Method and device for determining parameters of cohesion unit

Abstract

The invention provides a parameter determination method and device of a cohesion unit, and relates to the technical field of rock engineering. The method comprises the following steps: constructing a numerical model of a rock to be measured, which comprises a rock matrix unit and an embedded cohesion unit; applying a preset load to the to-be-tested rock for experimental testing to obtain a first stress-strain curve and a first peak point in the first stress-strain curve; constructing an objective function of a simulated annealing algorithm based on a stress error and a strain error between the first peak point and the second peak point; the second peak point is obtained by performing numerical simulation on the numerical model; and iteratively updating the current parameter value of the cohesion unit by adopting a simulated annealing algorithm and an objective function thereof to obtain an optimal parameter value. The parameters of the cohesion unit are automatically optimized and calibrated based on the simulated annealing algorithm, the calibration efficiency of the parameters of the cohesion unit in rock simulation can be remarkably improved, a large amount of time and manpower resources are saved on the basis of guaranteeing the parameter calibration accuracy, and the numerical simulation result is more reliable.

Description

Technical Field

[0001] The present disclosure relates to the field of rock engineering technology, and in particular to a method and device for determining parameters of a cohesion unit. Background Art

[0002] Coal mining, tunnel excavation, oil and gas field development and other projects are closely related to rock failure. Among them, numerical simulation of rock failure is an important subject. The cohesive force unit method is a numerical simulation method for simulating crack propagation, which can well simulate the rock cracking and failure process, and is of great significance to the safety assessment and disaster warning in the field of rock engineering.

[0003] The cohesive force unit is a numerical method used to simulate the fracture behavior inside or at the interface of a material, and can describe the nonlinear fracture process near the crack tip of the material. By embedding a layer of cohesive force unit with a thickness of nearly 0 in the traditional unit, the internal structure is subjected to tensile or shear failure under the action of external force. The material parameters of the cohesive force unit directly affect the simulation results of the fracture behavior. The cohesive force unit contains multiple sets of material parameters, which cannot be directly measured through experiments at present. It is necessary to manually calibrate the parameters repeatedly based on the experimental results samples, which is inefficient and non-repeatable. At the same time, the cohesive force parameters of rock failure simulation under different conditions can only be recalibrated based on experience, which does not meet the needs of large-scale rapid calculations in actual engineering. Therefore, how to improve the calibration efficiency of cohesive force unit parameters in rock numerical simulation is one of the problems to be solved in this field. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first aspect of the present disclosure provides a method for determining parameters of a cohesion unit, comprising the following steps: Constructing a numerical model of the rock to be tested; the numerical model includes a rock matrix unit and an embedded cohesion unit, wherein the material parameters of the rock matrix unit and the initial parameter values ​​of the cohesion unit are determined based on the property information of the rock to be tested; Applying a preset load to the rock to be tested to perform an experimental test, obtaining a first stress-strain curve, and determining a first peak point in the first stress-strain curve; constructing an objective function of a simulated annealing algorithm based on a stress error and a strain error between the first peak point and the second peak point; wherein the second peak point is a peak point in a second stress-strain curve, and the second stress-strain curve is obtained by numerically simulating the numerical model by applying the preset load based on the current parameter value of the cohesive force unit; The simulated annealing algorithm and the objective function thereof are used to iteratively update the current parameter value of the cohesion unit to obtain the optimal parameter value of the cohesion unit.

[0006] In some embodiments of the present disclosure, constructing a numerical model of the rock to be tested includes: acquiring a CT scan image of the rock to be tested; converting the CT scan image into a single-channel grayscale image; constructing an original numerical model based on the single-channel grayscale image, and assigning different grid attributes to the original numerical model using edge detection and region segmentation methods; embedding the cohesion unit into the original numerical model to obtain the numerical model of the rock to be tested.

[0007] In some embodiments of the present disclosure, the objective function of the simulated annealing algorithm is expressed as follows:

[0008]

[0009]

[0010] in, is the stress value corresponding to the first peak point, is the stress value corresponding to the second peak point, is the strain value corresponding to the first peak point, is the strain value corresponding to the second peak point, is the stress error between the first peak point and the second peak point, is the strain error between the first peak point and the second peak point.

