, an energy balance calculation module, a rheological effect verification module, and a result output display module.

[0029] In this invention, a ground-penetrating radar (GPR) scanning device, a deep deformation monitoring device, and a surrounding rock stress monitoring device are respectively installed in the same monitoring section. Multiple displacement gauges and stress sensors are vertically installed at the roadway roof and horizontally installed in the middle of both sides of the roadway. This allows for the formation of a three-dimensional sensing network within the same monitoring section, enabling comprehensive perception of multi-dimensional monitoring data. The GPR scanning device obtains the dielectric constant profile of the surrounding rock through scanning, effectively identifying the boundaries of the plastic zone. The synchronous monitoring process of the multiple displacement gauges and stress sensors allows for the simultaneous acquisition of the layered deformation and dynamic stress changes within the surrounding rock. With the addition of an intelligent computing terminal, based on the fusion of multi-source monitoring data, efficient and accurate identification of the plastic zone boundaries, calculation of the minimum reinforcement layer thickness, quantitative assessment of long-term stability, and calculation and output of the final critical reinforcement depth can be performed.

[0030] The system has a simple structure and a high degree of intelligence. It can accurately obtain the critical reinforcement depth. Based on the precise quantitative perception of the actual state of the surrounding rock on site, it can realize the transformation of reinforcement depth from experience-based judgment to timed calculation. It can effectively solve the problem of unreasonable reinforcement depth caused by neglecting the spatiotemporal evolution characteristics of the surrounding rock in traditional technology, and provide a scientific and reliable technical means for soft rock tunnel support design.

[0031] like Figure 2 As shown, the present invention also provides a method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels using a three-dimensional sensing system, comprising the following steps: Step 1: Establishment of monitoring sections and installation of the system; In a typical soft rock section 10 to 15 meters behind the tunnel excavation face, a ring-shaped monitoring section is established; a ground-penetrating radar scanning device, a deep deformation monitoring device, and a surrounding rock stress monitoring device are installed in the ring-shaped monitoring section; a communication connection is established between the intelligent computing terminal and the deep deformation monitoring device, the surrounding rock stress monitoring device, and the radar host using cables to form a three-dimensional sensing network. Step 2: Macroscopic structural scanning of the surrounding rock; The operator holds the antenna close to the rock surface and moves it along the central axis of the tunnel roof and the middle of the waistline on both sides of the roadway, with the ring monitoring section as the center. The operator continuously scans the rock surface at a set distance in front of the tunnel (in the direction of excavation) and behind the tunnel. Preferably, the set distance is not less than 20m. The operator obtains continuous profile data of the dielectric constant of the surrounding rock through the ground penetrating radar scanning device and sends the continuous profile data to the intelligent computing terminal to obtain information on the changes in the surrounding rock structure over a large area. Step 3: Synchronous acquisition and transmission of deformation stress data; The deep deformation monitoring device is used to monitor the stratified deformation inside the surrounding rock and the surrounding rock deformation data is sent to the intelligent computing terminal. As a preferred method, the sampling frequency of the deep deformation monitoring device is 1Hz. The surrounding rock stress monitoring device is used to monitor the dynamic changes of stress inside the surrounding rock and the surrounding rock stress data is sent to the intelligent computing terminal. As a preferred method, the sampling frequency of the surrounding rock stress monitoring device is 1Hz. Step 4: Identification of the plastic zone boundary; The intelligent computing terminal analyzes and processes continuous profile data through a plastic zone identification module, identifies significant abrupt change inflection points, determines the interface between the fractured and intact zones of the surrounding rock, and outputs the depth D of the plastic zone. p ; Step 5: Energy balance calculation; The intelligent computing terminal uses an energy balance calculation module based on the plastic zone depth D p and the average value of surrounding rock stress data Calculate the minimum stiffening ring thickness Among them, the average value of the surrounding rock stress data Based on the obtained surrounding rock stress data; Step Six: Long-term stability verification and determination of critical depth; S61: Parameter fitting; The intelligent computing terminal extracts the surrounding rock deformation-time series data from the surrounding rock deformation data through the rheological effect verification module, and uses the Burgers rheological model for fitting, and inversely calculates the elastic modulus and viscosity coefficient of the Burgers rheological model as a set of model parameters. S62: Long-term load prediction; Using model parameters as input data and the roadway service life as the duration, the Burgers rheological model is used to predict the radial pressure acting on the outer edge of the pre-set reinforcement ring at the end of the service life. ; S63: Safety assessment; calculate the long-term safety factor of the reinforcing ring. The final critical reinforcement depth d is determined; when K≥1.3, d=d min When 1.0 ≤ K < 1.3, take d = d min ×1.1; When K < 1.0, take d = d min ×1.2; S64: Result Output; The intelligent computing terminal outputs and displays the final critical reinforcement depth d through the result output display module.

