A method and system for grading and collaborative prevention of water-rich soft rock deep roadway

Abstract

This invention discloses a graded collaborative prevention and control method and system for deep roadways in water-rich soft rock, belonging to the field of mining engineering and safety technology. To address the problems of existing technologies' single prevention and control measures, reliance on experience, and lack of coordination, this invention obtains dynamic safe water head by acquiring confined water pressure, surrounding rock deformation, in-situ stress, and rock mechanics parameters, and corrects for these parameters through hydraulic softening reduction and in-situ stress non-uniformity influence coefficient. It constructs a multi-factor quantitatively integrated dynamic risk index and performs risk classification; based on the risk level, it selects templates from a template library and parameterizes them to generate an integrated prevention and control scheme including asymmetric support and graded water hazard response; the scheme is deployed and executed, and the risk index is updated in a closed loop based on real-time feedback, triggering dynamic adjustments to the scheme. This invention achieves a transformation from experience-based prevention to model-driven, precise collaboration, and closed-loop control, significantly improving the prevention and control efficiency and engineering safety of floor heave and water inrush disasters in deep roadways in water-rich soft rock.

Description

Technical Field

[0001] This invention belongs to the field of mining engineering and safety technology, and particularly relates to a graded collaborative prevention and control method and system for deep roadways in water-rich soft rock. Background Technology

[0002] The core risk of extended roadways in water-rich soft rock stems from the combined effects of water softening and changes in the stress field as the roadway deepens. Soft rock generally exhibits characteristics such as high clay mineral content, easy softening and expansion upon contact with water, and significant deterioration in mechanical strength. As extended roadways are excavated from shallow to deep with increasing burial depth, this leads to increased horizontal principal stress and enhanced stress non-uniformity, resulting in deeper floor failure. Under the superimposed pressure of confined water, floor deformation, floor heave, and even water inrush accidents are prone to occur, seriously threatening mine safety. Although existing prevention and control technologies have established criteria such as safe water head values ​​and measures such as support, grouting, and pressure relief, there are still many shortcomings in the scenario of extended roadways in water-rich soft rock.

[0003] Traditional safe head calculations typically employ theoretical formulas based on aquitard resistance to sudden water inrush, failing to adequately consider the softening of soft rock upon contact with water and the non-uniform coupling effect of in-situ stress. This leads to discrepancies between theoretically calculated safe head values ​​and actual risk conditions, resulting in a mismatch between safety margins and actual risks, and making it difficult to accurately reflect the true prevention and control needs under water-rich soft rock conditions. Regarding prevention and control measures, while existing technologies offer various methods such as support, grouting, and pressure relief, the selection of measures and parameter determination still rely on engineering experience. There is a lack of mechanisms for coordinated combination and parameterized output based on risk levels, and various prevention and control measures lack effective coordination, making it difficult to form targeted integrated prevention and control solutions. Furthermore, the monitoring, early warning, and response processes lack a closed-loop dynamic adjustment mechanism. After the plan is implemented, the prevention and control effect cannot be dynamically evaluated based on real-time changes in on-site monitoring data, and strategies cannot be adjusted in a timely manner, resulting in delayed responses and hindering timely and precise control.

[0004] Therefore, there is an urgent need for a hierarchical collaborative prevention and control method and system for water-rich soft rock deep roadways that can dynamically adjust the safety head, quantify and classify risks, output collaborative prevention and control schemes, and be able to be adjusted in a closed loop, in order to solve the problem of insufficient adaptability of prevention and control measures in existing technologies. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a graded collaborative prevention and control method and system for water-rich soft rock deep tunnels, thereby resolving the issues present in the prior art.

[0006] Firstly, to achieve the above objectives, the present invention provides a graded and coordinated prevention and control method for deep roadways in water-rich soft rock, comprising the following steps: S1. Collect the actual water pressure of the confined water in the tunnel floor, the real-time deformation rate of the surrounding rock, the geostress parameters, and the rock mechanics parameters. S2. Calculate and dynamically correct the safe head value based on the collected data; S3. Calculate the dynamic risk index based on the actual water pressure, dynamically corrected safety head value, geostress parameters, rock mechanics parameters, and deformation rate, and classify the risk level according to the dynamic risk index. S4. Select a template from the scheme template library according to the risk level, and generate an integrated prevention and control scheme that includes support sub-schemes and water hazard control sub-schemes; S5. Issue and implement the integrated prevention and control plan, and repeat S1 to S4 based on the monitoring feedback data after implementation to achieve closed-loop dynamic control.

