Support optimization method and system for spatial collaborative bearing structure of coal mine roadway

Abstract

This invention discloses a method and system for optimizing spatial collaborative bearing structure support in coal mine roadways, relating to the field of support parameter optimization technology. The method includes the following steps: collecting geomechanical parameters and constructing a three-dimensional initial geostress field numerical model; reconstructing, in the model, a first bearing structure formed by constrained concrete supports, a second bearing structure formed by high-strength UHPC spray coating, and a third bearing structure formed by grouting anchors and their reinforced surrounding rock, and defining their constitutive relations; applying tunneling disturbance and mining support pressure for simulation and extracting collaborative deformation response data; constructing a collaborative bearing capacity evaluation function to quantify the collaborative working performance of the structure; and optimizing the support parameters using an optimization algorithm, with the goal of maximizing the minimum value of the evaluation function, and outputting the optimal support parameters. This invention, through numerical reconstruction and dynamic collaborative coefficient inversion, achieves quantitative evaluation and parameter optimization of the bearing capacity of the triple structure, improving the stability of roadways in fractured soft rock.

Description

Technical Field

[0001] This invention relates to the field of support parameter optimization technology, specifically to a method and system for optimizing the support of spatially coordinated load-bearing structures for coal mine roadways. Background Technology

[0002] In deep coal mining, the surrounding rock of roadways is often in a complex geological environment characterized by fracture, softness, and high stress. After roadway excavation, the stress state of the surrounding rock changes from triaxial to biaxial, and this change in stress state disrupts the original equilibrium, leading to deformation or even instability of the surrounding rock. Currently, a combined support method of anchor bolts, U-shaped steel supports, and shotcrete is commonly used for such roadways.

[0003] However, traditional design methods have the following shortcomings: the design strength of U-shaped steel supports is difficult to meet the actual surrounding rock pressure requirements, and the anchor bolt parameters are mostly determined based on engineering experience, lacking scientific basis, resulting in poor support effect; the performance of ordinary concrete sprayed layer materials is insufficient, and cracking and spalling are prone to occur, making it impossible to effectively seal the surrounding rock; the anchor cables have poor anchorability in fractured surrounding rock, making it difficult to act on stable rock layers, and the anchoring effect is limited; the existing design methods treat anchor bolts, supports, and sprayed layers as independent components for strength verification, ignoring the spatiotemporal synergistic effect between the three, and failing to quantify the dynamic contribution of each structure in the deformation process of the surrounding rock.

[0004] Therefore, there is an urgent need for a support optimization method that can comprehensively consider the collaborative working mechanism of the triple load-bearing structure. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the shortcomings of the existing technology by providing a method and system for optimizing the support of spatial collaborative bearing structures for coal mine roadways.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] An optimization method for spatially coordinated load-bearing structure support in coal mine roadways includes the following steps:

[0008] Step S1: Collect the geomechanical parameters, geometric contour data and physical and mechanical indices of the surrounding rock of the target tunnel, and construct a three-dimensional initial geostress field numerical model including the original rock stress field.

[0009] Step S2: In the three-dimensional initial geostress field numerical model, reconstruct the first bearing structure formed by the confined concrete support, the second bearing structure formed by the high-strength UHPC spray coating, and the third bearing structure formed by the grouting anchor cable and its reinforced surrounding rock, and define the constitutive relationship and contact mechanical behavior of the first bearing structure, the second bearing structure and the third bearing structure according to their respective material properties.

[0010] Step S3: Apply simulated tunneling disturbance stress wave and mining support pressure to the three-dimensional initial geostress field numerical model to simulate the stress redistribution process of the entire space under the influence of tunnel excavation and subsequent mining, and extract the coordinated deformation response data of the first bearing structure, the second bearing structure and the third bearing structure in the spatiotemporal domain.

[0011] Step S4: Based on the cooperative deformation response data, construct a cooperative bearing capacity evaluation function to characterize the cooperative working performance of the first bearing structure, the second bearing structure and the third bearing structure. The cooperative bearing capacity evaluation function is obtained by combining the equivalent stress of the first bearing structure, the shear stress integral of the second bearing structure along the interface and the average anchorage stress of the third bearing structure.

[0012] Step S5: Taking the maximization of the minimum value of the collaborative bearing capacity evaluation function in the time domain as the optimization objective, and using the preset support strength and deformation limit as constraints, the optimization algorithm is used to optimize the support spacing, UHPC spray layer thickness and anchor cable spacing, and output the optimal support parameter combination.

