Deep well roadway surrounding rock area stress optimization control method

Abstract

The invention provides a deep well roadway surrounding rock area stress optimization control method, and belongs to the technical field of surrounding rock area stress optimization, and the method comprises the following steps: obtaining key data of deep well roadway surrounding rock; identifying a potential stress concentration area; performing grading evaluation on the identified potential stress concentration area, and determining the grade of the surrounding rock according to an evaluation result; according to the grade of the surrounding rock, a corresponding supporting strategy is formulated; the advanced presplitting blasting technology is applied to all grades of surrounding rocks; arranging a plurality of sensor nodes in the stress concentration area; the sensor nodes send monitoring data to the cloud processing center in a wireless mode, and the cloud processing center predicts the stability of surrounding rock and sends a prediction result to the expert terminal; and an expert formulates a new support strategy or adjusts an existing support strategy according to the prediction result of the expert terminal. According to the stress optimization control method for the surrounding rock area of the deep well roadway, the stability of the surrounding rock is improved through comprehensive technical means, and safe and efficient mining of a mine is guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of surrounding rock region stress optimization, and in particular to a method for optimizing and controlling stress in a surrounding rock region of a deep well tunnel. Background Art

[0002] During the excavation of deep mine tunnels, the stability of the surrounding rock is an important basis for ensuring safe production in mines. However, with the continuous increase in mining depth, the ground stress on deep mine tunnels has increased significantly. At the same time, it is affected by multiple factors such as complex geological structures, variable lithological characteristics, complex hydrogeological conditions, and historical mining and support activities, making the stress state of the tunnel surrounding rock extremely complex and difficult to predict. The combined effect of these factors can easily lead to stress concentration, deformation, and even destruction of the surrounding rock, which in turn causes safety accidents such as tunnel roof collapse and rock spalling, posing a serious threat to the safe production of mines.

[0003] Traditional tunnel surrounding rock stress control methods often rely on empirical judgments and lack scientific prediction and evaluation methods, making it difficult to fully and accurately grasp the stress state of the surrounding rock and its changing laws. Therefore, there is an urgent need for a scientific, systematic, and efficient method for optimizing the stress control of the surrounding rock area of ​​deep well tunnels to achieve comprehensive perception, accurate prediction, and effective control of surrounding rock stress, improve the stability of tunnel surrounding rocks, and ensure safe and efficient mining of mines. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing the stress control of the surrounding rock area of ​​a deep mine tunnel, propose a method for optimizing the stress control of the surrounding rock of a deep mine tunnel, improve the stability of the surrounding rock through comprehensive technical means, and ensure safe and efficient mining of the mine.

[0005] To achieve the above object, the present invention provides a method for optimizing and controlling stress in surrounding rock areas of deep well tunnels, comprising the following steps: S1. Collect key data of surrounding rock of deep well tunnels through geological exploration; S2. Analyze the collected key data using the finite element method, simulate the redistribution process of surrounding rock stress after tunnel excavation, and identify potential stress concentration areas; S3. Conduct a graded assessment of the identified potential stress concentration areas and determine the grade of the surrounding rock based on the assessment results; S4. Develop corresponding support strategies according to the level of surrounding rock; S5. For all levels of surrounding rock, the advanced pre-splitting blasting technology is implemented; S6. Arrange various sensor nodes in the stress concentration areas identified in the tunnel; S7, the sensor node sends the monitoring data to the cloud processing center by wireless means, and the cloud processing center predicts the stability of the surrounding rock based on the monitoring data and sends the prediction results to the expert terminal; S8. Experts formulate new support strategies or adjust existing support strategies based on the prediction results of the expert terminal.