[0007] Optionally, the process of calculating the dynamically corrected safety head value in S2 includes: calculating the theoretical safety head value based on the thickness of the impermeable layer, tensile strength, base plate width and unit weight of the impermeable layer; introducing a hydrological softening reduction coefficient based on clay mineral content and an influence coefficient based on the non-uniformity of geostress to correct the theoretical safety head value to a dynamically corrected safety head value.

[0008] Optionally, the process of calculating the dynamic risk index in S3 includes: weighting and summing the ratio of actual water pressure to dynamic corrected safety head, the ratio of maximum horizontal principal stress to saturated uniaxial compressive strength of surrounding rock, and normalized deformation rate to obtain the dynamic risk index, wherein the normalized deformation rate is obtained by dividing the real-time deformation rate of surrounding rock by the normalized reference value.

[0009] Optionally, the process of classifying risk levels in S3 includes: comparing the dynamic risk index with a first threshold and a second threshold; when the dynamic risk index is not greater than the first threshold, it is a low-risk level; when the dynamic risk index is greater than the first threshold but not greater than the second threshold, it is a medium-risk level; and when the dynamic risk index is greater than the second threshold, it is a high-risk level.

[0010] Optionally, the process of generating an integrated prevention and control scheme in S4 includes: selecting the corresponding template from the scheme template library according to the risk level, determining the specific values ​​of support parameters and water hazard control parameters based on the ratio of actual water pressure to dynamically corrected safe head value or dynamic risk index, then constraining and verifying the generated scheme, and outputting the integrated prevention and control scheme after the verification is passed.

[0011] Optionally, the process of generating a support sub-scheme in S4 includes: determining the priority reinforcement azimuth based on the azimuth of the maximum horizontal principal stress direction and the azimuth of the roadway direction; setting different support parameters for the priority reinforcement azimuth and non-priority reinforcement azimuth; and minimizing the weighted sum of surrounding rock displacement, deformation rate and support cost through numerical simulation iteration optimization, while satisfying the constraints that the actual water pressure is not greater than the dynamically corrected safety head value, the surrounding rock displacement and deformation rate do not exceed the allowable threshold, and the internal force of the support components does not exceed the allowable value.

[0012] Optionally, the process of generating a water hazard control sub-scheme in S4 includes: selecting one or more combinations of controllable pressure relief hole diversion, infiltration grouting reinforcement, or pressure relief hole densification measures according to the risk level, and determining the pressure relief hole spacing, single hole flow rate, grouting pressure, and grouting ratio according to the ratio of actual water pressure to dynamically corrected safety head value or dynamic risk index, so that these parameters fall within the preset range.

[0013] Secondly, the present invention also provides a graded collaborative prevention and control system for water-rich soft rock deep roadways, used to implement a graded collaborative prevention and control method for water-rich soft rock deep roadways, the system comprising: The data sensing module is used to collect the actual water pressure of the confined water on the tunnel floor, the real-time deformation rate of the surrounding rock, the geostress parameters, and the rock mechanics parameters. The intelligent analysis and decision-making module is used to calculate the dynamically corrected safe head value based on the collected data, calculate the dynamic risk index and classify the risk level based on the actual water pressure, the dynamically corrected safe head value, the ground stress parameters, the rock mechanics parameters and the deformation rate, and select a template from the scheme template library according to the risk level to generate an integrated prevention and control scheme that includes a support sub-scheme and a water hazard control sub-scheme. The collaborative control execution module is used to receive the integrated prevention and control plan and control the execution mechanism to carry out support construction and water hazard control; The human-computer interaction feedback module is used to display the risk level, plan content and execution status, and to send the monitoring feedback data back to the intelligent analysis and decision-making module to achieve closed-loop dynamic control.

