Roadway support parameter intelligent generation and optimization method for stress field diffusion maximization

Abstract

The application relates to a stress field diffusion maximization roadway support parameter intelligent generation and optimization method, belonging to the technical field of roadway surrounding rock control, and comprising the following steps: firstly, obtaining basic parameters such as rock mechanics, ground stress, rock mass integrity and roadway geometry through drilling sampling, indoor and field testing; secondly, defining a support stress field diffusion degree as an index for quantifying the effect of an effective pressure stress area of an anchor rod group; thirdly, establishing an analytic model of the diffusion degree and anchor rod length, spacing, row spacing and pre-tightening force containing a surrounding rock influence coefficient based on the superposition of elastic mechanics and the anchor rod load transmission mechanism; fourthly, solving optimal anchor rod parameters by using an intelligent algorithm with the diffusion degree maximization as a target and in combination with engineering constraints; fifthly, realizing three-dimensional visualization of a support stress field, checking a stress blank area and fine-tuning parameters as required; and finally, standardizing and outputting optimal support parameters and supporting visual graphics to form complete support design and construction files. The method can realize intelligent generation and dynamic optimization of roadway support parameters.

Description

Technical Field

[0001] This invention belongs to the field of mine roadway support technology, specifically relating to an intelligent generation and optimization method for roadway support parameters that maximizes stress field diffusion. Background Technology

[0002] Roadway bolt support is a core technical means to control the stability of surrounding rock in underground mining engineering. Its core mechanism is that through the synergistic deformation of the bolt and the surrounding rock, a reinforced load-bearing structure is constructed within the rock mass, thereby inhibiting the further development of loosening and damage of the surrounding rock and ensuring the safety of roadway construction and operation. As mining continues to extend into deeper strata, the surrounding rock environment of roadways exhibits multiple characteristics of high ground stress, strong mining disturbance, and complex geological structures, which places higher demands on the scientific and rational design of bolt support.

[0003] Currently, the determination of roadway anchor support parameters mainly relies on three conventional methods: engineering analogy, empirical formulas, and numerical simulation. However, all of these methods have certain technical limitations: engineering analogy depends on successful engineering cases under similar geological conditions; however, the discreteness and complexity of surrounding rock geological conditions often lead to large deviations in the analogy results, making it difficult to adapt to specific engineering scenarios. Empirical formulas oversimplify complex geological environments and stress processes, failing to accurately reflect the true stress state of anchors and surrounding rock under actual working conditions, resulting in insufficient design accuracy. Numerical simulation requires designers to repeatedly adjust parameters and perform multiple iterative calculations, which is not only time-consuming and labor-intensive, but also highly dependent on the designer's engineering experience and professional level, exhibiting strong subjectivity and making it difficult to guarantee the objectivity and scientific rigor of the design. Therefore, how to scientifically and accurately determine core support parameters such as anchor length, spacing, row spacing, and preload based on specific surrounding rock conditions, so that the support stress field formed by the anchor group within the surrounding rock reaches the optimal distribution state, thereby fully leveraging the reinforcement effectiveness of anchor support, is a core technical challenge that urgently needs to be solved in the field of roadway support design.

[0004] While some progress has been made in the optimization of anchor bolt support parameters, several shortcomings remain, and a comprehensive design system has not yet been established. The most prominent issue is the lack of quantitative correlation between the overall support stress distribution and support parameters formed by anchor bolt groups in the surrounding rock, and a lack of systematic quantitative analysis methods. Specifically, existing research often focuses on analyzing the stress characteristics of individual anchor bolts through numerical simulation or obtaining point data from the anchor bolt ends through on-site monitoring, thereby indirectly inferring the overall support effect. This approach fails to accurately quantify the continuity, coverage, and stress distribution level of the compressive stress zone formed by the anchor bolt group, making it difficult to determine the integrity and effectiveness of the anchored load-bearing structure. Furthermore, the determination of anchor bolt support parameters lacks a clear optimization objective. Existing designs often focus on meeting certain empirical indicators or safety factor requirements, failing to integrate the design philosophy of maximizing support effect and optimizing support cost into the entire design process, resulting in a difficulty in balancing the rationality and economy of the support design. In addition, existing support parameter design methods are mostly static design modes, which cannot be dynamically adjusted according to the actual geological state and stress characteristics of the surrounding rock gradually revealed during the tunnel excavation process. This results in a disconnect between the design scheme and the actual working conditions: either the support parameters are selected too conservatively, resulting in waste of support materials and reduced tunneling efficiency; or the support parameters are insufficient, making it impossible to effectively control the deformation of the surrounding rock, which in turn leads to safety hazards such as tunnel instability.

[0005] To overcome the shortcomings of the existing technology and solve problems such as inaccurate design of roadway anchor support parameters, poor dynamic adaptability, and insufficient support effectiveness, it is urgent to provide a method for intelligent generation and dynamic optimization of roadway support parameters based on maximizing stress field diffusion. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides an intelligent generation and optimization method for roadway support parameters that maximizes stress field diffusion. This method can achieve intelligent generation and dynamic optimization of roadway support parameters, overcome the subjectivity and inefficiency of traditional experience-based design, and provide an advanced technical tool for support design in mines and underground engineering.

[0007] To achieve the above objectives, the present invention provides an intelligent generation and optimization method for roadway support parameters that maximizes stress field diffusion, comprising the following steps:

[0008] Step 1: Measurement and acquisition of surrounding rock conditions and geostress parameters; through borehole sampling laboratory tests and in-situ field tests, rock mechanical parameters, geostress parameters, rock mass integrity parameters and tunnel geometric parameters are obtained to form a set of measured parameters;

[0009] Step 2: Define the support stress field diffusion degree; Define the diffusion degree To quantify the comprehensive effect of anchor bolt groups in forming an effective compressive stress zone in the surrounding rock, the area ratio of the effective compressive stress zone and the average stress level are taken into account.

[0010] Step 3: Construct an analytical model of diffusion degree based on the stress diffusion mechanism; establish the diffusion degree based on the superposition principle of elasticity and the load transfer mechanism of anchor bolts. With anchor bolt length ,spacing Row spacing Preload An analytical relationship model between them was developed, and a comprehensive influence coefficient of surrounding rock conditions was introduced;

[0011] Step 4: Generate optimal support parameters based on diffusion maximization; using diffusion degree With maximization as the objective and considering engineering constraints, particle swarm optimization or genetic algorithms are used to solve for the optimal combination of anchor bolt parameters.

