Anchor rod crack arrest and efficiency improvement method and system for lamellar rock mass tunnel

Abstract

The invention provides a lamellar rock mass tunnel anchor rod crack arrest synergistic method and system, and the method comprises the steps: collecting the geological parameters of a lamellar rock mass, including a bedding inclination angle, a rock mass elastic modulus, an anchor rod arrangement distance and an initial prestress value; calculating an anisotropic stress field in the rock mass through the three-dimensional stress distribution model, and generating an initial stress nephogram; in combination with an anchor rod parameter optimization algorithm, the corrected arrangement distance and the corrected prestress value of the anchor rods are determined; based on rock mass displacement data monitored in real time, an anchor rod prestress value is iteratively updated through a dynamic weight adjustment algorithm; calculating a rock mass crack propagation inhibition index after the anchor rods are arranged by utilizing the stress release effect prediction model; generating an anchor rod control instruction set through a multi-objective decision algorithm; and inputting the anchor rod control instruction set into a tunnel anchor rod control system, and feeding back and updating the three-dimensional stress distribution model in real time. The stability and safety of the tunnel can be improved, and the cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of geotechnical engineering and tunnel construction, and more particularly to a method and system for preventing cracks and increasing the efficiency of anchor rods in a thin-layered rock tunnel. Background Art

[0002] In the fields of geotechnical engineering and tunnel construction, the stability of thin-layered rock tunnels has always been a key concern for engineers and technicians. Thin-layered rock has a distinct bedding structure and exhibits strong anisotropy in its mechanical properties. This makes cracks prone to form and rapidly expand during tunnel excavation, thus affecting the stability and safety of the tunnel. While traditional anchor support technology can provide support to a certain extent, it often fails to effectively suppress crack propagation in thin-layered rock because it fails to fully consider the anisotropic properties of the rock mass and the complexity of crack propagation. Furthermore, the lack of a dynamic optimization and adjustment mechanism for anchor parameters during construction results in less than ideal support results and may even lead to engineering accidents due to improper anchor arrangement or insufficient prestressing.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the existing technology: the existing anchor support technology fails to fully consider the anisotropic characteristics of thin-layered rock masses, lacks accurate analysis of the internal stress field of the rock mass and dynamic optimization and adjustment mechanism of anchor parameters, resulting in poor crack suppression effect, insufficient support reliability, and inability to adapt to complex and changeable construction environments and rock conditions. Summary of the Invention

[0004] The invention provides a method and system for preventing cracks and increasing the efficiency of anchor rods in a thin-layered rock tunnel.

[0005] In a first aspect of the present invention, a method for arresting cracks and increasing the efficiency of anchor bolts in a thin-layered rock tunnel is provided, comprising:

[0006] Step S1: Collecting geological parameters of thin-layered rock mass, including bedding inclination, rock mass elastic modulus, anchor bolt spacing, and initial prestress value;

[0007] Step S2: Based on the geological parameters, the anisotropic stress field inside the rock mass is calculated using a three-dimensional stress distribution model, and an initial stress cloud map is generated;

[0008] Step S3: determining the corrected arrangement spacing and corrected prestress value of the anchor rods based on the initial stress cloud map and in combination with the anchor rod parameter optimization algorithm;

[0009] Step S4: Based on the real-time monitored rock displacement data, iteratively update the anchor rod prestress value through a dynamic weight adjustment algorithm;

[0010] Step S5: using the stress release effect prediction model, calculating the rock crack growth inhibition index after the anchor bolts are arranged;

[0011] Step S6: generating an anchor control instruction set through a multi-objective decision algorithm according to the crack growth inhibition index;

[0012] Step S7: inputting the anchor control instruction set into the tunnel anchor control system, and providing real-time feedback to update the three-dimensional stress distribution model.

[0013] Furthermore, in step S2, the calculation formula of the three-dimensional stress distribution model is:

[0014] σ eff =σ0·(1+β·sinθ)

[0015] Among them, σ eff is the effective stress of the rock mass, σ0 is the initial homogeneous stress, β is the anisotropy parameter, and θ is the bedding dip;

[0016] The anisotropy parameter β is calculated by the following formula:

[0017]

[0018] Among them, E v is the elastic modulus in the direction perpendicular to the bedding, E h is the elastic modulus parallel to the bedding direction.

[0019] Furthermore, in step S3, the anchor parameter optimization algorithm includes:

[0020] Sub-step S3.1: extracting the spatial coordinates of the stress concentration area based on the initial stress cloud map;

[0021] Sub-step S3.2: generating an anchor arrangement density gradient function according to the spatial coordinates;

[0022] Sub-step S3.3: Perform multi-objective optimization on the gradient function using a genetic algorithm, and output a corrected arrangement spacing and a corrected prestress value.

[0023] Furthermore, in step S4, the iterative formula of the dynamic weight adjustment algorithm is:

[0024]

[0025] Among them, P new is the updated anchor prestress value, P old is the current prestress value, α is the dynamic weight coefficient, ΔD is the difference between the real-time displacement monitoring value and the theoretical displacement value, is the sensitivity of effective stress to displacement.

[0026] Furthermore, in step S5, the crack growth inhibition index is calculated by the following formula:

[0027]

[0028] Among them, I c is the crack growth inhibition index, σ cr is the critical fracture stress of rock mass, k is the anchoring effect coefficient, and S is the anchor spacing.