[0014] Thirdly, the present invention also provides a computer terminal device, comprising: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the graded collaborative prevention and control method for water-rich soft rock deep tunnels in the first aspect described above.

[0015] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the graded collaborative prevention and control method for water-rich soft rock deep tunnels described in the first aspect above.

[0016] Compared with the prior art, the present invention has the following advantages and technical effects: This invention provides a graded collaborative prevention and control method and system for deep roadways in water-rich soft rock. By introducing a hydraulic softening reduction coefficient and a non-uniform in-situ stress influence coefficient to dynamically correct the theoretical safe head, it solves the problem of mismatch between safety margin and actual risk caused by the insufficient consideration of the coupling effect of hydraulic softening and non-uniform in-situ stress in traditional safe head calculations. By constructing a multi-factor quantitatively integrated dynamic risk index and classifying risks, it addresses the problem of existing technologies relying on experience and lacking quantitative basis for prevention and control measures. By selecting templates from a template library based on risk level and parameterizing support parameters and water hazard control parameters according to the dynamic risk index or the ratio of actual water pressure to corrected safe head, an integrated prevention and control scheme including asymmetric support sub-schemes and active water hazard control sub-schemes is formed, solving the problem of existing prevention and control measures lacking collaborative combination and parameterized output. By issuing and executing the integrated prevention and control scheme and updating the dynamic risk index in a closed loop based on real-time monitoring feedback data, triggering scheme upgrades, maintenance, or downgrades, it solves the problem of existing technologies lacking a closed-loop dynamic adjustment mechanism and difficulty in achieving timely response and precise control. This invention realizes the transformation from experience-based prevention and control to model-driven, precise collaboration, and closed-loop control, significantly improving the prevention and control efficiency and engineering safety of floor heave and water inrush disasters in water-rich soft rock deep roadways. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic flowchart of a graded and coordinated prevention and control method for deep roadways in water-rich soft rock, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a graded collaborative prevention and control system for deep tunnels in water-rich soft rock, according to an embodiment of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0020] Example 1 like Figure 1 As shown, this embodiment provides a graded collaborative prevention and control method for deep roadways in water-rich soft rock, including: S1. Collect the actual water pressure of the confined water in the tunnel floor, the real-time deformation rate of the surrounding rock, the geostress parameters, and the rock mechanics parameters. S2. Calculate and dynamically correct the safe head value based on the collected data; S3. Calculate the dynamic risk index based on the actual water pressure, dynamically corrected safety head value, geostress parameters, rock mechanics parameters, and deformation rate, and classify the risk level according to the dynamic risk index. S4. Select a template from the scheme template library according to the risk level, and generate an integrated prevention and control scheme that includes support sub-schemes and water hazard control sub-schemes; S5. Issue and implement the integrated prevention and control plan, and repeat S1 to S4 based on the monitoring feedback data after implementation to achieve closed-loop dynamic control.

[0021] Furthermore, the process of calculating the dynamically corrected safe head value in S2 includes: calculating the theoretical safe head value based on the thickness of the impermeable layer, tensile strength, base plate width, and unit weight of the impermeable layer; introducing a hydrodynamic softening reduction coefficient based on clay mineral content and an influence coefficient based on the non-uniformity of geostress to correct the theoretical safe head value to a dynamically corrected safe head value.

[0022] Specifically, the implementation process of this embodiment includes: Theoretical safe head value P 理论安全 The calculation is performed using one of the theoretical formulas for resisting sudden water inrush in a waterproof layer. For ease of engineering application, this embodiment preferably adopts the following form: P 理论安全 = 2 K P t 2 / L 2 + c t(1) in, K P t is the average tensile strength of the waterproof layer of the floor slab (MPa), t is the thickness of the waterproof layer (m), and L is the width of the roadway floor slab (m). c The average unit weight (MN / m³) of the waterproof layer of the base slab.

