Rockfill material compaction and breakage gradation evolution prediction method considering cushion effect

Abstract

The present application relates to the technical field of rockfill construction, and discloses a rockfill compaction and crushing gradation evolution prediction method considering the cushion effect, comprising the following steps: S1, grouping the rockfill according to particle size, and obtaining the initial mass fraction of each particle group, thereby obtaining the initial gradation information of the rockfill; S2, calculating the specific surface area index corresponding to the initial gradation according to the representative particle size and mass fraction of each particle group, and taking the specific surface area index as a variable representing the structure state of the particle group. In the case of known initial gradation and rolling energy, the present application dynamically predicts the gradation change of the rockfill under any rolling pass number; can be used for the rolling construction process control of the rockfill filling engineering, especially suitable for the wide-gradation rockfill working condition, and can provide a technical basis for the rolling pass number optimization, gradation overrun early warning and construction quality evaluation, and has good engineering application value.

Description

Technical Field

[0001] This invention relates to the field of rockfill construction technology, and in particular to a method for predicting the evolution of compaction and crushing gradation of rockfill considering the subbase effect. Background Technology

[0002] During the construction of high earth-rock dams, the rockfill undergoes particle breakage, fine material filling, and gradation refinement under vibratory compaction. Grading evolution directly affects the compaction effect, deformation characteristics, and construction quality control of the fill. Current engineering projects often rely on pit testing to determine the post-compaction gradation. This method is inefficient, time-consuming, and typically only provides gradation data under a single compaction condition, making dynamic prediction during continuous compaction difficult. While existing gradation evolution models can describe the particle breakage process to some extent, they usually do not consider the cushion layer effect formed by the mixing of multiple particle groups under on-site compaction. This leads to an overestimation of the effective breakage energy actually borne by large particles as the gradation refines and the contact network develops, thus easily overestimating the breakage rate of coarse particles and affecting prediction accuracy. Therefore, it is necessary to propose a method that can consider the particle group cushion layer effect and dynamically predict the gradation state under any number of compaction passes based on the initial gradation and compaction input energy. Summary of the Invention

[0003] To address the technical problems existing in the prior art, this invention provides a method for predicting the evolution of compaction and crushing gradation of rockfill materials that considers the cushion layer effect.

[0004] This invention is achieved using the following technical solution: a method for predicting the evolution of compaction and crushing gradation of rockfill considering the subbase effect, comprising the following steps:

[0005] S1. Group the riprap by particle size and obtain the initial mass fraction of each particle group to obtain the initial gradation information of the riprap.

[0006] S2. Based on the representative particle size and mass fraction of each particle size group, calculate the specific surface area index corresponding to the initial gradation, and use the specific surface area index as a variable to characterize the structural state of the particle population.

[0007] S3. Obtain the input energy parameters corresponding to the compaction conditions and calculate the input energy for riprap compaction.

[0008] S4. Compact the rockfill material N times, calculate the specific surface area index of the rockfill material after compaction, and construct a support function characterizing the particle group cushion layer effect based on the relationship between the specific surface area index of the rockfill material after compaction and the specific surface area index corresponding to the initial gradation.

[0009] S5. The effective energy after N compaction operations is reduced and corrected using the support function, thereby obtaining the effective crushing energy of each particle group after N compaction operations.

[0010] S6. Substitute the corrected effective crushing energy into the particle crushing probability model to obtain the crushing probability of each particle group under the current number of compaction passes.

[0011] S7. Based on the crushing probability of each particle group, combined with the distribution ratio of particle crushing products and the mass conservation relationship, calculate the remaining mass and supplementary mass of each particle group, update the mass fraction of each particle group, and obtain the gradation result under the next rolling pass.

[0012] S8. Repeat steps S4 to S7 until the target number of compaction passes is reached, and output the gradation evolution result of the rockfill material under the target number of compaction passes.

[0013] As a further improvement to the above scheme, in step S1, the rockfill is divided into multiple particle size groups according to a preset sieve aperture size or particle size range, and the initial mass fraction of each particle size group is calculated. And satisfy ,in, This represents the mass fraction of the i-th particle size group in the initial state.

