Unit strength parameter selection method for simulating rock mass fracture through discontinuous numerical method

Abstract

The invention relates to the technical field of calculation of rock mechanics, in particular to a unit strength parameter selection method for simulating rock mass fracture by a discontinuous numerical method, which comprises the following steps: acquiring calibration material mechanical parameters of a target rock, and calculating strength probability distribution functions of the parameters under different volumes; performing iterative fitting on the intensity probability distribution functions under different volumes to obtain Weibull shape functions and Weibull size functions of different volume grades; performing grid division on the calculation model of the target rock mass to calculate the size grade of each connection unit, and respectively inputting each size grade into the Weibull shape function and the Weibull size function of different volume grades to obtain the Weibull shape function and the Weibull size function of each connection unit, and the strength parameter of each connection unit is calculated. Therefore, the problem that when rock mass fracture is calculated through a discontinuous numerical method, the relation between rock macromechanical properties and unit strength parameters is unknown due to the influence of the size effect is solved.

Description

Technical Field

[0001] The invention relates to the technical field of computational rock mechanics, and in particular to a method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method. Background Art

[0002] The discontinuous numerical simulation method can effectively simulate the discontinuous deformation in the process of heterogeneous rock mass fracture evolution, and has been widely used in the study of geological disaster induction mechanism, assessment of geological disaster damage degree, and exploration of reasonable response strategies for geological disasters. The discontinuous simulation method simulates rock mass fracture by destroying the connecting units between the solid units in the model, and the destruction of the connecting bonds obeys the strength criterion. Therefore, inputting reliable unit strength parameters in the rock mass numerical model is the key to accurately simulate the evolution behavior of rock mass fracture.

[0003] At present, when using discontinuous numerical simulation methods, the strength parameters of the connection units are calibrated through indoor tests and directly applied to large-scale rock mass fracture calculations. Since the strength of rock materials has a typical size effect, the calibrated unit parameters are only accurate for the unit size of the calibration model, that is, the fracture behavior of rock materials can only be accurately simulated when the unit size of the calculation model and the calibration model are consistent. For large-scale rock mass fracture simulation, due to the limitation of computer performance, there are obvious differences in unit scale between the large-scale rock mass model and the indoor test calibration model. Using calibration parameters for engineering rock mass fracture evolution calculations inevitably leads to large calculation errors.

[0004] Therefore, a method for selecting unit strength parameters for simulating rock mass fracture using discontinuous numerical methods is urgently needed. Summary of the invention

[0005] The present invention provides a unit strength parameter selection method for simulating rock mass fracture using a discontinuous numerical method, so as to solve the problem that the relationship between the macroscopic mechanical properties of rocks and the unit strength parameters is unclear due to the influence of size effect when calculating rock mass fracture using a discontinuous numerical method.

[0006] The first aspect of the present invention provides a method for selecting unit strength parameters for simulating rock fracture using a non-continuous numerical method, comprising the following steps: obtaining the calibrated material mechanical parameters of the target rock, and calculating the strength probability distribution function of the calibrated material mechanical parameters at different volumes; iteratively fitting the strength probability distribution function at different volumes to obtain Weibull shape functions and Weibull size functions at different volume levels; gridding the calculation model of the target rock mass to calculate the size level of each connection unit; inputting the size level of each connection unit into the Weibull shape function and Weibull size function of the different volume levels, respectively, to obtain the Weibull shape function and Weibull size function of each connection unit; and calculating the strength parameters of each connection unit based on the Weibull shape function and Weibull size function of each connection unit.

[0007] Optionally, the obtaining of calibration material mechanical parameters of the target rock and calculating the strength probability distribution function of the calibration material mechanical parameters at different volumes includes:

[0008] Calibrate material mechanical parameters using indoor test data of the target rock to obtain the calibrated material mechanical parameters, wherein the calibrated material mechanical parameters include tensile strength, shear strength and friction coefficient;

[0009] The mechanical parameters of the calibration material are input into a pre-built multi-level recursive analytical model of strength size effect that takes into account the rock's own heterogeneity and fracture self-similarity, so as to calculate the strength probability distribution function of the mechanical parameters of the calibration material in different volumes.

[0010] Optionally, the iterative fitting of the intensity probability distribution functions at different volumes to obtain Weibull shape functions and Weibull size functions at different volume levels includes:

[0011] Converting the intensity probability distribution functions at different volumes into unit lengths at different volumes;

[0012] The unit lengths at different volumes are fitted using a two-parameter Weibull distribution function to obtain Weibull shape functions and Weibull size functions at different volume levels.

