Microscopic simulation method for surrounding rock similar material

Abstract

The invention belongs to the field of mechanical analysis of surrounding rocks, and relates to a surrounding rock similar material mesoscopic simulation method which comprises the following steps: 1) preparing surrounding rock similar materials in different proportions; 2) respectively carrying out physical experiments to obtain physical parameters of the surrounding rock similar material under different proportions; 3) determining a contact model between particles; 4) based on the physical parameters and the contact model of the surrounding rock similar materials under different proportions, constructing numerical models of the surrounding rock similar materials under different proportions; and 5) performing macroscopic destruction on the numerical model of the similar surrounding rock material in different proportions to obtain the mechanical behavior and destruction mechanism of the similar surrounding rock material, and completing the microscopic simulation of the similar surrounding rock material. The invention provides the mesoscopic simulation method for the surrounding rock similar material, which can reasonably reproduce mechanical behaviors of the surrounding rock similar material and can provide a relatively reasonable simulation means for stability analysis and support design of weak surrounding rock in tunnel engineering.

Description

Technical Field

[0001] The invention belongs to the field of mechanical analysis of surrounding rocks, and relates to a mesoscopic simulation method of similar materials of surrounding rocks, and in particular to a mesoscopic simulation method of similar materials of surrounding rocks taking into account particle density and radius. Background Art

[0002] In the fields of tunnel engineering, geological engineering and mining, the stability of surrounding rock is crucial to engineering safety. Model testing is an important means to study the stability of surrounding rock and explore the mechanical behavior of surrounding rock under load. Among them, the accurate simulation of the mechanical behavior of similar materials of surrounding rock is of great significance for predicting and evaluating the stability of engineering surrounding rock. The traditional method for measuring the mechanical behavior of similar materials of surrounding rock is physical testing, including uniaxial compression test, direct shear test, etc., but the traditional physical testing method has problems such as high cost, low efficiency, long test period, and difficulty in simulating complex geological environment.

[0003] Compared with traditional physical test methods, numerical simulation methods have lower costs and shorter time cycles, can greatly improve the efficiency of research and design, and have become an important means of studying the mechanical behavior of similar materials in surrounding rocks. Discrete element method (DEM) is a numerical technique that can simulate the interaction between particles inside the material and can simulate the microscopic mechanism of material failure, such as crack initiation, expansion and penetration, which are often difficult to observe in macroscopic tests. PFC 2D As a discrete element simulation tool, the software is widely used in simulation research of similar materials of surrounding rocks because it can accurately simulate the mechanical behavior of the granular medium inside the model.

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

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

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

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

[0008] 1) Make similar surrounding rock materials with different proportions;

[0009] 2) performing 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;

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

[0011] 4) Based on the physical parameters of the surrounding rock similar materials at different proportions obtained in step 2) and the contact model determined in step 3), a numerical model of the surrounding rock similar materials at different proportions is constructed;

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

[0013] Preferably, the specific implementation method of step 1) adopted in the present invention is:

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

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

[0016] Preferably, step 1.1) adopted in the present invention is specifically: setting the mechanical parameters of the surrounding rock material according to GB / T50218-2014 "Engineering Rock 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 materials in step 1.2) adopted in the present invention include fine aggregate, coarse aggregate, binder and regulator; the fine aggregate includes barite and fly ash; the coarse aggregate is river sand, and the particle size of the river sand is not more than 2mm; the binder is a saturated solution of rosin in alcohol; the alcohol is industrial alcohol with a volume fraction of 95%; the regulator is fully synthetic motor oil, semi-synthetic motor oil or mineral motor oil.

[0018] Preferably, the physical experiment in step 2) of the present invention includes a uniaxial compression test and a direct shear test, and the physical parameters include gravity γ, uniaxial compressive strength σ, elastic modulus E, cohesion c and internal friction angle

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

[0020] Preferably, the specific implementation method of step 4) adopted by the present invention is:

[0021] 4.1) Determine the mesoscopic parameters used for calibration;

[0022] 4.2) Based on the shear surface of the direct shear test conducted in step 2), a scanning electron microscope test is performed to determine the radius and distribution of particles inside the material; the mass and volume of each material are measured and the density of each material is calculated; the volume fraction of different materials is calculated; based on the volume and particle radius of different particles, the number ratio of different particles in different formulas is obtained; based on the number ratio of different particles, the discrete element software PFC is used to calculate the particle size distribution of different particles. 2D Randomly generate particles inside the middle;

[0023] 4.3) On the basis of step 4.2), a uniaxial compression test is performed according to the uniaxial compressive strength and elastic modulus measured in the uniaxial compression test in step 2), and the mesoscopic parameters used for calibration obtained in step 4.1) are used to calibrate the particle contact modulus and particle bonding strength corresponding to different samples to complete the modeling of the uniaxial compression test and obtain the numerical model of similar surrounding rock materials under different proportions.