[0014] As a further improvement to the above scheme, in step S2, the specific surface area index corresponding to the initial gradation satisfies the following condition: ,in, Specific surface area index. The mass fraction of the i-th particle size group. For the first The representative particle size of each particle size group This represents the total number of particle size groups.

[0015] Representative particle size Take the arithmetic mean of the upper and lower limits of the particle size group, that is: ,in, For the upper limit of the i-th particle size group, is the lower limit particle size of the i-th particle size group.

[0016] As a further improvement to the above scheme, in step S3, the input energy parameters include compaction equipment parameters and construction parameters. The compaction equipment parameters include the static weight, excitation force, vibration frequency, amplitude, travel speed, and roller width of the compaction equipment. The construction parameters include the layer thickness of the riprap.

[0017] The energy input for riprap compaction must meet the following requirements: ;

[0018] in, Input energy for compacting riprap. , These are the static weight and excitation force of the vibrating roller, respectively, in N; It is the vibration frequency of the vibratory roller, measured in Hz; , These are the amplitude and width of the roller, respectively, in cm; The thickness is measured in cm. This is the speed at which the vibratory roller travels, measured in cm / s. It is the phase angle difference;

[0019] The initial nominal crushing energy borne by particles of different size groups can be expressed as: ,

[0020] in, For the first The initial nominal crushing energy borne by particles in each size group.

[0021] As a further improvement to the above scheme, in step S4, the support function is: ,

[0022] in, The specific surface area index is the initial gradation. It is a structural index that reflects the degree to which finer gradation inhibits crushing. It is the surface area index.

[0023] As a further improvement to the above scheme, in step S5, the effective crushing energy of each particle group after N compaction operations is: .

[0024] As a further improvement to the above scheme, in step S6, the particle breakage probability model is as follows: ;

[0025] in, For the first The probability of a particle size group breaking after the Nth compaction. For the first Characteristic crushing energy of particles in each size group For Weibull modulus parameters;

[0026] Characteristic fragmentation energy The relationship between particle size and particle size is a power function: , Size effect constant, For material constants, For the first The representative particle size of each particle size group.

[0027] As a further improvement to the above scheme, in step S7, the remaining amount of particles in each particle size group after the Nth compaction satisfies the following condition: ;

[0028] At the same time, the particle size group also receives transferred mass from the crushed coarser particle size group. The mass increment obtained by each particle size group from the coarser particle size group is: ;

[0029] in, Let be the mass distribution coefficient of the j-th particle size group after crushing and being transferred to interval i.

[0030] , For fractal dimension, , These are the upper and lower limits of the particle size for the target interval (the i-th particle group), respectively.

[0031] As a further improvement to the above scheme, in step S7, the mass fraction of each particle size group after the N+1th compaction is: ,in, .

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] 1. This invention introduces the cushion layer effect generated by multi-particle mixing into the effective crushing energy correction process, no longer equating the externally input energy with the energy actually used for crushing, thus more reasonably describing the evolution law of the gradual weakening of crushing of riprap particles under high density, while avoiding overestimating the crushing rate of large particles, improving the accuracy of gradation prediction after riprap compaction, providing data support for the construction of the fill body, and facilitating the later construction operations of the fill body.

[0034] 2. This invention combines the particle breakage probability model, the breakage product distribution relationship and the mass conservation relationship to establish a set of gradation update methods for rockfill under continuous rolling. This method can dynamically predict the gradation under any number of rolling passes given the initial gradation and rolling energy.

[0035] 3. This invention can be used for the compaction process control of high earth-rock dams, pumped storage power station dams and other rockfill filling projects. It is particularly suitable for wide-gradient rockfill conditions commonly found in actual engineering projects. It can provide technical basis for optimizing the number of compaction passes, early warning of gradation exceeding limits, and evaluation of construction quality, and has good engineering application value. Attached Figure Description

[0036] Figure 1 A flowchart of a method for predicting the evolution of compaction and crushing gradation of rockfill considering the subbase effect, provided by the present invention;

[0037] Figure 2 A schematic diagram of the cushion layer effect mechanism in Embodiment 2 of the present invention;

