Microscopic simulation method of surrounding rock similar material

Abstract

The present application belongs to the field of surrounding rock mechanics analysis, and relates to a surrounding rock similar material mesoscopic simulation method, comprising: 1) preparing surrounding rock similar materials under different proportions; 2) respectively performing physical experiments to obtain physical parameters of the surrounding rock similar materials under different proportions; 3) determining a contact model between particles; 4) based on the physical parameters of the surrounding rock similar materials under different proportions and the contact model, constructing numerical models of the surrounding rock similar materials under different proportions; 5) performing macroscopic damage on the numerical models of the surrounding rock similar materials under different proportions to obtain mechanical behaviors and damage mechanisms of the surrounding rock similar materials, and completing mesoscopic simulation of the surrounding rock similar materials. The present application provides a surrounding rock similar material mesoscopic simulation method which can reasonably reproduce mechanical behaviors of surrounding rock similar materials and provide a more reasonable simulation means for stability analysis and support design of soft surrounding rocks in tunnel engineering.

Description

Technical Field

[0001] This invention belongs to the field of mechanical analysis of surrounding rocks, and relates to a microscopic simulation method for surrounding rock similar materials, and more particularly to a microscopic simulation method for surrounding rock similar materials that considers particle density and radius. Background Technology

[0002] In fields such as tunnel engineering, geological engineering, and mining, the stability of surrounding rock is crucial to engineering safety. Model testing is an important means of studying the stability of surrounding rock and exploring its mechanical behavior under load. Among these methods, accurate simulation of the mechanical behavior of similar materials to the surrounding rock is of great significance for predicting and evaluating the stability of the surrounding rock in engineering projects. Traditional methods for determining the mechanical behavior of similar materials to the surrounding rock are physical testing methods, including uniaxial compressive strength tests and direct shear tests. However, traditional physical testing methods suffer from problems such as high cost, low efficiency, long testing time, and difficulty in simulating complex geological environments.

[0003] Compared to traditional physical testing methods, numerical simulation methods offer lower costs and shorter timeframes, significantly improving research and design efficiency. They have become an important tool for studying the mechanical behavior of materials similar to surrounding rocks. The Discrete Element Method (DEM) is a numerical technique capable of simulating the interactions between particles within a material. It can simulate the microscopic mechanisms of material failure, such as crack initiation, propagation, and penetration—phenomena often difficult to observe in macroscopic experiments. PFC 2D As a discrete element simulation tool, the software is widely used in the simulation research of similar surrounding rock materials because it can accurately simulate the mechanical behavior of granular media inside the model.

[0004] However, existing PFC 2D Simulation methods still have some problems when simulating similar materials to surrounding rocks. For example, most of them only use one or two types of particles, which leads to a large gap between the simulated material failure process and the actual situation. Most existing numerical simulation methods do not consider the size and density of particles, making the model not precise enough and unable to accurately reflect the mechanical behavior of the material. Summary of the Invention

[0005] In order to solve the above-mentioned technical problems in the background art, the present invention provides a microscopic simulation method for surrounding rock similar materials that can reasonably reproduce the mechanical behavior of surrounding rock similar materials and can provide a more reasonable simulation means for stability analysis and support design of weak surrounding rock in tunnel engineering.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for microscopic simulation of surrounding rock similar materials, characterized in that: the method includes the following steps:

[0008] 1) Prepare materials with similar surrounding rock ratios;

[0009] 2) Physical experiments were conducted on the surrounding rock similar materials with different proportions prepared in step 1) to obtain the physical properties of the surrounding rock similar materials with different proportions and to record the physical parameters of the surrounding rock similar materials with different proportions;

[0010] 3) Determine the contact model between particles;

[0011] 4) Based on the physical parameters of the surrounding rock similar materials with different sizing ratios obtained in step 2) and the contact model determined in step 3), construct numerical models of the surrounding rock similar materials with different sizing ratios;

[0012] 5) Perform macroscopic destruction on the numerical models of the surrounding rock similar materials with different proportions obtained in step 4) to obtain the mechanical behavior and failure mechanism of the surrounding rock similar materials, and complete the microscopic simulation of the surrounding rock similar materials.