[0013] Furthermore, in step S2, when constructing the constitutive relation of the first load-bearing structure, a damage plasticity model considering the confining pressure effect is adopted. The evolution law of the damage factor in the damage plasticity model is jointly determined by the ratio of the axial compressive stress of the confined concrete to the uniaxial compressive strength and the stiffness recovery variable, and is described by an exponential damage evolution equation.

[0014] Furthermore, in step S2, when constructing the constitutive relation of the second load-bearing structure, a stochastic damage model based on the Weibull distribution is adopted. The stochastic damage model based on the Weibull distribution describes the reinforcing effect of steel fibers on the tensile strength of UHPC by introducing fiber orientation coefficient, fiber volume content and fiber aspect ratio. The increase in tensile strength follows a Weibull function shape that first increases and then decreases with the increase of tensile strain.

[0015] Furthermore, in step S2, when constructing the constitutive relation of the third load-bearing structure, a composite element model is adopted, and a load transfer function is defined at the anchorage interface. The load transfer function correlates the interface shear stress with the relative displacement between the anchor cable and the surrounding rock, and considers the influence of the grout shear modulus, the anchor cable radius, the anchorage hole radius, and the interface ultimate bond strength. At the same time, a damage factor is introduced to reflect the attenuation of shear stress during the progressive failure of the interface.

[0016] Furthermore, in step S4, the weight coefficients of each term in the collaborative bearing capacity evaluation function are dynamic collaborative coefficients determined through inversion analysis. The dynamic collaborative coefficients are obtained through multidimensional time-varying function inversion. The multidimensional time-varying function introduces exponential and proportional correction terms for the volumetric strain rate of the surrounding rock, the integral of the rate of change of fracture aperture, and the change of water pressure, respectively. The sensitivity coefficients in the exponential and proportional correction terms are calibrated by comparing the measured displacement data on site with the simulated displacement data.

[0017] Furthermore, in step S5, the optimization algorithm is a particle swarm optimization algorithm, and the constraints include: the maximum Mises stress of the first load-bearing structure does not exceed the allowable stress, the maximum shear stress of the second load-bearing structure does not exceed the shear strength, and the maximum elongation of the anchor cable of the third load-bearing structure does not exceed the allowable deformation.

[0018] A spatial collaborative load-bearing structure support optimization system for coal mine roadways, used to implement any of the aforementioned spatial collaborative load-bearing structure support optimization methods for coal mine roadways, including:

[0019] The data acquisition and model building module is used to collect the geomechanical parameters, geometric contour data and physical and mechanical indicators of the surrounding rock of the target tunnel, and to build a three-dimensional initial geostress field numerical model including the original rock stress field.

[0020] The structural reconstruction module, connected to the data acquisition and model building module, is used to reconstruct the first load-bearing structure, the second load-bearing structure, and the third load-bearing structure in the three-dimensional model, and to build numerical models of the first load-bearing structure, the second load-bearing structure, and the third load-bearing structure, and define the corresponding constitutive relations and contact mechanical behaviors.

[0021] The dynamic loading and simulation module, connected to the structural reconstruction module, is used to apply simulated tunneling disturbance stress waves and mining support pressure, simulate the stress redistribution process in the whole space, and output the coordinated deformation response data of the first bearing structure, the second bearing structure and the third bearing structure.

[0022] The collaborative analysis and optimization module, connected to the dynamic loading and simulation module, is used to receive the collaborative deformation response data, construct the collaborative bearing capacity evaluation function, and use the maximization of the minimum value of the collaborative bearing capacity evaluation function in the time domain as the optimization objective to solve for the optimal support matching parameters through the built-in optimization algorithm.

[0023] The visualization and output module, connected to the collaborative analysis and optimization module, is used to display the three-dimensional stress field cloud map, the structural damage evolution process, and the final support parameter design report.

[0024] Furthermore, the structural reconstruction module specifically includes:

[0025] The first structural unit is used to construct a discrete element form of a constrained concrete support model based on a damage plasticity model, so as to reconstruct the first load-bearing structure;

[0026] The second structural unit is used to construct a UHPC spray coating model in the form of a shell element based on a random damage model of the Weibull distribution, so as to reconstruct the second load-bearing structure;

[0027] The third structural unit is used to construct a grouting anchorage zone model in the form of a composite unit based on the interface load transfer function, so as to reconstruct the third load-bearing structure.

[0028] Furthermore, the collaborative analysis and optimization module includes a dynamic coefficient inversion unit, which is used to determine the dynamic collaborative coefficients based on the inversion of multidimensional time-varying functions and to iteratively solve the constraints.