[0029] Furthermore, the anchoring effect coefficient k is determined by combining experimental calibration with numerical simulation, and satisfies the relationship:

[0030]

[0031] Among them, Δε is the strain change after the anchor is applied, and ε0 is the initial strain.

[0032] Furthermore, in step S6, the multi-objective decision-making algorithm includes the following constraints:

[0033] Constraint 1: The maximum prestress of the anchor shall not exceed 70% of the tensile strength of the rock mass;

[0034] Constraint 2: The product of the spacing between adjacent anchor bolts and the prestress value maintains a monotonically decreasing relationship;

[0035] Constraint 3: Crack Growth Inhibition Index I c Greater than the set threshold.

[0036] Furthermore, in step S7, the feedback update includes:

[0037] Sub-step S7.1: Correcting the anisotropy parameter β in the three-dimensional stress distribution model according to actual anchor bolt stress data;

[0038] Sub-step S7.2: Recalculate the crack growth inhibition index I using the corrected β value c ;

[0039] Sub-step S7.3: When I c When the deviation from the theoretical value exceeds 10%, the anchor parameter optimization algorithm is triggered to be re-executed.

[0040] Furthermore, the geological parameters collected in step S1 also include rock permeability, and a permeability correction factor η is further introduced in step S5. The calculation formula of the crack growth inhibition index is expanded to:

[0041] I c ′=I c ·η

[0042] Wherein, η = 1-0.2·(K / K0), K is the current permeability, and K0 is the initial permeability.

[0043] In a second aspect of the present invention, a thin-layered rock tunnel anchor crack arresting and efficiency-enhancing system is provided, comprising:

[0044] Data acquisition module, used to collect geological parameters of thin-layered rock mass, including bedding inclination, rock mass elastic modulus, anchor spacing and initial prestress value;

[0045] A stress analysis module is used to calculate the anisotropic stress field inside the rock mass based on the geological parameters through a three-dimensional stress distribution model and generate an initial stress cloud map;

[0046] A parameter optimization module is used to determine the corrected arrangement spacing and corrected prestress value of the anchor rods based on the initial stress cloud map and in combination with an anchor rod parameter optimization algorithm;

[0047] Dynamic adjustment module, used to iteratively update anchor prestress value through dynamic weight adjustment algorithm based on real-time monitored rock displacement data;

[0048] A prediction module is used to calculate the rock crack growth inhibition index after anchor bolt placement using a stress release effect prediction model;

[0049] A decision module, configured to generate an anchor control instruction set through a multi-objective decision algorithm according to the crack growth inhibition index;

[0050] The control feedback module is used to input the anchor control instruction set into the tunnel anchor control system and provide real-time feedback to update the three-dimensional stress distribution model.

[0051] According to the above-mentioned embodiment of the present invention, there are at least the following beneficial effects: the present invention provides a method and system for increasing the effectiveness of anchor crack arrest in thin-layered rock tunnels, which can effectively improve the stability of thin-layered rock tunnels. By collecting the geological parameters of the rock mass and combining it with a three-dimensional stress distribution model, the anisotropic stress field inside the rock mass can be accurately calculated, and an initial stress cloud map can be generated to provide a scientific basis for anchor arrangement. Based on the initial stress cloud map, the anchor parameter optimization algorithm is used to determine the corrected arrangement spacing and corrected prestress value of the anchor, ensuring that the anchor can accurately act on the stress concentration area and enhance the support effect. At the same time, based on the real-time monitored rock displacement data, the dynamic weight adjustment algorithm can iteratively update the anchor prestress value, so that the anchor prestress is always maintained in the optimal state, further improving the reliability of the support.

[0052] Furthermore, a stress release effect prediction model is used to calculate the rock crack growth inhibition index after anchor bolt placement. This, combined with a multi-objective decision-making algorithm, generates an anchor control instruction set, enabling refined control of anchor support. The system also provides real-time feedback and updates the three-dimensional stress distribution model, ensuring its accuracy and timeliness. The combined application of these technical measures not only effectively inhibits rock crack growth and extends the service life of tunnels, but also optimizes anchor usage, reduces project costs, and improves construction efficiency, providing strong support for the construction and operation of tunnels in thin rock masses. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which:

[0054] Figure 1 A schematic diagram of a process for preventing cracks and increasing the efficiency of anchor bolts in thin-layered rock tunnels according to an embodiment of the present invention;

[0055] Figure 2 A schematic structural diagram of a thin-layered rock tunnel anchor crack arresting and efficiency-enhancing system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0056] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0057] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0058] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0059] Reference below Figure 1 , Figure 1 The flow chart of the method for preventing cracks and increasing the efficiency of anchor bolts in thin-layered rock tunnels provided by one embodiment of the present invention is as follows. Figure 1 As shown, a method 100 for preventing cracks and increasing the efficiency of anchor bolts in a thin-layered rock tunnel includes:

[0060] Step S1: Collecting geological parameters of thin-layered rock mass, including bedding inclination, rock mass elastic modulus, anchor bolt spacing, and initial prestress value;

[0061] Step S2: Based on the geological parameters, the anisotropic stress field inside the rock mass is calculated using a three-dimensional stress distribution model, and an initial stress cloud map is generated;

[0062] Step S3: determining the corrected arrangement spacing and corrected prestress value of the anchor rods based on the initial stress cloud map and in combination with the anchor rod parameter optimization algorithm;

[0063] Step S4: Based on the real-time monitored rock displacement data, iteratively update the anchor rod prestress value through a dynamic weight adjustment algorithm;

[0064] Step S5: using the stress release effect prediction model, calculating the rock crack growth inhibition index after the anchor bolts are arranged;

[0065] Step S6: generating an anchor control instruction set through a multi-objective decision algorithm according to the crack growth inhibition index;

[0066] Step S7: inputting the anchor control instruction set into the tunnel anchor control system, and providing real-time feedback to update the three-dimensional stress distribution model.