[0013] Optionally, the gridding of the computational model of the target rock mass to calculate the size grade of each connection unit includes:

[0014] Meshing the computational model of the target rock mass to calculate the volume of each solid unit;

[0015] Calculating the representative volume of each connected unit according to the volume of each solid unit;

[0016] The size class of each connection unit is determined according to the representative volume of each connection unit.

[0017] Optionally, the representative volume of each connection unit is an average value of the volumes of two solid units connected by each connection unit.

[0018] Optionally, calculating the strength parameter of each connection unit according to the Weibull shape function and the Weibull size function of each connection unit includes:

[0019] Inputting the Weibull shape function and the Weibull size function of each connection unit into a two-parameter Weibull distribution function to obtain a strength probability distribution function of each connection unit;

[0020] Any numerical value between 0 and 1 is input into the inverse function of the strength probability distribution function of each connection unit to calculate the strength parameter of each connection unit.

[0021] The second aspect of the present invention provides a unit strength parameter selection device for simulating rock fracture by a non-continuous numerical method, including: a calibration module, used to obtain the calibration material mechanical parameters of the target rock, and calculate the strength probability distribution function of the calibration material mechanical parameters at different volumes; an iterative fitting module, used to iteratively fit the strength probability distribution function at different volumes to obtain Weibull shape functions and Weibull size functions of different volume levels; a partitioning module, used to grid the target rock calculation model to calculate the size level of each connection unit; a solution module, used to input the size level of each connection unit into the Weibull shape function and Weibull size function of the different volume levels respectively to solve the Weibull shape function and Weibull size function of each connection unit; a calculation module, used to calculate the strength parameters of each connection unit according to the Weibull shape function and Weibull size function of each connection unit.

[0022] The third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a unit strength parameter selection method for simulating rock fracture using a non-continuous numerical method as described in the above embodiment.

[0023] A fourth aspect of the present invention provides a computer program product, which, when executed by a processor, implements the above-mentioned method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method.

[0024] A fifth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for selecting unit strength parameters for simulating rock fracture using a discontinuous numerical method.

[0025] The method for selecting unit strength parameters for simulating rock fracture using a non-continuous numerical method proposed in an embodiment of the present invention establishes a correlation between unit strength and volume in the non-continuous numerical simulation method, which not only solves the problem that unit strength parameters in the current non-continuous numerical simulation method are difficult to determine due to the influence of rock size effects, but also allows the integration of calculation units to improve calculation speed, thereby achieving accurate calculation of fracture evolution processes of engineering rock masses across scales from a few centimeters to tens of meters.

[0026] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A flow chart of a method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method provided by an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of the distribution of grain boundary tensile and shear strengths provided by a specific embodiment of the present invention;

[0030] Figure 3 A schematic diagram of calculation results provided by a specific embodiment of the present invention;

[0031] Figure 4 A schematic diagram of a hydraulic fracturing calculation model provided by another specific embodiment of the present invention;

[0032] Figure 5 A schematic diagram of calculation results provided by another specific embodiment of the present invention;

[0033] Figure 6 A schematic block diagram of a unit strength parameter selection device for simulating rock mass fracture using a discontinuous numerical method provided by an embodiment of the present invention;

[0034] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0036] The following describes the selection of unit strength parameters for simulating rock mass fracture using a discontinuous numerical method according to an embodiment of the present invention with reference to the accompanying drawings.

[0037] Specifically, Figure 1 A schematic flow chart of a method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method provided in an embodiment of the present invention.

[0038] like Figure 1 As shown, the unit strength parameter selection method for simulating rock mass fracture by the discontinuous numerical method includes the following steps:

[0039] In step S101, the calibration material mechanical parameters of the target rock are obtained, and the strength probability distribution function of the calibration material mechanical parameters in different volumes is calculated.

[0040] In some embodiments, obtaining the calibration material mechanical parameters of the target rock and calculating the strength probability distribution function of the calibration material mechanical parameters at different volumes include:

[0041] Calibrate the material mechanical parameters using the indoor test data of the target rock to obtain the calibrated material mechanical parameters, wherein the calibrated material mechanical parameters include tensile strength, shear strength and friction coefficient;

[0042] The mechanical parameters of the calibration materials are input into a pre-built multi-level recursive analytical model of strength size effect that takes into account the rock's own heterogeneity and fracture self-similarity to calculate the strength probability distribution function of the mechanical parameters of the calibration materials in different volumes.