[0012] Step 5: 3D visualization and blank area verification of the support stress field; input the optimal support parameters into the numerical model, calculate and visualize the additional stress field of the support, identify the stress blank area, and if the requirements are not met, adjust the spacing or row spacing and recalculate.

[0013] Step 6: Output of support parameters and generation of construction documents; Output the finalized optimal support parameters in a standardized table and attach a 3D visualization graphic to form a complete support design document.

[0014] As a preferred option, step 7 is also included: dynamic optimization feedback based on measured data;

[0015] After the tunnel excavation is completed, the actual support effect is judged by the deviation between the measured anchor bolt axial force and the design preload. When the relative deviation between the measured anchor bolt axial force and the design preload exceeds 30%, a displacement gauge is installed, and the displacement back analysis method is used to correct the elastic modulus and lateral pressure coefficient of the surrounding rock, and the support parameters of the subsequent sections are re-optimized.

[0016] As a preferred option, the process of measuring and obtaining the surrounding rock conditions and geostress parameters in step 1 is as follows:

[0017] S11: Obtain rock mechanical parameters; obtain the uniaxial compressive strength of the rock through indoor rock mechanics tests after core drilling. Rock mass elastic modulus Cohesion internal friction angle ;

[0018] S12: Obtain in-situ stress parameters; obtain vertical principal stress through field testing using stress relief methods or hydraulic fracturing methods. and maximum horizontal principal stress And calculate the lateral pressure coefficient. ;

[0019] S13: Obtain rock mass integrity parameters; obtain longitudinal wave velocity of the rock mass through single-hole acoustic wave testing. Longitudinal wave velocity of rocks was obtained through indoor rock sample testing. Calculate the rock mass integrity factor The maximum depth of the area where wave velocity significantly decreases was determined by borehole television, thus obtaining the thickness of the loosened zone of the surrounding rock. ;

[0020] S14: Obtain tunnel geometry parameters; measure and record tunnel width. With height .

[0021] As a preferred embodiment, the process of defining the support stress field diffusion degree in step 2 is as follows:

[0022] S21: Define the anchorage zone; the anchorage zone is defined as the distance from the roadway surface. to The annular region, in which The anchor bolt length; the total area of ​​the anchorage zone. Calculated based on the cross-sectional shape of the tunnel;

[0023] S22: Calculate the effective area; extract the additional support stress of each unit in the anchorage zone through numerical model post-processing. ; Statistical satisfaction The effective area is obtained by summing the surface areas of the units. ;

[0024] S23: Calculate the effective mean stress; calculate all conditions that satisfy... arithmetic mean of the additional stress of the unit support ;

[0025] S24: Define the diffusivity; the diffusivity is defined according to the following formula:

[0026] ;

[0027] In the formula, .

[0028] As a preferred option, in step 3, the process of constructing the analytical model of diffusivity based on the stress diffusion mechanism is as follows:

[0029] S31: Construct an analytical model for diffusion; construct the analytical model for diffusion based on the following formula:

[0030] ;

[0031] In the formula, , , , These are the influence indices of length, spacing, row spacing, and preload; This is the comprehensive influence coefficient of the surrounding rock conditions;

[0032] S32: Perform coefficient calibration;

[0033] Based on rock mass integrity coefficient The surrounding rock was divided into three categories; for each category, no fewer than 25 sets of orthogonal numerical tests were conducted, and the length influence index was determined using a multivariate nonlinear regression method. Spacing Influence Index Row spacing influence index Preload Influence Index and the comprehensive influence coefficient of surrounding rock conditions Regression coefficients in the expression , , , , , , , ;

[0034] S33: Determine the comprehensive influence coefficient of surrounding rock conditions; substitute the calibrated regression coefficients into... The comprehensive influence coefficient under different surrounding rock conditions was obtained. .

[0035] As a preferred option, in step 4, the process of generating the optimal support parameters based on diffusion maximization is as follows:

[0036] S41: Establish an optimization model; construct the objective function based on the following formula:

[0037] ;

[0038] In the formula, This is the set of measured parameters;

[0039] S42: Establish constraints; the constraints are as follows:

[0040] Anchor bolt length: ;

[0041] Anchor spacing: ;

[0042] Anchor bolt spacing: ;

[0043] Spacing ratio: ;

[0044] Preload: ,in , The anchor rod's yield strength. The cross-sectional area of ​​the anchor bolt;

[0045] S43: Optimize algorithm parameters; solve using either particle swarm optimization (PSO) or genetic algorithm; when using PSO, the number of particles is 30, the number of iterations is 50, the inertia weight is 0.7, and the learning factor is 1.5; when using genetic algorithm, the population size is 30, the crossover probability is 0.8, the mutation probability is 0.1, and the number of iterations is 50; the optimal parameter combination is obtained after solving. .

[0046] As a preferred option, the three-dimensional visualization and blank area verification process of the support stress field in step 5 is as follows:

[0047] S51: Establish a numerical model; the model range is 5 times the tunnel span, and the boundary condition is: vertical principal stress is applied at the top. Lateral application of horizontal principal stress The bottom is fixed with vertical displacement; the constitutive model of the surrounding rock is adopted using the Mohr-Coulomb model, and the anchor bolt is simulated using cable elements;

[0048] S52: Stress field visualization; generates 3D stress cloud maps, compressive stress zone contour maps, and anchor bolt force distribution maps;

[0049] S53: Blank area identification; discretize the anchorage area into grid cells; mark areas located inside the quadrilateral formed by four adjacent anchor bolts and with additional support stress. The units are counted to determine the total area of ​​these units. ;

[0050] S54: Calculation and adjustment of blank area proportion; the blank area proportion is defined according to the following formula:

[0051] ;

[0052] like Then reduce the spacing by 10% increments. or row spacing Re-execute S51 to S53 until... .