[0067] It should be noted that when implementing the crack arrest and efficiency enhancement method for thin-layered rock tunnels, it is first necessary to collect the geological parameters of the thin-layered rock mass. These parameters include bedding inclination, rock mass elastic modulus, anchor spacing, and initial prestress value. Bedding inclination refers to the inclination angle of each rock layer in the rock mass. It reflects the bedding direction of the rock mass and has a significant impact on the mechanical properties of the rock mass. The rock mass elastic modulus is a physical quantity that measures the rock mass's ability to resist elastic deformation. It characterizes the ease with which the rock mass undergoes elastic deformation when subjected to stress. Anchor spacing refers to the distance between adjacent anchors, while the initial prestress value is the stress value pre-applied to the anchor during installation. These parameters are crucial to the effectiveness of anchor support. By accurately collecting these geological parameters, basic data can be provided for subsequent stress distribution calculations and anchor parameter optimization.

[0068] Specifically, the process of collecting geological parameters can be carried out in a variety of ways. For example, the bedding inclination can be obtained through compass measurement in geological exploration, and its value is usually between 0° and 90°. Different bedding inclinations will significantly affect the mechanical behavior of the rock mass. The elastic modulus of the rock mass can be obtained through indoor rock mechanics tests or on-site in-situ tests. Its numerical range varies depending on the rock mass type, generally between several GPa and tens of GPa. The anchor arrangement spacing can be preliminarily set based on engineering experience, usually between 0.5 meters and 2 meters, while the initial prestress value is determined according to the mechanical properties of the rock mass and the support requirements, generally between tens of kN and hundreds of kN. The collection and setting of these parameters requires comprehensive consideration of the tunnel's geological conditions, construction technology, and expected support effect.

[0069] Preferably, when collecting geological parameters, measurements of rock permeability can be added, as permeability has a significant impact on crack propagation in rock. Furthermore, when determining anchor bolt spacing, optimization can be performed based on the tunnel's cross-sectional shape and dimensions. For example, anchor bolt spacing can be appropriately reduced in stress-concentrated areas such as the tunnel's vault and sidewalls to enhance support effectiveness. Furthermore, when calculating the initial stress contour map, advanced numerical simulation software, such as finite element analysis software, can be used to improve accuracy and efficiency. After generating the initial stress contour map, verification and adjustments can be performed based on the experience of geological experts to ensure that the stress contour map accurately reflects the actual stress distribution within the rock mass.

[0070] In some embodiments, in step S2, the calculation formula of the three-dimensional stress distribution model is:

[0071] σ eff =σ0·(1+β·sinθ)

[0072] Among them, σ eff is the effective stress of the rock mass, σ0 is the initial homogeneous stress, β is the anisotropy parameter, and θ is the bedding dip;

[0073] The anisotropy parameter β is calculated by the following formula:

[0074]

[0075] Among them, E v is the elastic modulus in the direction perpendicular to the bedding, E h is the elastic modulus parallel to the bedding direction.

[0076] It should be noted that when implementing the crack arrest and efficiency enhancement method for thin-layered rock tunnel anchor bolts, it is necessary to calculate the anisotropic stress field inside the rock mass through a three-dimensional stress distribution model and generate an initial stress cloud map. The three-dimensional stress distribution model is a mathematical model used to describe the distribution of stress inside the rock mass in three-dimensional space. The anisotropic stress field refers to the different characteristics of stress in different directions due to the bedding structure of the rock mass. The initial stress cloud map is a graph generated by numerical simulation, which intuitively shows the distribution of stress inside the rock mass and provides a basis for subsequent optimization of anchor bolt parameters. The anisotropy parameter is an indicator to measure the difference in mechanical properties of the rock mass in different directions. It is calculated by the difference in elastic modulus in the direction perpendicular to the bedding direction and the direction parallel to the bedding direction, reflecting the degree of anisotropy of the rock mass.

[0077] Specifically, the calculation formula of the three-dimensional stress distribution model is based on the initial homogeneous stress and anisotropic parameters of the rock mass. The initial homogeneous stress refers to the stress state when the anisotropy of the rock mass is not taken into account, which can usually be obtained through geomechanical model tests or empirical formulas. The calculation of anisotropic parameters involves the elastic modulus in the direction perpendicular to the bedding and in the direction parallel to the bedding. These two elastic moduli can be measured separately through indoor rock mechanics tests. The elastic modulus in the direction perpendicular to the bedding is usually larger, while the elastic modulus in the direction parallel to the bedding is relatively smaller. This difference leads to the anisotropy of the rock mass. In actual calculations, appropriate parameter values can be selected for model calculation based on the specific rock type and geological conditions to generate an accurate initial stress cloud map.

