A compactor wheel vibration control method and system considering the load transmission mechanism of the meso-skeleton

Abstract

The application discloses a compaction wheel vibration control method and system considering a mesoscopic skeleton load transmission mechanism, and the method comprises the following steps: a fitting model between aggregate movement parameters and mesoscopic structure parameters is constructed based on indoor tests; initial vibration parameters of a compaction wheel are determined based on a test road section at a construction site; an actual compaction process is monitored, the initial vibration parameters are input, the aggregate movement parameters are acquired in real time, the measured values of the mesoscopic structure parameters of the asphalt mixture are solved according to the fitting model, and the compaction degree of the pavement is measured; whether the compaction is up to the standard is judged, and if not, the vibration parameters of the compaction wheel are adjusted in real time. The fitting model reflecting the aggregate skeleton load transmission mechanism is constructed, the measured values of the mesoscopic structure parameters are solved based on the model, and then whether the compaction is up to the standard is judged, so that the vibration parameters of the compaction wheel are quickly and accurately updated, the controllability and uniformity of the compaction quality of the asphalt pavement can be effectively improved, and the application value and popularization prospect in engineering are remarkable.

Description

Technical Field

[0001] This invention belongs to the field of asphalt pavement compaction technology, and particularly relates to a compaction wheel vibration control method and system that considers the microscopic skeleton load transfer mechanism. Background Technology

[0002] In asphalt pavement construction, compaction is crucial for ensuring the pavement's structural integrity and service life. Compaction quality not only affects the pavement's density and smoothness but also directly determines the interlayer bonding and overall structural load-bearing capacity. Currently, vibratory rollers are commonly used in engineering practice to compact asphalt mixtures. Vibratory rollers input energy into the pavement material through the periodic vibration of the compaction drum, promoting the rearrangement of mixture particles and the formation of the structural framework, thereby achieving the designed compaction level.

[0003] Existing vibration control methods for compaction rollers mainly include those based on manual experience in setting parameters and those based on accelerometer signal feedback. The former relies on operators selecting vibration frequency and amplitude based on their construction experience, which can easily lead to insufficient or excessive compaction energy input, making the compaction effect highly susceptible to human factors. While the latter introduces accelerometers to monitor the vibration state of the compaction roller, its control logic is primarily based on the overall vibration response signal of the compaction roller, failing to fully reflect the actual mechanical interaction between the compaction roller and the mixture. This results in low accuracy in identifying the compaction state and significant vibration control errors.

[0004] In actual construction, the compaction characteristics and skeleton formation patterns of asphalt mixtures vary significantly under different pavement thicknesses, material moduli, and temperature conditions. Ideal compaction requires the compaction roller to operate at appropriate amplitude and frequency to ensure uniform compaction and a stable skeleton structure. However, existing vibratory rollers typically use fixed vibration parameters or preset speeds for control, making it impossible to dynamically adjust the compaction roller amplitude and frequency according to the real-time compaction status at the construction site. This can easily lead to localized over- or under-vibration, resulting in quality problems such as asphalt mixture segregation or uneven compaction.

[0005] Therefore, there is an urgent need for a technical solution that can monitor the compaction process in real time and dynamically adjust the vibration parameters of the compaction wheel based on the load transfer mechanism of the asphalt mixture microstructure, so as to achieve precise control of the asphalt mixture compaction process and quantifiable judgment of the forming quality, thereby breaking through the technical bottlenecks of existing compaction control that rely on experience, have lag response, and lack sufficient control precision. Summary of the Invention

[0006] The main objective of this invention is to provide a compaction wheel vibration control method and system that considers the load transfer mechanism of the microstructure. This method can fully consider the influence of the microstructure parameters of asphalt mixture on the vibration parameters of the compaction wheel, and realize the intelligent adjustment of the vibration frequency and amplitude parameters of the compaction wheel. This enables the compaction wheel vibration control to shift from experience-guided to mechanically determined, thereby improving the compaction quality of asphalt pavement and the level of intelligent construction.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] Firstly, a vibration control method for compaction wheels considering the load transfer mechanism of the microstructure skeleton is provided, including the following steps:

[0009] Step S1: Conduct indoor compaction tests on asphalt mixtures, collect aggregate motion parameters under different compaction degrees, and construct a fitting model F1 between compaction degree and aggregate motion parameters; perform non-destructive scanning on the compacted specimens obtained from the tests, extract aggregate microstructure parameters, and construct a fitting model F2 between compaction degree and aggregate microstructure parameters. Simultaneously, determine the values ​​of the target microstructure parameters based on the target compaction degree; based on fitting models F1 and F2, derive a fitting model F3 between aggregate motion parameters and microstructure parameters; the motion parameters include acceleration and rotation angle, and the microstructure parameters include average coordination number and partial texture.