[0013] Preferably, the specific implementation of step 1) in this invention is as follows:

[0014] 1.1) Determine the target parameters for materials similar to the surrounding rock;

[0015] 1.2) Based on the target parameters obtained in step 1.1), prepare similar materials of surrounding rock with different proportions.

[0016] Preferably, step 1.1) of the present invention specifically involves: setting the mechanical parameters of the surrounding rock material according to GB / T50218-2014 "Engineering Rock Mass Classification Standard", and determining the target parameters of the surrounding rock similar material according to the basic theory of similarity ratio and geometric similarity ratio.

[0017] Preferably, the surrounding rock similar material used in step 1.2) of the present invention includes fine aggregate, coarse aggregate, cementing agent, and modifier; the fine aggregate includes barite and fly ash; the coarse aggregate is river sand with a particle size of no more than 2 mm; the cementing agent is a saturated solution of rosin in alcohol; the alcohol is industrial alcohol with a volume fraction of 95%; and the modifier is fully synthetic motor oil, semi-synthetic motor oil, or mineral motor oil.

[0018] Preferably, the physical experiments in step 2) of this invention include uniaxial compression tests and direct shear tests, and the physical parameters include specific weight γ, uniaxial compressive strength σ, elastic modulus E, cohesion c, and internal friction angle.

[0019] Preferably, the contact model between particles in step 3) of the present invention is a linear parallel bonding model.

[0020] Preferably, the specific implementation of step 4) in this invention is as follows:

[0021] 4.1) Determine the microscopic parameters to be used for calibration;

[0022] 4.2) Based on the shear plane of the direct shear test conducted in step 2), scanning electron microscopy (SEM) experiments were performed to determine the radius and distribution of particles within the material; the mass and volume of each material were measured and the density of each material was calculated; the volume fraction of different materials was calculated; based on the volume and particle radius of different particles, the proportion of different particles in different formulations was obtained; based on the proportion of different particle numbers, the discrete element method (PFC) was used to analyze the particle distribution. 2D Randomly generated particles inside;

[0023] 4.3) Based on step 4.2), uniaxial compression tests are conducted according to the uniaxial compressive strength and elastic modulus measured in step 2). The micro-parameters obtained in step 4.1) are used to calibrate the micro-parameters of particle contact modulus and particle bond strength corresponding to different samples, thereby completing the modeling of the uniaxial compression test and obtaining numerical models of similar materials of surrounding rock under different proportions.

[0024] Preferably, the specific implementation of step 4.1) in this invention is as follows: using the discrete element method analysis software PFC. 2D To investigate the influence of different micro-parameters on macroscopic uniaxial compressive strength and elastic modulus, orthogonal experiments were designed. The micro-parameters were analyzed using range analysis to obtain the degree of influence of different micro-parameters on macroscopic uniaxial compressive strength and elastic modulus. Based on this degree of influence, the micro-parameters of the model corresponding to the physical experiment were adjusted until the uniaxial compressive strength and elastic modulus of the model matched the results of the physical experiment. Finally, the micro-parameters used for calibration were obtained.

[0025] Preferably, the specific method of calibration in step 4.3) of the present invention is as follows:

[0026] a) Through particle contact modulus E c The elastic modulus of the numerical model is initially determined until it matches the elastic modulus E of the actual material.

[0027] b) By particle bonding strength σ c Determine the uniaxial compressive strength σ of the model s Until it matches the uniaxial compressive strength σ obtained from the uniaxial compression test;

[0028] c) By particle contact stiffness ratio k n / k s The elastic modulus E of the numerical model was finally determined. s The particle contact stiffness ratio k n / k s The value is 0.9-1.1; where k nand k s These are the normal contact stiffness and tangential contact stiffness of the particles, respectively.

[0029] The advantages of this invention are:

[0030] This invention provides a method for microscopic simulation of surrounding rock similar materials, comprising: 1) preparing surrounding rock similar materials with different proportions; 2) conducting physical experiments on the surrounding rock similar materials with different proportions prepared in step 1) to obtain the physical properties of the surrounding rock similar materials with different proportions and recording the physical parameters of the surrounding rock similar materials with different proportions; 3) determining the contact model between particles; 4) constructing numerical models of surrounding rock similar materials with different proportions based on the physical parameters of the surrounding rock similar materials with different proportions obtained in step 2) and the contact model determined in step 3); 5) performing macroscopic destruction on the numerical models of surrounding rock similar materials with different proportions obtained in step 4) to obtain the mechanical behavior and failure mechanism of the surrounding rock similar materials, thereby completing the microscopic simulation of the surrounding rock similar materials. Compared with existing discrete element numerical simulation methods for surrounding rock similar materials, this invention fully considers the complexity of the microstructure of surrounding rock similar materials, employs various particle reduction test scenarios, and takes into account particle density and radius. This method can characterize different components of similar materials, such as barite, fly ash, river sand, and rosin particles. It explores the relationship between the uniaxial compressive strength and elastic modulus of the material and microscopic parameters such as particle radius, bond strength, and contact modulus. Through electron microscopy, this invention clarifies the radius of different particles and their internal distribution within the material, making the numerical simulation more realistic. This invention can simulate the movement and interaction of particles or blocks from a microscopic perspective, revealing the microscopic mechanisms of material failure and deformation. It can quickly assess the impact of different material ratios on the performance of surrounding rock similar materials, guiding experimental design and material selection, and improving research efficiency.

[0031] The microscopic simulation method for surrounding rock similar materials disclosed in this invention includes a particle flow program (PFC) based method. 2D The parallel bond model in this study analyzes the influence of different microscopic parameters on uniaxial compressive strength and elastic modulus; the radius and distribution of individual particles within the material are determined by scanning electron microscopy; a numerical model containing various particle types is established; microscopic parameters such as particle contact modulus and particle bond strength are calibrated for different samples; and the mechanical behavior and failure mechanism of similar surrounding rock materials are analyzed. This method can reasonably reproduce the mechanical behavior of similar surrounding rock materials, providing a relatively reasonable simulation method for stability analysis and support design of weak surrounding rock in tunnel engineering. Attached Figure Description

[0032] Figure 1 This is a flowchart illustrating the discrete element numerical simulation method for surrounding rock similar materials according to the present invention.

[0033] Figure 2This is a schematic diagram of the linear parallel bonding model used in this invention;

[0034] Figure 3 The results of scanning electron microscopy experiments are as follows;

[0035] Figure 4 The present invention comprises 16 formulation samples and their numerical models;

[0036] Figure 5 Stress-strain curves from some indoor tests and numerical models are shown.

[0037] Figure 6 The stress-strain curves and changes in the number of microcracks for the XY-15 group of formulation samples;

[0038] Figure 7 The crack evolution process of the XY-15 group formulation sample. Detailed Implementation

[0039] This example aims to address the shortcomings of existing technologies by providing a microscopic simulation method for similar materials that considers both particle density and radius. This example is applicable to discrete element numerical simulation studies of similar surrounding rock materials and includes the following steps:

[0040] Step 1. Determine the target parameters of the surrounding rock similar material and prepare the surrounding rock similar material.

[0041] The preparation of similar materials must follow similarity theory, ensuring geometric and physical similarity between the model and the prototype. According to the principle of similarity, if the model and prototype are similar, the following parameters must meet the requirements of geomechanical similarity:

[0042] α σ =α l α γ (1)

[0043] α X =α σ (2)

[0044] α σ =α E α ε (3)

[0045] α δ =α ε α l (4)

[0046]

[0047] In the formula: a σ For stress similarity ratio, a l Let a be the geometric similarity ratio. r For the high similarity ratio, aX Let a be the surface force similarity ratio. E For the similarity ratio of elastic modulus, a ε For strain similarity ratio, a δ For the deformation similarity ratio, a μ Let a be the similarity ratio of Poisson's ratio. φ This is the similarity ratio of the internal friction angles.

[0048] The mechanical parameters of the surrounding rock material are set with reference to GB / T50218-2014 "Engineering Rock Mass Classification Standard". To better reflect the properties of similar surrounding rock materials, the geometric similarity ratio is set to 1:12.5. Based on the basic theory of geometric similarity ratio and similarity ratio, the basic parameters of similar surrounding rock materials are determined.

