A method for improving the accuracy of numerical simulation of discrete elements of an engineering scale under a slope

Abstract

The present application relates to the field of geological disaster prevention, and particularly relates to a method for improving the accuracy of numerical simulation of discrete elements of a slope at an engineering scale, obtaining test results of geological conditions, topography and geomorphology and physical and mechanical parameters of rock-soil bodies of a target slope; constructing a discrete element numerical model of the same scale as the sample used in the physical and mechanical test, determining the minimum particle diameter, particle size distribution, maximum particle diameter and particle filling in the discrete element numerical model; calibrating the first micro parameter of the particle contact model in the discrete element numerical model based on the test results of the physical and mechanical parameters of rock-soil bodies; constructing a numerical analysis model of the evolution of the target slope at the engineering scale based on the geological conditions and topography and geomorphology information; determining the particle diameter in the numerical analysis model of the evolution of the target slope at the engineering scale and the second micro parameter in the particle contact model according to a preset rule; obtaining numerical simulation results by simulating the evolution process of the obtained model and parameters. The present application improves the accuracy of the simulation results.

Description

Technical Field

[0001] This invention relates to the field of geological disaster prevention and control, and specifically to a method for improving the accuracy of discrete element numerical simulation of slopes at the engineering scale. Background Technology

[0002] Geological disasters are geological phenomena that can cause severe damage to the environment, life, and property. The number of geological disasters occurring annually is substantial, exceeding 8,000, with landslides and collapses caused by slope instability accounting for over 70%, resulting in extremely serious loss of life and property. Currently, discrete element method (DEM) numerical simulation technology is one of the important tools for simulating the entire process of slope evolution, instability, and movement. Theoretically, the smaller the particle size of the discrete element, the more accurate the micro-parameter calibration of the particle contact model, and the closer the numerical simulation results are to the actual mechanical behavior and evolution laws of the slope.

[0003] However, due to limitations in current computing resources and discrete element method (DEM) simulation software, the number of particles that can be used in the discrete element numerical analysis model of slopes at the engineering scale is severely restricted, inevitably leading to a significant increase in particle diameter at the engineering scale. Furthermore, the accuracy of the micro-parameter calibration of the particle contact model in the engineering-scale discrete element numerical analysis model is also a crucial factor affecting the accuracy of the numerical simulation. Currently, the common practice is to calibrate the micro-parameters of the particle contact model based on the actual sample size and then directly use them as the micro-parameters for simulation calculations in the engineering-scale discrete element numerical analysis model. Due to the particle size effect, this method of small-scale calibration for large-scale application has significant errors, resulting in poor accuracy of the numerical simulation results. Summary of the Invention

[0004] This invention aims to provide a method for improving the accuracy of discrete element numerical simulation of slopes at the engineering scale. At the engineering scale, it enables the reasonable determination of particle diameter and quantity in the discrete element numerical analysis model of slopes, and the accurate calibration of microscopic parameters of the particle contact model. This solves the problems of difficulty in reasonably determining the diameter and quantity of filling particles in the discrete element numerical analysis model at the engineering scale, and difficulty in accurately mapping the calibrated microscopic parameters of the particle contact model at the small scale to the engineering scale, thus leading to low accuracy of discrete element numerical simulation of slopes at the engineering scale.

[0005] The method for improving the accuracy of discrete element numerical simulation of slopes at the engineering scale in this scheme includes the following steps:

[0006] S1, obtain the test results of geological conditions, topography and geomorphology and physical and mechanical parameters of soil and rock mass of the target slope;

[0007] S2, construct a discrete element numerical model of the same scale as the physical and mechanical test specimen used in the physical and mechanical test of the soil and rock obtained by S1, determine the minimum particle diameter, particle size distribution, and maximum particle diameter in the discrete element numerical model and perform particle filling.

[0008] S3, based on the test results of the physical and mechanical parameters of soil and rock obtained in S1, calibrates the first microscopic parameter in the particle contact model of the discrete element numerical model;

[0009] It also includes the following steps:

[0010] S4. Based on the geological conditions and topographic information of the target slope described in S1, construct a numerical analysis model of the target slope evolution at the engineering scale.