[0053] As a preferred option, the support parameter output and construction document generation process in step 6 is as follows:

[0054] S61: Parameter table output; The following parameters are output in tabular form: final values ​​of anchor bolt length, spacing, row spacing, preload force, anchor bolt diameter, model, length specifications, and reinforcement measures for special areas;

[0055] S62: Graphical output; Output the 3D stress cloud map, compressive stress zone isosurface map, and anchor bolt force distribution map generated in step 5;

[0056] S63: Document integration; The contents of S61 and S62 are summarized into a complete support design document for construction use.

[0057] As a preferred option, the dynamic optimization feedback process based on measured data in step 7 is as follows:

[0058] S71: Actual measurement and deviation calculation of anchor bolt axial force; Install anchor bolt force gauges at representative cross-sections to obtain the actual axial force of the anchor bolts. And calculate the relative deviation according to the following formula. :

[0059] ;

[0060] S72: Triggering dynamic optimization conditions; if Then, multiple displacement gauges are installed at this cross-section to measure the displacement of the surrounding rock at the surface of the tunnel and at depths of 1.5m and 3.0m.

[0061] S73: Correction parameters for displacement back analysis; based on measured displacement With calculation of displacement The objective is to minimize the sum of squared residuals, as shown in the following formula:

[0062] ;

[0063] The elastic modulus of the surrounding rock was inverted using an optimized algorithm. and lateral pressure coefficient The termination condition for the inversion iteration is: the sum of squared residuals < 1 × 10⁻⁶. -4 Or reach the maximum number of iterations, 30;

[0064] S74: Updated support design; the revised... and Substitute the measured parameters from step 1 into the set of parameters, and repeat steps 3 to 6 to generate a dynamic optimization support scheme for the unconstructed sections.

[0065] Compared with the prior art, the present invention has the following technical advantages:

[0066] 1. For the first time, a support stress field diffusion index was defined, realizing a quantitative description of the reinforcement effect of anchor bolt groups. When defining the diffusion degree, the area ratio of the effective compressive stress zone and the average stress level were comprehensively considered. For the first time, a quantitative evaluation of the continuity, coverage and stress level of the compressive stress zone formed by the anchor bolt group was realized, providing a clear numerical target for the optimization of support parameters and overcoming the shortcomings of traditional methods that rely on experience and lack quantitative basis.

[0067] 2. An analytical model based on the stress diffusion mechanism was established, revealing the quantitative influence of support parameters on the diffusion degree. Based on the superposition principle of elasticity and the load transfer mechanism of anchor bolts, an analytical relationship model was established between the diffusion degree and anchor bolt length, spacing, row spacing, and preload. The model coefficients were calibrated through orthogonal numerical experiments. This model reveals that the enhancing effect of length and preload on the diffusion degree exhibits an exponential saturation law, while the weakening effect of spacing and row spacing exhibits an exponential decay law, enabling the design of support parameters to shift from empirical calculations to scientific calculations.

[0068] 3. A comprehensive influence coefficient for surrounding rock conditions was introduced, enabling the model to adapt to different geological conditions. A comprehensive influence coefficient for surrounding rock conditions was constructed, incorporating the uniaxial compressive strength of rock, the elastic modulus of rock mass, the lateral pressure coefficient, the thickness of the loosened zone, the rock mass integrity coefficient, and the roadway geometric parameters into the model. Through graded calibration (intact / moderately broken / broken rock mass), the model can adapt to different surrounding rock conditions, significantly improving the engineering applicability of the method.

[0069] 4. An intelligent optimization algorithm is adopted to automatically generate the optimal support parameters, avoiding the subjective inefficiency of manual calculation. With the goal of maximizing the diffusion, the algorithm comprehensively considers the constraints such as the anchor length not being less than the loosening ring thickness plus 0.5m, the spacing between rows being 0.6 to 1.5m, and the preload not exceeding 80% of the yield strength. Particle swarm optimization or genetic algorithm is used to efficiently solve the global optimal parameter combination, avoiding the inefficiency and subjectivity of traditional manual repeated calculations.

[0070] 5. Three-dimensional visualization verification and quantitative identification of stress gaps ensure the intuitive reliability of the design results. Optimal support parameters are input into the numerical model to generate three-dimensional stress cloud maps and compressive stress zone isosurface maps. When defining the proportion of gaps, the spacing or row spacing is automatically reduced and recalculated until the requirements are met. This method makes the design results visible, verifiable, and adjustable, effectively avoiding the existence of weak support areas.

[0071] 6. Significantly improves the efficiency and reliability of support design under restricted geological conditions. Compared with the traditional empirical method, this method shortens the design time from several days to several hours and can quantitatively evaluate the support effect of different parameter combinations, avoiding support failure caused by insufficient experience or improper parameters. It is particularly suitable for roadway support design under complex geological conditions such as deep high stress and soft fracture.

[0072] This method, by defining diffusion index, establishing mechanical analytical model, introducing intelligent optimization algorithm, and implementing three-dimensional visualization verification, constructs a scientific and efficient intelligent generation and dynamic optimization method for roadway support parameters. It overcomes the subjectivity and inefficiency of traditional experience-based design and provides advanced technical tools for support design in mines and underground engineering. Attached Figure Description

[0073] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0074] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0075] like Figure 1 As shown, this invention provides an intelligent generation and optimization method for roadway support parameters to maximize stress field diffusion, comprising the following steps:

[0076] Step 1: Measure and obtain surrounding rock conditions and geostress parameters; through borehole sampling laboratory tests and in-situ field tests, obtain rock mechanical parameters, geostress parameters, rock mass integrity parameters and tunnel geometric parameters to form a set of measured parameters, providing basic data for subsequent analytical models and numerical calculations;

[0077] As a preferred option, the process of measuring and obtaining surrounding rock conditions and geostress parameters is as follows:

[0078] S11: Obtain rock mechanical parameters; obtain the uniaxial compressive strength of the rock through indoor rock mechanics tests after core drilling. Rock mass elastic modulus Cohesion internal friction angle ;

[0079] S12: Obtain in-situ stress parameters; obtain vertical principal stress through field testing using stress relief methods or hydraulic fracturing methods. and maximum horizontal principal stress And calculate the lateral pressure coefficient. ;

[0080] S13: Obtain rock mass integrity parameters; obtain longitudinal wave velocity of the rock mass through single-hole acoustic wave testing. Longitudinal wave velocity of rocks was obtained through indoor rock sample testing. Calculate the rock mass integrity factor The maximum depth of the area where wave velocity significantly decreases was determined by borehole television, thus obtaining the thickness of the loosened zone of the surrounding rock. ;

[0081] S14: Obtain tunnel geometry parameters; measure and record tunnel width. With height .

