A method for determining rock breaking parameters in super-large cross-section shaft excavation

Abstract

The present application relates to the vertical shaft construction technical field, solved the prior art did not fully consider the energy loss characteristics of the interaction between the cutterhead and the rock in the vertical shaft tunneling, which leads to a large deviation in the determination of the rock breaking parameters, especially relates to a method for determining the rock breaking parameters of super-large section vertical shaft tunneling, obtaining the target stratum lithology parameters, design section index and cutterhead configuration information of the vertical shaft to be tunneled and preprocessing, obtaining the target stratum feature vector and section geometric topology data set, based on the vertical tunneling condition, combining the target stratum feature vector to construct the energy loss function for representing the interaction between the cutterhead and the rock. The present application fully considers the secondary crushing of the rock residue and its energy consumption characteristics caused by the gravity effect in the vertical tunneling process of the super-large section vertical shaft, significantly improves the accuracy of the rock breaking energy consumption prediction under complex vertical conditions, and solves the energy consumption prediction distortion problem caused by ignoring the residue transport resistance in the traditional method under the vertical shaft condition.

Description

Technical Field

[0001] This invention relates to the field of shaft construction technology, and in particular to a method for determining rock-breaking parameters for excavating ultra-large cross-section shafts. Background Technology

[0002] With the continuous breakthroughs in my country's deep earth exploration projects and underground mining technologies, the construction scale and depth of ultra-large cross-section vertical shafts are constantly expanding. Due to the extremely complex geological conditions of ultra-large cross-section vertical shafts, which often traverse high-stress, heterogeneous rock strata and water-bearing strata, traditional blasting methods have been gradually replaced by full-face vertical shaft drilling technology. During the tunneling process, the scientific determination of rock-breaking parameters (such as thrust, rotation speed, cutterhead torque, etc.) is the core to ensuring tunneling efficiency and cutter life.

[0003] Currently, the main methods for determining rock-breaking parameters in the industry include empirical formulas, analogies, and simple indoor experimental analysis. However, for the specific working condition of ultra-large cross-section vertical shafts, existing technologies are usually derived from the working conditions of horizontal tunnels and do not fully consider the energy loss characteristics of the interaction between the cutter head and the rock during vertical shaft excavation. In vertical excavation, the gravity effect significantly changes the transport path of rock debris, causing rock debris to accumulate at the excavation interface and generate repeated crushing phenomena. This makes it difficult to truly reflect the path of rock-breaking energy flow, resulting in a large deviation in the determination of rock-breaking parameters and easily causing abnormal cutter head wear and low rock-breaking efficiency. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for determining rock-breaking parameters in ultra-large cross-section vertical shaft excavation. This method solves the problem that existing technologies do not fully consider the energy loss characteristics of the interaction between the cutter head and the rock during vertical shaft excavation, which leads to significant deviations in the determination of rock-breaking parameters. This method aims to improve the accuracy of rock-breaking energy consumption prediction under complex vertical working conditions and reduce the abnormal impact damage rate of the cutter head.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for determining rock-breaking parameters for ultra-large cross-section vertical shaft excavation, the method comprising the following steps:

[0006] S1. Obtain the target stratum lithological parameters, design cross-sectional indicators, and cutter configuration information of the vertical shaft to be excavated, and perform preprocessing to obtain the target stratum feature vector and cross-sectional geometric topology dataset.

[0007] S2. Based on the vertical tunneling condition, an energy loss function is constructed to characterize the interaction between the cutter and the rock by combining the target stratum feature vector, and a rock-breaking force transmission path matrix is ​​generated to reflect the flow of rock-breaking energy.

[0008] S3. Use the rock breaking force transmission path matrix to impose boundary condition constraints on the preset three-dimensional finite element model, and call the cross-sectional geometric topology dataset to perform mesh discretization processing on the three-dimensional finite element model to construct a finite element analytical environment.

[0009] S4. Load the cutter configuration information into the finite element analysis environment, and simulate the cutting path of the cutter on the three-dimensional finite element model by nonlinear incremental step iterative solution. During the simulation, extract the morphological characteristic coefficients of rock crack propagation, the time domain stress sequence data of the cutter, and the frequency domain feature vector simultaneously.

[0010] S5. Perform feature fusion on morphological characteristic coefficients, time-domain stress sequence data and frequency-domain feature vectors to construct a multi-dimensional rock-breaking evaluation feature vector, and determine the tunneling rock-breaking parameters that match the ultra-large cross-section vertical shaft according to the preset parameter mapping model.