[0023] Introducing a water softening reduction factor K ω Influence coefficient of geostress nonuniformity K σ Dynamic correction to the theoretical safe head: P 修正安全 = K ω · K σ · P 理论安全(2); K ω = 1 - c ω · oh (3); K σ = 1 - c σ · ( s max / s min - 1)(4) Among them, the dynamic correction of the safety head value P 修正安全 ; s max , s min Maximum / minimum horizontal principal stress (M P a); ω represents the total clay mineral content (%). c ω , c σ These are calibration coefficients, obtained from historical samples, experiments, or numerical simulations; and for K σ Limiting the amplitude to satisfy 0 < K σ ≤1, to ensure the rationality of the calculation.

[0024] Furthermore, the process of calculating the dynamic risk index in S3 includes: weighting and summing the ratio of actual water pressure to dynamic corrected safety head, the ratio of maximum horizontal principal stress to saturated uniaxial compressive strength of surrounding rock, and normalized deformation rate to obtain the dynamic risk index, wherein the normalized deformation rate is obtained by dividing the real-time deformation rate of surrounding rock by the normalized benchmark value.

[0025] Specifically, the implementation process of this embodiment includes: The Dynamic Risk Index (DRI) employs a multi-factor quantitative fusion model. DRI = α ·( P 实际 / P 修正安全 ) + β ·( s max / S c ) + c · e (5); in, e = e real / e 0; e This is a normalized index for the real-time deformation rate of the surrounding rock. e 0 is the normalized baseline value for deformation; α , β , c Let be the weight coefficient, and satisfy... α + β + c =1; P 实际 The actual water pressure of the pressurized water in the base plate (M) P a) S c The saturated uniaxial compressive strength of the surrounding rock (M P a) Weighting coefficient α , β , c The minimum misclassification rate is determined through methods such as regression / classification of historical monitoring samples, regression of numerical simulation samples, or minimum misclassification rate during field trials, and range constraints are preferably applied. α ∈[0.4,0.6]、 β ∈[0.3,0.4]、 c ∈[0.1,0.2].

[0026] Furthermore, the process of classifying risk levels in S3 includes: comparing the dynamic risk index with a first threshold and a second threshold; when the dynamic risk index is not greater than the first threshold, it is a low-risk level; when the dynamic risk index is greater than the first threshold but not greater than the second threshold, it is a medium-risk level; and when the dynamic risk index is greater than the second threshold, it is a high-risk level.

[0027] Specifically, the implementation process of this embodiment includes: This embodiment uses configurable hierarchical thresholds. T 1. T 2. Classify: DRI≤ T 1 indicates low risk; T 1 <DRI≤ T 2 indicates medium risk; DRI > T 2 indicates high risk. (Leveling threshold) T 1. T 2. Optimized range obtained through calibration using historical samples, numerical simulation samples, or trial operation samples. T 1∈[0.5,0.7]、 T 2∈[0.9,1.1].

[0028] Furthermore, the process of generating an integrated prevention and control scheme in S4 includes: selecting the corresponding template from the scheme template library according to the risk level, determining the specific values ​​of support parameters and water hazard control parameters based on the ratio of actual water pressure to dynamically corrected safe head value or dynamic risk index, then constraining and verifying the generated scheme, and outputting the integrated prevention and control scheme after the verification is passed.

[0029] Specifically, the implementation process of this embodiment includes: This embodiment's integrated prevention and control plan is generated through a process of "template selection—parameterized generation—constraint verification—rollback adjustment": a plan template is selected according to the risk level; based on DRI or... P 实际 / P 修正安全 Calculate the pressure relief hole spacing, single-hole flow rate, grouting pressure, grout mix ratio, and support parameters within the preset range; verify. P 实际 ≤ P 修正安全 The surrounding rock displacement / deformation rate must not exceed the allowable threshold, and the internal force of the support components must not exceed the allowable value. If the test fails, the parameters should be adjusted and the test repeated until the test is passed. In one optional implementation, a digital twin simulation unit is used to simulate and verify the scheme.