[0010] Step S2: Select a test section at the construction site, set different sets of vibration parameters at different locations to carry out on-site compaction tests, and keep the vibration parameters at the same location constant during the compaction process. Select the set of vibration parameters that requires the fewest compaction passes to achieve the target microstructure parameters from all test results, and use it as the initial vibration parameters of the compaction wheel; the vibration parameters include vibration frequency and amplitude.

[0011] Step S3: Conduct a monitoring test on the compaction process of asphalt mixture at the construction site. Use the initial vibration parameters obtained in step S2 as the vibration parameters during the first pass of the compaction wheel. Obtain the monitoring values ​​of the motion parameters of the aggregate after each pass of compaction in real time. Substitute the monitoring values ​​of the motion parameters of the aggregate into the fitting model F3 obtained in step S1 to obtain the measured values ​​of the microstructure parameters of the aggregate.

[0012] Step S4: Determine whether the measured values ​​of the microstructure parameters have reached the target microstructure parameters. If they have, further determine whether the measured compaction degree of the asphalt mixture has reached the target compaction degree and whether the rate of change of the microstructure parameters per pass is lower than the threshold. If so, the compaction is up to standard. Otherwise, adjust the vibration parameters of the compaction wheel and continue the next pass of compaction according to step S3 until the compaction is up to standard.

[0013] Furthermore, in step S1, the indoor compaction test of the asphalt mixture specifically includes the following steps:

[0014] Asphalt mixture specimens with different compaction degrees were molded under laboratory conditions according to the construction mix proportion. The selected compaction degree covered the key compaction stages in the construction process to simulate the complete process from initial compaction to final compaction. The compaction degree ranged from 86% to 98%, and the number of values ​​was no less than 4.

[0015] Before the compacted specimen is formed, smart particles are embedded in the middle of the compacted specimen. The smart particles are packaged into an aggregate form, and their size and hardness are similar to those of real aggregates. They are equipped with a triaxial accelerometer and a triaxial gyroscope, which are used to test the acceleration and rotation angle of the aggregates, respectively. The smart particles also have a wireless transmission sensing unit, and their sampling frequency is set according to the vibration frequency characteristics of the compaction process and the dynamic evolution characteristics of the micro-skeleton structure.

[0016] During the specimen compaction process, the acceleration and rotation angle data of the smart particles were collected to characterize the acceleration and rotation angle of the aggregate. Based on the changing trends of the aggregate acceleration and rotation angle under different compaction degrees, a fitting model F1 between the compaction degree and the aggregate motion parameters was established.

[0017] Furthermore, in step S1, indoor compaction tests revealed a good linear relationship between the compaction degree of the asphalt mixture and the aggregate acceleration, and a good exponential function relationship between the compaction degree of the asphalt mixture and the rotation angle. The fitting model F1 between the compaction degree of the asphalt mixture and the aggregate motion parameters was determined according to equations (1.1)-(1.2):

[0018] (1.1);

[0019] (1.2);

[0020] In the formula, K is the compaction degree of the asphalt mixture; k1, k2, c1, c2, and c3 are fitting parameters; a is the acceleration of the aggregate; and θ is the rotation angle of the aggregate.

[0021] Furthermore, in step S1, after performing non-destructive scanning on the compacted specimens and extracting the aggregate microstructure parameters, it was found that the compaction degree of the asphalt mixture had a good linear relationship with both the average coordination number and the partial texture; the fitting model F2 between the compaction degree of the asphalt mixture and the aggregate microstructure parameters was determined according to equations (1.3)-(1.4):

[0022] (1.3);

[0023] (1.4);

[0024] In the formula, α1, α2, β1, and β2 are fitting parameters; Z is the average coordination number of the aggregate; and D is the partial structure of the aggregate.

[0025] Furthermore, in step S1, based on the established fitting model F1 between compaction degree and kinematic parameters and the fitting model F2 between compaction degree and microstructure parameters, by using compaction degree as an intermediate variable, a fitting model F3 between aggregate kinematic parameters and aggregate microstructure parameters can be constructed as shown in equations (1.5)-(1.8):

[0026] (1.5);

[0027] (1.6);

[0028] (1.7);

[0029] (1.8);

[0030] In the formula, η1, η2, δ1, δ2, γ1, γ2, γ3, φ1, φ2, and φ3 are fitting parameters.

[0031] Furthermore, in step S1, when the compaction degree of the asphalt mixture reaches the target compaction degree (generally 98%), the microstructural parameters corresponding to the compacted specimen are considered as the target microstructural parameters. The range of target microstructural parameters is based on the statistical results of parameters from multiple compacted specimens at 98% compaction degree, and the mean ± standard deviation is used as the allowable range of values ​​for the target microstructural parameters. Therefore, based on the target compaction degree of 98%, the target microstructural parameters can be determined according to equations (1.9)-(1.10):

[0032] (1.9);

[0033] (1.10);

[0034] In the formula, Z 目标 The value of the target average coordination number of the aggregate; D 目标 The value of Z represents the target partial structure of the aggregate; 98% This represents the average distribution number of aggregates when the compaction degree is 98% in the indoor compaction test; D represents the standard deviation of the average coordination number of aggregates in the indoor compaction test. 98% This refers to the partial structure of aggregates when the compaction degree is 98% in an indoor compaction test; The standard deviation of aggregate texture in the indoor compaction test; the coefficient of variation C of the average coordination number Z and aggregate texture D in the indoor compaction test. v ≤0.1.

