Strength prediction method and preparation method of randomly distributed fracture rock

Abstract

The invention discloses a strength prediction method and a preparation method of randomly distributed fracture rock, which comprises the following steps: carrying out conventional uniaxial and triaxial compression tests on a randomly distributed fracture rock sample, and collecting strength data and stress-strain data of the sample in the test process; establishing and verifying a strength model; the water-soluble characteristic of the material avoids the operation of extra pulling out when the material is used for manufacturing cracks, so that the limitation of the prefabricated number, form, position range and distribution form of the cracks can be eliminated. The analysis of the fracture form is not only limited to a two-dimensional angle, but extends to a complex and more real three-dimensional space, so that the distribution information of the fracture is brought into the research category. Different types of randomly distributed fractured rock masses can be researched by matching different similar materials. The established strength prediction model of the randomly distributed fractured rock mass can reflect the influence of the three-dimensional fracture distribution information on the rock mass strength. The randomly distributed fracture rock strength predictor is simple and convenient to operate and high in prediction precision.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering technology, specifically to a method for predicting the strength of randomly distributed fractured rocks and a method for preparing such rocks. Background Technology

[0002] As natural geological materials, rock masses are influenced by a combination of factors, including temperature, engineering disturbances, chemical erosion, and tectonic movements. They contain structural defects spanning multiple orders of magnitude (from submicrometers to kilometers), such as pores, fissures, joints, and faults. The presence of defects like fissures causes rock materials to exhibit significant heterogeneity and anisotropy. Their quantity, type, and combination are directly related to rock mass strength. A lack of understanding of the strength characteristics of fractured rock masses will complicate the prevention and control of rock hazards, thereby posing safety risks to engineering projects such as rock salt gas storage, nuclear waste storage, and coal mining.

[0003] The laboratory preparation of fractured rock specimens is the physical basis for studying their strength properties and has always been a focus of attention in the field of rock mechanics. Directly cutting and processing rock materials is not easy, especially for standard cylindrical specimens. Due to the low success rate of fracture prefabrication, the number of prefabricated specimens is generally limited to no more than two. Furthermore, fracture prefabrication of rock specimens is usually achieved using diamond wire cutting or waterjet cutting techniques. Since the dimensions of the "wire" or "waterjet" itself are not negligible, coupled with processing errors, the resulting specimens are likely to only have open fractures; moreover, the processing defects penetrate directly through the specimen, essentially lacking three-dimensional characteristics.

[0004] Compared to natural rock, prefabricated fractures using rock-like materials (such as gypsum, resin, and cement mortar) offer several advantages. Embedded materials for fracture fabrication include metal sheets and mica sheets. However, to eliminate the interference of these embedded materials on the mechanical properties of the specimen, they generally need to be removed during the curing process, leading to additional damage. Furthermore, the pre-embedding method is currently mainly suitable for creating a small number of regular and open fractures. Based on these considerations, exploring a method for preparing randomly distributed fractured rock that more closely approximates the fracture distribution in actual engineering rock masses, and understanding the influence of fracture distribution within the rock on its strength characteristics, is crucial for ensuring the orderly construction of various projects and the safety of construction personnel and equipment. Therefore, this paper proposes a method for predicting the strength of randomly distributed fractured rock and its preparation method. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the strength of randomly distributed fractured rocks and a method for preparing such rocks, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting the strength of randomly distributed fractured rocks, characterized by comprising the following steps:

[0007] S1. Conduct conventional uniaxial and triaxial compression tests on randomly distributed fractured rock samples, and collect the strength data and stress-strain data of the samples during the test.

[0008] S2. Strength model establishment and verification;

[0009] S21. Based on the superposition of deformation of rock blocks and fracture structures, establish an analytical model of rock damage;

[0010] S23. Based on mathematical statistics methods, the analytical model is simplified;

[0011] S23. In view of the influence of confining pressure, establish a rock strength model that considers the coupling effect of confining pressure and distributed fractures;

[0012] S24. Compare the experimental results with the theoretical results to verify the reliability of the model;

[0013] S3. Input the intensity prediction model compiler into the instrument to form a predictor.