[0011] S5, based on the preset rules including the discrete element numerical model, minimum particle diameter, particle size distribution and maximum particle diameter in S2, determines the particle diameter and the second microscopic parameter in the particle contact model of the target slope evolution numerical analysis model at the engineering scale described in S4.

[0012] S6. Based on the numerical analysis model of the target slope evolution determined in S4 and the particle diameter and second microscopic parameters determined in S5, the evolution process of the target slope under the simulated working conditions is numerically simulated according to the simulated working conditions, and the numerical simulation results are obtained.

[0013] The beneficial effects of this plan are:

[0014] By rationally determining the diameter and number of particles in the discrete element numerical analysis model at the engineering scale, and mapping the calibrated mesoscopic parameters in the particle contact model at the small scale to the engineering scale, the accuracy and precision of discrete element numerical simulation of slopes at the engineering scale can be improved, enabling accurate geological hazard prediction in the future.

[0015] Furthermore, in step S1, survey data of the target slope to be simulated is collected, and the geological conditions, topography, and physical and mechanical parameters of the target slope are determined based on the survey data. If the survey data does not clearly show the physical and mechanical parameters of the target slope, physical and mechanical tests are conducted on the rock cores obtained from the rock and soil samples of the target slope through field tests and laboratory tests to obtain the physical and mechanical parameters of the target slope.

[0016] The beneficial effects are: to obtain geological conditions, geological and physical-mechanical parameters from the survey data of the target slope; if these cannot be obtained from the survey data, they can be obtained through experiments, thereby improving the accuracy of basic data acquisition.

[0017] Furthermore, in S2, the minimum particle diameter is determined based on the target slope space size to be simulated, computer computing resources, and simulation timeliness requirements. During particle filling, the particle size distribution and the maximum particle diameter are determined according to the particle gradation distribution of the target slope soil and rock mass. When the particle gradation distribution of the soil and rock mass is unclear or the computer computing resources are insufficient, the particle size distribution is determined by simplifying the process according to a uniform distribution.

[0018] The beneficial effect is that, based on the particle size distribution of the target slope soil and rock mass, the particle size distribution during particle filling is determined to be as close as possible to the actual engineering situation, thus ensuring the accuracy of the numerical simulation results.

[0019] Furthermore, in S2, when determining the particle size distribution according to a uniform distribution, the maximum particle diameter d filled in the discrete element numerical model is... max Determined according to the following formula:

[0020] d max =1.6d min ;

[0021] In the formula, d min The minimum particle diameter is the filler particle in the discrete element numerical model described in S2.

[0022] The beneficial effects are: it provides a consistent and clear method for determining particle size distribution and maximum particle diameter when the particle size distribution of soil and rock is unclear or the computing power resources are insufficient, which facilitates practical operation by engineers.

[0023] Furthermore, in S4, the stratigraphic structure in the numerical analysis model of the target slope evolution is basically consistent with the geological conditions and topography of the target slope.

[0024] The beneficial effects are: the model has a high degree of consistency with the actual geological conditions and topography, which makes the constructed discrete element numerical analysis model of the target slope closer to the actual engineering situation and ensures the accuracy of the numerical simulation results.

[0025] Furthermore, in S5, the preset rules include: the rules for determining particle diameter in the numerical analysis model of target slope evolution at the engineering scale and the rules for determining the second microscopic parameter in the particle contact model of the numerical analysis model of target slope evolution at the engineering scale.

[0026] The beneficial effect is that the preset rule settings include particle diameter and second microscopic parameters at the engineering scale, ensuring the accuracy of parameter determination.

[0027] Furthermore, in step S5, the rule for determining the particle diameter includes the following steps:

[0028] S5.1.1, based on the minimum particle diameter and particle size distribution filled in the discrete element numerical model in S2, calculate the minimum particle diameter d. min R is the ratio of the smallest geometric element g in the discrete element numerical model to the smallest geometric element g.

[0029] S5.1.2, retrieve and calculate the equivalent vertex nearest neighbor distance of the numerical analysis model of the target slope evolution at the engineering scale described in S4.

[0030] S5.1.3, Calculate and determine the minimum particle diameter D in the numerical analysis model of the target slope evolution at the engineering scale. min The calculation formula is:

[0031]

[0032] S5.1.4, based on the minimum particle diameter D determined in S5.1.3 min Following the same particle size distribution determination method as S2, the particle size distribution and maximum particle diameter D of the target slope evolution numerical analysis model are determined. max And then perform particle filling.