[0030] Furthermore, the process of generating support sub-schemes in S4 includes: determining the priority reinforcement azimuth based on the azimuth of the maximum horizontal principal stress direction and the azimuth of the roadway direction; setting different support parameters for the priority reinforcement azimuth and non-priority reinforcement azimuth; and minimizing the weighted sum of surrounding rock displacement, deformation rate and support cost through numerical simulation iteration optimization, while satisfying the constraints that the actual water pressure is not greater than the dynamically corrected safety head value, the surrounding rock displacement and deformation rate do not exceed the allowable threshold, and the internal force of the support components does not exceed the allowable value.

[0031] Specifically, the implementation process of this embodiment includes: Obtain the azimuth angle of the direction of the maximum horizontal principal stress f H Azimuth of the lane direction f R ,calculate: Δ f = min(| f H - f R |, 180°-| f H - f R |)(6) or i = cos( fH - f n,i (7); The tunnel cross-section boundary is divided into several azimuth segments, and the outward normal azimuth angle of the i-th segment is determined. f n,i Take η i The largest section corresponds to the side wall or bottom corner as the priority reinforcement direction, while the other side is the non-priority reinforcement direction.

[0032] The support parameters are obtained through iterative optimization via numerical simulation inversion and theoretical calculation. Design variables may include the anchor bolt inclination angle, length, and spacing for preferred and non-preferred orientations. x =[ i P , L P , s P , i n , L n , s n (8); constraint: P 实际 ≤ P 修正安全 ; u max ( x )≤ u allow ; s support ( x )≤σ allow (9); Establish the objective function J ( x The maximum displacement, deformation rate and support cost of the surrounding rock are minimized by weighting, and the support parameters are iteratively updated under the above constraints until convergence is achieved.

[0033] Numerical simulations are implemented using existing tools such as FLAC3D and ANSYS, which are optional implementation methods.

[0034] min J ( x )= w 1·(u max ( x ) / u allow ) + w 2·(ε pred ( x ) / ε allow ) + w 3·( C (x ) / C ref (10); in, u max ( x ( ) is for a given support parameter x The maximum displacement of the surrounding rock obtained from numerical simulation is shown below. e pred ( x The deformation rate of the surrounding rock is obtained from numerical simulation or monitoring and prediction models (monitoring values ​​can also be directly used for verification). C ( x ) represents the support cost function. C ref This serves as the baseline value for cost normalization. u allow , e allow These are the allowable displacement threshold and the allowable deformation rate threshold, respectively. w 1. w 2. w 3 is the weighting coefficient, which satisfies w 1+ w 2+ w 3=1, which can be determined through engineering experience or calibration.

[0035] In a preferred embodiment, the support cost function C ( x The simplified form related to the number of anchor bolts and material consumption is as follows: C ( x )= k 1·( A / s p 2 )·L p + k 2·( A / s n 2 )· L n (11); Where A is the support area per unit length of roadway (determined by cross-sectional dimensions). k 1. k 2 is the comprehensive cost coefficient (materials + construction) for anchor bolts per unit length, used to convert the support consumption of preferred and non-preferred orientations into cost.

[0036] In another preferred embodiment, to avoid constraints being difficult to satisfy directly, the constraints can be incorporated into the objective function using a penalty function: J ( x )= J 0( x )+ l 1·max(0,( u max ( x )- u allow ) / u allow ) 2 + l 2·max(0,( s support ( x )- s allow ) / s allow ) 2 (12); in J 0( x The first two terms or all three terms of equation (10) can be taken. l 1. l 2 represents the penalty coefficient. Iterative optimization can employ discrete step size search or other optimization strategies, updating the value in each iteration. x until satisfied | J ( x k+1 )-J( x k )| / J ( x k )< d Output the optimal support parameters until the constraints are satisfied. x .

[0037] Furthermore, the process of generating a water hazard control sub-plan in S4 includes: selecting one or more combinations of controllable pressure relief hole diversion, infiltration grouting reinforcement, or pressure relief hole densification measures according to the risk level, and determining the pressure relief hole spacing, single hole flow rate, grouting pressure, and grouting ratio according to the ratio of actual water pressure to dynamically corrected safety head value or dynamic risk index, so that these parameters fall within the preset range.