[0011] Furthermore, step S2 specifically includes the following steps:

[0012] S21. Extract the rock anisotropic strength features from the target stratum feature vector, and combine them with the gravity acceleration vector under vertical tunneling conditions to construct the energy dissipation tensor matrix of rock fracturing.

[0013] S22. Calculate the power consumption of the cutter during vertical pressing and radial shearing based on the energy dissipation tensor matrix, and construct an energy loss function to characterize the interaction between the cutter and the rock.

[0014] S23. Use the energy loss function to perform sensitivity analysis on the target stratum feature vector, determine the topological relationship of energy transfer between rock bedding, and generate a rock-breaking force transfer path matrix to reflect the flow of rock-breaking energy.

[0015] Furthermore, step S22 specifically includes the following steps:

[0016] S221, The formula for calculating the pressing power consumption of the hobbing cutter during the vertical pressing process is as follows:

[0017] ;

[0018] In the above formula, Indicates the power consumption of vertical push-in. Represents the vertical resistance function. This indicates the maximum indentation depth. Indicates the vertical indentation displacement;

[0019] S222, The calculation formula for the shearing power consumption of the hob during the radial shearing process is as follows:

[0020] ;

[0021] In the above formula, Indicates radial shear power consumption. Represents the shear torque function. Represents angular displacement variable. This indicates the step distance of a single arc cut by the hob;

[0022] S223. Combining the indentation power consumption and shear power consumption, an energy loss function is constructed to characterize the interaction between the cutter and the rock, as shown in the following expression:

[0023] ;

[0024] In the above formula, Represents the energy loss function. Represents the gravitational constant. Indicates the equivalent mass of rock slag. Indicates the current vertical excavation depth. This represents the system's transmission efficiency factor. This represents the weighting coefficient of the primary rock energy. This represents the gravity coupling compensation coefficient.

[0025] Furthermore, step S3 specifically includes the following steps:

[0026] S31. Using the energy transfer vector in the rock-breaking force transmission path matrix, generate the load boundary conditions for each element node in the three-dimensional finite element model.

[0027] S32. Based on the cross-sectional geometric topology dataset, extract the geometric complexity coefficients of each region in the super-large cross-section, and perform initial global mesh discretization processing on the three-dimensional finite element model.

[0028] S33. Calculate the energy density gradient of the mesh element along the energy flow path using the energy transfer vector, and adaptively refine the local mesh of the three-dimensional finite element model based on the energy density gradient. Calculate the size of the refined mesh element using the following formula:

[0029] ;

[0030] ;

[0031] In the above formula, Indicates the size of the encrypted grid cells. Indicates the initial global mesh size. Represents the natural constant. This represents the mesh adaptive adjustment coefficient. The modulus representing the energy density gradient; Represents the energy density gradient. This represents the energy density field function, used to describe the rock-breaking energy per unit volume carried by a grid cell at three-dimensional spatial coordinates (x, y, z), where x represents the first spatial dimension coordinate, y represents the second spatial dimension coordinate, and z represents the third spatial dimension coordinate. This represents the partial derivative of energy density with respect to the x-axis. This represents the partial derivative of energy density with respect to the y-axis. This represents the partial derivative of energy density with respect to the z-axis;

[0032] S34. Load the boundary conditions onto the discretized three-dimensional finite element model, and construct a finite element analytical environment that can simulate dynamic rock breaking response through numerical integration algorithm.

[0033] Furthermore, step S4 specifically includes the following steps:

[0034] S41. Map the hob configuration information to the initial load step features in the finite element analysis environment to establish the relationship between mechanical structure parameters and simulation discrete time-space step size, and simulate the indentation and cutting path of the hob on the three-dimensional finite element model through the incremental step iterative solver.

[0035] S42. Based on the initial load step characteristics, calculate the damage variables of the mesh elements in the three-dimensional finite element model, and determine the initiation and penetration state of rock cracks according to the damage evolution criterion. Calculate the morphological characteristic coefficients of rock crack propagation. The calculation formula is as follows:

[0036] ;

[0037] In the above formula, Represents morphological characteristic coefficients. Indicates the measurement scale. Indicates the number of covered elements. The logarithm of the reciprocal of the measurement scale;

[0038] S43. Synchronously acquire reaction force data of the hob contact interface in the finite element analysis environment, and perform time-domain signal smoothing processing through the sliding window algorithm to obtain the time-domain force sequence data of the hob.

[0039] S44. Use Fast Fourier Transform to extract the dominant frequency component from the time-domain force sequence data and generate a frequency domain feature vector.

[0040] Furthermore, the formula for calculating the damage variable of the mesh element in the three-dimensional finite element model is as follows:

[0041] ;

[0042] In the above formula, The damage variable represents the mesh element. Represents equivalent plastic strain. Indicates the initial strain of the initial damage. Indicates the fracture strain at failure. This represents the damage evolution rate factor.