[0033] The beneficial effect is that the ratio R of the equivalent vertex nearest neighbor distance to the minimum particle diameter to the minimum geometric element g in the discrete element numerical model can reasonably and quantitatively determine the particle diameter and particle size distribution at the engineering scale, while ensuring the effectiveness of the microscopic parameters of the particle contact model calibrated at the small scale to the greatest extent, thereby ensuring the effectiveness of the numerical simulation effect.

[0034] Furthermore, in S5.1.1, the minimum geometric element g is determined according to the following formula:

[0035]

[0036] Where Z and H are the base diameter and height of the cylindrical numerical simulation model, respectively, and e1, e2, and e3 are the side lengths of three adjacent edges at the same vertex in the cuboid discrete element numerical model.

[0037] The beneficial effect is that the minimum geometric elements are determined based on different geometric models, thus improving accuracy.

[0038] Furthermore, in S5.1.2, the method for retrieving and calculating the nearest neighbor distance of the equivalent vertex includes the following steps:

[0039] S5.1.2A, Read all vertex coordinates of the target slope evolution numerical analysis model.

[0040] S5.1.2B, calculate the spatial distance G between any two vertex coordinates using the following formula:

[0041]

[0042] S5.1.2C, based on S5.1.2B, obtains (i 2 Given a data set GS consisting of spatial distances Gi and G2, iterate through the elements in set GS and sort them in ascending order to obtain the following ascending spatial distance sequence: G1, G2, G3, ..., G4. n , where n = (i 2 -i) / 2;

[0043] S5.1.2D, calculate the equivalent vertex nearest neighbor distance of the numerical analysis model for the evolution of the target slope. The calculation formula is:

[0044]

[0045] In the formula, [] is the floor operator, and k∈(1,2,...,n).

[0046] The beneficial effects are: by introducing the equivalent vertex nearest neighbor distance, constraints are imposed on both the size and distribution of vertex nearest neighbor distances in the discrete element numerical analysis model at the engineering scale, eliminating the influence of irregular corners, thereby avoiding the unreasonable minimum particle size of the numerical simulation analysis model at the engineering scale caused by extreme geometric shapes, and further ensuring the accuracy of the numerical simulation results.

[0047] Furthermore, in step S5, the rule for determining the second microscopic parameter includes the following steps:

[0048] S5.2.1, Obtain the h first microscopic parameters MP calibrated in the particle contact model of the discrete element numerical model in S3. h Where h = 1, 2, 3, ...;

[0049] S5.2.2, based on the discrete element numerical model described in S2, the discrete element numerical model and its filling particles are enlarged proportionally by a factor of B according to the method of scaling up the minimum geometric element g, to obtain the enlarged discrete element numerical model M. B B takes values ​​of 1, 2, 5, 10, 15, and 20. When B = 1, M B This is the discrete element numerical model described in S2;

[0050] S5.2.3, according to the geometric dimensions of the discrete element numerical model described in S2, from the enlarged discrete element numerical model M described in S5.2.2. B The discrete element numerical model MM with the same geometric dimensions as the discrete element numerical model is extracted from the middle. B ;

[0051] S5.2.4, for any discrete element numerical model MM described in S5.2.3 B Repeat steps S2 and S3 to recalibrate the micromechanical parameters in the particle contact model, thus obtaining an arbitrary discrete element numerical model MM. B MM B The h microscopic parameters calibrated in the medium particle contact model are:

[0052] S5.2.5, for any one of the h micro-parameters The magnification factor B is used as the independent variable, where B = 1, 2, 5, 10, 15, 20. To observe the parameters in detail Using B as the dependent variable, we fit it using the least squares method to obtain the independent variable B and the two micro-parameters of the dependent variable B. The mapping relationship model F;

[0053] S5.2.6, Repeat step S5.2.5 for h micro-parameters to obtain h independent variables B and the micro-parameters that are the dependent variables. The mapping relationship model F h h = 1, 2, 3, ...;

[0054] S5.2.7, Based on the minimum particle diameter d in the discrete element numerical model determined in S2 min The minimum particle diameter D in the numerical analysis model of the target slope evolution determined in S5.1.3 min The magnification factor b of the numerical analysis model of the target slope evolution relative to the discrete element numerical model is calculated using the following formula:

[0055] b = D min / 100d min ;

[0056] S5.2.8, take the magnification factor b obtained in S5.2.7 as the independent variable and substitute it into the h mapping relationship model F described in S5.2.6. h The h microscopic parameters (MEP) required in the particle contact model of the numerical analysis model of the target slope evolution are obtained by solving. h .