[0038] Specifically, the implementation process of this embodiment includes: Active flood control sub-plans, categorized by risk level, should include at least one of the following or a combination thereof: controlled pressure relief hole drainage, infiltration grouting reinforcement, and increased pressure relief hole density; and should be based on DRI or P 实际 / P 修正安全The spacing between pressure relief holes, the flow rate of a single hole, the grouting pressure, and the grouting ratio are parameterized and determined so that the parameters fall within a preset range.

[0039] Example 2 like Figure 2 As shown, based on the same general inventive concept, this invention also provides a hierarchical collaborative prevention and control system for deep water-rich soft rock roadways. The hierarchical collaborative prevention and control system for deep water-rich soft rock roadways provided by this invention is described below. The hierarchical collaborative prevention and control system for deep water-rich soft rock roadways described below can be referred to in correspondence with the hierarchical collaborative prevention and control method for deep water-rich soft rock roadways described above. The system includes: The data sensing module is used to collect the actual water pressure of the confined water on the tunnel floor, the real-time deformation rate of the surrounding rock, the geostress parameters, and the rock mechanics parameters. The intelligent analysis and decision-making module is used to calculate the dynamically corrected safe head value based on the collected data, calculate the dynamic risk index and classify the risk level based on the actual water pressure, the dynamically corrected safe head value, the ground stress parameters, the rock mechanics parameters and the deformation rate, and select a template from the scheme template library according to the risk level to generate an integrated prevention and control scheme that includes a support sub-scheme and a water hazard control sub-scheme. The collaborative control execution module is used to receive the integrated prevention and control plan and control the execution mechanism to carry out support construction and water hazard control; The human-computer interaction feedback module is used to display the risk level, plan content and execution status, and to send the monitoring feedback data back to the intelligent analysis and decision-making module to achieve closed-loop dynamic control.

[0040] Specifically, the implementation process of this embodiment includes: a data sensing module for collecting... P 实际 , e real Geostress parameters S c Data such as ω; the intelligent analysis and decision-making module is used for calculation. P 理论安全 , P 修正安全 The system includes a DRI (Digital Risk Analysis and Decision-Making) module for risk level classification and generation of parameterized integrated prevention and control schemes; a collaborative control execution module for receiving integrated prevention and control schemes and controlling the execution mechanism to carry out support construction and water hazard control; a human-machine interaction feedback module for displaying risk levels, scheme content and execution status, and transmitting monitoring feedback data back to the intelligent analysis and decision-making module to achieve closed-loop control; and an intelligent analysis and decision-making module that optionally includes a digital twin simulation unit for scheme verification.

[0041] Example 3 In this embodiment, a computer terminal device is provided, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the above-described graded collaborative prevention and control method for deep roadways in water-rich soft rock.

[0042] In this embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the above-described graded collaborative prevention and control method for deep roadways in water-rich soft rock.

[0043] Implementation Case 1: Shallow Excavation Section Taking a water-rich, soft rock extended roadway in a coal mine as an example, the roadway has an inclination angle of 15°, a total excavation length of 3800m, and a burial depth of 260-720m. A shallow excavation section (300m burial depth) was selected for testing.

[0044] Indoor testing: K P =0.68 M P a, S c =12.5 M P a, ω=32%, c =0.023 MN / m³; Field measurement: s max =6.2 M P a, σ min =3.1 M P a, t = 13.8 m, L = 5.8 m, P 实际 =2.1 M P a; Monitoring: e real =1.8 mm / d, take e 0 = 1 mm / d, then e =1.8.

[0045] Pick c ω =0.01、 c σ =0.2, then K ω =0.68、 K σ =0.8. From equation (1), we get P 理论安全 ≈8.02 M P a; From equation (2), we get P 修正安全 ≈4.36 M P a.

[0046] Take weightα =0.45、 β =0.38、 c =0.17, from equation (5) we get DRI≈0.711. The preferred threshold is taken. T 1 = 0.6 T 2=1.0, which is classified as a medium risk level.