[0032] In order to accurately obtain multi-dimensional monitoring data of key nodes, the process of installing deep deformation monitoring devices and surrounding rock stress monitoring devices in the annular monitoring section in step one is as follows: In the ring monitoring section, a hole is drilled at the center of the roadway roof, and a set of multi-point displacement gauges containing three measuring points (shallow, middle, and deep) are vertically installed, along with a set of stress sensors containing two stress sensors (medium and deep). At the same time, in the middle of the left and right sides of the roadway, a set of multi-point displacement gauges containing three measuring points (shallow, middle, and deep) are horizontally installed, along with a set of stress sensors containing two stress sensors (medium and deep).

[0033] In order to accurately quantify and evaluate the minimum reinforcement ring thickness, in step five, the minimum reinforcement ring thickness is calculated according to formula (1). ; (1); In the formula, , These are two different empirical coefficients, calibrated beforehand through indoor experiments. As a preferred option, , The initial values ​​are 0.15 and 50 respectively.

[0034] In order to accurately quantify the long-term stability, in step S63 of step six, the long-term safety factor of the reinforcing ring is calculated according to formula (2). ; (2); In the formula, The long-term strength of the reinforced body is obtained through indoor creep testing.

[0035] In order to provide effective reminders in the event of an anomaly through timely warnings, in step S61 of step six, the least squares method is used to fit the parameters. When the fitting degree is lower than 90%, the intelligent computing terminal controls the alarm module connected to it to perform an anomaly warning action.

[0036] To achieve automated parameter updates and ensure monitoring accuracy during long-term service, the intelligent computing terminal uses new monitoring data to update parameters after continuous monitoring for more than 30 days. , Perform a calibration update.

[0037] This invention presents a three-dimensional sensing method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels, aiming to overcome the technical shortcomings of existing methods for determining the reinforcement depth of soft rock tunnels, which rely on empirical estimation and lack quantitative basis. First, by constructing a three-dimensional sensing network composed of a ground-penetrating radar (GPR) scanning device, a deep deformation monitoring device, and a surrounding rock stress monitoring device, multi-dimensional synchronous monitoring of the tunnel's surrounding rock structure, deformation, and stress state can be ensured. Next, GPR scanning acquires the dielectric constant profile data of the surrounding rock, and multi-point displacement gauges and stress sensors are used to synchronously monitor deformation and strain data. Then, an intelligent computing center fuses the multi-source monitoring data, sequentially performing plastic zone boundary identification, minimum reinforcement thickness calculation based on the energy balance principle, and dynamic verification of the long-term stability of the surrounding rock based on rheological effects. A reasonable critical reinforcement depth is dynamically determined by establishing a graded judgment criterion.

Claims

1. A three-dimensional sensing system for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels, characterized in that, Includes multiple sensing units and an intelligent computing center; The multi-sensor unit includes a ground-penetrating radar scanning device, a deep deformation monitoring device, and a surrounding rock stress monitoring device located on the same monitoring section. The ground-penetrating radar scanning device includes a radar host and an antenna, used to acquire continuous profile data of the dielectric constant of the surrounding rock. The deep deformation monitoring device includes at least three sets of multi-point displacement gauges. One set of multi-point displacement gauges is installed vertically at the center of the roadway roof, and the other two sets of multi-point displacement gauges are installed horizontally at the middle of the left and right sides of the roadway, respectively. The three measuring points of each set of multi-point displacement gauges are buried by drilling at three different depths: shallow, middle, and deep, to monitor the amount of layered deformation inside the surrounding rock. The surrounding rock stress monitoring device includes at least three sets of stress sensors. One set of stress sensors is installed vertically at the center of the roadway roof, and the other two sets of stress sensors are installed horizontally at the middle of the left and right sides of the roadway, respectively. Two stress sensors in each set are buried at two different depths, namely medium and deep, by drilling, to monitor the dynamic changes of stress inside the surrounding rock. The intelligent computing center is an intelligent computing terminal. The intelligent computing terminal is connected to the ground penetrating radar scanning device, the deep deformation monitoring device, and the surrounding rock stress monitoring device via wired or wireless means, respectively. It is used to identify the boundary of the plastic zone based on multi-source monitoring data, calculate the minimum reinforcement circle thickness based on the energy balance principle, perform a quantitative assessment of long-term stability, and output the final critical reinforcement depth.

2. The three-dimensional sensing critical reinforcement depth detection system for soft rock tunnel surrounding rock according to claim 1, characterized in that, The intelligent computing terminal is encapsulated in a protective box.

3. The three-dimensional sensing critical reinforcement depth detection system for soft rock tunnel surrounding rock according to claim 1, characterized in that, The operating frequency range of the ground-penetrating radar scanning device is 100MHz to 500MHz; the range of the multi-point displacement gauge is 0-200mm; and the range of the stress sensor is 0-10MPa.

