Roadway surrounding rock stability evaluation method

Abstract

The invention relates to the technical field of mine safety monitoring, and discloses a roadway surrounding rock stability evaluation method, which comprises the following steps: step 1, collecting roadway surrounding rock geological data, step 2, constructing a three-dimensional geomechanical model, step 3, inverting mechanical parameters, step 4, calculating a stability coefficient, and step 5, outputting an evaluation result. According to the method, rock mass types, structural plane occurrence, crustal stress vectors and permeability parameters are integrally collected through geological radar scanning, drilling coring and crustal stress measuring devices, the problem of model distortion caused by single data in a traditional method is solved, deformation of a stress concentration area is accurately captured by adopting a non-uniform grid division strategy, inversion mechanical parameters are combined, and the method is suitable for large-scale popularization and application. The network is trained by using in-situ direct shear and triaxial test data, the inversion efficiency is improved compared with manual trial and error, the whole surrounding rock fracture process is simulated based on a finite element-discrete element coupling algorithm, plastic zone distribution and a comprehensive index S are output, and automatic judgment of the stability level is realized.

Description

Technical Field

[0001] This invention relates to the field of mine safety monitoring technology, specifically a method for assessing the stability of surrounding rock in roadways. Background Technology

[0002] Coal mine underground mining roadways are crucial channels for coal transportation, mine ventilation, and personnel and material transport. Accurate prediction or assessment of the stability of the surrounding rock in these roadways is a prerequisite and foundation for support scheme design, and is of great significance to safe production. In recent years, research on methods for predicting the stability of the surrounding rock in mining roadways has become an important research topic in the field of coal mining safety technology. However, because the stability of the surrounding rock in mining roadways is the result of the combined effects of many factors, and these factors are interconnected and mutually influential, methods that rely on a single influencing factor for stability assessment inevitably have significant limitations and biases. Summary of the Invention

[0003] The purpose of this invention is to provide a method for assessing the stability of surrounding rock in roadways, in order to solve the problems mentioned in the background art.

[0004] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention relates to a method for assessing the stability of surrounding rock in roadways, the method comprising the following steps: Step 1: Collect geological data of the surrounding rock of the tunnel: Obtain the rock mass type, structural surface distribution, geostress field and hydrogeological parameters of the target tunnel. Through ground-penetrating radar scanning, core drilling and geostress measurement devices, obtain the rock mass type, structural surface occurrence, geostress vector distribution and permeability parameters of the target tunnel. Collect multi-source monitoring data of the surrounding rock of the tunnel in real time. Step 2: Construct a three-dimensional geomechanical model: Based on the data from Step 1, establish a three-dimensional numerical model of the surrounding rock of the tunnel, divide it into grid units, and use a non-uniform grid division strategy to construct the three-dimensional numerical model of the surrounding rock of the tunnel, wherein the grid accuracy of the roof and sidewall areas is higher than that of the floor area. Step 3: Inversion of mechanical parameters: Through in-situ field tests and laboratory tests, the rock mass mechanical parameters and structural surface strength criteria are determined. By combining in-situ direct shear test and rock triaxial test data, the rock mass elastic modulus, Poisson's ratio and structural surface strength parameters are corrected through backpropagation neural network. Step 4: Calculate the stability coefficient: The finite element-discrete element coupled method is used to simulate the deformation process of the surrounding rock, and the displacement field, stress field and plastic zone distribution are output. Based on the monitoring data, the comprehensive evaluation index S of the surrounding rock stability is calculated. Step 5: Output evaluation results: Determine the stability level based on the degree of plastic zone penetration and displacement threshold, determine the surrounding rock stability level based on the threshold range of the S value, output early warning decision, and generate an evaluation report.

[0005] Furthermore, the geostress measurement described in step one includes the following process: a. Hollow inclusion strain gauges were used to measure the geostress components in three orthogonal directions; b. Fitting the geostress gradient function based on well logging data ; in, For burial depth, The fitting coefficients refer to the multi-source monitoring data, which includes the displacement of the surrounding rock surface. Deep displacement Anchor bolt / anchor cable stress values and anchor bolt / anchor cable stress values .

[0006] Furthermore, step two also includes: a. Reconstructing the surface geometry of the tunnel using laser scanning point cloud data; b. Implement local mesh refinement in fault zones and fracture development areas, with the minimum element size ≤ 0.1m.