[0006] Preferably, in step S1, the key data includes geological structure data, lithological characteristic data, geostress data, hydrogeological data, mining history data and support history data; Among them, geological structure data include rock layer distribution and thickness, faults and joints, folds and uplifts; lithological characteristic data include rock type, rock strength parameters, rock physical properties, rock weathering and alteration; geostress data include original geostress state, stress gradient and change, local stress anomaly area; hydrogeological data include groundwater level and flow direction, aquifer and impermeable layer, water pressure and seepage pressure; mining history data include mining time, mining method, mining depth; support history data include support type and parameters.

[0007] Preferably, in step S2, analyzing the collected key data using the finite element method includes: creating a three-dimensional model of the tunnel surrounding rock, setting material properties, meshing, applying loads and setting boundary conditions, and submitting for analysis.

[0008] Preferably, in step S3, the basis for the graded assessment includes: stress level, geological structure characteristics, rock mechanical properties, external loads and historical engineering experience.

[0009] Preferably, in step S3, the specific operation of the graded evaluation is as follows: S31. Conduct preliminary assessment of identified potential stress concentration areas, including assessment of stress levels and geological structural characteristics, and determine the impact of stress concentration and geological structure on surrounding rock stability; S32. Re-evaluate the identified potential stress concentration areas, including testing the mechanical properties of the rock, analyzing the external load conditions, and drawing on historical engineering experience to evaluate the stability of the potential stress concentration areas; S33, based on the evaluation results of steps S31 and S32, comprehensively judging the stability of the potential stress concentration area; S34. Classify the surrounding rock based on the comprehensive judgment results.

[0010] Preferably, in step S4, a corresponding support strategy is formulated according to the level of the surrounding rock, and the specific operations are as follows: For Class I surrounding rock, only high prestressed super-strong anchor support is used to enhance the shear fracture resistance of the surrounding rock; For Class II surrounding rock, add delayed grouting consolidation repair steps on the basis of Class I surrounding rock support to fill the surrounding rock cracks; For Class III surrounding rock, prestressed anchor cable support is further added on the basis of Class II surrounding rock support, and the stress peak is transferred to the deep part and the surrounding rock bearing circle is expanded by optimizing the cross-sectional shape of the roadway; For Grade IV and Grade V surrounding rock, on the basis of Grade III surrounding rock support, pre-consolidation and pre-reinforcement of grouting anchor rods are carried out in advance on the construction surface to ensure that the anchor-grouting reinforcement area is tightly connected with the deep stable rock mass to form a stable bearing structure.

[0011] Preferably, prestressed anchor cable support is used in combination with anchor rod support, and the stress state of the surrounding rock is redistributed through the deep pulling effect of the anchor cable, thereby reducing the high stress peak value on the shallow surface of the tunnel.

[0012] Preferably, in step S5, the advanced pre-splitting blasting technology is implemented for all levels of surrounding rocks, including: The hard roof is blasted by long hole blasting to generate artificial blasting crack zone, so as to achieve the treatment and control of the integrity of the hard roof; The lateral top cutting pressure relief uses blasting pre-cracking technology to determine the pressure relief parameters and reduce the stress concentration of the rock mass around the tunnel; The pressure relief parameters include vertical pressure relief height, inclined pressure relief length and drilling parameters.

[0013] Preferably, the sensor nodes include stress sensor nodes, deformation sensor nodes, acoustic emission sensor nodes, and seepage sensor nodes.

[0014] Preferably, in step S7, the cloud processing center predicts the surrounding rock stability based on the monitoring data, specifically, the monitoring data is processed by a surrounding rock stability prediction model built into the cloud processing center to obtain a surrounding rock stability prediction result; The surrounding rock stability prediction model is a one-dimensional convolutional neural network model, which includes three one-dimensional convolution modules, namely one-dimensional convolution module one, one-dimensional convolution module two, and one-dimensional convolution module three; Among them, the one-dimensional convolution module 1 and the one-dimensional convolution module 2 are composed of a Conv1d, BatchNorm1d, ReLU and MaxPool1d respectively. The number of convolution kernels is 4 and 8 respectively, and the size of convolution kernel is , the kernel size of MaxPool1d is ; The one-dimensional convolution module 3 consists of a Conv1d, BatchNorm1d, ReLU and AveragePool1d, where the number of convolution kernels is 16 and the convolution kernel size is , the core size of AveragePool1d is .