[0057] The beneficial effect is that by constructing a mapping relationship model between h independent variables B and the microscopic parameters of the dependent variable, the microscopic mechanical parameters of the numerical analysis model at the engineering scale are corrected, thus realizing the problem of accurately mapping the microscopic mechanical parameters of the numerical model from the small scale to the large scale, thereby further ensuring the accuracy of the numerical analysis results. Attached Figure Description

[0058] Figure 1This is a flowchart illustrating an embodiment of the method for improving the accuracy of discrete element numerical simulation of slopes at an engineering scale according to the present invention.

[0059] Figure 2 This is a schematic diagram of a model of a mine slope, representing an embodiment of the method for improving the accuracy of discrete element numerical simulation of slopes at the engineering scale according to the present invention.

[0060] Figure 3 This is a schematic diagram of the discrete element numerical model M in an embodiment of the method for improving the accuracy of discrete element numerical simulation of slopes at the engineering scale according to the present invention;

[0061] Figure 4 This is a schematic diagram of the target slope evolution numerical analysis model ME in an embodiment of the method for improving the accuracy of discrete element numerical simulation of slopes at the engineering scale according to the present invention.

[0062] Figure 5 This is a numerical simulation result of the target slope during its evolution process, as shown in the embodiment of the method for improving the accuracy of discrete element numerical simulation of slopes at the engineering scale according to the present invention. Detailed Implementation

[0063] The following detailed description provides further details on specific implementation methods.

[0064] Example

[0065] like Figure 1 As shown, a method for improving the accuracy of discrete element numerical simulation of slopes at the engineering scale includes the following steps:

[0066] S1. Collect survey data of the target slope to be simulated, and based on the survey data, clarify the geological conditions, topography, and physical and mechanical parameters of the target slope. If the survey data does not clearly specify the physical and mechanical parameters of the target slope's soil and rock, conduct physical and mechanical tests on core samples obtained from the target slope's soil and rock through field and laboratory tests to obtain the test results of the target slope's soil and rock physical and mechanical parameters. Figure 2 As shown in the figure, an open-pit mine, after mining, has formed a mine slope with a height of 493m, a horizontal distance of 812m from the top to the bottom of the slope, and including 4 steps.

[0067] S2, based on the discrete element numerical simulation software PFC, constructs a discrete element numerical model M of the same scale as the specimen used in the physical and mechanical test described in S1, such as... Figure 3 As shown, the discrete element numerical model M is a standard cylindrical sample with a diameter of 50 mm and a height of 100 mm. The minimum particle diameter d to be filled in the discrete element numerical model M needs to be determined. min Particle size distribution, maximum particle diameter d maxThen, particle filling is performed. Particle filling is a function in the discrete element numerical simulation software PFC, which will not be described in detail here.

[0068] The minimum particle diameter is determined based on the target slope space dimensions to be simulated, computer computing resources, and simulation timeliness requirements. During particle filling, the particle size distribution and the maximum particle diameter d are considered. max The particle size distribution is determined based on the particle size distribution of the target slope soil and rock mass. When the particle size distribution is unclear or the computer computing resources are insufficient, a simplified method based on a uniform distribution is used to determine the particle size distribution. When determining the particle size distribution based on a uniform distribution, the maximum particle diameter d filled in the discrete element numerical model M is used. max Determined according to the following formula:

[0069] d max =1.6d min ;

[0070] In the formula, d min The minimum particle diameter is the filler particle in the discrete element numerical model described in S2.

[0071] In this embodiment, based on the computing power resources (CPU I9-194900), the target slope space size, and a calculation time of 24 hours, the discrete element numerical model M is estimated to contain 300,000 particles, thereby determining the minimum particle diameter d for filling the discrete element numerical model. min =0.00005m, particle size distribution is assumed to be uniform, maximum particle diameter d max =0.00008m.