[0038] Figure 3 The initial gradation curve obtained in Embodiment 2 of the present invention;

[0039] Figure 4 The measured and predicted grade comparison chart of the 26t roller compactor in Embodiment 2 of the present invention under 6, 8 and 10 compaction passes;

[0040] Figure 5 The measured and predicted grade comparison charts of the 32t roller in Embodiment 2 of the present invention under 6, 8 and 10 rolling passes. Detailed Implementation

[0041] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0042] Example 1:

[0043] Combination Figure 1 This embodiment of the method for predicting the evolution of compaction and crushing gradation of rockfill considering the cushion layer effect includes the following steps:

[0044] S1. Group the riprap by particle size and obtain the initial mass fraction of each particle group to obtain the initial gradation information of the riprap.

[0045] The rockfill material was divided into multiple particle size groups based on a preset sieve aperture size or particle size range, and the initial mass fraction of each particle size group was calculated. And satisfy ,in, This represents the mass fraction of the i-th particle size group in the initial state;

[0046] S2. Based on the representative particle size and mass fraction of each particle size group, calculate the specific surface area index corresponding to the initial gradation, and use the specific surface area index as a variable to characterize the structural state of the particle population.

[0047] The specific surface area index corresponding to the initial gradation satisfies the following condition: ,in, Specific surface area index. The mass fraction of the i-th particle size group. For the first The representative particle size of each particle size group This represents the total number of particle size groups.

[0048] Representative particle size Take the arithmetic mean of the upper and lower limits of the particle size group, that is: ,in, For the upper limit of the i-th particle size group, The lower limit particle size for the i-th particle size group;

[0049] S3. Obtain the input energy parameters corresponding to the compaction conditions and calculate the input energy for riprap compaction.

[0050] Input energy parameters include compaction equipment parameters and construction parameters. Compaction equipment parameters include the static weight, excitation force, vibration frequency, amplitude, travel speed, and roller width of the compaction equipment. Construction parameters include the thickness of the rockfill layer.

[0051] The energy input for riprap compaction must meet the following requirements: ;

[0052] in, Input energy for compacting riprap. , These are the static weight and excitation force of the vibrating roller, respectively, in N; It is the vibration frequency of the vibratory roller, measured in Hz; , These are the amplitude and width of the roller, respectively, in cm; The thickness is measured in cm. This is the speed at which the vibratory roller travels, measured in cm / s. It is the phase angle difference;

[0053] The initial nominal crushing energy borne by particles of different size groups can be expressed as: ,

[0054] in, For the first The initial nominal crushing energy borne by each particle size group;

[0055] S4. Compact the rockfill material N times, calculate the specific surface area index of the rockfill material after compaction, and construct a support function characterizing the particle group cushion layer effect based on the relationship between the specific surface area index of the rockfill material after compaction and the specific surface area index corresponding to the initial gradation.

[0056] The support function is: ,

[0057] in, The specific surface area index is the initial gradation. It is a structural index that reflects the degree to which finer gradation inhibits crushing. Specific surface area index;

[0058] S5. The effective energy after N compaction operations is reduced and corrected using the support function, thereby obtaining the effective crushing energy of each particle group after N compaction operations.

[0059] The effective crushing energy of each particle group after N compaction operations is: ;

[0060] S6. Substitute the corrected effective crushing energy into the particle crushing probability model to obtain the crushing probability of each particle group under the current number of compaction passes.

[0061] The particle breakage probability model is as follows: ;

[0062] in, For the first The probability of a particle size group breaking after the Nth compaction. For the first Characteristic crushing energy of particles in each size group For Weibull modulus parameters;

[0063] Characteristic fragmentation energy The relationship between particle size and particle size is a power function: , Size effect constant, For material constants, For the first Representative particle size of each particle size group;

[0064] S7. Based on the crushing probability of each particle group, combined with the distribution ratio of particle crushing products and the mass conservation relationship, calculate the remaining mass and supplementary mass of each particle group, update the mass fraction of each particle group, and obtain the gradation result under the next rolling pass.