[0049] The surrounding rock similarity material consists of aggregates, a binder, and a modifier. Barite and fly ash are used as fine aggregates, and river sand as coarse aggregates. Barite increases the material's density, while fly ash delays the setting time, increasing construction flexibility. Fine river sand with a particle size of 2mm is selected to increase the internal friction angle of the similarity material. To ensure a strong bond between aggregate particles, solid rosin and alcohol are mixed in a 1:3 ratio and stirred thoroughly. This rosin-alcohol solution serves as the binder. Since the binding effect comes from the rosin particles after alcohol evaporation, to ensure the rosin particles function effectively, the demolded samples are dried in an oven at 80°C for 12 hours. Fully synthetic, semi-synthetic, or mineral oil is used as a modifier; for example, 15W40 oil can be selected. This ensures thorough mixing of different materials during stirring, facilitating sample preparation and improving the material's plasticity.

[0050] Step 2. The physical properties of the surrounding rock similar materials under different proportions are measured by orthogonal tests (including direct shear tests and uniaxial compression tests) of the surrounding rock similar materials.

[0051] To comprehensively consider the influence of different raw materials and their proportions on the mechanical properties of the samples, this example sets influencing factors based on the material mass ratio. The main factors considered are the mass ratio of fine aggregate to coarse aggregate, the mass ratio of barite in fine aggregate, the mass ratio of aggregate to solution materials (rosin alcohol solution and machine oil), and the mass ratio of rosin alcohol to solution materials. These factors are incorporated into the orthogonal design in the forms of (barite + fly ash) / river sand, barite / (barite + fly ash), aggregate / (rosin alcohol + machine oil), and rosin alcohol / (rosin alcohol + machine oil), and are labeled as factors A, B, C, and D, respectively. A total of 16 proportioning experiments were conducted. Details of each factor and its level in the orthogonal experiments are shown in Table 1, and the proportioning schemes of the 16 similar materials are shown in Table 2. The physical parameters of each sample, including specific gravity γ, uniaxial compressive strength σ, elastic modulus E, cohesion c, and internal friction angle, were measured through direct shear tests and uniaxial compression tests. The experimental results are shown in Table 3.

[0052] Table 1. Factors and their levels in the orthogonal experiment.

[0053]

[0054] Table 2. Scheme for Proportioning Similar Materials for Surrounding Rock

[0055]

[0056]

[0057] Table 3 Mechanical parameters corresponding to different proportion schemes

[0058]

[0059] Step 3. Determine the contact model between particles.

[0060] The contact model used in this example is the linear parallel bond model, which is a discrete element method (PFC) for contact fusion. 2D This is a contact model used to simulate interparticle bonding behavior. It simulates cement-like materials by establishing an elastic interface between particles. Figure 2 The basic principle of the linear parallel bond model is based on two interfaces: a frictional interface that only bears forces, and a bond interface that bears both forces and moments. When the stress exceeds the bond strength, the bond breaks, and the associated forces and moments are removed from the system.

[0061] Step 4. Analyze using the discrete element method software PFC. 2D The influence of different microscopic parameters on the macroscopic uniaxial compressive strength and elastic modulus was investigated to determine the microscopic parameters that need to be calibrated. Analysis showed that the particle contact modulus E... c The stiffness of the particle contact point determines the degree of deformation of the numerical model under stress, thus affecting the uniaxial compressive strength σ of the numerical model. s and elastic modulus E s Particle radius R c The contact area between particles and the density of particle packing determine the density and porosity of the model, which in turn affects the uniaxial compressive strength σ of the model. s and elastic modulus E s Particle bonding strength σ c This reflects the adhesion between particles, which relates to the model's cohesiveness under stress and directly affects the model's uniaxial compressive strength σ. s Coefficient of friction v c This describes the frictional resistance between particles, a crucial parameter determining the frictional properties of the model particles and the overall stability. The particle stiffness ratio k is adjusted accordingly. n / ks It can alter the interactions between particles and the stress distribution of the model, thereby affecting the uniaxial compressive strength σ of the model. s and elastic modulus E s Therefore, before establishing the model, the particle contact modulus E is selected. c Particle radius R c Particle bonding strength σ c Friction coefficient v c and particle stiffness ratio k n / k s Five microscopic parameters were defined for the numerical model, and experiments were designed. The experimental results are listed in Table 4. Through range analysis, the effects of different parameters on the uniaxial compressive strength σ can be obtained. s and elastic modulus E s The extent of the impact is listed in Table 5.