[0082] In this technical solution, the uniaxial compressive strength, elastic modulus, cohesion, and internal friction angle of the rock are obtained through borehole core sampling laboratory tests. The vertical and maximum horizontal principal stresses are obtained through field tests using stress relief methods or hydraulic fracturing methods, and the lateral pressure coefficient is calculated. The rock mass integrity coefficient is obtained through single-hole acoustic wave and laboratory rock sample tests. The thickness of the loosened zone of the surrounding rock is determined through borehole television, and the width and height of the tunnel are recorded simultaneously. This achieves comprehensive, systematic, and accurate field measurement of the mechanical properties of the surrounding rock, the geostress field, the rock mass integrity, and the tunnel geometry, laying a reliable data foundation for subsequent diffusion modeling and support parameter optimization.

[0083] Step 2: Define the support stress field diffusion degree; Define the diffusion degree To quantify the comprehensive effect of anchor bolt groups in forming an effective compressive stress zone in the surrounding rock, the area ratio of the effective compressive stress zone and the average stress level are taken into account.

[0084] As a preferred option, the process for defining the support stress field diffusion degree is as follows:

[0085] S21: Define the anchorage zone; the anchorage zone is defined as the distance from the roadway surface. to The annular region, in which The anchor bolt length; the total area of ​​the anchorage zone. The radial depth is calculated based on the cross-sectional shape of the tunnel (e.g., for a straight-walled semi-circular arch tunnel, the radial depth is calculated by integration from...). arrive (area of ​​the annular region)

[0086] S22: Calculate the effective area; perform post-processing using numerical models such as FLAC3D or ABAQUS to extract the additional support stress of each unit within the anchorage zone. ; Statistical satisfaction The effective area is obtained by summing the surface areas of the units. ;

[0087] S23: Calculate the effective mean stress; calculate all conditions that satisfy... arithmetic mean of the additional stress of the unit support ;

[0088] S24: Define the diffusivity; the diffusivity is defined according to the following formula:

[0089] ;

[0090] In the formula, The larger the value, the more continuous the compressive stress zone, the wider its coverage, and the higher the stress level.

[0091] In this technical solution, the anchoring zone is defined as an annular area 0.5 to 1.5 times the length of the anchor bolt from the roadway surface. The total area is calculated based on the roadway cross-sectional shape. Then, the additional stress of the support is extracted based on the post-processing of the numerical model. The area of ​​the unit that satisfies not less than 0.1 times the vertical stress of the original rock is counted as the effective area, and its average stress is calculated. Finally, diffusion is defined. This index is the first to integrate the area ratio corresponding to the continuity of the compressive stress zone with the relative value of the average stress corresponding to the stress level into a single quantitative target. It realizes an objective, calculable, and normalized evaluation of the support effect of the anchor bolt group, providing a clear mathematical basis for subsequent analytical modeling and parameter optimization, and overcoming the limitations of traditional methods that rely on empirical qualitative judgment.

[0092] Step 3: Construct an analytical model of diffusion degree based on the stress diffusion mechanism;

[0093] Based on the superposition principle of elasticity and the load transfer mechanism of anchor bolts, a diffusion degree is established. With anchor bolt length (Unit: m), Spacing (Unit: m) Row spacing (Unit: m) Preload An analytical relationship model between (unit: kN) is established, and a comprehensive influence coefficient of surrounding rock conditions is introduced;

[0094] As a preferred option, the process of constructing an analytical model of diffusivity based on the stress diffusion mechanism is as follows:

[0095] S31: Construct an analytical model for diffusion; construct the analytical model for diffusion based on the following formula:

[0096] ;

[0097] In the formula, , , , The influence indices of length, spacing, row spacing, and preload are respectively determined through orthogonal numerical experiments and multivariate nonlinear regression. This is the comprehensive influence coefficient of the surrounding rock conditions;

[0098] The model reveals that the enhancing effect of anchor bolt length and preload on diffusion exhibits an exponential saturation law, while the weakening effect of spacing and row spacing on diffusion exhibits an exponential decay law.

[0099] S32: Perform coefficient calibration;

[0100] Based on rock mass integrity coefficient The surrounding rock is divided into three categories, for The surrounding rock is classified as a complete rock mass; for The surrounding rock is classified as moderately fractured rock mass; for The surrounding rock was classified into fractured rock masses; for three different types of surrounding rock—intact, moderately fractured, and fractured—no fewer than 25 sets of orthogonal numerical tests were conducted, covering the following ranges: , , , The method employs multiple nonlinear regression, requiring a goodness of fit. The length influence index was determined. Spacing Influence Index Row spacing influence index Preload Influence Index and the comprehensive influence coefficient of surrounding rock conditions Regression coefficients in the expression , , , , , , , ;

[0101] S33: Determine the comprehensive influence coefficient of surrounding rock conditions; substitute the calibrated regression coefficients into... The comprehensive influence coefficient under different surrounding rock conditions was obtained. ;

[0102] This technical solution, by constructing a diffusion degree analytical model, reveals for the first time the exponential saturation enhancement law of anchor bolt length and preload on support effect, as well as the exponential decay weakening law of spacing and row spacing. Based on the rock mass integrity coefficient, the surrounding rock is divided into three categories: intact, moderately fractured, and fractured. For each category, no fewer than 25 sets of orthogonal numerical tests were conducted, covering the range of commonly used engineering parameters. Multivariate nonlinear regression was used to calibrate the model coefficients, and a power product relationship was established between the comprehensive influence coefficient of surrounding rock conditions and rock strength, elastic modulus, lateral pressure coefficient, loosened zone thickness, integrity coefficient, and roadway geometric parameters. This method transforms the complex stress diffusion mechanism into a calculable, calibrable, and generalizable analytical expression, elevating support parameter design from empirical calculations to quantitative calculations based on mechanical models, providing a scientific and efficient objective function for subsequent intelligent optimization.