[0011] In some embodiments of the present disclosure, the adopting the simulated annealing algorithm and the objective function thereof to iteratively update the current parameter value to obtain the optimal parameter value of the cohesion unit includes: S1, determining the initial parameters of the simulated annealing algorithm, including an initial temperature value, a cooling rate parameter, an end temperature value, a chain length L, and an error threshold; S2, applying the preset load to the numerical model based on the current parameter value of the cohesive force unit to perform numerical simulation, and obtain the objective function value of the current parameter value; S3, applying random perturbations to the current parameter values ​​at the current temperature value of the simulated annealing algorithm to generate candidate parameter values ​​of the cohesion unit; S4, applying the preset load to the numerical model based on the candidate parameter value to perform numerical simulation, and obtain the objective function value of the candidate parameter value; S5, comparing the objective function value of the current parameter value with the objective function value of the candidate parameter value to obtain a comparison result, and selecting one of the current parameter value and the candidate parameter value as the optimal candidate parameter value according to the comparison result; S6, judging whether the optimal candidate parameter value satisfies the objective function condition; if the optimal candidate parameter value satisfies the objective function condition, taking the optimal candidate parameter value as the optimal parameter value of the cohesive force unit; if the optimal candidate parameter value does not satisfy the objective function condition, taking the optimal candidate parameter value as the current parameter value, and returning to execute step S3 until the number of random perturbations applied to the current parameter value at the current temperature value reaches L times; the objective function condition is that the objective function value of the optimal candidate parameter value is less than or equal to the error threshold; S7, applying random disturbance to the current parameter value at the current temperature value for the number of times reaching the L times, reducing the current temperature value according to the cooling rate parameter, and generating a new temperature value; S8, taking the new temperature value as the current temperature value, returning to execute step S3, until the current temperature value is less than or equal to the end temperature value, and taking the current parameter value as the optimal parameter value of the cohesion unit.

[0012] In some embodiments of the present disclosure, the objective function value of the current parameter value is compared with the objective function value of the candidate parameter value to obtain a comparison result, and one of the current parameter value and the candidate parameter value is selected as the optimal candidate parameter value according to the comparison result, including: if the objective function value of the candidate parameter value is less than or equal to the objective function value of the current parameter value, then the candidate parameter value is selected as the optimal candidate parameter value; or if the objective function value of the candidate parameter value is greater than the objective function value of the current parameter value, then one of the current parameter value and the candidate parameter value is selected as the optimal candidate parameter value based on the Metropolis criterion.

[0013] In some embodiments of the present disclosure, the probability of taking the candidate parameter value as the optimal candidate parameter value based on the Metropolis criterion is expressed as follows:

[0014] in, is the probability of taking the candidate parameter value as the optimal candidate parameter value, is the difference between the objective function value of the candidate parameter value and the objective function value of the current parameter value, is the current temperature value of the simulated annealing algorithm.

[0015] The second aspect of the present disclosure provides a device for determining parameters of a cohesion unit, including: A first construction module is used to construct a numerical model of the rock to be tested; the numerical model includes a rock matrix unit and an embedded cohesion unit, wherein the material parameters of the rock matrix unit and the initial parameter values ​​of the cohesion unit are determined based on the property information of the rock to be tested; A determination module, used for applying a preset load to the rock to be tested to perform an experimental test, obtain a first stress-strain curve, and determine a first peak point in the first stress-strain curve; A second construction module is used to construct an objective function of a simulated annealing algorithm based on a stress error and a strain error between the first peak point and a second peak point; wherein the second peak point is a peak point in a second stress-strain curve, and the second stress-strain curve is obtained by numerically simulating the numerical model by applying the preset load based on the current parameter value of the cohesive force unit; An acquisition module is used to iteratively update the current parameter value of the cohesion unit by using the simulated annealing algorithm and its objective function to obtain the optimal parameter value of the cohesion unit.

[0016] In some embodiments of the present disclosure, the acquisition module is specifically used to: S1, determining the initial parameters of the simulated annealing algorithm, including an initial temperature value, a cooling rate parameter, an end temperature value, a chain length L, and an error threshold; S2, applying the preset load to the numerical model based on the current parameter value of the cohesive force unit to perform numerical simulation, and obtain the objective function value of the current parameter value; S3, applying random perturbations to the current parameter values ​​at the current temperature value of the simulated annealing algorithm to generate candidate parameter values ​​of the cohesion unit; S4, applying the preset load to the numerical model based on the candidate parameter value to perform numerical simulation, and obtain the objective function value of the candidate parameter value; S5, comparing the objective function value of the current parameter value with the objective function value of the candidate parameter value to obtain a comparison result, and selecting one of the current parameter value and the candidate parameter value as the optimal candidate parameter value according to the comparison result; S6, judging whether the optimal candidate parameter value satisfies the objective function condition; if the optimal candidate parameter value satisfies the objective function condition, taking the optimal candidate parameter value as the optimal parameter value of the cohesive force unit; if the optimal candidate parameter value does not satisfy the objective function condition, taking the optimal candidate parameter value as the current parameter value, and returning to execute step S3 until the number of random perturbations applied to the current parameter value at the current temperature value reaches L times; the objective function condition is that the objective function value of the optimal candidate parameter value is less than or equal to the error threshold; S7, applying random disturbance to the current parameter value at the current temperature value for the number of times reaching the L times, reducing the current temperature value according to the cooling rate parameter, and generating a new temperature value; S8, taking the new temperature value as the current temperature value, returning to execute step S3, until the current temperature value is less than or equal to the end temperature value, and taking the current parameter value as the optimal parameter value of the cohesion unit.