[0065] The remaining amount of particles in each particle size group after the Nth compaction meets the following condition: ;

[0066] At the same time, the particle size group also receives transferred mass from the crushed coarser particle size group. The mass increment obtained by each particle size group from the coarser particle size group is: ;

[0067] in, Let be the mass distribution coefficient of the j-th particle size group (large particle size) after crushing and transferred to interval i.

[0068] , It is the fractal dimension;

[0069] The mass fraction of each particle size group after the (N+1)th compaction is as follows: ,in, ;

[0070] S8. Repeat steps S4 to S7 until the target number of compaction passes is reached, and output the gradation evolution result of the rockfill material under the target number of compaction passes.

[0071] Example 2:

[0072] This study focuses on the rockfill embankment construction project for a pumped storage power station dam in Gansu Province. Both indoor and field tests used the same material source, with the lithology of the embankment being biotite plagioclase gneiss (Gn1). This material source exhibits typical characteristics of dam rockfill and can be used for engineering verification of the compaction process of wide-graded rockfill.

[0073] (1) Preparation stage

[0074] Before the formal compaction test, the fill material to be used was sampled and analyzed, and a sieve analysis was conducted on the material before compaction to obtain the initial gradation curve and the initial mass fraction of each particle size group. ,like Figure 3 As shown.

[0075] Let the upper limit particle size of the i-th particle size group be . The lower limit particle size is The representative particle size of the particle size group is determined by the arithmetic mean of the upper and lower limits of the interval, i.e.: ;

[0076] Before the gradation iteration begins, based on the initial mass fraction and the representative particle size of each particle size group Calculate the initial specific surface area index It serves as a characterization of the initial structural state of a particle population. Its expression is: ;

[0077] in, The number of particle size groups is n=10 in this embodiment.

[0078] The on-site compaction equipment selected were two types of self-propelled vibratory rollers: 26t and 32t, namely the XS265 and YZ32SC models, respectively. According to the following formula, the energy density of the XS265 was calculated to be 23882J / m3, and the corresponding energy density of the YZ32SC was 47848J / m3.

[0079] The energy input for riprap compaction is: ;

[0080] In the formula, W and F0 are the static weight and excitation force of the vibratory roller, respectively, in N; f is the vibration frequency of the vibratory roller, in Hz; A and B are the amplitude and width of the roller, respectively, in cm; H is the compaction thickness, in cm; and v is the speed of the vibratory roller, in cm / s. It is the phase angle difference.

[0081] The required model parameters are mainly obtained through the following two parts: indoor single-particle crushing test and single field compaction test.

[0082] Indoor single-particle crushing tests determined the Weibull modulus, material constant, and size effect constant. The fractal dimension was determined by analyzing the sieving results of the crushed products. These parameters served as the foundational inputs for subsequent on-site gradation evolution prediction calculations. The final model parameters obtained through indoor experiments included a Weibull modulus m=1.5, a material constant n=4.4, a fractal dimension D=2.1, and a size effect constant. =0.15.

[0083] Before predicting the gradation of the quarry, a single on-site compaction test was conducted. The structural parameters in the breakage probability formula were analyzed using inversion analysis. Calibration was performed. The measured values ​​of the mass content of particles smaller than 5mm in diameter after 6 passes of compaction for both the XS265 and YZ32SC vibratory rollers were selected as the calibration benchmarks. The parameters for the XS265 model were then obtained. Specifications of the YZ32SC model Complete the parameter preparation before the formal compaction.

[0084] (2) Cyclic Phase

[0085] Starting with the initial gradation, perform the following iterative calculations in successive iterations according to the target number of iterations:

[0086] Under any number of compaction passes N, based on the current gradation Calculate the current surface area index , ;

[0087] Based on this, a support function is constructed, the expression of which is:

[0088] In the formula, The specific surface area index is the initial gradation. It is a structural index that reflects the degree to which finer gradation inhibits breakage.