[0015] Therefore, the present invention adopts the above-mentioned method for optimizing the stress control of the surrounding rock area of ​​a deep well tunnel, and the beneficial technical effects are as follows: (1) Through detailed geological exploration and finite element simulation analysis, the present invention can accurately identify potential stress concentration areas and formulate scientific and reasonable support strategies for surrounding rocks of different levels; these strategies include high prestressed anchor support, delayed grouting consolidation repair, prestressed anchor cable support, etc., which effectively enhance the bearing capacity and stability of the surrounding rock and reduce the risk of damage to the surrounding rock of the tunnel; (2) The hierarchical evaluation system of the present invention makes the support design more refined and personalized; according to the actual situation of the surrounding rock, the most appropriate support method and parameters are selected to avoid the problem of over-support or under-support, thereby improving the support efficiency and economy; (3) The application of the advanced pre-splitting blasting technology in the present invention can pre-treat the hard roof before tunnel excavation, thereby reducing stress concentration and rock burst risks during tunnel excavation; (4) The present invention arranges a variety of sensor nodes in the tunnel to achieve real-time monitoring of parameters such as surrounding rock stress, deformation, acoustic emission and seepage. These data are transmitted to the cloud processing center by wireless means, and are processed and analyzed using the built-in surrounding rock stability prediction model, which can timely discover and warn of potential problems in surrounding rock stability; this real-time monitoring and prediction mechanism provides strong support for timely response measures and effectively avoids accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of a method for optimizing stress control in surrounding rock areas of deep well tunnels. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0018] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0019] Embodiment 1 like Figure 1 FIG. 1 is a flow chart of a method for optimizing stress control in surrounding rock areas of deep well tunnels according to the present invention, which specifically includes the following steps: S1. Collect key data of surrounding rock of deep well tunnels through geological exploration; Key data include geological structure data, lithology data, geostress data, hydrogeological data, mining history data and support history data; Among them, geological structure data include rock layer distribution and thickness, faults and joints, folds and uplifts; Distribution and thickness of rock formations: including the types, thicknesses and relative positions of the rock formations that the tunnel crosses; Faults and joints: location, strike, dip, inclination of faults and density and direction of joints; Wrinkles and uplifts: whether there are fold structures in the tunnel area, as well as the shape and axis of the folds.

[0020] Lithological characteristic data include rock type, rock strength parameters, rock physical properties, rock weathering and alteration; Rock type: such as sandstone, mudstone, limestone, granite, etc.; Rock strength parameters: including uniaxial compressive strength, tensile strength, elastic modulus, Poisson's ratio, etc.; Rock physical properties: such as density, porosity, permeability, etc.; Rock weathering and alteration: Evaluate the degree of rock weathering, alteration type and its impact on mechanical properties.

[0021] The geostress data include the original geostress state, stress gradient and change, and local stress anomaly area; Original ground stress state: including the magnitude and direction of horizontal stress and vertical stress, and the relative relationship between them (such as lateral pressure coefficient); Stress gradient and change: the change trend of ground stress along the direction of the tunnel and the depth; Local stress abnormality area: Identify whether there are areas of local stress concentration or release.

[0022] Hydrogeological data include groundwater level and flow direction, aquifers and impermeable layers, water pressure and seepage pressure; Groundwater level and flow direction: the level, flow direction and velocity of groundwater in the tunnel area; Aquifers and aquitards: determine the distribution, thickness and permeability of aquifers, and the presence and nature of aquitards; Water pressure and seepage pressure: Assess the impact of groundwater on the stability of tunnel surrounding rock.