[0014] Preferably, in step S1, at least three sets of parallel tests are performed on the specimen under each test condition, the average strength value under each set of test conditions is obtained, and it is determined whether the strength value of a certain specimen exceeds the allowable error range of the average value. If it does, the specimen needs to be rejected and the test needs to be repeated until the error range is met.

[0015] Preferably: In step S21, the final expression of the rock damage analytical model established based on the superposition of deformations of rock blocks and fracture structures is:

[0016] ;

[0017] in, The degree of rock mass damage, This is a constraint coefficient, and its value ranges from 0 to 1. The elastic modulus of the rock block. For the height of the rock mass specimen, The diameter of the rock mass specimen is [missing information]. For the first The diameter of the crack, For the first The angle between the normal direction of the crack and the loading direction. and These are the normal stiffness and tangential stiffness of the fractured structural surface, respectively.

[0018] Preferably, in step S22, the analytical model is simplified based on mathematical statistics methods, and the simplified final expression is:

[0019] ;

[0020] in, The number of cracks, The diameter of a single crack. The expression is:

[0021] .

[0022] Preferably: In step S23, the final expression of the rock strength model considering the coupling effect of confining pressure and distributed fractures is:

[0023] ;

[0024] in, For the strength of rock masses containing distributed fractures, The allowable stress is the sum of the uniaxial compressive strength and the confining pressure of the intact specimen. The quantity to be determined is [L]. -1 ], Poisson's ratio of the rock block For confining pressure.

[0025] Preferably, in step S24, the verification of model reliability involves not only visually comparing the experimental results and theoretical results on the same coordinate system, but also calculating the correlation coefficient. Furthermore, error analysis of the data can effectively verify the reliability of the model.

[0026] A method for preparing randomly distributed fractured rocks as described in any one of the above-mentioned methods includes the following steps:

[0027] Sa, determine the proportion of rock-like materials, prepare the required amount of cement, standard sand and water, and cut the required size and quantity of water-soluble materials;

[0028] Sb. Put the prepared water-soluble materials, cement, and standard sand into a mortar mixer and dry mix for 5 minutes. Then pour in the weighed water and wet mix for 3 minutes.

[0029] Sc. Pour the well-mixed slurry into the mold, vibrate to form, and after initial setting, remove the mold, take out the sample, and place the sample in a constant temperature and humidity curing chamber for curing.

[0030] After the curing period, the sample is taken out and subjected to a warm bath treatment.

[0031] Preferably, in step Sa, the determination of the proportion of rock-like similar materials is achieved by obtaining the stress-strain curves of the original rock and rock-like similar materials with different proportions through conventional uniaxial and triaxial compression tests. The stress-strain curves, basic mechanical parameters and failure modes of the two are compared, and the proportion that is highly similar to the original rock is the final proportion.

[0032] The standard sand needs to be washed and then dried for 24 hours. After drying, it should be sieved to meet the particle size distribution requirements of the sample.

[0033] Preferably, in step Sa, the water-soluble material is PVA water-soluble material, which is in the form of a film. The main raw materials include PVA and starch. The water dissolution temperature is between 50 and 55°C. The dissolution products are mainly gas and liquid.

[0034] Preferably, in step Sd, the warm bath treatment refers to placing the cured sample in pure warm water, with the water temperature controlled within the range of 55±0.5℃, and soaking for 48 hours to ensure that the water-soluble material is completely dissolved.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. The water-soluble properties of the material avoid the need for additional extraction when using it to create cracks, thereby removing the limitations on the number, shape, location range and distribution of pre-made cracks.

[0037] 2. The analysis of crack morphology is not limited to two-dimensional perspective, but extends to the complex but more realistic three-dimensional space, so that the distribution information of cracks is included in the research scope.

[0038] 3. By using different proportions of similar materials, different types of randomly distributed fractured rock masses can be studied.

[0039] 4. The established strength prediction model for randomly distributed fractured rock masses can reflect the influence of three-dimensional fracture distribution information on rock mass strength.