[0089] For any particle size group Since the equipment parameters are fixed before compaction and will not be changed, the energy input for each compaction pass is also fixed. Specifically, the XS265 provides 23882 J / m³ per pass, and the YZ32SC provides 47848 J / m³ per pass. The effective crushing energy for different particle size groups can be expressed as: ; For the first Representative particle size of each particle size group;

[0090] By using a support function to reduce and correct the input energy under the current compaction conditions, the effective crushing energy of particles in each size group under the current number of compaction passes is obtained, and its expression is:

[0091] Simultaneously, the characteristic crushing energy of each particle size group was calculated: ;

[0092] In the formula, m=1.5, n=4.4, and the size effect constant. =0.15.

[0093] Subsequently, the effective crushing energy is substituted into the particle crushing probability model to calculate the crushing probability of particles of each size group under the current number of compaction passes: ;

[0094] Based on the fractal distribution relationship and mass conservation relationship of the crushed products, calculate the remaining mass of each particle size group and the mass increment transferred from the coarser particle size group to the current particle size group.

[0095] For the i-th particle size group, the mass retained in the original particle size group after this pass of compaction is: ;

[0096] in, Let represent the remaining crushed mass of the i-th particle size group during the Nth compaction process.

[0097] At the same time, this particle size group will also receive transferred mass from the coarser particle size group after crushing. The mass increment obtained by the i-th particle size group from the coarser particle size group is: ;

[0098] Preferably, the allocation relationship is described using a fractal distribution; wherein Let be the mass distribution coefficient of the j-th particle size group (large particle size) after crushing and being transferred to interval i.

[0099] in, ;

[0100] , These are the upper and lower limits of the particle size for the target interval (the i-th particle group), respectively.

[0101] represents the upper limit of the original particle size range. Here, it is assumed that after the j-th group of particles breaks, the maximum fragment size produced will not exceed the original maximum particle size of that group. D is the fractal dimension.

[0102] The mass fraction of each particle size group is updated according to the mass conservation law to obtain the gradation result for the next compaction pass. The mass fraction of the i-th particle size group after the (N+1)-th compaction pass is: ;

[0103] And satisfy: ;

[0104] After completing the above calculations, the mass fraction of each particle size group will be updated to obtain the gradation after the (N+1)th compaction. Repeat this process until the target number of passes is reached.

[0105] (3) Output stage

[0106] like Figure 4 and Figure 5 As shown, the target number of compaction passes is 6, 8, and 10. After the target number of compaction passes is reached, the final mass fraction of each particle group and the corresponding gradation curve are output.

[0107] like Figure 4 In section a, the measured and predicted grade pairings are compared under 6 passes of a 26t roller compactor.

[0108] like Figure 4 Figure b shows the comparison between the measured and predicted grade pairs under 8 passes of a 26t roller compactor.

[0109] like Figure 4 The figure in c is a comparison of the measured and predicted grade pairs under 10 passes of a 26t roller compactor.

[0110] like Figure 5 In section a, the measured and predicted grade pairings are compared under 6 passes of a 32t roller compactor.

[0111] like Figure 5 Figure b shows the comparison between the measured and predicted grade pairs under 8 passes of a 32t roller compactor.

[0112] like Figure 5 The figure in c is a comparison of the measured and predicted grade pairings under 10 passes of a 32t roller compactor.

[0113] Under the condition of 26t vibratory rolling (XS265), after calibration with 6 rolling passes, the model has high prediction accuracy in the small particle size range. Under the conditions of 8 and 10 rolling passes, the model has small prediction error for the 5 mm particle size content. For the medium and large particle size range, although there are some deviations in some particle size groups, the overall changes are consistent with the measured results, indicating that the model can describe the crushing and migration process of medium and large particle sizes well. The predicted values ​​are generally in good agreement with the measured values ​​on site, and can accurately reflect the crushing law of rockfill under different rolling passes.

[0114] Under the condition of 32t vibratory rolling (YZ32SC), the predicted results and the field measured results showed high consistency under the rolling conditions of 6, 8 and 10 passes. Although the predicted values ​​in the medium and fine particle size range were slightly higher than the measured values, they could still reflect the gradation refinement trend caused by the increase of excitation force. As the number of rolling passes increased from 6 to 10, the measured <5mm content increased from 24.29% to 26.32%, and the predicted value increased from 24.48% to 26.48%, which accurately reflected the law of continuous generation of fine particles under high energy input conditions.