[0072] S3, based on the test results of the physical and mechanical parameters of the soil and rock obtained in S1, uses the industry-standard trial-and-error method to calibrate the first microscopic parameter MP in the particle contact model of the discrete element numerical model M. The trial-and-error method is existing and will not be elaborated here.

[0073] S4, based on the geological conditions and topographic information of the target slope described in S1, constructs a numerical analysis model ME for the evolution of the target slope at the engineering scale in the discrete element numerical simulation software PFC, such as... Figure 4 As shown, the target slope evolution numerical analysis model ME maintains a basic consistency between the stratigraphic structure and the geological conditions and topography of the target slope. Maintaining basic consistency means that the evolution numerical analysis model is consistent with the actual target slope in terms of stratigraphy, geology, topography and thickness.

[0074] S5, based on the preset rules including the discrete element numerical model, minimum particle diameter, particle size distribution, and maximum particle diameter in S2, determines the particle diameter and the second mesoscopic parameter MEP in the particle contact model of the target slope evolution numerical analysis model ME at the engineering scale, as described in S4. The preset rules include: the rules for determining the particle diameter in the target slope evolution numerical analysis model ME at the engineering scale and the rules for determining the second mesoscopic parameter in the particle contact model of the target slope evolution numerical analysis model ME at the engineering scale. The first mesoscopic parameter and the second conventional parameter do not represent different parameters; the first and second are merely for differentiating the descriptions of mesoscopic parameters in the two models.

[0075] The rules for determining particle diameter include the following steps:

[0076] S5.1.1, the minimum particle diameter d in the discrete element numerical model M based on S2. min And particle size distribution, calculate the minimum particle diameter d min The ratio R between the minimum geometric element g in the discrete element numerical model M and the minimum geometric element g. The minimum geometric element g is determined according to the following formula:

[0077]

[0078] Where Z and H are the base diameter and height of the cylindrical numerical simulation model, respectively, and e1, e2, and e3 are the side lengths of three adjacent edges at the same vertex in the cuboid discrete element numerical model. In this embodiment, based on the simulated mine slope, g = 0.05m and R = 0.001 can be determined.

[0079] S5.1.2, retrieve and calculate the equivalent vertex nearest neighbor distance of the numerical analysis model ME for the evolution of the target slope at the engineering scale described in S4. Searching to calculate the equivalent vertex nearest neighbor distance The method includes the following steps:

[0080] S5.1.2A, Read all vertex coordinates of the target slope evolution numerical analysis model ME.

[0081] S5.1.2B, calculate the spatial distance G between any two vertex coordinates using the following formula:

[0082]

[0083] S5.1.2C, based on S5.1.2B, obtains (i 2 The data set GS consists of spatial distances G, where i and j represent two distinct vertices. A straight line is calculated based on these two points. 2-i) / 2 spatial distances are used to facilitate subsequent sorting and value retrieval. Elements in set GS are traversed and sorted in ascending order, resulting in the following ascending spatial distance sequence: G1, G2, G3, ..., G... n , where n = (i 2 -i) / 2;

[0084] S5.1.2D, calculate the equivalent vertex nearest neighbor distance of the numerical analysis model ME for the evolution of the target slope. The calculation formula is:

[0085]

[0086] In the formula, [] is the floor operator, and k∈(1,2,...,n).

[0087] This embodiment determines the equivalent vertex nearest neighbor distance of the numerical analysis model ME for the evolution of the target slope at the engineering scale, specifically for the simulated target slope.

[0088] S5.1.3, Calculate and determine the minimum particle diameter D in the numerical analysis model ME for the evolution of the target slope at the engineering scale. min The calculation formula is:

[0089]

[0090] This embodiment focuses on the simulated target slope, specifically the minimum particle diameter D in the numerical analysis model ME for target slope evolution. min =0.12m.

[0091] S5.1.4, based on the minimum particle diameter D determined in S5.1.3 min Following the same particle size distribution determination method as S2, the particle size distribution and maximum particle diameter D of the target slope evolution numerical analysis model ME are determined. max The particles are then filled. In this embodiment, for the simulated target slope, the particle size distribution of the target slope evolution numerical analysis model ME is uniform, with a maximum particle diameter D. max =0.192m.