[0024] Preferably, the specific implementation method of step 4.1) adopted by the present invention is: analyzing the discrete element software PFC 2D In order to understand the influence of different meso parameters on the macroscopic uniaxial compressive strength and elastic modulus, orthogonal experiments were designed and the meso parameters were analyzed by the range analysis method to obtain the influence of different meso parameters on the macroscopic uniaxial compressive strength and elastic modulus. According to the influence, the meso parameters of the model corresponding to the physical experiment were adjusted until the uniaxial compressive strength and elastic modulus of the model were consistent with the results of the physical experiment, and finally the meso parameters used for calibration were obtained.

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

[0026] a) Through particle contact modulus E c Preliminarily determine the elastic modulus of the numerical model until it matches the elastic modulus E of the actual material;

[0027] b) Through the particle bonding strength σ c Determine the uniaxial compressive strength σ of the model s , until it matches the uniaxial compressive strength σ obtained by the uniaxial compression test;

[0028] c) Through the particle contact stiffness ratio k n / k s Finally determine the elastic modulus E of the numerical model s ; The particle contact stiffness ratio k n / k s The value of is 0.9-1.1; among them, k nand k s are the normal contact stiffness and tangential contact stiffness of the particle respectively.

[0029] The advantages of the present invention are:

[0030] The present invention provides a method for microscopic simulation of surrounding rock similar materials, comprising: 1) preparing surrounding rock similar materials at different proportions; 2) performing physical experiments on the surrounding rock similar materials at different proportions prepared in step 1) to obtain physical properties of the surrounding rock similar materials at different proportions, and recording physical parameters of the surrounding rock similar materials at different proportions; 3) determining a contact model between particles; 4) constructing a numerical model of the surrounding rock similar materials at different proportions based on the physical parameters of the surrounding rock similar materials at different proportions obtained in step 2) and the contact model determined in step 3); 5) performing macroscopic destruction on the numerical model of the surrounding rock similar materials at different proportions obtained in step 4) to obtain mechanical behavior and destruction mechanism of the surrounding rock similar materials, and completing microscopic simulation of the surrounding rock similar materials. Compared with the existing discrete element numerical simulation methods for similar surrounding rock materials, the present invention fully considers the complexity of the microstructure of similar surrounding rock materials, adopts a variety of particle reduction test conditions, and considers particle density and radius. This method can characterize the different components of similar materials such as barite, fly ash, river sand and rosin particles; explores the relationship between the uniaxial compressive strength and elastic modulus of the material and microscopic parameters such as particle radius, bonding strength, and contact modulus; the present invention clarifies the radius of different particles and the distribution inside the material through electron microscope scanning, making the numerical simulation more realistic. The present invention can simulate the movement and interaction of particles or blocks from a microscopic perspective, reveal the microscopic mechanism of material damage and deformation, and can quickly evaluate the impact of different material ratios on the performance of similar surrounding rock materials, thereby guiding experimental design and material selection and improving research efficiency.

[0031] The present invention discloses a method for microscopic simulation of surrounding rock similar materials, including a method based on a particle flow program (PFC) 2D ) to analyze the influence of different mesoscopic parameters on uniaxial compressive strength and elastic modulus; determine the radius and distribution of each particle inside the material through electron microscope scanning; establish a numerical model containing different types of particles; calibrate the mesoscopic parameters such as particle contact modulus and particle bonding strength corresponding to different samples; and analyze the mechanical behavior and failure mechanism of similar surrounding rock materials. It can reasonably reproduce the mechanical behavior of similar surrounding rock materials, providing a more reasonable simulation method for stability analysis and support design of weak surrounding rock in tunnel engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic flow chart of the discrete element numerical simulation research method of surrounding rock similar materials of the present invention;

[0033] Figure 2Schematic diagram of the linear parallel bonding model adopted by the present invention;

[0034] Figure 3 The present invention is tested by scanning electron microscope.

