Method and device for constructing tailings pile liquefaction prediction model

Abstract

The embodiments of the present application are applicable to the field of mining technology and provide a method and device for constructing a tailings pile liquefaction prediction model. The method includes: determining relevant factors that affect tailings pile liquefaction, wherein the relevant factors include at least blasting distance and single-shot explosive quantity; determining a blasting parameter formula based on the blasting distance and the single-shot explosive quantity; obtaining vibration triaxial test results for the tailings pile, wherein the vibration triaxial test results include the unconfined compressive strength; constructing a nonlinear regression model based on the vibration triaxial test results, with the number of vibrations required for liquefaction of the tailings pile as the dependent variable; and constructing a liquefaction prediction model for the tailings pile based on the blasting parameter formula and the nonlinear regression model. Using the above method, a mathematical model that accurately predicts pile liquefaction can be constructed.

Description

Technical Field

[0001] The embodiments of the present application belong to the field of mining technology, and in particular to a method and device for constructing a tailings pile liquefaction prediction model. Background Art

[0002] Liquefaction of tailings piles refers to the transformation of tailings piles from a solid state to a liquid or liquid-like state under the influence of external forces or other factors. After liquefaction, tailings piles lose their ability to provide adequate support. For example, tailings piles used as underground backfill can no longer support the rock mass above or to the sides after liquefaction, potentially leading to rock collapse and other accidents.

[0003] Typically, multiple factors influence tailings pile liquefaction, each of which can be determined through static triaxial or vibration triaxial testing. However, triaxial testing cannot accurately determine the significance of each factor in tailings pile liquefaction. Therefore, it is impossible to accurately predict the liquefaction risk of tailings piles based on triaxial test results. Summary of the Invention

[0004] In view of this, embodiments of the present application provide a method and apparatus for constructing a tailings pile liquefaction prediction model, which is used to construct a mathematical model for accurately predicting pile liquefaction and quantitatively analyze and predict the risk of pile liquefaction.

[0005] A first aspect of an embodiment of the present application provides a method for constructing a tailings pile liquefaction prediction model, comprising:

[0006] Determining relevant factors affecting the liquefaction of the tailings pile, wherein the relevant factors include at least blasting distance and single-shot explosive quantity;

[0007] Determining a blasting parameter formula based on the blasting distance and the single-shot explosive quantity;

[0008] Obtaining a vibration triaxial test result for the tailings pile, wherein the vibration triaxial test result includes the unconfined compressive strength;

[0009] Based on the triaxial vibration test results, a nonlinear regression model is constructed with the number of vibrations required for liquefaction of the tailings pile as the dependent variable;

[0010] Based on the blasting parameter formula and the nonlinear regression model, a liquefaction prediction model of the tailings pile is constructed.

[0011] Optionally, the formula for determining blasting parameters based on the blasting distance and the single-shot explosive quantity includes:

[0012] Acquiring historical blasting monitoring data for the blasting distance and the single-shot explosive quantity;

[0013] The blasting parameter formula is determined based on the historical blasting monitoring data.

[0014] Optionally, the historical blasting monitoring data includes vibration peak accelerations measured at multiple monitoring points when blasting is performed using multiple different explosive amounts, and the distances between each monitoring point and the blasting point are not completely equal.

[0015] Optionally, determining the blasting parameter formula based on the historical blasting monitoring data includes:

[0016] The blasting parameter formula is obtained by performing regression analysis with the blasting distance and the single-shot explosive quantity as independent variables and the number of vibrations generated by the blasting as the dependent variable.

[0017] Optionally, constructing a nonlinear regression model based on the vibration triaxial test results with the number of vibrations required for liquefaction of the tailings pile as the dependent variable includes:

[0018] Determining a correspondence between a plurality of parameters of any one test in the vibration triaxial test results and the vibration number;

[0019] Based on the corresponding relationship, determining the correlation between the vibration frequency and the plurality of parameters;

[0020] Based on the correlation, the multivariate regression analysis method was applied to construct the nonlinear regression model.