[0035] Furthermore, in step S2, the on-site compaction test is carried out according to the following method:

[0036] Before formal construction, a test section consistent with the actual engineering conditions is selected at the construction site. Smart particles are laid out at test points at preset intervals within the test section. Different sets of vibration parameters are obtained according to the orthogonal principle. On-site compaction tests are conducted at different test points according to different sets of vibration parameters. The vibration frequency range is 20-60Hz, covering the low-frequency to high-frequency range, and the number of values ​​is no less than 5. The amplitude range is 0.2-2.0mm, covering the small amplitude to large amplitude range, and the number of values ​​is no less than 5.

[0037] Furthermore, in step S3, the monitoring test includes the following steps:

[0038] Several smart particles are pre-laid in the middle of the paved asphalt mixture so that the smart particles can be located in the representative structural area below the typical load transfer path; the smart particles are laid every 30-100m along the length of the road to adapt to different construction conditions and monitoring accuracy requirements, with a preferred value of 50m, and are laid along the width direction between the center line of each lane and the wheel track.

[0039] During the compaction process, after each pass of the compaction wheel, the intelligent particles collect the motion parameter monitoring values ​​recorded during the current pass of compaction based on the pressure change triggering mechanism.

[0040] Furthermore, in step S4, the rate of change of the aggregate microstructure parameters with each pass is calculated according to equations (4.1)-(4.2):

[0041] (4.1);

[0042] (4.2);

[0043] In the formula, and These are the average coordination number and the rate of change of partial texture of the aggregate during the i-th compaction pass, respectively; and These represent the average coordination number and partial texture of the aggregate during the i-th compaction pass, respectively; Z i-1 and D i-1 The compaction of the first i - The average coordination number and partial structure of the aggregate at the first pass; the threshold for the rate of change per pass is usually taken as 2%.

[0044] Furthermore, in step S4, the vibration parameters of the compaction wheel are adjusted in real time as follows:

[0045] like This indicates that the asphalt mixture skeleton structure is loose at this time. Therefore, the vibration frequency and amplitude should be increased during the next compaction to input more energy and promote particle movement.

[0046] like This indicates that the asphalt mixture skeleton has been initially formed but the orientation is not good. Therefore, the vibration frequency should be increased during the next compaction to optimize the particle orientation using the resonance principle.

[0047] like This indicates that the asphalt mixture skeleton has initially formed the desired arrangement orientation, but the number of effective contact points between particles is insufficient. Therefore, the amplitude should be increased during the next compaction to promote more effective contact between particles.

[0048] The vibration frequency during the next compaction pass is determined according to formulas (4.3)-(4.4):

[0049] (4.3);

[0050] (4.4);

[0051] Among them, f i f i+1 The vibration frequency during the i-th and i+1th compaction passes; This is the adjustment amount for the vibration frequency; This is the preset adjustment coefficient for the vibration frequency; , Let be the weighting coefficient, satisfying This is to demonstrate the dominant influence of vibration frequency on the off-center configuration.

[0052] The amplitude during the next compaction pass shall be determined according to formulas (4.5)-(4.6):

[0053] (4.5);

[0054] (4.6);

[0055] Among them, A i A i+1 The amplitude of the compaction at the i-th and i+1-th passes; This is the adjustment amount for the amplitude; This is the preset adjustment coefficient for the amplitude; , Let be the weighting coefficient, satisfying This is to reflect the dominant influence of amplitude on the mean coordination number.

[0056] Secondly, a compaction wheel vibration control system considering the microscopic skeleton load transfer mechanism is provided, including:

[0057] The smart particles, embedded in the asphalt mixture, are shaped to match the aggregate and have built-in triaxial accelerometers and triaxial gyroscopes to monitor the motion parameters of the aggregate, including acceleration and rotation angle.

[0058] The data acquisition module is used to collect the motion parameters of smart particles in real time during the actual compaction of asphalt mixtures, so as to characterize the motion parameter monitoring values ​​of aggregates;

[0059] The data processing module is used to substitute the monitored values ​​of the aggregate's motion parameters into the fitting model F3 to solve for the measured values ​​of the aggregate's microstructure parameters. Then, it determines whether the target microstructure parameters, the target compaction degree, and the threshold of the rate of change of microstructure parameters per pass have been reached. If so, it outputs that the compaction meets the standard; otherwise, it calculates and outputs the vibration parameters for the next pass of compaction. The microstructure parameters include the average coordination number and the partial texture, and the vibration parameters include the vibration frequency and amplitude.