[0043] Specifically, the material mechanical parameters are calibrated with preset size units using the indoor test data of the target rock to obtain the calibrated material mechanical parameters, wherein the calibrated material mechanical parameters include tensile strength, shear strength and friction coefficient. The tensile and shear strength of rock mineral particles tested in the experiment can also be directly used, and the corresponding unit size is the mineral particle size.

[0044] Furthermore, the tensile strength and shear strength are input into the multi-level recursive analytical model of strength size effect considering the rock's own heterogeneity and fracture self-similarity, and the probability distribution of the tensile strength and shear strength of the rock in different volumes is calculated. The specific formula is:

[0045]

[0046] Among them, S is the probability that the target rock will not be damaged under a specific load, i is the number of iterations, u is the uniform stress state, k is the stress concentration state, and r is the length from the stress concentration state to the uniform stress state.

[0047] Among them, the survival probability under uniform stress state can be expressed as:

[0048]

[0049] In the formula, l r is the stress recovery length from the stress concentration state to the uniform stress state, l0 is the dimension corresponding to the initial strength, X is the applied load, X0 and m are the Weibull size parameter and Weibull shape parameter, respectively.

[0050] Among them, the relationship between the survival probability under stress concentration state and uniform stress state can be expressed as:

[0051]

[0052] In the formula, x is the distance from the fracture position, α is an intermediate variable, and when α is used to calculate the tensile strength, it can be expressed as:

[0053]

[0054] In the formula, G m is the mineral interface shear modulus, E1 is the mineral grain elastic modulus, t m is the mineral interface thickness, and t1 is the mineral grain size.

[0055] When calculating the shear strength, α can be expressed as:

[0056]

[0057] Where G1 is the shear modulus of mineral grains, E m is the elastic modulus of the mineral interface.

[0058] Among them, the stress recovery length from the stress concentration state to the uniform stress state can be expressed as:

[0059]

[0060] In step S102, the intensity probability distribution functions at different volumes are iteratively fitted to obtain Weibull shape functions and Weibull size functions at different volume levels.

[0061] In some embodiments, the intensity probability distribution function at different volumes is iteratively fitted to obtain Weibull shape functions and Weibull size functions at different volume levels, including:

[0062] Convert the intensity probability distribution function under different volumes into unit length under different volumes;

[0063] The two-parameter Weibull distribution function is used to fit the unit lengths at different volumes, and the Weibull shape function and Weibull size function of different volume levels are obtained.

[0064] Specifically, the weakest link theory is used to transform the theoretical model length of the strength probability distribution function under different volumes into the unit length, and the tensile strength and shear strength distribution functions of different volume units are obtained. The specific formula is as follows:

[0065]

[0066] In the formula, l r is the stress recovery length from the stress concentration state to the uniform stress state, l0 is the size corresponding to the initial strength, and E is the unit strength distribution.

[0067] Furthermore, the two-parameter Weibull distribution function is used to fit the tensile strength and shear strength distribution functions of different volume units, and the Weibull shape function and scale function of the corresponding volume level are obtained and recorded.

[0068] In step S103, the computational model of the target rock mass is meshed to calculate the size class of each connection unit.

[0069] In some embodiments, meshing the computational model of the target rock mass to calculate the size level of each connection unit includes:

[0070] Meshing the computational model of the target rock mass to calculate the volume of each solid unit;

[0071] Calculate the representative volume of each connected unit based on the volume of each solid unit;

[0072] The size class of each connection unit is determined according to its representative volume.

[0073] Specifically, a computational model of the target rock mass is pre-constructed using a discontinuous numerical method, the computational model is gridded, the volume of each solid unit is obtained, the average value of the volumes of the two solid units connected by the volume of each solid unit is solved as the representative volume of each connected unit, and the size grade of each connected unit is determined according to the representative volume of each connected unit.

[0074] In step S104, the size level of each connection unit is input into the Weibull shape function and Weibull size function of different volume levels respectively to obtain the Weibull shape function and Weibull size function of each connection unit.

[0075] Specifically, the Weibull shape function and the Weibull size function of each connection unit are input into the two-parameter Weibull distribution function, and the Weibull shape function and the scale function corresponding to each connection unit are calculated using the interpolation method.