[0078] Preferably, when generating the initial stress cloud map, finite element analysis software can be used for numerical simulation. For example, using software such as ANSYS or ABAQUS, a three-dimensional geometric model of the rock mass is established based on the collected geological parameters, and corresponding boundary conditions and loads are applied. Through the solving function of the software, the stress distribution inside the rock mass is obtained, and an initial stress cloud map is generated. In addition, in order to improve the accuracy of the calculation, factors such as the heterogeneity and crack distribution of the rock mass can be considered in the model. When calculating the anisotropic parameters, if the bedding structure of the rock mass is relatively complex, the average value of the multi-layer elastic modulus can be used instead of the single elastic modulus in the perpendicular and parallel bedding directions to more accurately reflect the actual mechanical properties of the rock mass.

[0079] In some embodiments, in step S3, the anchor parameter optimization algorithm includes:

[0080] Sub-step S3.1: extracting the spatial coordinates of the stress concentration area based on the initial stress cloud map;

[0081] Sub-step S3.2: generating an anchor arrangement density gradient function according to the spatial coordinates;

[0082] Sub-step S3.3: Perform multi-objective optimization on the gradient function using a genetic algorithm, and output a corrected arrangement spacing and a corrected prestress value.

[0083] It is important to note that when implementing the anchor bolt crack arrest and efficiency enhancement method for thin-layered rock tunnels, the anchor bolt parameter optimization algorithm is a key step. Its purpose is to determine the corrected anchor bolt spacing and prestress value based on the initial stress contour. The initial stress contour intuitively displays the stress distribution within the rock mass. By analyzing areas of stress concentration, the anchor bolt placement scheme can be optimized. The anchor bolt parameter optimization algorithm consists of three sub-steps: extracting the spatial coordinates of stress concentration areas, generating an anchor bolt placement density gradient function, and performing multi-objective optimization using a genetic algorithm. These steps work together to ensure that anchor bolts are precisely placed in critical stress areas, thereby improving support effectiveness.

[0084] Specifically, in substep S3.1, the spatial coordinates of stress concentration areas are extracted by analyzing the initial stress contour. Stress concentration areas are locations within the rock mass where stress values are significantly higher than those in the surrounding areas. These areas are often prone to crack initiation and propagation. In the initial stress contour, stress concentration areas typically appear as clusters of high stress values. Using image processing techniques or numerical analysis methods, the spatial coordinates of these areas can be extracted, providing precise location information for subsequent anchor placement. In substep S3.2, the anchor density gradient function is generated based on the extracted spatial coordinates. This function describes the variation in anchor density within the rock mass. Typically, the density is higher near the stress concentration area and gradually decreases in other areas. In substep S3.3, the gradient function is optimized using a genetic algorithm (GA). A GA is a search algorithm based on natural selection and genetics that simulates biological evolution to find the optimal solution. In this method, the GA aims to optimize anchor spacing and prestressing to achieve optimal support.

[0085] Preferably, when extracting the spatial coordinates of the stress concentration area, advanced image recognition technology, such as an image segmentation algorithm based on deep learning, can be used to improve the accuracy and efficiency of the extraction. When generating the density gradient function of the anchor arrangement, more constraints can be introduced according to the actual engineering needs, such as considering factors such as the shape, size and construction process of the tunnel, so that the gradient function is more in line with the actual engineering. When performing genetic algorithm optimization, multiple optimization objectives can be set, such as minimizing the amount of anchors used, maximizing the crack suppression effect, etc., and these objectives can be balanced through weight distribution. In addition, other optimization algorithms, such as particle swarm optimization algorithm, can be combined with genetic algorithm for hybrid optimization to improve the convergence speed and global search capability of the optimization.

[0086] In some embodiments, in step S4, the iterative formula of the dynamic weight adjustment algorithm is:

[0087]

[0088] Among them, P new is the updated anchor prestress value, P old is the current prestress value, α is the dynamic weight coefficient, ΔD is the difference between the real-time displacement monitoring value and the theoretical displacement value, is the sensitivity of effective stress to displacement.

[0089] It is important to note that when implementing the crack arrest and efficiency enhancement method for thin-layered rock tunnel anchor bolts, the dynamic weight adjustment algorithm is a key step in iteratively updating the anchor bolt prestress value based on real-time rock displacement data. This algorithm monitors rock displacement changes in real time and, based on the current anchor bolt prestress value and displacement difference, dynamically adjusts the anchor bolt prestress to ensure it always maintains optimal working conditions. The core of this algorithm lies in the setting of the dynamic weight coefficient, which determines the magnitude and direction of prestress adjustment, thereby effectively controlling rock displacement.

[0090] Specifically, the iterative formula of the dynamic weight adjustment algorithm involves several key parameters: the updated anchor prestress value, the current prestress value, the dynamic weight coefficient, the difference between the real-time displacement monitoring value and the theoretical displacement value, and the sensitivity of the effective stress to the displacement. The dynamic weight coefficient is a value between 0 and 1 that is used to balance the amplitude and stability of the prestress adjustment. The real-time displacement monitoring value is the rock displacement data obtained in real time by a high-precision displacement sensor, while the theoretical displacement value is pre-calculated through numerical simulation or empirical formula. The displacement difference reflects the deviation between the actual displacement of the rock mass and the expected displacement, and the sensitivity of the effective stress to the displacement describes the degree of influence of stress changes on the displacement. In practical applications, the dynamic weight coefficient can be adjusted according to the mechanical properties of the rock mass and the construction environment. For example, in weak rock mass, the weight coefficient can be appropriately increased to speed up the adjustment of the prestress.