[0007] Furthermore, the process of inverting mechanical parameters described in step three is as follows: a. Calculation of rock mass strength parameters based on the Hoek-Brown criterion

[0008] in, The uniaxial compressive strength of the surrounding rock is MPa. , , It is a function of the Geological Strength Index (GSI); b. Optimizing the cohesion of the structural surfaces using the particle swarm optimization algorithm. and internal friction angle The objective function is to minimize the root mean square error between the field displacement monitoring data and the simulated values.

[0009] Furthermore, the formula for the stability coefficient S mentioned in step four is:

[0010] in, This is the critical threshold for surface displacement. This represents the maximum allowable value for deep displacement. The anchor bolt yield strength, The initial loosening ring reference thickness, Let be the weight coefficient, and satisfy... .

[0011] Furthermore, the method for determining the weighting coefficients is as follows: a. Construct a judgment matrix containing geological conditions, support parameters, and factors influencing surrounding rock stability using the analytic hierarchy process. ; b. Solve the judgment matrix Maximum eigenvalue corresponding feature vector ; c. For eigenvectors After normalization .

[0012] Furthermore, the surrounding rock stability levels described in step five are specifically divided into: when When, it is determined to be stable at level one; when When, it is determined to be basically stable at level two; when At that time, it was determined to be level three unstable; when At that time, it was determined to be a level four instability; The aforementioned early warning decision-making is divided into four levels: A green safety signal is output at level one. At level two, a blue monitoring enhancement command is output; At level three, a yellow support reinforcement command is output; At level four, a red emergency evacuation command will be issued.

[0013] Furthermore, the calculation of surrounding rock stability also includes calculating the surrounding rock stress release rate. The formula is in, This represents the initial elastic modulus of the surrounding rock. For the real-time equivalent elastic modulus, Introduced as a correction factor into the comprehensive evaluation index Generate correction indicators , The rock mass deterioration coefficient is determined by the following formula: in, This is a rock quality indicator.

[0014] Furthermore, the process of constructing the three-dimensional geomechanical model is as follows: a. Establish a three-dimensional geomechanical model of the surrounding rock of the tunnel; b. Calculate the theoretical displacement field using finite element numerical simulation. ; c. Define the model credibility factor when At that time, model calibration is triggered.

[0015] Furthermore, it also includes long-term stability predictions: a. Establish a time series of evaluation indicators ; b. Predicting instability time using differential equations ,

[0016] Where a, b, and c are the rheological properties parameters of the surrounding rock, determined by least squares fitting.

[0017] in, The critical threshold for instability This refers to the current moment.

[0018] The present invention has the following beneficial effects: (1) This invention constructs a closed-loop system of "monitoring-modeling-assessment-early warning" by using multi-source data-driven (geology + monitoring), intelligent algorithm fusion (neural network + particle swarm), dynamic model calibration (confidence factor η), and timely prediction (rheological equation). Compared with traditional methods, it improves assessment accuracy, shortens early warning response time, and significantly reduces the risk of roadway collapse.

[0019] (2) This invention integrates the collection of rock mass type, structural plane orientation, geostress vector and permeability parameters by ground radar scanning, borehole core sampling and geostress measurement device, solves the problem of model distortion caused by single data in traditional methods, adopts non-uniform grid division strategy to accurately capture the deformation of stress concentration area, optimizes the grid size from the conventional 0.5m to 0.15m, reduces the displacement simulation error of key area, combines inversion mechanical parameters, uses in-situ direct shear and triaxial test data to train network, improves inversion efficiency compared with manual trial and error, simulates the whole process of surrounding rock fracture based on finite element-discrete element coupling algorithm, outputs plastic zone distribution and comprehensive index S, realizes automatic determination of stability level.

[0020] (3) The present invention obtains data synchronously using a hollow cavity strain gauge. Nine stress components are used, and the geostress gradient function is fitted with well logging data. This overcomes the limitation of insufficient spatial coverage in traditional single-point measurement, reduces the stress prediction error in unexplored areas, and establishes a multi-source monitoring data coupling verification mechanism. The displacement of the surrounding rock surface (laser rangefinder ±0.1mm), the deep displacement (borehole inclinometer ±0.5mm), and the anchor stress (fiber optic sensor ±1MPa) are connected to the evaluation system in real time, forming a "measurement-simulation-feedback" closed loop. Application results show that the reduction in the number of boreholes improves the efficiency of geostress field reconstruction.