[0021] Optionally, the multiple parameters include unconfined compressive strength, cyclic stress ratio, and confining pressure, the unconfined compressive strength and the confining pressure are positively correlated with the number of vibrations, and the cyclic stress ratio is negatively correlated with the number of vibrations, and the nonlinear regression model is constructed based on the correlations using a multivariate regression analysis method, including:

[0022] Based on the correlation, constructing a model equation of the nonlinear regression model, wherein the model equation includes a plurality of equation parameters to be estimated;

[0023] Applying a multiple regression analysis method to process the triaxial vibration test results to determine estimated values ​​of the equation parameters;

[0024] The nonlinear regression model is constructed based on the supplied estimates of the equation parameters.

[0025] Optionally, constructing the tailings pile liquefaction prediction model based on the blasting parameter formula and the nonlinear regression model includes:

[0026] The blasting parameter formula and the nonlinear regression model are solved simultaneously to obtain a liquefaction prediction model of the tailings pile, which takes the blasting distance, the single-shot explosive amount and the unconfined compressive strength as independent variables and the number of vibrations required for the liquefaction of the tailings pile as the dependent variable.

[0027] A second aspect of the embodiments of the present application provides a device for constructing a tailings pile liquefaction prediction model, comprising:

[0028] A related factor determination module is used to determine the related factors that affect the liquefaction of the tailings pile, wherein the related factors include at least the blasting distance and the amount of single-shot explosives;

[0029] a blasting parameter formula determination module, configured to determine a blasting parameter formula based on the blasting distance and the single-shot explosive quantity;

[0030] a vibration triaxial test result acquisition module, configured to acquire vibration triaxial test results for the tailings pile, wherein the vibration triaxial test results include the unconfined compressive strength;

[0031] A nonlinear regression model building module is used to build a nonlinear regression model with the number of vibrations required for liquefaction of the tailings pile as a dependent variable based on the vibration triaxial test results;

[0032] The liquefaction prediction model building module is used to build a liquefaction prediction model of the tailings pile based on the blasting parameter formula and the nonlinear regression model.

[0033] A third aspect of an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for constructing a tailings pile liquefaction prediction model as described in any one of the first aspects above is implemented.

[0034] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for constructing a tailings pile liquefaction prediction model as described in any one of the first aspects above.

[0035] A fifth aspect of the embodiments of the present application provides a computer program product, which, when executed on a computer, enables the computer to execute the method for constructing a tailings pile liquefaction prediction model according to any one of the first aspects.

[0036] Compared with the prior art, the embodiments of the present application have the following advantages:

[0037] In the embodiment of the present application, by determining the relevant factors that affect the liquefaction of the tailings pile, such as the blasting distance and the amount of explosives per shot, a blasting parameter formula can be determined for the blasting distance and the amount of explosives per shot. After obtaining the vibration triaxial test results for the tailings pile, a nonlinear regression model with the number of vibrations required for the liquefaction of the tailings pile as the dependent variable can be constructed based on the vibration triaxial test results. On this basis, a liquefaction prediction model for the tailings pile can be constructed by performing a joint solution based on the blasting parameter formula and the nonlinear regression model. By applying the method provided in the embodiment of the present application, the influence of various factors on the liquefaction of the tailings pile can be quantitatively determined, so that the possibility of liquefaction of the tailings pile can be predicted based on the model under any parameter values. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0039] Figure 1 Schematic diagram of a method for constructing a tailings pile liquefaction prediction model provided in an embodiment of the present application;

[0040] Figure 2 This is a schematic diagram of an implementation of S104 in a method for constructing a tailings pile liquefaction prediction model provided in an embodiment of the present application;

[0041] Figure 3 This is a schematic diagram of an implementation of S1043 in a method for constructing a tailings pile liquefaction prediction model provided in an embodiment of the present application;

[0042] Figure 4 Schematic diagram of a device for constructing a tailings pile liquefaction prediction model provided in an embodiment of the present application;

[0043] Figure 5 This is a schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

[0045] The technical solution of this application is described below through specific embodiments.

[0046] Reference Figure 1 , which shows a schematic diagram of a method for constructing a tailings pile liquefaction prediction model provided in an embodiment of the present application, which may specifically include the following steps:

[0047] S101. Determine relevant factors that affect the liquefaction of the tailings pile, wherein the relevant factors at least include blasting distance and single-shot explosive quantity.