[0035] Figure 4 16 groups of formula samples and their numerical models of the present invention;

[0036] Figure 5 The stress-strain curves of some indoor tests and numerical models are shown;

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

[0038] Figure 7 This is the crack evolution process of the XY-15 group formula specimen. DETAILED DESCRIPTION

[0039] This example aims to solve the defects in the existing technology and provide a mesoscopic simulation method of similar materials considering particle density and radius. This example is applicable to the discrete element numerical simulation research method of surrounding rock similar materials, including the following steps:

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

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

[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] Where: a σ is the stress similarity ratio, a l is the geometric similarity ratio, a r is the heavy similarity ratio, aX is the surface force similarity ratio, a E is the elastic modulus similarity ratio, a ε is the strain similarity ratio, a δ is the deformation similarity ratio, a μ is the Poisson's ratio similarity ratio, a φ is the similarity ratio of the internal friction angle.

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

[0049] The surrounding rock similar materials are composed of aggregate, binder and regulator, with barite and fly ash as fine aggregates and river sand as coarse aggregate, among which barite is used to increase the weight of the material, fly ash is used to delay the setting time of similar materials and increase the flexibility of construction, and fine river sand with a particle size of 2mm is selected to increase the internal friction angle of similar materials. In order to ensure a firm bond between aggregate particles, solid rosin and alcohol are mixed and stirred evenly in a ratio of 1:3, and the prepared rosin alcohol solution is the binder of the material. Since the binder is the rosin particles after alcohol volatilization, in order to ensure that the rosin particles can fully play their role, the demoulded sample is placed in an oven and dried at 80℃ for 12h. Fully synthetic motor oil, semi-synthetic motor oil or mineral motor oil is used as a regulator. For example, the motor oil can be selected as 15W40 to ensure that different materials are fully mixed during stirring, which is convenient for sample formation and improves the plasticity of the material.

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

[0051] In order to comprehensively consider the influence of different raw materials, their proportions and their proportions on the mechanical properties of the specimens, in this example, influencing factors were set based on the mass ratio of the materials. The four factors are mainly considered, namely the mass ratio of fine aggregate to coarse aggregate, the mass ratio of barite in fine aggregate, the mass ratio of aggregate to solution material (rosin alcohol solution and engine oil), and the mass ratio of rosin alcohol to solution material. These factors are included in the orthogonal design in the form of (barite + fly ash) / river sand, barite / (barite + fly ash), aggregate / (rosin alcohol + engine oil) and rosin alcohol / (rosin alcohol + engine oil), and are marked as factors A, B, C and D respectively. A total of 16 groups of proportioning tests were conducted. The factors and their levels in the orthogonal test are shown in Table 1, and the proportioning schemes of 16 groups of similar materials are shown in Table 2. The physical parameters of each group of specimens were measured by direct shear test and uniaxial compression test, including specific gravity γ, uniaxial compressive strength σ, elastic modulus E, cohesion c, internal friction angle The test results are shown in Table 3.

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

[0053]

[0054] Table 2 Proportioning scheme of similar materials for surrounding rocks

[0055]

[0056]

[0057] Table 3 Mechanical parameters corresponding to different ratios

[0058]

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

[0060] The contact model used in this example is the linear parallel bonding model, which is a discrete element method for simulating PFC. 2D A contact model used in simulating the bonding behavior between particles. It simulates cement-like materials by establishing an elastic interface between particles. Figure 2 The basic principle of the linear parallel bond model is that the model contains two interfaces: one is a friction interface that only bears force, and the other is a bonding interface that bears both force and moment. When the stress exceeds the bond strength, the bond breaks and the associated force and moment are removed from the system.