[0040] 5. The strength predictor for randomly distributed fractured rocks is easy to operate and has high prediction accuracy. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method for preparing randomly distributed fractured rocks and the method for predicting their strength according to the present invention;

[0042] Figure 2 An image showing the effect of preparing a randomly distributed fractured rock mass;

[0043] Figure 3 For comparison of specimen test and theoretical strength distribution;

[0044] Figure 4The linear regression error between theoretical and experimental results;

[0045] Figure 5 A strength predictor for randomly distributed fractured rocks;

[0046] Figure 6 To illustrate the four-dimensional spatial flow law of rock strength based on the output data of the strength predictor;

[0047] Figure 7 This is a comparison chart of different crack diameters when the number of cracks is 400. Detailed Implementation

[0048] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0049] Please see Figure 1-7 This invention provides a technical solution: a method for preparing randomly distributed fractured rocks, the process of which is as follows:

[0050] First, consult relevant materials to determine the proportional relationships between several groups of cement, standard sand, and water, for example, cement:standard sand:water = 1:1:0.35. For each mix proportion, prepare at least three specimens of similar materials and the original rock used for the study. Obtain the average mechanical parameters of the specimens through conventional uniaxial and triaxial compression tests. Compare the stress-strain curves, basic mechanical parameters, and failure modes of the similar materials and the original rock. The mix proportion highly similar to the original rock is the final mix proportion. Wash the standard sand and dry it for 24 hours to avoid the water content in the standard sand affecting the water-cement ratio of the specimen. After drying, sieve the sand to meet the particle size distribution requirements of the specimen. Prepare the cement, standard sand, and water according to the required experimental quantities. Cut the PVA water-soluble material according to the required quantity; the shape and size of the cut can be customized to meet the experimental requirements.

[0051] Sb. Put the water-soluble material, cement, and standard sand into a mortar mixer and dry mix for 5 minutes to ensure that the three are mixed evenly and that the distribution and angle of the water-soluble material are as random as possible. Then pour in the weighed water and wet mix for 3 minutes to ensure that the materials are mixed evenly and form a uniform slurry.

[0052] Sc. Pour the well-mixed slurry into a mold and vibrate it on a high-frequency vibration table for 15 seconds to reduce air bubbles in the slurry and improve the uniformity of the sample. After initial setting, remove the mold and take out the sample. Place the sample in a constant temperature and humidity curing chamber for curing. The curing conditions are generally standard curing, i.e., the temperature inside the chamber is 20℃±1℃ and the humidity is 95%±2%, and the curing is carried out under these constant temperature and humidity conditions for 28 days. The curing conditions can also be changed according to the needs of the research.

[0053] After the curing period, the sample was removed and placed in pure warm water, with the water temperature controlled within the range of 55±0.5℃, for 48 hours. The water-soluble material was completely dissolved, and the randomly distributed fractured rock was prepared. The preparation effect was referenced... Figure 2 .

[0054] This invention also relates to a method for predicting the strength of randomly distributed fractured rocks, the process of which is as follows:

[0055] S1. Test treatment: Conventional uniaxial and triaxial compression tests were conducted on randomly distributed fractured rock samples. During the test, the strength data and stress-strain data of the samples were collected. At least three sets of parallel tests were performed on the samples under each test condition. The average strength value under each set of test conditions was calculated, and it was determined whether the strength value of a certain sample exceeded the allowable error range of the average value. If it exceeded the allowable error range, the sample was rejected and the test was repeated until it met the error range.

[0056] S2. Strength model establishment and verification;

[0057] S21. Superimposed deformation of rock blocks and fracture structures refers to a fractured rock mass structurally composed of intact rock blocks and fractured composite structures, whose overall deformation includes both rock block deformation and fracture deformation. By decomposing the fracture deformation along the normal and tangential directions, the total displacement along the load direction under uniaxial loading is obtained. It can be divided into three parts, namely the displacement of the rock block along the load direction. The component of the normal displacement of the cracked structure along the load direction and the component of shear displacement along the load direction in the fractured structure. Therefore, the total displacement It can be characterized as:

[0058] ;

[0059] Based on formula (4), considering the fracture morphology and dip angle, we can obtain:

[0060]

[0061]

[0062]

[0063] in, This refers to the stress along the axial direction of the specimen.