[0060] The interactive module is used to input the initial vibration parameters of the compaction wheel and the measured compaction degree of the asphalt mixture during the actual compaction process. It is used to display whether the target microstructure parameters have been reached, whether the target compaction degree has been reached, whether it is lower than the threshold of the rate of change of microstructure parameters per pass, and whether the compaction meets the standards.

[0061] The data transmission module is used to transmit the vibration parameters for the next compaction cycle to the vibration controller of the compaction wheel.

[0062] The beneficial effects achieved by this invention are as follows:

[0063] (1) This invention introduces intelligent particles during the compaction process to obtain aggregate motion parameters in real time. The average coordination number and partial structure are used as key indicators to characterize the microstructure of the asphalt mixture. These indicators can directly reflect the contact relationship between aggregates, the stability of the skeleton and its load transfer state. Thus, the vibration control of the compaction wheel is based on the real mechanical response of the mixture. This overcomes the problem that traditional methods rely only on macroscopic indicators such as compaction degree and density and are difficult to reflect the load transfer mechanism of the skeleton. It realizes the transformation of compaction control from empirical parameter adjustment to scientific control based on micromechanical judgment, improves the accuracy of vibration parameter control and can quickly achieve the target compaction degree.

[0064] (2) Based on the deviation between the microstructure parameters obtained in real time and the target microstructure parameters, the present invention adaptively calculates the adjustment amount of vibration frequency and amplitude, and updates the vibration parameters of the compaction wheel before the next rolling starts, so that the vibration parameters can be dynamically optimized with the evolution of the skeleton, thereby effectively avoiding over-compaction, under-compaction and local segregation, and improving compaction uniformity and molding quality.

[0065] (3) The method and system adopted in this invention can be implemented on the basis of existing vibratory rollers without complex modifications to the compaction equipment. It can be implemented simply through vibration parameter control and monitoring algorithms, and has good engineering adaptability. The smart particles used can be recycled and reused after construction by core sampling, which greatly reduces the sensing cost of a single construction and realizes a leap in compaction quality, and has broad engineering application prospects. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating a compaction wheel vibration control method that considers the microscopic skeleton load transfer mechanism according to the present invention.

[0067] Figure 2 This is a schematic diagram of the structure of a compaction wheel vibration control system that considers the microscopic skeleton load transfer mechanism according to the present invention;

[0068] Figure 3 This is a graph showing the relationship between the compaction degree of the asphalt mixture and the acceleration of the aggregate in this invention.

[0069] Figure 4 This is a graph showing the relationship between the compaction degree of the asphalt mixture and the rotation angle of the aggregate according to the present invention.

[0070] Figure 5 This is a graph showing the relationship between the compaction degree of the asphalt mixture and the average aggregate distribution number in this invention.

[0071] Figure 6 This is a diagram showing the relationship between the compaction degree of the asphalt mixture and the aggregate texture in this invention.

[0072] Where n represents the fitted function value, i.e., the compaction degree K of the asphalt mixture; R 2 This represents the correlation coefficient.

[0073] In the diagram, 1001 is the data acquisition module; 1002 is the data processing module; 1003 is the data transmission module; 1004 is the interaction module; 1005 is the smart particle; and 1006 is the vibration controller. Detailed Implementation

[0074] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0075] Based on the micro-stress mechanism and compaction process evolution law of asphalt mixtures, this invention uses the average coordination number Z and partial texture D of aggregates as the criteria for the micro-skeleton structure state of asphalt mixtures.

[0076] The average coordination number Z is a physical quantity that describes the average number of contact points between each aggregate particle and its adjacent aggregate particles. It is a key indicator for measuring the load-bearing capacity and stability of the skeleton. The calculation formula is shown in the following formula (1).

[0077] (1);

[0078] In the formula, N is the total number of aggregates; Z j Let be the coordination number of the j-th aggregate.

[0079] During compaction, as aggregate particles rearrange, crush, and interlock, the average coordination number gradually increases. This means there are more effective contact points between aggregates, resulting in a more stable force chain network, which macroscopically manifests as a stronger load-bearing capacity of the mixture. Therefore, the average coordination number is used as a core indicator for judging whether a skeleton has formed and its stability during compaction.

[0080] The partial structure D is used to characterize the orientation and anisotropy of the aggregate skeleton, and is an important microscopic parameter reflecting the particle orientation structure. The principal inertial axis direction vector of each aggregate particle is extracted using industrial CT imaging and image processing algorithms, and a second-order orientation tensor is constructed based on the principal inertial axis direction vector. Eigenvalue decomposition of the second-order orientation tensor yields three eigenvalues ​​λ1, λ2, and λ3 (satisfying...). The calculation formula for the partial structure is shown in equation (2) below:

[0081] (2);

[0082] During vibration compaction, aggregate particles are not randomly arranged, but rather develop a dominant orientation under the influence of vibration. An ideal microscopic load-transfer skeleton requires that the orientation of the aggregate particles facilitates efficient load transfer along the compaction depth. The magnitude of the partial structure directly characterizes the strength of this oriented order; therefore, the partial structure is used as a basis for judging whether the skeleton orientation is reasonable and whether it possesses an effective force chain during compaction.