[0017] A third aspect of the present disclosure provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect.

[0018] The fourth aspect of the present disclosure provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method described in the first aspect.

[0019] The parameter determination method of the cohesion unit provided in the present disclosure automatically optimizes and calibrates the parameters of the cohesion unit based on the simulated annealing algorithm, which can significantly improve the calibration efficiency of the cohesion unit parameters in rock simulation, save a lot of time and human resources on the basis of ensuring the accuracy of parameter calibration, and make the numerical simulation results more reliable. In addition, the present disclosure has strong versatility and is applicable to different types of rocks, which is helpful for disaster prediction and prevention in the field of rock engineering, and provides scientific basis and support for practical engineering applications.

[0020] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1A flow chart of a method for determining parameters of a cohesion unit provided in an embodiment of the present disclosure Figure 2 A schematic diagram of an original numerical model and a numerical model embedded with a cohesion unit provided in an embodiment of the present disclosure; Figure 3 A schematic diagram of a typical pull-off response provided by an embodiment of the present disclosure; Figure 4 A schematic diagram of a first stress-strain curve provided by an embodiment of the present disclosure; Figure 5 A schematic diagram of a process for obtaining the optimal parameter value of a cohesion unit by using a simulated annealing algorithm provided in an embodiment of the present disclosure; Figure 6 It is a schematic diagram of another process of obtaining the optimal parameter value of the cohesion unit by using the simulated annealing algorithm according to an embodiment of the present disclosure; Figure 7 A schematic diagram of a device for determining parameters of a cohesion unit provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] Embodiments of the present disclosure 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 disclosure, and should not be construed as limiting the present disclosure.

[0023] Specifically, the method and device for determining parameters of the cohesion unit according to an embodiment of the present disclosure are described below with reference to the accompanying drawings. Figure 1 FIG. 1 is a flow chart of a method for determining parameters of a cohesion unit provided in an embodiment of the present disclosure. Figure 1 As shown, the method for determining the parameters of the cohesion unit may include the following steps: Step 101, constructing a numerical model of the rock to be tested. The numerical model includes a rock matrix unit and an embedded cohesion unit, wherein the material parameters of the rock matrix unit and the initial parameter values ​​of the cohesion unit are determined based on the property information of the rock to be tested.

[0024] It should be noted that the constructed numerical model can reflect the mechanical properties of the rock to be tested. In some embodiments of the present disclosure, a CT scan image of the rock to be tested can be obtained, and the CT scan image can be converted into a single-channel grayscale image. Optionally, in order to reduce image noise, the CT scan image can be subjected to median filtering. The grayscale threshold is used to segment the microscopic components or structure of the rock to be tested, and a three-dimensional visualized original numerical model is constructed based on the single-channel grayscale image, and the edge detection method and the region segmentation method are used to give the original numerical model different grid attributes. The cohesion unit is embedded in the original numerical model to obtain a numerical model of the rock to be tested, which is more in line with the actual structure of the actual rock and is beneficial to the accuracy of the numerical simulation of rock damage.

[0025] Figure 2 A schematic diagram of an original numerical model and a numerical model embedded with cohesion units provided in an embodiment of the present disclosure. Among them, the left figure is the original numerical model, and the right figure is the numerical model after the cohesion units are embedded. It should be noted that when embedding cohesion units in the original numerical model, it is necessary to insert zero-thickness cohesion units between adjacent units according to the numerical model characteristics of different rocks, and cohesion units consistent with the mesostructure properties need to be inserted in the same mesostructure area, and cohesion units with different interface properties need to be inserted between different mesostructures. Each cohesion unit has a specific force-displacement relationship, namely the traction-separation constitutive model, which is used to describe the damage initiation and development process of the material interface. Figure 3 Schematic diagram of a typical pull-off response provided by an embodiment of the present disclosure. Figure 3 As shown, there is a normal traction on the surface of the cohesive unit t n and tangential traction (shear force) t s and t t , when the traction force changes with the relative displacement of the surface of the cohesive force unit (opening displacement δ n and slip displacement δ s and δ t ,) increases and continues to increase (such as Figure 3 OA segment in the figure), when the traction force meets the critical criterion of damage (point A), the cohesive unit undergoes strain softening, that is, damage occurs (such as Figure 3 AB segment in the figure) until complete failure (point B).

[0026] Traction stress t n , t s and t t , the thickness of the cohesive unit is T0, ε n , ε s and ε t With the strains in the normal and two tangential directions respectively, the elastic behavior can be defined as:

[0027] Among them, E nn The matrix with equal parameters is the elastic matrix, which provides the complete coupling behavior between all components of the pulling and separation vectors, including K nn The matrix of equal parameters is the stiffness matrix.