[0064] Combining equation (5), the definition of elastic modulus and damage degree, we can obtain the analytical model of rock damage based on the superposition of deformation of rock block and fracture structure:

[0065] ;

[0066] S22. Since the pre-fabricated crack inclination angles in the sample are randomly and uniformly distributed in the range of 0 to 360°, and the number of pre-fabricated cracks is sufficient, the number of cracks in this invention example is selected as 100 to 400. Therefore, the crack angle trigonometric function in equation (1) does not need to be calculated for each pre-fabricated crack individually, but can be directly calculated using probability expectation. Solve it, and the result is as follows:

[0067] ;

[0068] Substituting formula (6) into formula (1) yields a simplified analytical model based on mathematical statistics:

[0069] ;

[0070] S23. Combining formula (2), the definition of damage degree, and Hooke's law, we can obtain a rock strength model that considers the coupling effect of confining pressure and distributed fractures:

[0071] ;

[0072] Considering that both the size of individual cracks and the distance between cracks within the specimen affect the constraint coefficient, the parameters that may influence this effect are nothing more than the number of cracks and the size of individual cracks. However, analysis shows that if the size of individual cracks changes, the impact of the number of cracks on the constraint coefficient is unclear; while regardless of the number of cracks, the size of individual cracks, by affecting the boundary length, has a clear negative correlation with the constraint coefficient. Based on the above analysis, it can be assumed that the constraint coefficient is linearly related to the crack diameter; furthermore, considering that the constraint coefficient corresponding to a complete specimen must be 0, the following correspondence can be established:

[0073] ;

[0074] Substituting formula (8) into formula (7) yields the modified strength model, as shown below:

[0075] ;

[0076] S24, such as Figure 3 , Figure 4 As shown, the theoretical strength and experimental strength of the specimen are in high agreement, with a correlation coefficient of [missing value]. The value reached 0.971, verifying the rationality of the intensity model.

[0077] S3, Predictor Formation: Refer to Figure 5 The random distribution fracture rock strength predictor includes:

[0078] Memory: Used to store the randomly distributed fracture parameters recorded in the input;

[0079] Program: Used to read data and input it into the intensity prediction model;

[0080] CPU processor: Used to perform calculations, calculate and output intensity data calculated by the intensity prediction model.

[0081] The random distribution fracture rock strength predictor is easy to operate. Simply input the random distribution fracture parameters required for the study, and you can obtain the corresponding strength prediction data.

[0082] Furthermore, the strength predictor for randomly distributed fractured rocks can output multiple sets of strength prediction data at once. Specifically, after inputting one set of randomly distributed fracture parameters and clicking "Confirm," you can continue to input another set of parameters. When you need to output the results, simply click "Confirm" after inputting the last set of parameters, and then click "Calculate."

[0083] like Figure 6 As shown, the four-dimensional spatial flow law of rock strength is illustrated, with all data points being output data from a strength predictor. Specifically, the area of ​​a single fracture is used as an example. , and confining pressure Number of cracks Using these variables as independent variables and further enriching and encrypting the data, the four-dimensional spatial relationship of the compressive strength of the cracked specimen can be calculated using a strength predictor.

[0084] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the strength of randomly distributed fractured rocks, characterized in that, Includes the following steps: S1. Conduct conventional uniaxial and triaxial compression tests on randomly distributed fractured rock samples, and collect the strength data and stress-strain data of the samples during the test. S2. Strength model establishment and verification; S21. Based on the superposition of deformation of rock blocks and fracture structures, establish an analytical model of rock damage; S23. Based on mathematical statistics methods, the analytical model is simplified; S23. In view of the influence of confining pressure, establish a rock strength model that considers the coupling effect of confining pressure and distributed fractures; S24. Compare the experimental results with the theoretical results to verify the reliability of the model; S3. Input the intensity prediction model compiler into the instrument to form a predictor.