[0092] The second microscopic parameter determination rule includes the following steps:

[0093] S5.2.1 Obtain the h first microscopic parameters MP calibrated in the particle contact model of the discrete element numerical model M in S3. h Where h = 1, 2, 3, ..., the value of h can be selected according to the actual needs of the project.

[0094] S5.2.2, based on the discrete element numerical model M described in S2, by scaling up the minimum geometric element g by a factor of B, the discrete element numerical model M and its filling particles are scaled up proportionally by a factor of B to obtain the scaled-up discrete element numerical model M. B B takes values ​​of 1, 2, 5, 10, 15, and 20 in sequence. When B = 1, M B This is the discrete element numerical model described in S2;

[0095] S5.2.3, according to the geometric dimensions of the discrete element numerical model M described in S2, from the enlarged discrete element numerical model M described in S5.2.2. B Extract a discrete element numerical model MM with the same geometric dimensions as the discrete element numerical model M. B ;

[0096] S5.2.4, for any discrete element numerical model MM described in S5.2.3 B Repeat steps S2 and S3 to recalibrate the micromechanical parameters in the particle contact model, thus obtaining an arbitrary discrete element numerical model MM. B MM B The h microscopic parameters calibrated in the medium particle contact model are:

[0097] S5.2.5, for any one of the h micro-parameters The magnification factor B is used as the independent variable, where B = 1, 2, 5, 10, 15, 20. To observe the parameters in detail Using B as the dependent variable, we fit it using the least squares method to obtain the independent variable B and the micro-parameters of the dependent variable. The mapping relationship model F;

[0098] S5.2.6, Repeat step S5.2.5 for h micro-parameters to obtain h independent variables B and the micro-parameters that are the dependent variables. The mapping relationship model F h h = 1, 2, 3, ..., n;

[0099] S5.2.7, Based on the minimum particle diameter d in the discrete element numerical model determined in S2 min The minimum particle diameter D in the numerical analysis model of the target slope evolution determined in S5.1.3 min The magnification factor b of the numerical analysis model of the target slope evolution relative to the discrete element numerical model is calculated using the following formula:

[0100] b = D min / 100d min ;

[0101] S5.2.8, take the magnification factor b obtained in S5.2.7 as the independent variable and substitute it into the h mapping relationship model F described in S5.2.6. h The h microscopic parameters (MEP) required in the particle contact model of the numerical analysis model of the target slope evolution are obtained by solving. h .

[0102] In this embodiment, the two mesoscopic parameters, emod, pb_ten, and pb_coh, which are significantly affected by scale effects, were calibrated according to the method described above, with h = 1, 2, and 3. The calibration results are shown in Table 1.

[0103] Table 1 Calibration Results

[0104] h Detailed parameters B＝1 B＝2 B＝5 B＝10 B＝15 B＝20 1 emod 7.051E7 7.063E7 7.072E7 7.145E7 7.271E7 7.422E7 2 pb_ten 9.272E5 9.203E5 8.844E5 8.385E5 8.021E5 7.775E5 3 pb_coh 2.267E7 2.249E7 2.162E7 2.050E7 1.961E7 1.901E7

[0105] Mapping relationship F1: emod = 9004.3B 2 +6565.4B+7E7;

[0106] Mapping relationship F2: pb_ten = 209.96B 2 -12417B+941594;

[0107] Mapping relationship F2: pb_coh = 5122.4B 2 -303390B+2E+07.

[0108] The magnification factor b = 24 of the numerical analysis model for the evolution of the target slope compared to the discrete element numerical model.

[0109] Based on the mapping relationship F, the microscopic parameters in the ME particle contact model of the target slope evolution numerical analysis model are determined as follows: emod = 7.534E7; pb_ten = 7.645E5; pb_coh = 1.869E7.

[0110] S6, based on the target slope evolution numerical analysis model ME determined in S4 and the particle diameter and second microscopic parameters determined in S5, numerical simulations are performed on the evolution process of the target slope under simulated working conditions, obtaining numerical simulation results. For example, the failure process of a landslide under simulated working conditions is simulated, yielding simulation results of the landslide instability influence range. Figure 5 As shown.