[0048] This method can be applied to a computer device, that is, the execution subject of the embodiment of the present application is a computer device. By executing the various steps in the embodiment of the present application, the computer device can accurately construct a tailings pile liquefaction prediction model to analyze and predict the liquefaction risk of the tailings pile.

[0049] In the embodiment of the present application, relevant factors affecting the liquefaction of the tailings pile may be determined first. These relevant factors may be factors that have a significant impact on the liquefaction of the tailings pile.

[0050] Specifically, static triaxial tests and vibration triaxial tests can be used to investigate whether various factors affect the liquefaction of the tailings pile, and to obtain relevant factors affecting the liquefaction of the tailings pile.

[0051] In underground stopes, blasting is commonly used. The location of the blasting point and the amount of single-shot explosive used will result in varying vibration intensities, which in turn affect the fill material in the mined void. When tailings are used as fill material, this fill material is the tailings pile that fills the void.

[0052] For example, the farther the blasting point is from the tailings pile, the less impact the vibration generated by the blasting has on the liquefaction of the tailings pile. When the blasting distance is closer, the blasting is more likely to cause the tailings pile to liquefy. The term "single-shot explosive charge" refers to the amount of explosives used in a single blast. When a larger amount of explosives is used in a single blast, i.e., a larger single-shot explosive charge, the blasting will generate greater vibration. The corresponding vibration-generated stress, when acting on the tailings pile, is more likely to cause the tailings pile to liquefy.

[0053] S102: Determine a blasting parameter formula according to the blasting distance and the single-shot explosive quantity.

[0054] In the embodiment of the present application, a blasting parameter formula can be determined based on the blasting distance and the amount of explosives used in a single blast. The blasting parameter formula can reflect the relationship between different blasting distances and different single-shot explosive amounts and the intensity generated by the blasting.

[0055] In a possible implementation of the embodiment of the present application, when determining the blasting parameter formula, it can be based on historical blasting monitoring data.

[0056] Specifically, historical blasting monitoring data can be obtained for blasting distance and explosive charge per shot. This historical blasting monitoring data can be a series of data obtained from monitoring past blasting operations. This data may include the distance between the blasting point and the tailings pile, the amount of explosives used, the magnitude of the seismic waves generated by the blasting, and so on.

[0057] In one example, historical blasting monitoring data may include peak vibration accelerations measured at multiple monitoring points during blasting operations using different explosive charges. The distances between each monitoring point and the blasting site may not be exactly equal. For example, after determining the blasting site, monitoring points may be set at 50 meters, 100 meters, 150 meters, 200 meters, and so on, to monitor the peak acceleration of vibration generated by the blasting at each monitoring point.

[0058] Based on the acquired historical blasting monitoring data, the blasting parameter formula can be determined.

[0059] In one possible implementation of the present embodiment, the blasting parameter formula can be a formula obtained by performing a regression analysis using the blasting distance and the explosive charge per shot as independent variables and the number of vibrations generated by the blasting as the dependent variable. This formula can represent the relationship between the blasting distance, the explosive charge per shot, and the number of vibrations.

[0060] As an example, the blasting parameter formula can be expressed as:

[0061]

[0062] Among them, N is the number of vibrations, that is, the number of vibrations generated by a single blast; R is the blasting distance, that is, the distance between the blasting point and the center point of the tailings pile; Q is the amount of explosives per shot, that is, the amount of explosives used in a single blast.

[0063] From the above formula, we can see that during blasting, the number of vibrations is positively correlated with the amount of explosives per shot and negatively correlated with the blasting distance.

[0064] S103: Obtain vibration triaxial test results for the tailings pile, where the vibration triaxial test results include the unconfined compressive strength.

[0065] In an embodiment of the present application, a tailings pile liquefaction prediction model can be constructed by combining the results of a vibration triaxial test of the tailings pile, wherein the vibration triaxial test results can include the number of vibrations required for the tailings pile to liquefy at multiple different unconfined compressive strengths of the tailings pile.

[0066] As shown in Table 1, this is an example of a vibration triaxial test result provided in an embodiment of the present application.