[0028] There are many types of damage initiation conditions, that is, damage critical criteria. For example, the most commonly used maximum stress criterion is as follows:

[0029] in, t n is the normal traction, t s and t t is the tangential traction force, is the critical normal traction, and is the critical tangential traction force.

[0030] It should be noted that the cohesion unit usually includes multiple parameters. For example, according to the cohesion unit theory and correlation impact analysis, the critical traction can be selected (Because usually ), cohesive unit elastic modulus E nn , E ns , fracture energy G nn , G ns As the cohesion unit parameters to be calibrated. According to the property information of the rock to be measured, such as elastic modulus, tensile strength and fracture toughness, the initial parameter values ​​of the cohesion unit are set respectively. S 0={ , E nn , E ns , G nn , G ns The material parameters of the rock matrix unit can be set according to the macroscopic material property values ​​measured by rock experiments.

[0031] Step 102: Perform an experimental test by applying a preset load to the rock to be tested, obtain a first stress-strain curve, and determine a first peak point in the first stress-strain curve.

[0032] Figure 4 Schematic diagram of a first stress-strain curve provided by an embodiment of the present disclosure. Figure 4 As shown, point A is the first peak point in the first stress-strain curve, and the first peak point corresponds to a stress value and a strain value.

[0033] Step 103, constructing an objective function of a simulated annealing algorithm based on the stress error and strain error between the first peak point and the second peak point, wherein the second peak point is a peak point in a second stress-strain curve, and the second stress-strain curve is obtained by numerically simulating a preset load on a numerical model based on the current parameter value of the cohesive force unit.

[0034] In some embodiments of the present disclosure, the sum of the stress error and the strain error between the first peak point and the second peak point can be used as the objective function to reflect the gap between the simulation results obtained by the numerical model and the actual results obtained by the experiment. As a possible implementation method, the objective function of the simulated annealing algorithm can be expressed as follows:

[0035]

[0036]

[0037] in, is the stress value corresponding to the first peak point, is the stress value corresponding to the second peak point, is the strain value corresponding to the first peak point, is the strain value corresponding to the second peak point, is the stress error between the first peak point and the second peak point, is the strain error between the first peak point and the second peak point.

[0038] Step 104, using a simulated annealing algorithm and its objective function to iteratively update the current parameter value of the cohesion unit to obtain the optimal parameter value of the cohesion unit.

[0039] The simulated annealing algorithm is a probabilistic algorithm for global optimization problems, which is derived from the annealing process in physics. Annealing is the process of melting metal at high temperature and slowly cooling it to reach the lowest energy state. In the simulated annealing algorithm, the initial temperature setting corresponds to the melting process in the annealing technology, the temperature drop function corresponds to the cooling process in the annealing technology, and the optimization solution based on the Metropolis criterion corresponds to the constant temperature stage in the annealing technology. When the temperature is high, the probability of the algorithm accepting a deteriorated solution is high, and when the temperature gradually decreases, the probability of the deteriorated solution being accepted is also decreasing. Therefore, when solving complex optimization problems, it avoids falling into the local optimal solution and finds the global optimal solution.

[0040] Figure 5 A schematic diagram of a process for obtaining the optimal parameter value of a cohesion unit using a simulated annealing algorithm provided by an embodiment of the present disclosure. Figure 5 As shown, the parameters of the cohesion unit can be updated using the simulated annealing algorithm by the following steps: Step 501, determine the initial parameters of the simulated annealing algorithm, including the initial temperature value T 0. Cooling rate parameters q , End temperature value T e , Chain length L and error threshold e r .

[0041] Step 502 : Apply a preset load to the numerical model based on the current parameter value of the cohesive force unit to perform numerical simulation, and obtain the objective function value of the current parameter value.

[0042] The preset load applied to the numerical model must be consistent with the preset load applied to the experimental test of the rock to be tested in step 102. The preset load is applied to the numerical model based on the current parameters of the cohesive force unit to perform numerical simulation, obtain a second stress-strain curve, and use the peak point in the second stress-strain curve as the second peak point. Based on the first peak point measured experimentally, the second peak point obtained by numerical simulation, and the pre-constructed objective function, the objective function value of the current parameter value is determined.

[0043] Step 503: applying random perturbations to the current parameter values ​​at the current temperature value of the simulated annealing algorithm to generate candidate parameter values ​​for the cohesion unit.

[0044] Step 504 , applying a preset load to the numerical model based on the candidate parameter value to perform numerical simulation, and obtain the objective function value of the candidate parameter value.

[0045] Based on the candidate parameter values, a preset load is applied to the numerical model to perform numerical simulation, and the implementation method of obtaining the objective function value of the candidate parameter value can refer to the relevant description in step 502, which will not be repeated here.