[0021] (4) Based on laser scanning point cloud reconstruction technology (accuracy ±2mm), this invention accurately restores the roadway geometry, eliminates cross-sectional distortion errors caused by manual surveying, develops a fault zone intelligent densification algorithm, automatically implements local grid densification (minimum unit 0.05m) in fracture development areas, saves computing resources while effectively improving the accuracy of fault slip prediction, and constructs a grid adaptive optimization system. When the monitored displacement deviation increases, it triggers grid reconstruction to ensure the reliability of the model throughout its entire life cycle. In a coal mine application, the roof delamination simulation error is controlled within 3mm.

[0022] (5) This invention calculates rock mass strength parameters based on the Hoek-Brown criterion, quantifies rock mass integrity through the geological strength index GSI, and then uses a particle swarm optimization algorithm (population size 100, iterations 500) to invert the cohesion c and internal friction angle of the structural surface. The objective function is set as minimizing the root mean square error (RMSE) between the field displacement monitoring value and the simulated value (RMSE < 1.2 mm). Compared with the empirical formula method, the reliability of parameters is improved and the inversion time is effectively shortened. At the same time, an innovative comprehensive index S formula integrating surface displacement, deep displacement, anchor stress and loosening zone thickness is proposed. Through dynamic weight allocation, the characteristics of surrounding rock failure are fully characterized, providing a quantitative basis for graded early warning.

[0023] (6) The weight coefficient determination of this invention adopts the AHP-entropy method fusion model: a three-layer judgment matrix (target layer-criteria layer-index layer) is constructed, covering nine factors including geological conditions (rock mass strength, fracture density, etc.), support parameters (anchor spacing, preload, etc.), and surrounding rock conditions (displacement rate, acoustic emission energy, etc.). The weight vector is solved by the eigenvector method. The judgment matrix satisfies the consistency test CR<0.1. A dynamic weight correction module is developed. When the abnormal deviation of the monitoring data increases, the weight is automatically triggered for redistribution. This mechanism improves the objectivity of weight allocation and adapts to different mine conditions.

[0024] (7) This invention introduces the surrounding rock stress release rate and rock mass deterioration coefficient (Related to RQD index), the dynamic correction assessment model quantifies the time-dependent damage of surrounding rock, breaking through the limitations of traditional static models. In addition, the proposed time-dependent damage correction model defines the stress release rate of surrounding rock, quantifies the degree of energy release of surrounding rock, constructs a rock mass deterioration system, and transforms the rock quality index RQD into a mechanical parameter attenuation factor to correct the stability index, thereby improving the accuracy of long-term stability prediction and effectively warning of rockburst accidents. It defines four-level stability intervals and associates them with graded early warning instructions (green safety - red evacuation), realizing intuitive judgment of risk level and rapid response of emergency measures. The early warning instructions are linked with the underground broadcasting system and support equipment controller for emergency response. In a coal mine application, it successfully warned of three roof collapse accidents with zero casualties.

[0025] (8) This invention establishes a prediction model for the time of surrounding rock rheological instability, describes the accelerated damage law of surrounding rock through differential equations, and uses the sliding time window least squares method (window length 7 days) to dynamically fit parameters and instability time formula. By outputting early warning time points accurate to the hour, it can provide early warning of floor heave instability in a certain deep roadway.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic flowchart of the present invention; Figure 2 This is a three-dimensional numerical model of the surrounding rock of the present invention; Figure 3 This is a location diagram of the geostress measuring point 1 of the present invention; Figure 4 This is a location diagram of the ground stress measuring point 2 of the present invention; Figure 5 This is a graph showing the relationship between surrounding rock stress and depth in this invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below 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.

[0030] This invention relates to a method for assessing the stability of surrounding rock in roadways, the method comprising the following steps: Step 1: Collect geological data of the surrounding rock of the tunnel: Obtain the rock mass type, structural surface distribution, geostress field and hydrogeological parameters of the target tunnel. Through ground-penetrating radar scanning, core drilling and geostress measurement devices, obtain the rock mass type, structural surface occurrence, geostress vector distribution and permeability parameters of the target tunnel. Collect multi-source monitoring data of the surrounding rock of the tunnel in real time. The geostress measurement in this step includes the following procedures: a. Hollow inclusion strain gauges were used to measure the geostress components in three orthogonal directions; b. Fitting the geostress gradient function based on well logging data ; in, For burial depth, The fitting coefficients refer to the multi-source monitoring data, which includes the displacement of the surrounding rock surface. Deep displacement Anchor bolt / anchor cable stress values and anchor bolt / anchor cable stress values .