[0076] In step S105 , the strength parameter of each connection unit is calculated according to the Weibull shape function and the Weibull size function of each connection unit.

[0077] In some embodiments, calculating the strength parameter of each connection unit according to the Weibull shape function and the Weibull size function of each connection unit includes:

[0078] The Weibull shape function and the Weibull size function of each connection unit are input into the two-parameter Weibull distribution function to obtain the strength probability distribution function of each connection unit;

[0079] Any value between 0 and 1 is input into the inverse function of the strength probability distribution function of each connection unit to calculate the strength parameter of each connection unit.

[0080] Specifically, the Weibull shape function and scale function corresponding to each connection unit are substituted into the two-parameter Weibull distribution function to obtain the strength probability distribution function of the corresponding connection unit, and any random number between 0 and 1 is substituted into the inverse function of the strength probability distribution function of each connection unit to calculate the strength parameter of each connection unit.

[0081] The unit strength parameter selection method for simulating rock mass fracture using the discontinuous numerical method proposed in the embodiment of the present invention is further described below through two specific embodiments.

[0082] Example 1: Yunnan marble with a grain size of 0.4 mm was selected, and a uniaxial compression test simulation was performed using discrete element models with different unit sizes, wherein the strength parameters of the connection unit were determined using the proposed unit parameter selection method for simulating rock mass fracture using a discontinuous numerical method.

[0083] Step 1, such as Figure 2 As shown in the figure, the intercrystalline tensile strength, shear strength and friction coefficient of calcite minerals are directly tested by the positive and negative symmetric four-point bending test. The shear friction coefficient of calcite is 3.44.

[0084] Step 2: Use PCF discrete element calculation software to establish a set of numerical models of marble uniaxial compression models. The sizes of the model rigid balls are 0.4 mm, 0.8 mm, and 1.6 mm respectively.

[0085] Step three, assign a value to the connecting bond unit strength in the model with a rigid sphere diameter of 0.4 mm, and directly use the measured grain boundary strength distribution function to generate the connecting bond unit strength.

[0086] Step 4: Calculate the strength distribution function of the connecting bond unit in the model with a rigid sphere diameter of 0.8 mm and 1.6 mm, substitute the measured grain boundary strength distribution into the recursive formula and perform 2 and 4 recursions respectively, and then use the weakest link theory to convert the recursive strength distribution function to the size of the particle diameter.

[0087] Step 5: Generate corresponding connection unit strength parameters according to the corresponding distribution function, and substitute the remaining parameters required for calculation as shown in Table 1 below into the calculation model.

[0088] Table 1 Some parameters used in the uniaxial compression simulation of marble

[0089]

[0090] Step 6: Input the three sets of calculation models into the PFC discrete element calculation platform for calculation, and obtain the following Figure 3 The calculation results are shown.

[0091] It can be seen that the three sets of simulation results are consistent with the experimental results, which shows that this method can give corresponding reasonable strength parameters of the connecting key unit for numerical models with different unit sizes, so that the calculation results of models with different unit sizes can be consistent with the experimental results. The calculation time of the three sets of models is shown in Figure 3 , while ensuring the calculation accuracy, this method allows the use of larger computing units, which improves the calculation efficiency by about 100 times.

[0092] Example 2: The ground stress at 3700 meters of the GR1 well of the Gonghe Enhanced Geothermal System in Qinghai is about 70MPa. The on-site hydraulic fracturing data shows that the inner diameter pressure of the fracture is 0.23MPa to 1.89MPa. The unit grid with a size of 3.2mm to 4.5m is used to simulate the fracturing process.

[0093] Step 1: Take the granite at 3000 meters of the GR1 geothermal well for uniaxial compression, direct shear, and Brazilian splitting tests, and use a model with a unit size of 1.6 mm for parameter calibration. The obtained input parameters are shown in Table 2 below.

[0094] Table 2 Mechanical parameters of granite with a unit size of 1.6 mm obtained by calibration

[0095]

[0096]

[0097] Step 2: Substitute the calibrated unit strength distribution function into the multi-level recursive model of rock strength size effect and record the results of each iteration.

[0098] Step three, convert the material strength distribution obtained in each iteration to the unit length, and use the two-parameter Weibull distribution function to fit the iteration results to obtain the Weibull shape parameters and Weibull size parameters obtained in each iteration.