[0062] Table 4 Uniaxial compressive strength σ of the model under different microscopic parameters s and elastic modulus E s

[0063]

[0064]

[0065] Wherein: particle contact modulus E c The unit is GPa; particle radius R min -R max The unit is mm; particle bonding strength σ c The unit is MPa; the coefficient of friction is v. c Particle stiffness ratio k n / k s Uniaxial compressive strength σ s The unit is MPa; the elastic modulus E s The unit is GPa.

[0066] Table 5 Range analysis of different micro-parameters

[0067]

[0068] Therefore, the uniaxial compressive strength σ of the model s Subject to particle radius R c Particle bonding strength σ c Contact modulus E of particles c The impact is significant, with corresponding ranges of 0.0269, 0.0235, and 0.0149, respectively, and the elastic modulus E s Subject to particle radius R c Particle stiffness ratio k n / k s and particle contact modulus Ec The influence of this is significant, with corresponding ranges of 1.2330, 0.6110, and 0.5665, respectively. Therefore, the particle radius R... c Uniaxial compressive strength σ s and elastic modulus E s Both have a significant impact, but for the same model (with unchanged particle composition), the particle bonding strength σ can be adjusted. c Determine the uniaxial compressive strength σ of the model s Adjust the particle size and contact modulus E c Compared with particle stiffness ratio k n / k s Determine the elastic modulus E of the model s Size.

[0069] Step 5. Scanning electron microscopy (SEM) is performed on the shear surfaces of some directly sheared test specimens to obtain the distribution patterns of different particles in the model. The SEM results are as follows: Figure 3 As shown in (a), the particles in the figure can be roughly divided into two types: small-sized, concentrated polygonal particles (barite and rosin particles) and large-sized, relatively flat spherical particles (fly ash and river sand particles).

[0070] The density and radius of different particles were determined. The numerical model included four types of particles: barite particles, fly ash particles, river sand particles, and rosin particles. By measuring the mass and volume of each material, the density of the four materials was calculated to be approximately 4500 kg / m³. 3 700kg / m 3 1600kg / m 3 and 900kg / m 3 Based on electron microscopy results, the approximate radii of the four particle types were determined to be 2 μm, 25 μm, 45 μm, and 1 μm. Since a large number of particles in the model would lead to excessively long numerical calculation times, the particle radii in the model needed to be enlarged to ensure reasonable experimental time and accurate results. Considering the interactions between small particles within the model (such as the interlocking effect between barite particles and the bonding effect between rosin particles), the final radii of the four particle types—barite, fly ash, river sand, and rosin—were determined to be 0.2 mm–0.4 mm, 0.5 mm–0.6 mm, 0.8 mm–1 mm, and 0.1 mm–0.2 mm, respectively. The generated particles were randomly distributed in the model, and different types of particles were distinguished by color according to their density, such as… Figure 3 As shown in (b), the distribution pattern of particles in the model is similar to that in reality.

[0071] Step 6. Establish a numerical model. To more realistically reflect the particle system and internal structure of the materials, the volume fraction of different materials is calculated using the mass fraction and density of the materials in the formulation. For example, the volume fraction can be calculated as follows: measure the density of the materials. Since the proportions of materials in the formulation are known, the volume of the material = mass of the material / density, thus obtaining the volume fraction. Simultaneously, determine the particle radius and volume of different materials using electron microscopy. The volume of different materials / individual particle volume = number of particles of different materials, and the number of particles of different materials / total number of particles = percentage of particles of different materials. Based on the volume and radius of different particles, the percentage of different particles in different formulations can be obtained. Based on the percentage of the four particle types, particles are generated through gradation within the model and randomly distributed in a 50mm × 100mm region. Figure 4 Sixteen sets of formulation test models generated using this method are shown.

[0072] Step 7. Numerical experiments are conducted based on the uniaxial compressive strength and elastic modulus measured in actual uniaxial compression tests to calibrate microscopic parameters such as particle contact modulus and particle bond strength for different specimens. In this example, to ensure that the loading method of the numerical model is consistent with the actual experiment, walls are set at the top and bottom of the model. During the experiment, the two walls move towards each other at the same speed, applying pressure to the specimen to achieve uniaxial compression. At the same time, the force, displacement, and number of microcracks of the walls are recorded and saved in real time for subsequent analysis and processing of the test results.