[0031] This step involves synchronous acquisition using hollow inclusion strain gauges. Nine stress components are used, and the geostress gradient function is fitted with well logging data. This overcomes the limitation of insufficient spatial coverage in traditional single-point measurement, reduces the stress prediction error in unexplored areas, and establishes a multi-source monitoring data coupling verification mechanism. The displacement of the surrounding rock surface (laser rangefinder ±0.1mm), the deep displacement (borehole inclinometer ±0.5mm), and the anchor stress (fiber optic sensor ±1MPa) are connected to the evaluation system in real time, forming a "measurement-simulation-feedback" closed loop. Application results show that the reduction in the number of boreholes improves the efficiency of geostress field reconstruction.

[0032] In a coal mine application, two measuring points were set up for in-situ stress testing, with three boreholes at each measuring point. The measuring points were located in the track skin triple roadway and the main track roadway, respectively. Their specific locations are as follows: Figure 3 and Figure 4 As shown, the stress components are as follows: =5.68 , =5.68 , =9.69 ; =2.93 , =0.86 , =0.25 The lateral pressure coefficient of the main track roadway in this coal mine is λ=0.59, and the magnitude of the principal stress in the main track roadway is: =11.4 , =6.14 , =4.78 .

[0033] Step 2: Construct a three-dimensional geomechanical model: Based on the data from Step 1, establish a three-dimensional numerical model of the surrounding rock of the tunnel, divide it into grid units, and use a non-uniform grid division strategy to construct the three-dimensional numerical model of the surrounding rock of the tunnel, wherein the grid accuracy of the roof and sidewall areas is higher than that of the floor area. In this step, a three-dimensional numerical model of the surrounding rock of the tunnel is established. Specifically, a. Reconstructing the surface geometry of the tunnel using laser scanning point cloud data; b. Implement local mesh refinement in fault zones and fracture development areas, with the minimum element size ≤ 0.1m.

[0034] This step accurately restores the roadway geometry using laser scanning point cloud reconstruction technology (accuracy ±2mm), eliminating cross-sectional distortion errors caused by manual surveying. An intelligent fault zone densification algorithm is developed to automatically implement local mesh densification (minimum unit 0.05m) in fracture-developed areas. Compared to a uniform grid across the entire region, this saves computational resources while effectively improving the accuracy of fault slip prediction. A mesh adaptive optimization system is constructed, triggering mesh reconstruction when the monitored displacement deviation increases, ensuring the reliability of the model throughout its entire lifecycle. In a coal mine application, the roof delamination simulation error is controlled within 3mm.

[0035] The process of constructing a three-dimensional geomechanical model is as follows: a. Establish a three-dimensional geomechanical model of the surrounding rock of the tunnel; b. Calculate the theoretical displacement field using finite element numerical simulation. ; c. Define the model credibility factor when At that time, model calibration is triggered.

[0036] By designing a model confidence factor η calibration mechanism (triggered when deviation > 15%), the prediction accuracy of the numerical model is ensured throughout its entire life cycle, avoiding the risk of failure caused by geological mutations.

[0037] Step 3: Inversion of mechanical parameters: Through in-situ field tests and laboratory tests, the rock mass mechanical parameters and structural surface strength criteria are determined. By combining in-situ direct shear test and rock triaxial test data, the rock mass elastic modulus, Poisson's ratio and structural surface strength parameters are corrected through backpropagation neural network. In this step, the process of inverting mechanical parameters is as follows: a. Calculation of rock mass strength parameters based on the Hoek-Brown criterion

[0038] in, The uniaxial compressive strength of the surrounding rock is MPa. , , It is a function of the Geological Strength Index (GSI); b. Optimizing the cohesion of the structural surfaces using the particle swarm optimization algorithm. and internal friction angle The objective function is to minimize the root mean square error between the field displacement monitoring data and the simulated values.

[0039] This step first calculates rock mass strength parameters based on the Hoek-Brown criterion, quantifies rock mass integrity using the Geological Strength Index (GSI), and then employs a particle swarm optimization algorithm (population size 100, 500 iterations) to invert the cohesion c and internal friction angle of structural surfaces. The objective function is set as minimizing the root mean square error (RMSE) between the field displacement monitoring value and the simulated value (RMSE < 1.2 mm). Compared with the empirical formula method, the reliability of the parameters is improved and the inversion time is effectively shortened.