[0083] The average coordination number Z and the partial structure D comprehensively reflect the microstructure of the aggregate from two complementary dimensions. Compared with the existing technology, which only controls compaction vibration based on macroscopic or single indicators such as compaction degree and acceleration, this invention can dynamically adjust the vibration parameters of the compaction wheel based on the evolution law of the true morphology of the aggregate microstructure. This not only identifies whether the aggregate microstructure has been formed, but also whether its structure is reasonable, effectively avoiding under-compaction and over-compaction of asphalt mixture, thereby significantly improving the compaction quality of asphalt mixture and the service performance of the pavement.

[0084] Example 1;

[0085] like Figure 1 As shown, this embodiment of the invention provides a method for controlling the vibration of a compaction wheel that considers the load transfer mechanism of a microstructure, specifically including the following steps:

[0086] Step S1: Conduct indoor compaction tests on asphalt mixtures, collect aggregate motion parameters under different compaction degrees, and construct a fitting model F1 between compaction degree and aggregate motion parameters; perform non-destructive scanning on the compacted specimens obtained from the tests to extract aggregate microstructure parameters, construct a fitting model F2 between compaction degree and aggregate microstructure parameters, and determine the values ​​of target microstructure parameters based on the target compaction degree; based on fitting models F1 and F2, derive a fitting model F3 between aggregate motion parameters and microstructure parameters; the motion parameters include acceleration and rotation angle, and the microstructure parameters include average coordination number and partial texture.

[0087] Rotary compaction specimens of asphalt mixtures with compaction degrees of 86%, 89%, 92%, 95%, and 98% were prepared under laboratory conditions according to the construction mix proportion (such as SMA-13 ​​type gradation). The compaction degree covered the key compaction stages in the construction process to simulate the complete process from initial compaction to final compaction.

[0088] Before the compacted specimen is formed, smart particles are embedded in the center of the specimen. These smart particles are encapsulated in an aggregate form, with dimensions and hardness similar to real aggregate. They incorporate a triaxial accelerometer and a triaxial gyroscope to measure the aggregate's acceleration and rotation angle, respectively. The smart particles also have a wireless transmitting sensor unit. The sampling frequency is set based on the vibration frequency characteristics of the compaction process and the dynamic evolution of the microstructure, preferably 500-1000Hz, to ensure accurate capture of key motion features.

[0089] During the specimen compaction process, the acceleration and rotation angle data of the smart particles were simultaneously collected to characterize the aggregate's acceleration and rotation angle. A scatter plot analysis was then performed based on the actual compaction degree of the compacted specimen. The results, based on the changing trends of the aggregate motion parameters under different compaction degrees, are as follows: Figure 3 and Figure 4 As shown, a good linear relationship was found between the compaction degree of asphalt mixture and the aggregate acceleration, and a good exponential function relationship was found between the compaction degree of asphalt mixture and the rotation angle. Therefore, a fitting model F1 between the compaction degree and the aggregate motion parameters was established, and the model parameters were fitted based on the test results of this embodiment to obtain the specific fitting relationships between the compaction degree of asphalt mixture and the aggregate acceleration and rotation angle, respectively:

[0090] (3);

[0091] (4);

[0092] In the formula, K is the compaction degree of the asphalt mixture; a is the acceleration of the aggregate; and θ is the rotation angle of the aggregate.

[0093] Based on the compacted specimens obtained from the aforementioned indoor compaction tests, non-destructive scanning of compacted specimens under different compaction degrees was performed using industrial CT scanning equipment under laboratory conditions. Key microstructural parameters of the aggregates were extracted using image processing technology, including: the average coordination number characterizing the stability of the skeleton connection and the off-matrix characterizing the orientation of the aggregate arrangement.

[0094] By performing scatter plot analysis on the microstructural parameters of aggregates under different compaction degrees and their corresponding compaction degrees, and based on the changing trends of the microstructural parameters of aggregates under different compaction degrees, the results are as follows: Figure 5 and Figure 6 As shown, a good linear relationship was found between the compaction degree of asphalt mixture and the average coordination number and partial texture. Therefore, a fitting model F2 between compaction degree and aggregate motion parameters was established, and the model parameters were fitted based on the test results of this embodiment. The specific fitting relationships between the compaction degree of asphalt mixture and the average coordination number and partial texture of aggregate were obtained as follows:

[0095] (5);

[0096] (6);

[0097] In the formula, Z is the average coordination number of the aggregate; D is the partial structure of the aggregate.

[0098] Based on the established fitting models F1 for compaction degree and kinematic parameters and F2 for compaction degree and microstructure parameters, namely equations (3)-(4) and (5)-(6), by using compaction degree as an intermediate variable, the specific fitting models F3 between aggregate kinematic parameters and aggregate microstructure parameters can be obtained as follows:

[0099] (7);

[0100] (8);

[0101] (9);

[0102] (10).