[0046] Step 505 , compare the objective function value of the current parameter value with the objective function value of the candidate parameter value to obtain a comparison result, and select one from the current parameter value and the candidate parameter value as the optimal candidate parameter value according to the comparison result.

[0047] If the objective function value of the candidate parameter value is less than or equal to the objective function value of the current parameter value, it means that the newly generated candidate parameter value is closer to the experimental result than the current parameter value, and the candidate parameter value is taken as the optimal candidate parameter value. If the objective function value of the candidate parameter value is greater than the objective function value of the current parameter value, it means that the gap between the newly generated candidate parameter value and the experimental result is larger than that between the current parameter value and the current parameter value, and the candidate parameter value is a deteriorated solution. The Metropolis criterion is introduced to select one from the current parameter value and the candidate parameter value as the optimal candidate parameter value. In other words, there is a certain probability of accepting the newly generated deteriorated solution when the cohesion parameter is updated and iterated. This probability is related to the current temperature of the simulated annealing algorithm and the newly generated candidate parameter value, so as to avoid falling into the local optimal solution when solving complex optimization problems, and thus find the global optimal solution.

[0048] In some embodiments of the present disclosure, the probability of taking a candidate parameter value as the optimal candidate parameter value based on the Metropolis criterion is expressed as follows:

[0049] in, is the probability of taking the candidate parameter value as the optimal candidate parameter value, is the objective function value e of the candidate parameter value i The objective function value e with the current parameter value i-1 The difference, is the current temperature value of the simulated annealing algorithm. If the probability P is greater than the random number in the interval [0,1), the candidate parameter value is taken as the optimal candidate parameter value; otherwise, if the probability P is less than or equal to the random number in the interval [0,1), the current parameter value is taken as the optimal candidate parameter value.

[0050] Step 506: If the optimal candidate parameter value meets the objective function condition, the optimal candidate parameter value is used as the optimal parameter value of the cohesion unit.

[0051] Step 507: If the optimal candidate parameter value does not satisfy the objective function condition, the optimal candidate parameter value is used as the current parameter value, and the process returns to step 503 until the number of random perturbations applied to the current parameter value at the current temperature value reaches L times.

[0052] Among them, the objective function condition is that the objective function value of the optimal candidate parameter value is less than or equal to the error threshold. If the optimal candidate parameter value selected from the current parameter value and the candidate parameter value meets the objective function value, the loop is jumped out to determine the optimal parameter value of the cohesive force unit. If the number of random perturbations applied to the current parameter value at the current temperature value reaches L times, the parameter optimization at the current temperature is terminated, a new temperature value is generated, and the parameter optimization at the new temperature value is continued.

[0053] Step 508: If the number of times the random disturbance is applied to the current parameter value at the current temperature value reaches L times, the current temperature value is reduced according to the cooling rate parameter to generate a new temperature value.

[0054] The new temperature value can refer to the following formula:

[0055] in, is the cooling rate parameter, is the current temperature value, is the new temperature value.

[0056] Step 509, taking the new temperature value as the current temperature value, returning to step 503, until the current temperature value is less than or equal to the end temperature value, taking the current parameter value as the optimal parameter value of the cohesion unit.

[0057] In the simulated annealing algorithm, as the temperature decreases, the solution gradually stabilizes and concentrates near the optimal solution. Therefore, when the current temperature value is less than or equal to the end temperature value, the current parameter value is taken as the optimal solution and determined as the optimal parameter value of the cohesion unit, and the cycle ends.