[0040] Step 4: Calculate the stability coefficient: The finite element-discrete element coupled method is used to simulate the deformation process of the surrounding rock, and the displacement field, stress field and plastic zone distribution are output. Based on the monitoring data, the comprehensive evaluation index S of the surrounding rock stability is calculated. In this step, the formula for the stability coefficient S is:

[0041] in, This is the critical threshold for surface displacement. This represents the maximum allowable value for deep displacement. The anchor bolt yield strength, The initial loosening ring reference thickness, Let be the weight coefficient, and satisfy... .

[0042] This innovative step proposes a comprehensive index S formula that integrates surface displacement, deep displacement, anchor stress, and loosening zone thickness. Through dynamic weight allocation, it comprehensively characterizes the surrounding rock failure features and provides a quantitative basis for graded early warning.

[0043] Furthermore, the process for determining the aforementioned weighting coefficients is as follows: a. Construct a judgment matrix containing geological conditions, support parameters, and factors influencing surrounding rock stability using the analytic hierarchy process. ; b. Solve the judgment matrix Maximum eigenvalue corresponding feature vector ; c. For eigenvectors After normalization .

[0044] The above technical solution uses the AHP-entropy method to determine the weight coefficients. A three-layer judgment matrix (target layer, criterion layer, and index layer) is constructed, covering nine factors including geological conditions (rock mass strength, fracture density, etc.), support parameters (anchor spacing, preload, etc.), and surrounding rock conditions (displacement rate, acoustic emission energy, etc.). The weight vector is solved by the eigenvector method. The judgment matrix satisfies the consistency test CR<0.1. A dynamic weight correction module is developed. When the abnormal deviation of the monitoring data increases, the weight is automatically triggered for redistribution. This mechanism improves the objectivity of weight allocation and adapts to different mine conditions.

[0045] Step 5: Output evaluation results: Determine the stability level based on the degree of plastic zone penetration and displacement threshold, determine the surrounding rock stability level based on the threshold range of the S value, output early warning decision, and generate an evaluation report.

[0046] In this step, the surrounding rock stability level is specifically divided into: when When, it is determined to be stable at level one; when When, it is determined to be basically stable at level two; when At that time, it was determined to be level three unstable; when At that time, it was determined to be a level four instability.

[0047] In addition, early warning decision-making is divided into four levels: A green safety signal is output at level one. At level two, a blue monitoring enhancement command is output; At level three, a yellow support reinforcement command is output; At level four, a red emergency evacuation command will be issued.

[0048] This step defines four levels of stability ranges and associates them with graded early warning commands (green for safety and red for evacuation) to achieve intuitive judgment of risk level and rapid response to emergency measures. The early warning commands are linked with the underground broadcasting system and support equipment controller for emergency response. In a coal mine application, it successfully warned of three roof collapse accidents with zero casualties.

[0049] Stability Decision Matrix Chart Criterion type Level I Standard Level II Standard Level III Standard Plastic zone penetration <30% 30-60% >60% Maximum displacement (mm) <15 15-30 >30 S index value <0.3 0.3-0.6 ≥0.6 In this embodiment, a preferred long-term stability prediction method is also included: a. Establish a time series of evaluation indicators ; b. Predicting instability time using differential equations ,

[0050] Where a, b, and c are the rheological properties parameters of the surrounding rock, determined by least squares fitting.

[0051] in, The critical threshold for instability This refers to the current moment.

[0052] This model is used to establish a prediction model for the time of rheological instability of surrounding rock. It describes the acceleration law of surrounding rock damage through differential equations, and uses the sliding time window least squares method (window length 7 days) to dynamically fit the parameters and instability time formula. By outputting the early warning time point accurate to the hour, it can provide early warning of floor heave instability in a certain deep roadway.

[0053] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for assessing the stability of surrounding rock in roadways, characterized in that... The evaluation method includes the following steps: Step 1: Collect geological data of the surrounding rock of the tunnel: Obtain the rock mass type, structural surface distribution, geostress field and hydrogeological parameters of the target tunnel. Through ground-penetrating radar scanning, core drilling and geostress measurement devices, obtain the rock mass type, structural surface occurrence, geostress vector distribution and permeability parameters of the target tunnel. Collect multi-source monitoring data of the surrounding rock of the tunnel in real time. Step 2: Construct a three-dimensional geomechanical model: Based on the data from Step 1, establish a three-dimensional numerical model of the surrounding rock of the tunnel, divide it into grid units, and use a non-uniform grid division strategy to construct the three-dimensional numerical model of the surrounding rock of the tunnel, wherein the grid accuracy of the roof and sidewall areas is higher than that of the floor area. Step 3: Inversion of mechanical parameters: Through in-situ field tests and laboratory tests, the rock mass mechanical parameters and structural surface strength criteria are determined. By combining in-situ direct shear test and triaxial rock test data, the rock mass elastic modulus, Poisson's ratio and structural surface strength parameters are corrected through backpropagation neural network. Step 4: Calculate the stability coefficient: The finite element-discrete element coupled method is used to simulate the deformation process of the surrounding rock, and the displacement field, stress field and plastic zone distribution are output. Based on the monitoring data, the comprehensive evaluation index S of the surrounding rock stability is calculated. Step 5: Output evaluation results: Determine the stability level based on the degree of plastic zone penetration and displacement threshold, determine the surrounding rock stability level based on the threshold range of the S value, output early warning decision, and generate an evaluation report.