[0067] Table 1, triaxial vibration test results:

[0068]

[0069] The test result corresponding to any serial number in Table 1 indicates the number of vibrations required for the sample to liquefy when a vibration triaxial test is performed on a sample of corresponding sample density according to the unconfined compressive strength, cyclic stress reduction ratio and confining pressure in Table 1.

[0070] S104. Based on the triaxial vibration test results, a nonlinear regression model is constructed with the number of vibrations required for liquefaction of the tailings pile as the dependent variable.

[0071] In the embodiment of the present application, a model of the relationship between the number of vibrations required for tailings pile liquefaction and various parameters can be constructed based on the results of the triaxial vibration test. For example, a nonlinear regression model can be constructed with the number of vibrations required for tailings pile liquefaction as the dependent variable.

[0072] In a possible implementation of the embodiment of the present application, as Figure 2 As shown, in S104, based on the vibration triaxial test results, constructing a nonlinear regression model with the number of vibrations required for tailings pile liquefaction as the dependent variable may specifically include the following steps S1041-S1043:

[0073] S1041. Determine a correspondence between a plurality of parameters of any one test in the vibration triaxial test results and the vibration times.

[0074] For example, each parameter corresponding to any serial number in Table 1 is a plurality of parameters having a corresponding relationship. The plurality of parameters include unconfined compressive strength, cyclic stress reduction ratio, and confining pressure.

[0075] S1042: Determine the correlation between the vibration frequency and the plurality of parameters based on the corresponding relationship.

[0076] Among them, the unconfined compressive strength and confining pressure are positively correlated with the number of vibrations, while the cyclic stress reduction ratio is negatively correlated with the number of vibrations.

[0077] S1043. Based on the correlation, use multiple regression analysis to construct the nonlinear regression model.

[0078] In a possible implementation of the embodiment of the present application, as Figure 3 As shown, in S1043, based on the correlation, applying the multivariate regression analysis method to construct a nonlinear regression model can specifically include the following steps S1431-S1433:

[0079] S1431. Based on the correlation, construct a model equation of the nonlinear regression model, wherein the model equation includes a plurality of equation parameters to be estimated.

[0080] S1432. Apply a multivariate regression analysis method to process the vibration triaxial test results to determine the estimated values ​​of the equation parameters.

[0081] S1433. Construct the nonlinear regression model according to the sent estimated values ​​of the equation parameters.

[0082] In an embodiment of the present application, based on the correlation between various parameters and the number of vibrations and combined with empirical formulas, a model equation of a nonlinear regression model can be first constructed. The model equation can include multiple equation parameters to be estimated, and these equation parameters can be estimated based on the results of the triaxial vibration test.

[0083] In a specific implementation, the values ​​in Table 1 can be used to estimate the equation parameters using SPSS software and multiple regression analysis to determine the nonlinear regression model.

[0084] As an example of an embodiment of the present application, the nonlinear regression model can be expressed as:

[0085]

[0086] Where Y is the number of vibrations required for the pile to liquefy, X1 is the unconfined compressive strength, X2 is the cyclic stress reduction ratio, and X3 is the confining pressure.

[0087] Of course, the above expression is only an example of a nonlinear regression model. According to different vibration triaxial test results, the nonlinear regression model finally constructed may be different.

[0088] S105: Constructing a liquefaction prediction model for the tailings pile based on the blasting parameter formula and the nonlinear regression model.

[0089] In an embodiment of the present application, the blasting parameter formula and the nonlinear regression model can be solved simultaneously to obtain a tailings pile liquefaction prediction model with blasting distance, single-shot explosive quantity and unconfined compressive strength as independent variables and the number of vibrations required for tailings pile liquefaction as the dependent variable.

[0090] In a specific implementation, the relationship expression between the unconfined compressive strength, the cyclic stress reduction ratio, and the confining pressure can be determined, so that the unconfined compressive strength can be used to represent the cyclic stress reduction ratio and the confining pressure, that is, X2 and X3 in the above nonlinear regression model are represented by X1.

[0091] Then, combined with the blasting parameter formula, a tailings pile liquefaction prediction model was solved, with blasting distance, single-shot explosive quantity and unconfined compressive strength as independent variables and the number of vibrations required for tailings pile liquefaction as the dependent variable.