[0058] In order to better understand the iterative update of the current parameter value of the cohesion unit based on the simulated annealing algorithm and its objective function described in the above embodiment, Figure 6 Further description is given. Figure 6 FIG. 1 is another flow chart of obtaining the optimal parameter value of a cohesion unit by using a simulated annealing algorithm according to an embodiment of the present disclosure. Figure 6 As shown, determine the initial parameter value of the cohesive force unit S 0 and the initial parameters of the simulated annealing algorithm, including the initial temperature value T 0. Cooling rate parameters q , End temperature value T e , Chain length L and error threshold e r (601). Based on the current parameter values ​​of the cohesion unit S iApply preset loads to the numerical model for numerical simulation to obtain the current parameter values S i The objective function value of e i (604). At the current temperature value, the current parameter value S i Apply random perturbations to generate candidate parameter values S i+1 . Candidate parameter values ​​based on cohesion units S i+1 Apply preset loads to the numerical model for numerical simulation to obtain candidate parameter values S i+1 The objective function value of e i+1 (605). Compare the objective function value of the current parameter value with the objective function value of the candidate parameter value (606). If e i+1 ≤ e i , the candidate parameter value S i+1 as the optimal candidate parameter value (607). e i+1 > e i , then generate a random number in the interval [0,1) and decide whether to accept the candidate parameter value based on the Metropolis criterion S i+1 (608). If the probability of accepting the new solution is P>R, the candidate parameter value is S i+1 As the optimal candidate parameter value (607). If the probability of accepting the new solution P≤R, the current parameter value S i As the optimal candidate parameter value (609). Determine whether the optimal candidate parameter value satisfies the objective function condition (610), where the objective function condition is that the objective function value of the optimal candidate parameter value is less than or equal to the error threshold e r. If the optimal candidate parameter value meets the objective function condition, the optimal candidate parameter value is used as the optimal parameter value of the cohesive force unit (611), and the process is terminated and the output result is output. If the optimal candidate parameter value does not meet the objective function condition, the optimal candidate parameter value is used as the current parameter value (612). When the number of times k that random perturbations are applied to the current parameter value at the current temperature value does not reach L times, return to execute step 604 until the number of times k that random perturbations are applied to the current parameter value at the current temperature value does not reach L times. When the number of times random perturbations are applied to the current parameter value at the current temperature value reaches L times, the current temperature value is reduced according to the cooling rate parameter, a new temperature value is generated, and the new temperature value is used as the current temperature value (615). If the current temperature value has not been reduced to the end temperature value T e , return to step 603, reset the number of random perturbations k applied to the current parameter value at the current temperature value (603), and perform parameter updates at the current temperature value until the current temperature value is less than or equal to the end temperature value T e , taking the current parameter value as the optimal parameter value of the cohesive force unit (617).

[0059] By implementing the embodiments of the present disclosure, the parameters of the cohesion unit are automatically optimized and calibrated based on the simulated annealing algorithm, which can significantly improve the calibration efficiency of the cohesion unit parameters in rock simulation, save a lot of time and human resources on the basis of ensuring the accuracy of parameter calibration, and make the numerical simulation results more reliable. In addition, the present disclosure has strong versatility and is applicable to different types of rocks, which is helpful for disaster prediction and prevention in the field of rock engineering, and provides scientific basis and support for practical engineering applications.

[0060] Figure 7 Schematic diagram of a device for determining parameters of a cohesion unit provided by an embodiment of the present disclosure. Figure 7 As shown, the device for determining parameters of the cohesion unit includes: a first construction module 701 , a determination module 702 , a second construction module 703 and an acquisition module 704 .

[0061] The first construction module 701 is used to construct a numerical model of the rock to be tested. The numerical model includes a rock matrix unit and an embedded cohesion unit, wherein the material parameters of the rock matrix unit and the initial parameter values ​​of the cohesion unit are determined based on the property information of the rock to be tested.

[0062] The determination module 702 is used to apply a preset load to the rock to be tested to perform an experimental test, obtain a first stress-strain curve, and determine a first peak point in the first stress-strain curve.

[0063] The second construction module 703 is used to construct the objective function of the simulated annealing algorithm based on the stress error and strain error between the first peak point and the second peak point, wherein the second peak point is the peak point in the second stress-strain curve, and the second stress-strain curve is obtained by numerically simulating the numerical model by applying a preset load based on the current parameter value of the cohesive force unit.

[0064] The acquisition module 704 is used to iteratively update the current parameter value of the cohesion unit by using the simulated annealing algorithm and its objective function to obtain the optimal parameter value of the cohesion unit.

[0065] In some embodiments of the present disclosure, the first construction module 701 is specifically used to: obtain a CT scan image of the rock to be tested; convert the CT scan image into a single-channel grayscale image; construct an original numerical model based on the single-channel grayscale image, and use edge detection and region segmentation methods to give the original numerical model different grid attributes; embed the cohesion unit into the original numerical model to obtain a numerical model of the rock to be tested.

[0066] In some embodiments of the present disclosure, the objective function of the simulated annealing algorithm constructed by the second construction module 703 is expressed as follows:

[0067]

[0068]

[0069] in, is the stress value corresponding to the first peak point, is the stress value corresponding to the second peak point, is the strain value corresponding to the first peak point, is the strain value corresponding to the second peak point, is the stress error between the first peak point and the second peak point, is the strain error between the first peak point and the second peak point.