[0103] Due to errors in the fitting model, the average coordination number and partial structural parameters obtained by inverting aggregate acceleration and rotation angle are similar but not completely equal. To reduce the impact of fitting errors, this embodiment takes the average of the two as the current aggregate microstructure parameters.

[0104] When the compaction degree of asphalt mixture reaches the target compaction degree (generally 98%), the aggregate microstructure parameters corresponding to the compacted specimens at this point are considered as the target microstructure parameters. The range of target microstructure parameters is based on the statistical results of parameters from multiple compacted specimens at 98% compaction degree; therefore, the mean ± standard deviation is used as the allowable range for the target microstructure parameters. Based on the target compaction degree of 98%, the target microstructure parameters are determined as follows:

[0105] (11);

[0106] (12);

[0107] In the formula, Z 目标 The value of the target average coordination number of the aggregate; D 目标 The value of Z represents the target partial structure of the aggregate; 98% This represents the average distribution number of aggregates when the compaction degree is 98% in the indoor compaction test; D represents the standard deviation of the average coordination number of aggregates in the indoor compaction test. 98% This refers to the partial structure of aggregates when the compaction degree is 98% in an indoor compaction test; The standard deviation of aggregate texture in the indoor compaction test; the coefficient of variation C of the average coordination number Z and aggregate texture D in the indoor compaction test. v ≤0.1.

[0108] Step S2: Select a test section at the construction site, set different sets of vibration parameters at different locations to carry out on-site compaction tests, and keep the vibration parameters at the same location unchanged during the compaction process. Select the set of vibration parameters that requires the fewest compaction passes to achieve the target microstructure parameters from all test results, and use it as the initial vibration parameters of the compaction wheel; the vibration parameters include vibration frequency and amplitude.

[0109] Before formal construction, a representative test section consistent with the actual engineering conditions was selected at the construction site. The structural type, mixture gradation, and paving temperature of the test section were consistent with the main project to ensure the applicability of the test results to actual construction. Within the test section, smart particles were laid out at test points every 50m along the length of the road surface and at the location between the centerline of each lane and the wheel track in the width direction. Multiple sets of vibration frequencies and amplitudes were subjected to compaction tests within the test section. Different sets of vibration parameters were obtained using orthogonal experimental design principles, and compaction tests were conducted at different test points according to different sets of vibration parameters. The vibration frequency range was 20-60Hz, covering the low-frequency to high-frequency range, with no fewer than 5 values ​​taken. The amplitude range was 0.2-2.0mm, covering the small-amplitude to large-amplitude range, with no fewer than 5 values ​​taken.

[0110] During compaction according to vibration parameters, the response signals of the smart particles are simultaneously collected. Based on the fitting model F3 between the aggregate motion parameters and microstructure parameters established in step S1, the acceleration and rotation angle during compaction are converted into aggregate microstructure parameters. The response results of microstructure parameters corresponding to different vibration parameters are compared, and the set that produces the fastest on-site microstructure response characteristics, i.e., the set with the fewest compaction passes required to achieve the target microstructure parameters, is selected.

[0111] (13);

[0112] Where f0 is the initial vibration frequency; A0 is the initial amplitude; The optimal vibration parameters are the set of microstructure parameters that require the fewest compaction passes for asphalt mixtures.

[0113] These vibration parameters can quickly form a stable aggregate skeleton structure in the early stages of construction, thus serving as the initial vibration parameters for the compaction wheel when the main compaction project begins.

[0114] Step S3: Conduct a monitoring test on the compaction process of the asphalt mixture at the construction site. Use the initial vibration parameters obtained in step S2 as the vibration parameters during the first pass of the compaction wheel. Obtain the monitoring values ​​of the motion parameters of the aggregate after each pass of compaction in real time. Substitute the monitoring values ​​of the motion parameters of the aggregate into the fitting model F3 obtained in step S1 to obtain the measured values ​​of the microstructure parameters of the aggregate.

[0115] During the compaction process at the construction site, several smart particles are pre-placed in the middle of the paved asphalt mixture, ensuring that the smart particles are located in representative structural areas below typical load transfer paths. The smart particles are placed every 50 meters along the length of the road surface and along the width of each lane between the centerline and the wheel track, with 1-2 smart particles placed at each placement point.

[0116] The smart particles are placed at the same locations as the subsequent core sampling locations so that they can be recycled and reused after the construction is completed.

[0117] During the compaction process, after each pass of the compaction wheel, the smart particles collect the motion parameter monitoring values ​​recorded during the current pass based on a pressure change triggering mechanism, and then transmit them wirelessly to the data acquisition instrument. The data processing and transmission time of the smart particles per pass is less than the time required for the compaction wheel to complete one pass, thus ensuring that the data of the smart particles can be uploaded and processed before the start of the next pass, achieving real-time data closure on the compaction pass scale.