4. The three-dimensional sensing critical reinforcement depth detection system for soft rock tunnel surrounding rock according to claim 1, characterized in that, The intelligent computing terminal is a computer; the intelligent computing terminal integrates a plastic zone identification module, an energy balance calculation module, a rheological effect verification module, and a result output display module.

5. A method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels using a three-dimensional sensing system as described in any one of claims 1 to 4, characterized in that... Includes the following steps: Step 1: Establishment of monitoring sections and installation of the system; In the soft rock section behind the tunnel excavation face, a ring-shaped monitoring section is established; a ground-penetrating radar scanning device, a deep deformation monitoring device, and a surrounding rock stress monitoring device are installed in the ring-shaped monitoring section; a communication connection is established between the intelligent computing terminal and the deep deformation monitoring device, the surrounding rock stress monitoring device, and the radar host using cables to form a three-dimensional sensing network. Step 2: Macroscopic structural scanning of the surrounding rock; The operator holds the antenna close to the rock surface and continuously scans the roadway forward and backward at a set distance along the central axis of the roadway roof and the waistline of both sides, with the ring monitoring section as the center. The continuous profile data of the dielectric constant of the surrounding rock is obtained by the ground penetrating radar scanning device and then sent to the intelligent computing terminal. Step 3: Synchronous acquisition and transmission of deformation stress data; The deep deformation monitoring device is used to monitor the layered deformation inside the surrounding rock and send the deformation data to the intelligent computing terminal; the surrounding rock stress monitoring device is used to monitor the dynamic changes of stress inside the surrounding rock and send the stress data to the intelligent computing terminal. Step 4: Identification of the plastic zone boundary; The intelligent computing terminal analyzes and processes continuous profile data through a plastic zone identification module, identifies significant abrupt change inflection points, determines the interface between the fractured and intact zones of the surrounding rock, and outputs the depth D of the plastic zone. p ; Step 5: Energy balance calculation; The intelligent computing terminal uses an energy balance calculation module based on the plastic zone depth D p and the average value of surrounding rock stress data Calculate the minimum stiffening ring thickness ; Step Six: Long-term stability verification and determination of critical depth; S61: Parameter fitting; The intelligent computing terminal extracts the surrounding rock deformation-time series data from the surrounding rock deformation data through the rheological effect verification module, and uses the Burgers rheological model for fitting, and inversely calculates the elastic modulus and viscosity coefficient of the Burgers rheological model as a set of model parameters. S62: Long-term load prediction; Using model parameters as input data and the roadway service life as the time frame, the radial pressure acting on the outer edge of the pre-set reinforcement ring at the end of the service life is predicted using the Burgers rheological model. ; S63: Safety assessment; calculate the long-term safety factor of the reinforcing ring. The final critical reinforcement depth d is determined; when K≥1.3, d=d min When 1.0 ≤ K < 1.3, take d = d min ×1.1; When K < 1.0, take d = d min ×1.2; S64: Result Output; The intelligent computing terminal outputs and displays the final critical reinforcement depth d through the result output display module.

6. The method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels using three-dimensional sensing according to claim 5, characterized in that, In step one, the process of installing the deep deformation monitoring device and the surrounding rock stress monitoring device in the annular monitoring section is as follows: In the ring monitoring section, a hole is drilled at the center of the roadway roof, and a set of multi-point displacement gauges containing three measuring points (shallow, middle, and deep) are vertically installed, along with a set of stress sensors containing two stress sensors (medium and deep). At the same time, in the middle of the left and right sides of the roadway, a set of multi-point displacement gauges containing three measuring points (shallow, middle, and deep) are horizontally installed, along with a set of stress sensors containing two stress sensors (medium and deep).

7. The method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels using three-dimensional sensing according to claim 6, characterized in that, In step five, the minimum reinforcing ring thickness is calculated according to formula (1). ; （1）； In the formula, , These are two different empirical coefficients, which were calibrated in advance through indoor experiments.

8. The method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels using three-dimensional sensing according to claim 7, characterized in that, In step S63 of step six, the long-term safety factor of the reinforcing ring is calculated according to formula (2). ; （2）； In the formula, The long-term strength of the reinforced body is obtained through indoor creep testing.

9. The method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels using three-dimensional sensing according to claim 8, characterized in that, In step S61 of step six, the least squares method is used to fit the parameters. When the fitting degree is less than 90%, the intelligent computing terminal controls the alarm module connected to it to perform an abnormal alarm action.

10. The method for detecting the critical reinforcement depth of surrounding rock in soft rock tunnels using three-dimensional sensing according to claim 9, characterized in that, After continuous monitoring for more than 30 days, the intelligent computing terminal uses the new monitoring data to... , Perform a calibration update.

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