[0091] Preferably, when implementing the dynamic weight adjustment algorithm, an adaptive adjustment strategy can be used to optimize the dynamic weight coefficient. For example, the size of the weight coefficient is dynamically adjusted according to the displacement change rate monitored in real time. When the displacement change rate is large, the weight coefficient is appropriately increased to respond quickly; when the displacement change rate is small, the weight coefficient is reduced to maintain the stability of the prestress. In addition, multi-sensor data fusion technology can be introduced to combine various types of sensor data (such as strain sensors, pressure sensors, etc.) to improve the accuracy and reliability of displacement monitoring. When calculating the sensitivity of effective stress to displacement, finite element analysis software can be used for numerical simulation, and by simulating the stress and displacement changes under different working conditions, the precise value of the sensitivity can be obtained. This refined adjustment strategy can better adapt to complex rock conditions and construction environments, and improve the reliability and effectiveness of anchor support.

[0092] In some embodiments, in step S5, the crack growth inhibition index is calculated by the following formula:

[0093]

[0094] Among them, I c is the crack growth inhibition index, σ cr is the critical fracture stress of rock mass, k is the anchoring effect coefficient, and S is the anchor spacing.

[0095] It's important to note that when implementing bolting crack arrest and efficiency enhancement methods in thin-layered rock tunnels, calculating the crack growth inhibition index (CSI) is a key factor in evaluating the effectiveness of bolting support. The CSI is a dimensionless metric used to quantify the effectiveness of bolting on rock crack growth. The index is calculated based on parameters such as the rock's critical fracture stress, the anchoring effect coefficient, and bolt spacing. Using the CSI, the effectiveness of bolting solutions can be intuitively assessed and informed subsequent multi-objective decision-making.

[0096] Specifically, the calculation of the crack growth suppression index involves several key parameters: The critical fracture stress of the rock mass refers to the minimum stress value at which the rock mass will break when not supported by anchor rods, which is usually obtained through indoor rock mechanics tests or numerical simulations. The anchoring effect coefficient is a quantitative indicator that measures the change in the stress state of the rock mass caused by the anchor rod, which is determined jointly by experimental calibration and numerical simulation. The anchor spacing is the distance between adjacent anchor rods, which directly affects the support effect of the anchor rods. When calculating the crack growth suppression index, it is first necessary to determine the critical fracture stress and anchoring effect coefficient of the rock mass, and then calculate them in combination with the anchor spacing. The higher the value of this index, the better the anchor rod's suppression effect on crack growth.

[0097] Preferably, when calculating the crack growth inhibition index, a permeability correction factor can be introduced to take into account the influence of changes in rock permeability on crack growth. The permeability correction factor is a coefficient related to the permeability of the rock mass, which is used to correct the crack growth inhibition index to make it more consistent with actual engineering conditions. For example, when the permeability of the rock mass increases, the possibility of crack growth will also increase, so it is necessary to use a correction factor to reduce the value of the crack growth inhibition index. In addition, a variety of experimental methods can be used to jointly calibrate the anchoring effect coefficient, such as indoor uniaxial compression tests, anchor pull-out tests, etc., to improve the accuracy of the coefficient. In practical applications, the calculation method of the crack growth inhibition index can also be adjusted according to different rock mass types and construction conditions. For example, in weak rock mass, the weight of the anchoring effect coefficient can be appropriately increased to more accurately reflect the support effect of the anchor rod.

[0098] In some embodiments, the anchoring effect coefficient k is determined by combining experimental calibration with numerical simulation, and satisfies the relationship:

[0099]

[0100] Among them, Δε is the strain change after the anchor is applied, and ε0 is the initial strain.

[0101] It's important to note that when implementing anchor bolt crack arrest and efficiency enhancement methods in thin-layered rock tunnels, determining the anchor effect coefficient is a crucial step in calculating the crack growth inhibition index. The anchor effect coefficient is a quantitative measure of the effect of the anchor bolt on the rock mass's stress state. It is determined through a combination of experimental calibration and numerical simulation. Experimental calibration involves obtaining the strain change after anchor bolt application through experimental methods such as actual anchor bolt pullout tests, while numerical simulation utilizes computational methods such as finite element analysis to simulate the stress-strain state after anchor bolt support. Combining these two methods allows for a more accurate determination of the anchor effect coefficient, providing a reliable basis for calculating the crack growth inhibition index.

[0102] Specifically, the calculation of the anchoring effect coefficient involves the initial strain and the strain change after the anchor is applied. The initial strain refers to the strain state of the rock mass when no anchor support is applied, which is usually measured by a strain sensor. The strain change after the anchor is applied refers to the change in rock strain after the anchor is installed and prestressed. During the experimental calibration process, the strain change can be obtained by installing a strain sensor in the rock mass and measuring the strain change before and after the anchor is installed. Numerical simulation can use finite element analysis software such as ANSYS or ABAQUS to establish a geometric model of the rock mass and anchor, apply corresponding boundary conditions and loads, calculate the stress-strain distribution after the anchor is installed, and then obtain the strain change. The anchoring effect coefficient is determined using a logarithmic relationship based on the results of experiments and numerical simulations.