[0103] Step 4: Generate optimal support parameters based on diffusion maximization;

[0104] In terms of diffusivity With maximization as the objective and considering engineering constraints, particle swarm optimization or genetic algorithms are used to solve for the optimal combination of anchor bolt parameters.

[0105] As a preferred approach, the process of generating optimal support parameters based on diffusion maximization is as follows:

[0106] S41: Establish an optimization model; construct the objective function based on the following formula:

[0107] ;

[0108] In the formula, The set of measured parameters obtained in step 1;

[0109] S42: Establish constraints; the constraints are as follows:

[0110] Anchor bolt length: ;

[0111] Anchor spacing: ;

[0112] Anchor bolt spacing: ;

[0113] Spacing ratio: ;

[0114] Preload: ,in , The yield strength of the anchor bolt is taken as 400 MPa, using HRB400 as an example. The cross-sectional area of ​​the anchor bolt is 380mm, taking a 22mm diameter anchor bolt as an example. 2 ;

[0115] S43: Optimize algorithm parameters; solve using either particle swarm optimization (PSO) or genetic algorithm; when using PSO, the number of particles is 30, the number of iterations is 50, the inertia weight is 0.7, and the learning factor is 1.5; when using genetic algorithm, the population size is 30, the crossover probability is 0.8, the mutation probability is 0.1, and the number of iterations is 50; the optimal parameter combination is obtained after solving. ;

[0116] In this technical solution, an optimization model is established with the goal of maximizing diffusion. It comprehensively considers engineering constraints such as the anchor length not being less than the loosening ring thickness plus 0.5m, the spacing range (0.6–1.5m), the spacing ratio (0.5–2.0), and the preload not exceeding 80% of the yield strength. A particle swarm optimization or genetic algorithm is used, with a particle count or population size of 30 and 50 iterations, to efficiently solve for the globally optimal parameter combination. This method transforms support parameter design into a constrained nonlinear optimization problem, automatically searching for the optimal solution using intelligent algorithms. This avoids the inefficiency and subjectivity of traditional manual trial and error calculations. Simultaneously, the constraints ensure that the design results meet engineering safety requirements, achieving scientific, rapid, and accurate generation of support parameters.

[0117] Step 5: Three-dimensional visualization of the support stress field and verification of the blank area;

[0118] Input the optimal support parameters into the numerical model, calculate and visualize the additional stress field of the support, identify the stress gap area, and if the requirements are not met, adjust the spacing or row spacing and recalculate.

[0119] As a preferred option, the process of 3D visualization and blank area verification of the support stress field is as follows:

[0120] S51: Establish a numerical model; the model range is 5 times the tunnel span, and the boundary condition is: vertical principal stress is applied at the top. Lateral application of horizontal principal stress The bottom is fixed with vertical displacement; the constitutive model of the surrounding rock is adopted using the Mohr-Coulomb model, and the anchor bolt is simulated using cable elements;

[0121] S52: Stress field visualization; generates 3D stress cloud maps with color mapping, and displays stress patterns with a resolution of 0.5. Isosurface diagram of compressive stress zone with threshold value, and stress distribution diagram of anchor bolt;

[0122] S53: Blank area identification; discretize the anchorage area into grid cells (0.1m × 0.1m); mark the area located inside the quadrilateral formed by four adjacent anchor bolts and the additional stress of the support. The units are counted to determine the total area of ​​these units. ;

[0123] S54: Calculation and adjustment of blank area proportion; the blank area proportion is defined according to the following formula:

[0124] ;

[0125] like Then reduce the spacing by 10% increments. or row spacing Prioritize reducing the spacing, and re-execute S51 to S53 until... ;

[0126] In this technical solution, a numerical model is established, with the model's range being five times the tunnel span. Vertical and horizontal principal stresses are applied to the top and sides, respectively, while the bottom has a fixed vertical displacement. The surrounding rock adopts the Mohr-Coulomb constitutive model, and the anchor bolts are simulated using cable elements. This generates a three-dimensional stress cloud map and an isosurface map of the compressive stress zone, achieving intuitive visualization of the support stress field. Furthermore, the anchorage zone is discretized into 0.1m × 0.1m grid elements. Blank areas located within the quadrilaterals of four adjacent anchor bolts, where the additional support stress is less than 0.05 times the original rock vertical stress, are identified. The proportion of blank areas is calculated, and a threshold of 0.15 is used. If the threshold is exceeded, the spacing is reduced by 10% increments and recalculated until the requirements are met. This method elevates the design results from parameter output to a level of visualization, verification, and iteration, effectively eliminating weak support areas, significantly enhancing the continuity and integrity of the compressive stress zone formed by the anchor bolt group, and ensuring the reliability and engineering safety of the final design scheme.

[0127] Step 6: Output of support parameters and generation of construction documents;

[0128] The finalized optimal support parameters are output in a standardized table and accompanied by a three-dimensional visualization, forming a complete support design document.

[0129] As a preferred option, the process for outputting support parameters and generating construction documents is as follows:

[0130] S61: Parameter table output; The following parameters are output in tabular form: final values ​​of anchor bolt length, spacing, row spacing, preload force, anchor bolt diameter, model, length specifications, and reinforcement measures for special areas;

[0131] S62: Graphical output; Output the 3D stress cloud map, compressive stress zone isosurface map, and anchor bolt force distribution map generated in step 5;

[0132] S63: Document integration; The contents of S61 and S62 are summarized into a complete support design document for construction use;

[0133] In this technical solution, the optimized anchor bolt length, spacing, row spacing, preload, diameter, model, and reinforcement measures for special areas are output in standardized tables. Simultaneously, three-dimensional stress cloud maps, compressive stress zone isosurface maps, and anchor bolt layout plans are exported, ultimately integrating them into a complete support design document. This method achieves seamless transformation from optimization results to construction instructions, ensuring the traceability and accuracy of design parameters, avoiding the risks of information omission or misinterpretation in traditional methods, and significantly improving the operability of on-site construction and the engineering practicality of the design results.