[0061] Step 4. Analyze PFC with discrete element software 2D The influence of different microscopic parameters on the macroscopic uniaxial compressive strength and elastic modulus is then determined to determine the microscopic parameters that need to be calibrated. c It determines the stiffness of the particle contact point and the deformation degree of the numerical model when subjected to force, thus affecting the uniaxial compressive strength σ of the numerical model. s and elastic modulus E s . Particle radius R c Affects the contact area between particles and the compactness of particle arrangement, determines the density and porosity of the model, and further affects the uniaxial compressive strength σ of the model s and elastic modulus E s . Particle bonding strength σ c It reflects the adhesion between particles, is related to the cohesion of the model under stress, and directly affects the uniaxial compressive strength σ of the model. s . Friction coefficient v c It describes the friction resistance between particles and is an important parameter that determines the friction properties and overall stability of the model particles. n / ks The stress distribution between particles and the model can be changed, thereby affecting the uniaxial compressive strength σ of the model. s and elastic modulus E s Therefore, before building the model, the particle contact modulus E is selected c , particle radius R c , particle bonding strength σ c , friction coefficient v c and the particle stiffness ratio k n / k s are five microscopic parameters of the numerical model, and the experiment is designed. The test results are listed in Table 4. Through the range analysis, the effect of different parameters on the uniaxial compressive strength σ s and elastic modulus E s The degree of influence 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] Where: Particle contact modulus E c The unit is GPa; the particle radius R min -R max The unit is mm; particle bonding strength σ c The unit is MPa; the friction coefficient v c ; Particle stiffness ratio k n / k s ; Uniaxial compressive strengthσ s The unit is MPa; elastic modulus E s The unit is Gpa.

[0066] Table 5 Range analysis of different microscopic parameters

[0067]

[0068] Therefore, the uniaxial compressive strength σ of the model s Affected by particle radius R c , particle bonding strength σ c and particle contact modulus E c The influence of is relatively large, and the corresponding ranges are 0.0269, 0.0235 and 0.0149 respectively. The elastic modulus E s Affected by particle radius R c , particle stiffness ratio k n / k s and the particle contact modulus Ec The influence of is greater, and the corresponding ranges are 1.2330, 0.6110 and 0.5665 respectively. It can be seen that the particle radius R c Uniaxial compressive strengthσ s and elastic modulus E s However, for the same model (with unchanged particle composition), the particle bonding strength σ c To determine the uniaxial compressive strength σ of the model s The size of the particle contact modulus E c Ratio of particle stiffness to k n / k s To determine the model elastic modulus E s size.

[0069] Step 5. Perform a scanning electron microscope test on the shear surface of some direct shear test specimens to obtain the distribution pattern of different particles in the model. The scanning electron microscope results are as follows: Figure 3 As shown in (a), the particles in the figure can be roughly divided into two types, namely, polygonal particles with small particle size and concentrated distribution (baryte and rosin particles) and spherical particles with large particle size and relatively flat surface (fly ash and river sand particles).

[0070] Determine the density and radius of different particles. The numerical model contains four types of particles, namely 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 is calculated to be about 4500kg / m 3 , 700kg / m 3 , 1600kg / m 3 and 900kg / m 3 . Based on the results of electron microscope scanning, the radius of the four particles is determined to be approximately 2μm, 25μm, 45μm and 1μm. Since too many particles in the model will cause the numerical test calculation to take too long, in order to ensure the rationality of the test time and the accuracy of the test results, it is necessary to enlarge the radius of the particles in the model to a certain extent. Taking into account the interaction between the fine particles inside the model (such as the bite effect between the barite particles and the bonding effect between the rosin), it is finally determined that the radius of the four particles of barite, fly ash, river sand and rosin are 0.2mm~0.4mm, 0.5mm~0.6mm, 0.8mm~1mm and 0.1mm~0.2mm. The generated particles are randomly distributed in the model, and different types of particles are distinguished by color according to different densities, such as Figure 3 As shown in (b), it can be seen that the distribution pattern of particles in the model is similar to the actual one.

[0071] Step 6. Establish a numerical model. In order to more realistically reflect the particle system and internal structure of the material, the volume fraction of different materials is calculated through the mass fraction and density of the material in the ratio scheme. Exemplarily, the volume fraction can be calculated by measuring the density of the material. Since the ratio of the materials in the formula is known, the volume of the material = the mass of the material / density, and then the volume fraction can be obtained; at the same time, the particle radius and particle volume of different materials are determined according to the electron microscope scanning test, the volume of different materials / the volume of each particle = the number of particles of different materials, and the number of particles of different materials / the total number of particles = the proportion of the number of particles of different materials. According to the volume and particle radius of different particles, the proportion of the number of different particles in different formulas can be obtained. According to the particle number proportion of the four types of particles, particles are generated by grading inside the model and randomly distributed in an area of ​​50mm×100mm. Figure 4 Sixteen groups of formulation test models generated using this method are shown.

[0072] Step 7. Perform numerical tests based on the uniaxial compressive strength and elastic modulus measured by the actual uniaxial compression test to calibrate the microscopic parameters such as particle contact modulus and particle bonding strength corresponding to different samples. In this example, in order to ensure that the loading method of the numerical model is consistent with the actual test, walls are set at the upper and lower ends of the model. During the test, the two walls move toward each other at the same speed, applying pressure to the sample to achieve uniaxial compression. At the same time, the force, displacement, and number of microcracks of the wall are recorded and saved in real time to facilitate the subsequent analysis and processing of the test results.