[0023] Mining history data includes mining time, mining method, and mining depth; support history data includes support type and parameters; Mining conditions of adjacent tunnels or mining areas: including mining time, mining methods, mining depth, etc.; Support structure and effect: the type, parameters and support effect of previous tunnel support; Disaster records: record the disasters such as rock burst, roof fall, bottom heave and their causes in the tunnel area.

[0024] Other relevant data: Climate conditions: such as temperature and humidity, which may have an indirect impact on the stability of the surrounding rock of the tunnel; Seismic activity: frequency, magnitude and source distribution of regional seismic activity; Tunnel design and construction parameters: including the tunnel's cross-sectional dimensions, shape, support design, construction methods, etc.

[0025] S2. Analyze the collected key data using the finite element method (including creating a three-dimensional model of the tunnel surrounding rock, setting material properties, meshing, applying loads and setting boundary conditions, and submitting for analysis), simulate the redistribution process of surrounding rock stress after tunnel excavation, and identify potential stress concentration areas; S3. Conduct a graded assessment of the identified potential stress concentration areas and determine the grade of the surrounding rock based on the assessment results; The specific operations of the graded assessment are as follows: S31. Conduct preliminary assessment of identified potential stress concentration areas, including assessment of stress levels and geological structural characteristics, and determine the impact of stress concentration and geological structure on surrounding rock stability; It also includes the analysis of the role of geological structural characteristics on stress transfer and failure processes; S32. Re-evaluate the identified potential stress concentration areas, including testing the mechanical properties of the rock, analyzing the external load conditions, and drawing on historical engineering experience to evaluate the stability of the potential stress concentration areas; It also includes obtaining the mechanical parameters of rocks through experimental tests, and the mechanical parameters of rocks include uniaxial compressive strength, tensile strength and elastic modulus; It also includes the analysis of the effects of external load conditions such as topography, groundwater pressure, and seismic activity on the stability of the surrounding rock; S33, based on the evaluation results of steps S31 and S32, comprehensively judging the stability of the potential stress concentration area; S34. Classify the surrounding rock based on the comprehensive judgment results.

[0026] According to the above classification evaluation method, five levels of surrounding rock are determined, as shown in Table 1: Table 1 Classification of five surrounding rock types

[0027] S4. Develop corresponding support strategies according to the level of surrounding rock; For Class I surrounding rock, only high prestressed super-strong anchor support is used to enhance the shear fracture resistance of the surrounding rock; In high prestressed super-strong anchor support, the preload of the anchor is set high enough to limit the shear deformation of the surrounding rock along the primary cracks and secondary fracture slip surfaces.

[0028] For Class II surrounding rock, a delayed grouting consolidation repair step is added on the basis of Class I surrounding rock support to fill the surrounding rock cracks and improve its integrity; For Class III surrounding rock, prestressed anchor cable support is further added on the basis of Class II surrounding rock support, and the stress peak is transferred to the deep part and the surrounding rock bearing circle is expanded by optimizing the cross-sectional shape of the roadway; Prestressed anchor cable support is used in combination with anchor rod support. Through the deep pulling effect of the anchor cable, the stress state of the surrounding rock is redistributed, reducing the high stress peak on the shallow surface of the tunnel.

[0029] For Grade IV and Grade V surrounding rock, on the basis of Grade III surrounding rock support, pre-consolidation and pre-reinforcement of grouting anchor rods are carried out in advance on the construction surface to ensure that the anchor-grouting reinforcement area is tightly connected with the deep stable rock mass to form a stable bearing structure.

[0030] S5. For all levels of surrounding rock, advanced pre-splitting blasting technology is implemented, including: The hard roof is blasted by long hole blasting to generate artificial blasting crack zone, so as to achieve the treatment and control of the integrity of the hard roof; The lateral top cutting pressure relief uses blasting pre-cracking technology to determine the pressure relief parameters and reduce the stress concentration of the rock mass around the tunnel; The pressure relief parameters include vertical pressure relief height, inclined pressure relief length and drilling parameters.