[0134] Step 7: Dynamic optimization feedback based on measured data;

[0135] After the tunnel excavation is completed, the actual support effect is judged by the deviation between the measured anchor bolt axial force and the design preload. When the relative deviation between the measured anchor bolt axial force and the design preload exceeds 30%, a displacement gauge is installed, and the elastic modulus and lateral pressure coefficient of the surrounding rock are corrected by the displacement back analysis method. The support parameters of the subsequent sections are then re-optimized.

[0136] This establishes a dynamic feedback mechanism based on measured data, enabling coordinated optimization of design and construction. After construction, the actual axial force is obtained using anchor bolt force gauges, and the relative deviation is calculated. If the relative deviation exceeds a set threshold, multiple displacement gauges are installed, and the elastic modulus and lateral pressure coefficient of the surrounding rock are corrected using displacement back analysis. The corrected parameters are then re-introduced into the optimization process to regenerate support schemes for subsequent sections. This mechanism allows the support design to adapt to changes in surrounding rock conditions in a timely manner, significantly improving the long-term stability of the roadway support.

[0137] As a preferred option, the dynamic optimization feedback process based on measured data is as follows:

[0138] S71: Actual Measurement and Deviation Calculation of Anchor Bolt Axial Force; Since the anchor bolt axial force directly determines the magnitude of the additional stress field of the support, the deviation between its actual value and the design value can reflect the difference between the actual diffusion degree and the design expectation. Therefore, anchor bolt force gauges are installed at representative cross-sections to obtain the actual axial force of the anchor bolts. And calculate the relative deviation according to the following formula. :

[0139] ;

[0140] S72: Triggering dynamic optimization conditions; if Then, multiple displacement gauges are installed at this cross-section to measure the displacement of the surrounding rock at the surface of the tunnel and at depths of 1.5m and 3.0m.

[0141] S73: Parameter correction using displacement back analysis; Since the elastic modulus of the surrounding rock directly controls the magnitude of deformation and the lateral pressure coefficient determines the shape of the in-situ stress field, these two are the dominant factors affecting the anchor bolt's stress. Therefore, displacement back analysis is used to correct these two parameters first; based on measured displacement... With calculation of displacement The objective is to minimize the sum of squared residuals, as shown in the following formula:

[0142] ;

[0143] The elastic modulus of the surrounding rock was inverted using an optimized algorithm. and lateral pressure coefficient The termination condition for the inversion iteration is: the sum of squared residuals < 1 × 10⁻⁶. -4The maximum number of iterations can be reached, up to 30. The measured displacement is obtained through a multi-point displacement gauge, with the measuring points located at the surface of the roadway, 1.5m, and 3.0m, respectively.

[0144] S74: Updated support design; the revised... and Substitute the measured parameters from step 1 into the set of parameters, and re-execute step 3 (recalculate). Step 6 generates a dynamic optimization support scheme for the unconstructed sections.

[0145] In this technical solution, actual axial force is obtained by installing anchor bolt force gauges at representative cross-sections, and the relative deviation is calculated. Using this as a trigger, multiple displacement gauges are installed to obtain the surrounding rock displacement at the roadway surface and depths of 1.5m and 3.0m. Then, a displacement back-analysis method is employed, aiming to minimize the sum of squares of the measured and calculated displacement residuals. The two dominant parameters, the elastic modulus of the surrounding rock and the lateral pressure coefficient, are preferentially corrected. The corrected parameters are then substituted back into step 1, and steps 3 through 6 are repeated to generate a dynamically optimized support scheme for subsequent unconstructed sections. This method achieves a continuous feedback mechanism of design, construction, monitoring, inversion, and optimization, enabling timely adjustment of support parameters based on the actual surrounding rock response, effectively ensuring the long-term stability and adaptability of the roadway support.

[0146] Comparative experiments to verify:

[0147] To verify the effectiveness of the method of this invention, a support parameter design and effect comparison were carried out using a mining roadway in a certain mine as the engineering background. The roadway has a straight wall semi-circular arch cross section, with a width of 5.0m, a height of 4.0m, and a burial depth of approximately 600m.

[0148] I. Engineering conditions and measured parameters (Step 1);

[0149] The measured parameter set obtained through on-site sampling, indoor experiments, and in-situ testing is shown in Table 1:

[0150] Table 1: Set of Measured Parameters

[0151]

[0152] Determination of surrounding rock type: It belongs to a moderately fractured rock mass.

[0153] II. Comparison Scheme Setup;

[0154] The three schemes in Table 2 below are compared:

[0155] Table 2: Comparison of Schemes

[0156]

[0157] III. Implementation process of the method of the present invention;

[0158] Step 3: Diffusion analytical model and coefficient calibration:

[0159] The surrounding rock of this tunnel is moderately fractured rock mass, and the calibrated model parameters (for) are used. Similar to surrounding rock, the goodness of fit was determined through 25 sets of orthogonal numerical experiments and multivariate nonlinear regression calibration. =0.91):

[0160] Comprehensive influence coefficient of surrounding rock conditions The regression coefficients in the expression have been pre-calibrated; substituting them into the measured parameters yields... =0.72

[0161] Length Influence Index =0.85, Spacing Influence Index =1.20, Row spacing influence index =1.10, Preload Influence Index =0.03;

[0162] The analytical model is: ;

[0163] Step 4, Optimize diffusion maximization:

[0164] With the objective of maximizing D, the constraints are as follows: , , , .

[0165] The global optimal parameters were obtained using the particle swarm optimization algorithm (30 particles, 50 iterations), as shown in Table 3.

[0166] Table 3: Global Optimal Parameters

[0167]

[0168] The diffusivity corresponding to this parameter combination =0.68.

[0169] Step 5: Numerical verification and blank area elimination:

[0170] Input the optimal parameters into the FLAC3D numerical model (model size is 25m × 25m, with an application applied at the top). =15MPa, applied laterally =18MPa, Mohr-Coulomb constitutive model, anchor bolt simulated with cable elements), the calculation results are as follows:

[0171] Total area of ​​anchorage zone =8.74m² (based on 0.5L~1.5L) (ring region integral).