[0073] The calibration process of mesoscopic parameters is divided into four steps:

[0074] (1) Through the particle contact modulus E c Preliminary determination of the elastic modulus of the numerical model. In order to ensure the elastic modulus E s As far as possible, the stiffness ratio k n / k s Variation of the disturbance, using the particle stiffness ratio k n / k s When adjusting the elastic modulus of the model, keep it within a reasonable range and ensure that other microscopic parameters are determined. 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 the 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 Finally determine the elastic modulus E of the numerical model s Since the particle stiffness is higher than k n / k s Uniaxial compressive strengthσ s There is almost no effect, so the particle stiffness ratio k can be used n / k s Adjust the particle bonding strength σ c Elastic modulus E s The elastic modulus E of the numerical model is obtained s To ensure that the particle stiffness is higher than k n / k s The influence on other microscopic parameters is kept as small as possible, and the selection range is controlled within 0.9–1.1.

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

[0078] Based on the above steps, 16 sets of numerical models with different particle mesoscopic parameters are finally obtained. The uniaxial compression test results of 16 sets of different mesoscopic parameters are listed in Table 6. In order to compare the numerical test with the uniaxial compression test results of actual materials, 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 Uniaxial compression test results of 16 groups of different microscopic parameters

[0080]

[0081] Where: Particle contact modulus E c Unit, particle bonding strength σ c Units, simulated uniaxial compressive strength σ s Unit, simulated elastic modulus E s The unit of 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. Through the macroscopic failure process of the indoor test and the microcrack evolution law of the numerical model, as well as the distribution of the force chain field and displacement field, the mechanical behavior and failure mechanism of similar surrounding rock materials are obtained.

[0083] Some stress-strain curves of indoor tests and numerical simulations are shown in Figure 5As shown in the figure, by comparing the stress-strain curves obtained from the numerical test and the indoor test, it can be seen that the peak value and slope of the numerical simulation curve are basically consistent with the curve of the indoor test. In the early stage of loading, the stress of the indoor test curve increases slowly, and gradually returns to a stable state with the increase of load. This is because the specimens used in the indoor test are relatively soft. In the first half of the loading, the specimens are in the compaction stage. When the stress reaches the peak, the stress does not show an obvious downward trend, which also illustrates this point. The stress-strain curves obtained from the numerical test are linear before and after the peak. This is because the model uses a certain servo pressure when generating particles, so that the generated particle aggregates are in dense and uniform contact, resulting in a large stiffness of the numerical model.

[0084] Take the XY-15 group of samples as an example. Figure 6 The stress-strain curve and the change of the number of microcracks of the XY-15 group formula samples are shown in Figure 2. Figure 7 This is the crack evolution process of the XY-15 group formula specimen. Before the vertical stress reaches the peak strength, the numerical model and the indoor specimen are in a compression state, with no obvious signs of damage, and the number of microcracks grows slowly. When the loading intensity reaches the peak, the growth rate of microcracks gradually accelerates. At this time, the damage of the model and the specimen mainly occurs in the upper part, and a main crack begins to form that extends to the bottom of the specimen. When the specimen reaches the peak strength, as the microcrack rate grows rapidly, the main crack extends to the bottom of the specimen, and the damage in the upper part of the specimen intensifies until the loading ends.

[0085] Through the simulation of the above steps, the simulation results considering the particle density and radius under different proportions of similar surrounding rock materials are obtained. It can be seen that the numerical simulation and the indoor test are relatively consistent in the stress-strain curve, load-damage process and microcrack evolution law. The simulation method provided by the present invention can reasonably reproduce the mechanical behavior of similar surrounding rock materials, and provides a more reasonable simulation method for the stability analysis and support design of weak surrounding rock in tunnel engineering.

[0086] The above are only preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for microscopic simulation of surrounding rock similar materials, characterized by: The method for mesoscopic simulation of surrounding rock similar materials comprises the following steps: 1) Make similar surrounding rock materials with different proportions; 2) performing 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) Determine the contact model between particles; 4) Based on the physical parameters of the surrounding rock similar materials at different proportions obtained in step 2) and the contact model determined in step 3), a numerical model of the surrounding rock similar materials at different proportions is constructed; 5) Perform macroscopic destruction on the numerical model of the surrounding rock similar material under different proportions obtained in step 4) to obtain the mechanical behavior and failure mechanism of the surrounding rock similar material, and complete the microscopic simulation of the surrounding rock similar material.