[0103] Preferably, during the experimental calibration process, various types of strain sensors, such as fiber optic strain sensors or electrical resistance strain gauges, can be used to improve measurement accuracy and reliability. Numerical simulations can also take into account the heterogeneity and anisotropy of the rock mass, as well as the interaction between the anchor and the rock mass, to make the simulation results more realistic. Furthermore, the calculation method for the anchoring effect coefficient can be optimized by comparing the strain variation under different experimental conditions. For example, experiments and simulations can be conducted under different rock mass types, different anchor spacings, and different prestress values, and their impact on the strain variation can be analyzed to obtain a more accurate anchoring effect coefficient.

[0104] In some embodiments, in step S6, the multi-objective decision-making algorithm includes the following constraints:

[0105] Constraint 1: The maximum prestress of the anchor shall not exceed 70% of the tensile strength of the rock mass;

[0106] Constraint 2: The product of the spacing between adjacent anchor bolts and the prestress value maintains a monotonically decreasing relationship;

[0107] Constraint 3: Crack Growth Inhibition Index I c Greater than the set threshold.

[0108] It's important to note that when implementing the crack arrest and efficiency enhancement method for thin-layered rock tunnels, a multi-objective decision-making algorithm is a key step in generating the anchor control instruction set. This algorithm comprehensively considers multiple constraints to optimize the anchor support effectiveness. By setting multiple constraints, such as the maximum anchor prestress, the product of the spacing between adjacent anchors and the prestress value, and the threshold for the crack growth suppression index, the algorithm ensures that the anchor support solution achieves optimal crack suppression while meeting engineering safety and economic requirements.

[0109] Specifically, the constraints in the multi-objective decision-making algorithm include: the maximum prestress of the anchor bolts must not exceed 70% of the rock mass's tensile strength. This constraint ensures that excessive prestressing during anchor use prevents excessive deformation or damage to the rock mass. The product of the spacing between adjacent anchor bolts and the prestress value must maintain a monotonically decreasing relationship. This constraint aims to optimize anchor bolt placement, ensuring that anchor bolts play a greater role in stress-concentrated areas while avoiding overuse in non-critical areas. The crack growth suppression index must be greater than a set threshold. This constraint ensures that the anchor bolt support scheme effectively suppresses crack growth, thereby safeguarding tunnel stability. In practical applications, the specific parameters of these constraints need to be adjusted based on factors such as the mechanical properties of the rock mass, the geometry of the tunnel, and the construction process.

[0110] Preferably, when implementing a multi-objective decision-making algorithm, additional constraints can be introduced to further optimize the anchor support solution. For example, the durability of the anchor can be considered, with the durability index of the anchor material set as one of the constraints. Construction costs can also be considered, with an upper limit set on the amount of anchor used to control costs. In the specific implementation of the algorithm, advanced optimization algorithms such as genetic algorithms and particle swarm optimization can be employed to improve the efficiency and accuracy of decision-making. Furthermore, by establishing a sensitivity analysis of the multi-objective decision-making model, the impact of different constraints on the decision-making results can be evaluated, providing engineers with a more scientific basis for decision-making.

[0111] In some embodiments, in step S7, the feedback update includes:

[0112] Sub-step S7.1: Correcting the anisotropy parameter β in the three-dimensional stress distribution model according to actual anchor bolt stress data;

[0113] Sub-step S7.2: Recalculate the crack growth inhibition index I using the corrected β value c ;

[0114] Sub-step S7.3: When I c When the deviation from the theoretical value exceeds 10%, the anchor parameter optimization algorithm is triggered to be re-executed.

[0115] It is important to note that when implementing the crack arrest and efficiency enhancement method for thin-layered rock tunnels, the control feedback module inputs the anchor control instruction set into the tunnel anchor control system and provides real-time feedback to update the three-dimensional stress distribution model. This process ensures that the anchor support scheme can be dynamically adjusted according to actual construction conditions, improving the adaptability and reliability of the support system. The feedback update mechanism includes corrections to the anisotropic parameters in the three-dimensional stress distribution model and triggers the re-execution of the anchor parameter optimization algorithm when the crack growth suppression index deviates from the theoretical value, thereby ensuring continuous optimization of the support effect.

[0116] Specifically, the workflow of the control feedback module includes three sub-steps: First, the anisotropic parameters in the three-dimensional stress distribution model are corrected based on the actual anchor force data. The actual anchor force data can be obtained through strain sensors or pressure sensors installed on the anchor. These data reflect the force conditions of the anchor under actual working conditions. The correction of the anisotropic parameters is based on these actual data, adjusting the mechanical property description of the rock mass in the model to make it closer to the actual situation. Secondly, the crack growth inhibition index is recalculated using the corrected anisotropic parameters. This process ensures that the crack growth inhibition index can accurately reflect the current support effect by updating the model parameters. Finally, when the crack growth inhibition index deviates from the theoretical value by more than 10%, the anchor parameter optimization algorithm is triggered to be re-executed. This mechanism ensures that during the construction process, if the support effect is found to be unsatisfactory, the anchor arrangement and prestress value can be adjusted in time.

[0117] Preferably, when implementing the control feedback module, more advanced sensor technologies can be used to acquire anchor force data, such as high-precision fiber optic sensors, to improve data accuracy and real-time performance. When correcting anisotropic parameters, a comprehensive analysis can be conducted by combining multiple monitoring data, such as displacement and stress monitoring data, to more comprehensively reflect the actual mechanical state of the rock mass. Furthermore, more refined thresholds can be set to trigger the re-execution of the anchor parameter optimization algorithm. For example, the threshold range can be dynamically adjusted according to different construction stages and rock mass conditions to achieve more precise feedback control.