[0111] Compared with existing discrete element numerical simulation techniques, this embodiment constructs a discrete element numerical model of the target slope at the engineering scale as a small-scale calibration model to obtain the sample scale of the corresponding slope. Then, considering the spatial size of the target slope, balancing computing resources, and simulation timeliness, the diameter and number of particles in the determined discrete element numerical model at the engineering scale, as well as the microscopic parameters calibrated in the particle contact model at the small scale, are accurately mapped to the engineering scale. In other words, the small-scale calibrated model is transformed into a target slope evolution numerical analysis model for large-scale application. Compared with the existing method of directly using the experimental scale obtained by calibrating the small-scale model, the method of this embodiment greatly improves the rationality of the determination of various parameters, improves the accuracy of discrete element numerical simulation of slope at the engineering scale, and makes the simulation results more consistent with the actual situation of the target slope, so as to improve the accuracy of subsequent geological disaster simulation and prediction.

[0112] The above descriptions are merely embodiments of the present invention, and common knowledge regarding specific structures and characteristics is not elaborated upon here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for improving the accuracy of discrete element numerical simulation of slopes at the engineering scale, characterized in that, Includes the following steps: S1, obtain the test results of geological conditions, topography and geomorphology and physical and mechanical parameters of soil and rock mass of the target slope; S2, construct a discrete element numerical model of the same scale as the physical and mechanical test specimen used in the physical and mechanical test of the soil and rock obtained by S1, determine the minimum particle diameter, particle size distribution, and maximum particle diameter in the discrete element numerical model and perform particle filling. The minimum particle diameter is determined based on the target slope spatial dimensions, computer computing resources, and simulation timeliness requirements. During particle filling, the particle size distribution and maximum particle diameter are determined according to the particle gradation distribution of the target slope soil and rock mass. When the particle gradation distribution of the soil and rock mass is unclear or the computer computing resources are insufficient, a simplified process based on a uniform distribution is used to determine the particle size distribution. The maximum particle diameter filled in the discrete element numerical model... Determined according to the following formula: ； In the formula, The minimum particle diameter used to fill the discrete element numerical model; S3, based on the test results of the physical and mechanical parameters of soil and rock obtained in S1, calibrates the first microscopic parameter in the particle contact model of the discrete element numerical model; It also includes the following steps: S4. Based on the geological conditions and topographic information of the target slope described in S1, construct a numerical analysis model of the target slope evolution at the engineering scale. S5, based on the preset rules including the discrete element numerical model, minimum particle diameter, particle size distribution and maximum particle diameter in S2, determines the particle diameter and the second microscopic parameter in the particle contact model of the target slope evolution numerical analysis model at the engineering scale described in S4. The preset rules include: the rules for determining particle diameter in the numerical analysis model of target slope evolution at the engineering scale and the rules for determining the second microscopic parameter in the particle contact model of the numerical analysis model of target slope evolution at the engineering scale; The rules for determining particle diameter include the following steps: S5.1.1, based on the minimum particle diameter and particle size distribution filled in the discrete element numerical model in S2, calculate the minimum particle diameter. R is the ratio of the smallest geometric element g in the discrete element numerical model to the smallest geometric element g. S5.1.2, retrieve and calculate the equivalent vertex nearest neighbor distance of the numerical analysis model of the target slope evolution at the engineering scale described in S4. ; S5.1.3, Calculate and determine the minimum particle diameter in the numerical analysis model of the target slope evolution at the engineering scale. The calculation formula is: ； S5.1.4, based on the minimum particle diameter determined in S5.1.3 Following the same particle size distribution determination method as S2, the particle size distribution and maximum particle diameter of the target slope evolution numerical analysis model are determined. And then perform particle filling; The rules for determining the second micro-parameter include: scaling up the discrete element numerical model by a factor of B according to the minimum geometric element g, recalibrating the micro-mechanical parameters, fitting the mapping relationship model between the scaling factor B and the micro-parameters based on the least squares method, and then calculating the scaling factor b of the target slope evolution numerical analysis model relative to the discrete element numerical model and substituting it into the mapping relationship model to obtain the second micro-parameter at the engineering scale. S6. Based on the numerical analysis model of the target slope evolution determined in S4 and the particle diameter and second microscopic parameters determined in S5, the evolution process of the target slope under the simulated working conditions is numerically simulated according to the simulated working conditions, and the numerical simulation results are obtained.