[0099] Step 4: Figure 4 As shown, a hydraulic fracturing calculation model with a side length of 200m×200m×210m is established. The preset fracture surface is perpendicular to the xy plane and forms a 45-degree angle with the xz plane. The injection point is located at the center of the model. The unit size near the injection point is 3.2mm. The farther from the injection point, the larger the unit size. The outermost unit size is 4.5m, and the hydrostatic pressure outside the model is 70MP.

[0100] Step 5: Calculate the volume of the solid units on both sides of the connection unit in the preset fracture surface. It is known that the vertices of the tetrahedral solid units on both sides of the connection unit are ABCD and BCDE. The calculation formula for the representative volume V of the connection unit is:

[0101]

[0102] Step 6: Calculate the size level p of the connection unit. The calculation formula is:

[0103]

[0104] Step 7: Calculate the Weibull shape parameter m corresponding to the unit strength distribution function p and size parameter A p , the calculation formula is:

[0105] m p =(p-[p])·m [p] +([p+1]-p)·m [p+1]

[0106] A p =(p-[p])·A [p] +([p+1]-p)·A [p+1]

[0107] Among them, m [p] is the Weibull shape function for different volume levels, A [p] is the Weibull size function for different volume levels.

[0108] Step 8: Generate corresponding connection unit strength parameters according to the corresponding distribution function, and input the model into the 3DEC discrete element calculation platform for calculation to obtain Figure 5 The calculation results are shown.

[0109] Figure 4The black line in the middle is the calculation result of the proposed unit parameter selection method for simulating rock mass fracture using the discontinuous numerical method, the red line is the calculation result of directly using the calibration parameters, and the blue area is the static pressure value of the fracture monitored in the field. After using the proposed unit parameter selection method for simulating rock mass fracture using the discontinuous numerical method, the numerical calculation results can match the field monitoring data well and can capture the cross-scale fracture behavior of rocks from a few centimeters to tens of meters.

[0110] In summary, the unit strength parameter selection method for simulating rock fracture using a discontinuous numerical method proposed in an embodiment of the present invention establishes a correlation between the unit strength and volume of the discontinuous numerical simulation method, which not only solves the problem that the unit strength parameters in the current discontinuous numerical simulation method are difficult to determine due to the influence of the rock size effect, but also allows the integration of calculation units to improve the calculation speed, thereby realizing accurate calculation of the fracture evolution process of engineering rock masses across scales from a few centimeters to tens of meters.

[0111] Next, a unit strength parameter selection device for simulating rock mass fracture using a discontinuous numerical method according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0112] Figure 6 It is a block diagram of a unit strength parameter selection device for simulating rock mass fracture using a discontinuous numerical method according to an embodiment of the present invention.

[0113] like Figure 6 As shown, the unit strength parameter selection device 60 for simulating rock mass fracture using a discontinuous numerical method includes: a calibration module 601 , an iterative fitting module 602 , a division module 603 , a solution module 604 and a calculation module 605 .

[0114] Among them, the calibration module 601 is used to obtain the calibration material mechanical parameters of the target rock, and calculate the strength probability distribution function of the calibration material mechanical parameters at different volumes. The iterative fitting module 602 is used to iteratively fit the strength probability distribution function at different volumes to obtain the Weibull shape function and Weibull size function of different volume levels. The division module 603 is used to mesh the target rock mass calculation model to calculate the size level of each connection unit. The solution module 604 is used to input the size level of each connection unit into the Weibull shape function and Weibull size function of different volume levels respectively to solve the Weibull shape function and Weibull size function of each connection unit. The calculation module 605 is used to calculate the strength parameters of each connection unit based on the Weibull shape function and Weibull size function of each connection unit.

[0115] It should be noted that the above explanation of the embodiment of the method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method is also applicable to the device for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method in this embodiment, and will not be repeated here.

[0116] According to the unit strength parameter selection device for simulating rock fracture using a discontinuous numerical method proposed in an embodiment of the present invention, a correlation between unit strength and volume of the discontinuous numerical simulation method is established, which not only solves the problem that the unit strength parameters in the current discontinuous numerical simulation method are difficult to determine due to the influence of rock size effects, but also allows the integration of calculation units to improve the calculation speed, thereby achieving accurate calculation of the fracture evolution process of engineering rock masses across scales from a few centimeters to tens of meters.