[0043] Furthermore, step S5 specifically includes the following steps:

[0044] S51. The morphological characteristic coefficients, temporal stress sequence data, and frequency domain feature vectors are time-series aligned and dimension-normalized to construct a multi-dimensional rock-breaking evaluation feature vector for characterizing comprehensive rock-breaking performance. The expression is:

[0045] ;

[0046] In the above formula, This represents a multi-dimensional rock breaking evaluation feature vector. Represents morphological characteristic coefficients. This represents the mean of the force sequence. Indicates the standard deviation of the force sequence. Represents the transpose of the frequency domain eigenvector;

[0047] S52. Based on the preset rock-breaking efficiency objective function, calculate the dynamic contribution coefficient of each feature component in the multi-dimensional rock-breaking evaluation feature vector to the tunneling performance. The calculation formula is as follows:

[0048] ;

[0049] In the above formula, This represents the dynamic contribution coefficient of the i-th feature component. Let represent the objective function for rock-breaking efficiency. This represents the i-th element in the multi-dimensional rock breaking evaluation feature vector. This represents the partial derivative of the rock-breaking efficiency objective function with respect to its characteristic components. Represents the weighted smoothing factor. This represents the total number of dimensions of the feature components;

[0050] S53. Input the dynamic contribution coefficient into the preset parameter mapping model. By performing neuron activation operations on the parameter mapping model, determine the optimal tunneling and rock breaking parameters that match the target geological conditions of the ultra-large cross-section vertical shaft. The calculation formula is as follows:

[0051] ;

[0052] In the above formula, This represents the optimal rock-breaking parameters for tunneling. This represents a nonlinear parameter mapping model.

[0053] By employing the above technical solution, the present invention provides a method for determining rock-breaking parameters in ultra-large cross-section vertical shaft excavation, which has at least the following beneficial effects:

[0054] 1. This invention constructs an energy loss function and generates a rock-breaking force transmission path matrix, fully considering the secondary crushing of rock debris and its energy consumption characteristics caused by gravity effect during the vertical excavation of ultra-large cross-section vertical shafts. This dynamic energy field modeling method significantly improves the accuracy of rock-breaking energy consumption prediction under complex vertical working conditions and solves the problem of energy consumption prediction distortion caused by neglecting the resistance of debris transportation in traditional methods under vertical shaft working conditions.

[0055] 2. This invention utilizes the energy transfer path matrix to guide the boundary constraints of the three-dimensional finite element model and calls the cross-sectional geometric topology dataset for adaptive mesh discretization. This avoids the waste of computational power in performing high-intensity meshing on ultra-large cross-sectional models globally. While significantly improving the simulation accuracy of key rock-breaking areas, it also shortens the construction and computation time of the finite element analytical environment.

[0056] 3. This invention extracts the morphological feature coefficients and frequency domain feature vectors of rock crack propagation simultaneously through nonlinear incremental step iterative solution and multi-feature extraction. Combined with time-domain stress fluctuation and frequency-domain energy distribution characteristics, it enhances the quantitative characterization ability of the nonlinear rock fracturing process, solves the defect that a single physical index cannot comprehensively evaluate the rock breaking efficiency of ultra-large cross sections, and provides more technically in-depth underlying data support for parameter decision-making.

[0057] 4. This invention, through the construction of a multi-dimensional rock-breaking evaluation feature vector and parameter mapping model, can automatically calculate the optimal weight based on the comprehensive features of simulation feedback. This avoids the blindness exhibited by traditional determination methods when facing heterogeneous rock layers, effectively reduces the abnormal impact damage rate of the cutter head, and significantly improves the overall rock-breaking efficiency and construction safety of ultra-large cross-section vertical shaft excavation. Attached Figure Description

[0058] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0059] Figure 1 This is a flowchart of the parameter determination method of the present invention. Detailed Implementation

[0060] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0061] Traditional methods struggle to quantitatively characterize energy dissipation during vertical tunneling, leading to significant discrepancies between rock-breaking force boundary conditions and actual working conditions. This results in an inaccurate determination of rock-breaking parameters, failing to accurately reflect the path of rock-breaking energy flow. To improve the accuracy of rock-breaking energy consumption prediction under complex vertical working conditions and ensure more precise determination of subsequent rock-breaking parameters, this invention proposes a method for determining rock-breaking parameters in ultra-large cross-section vertical shaft tunneling. Figure 1 As shown, the method includes the following steps:

[0062] S1. First, obtain the target stratum lithological parameters (such as uniaxial compressive strength, quartz content, etc.), design cross-sectional indicators (shaft diameter, eccentricity), and cutter configuration information (cutter spacing, cutter position angle) of the target stratum lithological parameters. Perform tensor mapping and spatial interpolation on the target stratum lithological parameters to generate target stratum feature vectors characterizing the heterogeneity of the rock mass. Construct a geometric model of the shaft cross-section based on the design cross-sectional indicators, and map the cutter configuration information into the geometric model. Perform pose alignment processing to obtain the cross-sectional geometric topology dataset. Perform normalization processing on the target stratum feature vectors and the cross-sectional geometric topology dataset to eliminate numerical differences between different physical dimensions, thereby improving the accuracy and consistency of the initial data entry and ensuring that the boundary inputs for subsequent simulations are targeted.

[0063] S2. To avoid the distortion in energy distribution prediction caused by secondary crushing of rock debris and gravity interference during vertical tunneling, which is not considered in traditional methods, an energy loss function characterizing the interaction between the cutter head and the rock is constructed based on the vertical tunneling working condition and the target stratum feature vector. A rock-breaking force transmission path matrix reflecting the flow of rock-breaking energy is also generated. This more realistically restores the energy dissipation law in ultra-large cross-section tunneling and provides a physically consistent dynamic boundary for the finite element model. The specific steps include:

[0064] Anisotropic strength features of rocks are extracted from the feature vector of the target strata to characterize the strength differences of rock strata in different bedding directions. Combined with the gravity acceleration vector under vertical tunneling conditions, an energy dissipation tensor matrix for rock fracturing is constructed. Based on this energy dissipation tensor matrix, the power consumption of the cutter head during vertical indentation and radial shearing processes is calculated. An energy loss function characterizing the interaction between the cutter head and the rock is then constructed. This improves the robustness of predicting rock-breaking loads in ultra-large cross-sections and extends the service life of the cutter head. The specific steps include:

[0065] The power consumption of the hob during the vertical pressing process and the power consumption during the radial shearing process are calculated using the following formulas:

[0066] ;

[0067] ;

[0068] In the above formula, This represents the vertical indentation power consumption, used to quantify the energy consumed by the cutter as it vertically cuts into the rock mass. This represents the vertical resistance function, which is the vertical resistance that varies with the indentation depth and is used to characterize the feedback resistance of the rock to the intrusion of the cutter tip. This indicates the maximum indentation depth, used to define the displacement termination boundary of a single indentation action. It represents the vertical pressing displacement, which is used as an integral variable to determine the instantaneous position of the hob on the vertical axis; This represents radial shear power consumption, used to quantify the tangential mechanical energy generated by the rotary cutter cutting rock. The shear torque function represents the torque characteristic required for the cutter to overcome the shear strength of the rock, which varies with the rotation angle. This represents the angular displacement variable, used to define the instantaneous angular coordinate position of the hob on the cutting trajectory. This indicates the single-cutting arc step distance of the hob, used to determine the spatial arc span of the radial shearing action.

[0069] Combining the indentation power consumption and the shear power consumption, an energy loss function is constructed to characterize the interaction between the cutter and the rock, as shown in the following expression:

[0070] ;

[0071] In the above formula, This represents the energy loss function, used to quantify the sum of mechanical energy conversion and external environmental resistance losses per unit excavation volume. This represents the gravity constant, which serves as the calculation benchmark for the resistance to soil slag falling under vertical working conditions. It represents the equivalent rock debris mass and is used to characterize the additional energy consumption caused by secondary crushing as the tunneling depth increases. This indicates the current vertical tunneling depth, used to determine the correction benchmark for rock-breaking efficiency based on ground stress. This represents the system transmission efficiency factor, used to correct for transmission losses during the conversion of mechanical energy into rock-breaking energy. This represents the weighting coefficient of the primary rock-breaking energy, used to adjust the proportion of direct rock-breaking energy from the roller cutter in the total energy consumption. This represents the gravity coupling compensation coefficient, used to correct the increase in energy consumption caused by rock debris accumulation under vertical operating conditions.

[0072] By using the constructed energy loss function to perform sensitivity analysis on the target stratum feature vector, the topological relationship of energy transfer between rock bedding is determined, and a rock-breaking force transfer path matrix is ​​generated to reflect the flow of rock-breaking energy. This makes the crack propagation features extracted by simulation highly consistent with the rock-breaking cross-sectional morphology revealed by actual engineering, thus improving the fitting degree of simulation results to the actual fracture morphology.