[0118] In some embodiments, the geological parameters collected in step S1 further include rock permeability, and a permeability correction factor η is further introduced in step S5. The crack growth inhibition index calculation formula is expanded to:

[0119] I c ′=I c ·η

[0120] Wherein, η = 1-0.2·(K / K0), K is the current permeability, and K0 is the initial permeability.

[0121] It's important to note that when implementing the crack arrest enhancement method for thin-layered rock tunnels, incorporating rock permeability and its correction factor is a crucial measure for improving crack growth inhibition. Rock permeability, which refers to the ability of fluids (such as groundwater) to pass through a rock mass, significantly influences crack growth, as changes in permeability affect the mechanical properties of the rock mass and the crack propagation path. The permeability correction factor is a coefficient used to adjust the crack growth inhibition index. It accounts for the impact of changes in rock permeability on crack growth, thereby making the calculation of the crack growth inhibition index more accurate.

[0122] Specifically, the measurement of rock permeability can be completed through on-site water injection tests or indoor rock permeability tests. On-site water injection tests calculate permeability by injecting a certain amount of water into the rock and measuring the water penetration rate; indoor tests collect rock samples and conduct permeability tests in the laboratory. The calculation of the correction factor is based on the ratio of current permeability to initial permeability. The initial permeability refers to the permeability of the rock before any construction treatment, while the current permeability is obtained by real-time monitoring during the construction process. The value of the correction factor is usually between 0 and 1. When the rock permeability increases, the correction factor will decrease, thereby reducing the crack growth inhibition index to reflect the situation where cracks are more likely to grow.

[0123] Preferably, during the implementation process, high-precision permeability sensors can be used to monitor changes in rock permeability in real time to ensure that the correction factor is calculated more accurately. When calculating the correction factor, the calculation formula of the correction factor can be adjusted according to the type of rock mass and the construction environment, such as introducing a nonlinear correction relationship to better reflect the impact of permeability changes on crack propagation. In addition, the correction factor can be dynamically adjusted in combination with numerical simulation and field monitoring data to adapt to complex construction conditions. For example, during tunnel excavation, with changes in groundwater levels and construction disturbances, the rock permeability may change significantly. At this time, the correction factor needs to be updated in a timely manner to ensure that the crack propagation inhibition index can accurately reflect the actual support effect.

[0124] The above-mentioned various embodiments of the present invention have the following beneficial effects: by collecting the geological parameters of the thin-layered rock mass and combining it with the three-dimensional stress distribution model, the present invention can accurately calculate the anisotropic stress field inside the rock mass, generate an initial stress cloud map, and provide a scientific basis for anchor arrangement. Based on the initial stress cloud map, the anchor parameter optimization algorithm is used to determine the corrected arrangement spacing and corrected prestress value of the anchor rod, which can ensure that the anchor rod acts accurately on the stress concentration area and enhance the support effect. Based on the real-time monitored rock displacement data, the dynamic weight adjustment algorithm can iteratively update the anchor rod prestress value, so that the anchor rod prestress is always maintained in the optimal state, which can further improve the reliability of the support. The stress release effect prediction model is used to calculate the rock crack propagation inhibition index after the anchor rod is arranged, and the anchor rod control instruction set is generated in combination with the multi-objective decision algorithm, which can achieve refined control of the anchor rod support.

[0125] Furthermore, the combined determination of the anchoring effect coefficient through experimental calibration and numerical simulation can more accurately reflect the actual support effectiveness of anchor bolts and provide a reliable basis for the calculation of the crack growth inhibition index. Introducing a permeability correction factor into the calculation of the crack growth inhibition index can account for the impact of rock permeability changes on crack growth, making the calculation of the crack growth inhibition index more accurate and further improving the reliability of the anchor bolt's crack arrest effect. The combined application of these technical measures can not only effectively inhibit the growth of rock cracks and extend the service life of tunnels, but also optimize the use of anchor bolts, reduce project costs, and improve construction efficiency, providing strong support for the construction and operation of thin-layered rock tunnels.

[0126] like Figure 2 As shown, in some embodiments, a thin-layered rock tunnel anchor crack arresting and efficiency enhancement system 200 is provided, the system 200 comprising:

[0127] The data acquisition module 201 is used to collect geological parameters of thin-layered rock mass, including bedding inclination, rock mass elastic modulus, anchor arrangement spacing and initial prestress value;

[0128] The stress analysis module 202 is used to calculate the anisotropic stress field inside the rock mass based on the geological parameters using a three-dimensional stress distribution model and generate an initial stress cloud map;

[0129] The parameter optimization module 203 is used to determine the corrected arrangement spacing and corrected prestress value of the anchor rods based on the initial stress cloud map and in combination with the anchor rod parameter optimization algorithm;

[0130] A dynamic adjustment module 204 is used to iteratively update the anchor rod prestress value through a dynamic weight adjustment algorithm based on real-time monitored rock displacement data;

[0131] The prediction module 205 is used to calculate the rock crack growth inhibition index after the anchor rod is arranged using the stress release effect prediction model;

[0132] A decision module 206 is configured to generate an anchor control instruction set based on the crack growth inhibition index through a multi-objective decision algorithm;

[0133] The control feedback module 207 is used to input the anchor control instruction set into the tunnel anchor control system and provide real-time feedback to update the three-dimensional stress distribution model.