[0117] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0118] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0119] When the processor 702 executes the program, the unit strength parameter selection method for simulating rock mass fracture using the discontinuous numerical method provided in the above embodiment is implemented.

[0120] Furthermore, the electronic device further comprises:

[0121] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0122] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0123] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0124] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0125] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0126] The processor 702 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0127] An embodiment of the present invention further provides a computer program product. When the computer program / instructions are executed by a processor, the method for selecting unit strength parameters for simulating rock mass fracture using the above discontinuous numerical method is implemented.

[0128] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method.

[0129] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0130] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0131] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0132] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0133] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0134] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0135] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0136] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method, characterized in that: The following steps are involved: Obtaining calibration material mechanical parameters of the target rock, and calculating the strength probability distribution function of the calibration material mechanical parameters at different volumes; Iteratively fitting the intensity probability distribution functions under the different volumes to obtain Weibull shape functions and Weibull size functions of different volume levels; Meshing the computational model of the target rock mass to calculate the size class of each connection unit; Inputting the size grade of each connection unit into the Weibull shape function and Weibull size function of the different volume grades respectively to obtain the Weibull shape function and Weibull size function of each connection unit; The strength parameter of each connection unit is calculated according to the Weibull shape function and the Weibull size function of each connection unit.

2. The method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method according to claim 1, characterized in that: The step of obtaining the calibration material mechanical parameters of the target rock and calculating the strength probability distribution function of the calibration material mechanical parameters at different volumes includes: Calibrate material mechanical parameters using indoor test data of the target rock to obtain the calibrated material mechanical parameters, wherein the calibrated material mechanical parameters include tensile strength, shear strength and friction coefficient; The mechanical parameters of the calibration material are input into a pre-built multi-level recursive analytical model of strength size effect that takes into account the rock's own heterogeneity and fracture self-similarity, so as to calculate the strength probability distribution function of the mechanical parameters of the calibration material in different volumes.

3. The method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method according to claim 1, characterized in that: The iterative fitting of the intensity probability distribution functions under the different volumes to obtain Weibull shape functions and Weibull size functions of different volume levels includes: Converting the intensity probability distribution functions at different volumes into unit lengths at different volumes; The unit lengths at different volumes are fitted using a two-parameter Weibull distribution function to obtain Weibull shape functions and Weibull size functions at different volume levels.

4. The method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method according to claim 1, characterized in that: The gridding of the computational model of the target rock mass to calculate the size grade of each connection unit includes: Meshing the computational model of the target rock mass to calculate the volume of each solid unit; Calculating the representative volume of each connected unit according to the volume of each solid unit; The size class of each connection unit is determined according to the representative volume of each connection unit.

5. The method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method according to claim 4, characterized in that: The representative volume of each connection unit is the average volume of two solid units connected by each connection unit.

6. The method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method according to claim 1, characterized in that: The calculating the strength parameter of each connection unit according to the Weibull shape function and the Weibull size function of each connection unit comprises: Inputting the Weibull shape function and the Weibull size function of each connection unit into a two-parameter Weibull distribution function to obtain a strength probability distribution function of each connection unit; Any numerical value between 0 and 1 is input into the inverse function of the strength probability distribution function of each connection unit to calculate the strength parameter of each connection unit.

7. A unit strength parameter selection device for simulating rock mass fracture using a discontinuous numerical method, characterized in that: include: A calibration module, used to obtain the calibration material mechanical parameters of the target rock and calculate the strength probability distribution function of the calibration material mechanical parameters at different volumes; An iterative fitting module, used for iteratively fitting the intensity probability distribution function under the different volumes to obtain Weibull shape function and Weibull size function of different volume levels; A partitioning module is used to perform mesh partitioning on the target rock mass calculation model to calculate the size class of each connection unit; A solution module, used for inputting the size grade of each connection unit into the Weibull shape function and the Weibull size function of the different volume grades respectively, so as to solve the Weibull shape function and the Weibull size function of each connection unit; A calculation module is used to calculate the strength parameter of each connection unit according to the Weibull shape function and the Weibull size function of each connection unit.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the unit strength parameter selection method for simulating rock mass fracture using a discontinuous numerical method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program / instruction is executed by a processor, the method for selecting unit strength parameters for simulating rock mass fracture using a discontinuous numerical method as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a unit strength parameter selection method for simulating rock mass fracture using a discontinuous numerical method as described in any one of claims 1 to 6.

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