2. The method for assessing the stability of surrounding rock in a roadway according to claim 1, characterized in that... The geostress measurement described in step one includes the following process: a. Hollow inclusion strain gauges were used to measure the geostress components in three orthogonal directions; b. Fitting the geostress gradient function based on well logging data ; in, For burial depth, The fitting coefficients refer to the multi-source monitoring data, which includes the displacement of the surrounding rock surface. Deep displacement Anchor bolt / anchor cable stress values and anchor bolt / anchor cable stress values .

3. The method for assessing the stability of surrounding rock in a roadway according to claim 1, characterized in that... Step two also includes: a. Reconstructing the surface geometry of the tunnel using laser scanning point cloud data; b. Implement local mesh refinement in fault zones and fracture development areas, with the minimum element size ≤ 0.1m.

4. The method for assessing the stability of surrounding rock in a roadway according to claim 1, characterized in that... The process of inverting mechanical parameters described in step three is as follows: a. Calculation of rock mass strength parameters based on the Hoek-Brown criterion in, The uniaxial compressive strength of the surrounding rock is MPa. , , It is a function of the Geological Strength Index (GSI); b. Optimizing the cohesion of the structural surfaces using the particle swarm optimization algorithm. and internal friction angle The objective function is to minimize the root mean square error between the field displacement monitoring data and the simulated values.

5. The method for assessing the stability of surrounding rock in a roadway according to claim 1, characterized in that... The formula for the stability coefficient S mentioned in step four is: in, This is the critical threshold for surface displacement. This represents the maximum allowable value for deep displacement. The anchor bolt yield strength, The initial loosening ring reference thickness, Let be the weight coefficient, and satisfy... .

6. The method for assessing the stability of surrounding rock in a roadway according to claim 5, characterized in that... The method for determining the weighting coefficients is as follows: a. Construct a judgment matrix containing geological conditions, support parameters, and factors influencing surrounding rock stability using the analytic hierarchy process. ; b. Solve the judgment matrix Maximum eigenvalue corresponding feature vector ; c. For eigenvectors After normalization .

7. The method for assessing the stability of surrounding rock in a roadway according to claim 1, characterized in that... The rock stability levels mentioned in step five are specifically divided into: when When, it is determined to be stable at level one; when When, it is determined to be basically stable at level two; when At that time, it was determined to be level three unstable; when At that time, it was determined to be a level four instability; The aforementioned early warning decision-making is divided into four levels: A green safety signal is output at level one. At level two, a blue monitoring enhancement command is output; At level three, a yellow support reinforcement command is output; At level four, a red emergency evacuation command will be issued.

8. The method for assessing the stability of surrounding rock in a roadway according to claim 1, characterized in that... The calculation of surrounding rock stability also includes calculating the surrounding rock stress release rate. The formula is in, This represents the initial elastic modulus of the surrounding rock. For the real-time equivalent elastic modulus, Introduced as a correction factor into the comprehensive evaluation index Generate correction indicators , The rock mass deterioration coefficient is determined by the following formula: in, This is a rock quality indicator.

9. The method for assessing the stability of surrounding rock in a roadway according to claim 1, characterized in that... The process of constructing the three-dimensional geomechanical model is as follows: a. Establish a three-dimensional geomechanical model of the surrounding rock of the tunnel; b. Calculate the theoretical displacement field using finite element numerical simulation. ; c. Define the model credibility factor when At that time, model calibration is triggered.

10. The method for assessing the stability of surrounding rock in a roadway according to claim 1, characterized in that, It also includes long-term stability predictions: a. Establish a time series of evaluation indicators ; b. Predicting instability time using differential equations , Where a, b, and c are the rheological properties parameters of the surrounding rock, determined by least squares fitting. in, The critical threshold for instability This refers to the current moment.

Images