[0029] Furthermore, the system also includes a material parameter database, which stores historical data on the elastic modulus and interfacial ultimate bond strength of grouting materials with different mix proportions, used to update the characteristic parameters of the Weibull distribution-based stochastic damage model and the boundary conditions of the interfacial load transfer function.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] 1. This invention constructs a three-dimensional initial geostress field numerical model and reconstructs the triple load-bearing structure of the confined concrete support, high-strength UHPC spray coating, and grouting anchor cable reinforced surrounding rock in the model, thereby realizing a refined numerical characterization of the anchor-support-grouting support system and providing a basis for quantitative analysis of the load-bearing contribution of each structure.

[0032] 2. This invention proposes a collaborative bearing capacity evaluation function. By weighting and combining the equivalent stress of the first bearing structure, the shear stress integral of the second bearing structure along the interface, and the average anchorage stress of the third bearing structure, it achieves a quantitative evaluation of the collaborative performance of the triple bearing structure for the first time, overcoming the shortcomings of traditional methods that cannot quantify the collaborative effect.

[0033] 3. This invention introduces a dynamic synergy coefficient, which describes the time-varying influence of surrounding rock volumetric strain rate, cumulative fracture aperture, and water pressure changes on the synergy coefficient through a multidimensional time-varying function. This enables the evaluation function to truly reflect the dynamic adjustment of the contribution weights of each structure during the evolution of surrounding rock damage, significantly improving the engineering adaptability of the optimization results.

[0034] 4. This invention aims to maximize the collaborative bearing capacity assessment function and uses the particle swarm optimization algorithm to globally optimize the support spacing, spray layer thickness, and anchor cable spacing. Under the condition of satisfying strength and deformation constraints, it outputs the optimal combination of support parameters, realizing the transformation from empirical analogy to scientific calculation, which avoids material waste and ensures support safety. Attached Figure Description

[0035] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0036] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0037] Figure 2 This is a system schematic diagram according to an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] like Figure 1 As shown, the spatial collaborative bearing capacity support optimization method for coal mine roadways includes the following steps:

[0040] Step S1: Collect the geomechanical parameters, geometric contour data and physical and mechanical indices of the surrounding rock of the target tunnel, and construct a three-dimensional initial geostress field numerical model including the original rock stress field.

[0041] Step S2: In the three-dimensional initial geostress field numerical model, reconstruct the first bearing structure formed by the confined concrete support, the second bearing structure formed by the high-strength UHPC spray coating, and the third bearing structure formed by the grouting anchor cable and its reinforced surrounding rock, and define the constitutive relationship and contact mechanical behavior of the first bearing structure, the second bearing structure and the third bearing structure according to their respective material properties.

[0042] Step S3: Apply simulated tunneling disturbance stress wave and mining support pressure to the three-dimensional initial geostress field numerical model to simulate the stress redistribution process of the entire space under the influence of tunnel excavation and subsequent mining, and extract the coordinated deformation response data of the first bearing structure, the second bearing structure and the third bearing structure in the spatiotemporal domain.

[0043] Step S4: Based on the cooperative deformation response data, construct a cooperative bearing capacity evaluation function to characterize the cooperative working performance of the first bearing structure, the second bearing structure and the third bearing structure. The cooperative bearing capacity evaluation function is obtained by combining the equivalent stress of the first bearing structure, the shear stress integral of the second bearing structure along the interface and the average anchorage stress of the third bearing structure.

[0044] Step S5: Taking the maximization of the minimum value of the collaborative bearing capacity evaluation function in the time domain as the optimization objective, and using the preset support strength and deformation limit as constraints, the optimization algorithm is used to optimize the support spacing, UHPC spray layer thickness and anchor cable spacing, and output the optimal support parameter combination.

[0045] First, the geomechanical parameters of the rock strata where the target tunnel is located are obtained through on-site geological exploration and indoor rock mechanics tests, mainly including:

[0046] Rock mass density is usually obtained by on-site sampling and weighing, with a typical range of 2400~2800 kg / m³; elastic modulus is determined by uniaxial compression test, generally 1~10 GPa for soft rock and 20~40 GPa for hard rock; Poisson's ratio, ranging from 0.15 to 0.35, can be obtained by the ratio of transverse to longitudinal strain in uniaxial compression test; cohesion is obtained by triaxial compression test, generally 0.5~5 MPa for soft rock and 10~30 MPa for hard rock; internal friction angle is also determined by triaxial test, generally 20°~30° for soft rock and 35°~50° for hard rock; tensile strength is determined by Brazilian splitting test, and is usually 1 / 10~1 / 20 of the uniaxial compressive strength.