[0172] Effective area =7.82m² (unit area);

[0173] Effective mean stress =1.96MPa;

[0174] Numerical calculation of diffusion =(7.82 / 8.74)×(1.96 / 15)=0.67, which is in good agreement with the analytical model's predicted value of 0.68, with a deviation of only 1.5%;

[0175] Blank area identification: Discretize the anchorage area into a 0.1m × 0.1m grid, and count the number of blank areas within the quadrilaterals of four adjacent anchor bolts. unit area =0.68m²,

[0176] Percentage of blank areas =0.68 / 8.74=0.078 0.15, meets the requirements.

[0177] IV. Comparison of the effects of each plan;

[0178] Numerical simulations were performed on the three schemes, and the diffusion degree and blank area ratio were extracted. The results are shown in Tables 4 and 5.

[0179] Table 4: Comparison of Support Effects of Different Schemes

[0180]

[0181] Table 5: Comparison of Anchor Bolt Consumption Per Meter of Tunnel for Each Scheme

[0182]

[0183] Note: Although Comparative Examples 1 and 2 used fewer anchor bolts per meter, their diffusion degree was below 0.50 and the blank area ratio exceeded the threshold of 0.15, indicating significant weak areas in the compressive stress zone, making it difficult to ensure support safety. This invention, by appropriately increasing the anchor bolt length and preload, improves the diffusion degree to 0.68 and reduces the blank area ratio to 0.078, significantly improving support reliability and effectively avoiding repair costs and safety accidents caused by support failure.

[0184] V. Dynamic optimization feedback verification (step 7);

[0185] In the subsequent excavation section of this roadway, anchor bolt force gauges were installed at representative cross-sections. The axial force of the anchor bolts was measured after construction. =55kN, which is consistent with the optimal preload design. =95kN relative deviation =|55-95| / 95×100%=42.1%>30%, triggering dynamic optimization.

[0186] Multiple displacement gauges were installed at this cross-section to obtain the surface displacement of the roadway. Displacement at a depth of 18.2 mm and 1.5 m Displacement at a depth of 3.0m and a diameter of 9.6mm =3.1mm. The corrected elastic modulus of the surrounding rock was obtained by inversion using the displacement inverse analysis method. =8GPa (originally 12GPa), lateral pressure coefficient =1.4 (originally 1.2), indicating that the surrounding rock in this section is weaker and the horizontal tectonic stress is higher.

[0187] The revised and Substitute into step 1 to update the measured parameter set Recalculate step 3 Worth it =0.61, and step 4 was re-executed to obtain the dynamic optimization scheme shown in Table 6:

[0188] Table 6: Dynamic Optimization Scheme

[0189]

[0190] The dynamically optimized support scheme improved the diffusion rate to 0.71, further reducing the proportion of blank areas to 0.062, fully adapting to changes in geological conditions. After adopting this scheme in subsequent construction sections, the deviation between the measured and designed values ​​of the anchor bolt axial force remained stable within 15%, and tunnel deformation was effectively controlled.

[0191] VI. Overall Conclusion;

[0192] Comparative experimental results show that the method of the present invention has the following significant advantages over the traditional engineering analogy method and the equidistant design method:

[0193] 1. Significantly improved diffusion: The solution of this invention =0.68, which is 62% higher than Comparative Example 1 and 42% higher than Comparative Example 2, indicating that the effective compressive stress zone is more continuous and the stress level is higher.

[0194] 2. Blank areas were reliably eliminated: all comparative examples showed blank areas exceeding the standard ( (>0.15), through numerical verification and adjustment, this invention controls the proportion of blank area at 0.078, ensuring that there are no weak links in the compressive stress zone.

[0195] 3. Strong dynamic adaptability: Through axial force monitoring and displacement back analysis, it can automatically sense changes in surrounding rock conditions and adjust support parameters. After dynamic optimization, the diffusion degree is further improved, ensuring the support safety of geological change sections.

[0196] 4. Scientific and efficient design: The combination of analytical models and intelligent optimization algorithms upgrades the design of support parameters from empirical calculations to quantitative calculations, and the design process is traceable and reproducible.

[0197] The above results show that the present invention is significantly superior to the prior art in terms of support effect, safety and reliability, and dynamic adaptability, and has good engineering application value.

[0198] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for intelligent generation and optimization of roadway support parameters to maximize stress field diffusion, characterized in that, Includes the following steps: Step 1: Measurement and acquisition of surrounding rock conditions and geostress parameters; through borehole sampling laboratory tests and in-situ field tests, rock mechanical parameters, geostress parameters, rock mass integrity parameters and tunnel geometric parameters are obtained to form a set of measured parameters; S11: Obtain rock mechanical parameters; obtain the uniaxial compressive strength of the rock through indoor rock mechanics tests after core drilling. Rock mass elastic modulus Cohesion internal friction angle ; S12: Obtain in-situ stress parameters; obtain the vertical principal stress through field testing using stress relief methods or hydraulic fracturing methods. and maximum horizontal principal stress And calculate the lateral pressure coefficient. ; S13: Obtain rock mass integrity parameters; Longitudinal wave velocity of rock mass was obtained by single-hole acoustic wave testing. Longitudinal wave velocity of rocks was obtained through indoor rock sample testing. Calculate the rock mass integrity factor The maximum depth of the area where wave velocity significantly decreases was determined by borehole television, thus obtaining the thickness of the loosened zone of the surrounding rock. ; S14: Obtain tunnel geometry parameters; measure and record tunnel width. With height ; Step 2: Define the support stress field diffusion degree; Define diffusion degree To quantify the comprehensive effect of anchor bolt groups in forming an effective compressive stress zone in the surrounding rock, the area ratio of the effective compressive stress zone and the average stress level are taken into account. Step 3: Construct an analytical model of diffusion degree based on the stress diffusion mechanism; establish the diffusion degree based on the superposition principle of elasticity and the load transfer mechanism of anchor bolts. With anchor bolt length ,spacing Row spacing Preload An analytical relationship model between them was developed, and a comprehensive influence coefficient of surrounding rock conditions was introduced; S31: Construct an analytical model for diffusion; construct the analytical model for diffusion based on the following formula: ； In the formula, , , , These are the influence indices of length, spacing, row spacing, and preload; This is the comprehensive influence coefficient of the surrounding rock conditions; S32: Perform coefficient calibration; Based on rock mass integrity coefficient The surrounding rock was divided into three categories; for each category, no fewer than 25 sets of orthogonal numerical tests were conducted, and the length influence index was determined using a multivariate nonlinear regression method. Spacing Influence Index Row spacing influence index Preload Influence Index and the comprehensive influence coefficient of surrounding rock conditions Regression coefficients in the expression , , , , , , , ; S33: Determine the comprehensive influence coefficient of surrounding rock conditions; substitute the calibrated regression coefficients into... The comprehensive influence coefficient under different surrounding rock conditions was obtained. ; Step 4: Generate optimal support parameters based on diffusion maximization; using diffusion degree With maximization as the objective and considering engineering constraints, particle swarm optimization or genetic algorithms are used to solve for the optimal combination of anchor bolt parameters. Step 5: 3D visualization and blank area verification of the support stress field; input the optimal support parameters into the numerical model, calculate and visualize the additional stress field of the support, identify the stress blank area, and if the requirements are not met, adjust the spacing or row spacing and recalculate. Step 6: Output of support parameters and generation of construction documents; Output the finalized optimal support parameters in a standardized table and attach a 3D visualization graphic to form a complete support design document.