2. The method for predicting the strength of randomly distributed fractured rocks according to claim 1, characterized in that: In step S1, at least three sets of parallel tests are performed on the specimen under each test condition. The average strength value under each set of test conditions is obtained, and it is determined whether the strength value of a certain specimen exceeds the allowable error range of the average value. If it does, the specimen needs to be rejected and the test needs to be repeated until the error range is met.

3. The method for predicting the strength of randomly distributed fractured rocks according to claim 1, characterized in that: In step S21, the final expression of the rock damage analytical model established based on the superposition of deformations of rock blocks and fracture structures is: ； in, The degree of rock mass damage, This is a constraint coefficient, and its value ranges from 0 to 1. The elastic modulus of the rock block. For the height of the rock mass specimen, The diameter of the rock mass specimen is [missing information]. For the first The diameter of the crack, For the first The angle between the normal direction of the crack and the loading direction. and These are the normal stiffness and tangential stiffness of the fractured structural surface, respectively.

4. The method for predicting the strength of randomly distributed fractured rocks according to claim 1, characterized in that: In step S22, the analytical model is simplified based on mathematical statistics methods, and the simplified final expression is: ； in, The number of cracks, The diameter of a single crack. The expression is: 。 5. The method for predicting the strength of randomly distributed fractured rocks according to claim 1, characterized in that: In step S23, the final expression for the rock strength model considering the coupling effect of confining pressure and distributed fractures is: ； in, For the strength of rock masses containing distributed fractures, The allowable stress is the sum of the uniaxial compressive strength and the confining pressure of the intact specimen. The quantity to be determined is [L]. -1 ], Poisson's ratio of the rock block For confining pressure.

6. The method for predicting the strength of randomly distributed fractured rocks according to claim 1, characterized in that: In step S24, the verification of model reliability involves not only visually comparing the experimental results and theoretical results on the same coordinate system, but also calculating the correlation coefficient. Furthermore, error analysis of the data can effectively verify the reliability of the model.

7. A method for preparing the randomly distributed fractured rock according to any one of claims 1-6, characterized in that, Includes the following steps: Sa, determine the proportion of rock-like materials, prepare the required amount of cement, standard sand and water, and cut the required size and quantity of water-soluble materials; Sb. Put the prepared water-soluble materials, cement, and standard sand into a mortar mixer and dry mix for 5 minutes. Then pour in the weighed water and wet mix for 3 minutes. Sc. Pour the well-mixed slurry into the mold, vibrate to form, and after initial setting, remove the mold, take out the sample, and place the sample in a constant temperature and humidity curing chamber for curing. After the curing period, the sample is taken out and subjected to a warm bath treatment.

8. The method for predicting the strength of randomly distributed fractured rocks and its preparation method according to claim 7, characterized in that: In step Sa, the determination of the proportion of rock-like similar materials is achieved by obtaining the stress-strain curves of the original rock and rock-like similar materials with different proportions through conventional uniaxial and triaxial compression tests. The stress-strain curves, basic mechanical parameters and failure modes of the two are compared, and the proportion that is highly similar to the original rock is the final proportion. The standard sand needs to be washed and then dried for 24 hours. After drying, it should be sieved to meet the particle size distribution requirements of the sample.

9. The method for predicting the strength of randomly distributed fractured rocks and its preparation method according to claim 1, characterized in that: In step Sa, the water-soluble material is PVA water-soluble material, which is in the form of a film. The main raw materials include PVA and starch. The water dissolution temperature is between 50 and 55°C. The dissolution products are mainly gas and liquid.

10. The method for predicting the strength of randomly distributed fractured rocks and its preparation method according to claim 1, characterized in that: In step Sd, the warm bath treatment refers to placing the cured sample in pure warm water, with the water temperature controlled within the range of 55±0.5℃, and soaking for 48 hours to ensure that the water-soluble material is completely dissolved.

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