[0047] A "local reinforcement and drainage control" plan was generated and issued for implementation: controllable pressure relief holes were constructed at 50m intervals on the base slab, with the flow rate of a single hole controlled at 5-10 m³ / h; low-pressure permeable grouting was carried out in the water-rich sections and the fractured zones of the base slab, with a grouting pressure of 1.0-1.5 MPa. P a; The support was implemented according to the asymmetric design parameters. Continuous monitoring for 15 days ensured the water pressure remained stable between 1.9 and 2.1 MPa. P a. If the deformation rate is ≤2.0mm / d and the DRI is maintained in the range of 0.5 to 0.55, the solution remains the same and no upgrade is required.

[0048] Implementation Case 2: Deep Excavation Section The test was conducted on a deep tunnel section (600m deep).

[0049] Indoor testing: K P =0.75 M P a, S c =9.8 M P a, ω=42%, c =0.024 MN / m³; Field measurement: s max =9.5M P a, σ min =4.0 M P a, t = 14.5 m, L = 5.8 m, P 实际 =3.5 M P a; Monitoring: e real =7.3 mm / d, take e 0 = 1 mm / d, then e =7.3.

[0050] Pick c ω =0.01、 c σ =0.2, then K ω =0.58、 K σ =0.725. From equation (1), we get P 理论安全 ≈9.723 MP a; From equation (2), we get P 修正安全 ≈4.09 M P a.

[0051] Take weight α =0.53、 β =0.32、 c =0.15, and from equation (5) we get DRI≈1.86, which is judged as a high-risk level.

[0052] A "powerful pressure relief and structural synergy" solution was generated and implemented: the spacing between pressure relief holes in the base plate was increased to 25-30m, and the flow rate per hole was increased to 15-20m³ / h; local grouting and asymmetric support reinforcement measures were implemented simultaneously; optional implementation was performed after verification by digital twin simulation. After 15 days of continuous monitoring, the pressurized water pressure in the base plate stabilized at 3.2-3.4M. P a (lower than) P 修正安全 The surrounding rock deformation rate was reduced to 2.6–2.9 mm / d; the DRI was recalculated and reduced to the second level, triggering a downgrade scheme: retain the asymmetric support structure, reduce the flow rate of the pressure relief hole to 8–10 m³ / h, stop full-section grouting, and only reinforce the local seepage areas at specific points; the subsequent roadway was safely excavated.

[0053] This invention provides a graded collaborative prevention and control method and system for deep roadways in water-rich soft rock. By introducing a hydraulic softening reduction coefficient and a non-uniform in-situ stress influence coefficient to dynamically correct the theoretical safe head, it solves the problem of mismatch between safety margin and actual risk caused by the insufficient consideration of the coupling effect of hydraulic softening and non-uniform in-situ stress in traditional safe head calculations. By constructing a multi-factor quantitatively integrated dynamic risk index and classifying risks, it addresses the problem of existing technologies relying on experience and lacking quantitative basis for prevention and control measures. By selecting templates from a template library based on risk level and parameterizing support parameters and water hazard control parameters according to the dynamic risk index or the ratio of actual water pressure to corrected safe head, an integrated prevention and control scheme including asymmetric support sub-schemes and active water hazard control sub-schemes is formed, solving the problem of existing prevention and control measures lacking collaborative combination and parameterized output. By issuing and executing the integrated prevention and control scheme and updating the dynamic risk index in a closed loop based on real-time monitoring feedback data, triggering scheme upgrades, maintenance, or downgrades, it solves the problem of existing technologies lacking a closed-loop dynamic adjustment mechanism and difficulty in achieving timely response and precise control. This invention realizes the transformation from experience-based prevention and control to model-driven, precise collaboration, and closed-loop control, significantly improving the prevention and control efficiency and engineering safety of floor heave and water inrush disasters in water-rich soft rock deep roadways.

[0054] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A graded and coordinated prevention and control method for deep roadways in water-rich soft rock, characterized in that, Includes the following steps: S1. Collect the actual water pressure of the confined water in the tunnel floor, the real-time deformation rate of the surrounding rock, the geostress parameters, and the rock mechanics parameters. S2. Calculate and dynamically correct the safe head value based on the collected data; S3. Calculate the dynamic risk index based on the actual water pressure, dynamically corrected safety head value, geostress parameters, rock mechanics parameters, and deformation rate, and classify the risk level according to the dynamic risk index. S4. Select a template from the scheme template library according to the risk level, and generate an integrated prevention and control scheme that includes support sub-schemes and water hazard control sub-schemes; S5. Issue and implement the integrated prevention and control plan, and repeat S1 to S4 based on the monitoring feedback data after implementation to achieve closed-loop dynamic control.