[0092] As an example of an embodiment of the present application, a possible liquefaction prediction model of a tailings pile can be expressed as:

[0093]

[0094] Among them, Q is the amount of explosives per shot, R is the blasting distance, n is the number of blastings, UCS is the unconfined compressive strength, and H is the stope height.

[0095] In an embodiment of the present application, by determining the relevant factors that affect the liquefaction of the tailings pile, such as the blasting distance and the amount of explosives per shot, the blasting parameter formula can be determined for the blasting distance and the amount of explosives per shot. After obtaining the vibration triaxial test results for the tailings pile, a nonlinear regression model with the number of vibrations required for the liquefaction of the tailings pile as the dependent variable can be constructed based on the vibration triaxial test results. On this basis, a liquefaction prediction model for the tailings pile can be constructed by performing a joint solution based on the blasting parameter formula and the nonlinear regression model. By applying the method provided in the embodiment of the present application, the influence of various factors on the liquefaction of the tailings pile can be quantitatively determined, so that the possibility of liquefaction of the tailings pile can be predicted based on the model under any parameter values.

[0096] It should be noted that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0097] Reference Figure 4 , shows a schematic diagram of a device for constructing a tailings pile liquefaction prediction model provided in an embodiment of the present application, which may specifically include a relevant factor determination module 401, a blasting parameter formula determination module 402, a vibration triaxial test result acquisition module 403, a nonlinear regression model construction module 404, and a liquefaction prediction model construction module 405, wherein:

[0098] The relevant factors determination module 401 is used to determine the relevant factors that affect the liquefaction of the tailings pile, wherein the relevant factors include at least the blasting distance and the amount of explosives per shot;

[0099] a blasting parameter formula determination module 402, configured to determine a blasting parameter formula based on the blasting distance and the single-shot explosive quantity;

[0100] A vibration triaxial test result acquisition module 403 is used to acquire vibration triaxial test results for the tailings pile, wherein the vibration triaxial test results include the unconfined compressive strength;

[0101] A nonlinear regression model building module 404 is used to build a nonlinear regression model with the number of vibrations required for liquefaction of the tailings pile as a dependent variable based on the vibration triaxial test results;

[0102] The liquefaction prediction model building module 405 is used to build a liquefaction prediction model of the tailings pile based on the blasting parameter formula and the nonlinear regression model.

[0103] In a possible implementation of the embodiment of the present application, the blasting parameter formula determination module 402 may be specifically configured to:

[0104] Acquiring historical blasting monitoring data for the blasting distance and the single-shot explosive quantity;

[0105] The blasting parameter formula is determined based on the historical blasting monitoring data.

[0106] In an embodiment of the present application, the historical blasting monitoring data may include vibration peak accelerations measured at multiple monitoring points when blasting was performed using multiple different amounts of explosives, and the distances between each monitoring point and the blasting point may not be completely equal.

[0107] In the embodiment of the present application, the blasting parameter formula determination module 402 may also be used to:

[0108] The blasting parameter formula is obtained by performing regression analysis with the blasting distance and the single-shot explosive quantity as independent variables and the number of vibrations generated by the blasting as the dependent variable.

[0109] In a possible implementation of the embodiment of the present application, the nonlinear regression model construction module 404 may be specifically used to:

[0110] Determining a correspondence between a plurality of parameters of any one test in the vibration triaxial test results and the vibration number;

[0111] Based on the corresponding relationship, determining the correlation between the vibration frequency and the plurality of parameters;

[0112] Based on the correlation, the multivariate regression analysis method was applied to construct the nonlinear regression model.

[0113] In the embodiment of the present application, the multiple parameters may include unconfined compressive strength, cyclic stress ratio, and confining pressure. The unconfined compressive strength and the confining pressure are positively correlated with the number of vibrations, and the cyclic stress ratio is negatively correlated with the number of vibrations. The nonlinear regression model construction module 404 may also be used to:

[0114] Based on the correlation, constructing a model equation of the nonlinear regression model, wherein the model equation includes a plurality of equation parameters to be estimated;

[0115] Applying a multiple regression analysis method to process the triaxial vibration test results to determine estimated values ​​of the equation parameters;

[0116] The nonlinear regression model is constructed based on the supplied estimates of the equation parameters.