[0073] The calibration process for the detailed parameters consists of four steps:

[0074] (1) Through particle contact modulus E c The elastic modulus of the numerical model was initially determined. To ensure the elastic modulus E of the model... s As unaffected as possible by stiffness ratio k n / k s The changing disturbance, when using particle stiffness ratio k n / k s When adjusting the elastic modulus of the model, it should be kept within a reasonable range, and all other microscopic parameters should be determined. This is achieved by adjusting the particle contact modulus E. c To control the elastic modulus E of the model s Until it matches the elastic modulus E of the actual material.

[0075] (2) Through particle bonding strength σ c Determine the uniaxial compressive strength σ of the model s Until it matches the uniaxial compressive strength σ obtained from the experiment.

[0076] (3) Through the particle contact stiffness ratio k n / ks The elastic modulus E of the numerical model was finally determined. s Because the particle stiffness is greater than k n / k s Uniaxial compressive strength σ s It has almost no impact, so the particle stiffness ratio k can be used. n / k s The particle bonding strength σ is adjusted back and forth. c For elastic modulus E s The influence of this was considered, and the elastic modulus E of the numerical model was obtained. s To ensure the particle stiffness ratio k n / k s The impact on other microscopic parameters should be minimized, with the range controlled within 0.9–1.1.

[0077] (4) Record the micro-parameters of the numerical model at this time and simulate the next set of formulation experiments.

[0078] Based on the above steps, 16 numerical models with different microscopic parameters were finally obtained. The uniaxial compression test results of the 16 sets of different microscopic parameters are listed in Table 6. To facilitate the comparison between the numerical test and the actual material uniaxial compression test results, the last two columns of Table 6 show the uniaxial compressive strength σ and elastic modulus E of the 16 sets of indoor specimens.

[0079] Table 6. Results of uniaxial compression tests for 16 groups with different microscopic parameters.

[0080]

[0081] Wherein: particle contact modulus E c The unit of particle bond strength σ c The unit of simulated uniaxial compressive strength σ s The unit of simulated elastic modulus E s The units for the measured uniaxial compressive strength σ and the measured elastic modulus E are all GPa; the particle stiffness ratio is k. n / k s .

[0082] Step 8. By observing the macroscopic failure process of indoor tests and the microcrack evolution law of numerical models, as well as the distribution of force chain field and displacement field, the mechanical behavior and failure mechanism of similar materials in the surrounding rock are obtained.

[0083] Stress-strain curves from some indoor tests and numerical simulations are shown below. Figure 5As shown, comparing the stress-strain curves obtained from numerical experiments and laboratory tests reveals that the peak value and slope of the numerical simulation curve are basically consistent with those of the laboratory test curve. In the initial loading stage, the stress increase in the laboratory test curve is relatively slow, gradually returning to a stable state as the load increases. This is because the specimens used in the laboratory tests are relatively soft, and in the first half of the loading stage, the specimens are in the compaction phase. The fact that the stress does not show a significant downward trend when it reaches its peak also illustrates this point. The stress-strain curves obtained from the numerical experiments show a linear relationship both before and after the peak value. This is because the model uses a certain servo pressure when generating particles, resulting in a dense and uniform particle aggregate, leading to a higher stiffness in the numerical model.

[0084] Taking the XY-15 group of samples as an example, Figure 6 The stress-strain curves and microcrack number variations of the XY-15 group formulation samples are shown. Figure 7 This describes the crack evolution process of the XY-15 group of formulation specimens. Before the vertical stress reaches the peak strength, the numerical model and the laboratory specimen are in a state of compression, showing no obvious signs of failure, and the number of microcracks increases slowly. When the loading strength reaches the peak, the microcrack growth rate gradually accelerates. At this point, the failure of the model and specimen mainly occurs in the upper half, and a main crack begins to extend towards the bottom of the specimen. After the specimen reaches the peak strength, as the microcrack velocity increases rapidly, the main crack extends to the bottom of the specimen, and the failure of the upper half of the specimen intensifies until the loading ends.

[0085] Through the simulation steps described above, simulation results considering particle density and radius were obtained under different proportions of similar surrounding rock materials. It can be seen that the numerical simulation and laboratory tests show good consistency in stress-strain curves, load-bearing failure processes, and microcrack evolution. The simulation method provided by this invention can reasonably reproduce the mechanical behavior of similar surrounding rock materials, providing a more reasonable simulation means for the stability analysis and support design of weak surrounding rock in tunnel engineering.