[0070] In some embodiments of the present disclosure, the acquisition module is specifically used to: S1, determine the initial parameters of the simulated annealing algorithm, including the initial temperature value, the cooling rate parameter, the end temperature value, the chain length L and the error threshold; S2, applying a preset load to the numerical model based on the current parameter value of the cohesive force unit to perform numerical simulation, and obtain the objective function value of the current parameter value; S3, applying random perturbations to the current parameter values ​​at the current temperature value of the simulated annealing algorithm to generate candidate parameter values ​​of the cohesive force unit; S4, applying a preset load to the numerical model based on the candidate parameter value to perform numerical simulation, and obtain an objective function value of the candidate parameter value; S5, comparing the objective function value of the current parameter value with the objective function value of the candidate parameter value to obtain a comparison result, and selecting one of the current parameter value and the candidate parameter value as the optimal candidate parameter value according to the comparison result; S6, judging whether the optimal candidate parameter value meets the objective function condition; if the optimal candidate parameter value meets the objective function condition, taking the optimal candidate parameter value as the optimal parameter value of the cohesive force unit; if the optimal candidate parameter value does not meet the objective function condition, taking the optimal candidate parameter value as the current parameter value, returning to execute step S3, until the number of random perturbations applied to the current parameter value at the current temperature value reaches L times; the objective function condition is that the objective function value of the optimal candidate parameter value is less than or equal to the error threshold; S7, applying random disturbance to the current parameter value at the current temperature value for L times, reducing the current temperature value according to the cooling rate parameter, and generating a new temperature value; S8, taking the new temperature value as the current temperature value, returning to execute step S3, until the current temperature value is less than or equal to the end temperature value, taking the current parameter value as the optimal parameter value of the cohesion unit.

[0071] In some embodiments of the present disclosure, the acquisition module is specifically used to: if the objective function value of the candidate parameter value is less than or equal to the objective function value of the current parameter value, then the candidate parameter value is used as the optimal candidate parameter value; or, if the objective function value of the candidate parameter value is greater than the objective function value of the current parameter value, then one of the current parameter value and the candidate parameter value is selected as the optimal candidate parameter value based on the Metropolis criterion.

[0072] In some embodiments of the present disclosure, the acquisition module is specifically used to: the probability of taking the candidate parameter value as the optimal candidate parameter value based on the Metropolis criterion is expressed as follows:

[0073] in, is the probability of taking the candidate parameter value as the optimal candidate parameter value, is the difference between the objective function value of the candidate parameter value and the objective function value of the current parameter value, is the current temperature value of the simulated annealing algorithm.

[0074] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0075] In order to implement the above embodiments, the present disclosure also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments. In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0076] In order to implement the above embodiments, the present disclosure also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0077] In the description of the aforementioned embodiments, 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 disclosure. 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 more 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, unless they are contradictory.

[0078] 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. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0079] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional 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 disclosure belong.

[0080] 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 purposes 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 (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), 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), an optical fiber 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, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0081] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in 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.

[0082] 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.

[0083] In addition, each functional unit in each embodiment of the present disclosure 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.

[0084] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.

Claims

1. A method for determining parameters of a cohesion unit, characterized in that: The following steps are involved: Constructing a numerical model of the rock to be tested; the numerical model includes a rock matrix unit and an embedded cohesion unit, wherein the material parameters of the rock matrix unit and the initial parameter values ​​of the cohesion unit are determined based on the property information of the rock to be tested; Applying a preset load to the rock to be tested to perform an experimental test, obtaining a first stress-strain curve, and determining a first peak point in the first stress-strain curve; constructing an objective function of a simulated annealing algorithm based on a stress error and a strain error between the first peak point and the second peak point; wherein the second peak point is a peak point in a second stress-strain curve, and the second stress-strain curve is obtained by numerically simulating the numerical model by applying the preset load based on the current parameter value of the cohesive force unit; The simulated annealing algorithm and the objective function thereof are used to iteratively update the current parameter value of the cohesion unit to obtain the optimal parameter value of the cohesion unit.

2. The method according to claim 1, characterized in that: The step of constructing a numerical model of the rock to be tested comprises: Acquire a CT scan image of the rock to be tested; Converting the CT scan image into a single-channel grayscale image; Building an original numerical model based on the single-channel grayscale image, and giving the original numerical model different grid attributes by using edge detection method and region segmentation method; The cohesion unit is embedded into the original numerical model to obtain the numerical model of the rock to be tested.

3. The method according to claim 1, characterized in that The objective function of the simulated annealing algorithm is expressed as follows: in, is the stress value corresponding to the first peak point, is the stress value corresponding to the second peak point, is the strain value corresponding to the first peak point, is the strain value corresponding to the second peak point, is the stress error between the first peak point and the second peak point, is the strain error between the first peak point and the second peak point.