[0115] In the initial prediction stage, this method first obtains the initial gradation information of the rockfill to be tested, determines the initial mass of each particle size group according to the design particle size range, and calibrates the model parameters through indoor single-particle crushing tests and field tests. Then, it calculates the initial specific surface area index corresponding to the initial gradation, and finally calculates the input energy of a single compaction based on the compaction equipment parameters.

[0116] In the iterative prediction process, based on the cushion layer effect mechanism, such as Figure 2 As shown, as the number of compaction passes increases, a large number of fine gravel particles are generated around the large particles. These fine particles form a cushion layer effect around the large particles, causing the external compaction energy to be dissipated during transmission, which reduces the effective stress on the large particles.

[0117] At the beginning of each cycle, the dynamic specific surface area index is calculated based on the latest gradation data. Then, a support function reflecting the current particle population structure is constructed. This support function is used to reduce and correct the basic input energy to obtain the effective crushing energy actually allocated to each particle size group. The characteristic crushing energy and effective crushing energy of each group are substituted into the probability model to calculate the crushing probability of each particle size group in the current cycle. Finally, based on the mass transfer ratio allocated to each small particle size group after the large particles are crushed and the principle of mass conservation, the increase or decrease of the mass fraction of each particle size group is summarized to obtain the new gradation data.

[0118] After each gradation update, it is determined whether the current number of rolling passes has reached the target number of rolling passes. If the target number of rolling passes has not been reached, the iterative calculation is restarted. If the target number of rolling passes has been reached, the calculation is terminated and the gradation data is output.

[0119] This invention incorporates the cushion layer effect generated by multi-particle mixing into the effective crushing energy correction process, no longer equating externally input energy with the actual energy used for particle crushing. This more rationally describes the evolution law of the gradual weakening of crushing of riprap particles under high-density conditions, while avoiding overestimation of the crushing rate of large particles. This improves the prediction accuracy of the gradation of riprap after compaction, providing data support for the construction of the fill structure and facilitating subsequent construction operations. By combining the particle crushing probability model, the crushing product distribution relationship, and the mass conservation relationship, a set of gradation update methods for riprap under continuous compaction is established. This method can dynamically predict the gradation under any number of compaction passes given the initial gradation and compaction energy. It can be used for the compaction construction process control of high earth-rock dams, pumped storage power station dams, and other riprap filling projects. It is particularly suitable for the wide-gradation riprap conditions commonly encountered in actual engineering, providing technical basis for compaction pass optimization, gradation over-limit early warning, and construction quality evaluation, and has good engineering application value.

[0120] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A method for predicting the evolution of compaction and crushing gradation of rockfill considering the subbase effect, characterized in that, Includes the following steps: S1. Group the riprap by particle size and obtain the initial mass fraction of each particle group to obtain the initial gradation information of the riprap. S2. Based on the representative particle size and mass fraction of each particle size group, calculate the specific surface area index corresponding to the initial gradation, and use the specific surface area index as a variable to characterize the structural state of the particle population. S3. Obtain the input energy parameters corresponding to the compaction conditions and calculate the input energy for riprap compaction. S4. Compact the rockfill material N times, calculate the specific surface area index of the rockfill material after compaction, and construct a support function characterizing the particle group cushion layer effect based on the relationship between the specific surface area index of the rockfill material after compaction and the specific surface area index corresponding to the initial gradation. S5. The effective energy after N compaction operations is reduced and corrected using the support function, thereby obtaining the effective crushing energy of each particle group after N compaction operations. S6. Substitute the corrected effective crushing energy into the particle crushing probability model to obtain the crushing probability of each particle group under the current number of compaction passes. S7. Based on the crushing probability of each particle group, combined with the distribution ratio of particle crushing products and the mass conservation relationship, calculate the remaining mass and supplementary mass of each particle group, update the mass fraction of each particle group, and obtain the gradation result under the next rolling pass. S8. Repeat steps S4 to S7 until the target number of compaction passes is reached, and output the gradation evolution result of the rockfill material under the target number of compaction passes.