[0047] Simultaneously, the geometric contour data of the tunnel, including cross-sectional shape, cross-sectional dimensions, and tunnel depth, are acquired. Based on the above data, a three-dimensional numerical model incorporating the original rock stress field is constructed using finite element software such as ABAQUS or finite difference software such as FLAC3D. The model dimensions should meet the following requirement: the outward extension distance around the tunnel perimeter should be no less than 3 to 5 times the tunnel width to eliminate boundary effects. The initial geostress field is applied based on measured geostress data or empirical formulas. Vertical stress is calculated based on self-weight stress, and horizontal stress is applied based on the lateral pressure coefficient. The lateral pressure coefficient generally ranges from 0.5 to 1.5, and specific values ​​can be obtained by referring to regional geostress measurement data or the Hoek-Brown empirical formula.

[0048] In step S2, when constructing the constitutive relationship of the first load-bearing structure, a damage plasticity model considering the confining pressure effect is adopted. The evolution law of the damage factor in the damage plasticity model is jointly determined by the ratio of the axial compressive stress of the confined concrete to the uniaxial compressive strength and the stiffness recovery variable, and is described by an exponential damage evolution equation.

[0049] The confined concrete support is discretized into beam elements or solid elements. The constitutive relation adopts a damage-plastic model considering the confining pressure effect. The core of this model is the evolution equation of the damage factor, which describes the stiffness degradation of concrete during the compressive damage process. The formula for calculating the damage factor is:

[0050]

[0051] in, This represents the damage factor of confined concrete, and is dimensionless. This represents the compressive stiffness recovery variable, reflecting the degree of damage accumulation in concrete under cyclic loading, and is obtained through equivalent plastic strain calculation. This represents the damage threshold, i.e., the equivalent plastic strain value at which damage begins to occur. It is typically taken as 0.0001 to 0.0005, and the specific value can be determined by the stress-strain curve of a uniaxial compression test. This parameter represents the brittleness of the material and controls the rate of damage development. For C40 concrete, it is typically found by fitting a uniaxial compression test curve, with a value ranging from 0.005 to 0.02. For high-strength concrete, it can be taken as 0.001 to 0.01. This represents the axial compressive stress of the support, which changes during the loading process. It represents the uniaxial compressive strength of concrete, determined through indoor testing;

[0052] The above formula describes that the damage factor increases exponentially with the equivalent plastic strain, and that the higher the axial stress level, the faster the damage develops, which is consistent with the physical law of accelerated failure of concrete under high stress.

[0053] In step S2, when constructing the constitutive relation of the second load-bearing structure, a stochastic damage model based on the Weibull distribution is adopted. The stochastic damage model based on the Weibull distribution describes the reinforcing effect of steel fibers on the tensile strength of UHPC by introducing fiber orientation coefficient, fiber volume content and fiber aspect ratio. The increase in tensile strength follows a Weibull function shape that first increases and then decreases with the increase of tensile strain.

[0054] The UHPC coating layer is simulated as a shell element, and the constitutive relation adopts a stochastic damage model based on the Weibull distribution to reflect the enhancing effect of the random distribution of steel fibers on the tensile properties of the material. The formula for calculating the tensile strength of UHPC is as follows:

[0055]

[0056] in, Indicates the tensile strength of UHPC. This indicates the tensile strength of the matrix material; the UHPC matrix typically has a tensile strength of 8~12 MPa. This represents the fiber orientation coefficient, which depends on the fiber distribution and orientation in the sprayed coating. For spraying, the fibers tend to be randomly distributed in two dimensions, and are generally taken as 0.5~0.8. If the application process is good, the fibers can be oriented towards the principal stress direction, and can be taken as 0.8~1.0. This indicates the fiber volume content, which is the percentage of steel fiber in the total volume. and These represent fiber length and diameter, respectively. Indicates the current tensile strain. This represents the initial crack strain of the matrix, i.e., the strain value at which microcracks begin to appear in the matrix. For UHPC matrices, this is typically 0.0003~0.0005. UHPC represents the ultimate tensile strain, which is the strain value at which a material reaches its peak tensile strength. UHPC is typically 0.003~0.005. This parameter represents the shape of the Weibull distribution and controls the steepness of the strength decay curve. For steel fiber reinforced materials, the value range is generally 2 to 5.

[0057] The above formula shows that the fiber reinforcement effect increases exponentially when the strain is small, reaches a peak and then decays with increasing strain, simulating the softening process of the fiber being gradually pulled out of the matrix.