[0134] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0135] The above descriptions are merely some preferred embodiments of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also encompass other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by mutually replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for increasing the effectiveness of crack arrest of thin-layered rock tunnel anchor bolts, characterized in that: The following steps are involved: Step S1: Collecting geological parameters of thin-layered rock mass, including bedding inclination, rock mass elastic modulus, anchor bolt spacing, and initial prestress value; Step S2: Based on the geological parameters, the anisotropic stress field inside the rock mass is calculated using a three-dimensional stress distribution model, and an initial stress cloud map is generated; Step S3: determining the corrected arrangement spacing and corrected prestress value of the anchor rods based on the initial stress cloud map and in combination with the anchor rod parameter optimization algorithm; Step S4: Based on the real-time monitored rock displacement data, iteratively update the anchor rod prestress value through a dynamic weight adjustment algorithm; Step S5: using the stress release effect prediction model, calculating the rock crack growth inhibition index after the anchor bolts are arranged; Step S6: generating an anchor control instruction set through a multi-objective decision algorithm according to the crack growth inhibition index; Step S7: inputting the anchor control instruction set into the tunnel anchor control system, and providing real-time feedback to update the three-dimensional stress distribution model.

2. The method according to claim 1, characterized in that In step S2, the calculation formula of the three-dimensional stress distribution model is: s eff =σ0·(1+β·sinθ) Among them, σ eff is the effective stress of the rock mass, σ0 is the initial homogeneous stress, β is the anisotropy parameter, and θ is the bedding dip; The anisotropy parameter β is calculated by the following formula: Among them, E v is the elastic modulus in the direction perpendicular to the bedding, E h is the elastic modulus parallel to the bedding direction.

3. The method according to claim 1, characterized in that In step S3, the anchor parameter optimization algorithm includes: Sub-step S3.1: extracting the spatial coordinates of the stress concentration area based on the initial stress cloud map; Sub-step S3.2: generating an anchor arrangement density gradient function according to the spatial coordinates; Sub-step S3.3: Perform multi-objective optimization on the gradient function using a genetic algorithm, and output a corrected arrangement spacing and a corrected prestress value.

4. The method according to claim 1, wherein In step S4, the iterative formula of the dynamic weight adjustment algorithm is: Among them, P new is the updated anchor prestress value, P old is the current prestress value, α is the dynamic weight coefficient, ΔD is the difference between the real-time displacement monitoring value and the theoretical displacement value, is the sensitivity of effective stress to displacement.

5. The method according to claim 1, wherein In step S5, the crack growth inhibition index is calculated by the following formula: Among them, I c is the crack growth inhibition index, σ cr is the critical fracture stress of rock mass, k is the anchoring effect coefficient, and S is the anchor spacing.

6. The method according to claim 5, characterized in that The anchoring effect coefficient k is determined by experimental calibration and numerical simulation, and satisfies the relationship: Among them, Δε is the strain change after the anchor is applied, and ε0 is the initial strain.

7. The method according to claim 1, characterized in that In step S6, the multi-objective decision-making algorithm includes the following constraints: Constraint 1: The maximum prestress of the anchor shall not exceed 70% of the tensile strength of the rock mass; Constraint 2: The product of the spacing between adjacent anchor bolts and the prestress value maintains a monotonically decreasing relationship; Constraint 3: Crack Growth Inhibition Index I c Greater than the set threshold.

8. The method according to claim 1, characterized in that In step S7, the feedback update includes: Sub-step S7.1: Correcting the anisotropy parameter β in the three-dimensional stress distribution model according to actual anchor bolt stress data; Sub-step S7.2: Recalculate the crack growth inhibition index I using the corrected β value c ; Sub-step S7.3: When I c When the deviation from the theoretical value exceeds 10%, the anchor parameter optimization algorithm is triggered to be re-executed.

9. The method according to claim 1, characterized in that The geological parameters collected in step S1 also include rock permeability. In step S5, a permeability correction factor η is further introduced, and the calculation formula of the crack growth inhibition index is expanded to: I′ c =I c ·η Wherein, η = 1-0.2·(K / K0), K is the current permeability, and K0 is the initial permeability.

10. A thin-layered rock tunnel anchor crack arrest efficiency enhancement system, characterized in that: include: Data acquisition module, used to collect geological parameters of thin-layered rock mass, including bedding inclination, rock mass elastic modulus, anchor spacing and initial prestress value; A stress analysis module is used to calculate the anisotropic stress field inside the rock mass based on the geological parameters through a three-dimensional stress distribution model and generate an initial stress cloud map; A parameter optimization module is used to determine the corrected arrangement spacing and corrected prestress value of the anchor rods based on the initial stress cloud map and in combination with an anchor rod parameter optimization algorithm; Dynamic adjustment module, used to iteratively update anchor prestress value through dynamic weight adjustment algorithm based on real-time monitored rock displacement data; A prediction module is used to calculate the rock crack growth inhibition index after anchor bolt placement using a stress release effect prediction model; A decision module, configured to generate an anchor control instruction set through a multi-objective decision algorithm according to the crack growth inhibition index; The control feedback module is used to input the anchor control instruction set into the tunnel anchor control system and provide real-time feedback to update the three-dimensional stress distribution model.

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