[0073] S3. Using the rock-breaking force transmission path matrix as a constraint criterion, boundary conditions are constrained on the preset three-dimensional finite element model. The cross-sectional geometric topology dataset is then used to discretize the three-dimensional finite element model into a mesh, constructing a finite element analytical environment. This significantly reduces the hardware overhead and computation time of large-scale simulation tasks while ensuring the ability to capture crack propagation details. The specific steps include the following:

[0074] The energy transfer vector is extracted from the rock-breaking force transmission path matrix. This vector contains the energy flow direction operator and the intensity attenuation coefficient. The load boundary conditions of each element node in the three-dimensional finite element model are calculated using the following formula:

[0075] ;

[0076] In the above formula, This represents the load boundary condition of the i-th element node, used to define the force constraints on the model at that node. The elements in the rock-breaking force transmission path matrix represent the force transmission efficiency weights from the j-th cutter action point to the i-th node. This represents the initial force vector at the j-th hob's point of application, used to characterize the direction and magnitude of the input energy at the mechanical end. This indicates the total number of points of contact of the hob, used to define the range of energy sources;

[0077] Based on the cross-sectional geometric topology dataset, the geometric complexity coefficients of each region in the ultra-large cross-section are extracted. The 3D finite element model is then initially discretized into a global mesh. The energy density gradient of the mesh elements along the energy flow path is calculated using energy transfer vectors. Adaptive local mesh refinement is then applied to the 3D finite element model based on the energy density gradient, and the size of the refined mesh elements is calculated using the following formula:

[0078] ;

[0079] ;

[0080] In the above formula, The modulus represents the energy density gradient and is used to quantify the maximum rate of change of energy density per unit distance in three-dimensional space. This represents the energy density field function, used to describe the rock-breaking energy per unit volume carried by a grid cell at three-dimensional spatial coordinates (x, y, z). x represents the first spatial dimension coordinate, used to define the spatial position of the grid cell in the horizontal direction of the shaft cross-section; y represents the second spatial dimension coordinate, used to define the spatial position of the grid cell in the direction perpendicular to the x-axis of the shaft cross-section; and z represents the third spatial dimension coordinate, used to define the spatial position of the grid cell in the direction of shaft depth. This represents the partial derivative of energy density with respect to the x-axis, and its function is to extract the intensity characteristics of energy variation in the horizontal direction. This represents the partial derivative of energy density with respect to the y-axis; its function is to extract the intensity characteristics of energy variation along the longitudinal direction of the cross-section. It represents the partial derivative of energy density with respect to the z-axis; its function is to extract the intensity characteristics of energy variation in the depth direction.

[0081] This indicates the size of the refined mesh cells, used to improve simulation accuracy in areas of high energy concentration. This represents the initial global mesh size, serving as the basic scale for mesh subdivision. This represents the natural constant, used to construct a nonlinear shrinkage function where the mesh size varies with the gradient. This represents the mesh adaptive adjustment coefficient, used to control the sensitivity of the mesh to changes in energy gradient.

[0082] After the mesh discretization is completed, the load boundary conditions are applied to the discretized three-dimensional finite element model. By using a preset numerical integration algorithm (such as the central difference method), the dynamic equilibrium equation is solved by discretization within the time step, and a finite element analytical environment capable of simulating dynamic rock breaking response is constructed. This enables accurate prediction of the load fluctuation of the equipment and improves the physical realism of the rock breaking process simulation.

[0083] S4. To address the limitations of single mechanical indicators in comprehensively characterizing rock-breaking efficiency and quantifying the topological complexity of crack propagation, the cutter configuration information is loaded into the finite element analysis environment. The cutting path of the cutter on the three-dimensional finite element model is simulated through nonlinear incremental step iterative solutions. Simultaneously, the morphological characteristic coefficients of rock crack propagation, the temporal stress sequence data of the cutter, and the frequency domain feature vector are extracted during the simulation. By capturing the fractal characteristics of the cracks, the degree of rock fragmentation can be more fundamentally reflected. The specific steps include the following:

[0084] The hob configuration information is mapped to the initial load step features in the finite element analysis environment. The hob configuration information (such as installation radius and tilt angle) are static geometric parameters, while the load step is the time / space discrete unit of the finite element analysis. The purpose of the mapping is to transform the geometric center of the hob into a set of compression nodes in the finite element mesh, ensuring that the force application position in the simulation process is completely consistent with the actual mechanical arrangement, so as to establish the correlation between the mechanical structure parameters and the simulation discrete time and space step. The incremental step iterative solver is used to simulate the pressing and cutting path of the hob on the three-dimensional finite element model. The calculation formula is:

[0085] ;