[0058] In step S2, when constructing the constitutive relationship of the third load-bearing structure, a composite element model is adopted, and a load transfer function is defined at the anchorage interface. The load transfer function associates the interface shear stress with the relative displacement between the anchor cable and the surrounding rock, and considers the influence of the grout shear modulus, the anchor cable radius, the anchorage hole radius and the interface ultimate bond strength. At the same time, a damage factor is introduced to reflect the attenuation of shear stress during the progressive failure of the interface.

[0059] The grouting anchor cable and the surrounding rock within its influence range are considered as a composite unit. The anchor cable is simulated using rod elements or cable elements, and the interface between the grouting body and the surrounding rock is simulated using cohesive elements. The specific formula for the interface shear stress transfer function is as follows:

[0060]

[0061] in, This represents the interfacial shear stress at the anchorage depth z. This indicates the shear modulus of the grout. Indicates the anchor cable radius. Indicates the radius of the anchor hole. This represents the displacement of the anchor cable along depth z. This represents the displacement of the surrounding rock along depth z. Indicates the ultimate interfacial bond strength. The length of the anchorage section is typically the total length of the anchor cable minus the length of the free section. This represents the cumulative integral of shear stress from the anchorage point to depth z. Represents the integral variable, indicating the position along the anchorage depth;

[0062] The transfer function described above consists of two parts: the first part represents the linear relationship between shear stress and relative displacement, similar to the shear stiffness in the elastic stage; the second part is the damage factor, which indicates that the interfacial bonding capacity gradually decreases as the cumulative shear stress increases, reaching a certain level when the cumulative shear stress reaches a certain threshold. At that time, the damage factor is 0, and the interface is completely destroyed.

[0063] After completing the three-dimensional initial geostress field numerical model and the reconstruction of the triple bearing structure, dynamic loads reflecting the actual engineering environment need to be applied to the model to simulate the full-space stress redistribution process under the influence of tunnel excavation disturbance and subsequent mining. Specific implementation methods include:

[0064] 1. During tunnel excavation, blasting or mechanical cutting releases energy to the surrounding rock in the form of stress waves, causing transient adjustments in the stress of the surrounding rock. This invention uses a method of applying dynamic unloading force at the excavation boundary to simulate the excavation disturbance. Specifically, it includes applying a dynamic surface force equal in magnitude and opposite in direction to the initial ground stress on the excavation boundary surface of the model to simulate the effect of sudden release of the original stress after the rock mass is excavated. The change of dynamic unloading force over time can be described by an exponential decay function or a measured blasting vibration waveform.

[0065] 2. Due to the impact of mining operations, the supporting pressure on the surrounding rock of the roadway will dynamically change, exhibiting a characteristic of first increasing and then decreasing. This invention uses a sine function to approximate the vertical stress increment caused by mining, applying it as a distributed force to the upper boundary of the model, corresponding to the mining-affected area. The specific formula is as follows:

[0066]

[0067] in, This represents the total vertical stress acting on the upper boundary of the model under the influence of mining. Indicates the vertical stress of the original rock. This represents the stress increase factor, reflecting the degree of maximum stress concentration caused by mining. Based on field measurements or empirical formulas, it is generally taken as 1.2 to 2.0. Represents a time variable. This indicates the total time of impact from mining, that is, the duration from when the working face begins to affect the cross section until it moves away from the cross section;

[0068] 3. The excavation of the tunnel is achieved by the killing element method, that is, after calculating the initial ground stress balance, the corresponding element inside the tunnel is removed at once, and the above-mentioned excavation disturbance stress wave is applied at the same time. To simulate step-by-step excavation, the element can also be removed step by step according to the excavation cycle.

[0069] 4. Use an explicit dynamic solver for time history analysis. The time step should satisfy the Courant stability condition to ensure calculation convergence. The total simulation time should cover the entire process from excavation to the end of mining impact. During the solution process, output the following cooperative deformation response data at fixed time intervals: equivalent stress and displacement of key sections of the first load-bearing structure (confined concrete support); shear stress distribution along the interface of the second load-bearing structure (UHPC spray coating); average anchorage stress and axial force distribution of the third load-bearing structure (grouting anchor cable); volumetric strain and fracture aperture change rate of the surrounding rock. These data will be used as inputs to the cooperative bearing capacity evaluation function in step S4 to dynamically evaluate the cooperative working performance of the triple structure.

[0070] In step S4, the weight coefficients of each term in the collaborative bearing capacity evaluation function are dynamic collaborative coefficients determined by inversion analysis. The dynamic collaborative coefficients are obtained by inversion using a multidimensional time-varying function. The multidimensional time-varying function introduces exponential and proportional correction terms for the volumetric strain rate of the surrounding rock, the integral of the rate of change of fracture aperture, and the change of water pressure, respectively. The sensitivity coefficients in the exponential and proportional correction terms are calibrated by comparing the measured displacement data with the simulated displacement data.