2. The method for predicting the evolution of compaction and crushing gradation of rockfill considering the cushion layer effect as described in claim 1, characterized in that, In step S1, the rockfill is divided into multiple particle size groups according to a preset sieve aperture size or particle size range, and the initial mass fraction of each particle size group is calculated. And satisfy ,in, This represents the mass fraction of the i-th particle size group in the initial state.

3. The method for predicting the evolution of compaction and crushing gradation of riprap considering the subbase effect as described in claim 1, characterized in that, In step S2, the specific surface area index corresponding to the initial gradation satisfies the following condition: ,in, Specific surface area index. The mass fraction of the i-th particle size group. For the first The representative particle size of each particle size group This represents the total number of particle size groups. Representative particle size Take the arithmetic mean of the upper and lower limits of the particle size group, that is: ,in, For the upper limit of the i-th particle size group, is the lower limit particle size of the i-th particle size group.

4. The method for predicting the evolution of compaction and crushing gradation of rockfill considering the cushion layer effect as described in claim 1, characterized in that, In step S3, the input energy parameters include compaction equipment parameters and construction parameters. The compaction equipment parameters include the static weight, excitation force, vibration frequency, amplitude, travel speed, and roller width of the compaction equipment. The construction parameters include the layer thickness of the riprap. The energy input for riprap compaction must meet the following requirements: ; in, Input energy for compacting riprap. , These are the static weight and excitation force of the vibrating roller, respectively, in N; It is the vibration frequency of the vibratory roller, measured in Hz; , These are the amplitude and width of the roller, respectively, in cm; The thickness is measured in cm. This is the speed at which the vibratory roller travels, measured in cm / s. It is the phase angle difference; The initial nominal crushing energy borne by particles of different size groups can be expressed as: , in, For the first The initial nominal crushing energy borne by particles in each size group.

5. The method for predicting the evolution of compaction and crushing gradation of rockfill considering the cushion layer effect as described in claim 1, characterized in that, In step S4, the support function is: , in, The specific surface area index is the initial gradation. It is a structural index that reflects the degree to which finer gradation inhibits crushing. It is the surface area index.

6. The method for predicting the evolution of compaction and crushing gradation of riprap considering the cushion layer effect as described in claim 1, characterized in that, In step S5, the effective crushing energy of each particle group after N compaction operations is: ; Among them, E eff Let E be the effective crushing energy of the i-th particle size group after the Nth compaction pass, E be the initial nominal crushing energy borne by the i-th particle size group, Ω(N) be the support function corresponding to the Nth compaction pass, and I be the effective crushing energy of the i-th particle size group after the Nth compaction pass. s (N) is the specific surface area index after the Nth compaction, I s (0) is the specific surface area index corresponding to the initial gradation, and β is the structure index.

7. The method for predicting the evolution of compaction and crushing gradation of rockfill considering the cushion layer effect as described in claim 1, characterized in that, In step S6, the particle breakage probability model is as follows: ; in, For the first The probability of a particle size group breaking after the Nth compaction. For the first Characteristic crushing energy of particles in each size group For Weibull modulus parameters; Characteristic fragmentation energy The relationship between particle size and particle size is a power function: , Size effect constant, For material constants, For the first The representative particle size of each particle size group.

8. The method for predicting the evolution of compaction and crushing gradation of rockfill considering the cushion layer effect as described in claim 1, characterized in that, In step S7, the remaining amount of particles in each particle size group after the Nth compaction meets the following condition: ; in For the Nth pass, P represents the original content of this particle size range. i (N) is an existing breakage probability model, representing the portion lost due to breakage in this particle size range; At the same time, the particle size group also receives transferred mass from the crushed coarser particle size group. The mass increment obtained by each particle size group from the coarser particle size group is: ; in, Let be the mass distribution coefficient of the j-th particle size group after crushing and being transferred to interval i. and , For fractal dimension, , These are the upper and lower limits of the particle size of the i-th particle group in the target interval, respectively.

9. The method for predicting the evolution of compaction and crushing gradation of riprap considering the cushion layer effect as described in claim 1, characterized in that, In step S7, the mass fraction of each particle size group after the (N+1)th compaction is: ,in, .

Images