[0086] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for microscopic simulation of surrounding rock similar materials, characterized in that: the method for microscopic simulation of surrounding rock similar materials includes the following steps: 1) To prepare materials similar to surrounding rock with different proportions, the specific implementation method is as follows: 1.1) Set the mechanical parameters of the surrounding rock material according to GB / T50218-2014 "Engineering Rock Mass Classification Standard", and determine the target parameters of the surrounding rock similar material according to the basic theory of similarity ratio and geometric similarity ratio. 1.2) Based on the target parameters obtained in step 1.1), prepare surrounding rock similar materials with different proportions; the surrounding rock similar materials include fine aggregate, coarse aggregate, cementing agent, and modifier; the fine aggregate includes barite and fly ash; the coarse aggregate is river sand with a particle size of no more than 2 mm; the cementing agent is a saturated solution of rosin in alcohol; the alcohol is 95% industrial alcohol by volume; the modifier is fully synthetic motor oil, semi-synthetic motor oil, or mineral motor oil; 2) Physical experiments were conducted on the surrounding rock similar materials with different proportions prepared in step 1) to obtain the physical properties of the surrounding rock similar materials with different proportions and to record the physical parameters of the surrounding rock similar materials with different proportions. The physical experiments included uniaxial compression test and direct shear test. The physical parameters included specific weight γ, uniaxial compressive strength σ, elastic modulus E, cohesion c and internal friction angle φ. 3) Determine the contact model between particles; 4) Based on the physical parameters of the surrounding rock similar materials with different mix proportions obtained in step 2) and the contact model determined in step 3), a numerical model of the surrounding rock similar materials with different mix proportions is constructed. The specific implementation method is as follows: 4.1) Determine the microscopic parameters used for calibration. Specifically, this is achieved by analyzing the discrete element method software PFC. 2D To investigate the influence of different micro-parameters on macroscopic uniaxial compressive strength and elastic modulus, an orthogonal experiment was designed. The micro-parameters were analyzed using range analysis to obtain the degree of influence of different micro-parameters on macroscopic uniaxial compressive strength and elastic modulus. Based on this degree of influence, the micro-parameters of the model corresponding to the physical experiment were adjusted until the uniaxial compressive strength and elastic modulus of the model matched the results of the physical experiment. Finally, the micro-parameters used for calibration were obtained. 4.2) Based on the shear plane of the direct shear test conducted in step 2), a scanning electron microscope (SEM) experiment was performed to determine the radius and distribution of particles inside the material; the mass and volume of each material were measured and the density of each material was calculated; the volume fraction of different materials was calculated; based on the volume and particle radius of different particles, the proportion of different particles in different formulations was obtained; based on the proportion of different particle numbers, the discrete element method (PFC) was used to analyze the particle distribution. 2D Randomly generated particles inside; 4.3) Based on step 4.2), uniaxial compression tests are conducted according to the uniaxial compressive strength and elastic modulus measured in step 2). The micro-parameters obtained in step 4.1) are used to calibrate the micro-parameters of particle contact modulus and particle bond strength corresponding to different samples, thereby completing the modeling of the uniaxial compression test and obtaining numerical models of similar materials of surrounding rock under different proportions. The specific method of calibration is as follows: a) Through particle contact modulus E c The elastic modulus of the numerical model is initially determined until it matches the elastic modulus E of the actual material. b) By particle bonding strength σ c Determine the uniaxial compressive strength σ of the model s Until it matches the uniaxial compressive strength σ obtained from the uniaxial compression test; c) By particle contact stiffness ratio k n / k s The elastic modulus E of the numerical model was finally determined. s The particle contact stiffness ratio k n / k s The value is 0.9-1.1; where, k n and k s These are the normal contact stiffness and tangential contact stiffness of the particles, respectively. 5) Perform macroscopic destruction on the numerical models of the surrounding rock similar materials with different proportions obtained in step 4) to obtain the mechanical behavior and failure mechanism of the surrounding rock similar materials, and complete the microscopic simulation of the surrounding rock similar materials.

2. The method for microscopic simulation of surrounding rock similar materials according to claim 1, characterized in that: The contact model between particles in step 3) is a linear parallel bonding model.

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