[0071] A collaborative bearing capacity assessment function is constructed to quantify the overall collaborative bearing capacity of the triple structure at time t. The specific formula is as follows:

[0072]

[0073] in, This represents the collaborative bearing capacity assessment function; a larger value indicates a stronger collaborative bearing capacity. This represents the equivalent stress of the first load-bearing structure, taken as the maximum value at the critical section. This represents the shear stress integral of the second load-bearing structure along the entire spray layer interface S. Indicates the interface integral variable. This represents the average anchorage stress of the third load-bearing structure. This indicates the elastic modulus of the grout. This represents the elastic modulus of the original rock. The dynamic synergy coefficient is obtained through inversion using a multidimensional time-varying function, and the specific formula is as follows:

[0074]

[0075] in, This represents the volumetric strain of the surrounding rock. This represents the first sensitivity coefficient, typically ranging from 100 to 1000 seconds. This represents the second sensitivity coefficient, typically ranging from 100 to 1000m. - ¹, This represents the third sensitivity coefficient, typically ranging from 0.1 to 1.0 MPa. - ¹, This represents the rate of change of fracture aperture, where Represents the local coordinates along the crack. Represents the time integral variable, This represents the cumulative change in fracture aperture from the initial time to the current time t, i.e., the total opening of the fracture. This represents the change in water pressure, i.e., the change relative to the initial water pressure. The sensitivity coefficient calibration method specifically includes: setting a set of initial values ​​in the numerical model, calculating the displacement time history curve, comparing it with the actual measured displacement on site, and using the least squares method or genetic algorithm for optimization to minimize the error between the two.

[0076] In step S5, the optimization algorithm is a particle swarm optimization algorithm, and the constraints include: the maximum Mises stress of the first load-bearing structure does not exceed the allowable stress, the maximum shear stress of the second load-bearing structure does not exceed the shear strength, and the maximum elongation of the anchor cable of the third load-bearing structure does not exceed the allowable deformation.

[0077] The minimum value of the collaborative bearing capacity assessment function over the entire time domain is used as the optimization index, and the optimization objective is to maximize the minimum value of the collaborative bearing capacity assessment function over the entire time domain, i.e., to maximize the collaborative bearing capacity under the worst-case scenario. The design variables are the support spacing, UHPC spray layer thickness, and anchor cable spacing. The specific constraints include: the maximum Mises stress of the first bearing structure does not exceed its allowable stress, which is taken as 0.6 to 0.7 times the compressive strength of concrete; the maximum shear stress of the second bearing structure does not exceed its shear strength, which is taken as the shear strength of UHPC, generally 0.1 to 0.15 times its compressive strength; the maximum elongation of the anchor cable of the third bearing structure does not exceed the allowable deformation, which is determined according to the requirements of the roadway cross-section, for example, 50 to 100 mm.

[0078] The minimum value of the time-domain collaborative bearing capacity assessment function is defined as:

[0079]

[0080] The optimization objective is to maximize this minimum value. ;

[0081] in, This represents the minimum value of the collaborative bearing capacity assessment function in the time domain;

[0082] The optimization process using the particle swarm optimization algorithm includes: 1. Randomly generating N sets of parameter combinations, where N is typically 20-40, and the range of design variables is set based on engineering experience; 2. For each set of parameters, calling the established numerical model to calculate... 3. For each particle, if the current If the value is greater than its historical best, then update the individual's optimal position; the maximum among all particles. The corresponding position is the global optimal position; 4. According to the standard velocity-position update formula of the particle swarm algorithm, adjust the search direction and step size of each particle; 5. Repeat steps 2 to 4 until the maximum number of iterations is reached, such as 100 times or the change of the objective function is less than the threshold of 1%; The final output of the global optimal parameter combination is the optimal support parameter, which can be directly used to guide on-site construction and achieve the optimal collaborative support effect of the anchor-frame-filling triple load-bearing structure.

[0083] like Figure 2 As shown, a spatial collaborative load-bearing structure support optimization system for coal mine roadways is used to implement any of the aforementioned spatial collaborative load-bearing structure support optimization methods for coal mine roadways, including:

[0084] The data acquisition and model building module is used to collect the geomechanical parameters, geometric contour data and physical and mechanical indicators of the surrounding rock of the target tunnel, and to build a three-dimensional initial geostress field numerical model including the original rock stress field.