2. The method for improving the accuracy of discrete element numerical simulation of slopes at an engineering scale according to claim 1, characterized in that: In step S1, survey data of the target slope to be simulated is collected, and the geological conditions, topography, and physical and mechanical parameters of the target slope are determined based on the survey data. If the survey data does not clearly show the physical and mechanical parameters of the target slope, physical and mechanical tests are conducted on the rock cores obtained by sampling the target slope through field tests and laboratory tests to obtain the physical and mechanical parameters of the target slope.

3. The method for improving the accuracy of discrete element numerical simulation of slopes at an engineering scale according to claim 1, characterized in that: In S4, the stratigraphic structure in the numerical analysis model of the target slope evolution is consistent with the geological conditions and topography of the target slope.

4. The method for improving the accuracy of discrete element numerical simulation of slopes at an engineering scale according to claim 1, characterized in that: In S5.1.1, the minimum geometric element g is determined according to the following formula: ； Where Z and H are the base diameter and height of the cylindrical numerical simulation model, respectively. These are the side lengths of the three adjacent edges at the same vertex in the discrete element numerical model of a cuboid.

5. The method for improving the accuracy of discrete element numerical simulation of slopes at an engineering scale according to claim 1, characterized in that: In S5.1.2, the method for retrieving and calculating the nearest neighbor distance of the equivalent vertex includes the following steps: S5.1.2A, Read all vertex coordinates of the target slope evolution numerical analysis model. ; S5.1.2B, calculate the spatial distance G between any two vertex coordinates using the following formula: ； S5.1.2C, based on S5.1.2B, yields... A data set GS consisting of spatial distances G is obtained by iterating through the elements of set GS and sorting them in ascending order, resulting in the following ascending spatial distance sequence. ,in, ; S5.1.2D, calculate the equivalent vertex nearest neighbor distance of the numerical analysis model for the evolution of the target slope. The calculation formula is: ； In the formula, [ ] is the floor function. .

6. The method for improving the accuracy of discrete element numerical simulation of slopes at an engineering scale according to claim 1, characterized in that: In step S5, the rule for determining the second microscopic parameter includes the following steps: S5.2.1 Obtain the h first microscopic parameters calibrated in the particle contact model of the discrete element numerical model in S3. Where h = 1, 2, 3, ..., n; S5.2.2, based on the discrete element numerical model described in S2, the discrete element numerical model and its filling particles are enlarged proportionally by a factor of B according to the method of scaling up the minimum geometric element g, to obtain the enlarged discrete element numerical model. The value of B is 1, 2, 5, 10, 15, 20. When B=1, This is the discrete element numerical model described in S2; S5.2.3, according to the geometric dimensions of the discrete element numerical model described in S2, from the enlarged discrete element numerical model described in S5.2.

2. Extracting a discrete element numerical model with the same geometric dimensions as the discrete element numerical model. ; S5.2.4, for any discrete element numerical model described in S5.2.3 Repeat steps S2 and S3 to recalibrate the micromechanical parameters in the particle contact model, thus obtaining any discrete element numerical model. , The h microscopic parameters calibrated in the medium particle contact model are: ; S5.2.5, for any one of the h micro-parameters The magnification factor B is used as the independent variable, where B = 1, 2, 5, 10, 15, 20. To observe the parameters in detail Using B as the dependent variable, we fit it using the least squares method to obtain the independent variable B and the micro-parameters of the dependent variable. The mapping relationship model F; S5.2.6, Repeat step S5.2.5 for h micro-parameters to obtain h independent variables B and the micro-parameters that are the dependent variables. Mapping relationship model h = 1, 2, 3, ..., n; S5.2.7, Minimum particle diameter in the discrete element numerical model determined in S2 The minimum particle diameter in the numerical analysis model of the target slope evolution determined in S5.1.3 The magnification factor b of the numerical analysis model of the target slope evolution relative to the discrete element numerical model is calculated using the following formula: ； S5.2.8, take the magnification factor b obtained in S5.2.7 as the independent variable and substitute it into the mapping relationship model described in S5.2.

6. The h microscopic parameters required in the particle contact model of the numerical analysis model of the target slope evolution are obtained by solving. .

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