2. The method for mesoscopic simulation of surrounding rock similar materials according to claim 1, characterized in that: The specific implementation method of step 1) is: 1.1) Determine the target parameters of similar surrounding rock materials; 1.2) Based on the target parameters obtained in step 1.1), similar materials of surrounding rocks with different proportions are produced.

3. The method for microscopic simulation of surrounding rock similar materials according to claim 2, characterized in that: The step 1.1) specifically includes: setting the mechanical parameters of the surrounding rock materials according to GB / T50218-2014 "Engineering Rock Mass Classification Standard", and determining the target parameters of the surrounding rock similar materials according to the basic theory of similarity ratio and geometric similarity ratio.

4. The method for mesoscopic simulation of surrounding rock similar materials according to claim 3 is characterized in that: The surrounding rock similar materials in the step 1.2) include fine aggregate, coarse aggregate, binder and regulator; the fine aggregate includes barite and fly ash; the coarse aggregate is river sand, and the particle size of the river sand is not more than 2mm; the binder is a saturated solution of rosin in alcohol; the alcohol is industrial alcohol with a volume fraction of 95%; the regulator is fully synthetic motor oil, semi-synthetic motor oil or mineral motor oil.

5. The method for microscopic simulation of surrounding rock similar materials according to claim 4, characterized in that: The physical experiment in step 2) includes a uniaxial compression test and a direct shear test, and the physical parameters include gravity γ, uniaxial compressive strength σ, elastic modulus E, cohesion c and internal friction angle φ.

6. The method for microscopic simulation of surrounding rock similar materials according to claim 5, characterized in that: The contact model between particles in step 3) is a linear parallel bonding model.

7. The method for microscopic simulation of surrounding rock similar materials according to claim 6, characterized in that: The specific implementation method of step 4) is: 4.1) Determine the mesoscopic parameters used for calibration; 4.2) Based on the shear surface of the direct shear test conducted in step 2), a scanning electron microscope test is performed to determine the radius and distribution of particles inside the material; the mass and volume of each material are measured and the density of each material is calculated; the volume fraction of different materials is calculated; based on the volume and particle radius of different particles, the number ratio of different particles in different formulas is obtained; based on the number ratio of different particles, the discrete element software PFC is used to calculate the particle size distribution of different particles. 2D Randomly generate particles inside the middle; 4.3) On the basis of step 4.2), a uniaxial compression test is performed according to the uniaxial compressive strength and elastic modulus measured in the uniaxial compression test in step 2), and the mesoscopic parameters used for calibration obtained in step 4.1) are used to calibrate the particle contact modulus and particle bonding strength corresponding to different samples to complete the modeling of the uniaxial compression test and obtain the numerical model of similar surrounding rock materials under different proportions.

8. The method for microscopic simulation of surrounding rock similar materials according to claim 7, characterized in that: The specific implementation method of step 4.1) is: analyzing the discrete element software PFC 2D In order to understand the influence of different meso parameters on the macroscopic uniaxial compressive strength and elastic modulus, orthogonal experiments were designed and the meso parameters were analyzed by the range analysis method to obtain the influence of different meso parameters on the macroscopic uniaxial compressive strength and elastic modulus. According to the influence, the meso parameters of the model corresponding to the physical experiment were adjusted until the uniaxial compressive strength and elastic modulus of the model were consistent with the results of the physical experiment, and finally the meso parameters used for calibration were obtained.

9. The method for mesoscopic simulation of surrounding rock similar materials according to claim 8, characterized in that: The specific method of calibration in step 4.3) is: a) Through particle contact modulus E c Preliminarily determine the elastic modulus of the numerical model until it matches the elastic modulus E of the actual material; b) Through the particle bonding strength σ c Determine the uniaxial compressive strength σ of the model s , until it matches the uniaxial compressive strength σ obtained by the uniaxial compression test; c) Through the particle contact stiffness ratio k n / k s Finally determine the elastic modulus E of the numerical model s ; The particle contact stiffness ratio k n / k s The value of is 0.9-1.1; among them, k n and k s are the normal contact stiffness and tangential contact stiffness of the particle respectively.

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