[0118] The real-time motion parameter monitoring values ​​uploaded by each smart particle are input into the fitting model F3 between aggregate motion parameters and microstructure parameters constructed in step S1. The average coordination number and partial texture of the aggregate in the asphalt mixture below the current compaction surface are calculated in real time. The microstructure parameters of multiple smart particles can be obtained by weighted averaging to obtain the microstructure parameters of representative areas, realizing online identification and real-time diagnosis of the evolution of the microstructure of the asphalt mixture.

[0119] Step S4: Determine whether the measured values ​​of the microstructure parameters have reached the target microstructure parameters. If they have, further determine whether the measured compaction degree of the asphalt mixture has reached the target compaction degree and whether the rate of change of the microstructure parameters per pass is lower than the threshold. If so, the compaction is up to standard. Otherwise, adjust the vibration parameters of the compaction wheel and continue the next pass of compaction according to step S3 until the compaction is up to standard.

[0120] The smaller the threshold of the rate of change per pass, the higher the compaction degree of the asphalt mixture, but the more energy and resources are required. Conversely, the larger the threshold of the rate of change per pass, the lower the compaction degree of the asphalt mixture. The threshold of the rate of change per pass is usually set at 2%, but it can also be selected based on pavement performance requirements and engineering costs.

[0121] The measured values ​​of the microstructure parameters obtained in real time in step S3 are compared with the target microstructure parameters:

[0122] like This indicates that the asphalt mixture skeleton structure is loose at this time. Therefore, the vibration frequency and amplitude should be increased during the next compaction to input more energy and promote particle movement.

[0123] like This indicates that the asphalt mixture skeleton has been initially formed but the orientation is not good. Therefore, the vibration frequency should be increased during the next compaction to optimize the particle orientation using the resonance principle.

[0124] like This indicates that the asphalt mixture skeleton has initially formed the desired arrangement orientation, but the number of effective contact points between particles is insufficient. Therefore, the amplitude is increased during the next compaction to promote more effective contact between particles.

[0125] in, and These are the average coordination number and partial structure of the aggregate during the i-th compaction pass, respectively; Z 目标 The value of the target average coordination number of the aggregate; D 目标 The value is determined by the target structure of the aggregate.

[0126] When the real-time acquired microstructure parameters reach the target microstructure parameter Z 目标 D 目标If the measured compaction degree of the asphalt mixture reaches the target compaction degree and the pass change rate of the structural parameters is less than the threshold, then the compaction is up to standard. If either the target compaction degree or the pass change rate is not met, the compaction is considered not up to standard, and the vibration parameters of the compaction wheel need to be adjusted for the next pass.

[0127] The macro-compaction degree of asphalt mixture in the compacted area was detected in real time using a nucleus-free density meter. .

[0128] The rate of change of aggregate microstructure parameters per pass is calculated using the following formula:

[0129] (14);

[0130] (15);

[0131] In the formula, and These are the average coordination number and the rate of change of partial texture of the aggregate during the i-th compaction pass, respectively; and These represent the average coordination number and partial texture of the aggregate during the i-th compaction pass, respectively; Z i-1 and D i-1 The compaction of the first i -Average coordination number and partial structure of aggregates at 1 pass.

[0132] When the real-time acquired mesostructure parameters do not reach the target mesostructure parameter Z 目标 D 目标 At the same time, it is also necessary to adjust the vibration parameters of the compaction wheel for the next compaction.

[0133] The vibration frequency of the compaction wheel during the next compaction pass is determined by the following formula:

[0134] (16);

[0135] (17);

[0136] in, f i , f i+1 To compact the first i , i Vibration frequency at +1 repetition; This is the adjustment amount for the vibration frequency; This is the preset adjustment coefficient for the vibration frequency; , For the weighting coefficients, satisfying This is to demonstrate the dominant influence of vibration frequency on the off-center configuration.

[0137] The amplitude of the compaction wheel during the next compaction pass is determined by the following formula:

[0138] (18);

[0139] (19);

[0140] in, A i , A i+1 To compact the first i , i Amplitude at +1 pass; This is the adjustment amount for the amplitude; This is the preset adjustment coefficient for the amplitude; , For the weighting coefficients, satisfying This is to reflect the dominant influence of amplitude on the mean coordination number.

[0141] The adjustment calculation formula adopts a weighted normalized deviation feedback form. The normalized deviation term is used to unify the dimensions and improve the stability of the model, and the weight coefficient is used to quantify the contribution of different microstructural parameter deviations to the vibration parameter adjustment, so as to adapt to the needs of different compaction stages.

[0142] Immediately after each compaction cycle... and Perform calculations and update the results before the next compaction begins. and By incorporating the compaction wheel, the vibration parameters of the compaction wheel can be adjusted in real time.

[0143] After each compaction, if , and If all the above conditions are met, the compaction is considered satisfactory, and the compaction operation is stopped. If not all the above conditions are met, the vibration parameters of the compaction wheel are readjusted, and the next compaction cycle is performed, realizing closed-loop iterative adjustment of the compaction wheel vibration parameters until compaction is completed.