2. The intelligent generation and optimization method for roadway support parameters to maximize stress field diffusion according to claim 1, characterized in that, It also includes step 7: dynamic optimization feedback based on measured data; After the tunnel excavation is completed, the actual support effect is judged by the deviation between the measured anchor bolt axial force and the design preload. When the relative deviation between the measured anchor bolt axial force and the design preload exceeds 30%, the displacement back analysis method is used to correct the elastic modulus and lateral pressure coefficient of the surrounding rock, and the support parameters of the subsequent sections are re-optimized.

3. The intelligent generation and optimization method for roadway support parameters to maximize stress field diffusion according to claim 1, characterized in that, In step 2, the process of defining the support stress field diffusion degree is as follows: S21: Define the anchorage zone; the anchorage zone is defined as the distance from the roadway surface. to The annular region, in which The anchor bolt length; the total area of ​​the anchorage zone. Calculated based on the cross-sectional shape of the tunnel; S22: Calculate the effective area; extract the additional support stress of each unit in the anchorage zone through numerical model post-processing. ; Statistical satisfaction The effective area is obtained by summing the surface areas of the units. ; S23: Calculate the effective mean stress; calculate all conditions that satisfy... arithmetic mean of the additional stress of the support unit ; S24: Define the diffusivity; the diffusivity is defined according to the following formula: ； In the formula, .

4. The intelligent generation and optimization method for roadway support parameters to maximize stress field diffusion according to claim 1, characterized in that, In step 4, the process of generating the optimal support parameters based on diffusion maximization is as follows: S41: Establish an optimization model; construct the objective function based on the following formula: ； In the formula, This is the set of measured parameters; S42: Establish constraints; the constraints are as follows: Anchor bolt length: ; Anchor spacing: ; Anchor bolt spacing: ; Spacing ratio: ; Preload: ,in , The anchor rod's yield strength. The cross-sectional area of ​​the anchor bolt; S43: Optimize algorithm parameters; solve using either particle swarm optimization (PSO) or genetic algorithm; when using PSO, the number of particles is 30, the number of iterations is 50, the inertia weight is 0.7, and the learning factor is 1.5; when using genetic algorithm, the population size is 30, the crossover probability is 0.8, the mutation probability is 0.1, and the number of iterations is 50; the optimal parameter combination is obtained after solving. .

5. The intelligent generation and optimization method for roadway support parameters to maximize stress field diffusion according to claim 1, characterized in that, In step 5, the three-dimensional visualization of the support stress field and the verification of the blank area are as follows: S51: Establish a numerical model; the model range is 5 times the tunnel span, and the boundary condition is: vertical principal stress is applied at the top. Lateral application of horizontal principal stress The bottom is fixed with vertical displacement; the constitutive model of the surrounding rock is adopted using the Mohr-Coulomb model, and the anchor bolt is simulated using cable elements; S52: Stress field visualization; generates 3D stress cloud maps, compressive stress zone contour maps, and anchor bolt force distribution maps; S53: Blank area identification; discretize the anchorage area into grid cells; mark areas located inside the quadrilateral formed by four adjacent anchor bolts and with additional support stress. The units are counted to determine the total area of ​​these units. ; S54: Calculation and adjustment of blank area proportion; the blank area proportion is defined according to the following formula: ； like Then reduce the spacing by 10% increments. or row spacing Re-execute S51 to S53 until... .

6. The intelligent generation and optimization method for roadway support parameters to maximize stress field diffusion according to claim 1, characterized in that, In step 6, the process of outputting support parameters and generating construction documents is as follows: S61: Parameter table output; The following parameters are output in tabular form: final values ​​of anchor bolt length, spacing, row spacing, preload force, anchor bolt diameter, model, length specifications, and reinforcement measures for special areas; S62: Graphical output; Output the 3D stress cloud map, compressive stress zone isosurface map, and anchor bolt force distribution map generated in step 5; S63: Document integration; The contents of S61 and S62 are summarized into a complete support design document for construction use.

7. The intelligent generation and optimization method for roadway support parameters to maximize stress field diffusion according to claim 2, characterized in that, In step 7, the dynamic optimization feedback process based on measured data is as follows: S71: Actual measurement and deviation calculation of anchor bolt axial force; Install anchor bolt force gauges at representative cross-sections to obtain the actual axial force of the anchor bolts. And calculate the relative deviation according to the following formula. : ； S72: Triggering dynamic optimization conditions; if Then, multiple displacement gauges are installed at this cross-section to measure the displacement of the surrounding rock at the surface of the tunnel and at depths of 1.5m and 3.0m. S73: Correction parameters for displacement inverse analysis method; Based on measured displacement With calculation of displacement The objective is to minimize the sum of squared residuals, as shown in the following formula: ； The elastic modulus of the surrounding rock was inverted using an optimized algorithm. and lateral pressure coefficient The termination condition for the inversion iteration is: the sum of squared residuals < 1 × 10⁻⁶. -4 Or reach the maximum number of iterations, 30; S74: Updated support design; the revised... and Substitute the measured parameters from step 1 into the set of parameters, and repeat steps 3 to 6 to generate a dynamic optimization support scheme for the unconstructed sections.

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