[0086] In the above formula, This represents the damage variable of the mesh element, used to quantify the degree of material degradation. When D reaches a preset threshold, the mesh element is determined to have failed, serving as a criterion for crack formation. Represents the equivalent plastic strain, used to characterize the degree of cumulative plastic deformation of mesh elements under hobbing load. This represents the initial damage initiation strain, used to define the critical threshold for a material to enter the damage evolution stage. It represents the failure fracture strain and is used to define the ultimate deformation state when a material completely loses its load-bearing capacity. It represents the damage evolution rate factor, which is used to adjust the brittle / ductile evolution rate of rock materials under different stress states;

[0087] Damage variables of mesh elements in a three-dimensional finite element model are calculated based on the initial load step characteristics. The initiation and penetration states of rock cracks are determined according to the damage evolution criterion, and the morphological characteristic coefficients of rock crack propagation are calculated. The calculation formula is as follows:

[0088] ;

[0089] In the above formula, The morphological characteristic coefficient is used to quantitatively characterize the degree of fragmentation in rock fragmentation morphology through fractal dimension. This represents the measurement scale, used to define the smallest spatial unit size covering the cracked region. This represents the number of covering elements, used to quantify the spatial topological extent occupied by the crack at scale r. It represents the logarithmic value of the reciprocal of the measurement scale, specifically, in the finite element analysis environment, it is the benchmark for measuring the fineness of spatial division of rock cracks in logarithmic space.

[0090] The reaction force data of the cutter contact interface in the finite element analysis environment are collected synchronously. The time domain signal is smoothed by the sliding window algorithm to obtain the time domain force sequence data of the cutter. The main frequency component in the time domain force sequence data is extracted by fast Fourier transform to generate a frequency domain feature vector to characterize the rock breaking stability.

[0091] S5. Based on morphological characteristic coefficients, time-domain stress sequence data, and frequency-domain feature vectors, a multi-dimensional rock-breaking evaluation feature vector is constructed. According to a pre-set parameter mapping model, the rock-breaking parameters for tunneling matching the ultra-large cross-section vertical shaft are determined. This achieves deep decoupling and precise matching of tunneling parameters with complex working conditions, effectively reducing abnormal wear of the cutter head and significantly improving the overall rock-breaking efficiency of vertical shaft tunneling. Specifically, the steps include:

[0092] By performing temporal alignment and dimensional regularization on morphological characteristic coefficients, temporal stress sequence data, and frequency domain feature vectors, a multi-dimensional rock-breaking evaluation feature vector is constructed to characterize the overall rock-breaking effectiveness. The expression is as follows:

[0093] ;

[0094] In the above formula, This represents a multi-dimensional rock breaking evaluation feature vector, used to integrate simulation response data from different dimensions, serving as the standard input for subsequent parameter mapping models. The morphological characteristic coefficient is used to quantitatively characterize the geometric spatial complexity of rock fragmentation using fractal dimension. This represents the mean of the force sequence, used to characterize the average load intensity during the tunneling process. This represents the standard deviation of the force sequence, used to characterize the severity of load fluctuations during rock breaking. This represents the transpose of the frequency domain eigenvector, used to provide the frequency domain eigencomponents for rock breaking stability.

[0095] Based on a preset rock-breaking efficiency objective function, which is a joint loss function aimed at minimizing energy consumption and maximizing load stability, the dynamic contribution coefficient of each feature component in the multi-dimensional rock-breaking evaluation feature vector to tunneling performance is calculated. The calculation formula is as follows:

[0096] ;

[0097] In the above formula, This represents the dynamic contribution coefficient of the i-th feature component, quantifying the weight of the i-th feature term's influence on the rock-breaking efficiency objective function, and is used to adjust the decision priority in subsequent parameter mapping processes. The objective function for rock-breaking efficiency is used as an evaluation benchmark. It defines the optimal state of tunneling performance by minimizing energy loss and load fluctuation. This represents the i-th element in the multi-dimensional rock breaking evaluation feature vector, used as an input independent variable, representing a specific physical dimension in the morphological feature coefficient, force sequence statistics, or frequency domain features. This represents the partial derivative of the rock-breaking efficiency objective function with respect to the characteristic components. It is used to extract the sensitivity of the characteristic components and characterizes the rate of change in rock-breaking performance caused by small changes in the characteristic terms. This represents a weighting smoothing factor, used to adjust the dispersion of the contribution distribution among various feature terms, preventing any single feature from having too high a weight. This represents the total dimension of the feature components and is used to define the range of the feature space involved in weight allocation.