[0117] In a possible implementation of the embodiment of the present application, the liquefaction prediction model building module 405 may be specifically used to:

[0118] The blasting parameter formula and the nonlinear regression model are solved simultaneously to obtain a liquefaction prediction model of the tailings pile, which takes the blasting distance, the single-shot explosive amount and the unconfined compressive strength as independent variables and the number of vibrations required for the liquefaction of the tailings pile as the dependent variable.

[0119] The embodiments of the present application also provide a device for constructing a tailings pile liquefaction prediction model, and the device can be used to implement the steps in the aforementioned method embodiments.

[0120] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment part.

[0121] Reference Figure 5 , shows a schematic diagram of a computer device provided by an embodiment of the present application. Figure 5 As shown, the computer device 500 in the embodiment of the present application includes: a processor 510, a memory 520, and a computer program 521 stored in the memory 520 and executable on the processor 510. When the processor 510 executes the computer program 521, the steps of each embodiment of the method for constructing the tailings pile liquefaction prediction model are implemented, such as Figure 1 Alternatively, when the processor 510 executes the computer program 521, the functions of the modules / units in the above-mentioned device embodiments are realized, for example, Figure 4 Functions of modules 401 to 405 are shown.

[0122] Exemplarily, the computer program 521 can be divided into one or more modules / units, which are stored in the memory 520 and executed by the processor 510 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments can be used to describe the execution process of the computer program 521 in the computer device 500. For example, the computer program 521 can be divided into a relevant factor determination module, a blasting parameter formula determination module, a vibration triaxial test result acquisition module, a nonlinear regression model construction module, and a liquefaction prediction model construction module. The specific functions of each module are as follows:

[0123] A related factor determination module is used to determine the related factors that affect the liquefaction of the tailings pile, wherein the related factors include at least the blasting distance and the amount of single-shot explosives;

[0124] a blasting parameter formula determination module, configured to determine a blasting parameter formula based on the blasting distance and the single-shot explosive quantity;

[0125] a vibration triaxial test result acquisition module, configured to acquire vibration triaxial test results for the tailings pile, wherein the vibration triaxial test results include the unconfined compressive strength;

[0126] A nonlinear regression model building module is used to build a nonlinear regression model with the number of vibrations required for liquefaction of the tailings pile as a dependent variable based on the vibration triaxial test results;

[0127] The liquefaction prediction model building module is used to build a liquefaction prediction model of the tailings pile based on the blasting parameter formula and the nonlinear regression model.

[0128] The computer device 500 may be a device for implementing the steps in the above-mentioned various method embodiments. The computer device 500 may be a computing device such as a desktop computer, a cloud server, etc. The computer device 500 may include, but is not limited to, a processor 510 and a memory 520. It will be understood by those skilled in the art that Figure 5 This is merely an example of the computer device 500 and does not constitute a limitation of the computer device 500 . The computer device 500 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device 500 may also include input and output devices, network access devices, buses, etc.

[0129] The processor 510 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0130] The memory 520 may be an internal storage unit of the computer device 500, such as a hard disk or memory of the computer device 500. The memory 520 may also be an external storage device of the computer device 500, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device 500. Furthermore, the memory 520 may include both an internal storage unit of the computer device 500 and an external storage device. The memory 520 is used to store the computer program 521 and other programs and data required by the computer device 500. The memory 520 may also be used to temporarily store data that has been output or is about to be output.

[0131] An embodiment of the present application also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for constructing a tailings pile liquefaction prediction model as described in the aforementioned embodiments is implemented.

[0132] The embodiments of the present application further disclose a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for constructing a tailings pile liquefaction prediction model as described in the aforementioned embodiments is implemented.

[0133] The embodiments of the present application further disclose a computer program product. When the computer program product is run on a computer, the computer is enabled to execute the method for constructing the tailings pile liquefaction prediction model described in the aforementioned embodiments.

[0134] The above embodiments are intended only to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application and should be included within the scope of protection of the present application.