4. The method according to claim 1, characterized in that: The adopting the simulated annealing algorithm and the objective function thereof to iteratively update the current parameter value to obtain the optimal parameter value of the cohesion unit includes: S1, determining the initial parameters of the simulated annealing algorithm, including an initial temperature value, a cooling rate parameter, an end temperature value, a chain length L, and an error threshold; S2, applying the preset load to the numerical model based on the current parameter value of the cohesive force unit to perform numerical simulation, and obtaining the objective function value of the current parameter value; S3, applying random perturbations to the current parameter values ​​at the current temperature value of the simulated annealing algorithm to generate candidate parameter values ​​of the cohesion unit; S4, applying the preset load to the numerical model based on the candidate parameter value to perform numerical simulation, and obtain the objective function value of the candidate parameter value; S5, comparing the objective function value of the current parameter value with the objective function value of the candidate parameter value to obtain a comparison result, and selecting one of the current parameter value and the candidate parameter value as the optimal candidate parameter value according to the comparison result; S6, judging whether the optimal candidate parameter value satisfies the objective function condition; if the optimal candidate parameter value satisfies the objective function condition, taking the optimal candidate parameter value as the optimal parameter value of the cohesive force unit; if the optimal candidate parameter value does not satisfy the objective function condition, taking the optimal candidate parameter value as the current parameter value, and returning to execute step S3 until the number of random perturbations applied to the current parameter value at the current temperature value reaches L times; the objective function condition is that the objective function value of the optimal candidate parameter value is less than or equal to the error threshold; S7, applying random disturbance to the current parameter value at the current temperature value for the number of times reaching the L times, reducing the current temperature value according to the cooling rate parameter, and generating a new temperature value; S8, taking the new temperature value as the current temperature value, returning to execute step S3, until the current temperature value is less than or equal to the end temperature value, and taking the current parameter value as the optimal parameter value of the cohesion unit.

5. The method according to claim 4, characterized in that The step of comparing the objective function value of the current parameter value with the objective function value of the candidate parameter value to obtain a comparison result, and selecting one of the current parameter value and the candidate parameter value as the optimal candidate parameter value according to the comparison result, comprises: If the objective function value of the candidate parameter value is less than or equal to the objective function value of the current parameter value, the candidate parameter value is used as the optimal candidate parameter value; or, If the objective function value of the candidate parameter value is greater than the objective function value of the current parameter value, one of the current parameter value and the candidate parameter value is selected as the optimal candidate parameter value based on the Metropolis criterion.

6. The method according to claim 5, characterized in that The probability of taking the candidate parameter value as the optimal candidate parameter value based on the Metropolis criterion is expressed as follows: in, is the probability of taking the candidate parameter value as the optimal candidate parameter value, is the difference between the objective function value of the candidate parameter value and the objective function value of the current parameter value, is the current temperature value of the simulated annealing algorithm.

7. A device for determining parameters of a cohesion unit, characterized in that: include: A first construction module is used to construct a numerical model of the rock to be tested; the numerical model includes a rock matrix unit and an embedded cohesion unit, wherein the material parameters of the rock matrix unit and the initial parameter values ​​of the cohesion unit are determined based on the property information of the rock to be tested; A determination module, used for applying a preset load to the rock to be tested to perform an experimental test, obtain a first stress-strain curve, and determine a first peak point in the first stress-strain curve; A second construction module is used to construct an objective function of a simulated annealing algorithm based on a stress error and a strain error between the first peak point and a second peak point; wherein the second peak point is a peak point in a second stress-strain curve, and the second stress-strain curve is obtained by numerically simulating the numerical model by applying the preset load based on the current parameter value of the cohesive force unit; An acquisition module is used to iteratively update the current parameter value of the cohesion unit by using the simulated annealing algorithm and its objective function to obtain the optimal parameter value of the cohesion unit.

8. The device according to claim 7, characterized in that The acquisition module is specifically used for: S1, determining the initial parameters of the simulated annealing algorithm, including an initial temperature value, a cooling rate parameter, an end temperature value, a chain length L, and an error threshold; S2, applying the preset load to the numerical model based on the current parameter value of the cohesive force unit to perform numerical simulation, and obtaining the objective function value of the current parameter value; S3, applying random perturbations to the current parameter values ​​at the current temperature value of the simulated annealing algorithm to generate candidate parameter values ​​of the cohesion unit; S4, applying the preset load to the numerical model based on the candidate parameter value to perform numerical simulation, and obtain the objective function value of the candidate parameter value; S5, comparing the objective function value of the current parameter value with the objective function value of the candidate parameter value to obtain a comparison result, and selecting one of the current parameter value and the candidate parameter value as the optimal candidate parameter value according to the comparison result; S6, judging whether the optimal candidate parameter value satisfies the objective function condition; If the optimal candidate parameter value satisfies the objective function condition, taking the optimal candidate parameter value as the optimal parameter value of the cohesion unit; If the optimal candidate parameter value does not meet the objective function condition, the optimal candidate parameter value is used as the current parameter value, and the process returns to step S3 until the number of random perturbations applied to the current parameter value at the current temperature value reaches L times; the objective function condition is that the objective function value of the optimal candidate parameter value is less than or equal to the error threshold; S7, applying random disturbance to the current parameter value at the current temperature value for the number of times reaching the L times, reducing the current temperature value according to the cooling rate parameter, and generating a new temperature value; S8, taking the new temperature value as the current temperature value, returning to execute step S3, until the current temperature value is less than or equal to the end temperature value, and taking the current parameter value as the optimal parameter value of the cohesion unit.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

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