[0098] The dynamic contribution coefficient is input into a preset parameter mapping model, which employs a deep neural network architecture, specifically including: one input layer, three hidden layers, and one output layer. The hidden layers use the ReLU activation function to perform neuron activation operations, thereby mapping multi-dimensional feature vectors from a high-dimensional feature space to a low-dimensional decision space. By performing neuron activation operations on the parameter mapping model, the optimal tunneling and rock-breaking parameters matching the target geological conditions of the ultra-large cross-section vertical shaft are determined. The calculation formula is as follows:

[0099] ;

[0100] In the above formula, This represents the optimal rock-breaking parameters for tunneling. Its purpose is to output the cutterhead rotation speed, tunneling speed, and thrust parameters that are ultimately used to guide actual engineering projects. This represents a nonlinear parameter mapping model, which aims to achieve cross-dimensional nonlinear mapping from multidimensional simulation features to engineering decision parameters.

[0101] The parameter determination method first integrates stratigraphic lithology, cross-sectional indices, and cutter configuration through preprocessing to construct a target stratigraphic feature vector and cross-sectional geometric topology dataset. Then, based on the energy evolution law of vertical tunneling, an energy loss function is constructed and the rock-breaking force transmission path matrix is ​​solved. The path matrix is ​​used to drive the mesh discretization and boundary constraints of the three-dimensional finite element model to construct a dynamic finite element analytical environment. Subsequently, the tunneling path is simulated through nonlinear incremental step iteration, and morphological feature coefficients characterizing the degree of rock fragmentation, time-domain stress sequences reflecting load fluctuations, and frequency-domain feature vectors are extracted simultaneously. Finally, an evaluation feature vector is constructed based on the above multi-dimensional features, and the optimal tunneling rock-breaking parameters are precisely matched with the target working conditions through a preset parameter mapping model.

[0102] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Since the above embodiments are substantially similar to the method embodiments, their descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0104] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for determining rock-breaking parameters in ultra-large cross-section vertical shaft excavation, characterized in that, The method includes the following steps: S1. Obtain the target stratum lithological parameters, design cross-sectional indicators, and cutter configuration information of the vertical shaft to be excavated, and perform preprocessing to obtain the target stratum feature vector and cross-sectional geometric topology dataset. S2. Based on the vertical tunneling condition, an energy loss function is constructed to characterize the interaction between the cutter head and the rock, combined with the target stratum feature vector, and a rock-breaking force transmission path matrix is ​​generated. This includes the following steps: S21. Extract the rock anisotropic strength features from the target stratum feature vector, and combine them with the gravity acceleration vector under vertical tunneling conditions to construct the energy dissipation tensor matrix of rock fracturing. S22. Calculate the power consumption of the cutter during vertical indentation and radial shearing based on the energy dissipation tensor matrix, and construct an energy loss function to characterize the interaction between the cutter and the rock. This includes the following steps: S221, The formula for calculating the pressing power consumption of the hobbing cutter during the vertical pressing process is as follows: ； In the above formula, Indicates the power consumption of vertical push-in. Represents the vertical resistance function. This indicates the maximum indentation depth. Indicates the vertical indentation displacement; S222, The calculation formula for the shearing power consumption of the hob during the radial shearing process is as follows: ； In the above formula, Indicates radial shear power consumption. Represents the shear torque function. Represents angular displacement variable. This indicates the step distance of a single arc cut by the hob; S223. Combining the indentation power consumption and shear power consumption, an energy loss function is constructed to characterize the interaction between the cutter and the rock, as shown in the following expression: ； In the above formula, Represents the energy loss function. Represents the gravitational constant. Indicates the equivalent mass of rock slag. Indicates the current vertical excavation depth. This represents the system's transmission efficiency factor. This represents the weighting coefficient of the primary rock energy. This represents the gravity coupling compensation coefficient; S23. Use the energy loss function to perform sensitivity analysis on the target stratum feature vector, determine the topological relationship of energy transfer between rock bedding, and generate a rock-breaking force transfer path matrix to reflect the flow of rock-breaking energy. S3. Use the rock breaking force transmission path matrix to apply boundary constraints to the preset three-dimensional finite element model, and call the cross-sectional geometric topology dataset to perform mesh discretization processing on the three-dimensional finite element model to construct a finite element analytical environment. S4. Load the cutter configuration information into the finite element analysis environment, and simulate the cutting path of the cutter on the three-dimensional finite element model by nonlinear incremental step iterative solution. During the simulation, extract the morphological characteristic coefficients of rock crack propagation, the time domain stress sequence data of the cutter, and the frequency domain feature vector simultaneously. S5. Perform feature fusion on morphological characteristic coefficients, time-domain stress sequence data, and frequency-domain feature vectors to construct a multi-dimensional rock-breaking evaluation feature vector, and determine the tunneling rock-breaking parameters according to the preset parameter mapping model. The specific steps include the following: S51. The morphological characteristic coefficients, temporal stress sequence data, and frequency domain feature vectors are time-series aligned and dimension-normalized to construct a multi-dimensional rock-breaking evaluation feature vector for characterizing comprehensive rock-breaking performance. The expression is: ； In the above formula, This represents a multi-dimensional rock breaking evaluation feature vector. Represents morphological characteristic coefficients. This represents the mean of the force sequence. Indicates the standard deviation of the force sequence. Represents the transpose of the frequency domain eigenvector; S52. Based on the preset rock-breaking efficiency objective function, calculate the dynamic contribution coefficient of each feature component in the multi-dimensional rock-breaking evaluation feature vector to the tunneling performance. The calculation formula is as follows: ； In the above formula, This represents the dynamic contribution coefficient of the i-th feature component. Let represent the objective function for rock-breaking efficiency. This represents the i-th element in the multi-dimensional rock breaking evaluation feature vector. This represents the partial derivative of the rock-breaking efficiency objective function with respect to its characteristic components. Represents the weighted smoothing factor. This represents the total number of dimensions of the feature components; S53. Input the dynamic contribution coefficient into the preset parameter mapping model. By performing neuron activation operations on the parameter mapping model, determine the optimal tunneling and rock breaking parameters that match the target geological conditions of the ultra-large cross-section vertical shaft. The calculation formula is as follows: ； In the above formula, This represents the optimal rock-breaking parameters for tunneling. This represents a nonlinear parameter mapping model.