Claims

1. A method for constructing a tailings pile liquefaction prediction model, characterized in that: include: Determining relevant factors affecting the liquefaction of the tailings pile, wherein the relevant factors include at least blasting distance and single-shot explosive quantity; Determining a blasting parameter formula based on the blasting distance and the single-shot explosive quantity; Obtaining vibration triaxial test results for the tailings pile, wherein the vibration triaxial test results include unconfined compressive strength; Based on the triaxial vibration test results, a nonlinear regression model is constructed with the number of vibrations required for liquefaction of the tailings pile as the dependent variable; The blasting parameter formula and the nonlinear regression model are solved simultaneously to obtain a tailings pile liquefaction prediction model with the blasting distance, the single-shot explosive quantity, and the unconfined compressive strength as independent variables and the number of vibrations required for the tailings pile to liquefy as a dependent variable; Wherein, the nonlinear regression model is constructed based on the vibration triaxial test results, with the number of vibrations required for liquefaction of the tailings pile as the dependent variable, including: Determining a correspondence between a plurality of parameters of any one test in the vibration triaxial test results and the vibration number, and determining a correlation between the vibration number and the plurality of parameters based on the correspondence, the plurality of parameters comprising unconfined compressive strength, cyclic stress ratio, and confining pressure, the unconfined compressive strength and the confining pressure being positively correlated with the vibration number, and the cyclic stress ratio being negatively correlated with the vibration number; Based on the correlation, constructing a model equation of the nonlinear regression model, wherein the model equation includes a plurality of equation parameters to be estimated; Applying a multiple regression analysis method to process the triaxial vibration test results to determine estimated values ​​of the equation parameters; The nonlinear regression model is constructed based on the estimated values ​​of the equation parameters.

2. The method according to claim 1, characterized in that The formula for determining blasting parameters based on the blasting distance and the single-shot explosive quantity includes: Acquiring historical blasting monitoring data for the blasting distance and the single-shot explosive quantity; The blasting parameter formula is determined based on the historical blasting monitoring data.

3. The method according to claim 2, characterized in that The historical blasting monitoring data includes vibration peak accelerations measured at multiple monitoring points when blasting was performed using multiple different explosive amounts, and the distances between each monitoring point and the blasting point are not completely equal.

4. The method according to claim 2, characterized in that The step of determining the blasting parameter formula based on the historical blasting monitoring data includes: The blasting parameter formula is obtained by performing regression analysis with the blasting distance and the single-shot explosive quantity as independent variables and the number of vibrations generated by the blasting as the dependent variable.

5. A device for constructing a tailings pile liquefaction prediction model, characterized in that: include: A related factor determination module is used to determine the related factors that affect the liquefaction of the tailings pile, wherein the related factors include at least the blasting distance and the amount of single-shot explosives; a blasting parameter formula determination module, configured to determine a blasting parameter formula based on the blasting distance and the single-shot explosive quantity; a vibration triaxial test result acquisition module, configured to acquire vibration triaxial test results for the tailings pile, wherein the vibration triaxial test results include unconfined compressive strength; A nonlinear regression model building module is used to build a nonlinear regression model with the number of vibrations required for liquefaction of the tailings pile as a dependent variable based on the vibration triaxial test results; a liquefaction prediction model construction module for simultaneously solving the blasting parameter formula and the nonlinear regression model to obtain a tailings pile liquefaction prediction model with the blasting distance, the single-shot explosive quantity, and the unconfined compressive strength as independent variables and the number of vibrations required for liquefaction of the tailings pile as a dependent variable; The nonlinear regression model building module is specifically used to: Determining a correspondence between a plurality of parameters of any one test in the vibration triaxial test results and the vibration number, and determining a correlation between the vibration number and the plurality of parameters based on the correspondence, the plurality of parameters comprising unconfined compressive strength, cyclic stress ratio, and confining pressure, the unconfined compressive strength and the confining pressure being positively correlated with the vibration number, and the cyclic stress ratio being negatively correlated with the vibration number; Based on the correlation, constructing a model equation of the nonlinear regression model, wherein the model equation includes a plurality of equation parameters to be estimated; Applying a multiple regression analysis method to process the triaxial vibration test results to determine estimated values ​​of the equation parameters; The nonlinear regression model is constructed based on the estimated values ​​of the equation parameters.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for constructing a tailings pile liquefaction prediction model according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for constructing a tailings pile liquefaction prediction model according to any one of claims 1 to 4 is implemented.

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