[0085] The structural reconstruction module, connected to the data acquisition and model building module, is used to reconstruct the first load-bearing structure, the second load-bearing structure, and the third load-bearing structure in the three-dimensional model, and to build numerical models of the first load-bearing structure, the second load-bearing structure, and the third load-bearing structure, and define the corresponding constitutive relations and contact mechanical behaviors.

[0086] The dynamic loading and simulation module, connected to the structural reconstruction module, is used to apply simulated tunneling disturbance stress waves and mining support pressure, simulate the stress redistribution process in the whole space, and output the coordinated deformation response data of the first bearing structure, the second bearing structure and the third bearing structure.

[0087] The collaborative analysis and optimization module, connected to the dynamic loading and simulation module, is used to receive the collaborative deformation response data, construct the collaborative bearing capacity evaluation function, and use the maximization of the minimum value of the collaborative bearing capacity evaluation function in the time domain as the optimization objective to solve for the optimal support matching parameters through the built-in optimization algorithm.

[0088] The visualization and output module, connected to the collaborative analysis and optimization module, is used to display the three-dimensional stress field cloud map, the structural damage evolution process, and the final support parameter design report.

[0089] The structural reconstruction module specifically includes:

[0090] The first structural unit is used to construct a discrete element form of a constrained concrete support model based on a damage plasticity model, so as to reconstruct the first load-bearing structure;

[0091] The second structural unit is used to construct a UHPC spray coating model in the form of a shell element based on a random damage model of the Weibull distribution, so as to reconstruct the second load-bearing structure;

[0092] The third structural unit is used to construct a grouting anchorage zone model in the form of a composite unit based on the interface load transfer function, so as to reconstruct the third load-bearing structure.

[0093] The collaborative analysis and optimization module includes a dynamic coefficient inversion unit, which is used to determine the dynamic collaborative coefficients based on the inversion of multidimensional time-varying functions and to iteratively solve the constraints.

[0094] The system also includes a material parameter database, which stores historical data on the elastic modulus and interfacial ultimate bond strength of grouting materials with different mix proportions. This data is used to update the characteristic parameters of the Weibull distribution-based stochastic damage model and the boundary conditions of the interfacial load transfer function.

[0095] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0096] The examples described herein are merely preferred embodiments of the invention and are not intended to limit the concept and scope of the invention. Any modifications and improvements made by those skilled in the art to the technical solutions of the invention without departing from the design concept of the invention should fall within the protection scope of the invention.

Claims

1. A method for optimizing spatial collaborative load-bearing structure support in coal mine roadways, characterized in that, Includes the following steps: Step S1: Collect the geomechanical parameters, geometric contour data and physical and mechanical indices of the surrounding rock of the target tunnel, and construct a three-dimensional initial geostress field numerical model including the original rock stress field. Step S2: In the three-dimensional initial geostress field numerical model, reconstruct the first bearing structure formed by the confined concrete support, the second bearing structure formed by the high-strength UHPC spray coating, and the third bearing structure formed by the grouting anchor cable and its reinforced surrounding rock, and define the constitutive relationship and contact mechanical behavior of the first bearing structure, the second bearing structure and the third bearing structure according to their respective material properties. Step S3: Apply simulated tunneling disturbance stress wave and mining support pressure to the three-dimensional initial geostress field numerical model to simulate the stress redistribution process of the entire space under the influence of tunnel excavation and subsequent mining, and extract the coordinated deformation response data of the first bearing structure, the second bearing structure and the third bearing structure in the spatiotemporal domain. Step S4: Based on the cooperative deformation response data, construct a cooperative bearing capacity evaluation function to characterize the cooperative working performance of the first bearing structure, the second bearing structure and the third bearing structure. The cooperative bearing capacity evaluation function is obtained by combining the equivalent stress of the first bearing structure, the shear stress integral of the second bearing structure along the interface and the average anchorage stress of the third bearing structure. Step S5: Taking the maximization of the minimum value of the collaborative bearing capacity evaluation function in the time domain as the optimization objective, and using the preset support strength and deformation limit as constraints, the optimization algorithm is used to optimize the support spacing, UHPC spray layer thickness and anchor cable spacing, and output the optimal support parameter combination.

2. The method according to claim 1, characterized in that, In step S2, when constructing the constitutive relationship of the first load-bearing structure, a damage plasticity model considering the confining pressure effect is adopted. The evolution law of the damage factor in the damage plasticity model is jointly determined by the ratio of the axial compressive stress of the confined concrete to the uniaxial compressive strength and the stiffness recovery variable, and is described by an exponential damage evolution equation.