2. The parameter determination method according to claim 1, characterized in that, Step S3 specifically includes the following steps: S31. Using the energy transfer vector in the rock-breaking force transmission path matrix, generate the load boundary conditions for each element node in the three-dimensional finite element model. S32. Based on the cross-sectional geometric topology dataset, extract the geometric complexity coefficients of each region in the super-large cross-section, and perform initial global mesh discretization processing on the three-dimensional finite element model. S33. Calculate the energy density gradient of the mesh element along the energy flow path using the energy transfer vector, and adaptively refine the local mesh of the three-dimensional finite element model based on the energy density gradient. Calculate the size of the refined mesh element using the following formula: ； In the above formula, Indicates the size of the encrypted grid cells. Indicates the initial global mesh size. Represents the natural constant. This represents the mesh adaptive adjustment coefficient. The modulus representing the energy density gradient; S34. Load the boundary conditions onto the discretized three-dimensional finite element model, and construct a finite element analytical environment that can simulate dynamic rock breaking response through numerical integration algorithm.

3. The parameter determination method according to claim 2, characterized in that, The formula for calculating the energy density gradient of the grid cell along the energy flow path is: ； In the above formula, Represents the energy density gradient. This represents the energy density field function, used to describe the rock-breaking energy per unit volume carried by a grid cell at three-dimensional spatial coordinates (x, y, z), where x represents the first spatial dimension coordinate, y represents the second spatial dimension coordinate, and z represents the third spatial dimension coordinate. This represents the partial derivative of energy density with respect to the x-axis. This represents the partial derivative of energy density with respect to the y-axis. This represents the partial derivative of energy density with respect to the z-axis.

4. The parameter determination method according to claim 1, characterized in that, Step S4 specifically includes the following steps: S41. Map the hob configuration information to the initial load step features in the finite element analysis environment to establish the relationship between mechanical structure parameters and simulation discrete time-space step size, and simulate the indentation and cutting path of the hob on the three-dimensional finite element model through the incremental step iterative solver. S42. Based on the initial load step characteristics, calculate the damage variables of the mesh elements in the three-dimensional finite element model, and determine the initiation and penetration state of rock cracks according to the damage evolution criterion. Calculate the morphological characteristic coefficients of rock crack propagation. The calculation formula is as follows: ； In the above formula, Represents morphological characteristic coefficients. Indicates the measurement scale. Indicates the number of covered elements. The logarithm of the reciprocal of the measurement scale; S43. Synchronously acquire reaction force data of the hob contact interface in the finite element analysis environment, and perform time-domain signal smoothing processing through the sliding window algorithm to obtain the time-domain force sequence data of the hob. S44. Use Fast Fourier Transform to extract the dominant frequency component from the time-domain force sequence data and generate a frequency domain feature vector.

5. The parameter determination method according to claim 4, characterized in that, The formula for calculating the damage variable of the mesh element in the three-dimensional finite element model is as follows: ； In the above formula, The damage variable represents the mesh element. Represents equivalent plastic strain. Indicates the initial strain of the initial damage. Indicates the fracture strain at failure. This represents the damage evolution rate factor.

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