2. The method according to claim 1, characterized in that, The process of calculating the dynamically corrected safe head value in S2 includes: calculating the theoretical safe head value based on the thickness of the impermeable layer, tensile strength, base plate width and unit weight of the impermeable layer; introducing the hydrophysical softening reduction coefficient based on clay mineral content and the influence coefficient based on the non-uniformity of geostress to correct the theoretical safe head value to the dynamically corrected safe head value.

3. The method according to claim 2, characterized in that, The process of calculating the dynamic risk index in S3 includes: weighting and summing the ratio of actual water pressure to dynamic corrected safety head, the ratio of maximum horizontal principal stress to saturated uniaxial compressive strength of surrounding rock, and normalized deformation rate to obtain the dynamic risk index, wherein the normalized deformation rate is obtained by dividing the real-time deformation rate of surrounding rock by the normalized reference value.

4. The method according to claim 3, characterized in that, The process of classifying risk levels in S3 includes: comparing the dynamic risk index with a first threshold and a second threshold; when the dynamic risk index is not greater than the first threshold, it is a low-risk level; when the dynamic risk index is greater than the first threshold but not greater than the second threshold, it is a medium-risk level; and when the dynamic risk index is greater than the second threshold, it is a high-risk level.

5. The method according to claim 4, characterized in that, The process of generating an integrated prevention and control scheme in S4 includes: selecting the corresponding template from the scheme template library according to the risk level, determining the specific values ​​of support parameters and water hazard control parameters based on the ratio of actual water pressure to dynamically corrected safe head value or dynamic risk index, then constraining and verifying the generated scheme, and outputting the integrated prevention and control scheme after the verification is passed.

6. The method according to claim 5, characterized in that, The process of generating support sub-schemes in S4 includes: determining the priority reinforcement azimuth based on the azimuth of the maximum horizontal principal stress direction and the azimuth of the roadway direction; setting different support parameters for the priority reinforcement azimuth and non-priority reinforcement azimuth; and minimizing the weighted sum of surrounding rock displacement, deformation rate and support cost through numerical simulation iteration optimization, while satisfying the constraints that the actual water pressure is not greater than the dynamically corrected safety head value, the surrounding rock displacement and deformation rate do not exceed the allowable threshold, and the internal force of the support components does not exceed the allowable value.

7. The method according to claim 5, characterized in that, The process of generating a water hazard control sub-plan in S4 includes: selecting one or more combinations of controllable pressure relief hole diversion, infiltration grouting reinforcement, or pressure relief hole densification measures according to the risk level, and determining the pressure relief hole spacing, single hole flow rate, grouting pressure, and grouting ratio according to the ratio of actual water pressure to dynamically corrected safety head value or dynamic risk index, so that these parameters fall within the preset range.

8. A graded collaborative prevention and control system for deep roadways in water-rich soft rock, characterized in that, The system for implementing the method of any one of claims 1-7 comprises: The data sensing module is used to collect the actual water pressure of the confined water on the tunnel floor, the real-time deformation rate of the surrounding rock, the geostress parameters, and the rock mechanics parameters. The intelligent analysis and decision-making module is used to calculate the dynamically corrected safe head value based on the collected data, calculate the dynamic risk index and classify the risk level based on the actual water pressure, the dynamically corrected safe head value, the ground stress parameters, the rock mechanics parameters and the deformation rate, and select a template from the scheme template library according to the risk level to generate an integrated prevention and control scheme that includes a support sub-scheme and a water hazard control sub-scheme. The collaborative control execution module is used to receive the integrated prevention and control plan and control the execution mechanism to carry out support construction and water hazard control; The human-computer interaction feedback module is used to display the risk level, plan content and execution status, and to send the monitoring feedback data back to the intelligent analysis and decision-making module to achieve closed-loop dynamic control.

9. A computer terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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