[0031] S6. Arrange various sensor nodes in the stress concentration areas identified in the tunnel; The sensor nodes include stress sensor nodes, deformation sensor nodes, acoustic emission sensor nodes, and seepage sensor nodes.

[0032] S7, the sensor node sends the monitoring data to the cloud processing center by wireless means, and the cloud processing center predicts the stability of the surrounding rock based on the monitoring data and sends the prediction results to the expert terminal; The cloud processing center predicts the surrounding rock stability based on the monitoring data. Specifically, the monitoring data is processed through the surrounding rock stability prediction model built into the cloud processing center to obtain the surrounding rock stability prediction result; The surrounding rock stability prediction model is a one-dimensional convolutional neural network model, which includes three one-dimensional convolution modules, namely one-dimensional convolution module one, one-dimensional convolution module two, and one-dimensional convolution module three; Among them, the one-dimensional convolution module 1 and the one-dimensional convolution module 2 are composed of a Conv1d, BatchNorm1d, ReLU and MaxPool1d respectively. The number of convolution kernels is 4 and 8 respectively, and the size of convolution kernel is , the kernel size of MaxPool1d is ; The one-dimensional convolution module 3 consists of a Conv1d, BatchNorm1d, ReLU and AveragePool1d, where the number of convolution kernels is 16 and the convolution kernel size is , the core size of AveragePool1d is .

[0033] S8. Experts formulate new support strategies or adjust existing support strategies based on the prediction results of the expert terminal.

[0034] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0035] Therefore, the present invention adopts the above-mentioned method for optimizing the stress control of the surrounding rock area of ​​the deep well tunnel, which realizes the comprehensive optimization control of the stress of the surrounding rock area of ​​the deep well tunnel by comprehensively considering the hierarchical evaluation of the surrounding rock, the optimization of the support strategy, the improvement of the advanced pre-splitting blasting technology, and the establishment of the monitoring and feedback mechanism. This method will help improve the support effect of the tunnel, reduce the support cost, improve the construction efficiency, and ensure the safe and stable operation of the tunnel.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels, characterized in that: The following steps are involved: S1. Collect key data of surrounding rock of deep well tunnels through geological exploration; S2. Analyze the collected key data using the finite element method, simulate the redistribution process of surrounding rock stress after tunnel excavation, and identify potential stress concentration areas; S3. Conduct a graded assessment of the identified potential stress concentration areas and determine the grade of the surrounding rock based on the assessment results; S4. Develop corresponding support strategies according to the level of surrounding rock; S5. For all levels of surrounding rock, the advanced pre-splitting blasting technology is implemented; S6. Arrange various sensor nodes in the stress concentration areas identified in the tunnel; S7, the sensor node sends the monitoring data to the cloud processing center by wireless means, and the cloud processing center predicts the stability of the surrounding rock based on the monitoring data and sends the prediction results to the expert terminal; S8. Experts formulate new support strategies or adjust existing support strategies based on the prediction results of the expert terminal.

2. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels according to claim 1, characterized in that: In step S1, the key data include geological structure data, lithological characteristic data, geostress data, hydrogeological data, mining history data and support history data; Among them, geological structure data include rock layer distribution and thickness, faults and joints, folds and uplifts; lithological characteristic data include rock type, rock strength parameters, rock physical properties, rock weathering and alteration; geostress data include original geostress state, stress gradient and change, local stress anomaly area; hydrogeological data include groundwater level and flow direction, aquifer and impermeable layer, water pressure and seepage pressure; mining history data include mining time, mining method, mining depth; support history data include support type and parameters.

3. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels according to claim 2, characterized in that: In step S2, the collected key data are analyzed using the finite element method, including: creating a three-dimensional model of the tunnel surrounding rock, setting material properties, meshing, applying loads and setting boundary conditions, and submitting for analysis.

4. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels according to claim 3, characterized in that: In step S3, the basis for the graded assessment includes: stress level, geological structure characteristics, rock mechanical properties, external loads and historical engineering experience.

5. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels according to claim 4, characterized in that: In step S3, the specific operations of the hierarchical evaluation are as follows: S31. Conduct preliminary assessment of identified potential stress concentration areas, including assessment of stress levels and geological structural characteristics, and determine the impact of stress concentration and geological structure on surrounding rock stability; S32. Re-evaluate the identified potential stress concentration areas, including testing the mechanical properties of the rock, analyzing the external load conditions, and drawing on historical engineering experience to evaluate the stability of the potential stress concentration areas; S33, based on the evaluation results of steps S31 and S32, comprehensively judging the stability of the potential stress concentration area; S34. Classify the surrounding rock based on the comprehensive judgment results.

6. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels according to claim 5, characterized in that: In step S4, a corresponding support strategy is formulated according to the level of the surrounding rock. The specific operations are as follows: For Class I surrounding rock, only high prestressed super-strong anchor support is used to enhance the shear fracture resistance of the surrounding rock; For Class II surrounding rock, add delayed grouting consolidation repair steps on the basis of Class I surrounding rock support to fill the surrounding rock cracks; For Class III surrounding rock, prestressed anchor cable support is further added on the basis of Class II surrounding rock support, and the stress peak is transferred to the deep part and the surrounding rock bearing circle is expanded by optimizing the cross-sectional shape of the roadway; For Grade IV and Grade V surrounding rock, on the basis of Grade III surrounding rock support, pre-consolidation and pre-reinforcement of grouting anchor rods are carried out in advance on the construction surface to ensure that the anchor-grouting reinforcement area is tightly connected with the deep stable rock mass to form a stable bearing structure.

7. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels according to claim 6, characterized in that: Prestressed anchor cable support is used in combination with anchor rod support. Through the deep pulling effect of the anchor cable, the stress state of the surrounding rock is redistributed, reducing the high stress peak on the shallow surface of the tunnel.

8. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels according to claim 7, characterized in that: In step S5, the advanced pre-splitting blasting technology is implemented for all levels of surrounding rocks, including: The hard roof is blasted by long hole blasting to generate artificial blasting crack zone, so as to achieve the treatment and control of the integrity of the hard roof; The lateral top cutting pressure relief uses blasting pre-cracking technology to determine the pressure relief parameters and reduce the stress concentration of the rock mass around the tunnel; The pressure relief parameters include vertical pressure relief height, inclined pressure relief length and drilling parameters.

9. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels according to claim 8, characterized in that: The sensor nodes include stress sensor nodes, deformation sensor nodes, acoustic emission sensor nodes and seepage sensor nodes.

10. A method for optimizing and controlling stress in surrounding rock areas of deep well tunnels according to claim 9, characterized in that: In step S7, the cloud processing center predicts the surrounding rock stability based on the monitoring data, specifically, the monitoring data is processed by the surrounding rock stability prediction model built into the cloud processing center to obtain the surrounding rock stability prediction result; The surrounding rock stability prediction model is a one-dimensional convolutional neural network model, which includes three one-dimensional convolution modules, namely one-dimensional convolution module one, one-dimensional convolution module two, and one-dimensional convolution module three; Among them, the one-dimensional convolution module 1 and the one-dimensional convolution module 2 are composed of a Conv1d, BatchNorm1d, ReLU and MaxPool1d respectively. The number of convolution kernels is 4 and 8 respectively, and the size of convolution kernel is , the kernel size of MaxPool1d is ; The one-dimensional convolution module 3 consists of a Conv1d, BatchNorm1d, ReLU and AveragePool1d, where the number of convolution kernels is 16 and the convolution kernel size is , the core size of AveragePool1d is .

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