3. The method according to claim 2, characterized in that, In step S2, when constructing the constitutive relation of the second load-bearing structure, a stochastic damage model based on the Weibull distribution is adopted. The stochastic damage model based on the Weibull distribution describes the reinforcing effect of steel fibers on the tensile strength of UHPC by introducing fiber orientation coefficient, fiber volume content and fiber aspect ratio. The increase in tensile strength follows a Weibull function shape that first increases and then decreases with the increase of tensile strain.

4. The method according to claim 3, characterized in that, In step S2, when constructing the constitutive relationship of the third load-bearing structure, a composite element model is adopted, and a load transfer function is defined at the anchorage interface. The load transfer function associates the interface shear stress with the relative displacement between the anchor cable and the surrounding rock, and considers the influence of the grout shear modulus, the anchor cable radius, the anchorage hole radius and the interface ultimate bond strength. At the same time, a damage factor is introduced to reflect the attenuation of shear stress during the progressive failure of the interface.

5. The method according to claim 4, characterized in that, In step S4, the weight coefficients of each term in the collaborative bearing capacity evaluation function are dynamic collaborative coefficients determined by inversion analysis. The dynamic collaborative coefficients are obtained by inversion using a multidimensional time-varying function. The multidimensional time-varying function introduces exponential and proportional correction terms for the volumetric strain rate of the surrounding rock, the integral of the rate of change of fracture aperture, and the change of water pressure, respectively. The sensitivity coefficients in the exponential and proportional correction terms are calibrated by comparing the measured displacement data with the simulated displacement data.

6. The method according to claim 5, characterized in that, In step S5, the optimization algorithm is a particle swarm optimization algorithm, and the constraints include: the maximum Mises stress of the first load-bearing structure does not exceed the allowable stress, the maximum shear stress of the second load-bearing structure does not exceed the shear strength, and the maximum elongation of the anchor cable of the third load-bearing structure does not exceed the allowable deformation.

7. A spatial collaborative load-bearing structure support optimization system for coal mine roadways, used to implement the spatial collaborative load-bearing structure support optimization method for coal mine roadways as described in any one of claims 1-6, characterized in that, include: The data acquisition and model building module is used to collect the geomechanical parameters, geometric contour data and physical and mechanical indicators of the surrounding rock of the target tunnel, and to build a three-dimensional initial geostress field numerical model including the original rock stress field. The structural reconstruction module, connected to the data acquisition and model building module, is used to reconstruct the first load-bearing structure, the second load-bearing structure, and the third load-bearing structure in the three-dimensional model, and to build numerical models of the first load-bearing structure, the second load-bearing structure, and the third load-bearing structure, and define the corresponding constitutive relations and contact mechanical behaviors. The dynamic loading and simulation module, connected to the structural reconstruction module, is used to apply simulated tunneling disturbance stress waves and mining support pressure, simulate the stress redistribution process in the whole space, and output the coordinated deformation response data of the first bearing structure, the second bearing structure and the third bearing structure. The collaborative analysis and optimization module, connected to the dynamic loading and simulation module, is used to receive the collaborative deformation response data, construct the collaborative bearing capacity evaluation function, and use the maximization of the minimum value of the collaborative bearing capacity evaluation function in the time domain as the optimization objective to solve for the optimal support matching parameters through the built-in optimization algorithm. The visualization and output module, connected to the collaborative analysis and optimization module, is used to display the three-dimensional stress field cloud map, the structural damage evolution process, and the final support parameter design report.

8. The system according to claim 7, characterized in that, The structural reconstruction module specifically includes: The first structural unit is used to construct a discrete element form of a constrained concrete support model based on a damage plasticity model, so as to reconstruct the first load-bearing structure; The second structural unit is used to construct a UHPC spray coating model in the form of a shell element based on a random damage model of the Weibull distribution, so as to reconstruct the second load-bearing structure; The third structural unit is used to construct a grouting anchorage zone model in the form of a composite unit based on the interface load transfer function, so as to reconstruct the third load-bearing structure.

9. The system according to claim 8, characterized in that, The collaborative analysis and optimization module includes a dynamic coefficient inversion unit, which is used to determine the dynamic collaborative coefficients based on the inversion of multidimensional time-varying functions and to iteratively solve the constraints.

10. The system according to claim 9, characterized in that, The system also includes a material parameter database, which stores historical data on the elastic modulus and interfacial ultimate bond strength of grouting materials with different mix proportions. This data is used to update the characteristic parameters of the Weibull distribution-based stochastic damage model and the boundary conditions of the interfacial load transfer function.

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