[0144] The beneficial effects achieved by the embodiments of the present invention are as follows:

[0145] This invention selects average coordination number and partial texture as key indicators to characterize the microstructure of asphalt mixtures, achieving a fundamental shift from macroscopic empirical control to microstructural control. Average coordination number reflects the number of contacts and connection stability between aggregates within the skeleton, serving as a core parameter for evaluating the load-bearing capacity of the skeleton. Partial texture reflects the spatial orientation of aggregates, characterizing the orientation of the skeleton structure and its force chain distribution during load-bearing. Compared to traditional macroscopic indicators such as density, porosity, and compaction degree, average coordination number and partial texture can truly reflect the evolution of the aggregate skeleton during compaction, fundamentally revealing the load-bearing mechanism and long-term performance causes of the mixture.

[0146] After construction is completed, the smart particles can be recycled and reused by core sampling, eliminating the need to invest in new sensor particles for each construction project, thus significantly reducing material costs and experimental consumption and improving the economic efficiency of the project.

[0147] This invention adaptively calculates the vibration parameter adjustment based on the deviation between real-time microstructural parameters and target microstructural indices, and automatically updates the vibration frequency and amplitude before the next compaction pass. This achieves dynamic optimization of the compaction wheel vibration parameters for each pass, ensuring that each compaction pass is optimized and adjusted according to the skeleton evolution state, effectively avoiding over-vibration, under-vibration, and local segregation, and improving compaction uniformity and construction efficiency.

[0148] By combining microstructural indicators with macro compaction indicators, a dual-scale evaluation of compaction quality can be achieved. This allows the compaction state to be judged not only by the degree of compaction but also by the stability of the aggregate skeleton and the rationality of the aggregate arrangement, thereby effectively improving the scientificity and accuracy of compaction quality evaluation.

[0149] Example 2;

[0150] like Figure 2 As shown, an embodiment of the present invention provides a compaction wheel vibration control system that considers the load transfer mechanism of the microstructure skeleton, comprising:

[0151] The Smart Particle 1005 is embedded in the asphalt mixture. It has the same shape as the aggregate and is equipped with a triaxial accelerometer and a triaxial gyroscope to monitor the motion parameters of the aggregate, including acceleration and rotation angle.

[0152] The data acquisition module 1001 is used to collect the motion parameters of smart particles in real time during the actual compaction of asphalt mixtures, so as to characterize the motion parameter monitoring values ​​of aggregates.

[0153] The data processing module 1002 is used to substitute the monitored values ​​of the aggregate's motion parameters into the fitting model F3 to solve for the measured values ​​of the aggregate's microstructure parameters. Then, it determines whether the target microstructure parameters, the target compaction degree, and the threshold of the rate of change of microstructure parameters per pass are reached. If so, it outputs that the compaction meets the standard; otherwise, it calculates and outputs the vibration parameters for the next pass of compaction. The microstructure parameters include the average coordination number and the partial texture, and the vibration parameters include the vibration frequency and amplitude.

[0154] The interactive module 1004 is used to input the initial vibration parameters of the compaction wheel and the measured compaction degree of the asphalt mixture during the actual compaction process, and to display whether the target microstructure parameters are reached, whether the target compaction degree is reached, whether it is lower than the threshold of the rate of change of microstructure parameters per pass, and whether the compaction meets the standard.

[0155] The data transmission module 1003 is used to transmit the vibration parameters of the next compaction cycle to the vibration controller 1006 of the compaction wheel.

[0156] The compaction wheel vibration control system in this embodiment can be implemented by electronic devices, which are connected to the vibration controller of the compaction wheel on the road roller and are used to send vibration parameters to the compaction wheel. The electronic devices include a processor, a memory, and a communication interface. The memory is used to store the computer program instructions of each module, and the processor implements the corresponding functions of each module when executing the computer program instructions.

[0157] The modules are connected through data interfaces to form a closed data flow loop. The input end of the data processing module is electrically connected to the output end of the data acquisition module, the input end of the data acquisition module is communicatively connected to the smart particle, the output end of the data processing module is electrically connected to the data transmission module, and the data processing module is also electrically connected to the interaction module.

[0158] Specifically, the smart pellets are used to simultaneously collect acceleration and rotation angle data of the aggregate during the compaction process. The smart pellets are packaged into an aggregate form, with dimensions and hardness similar to real aggregates, and incorporate a triaxial accelerometer and a triaxial gyroscope. The smart pellets also include a wireless data communication unit, which packages and transmits the motion data collected by the smart pellets to the data acquisition module after each compaction pass. The processing and transmission time for a single data transfer is less than the time required for the compaction wheel to complete one compaction pass.

[0159] Specifically, the data acquisition module is used to receive motion parameter data from smart particles in real time, mark the data source and cache it, provide a data interface for subsequent microstructure calculation, and then transmit the motion parameter monitoring values ​​of the aggregate to the data processing module.

[0160] Specifically, the data processing module includes:

[0161] (1) Used to call the fitting model F3 of aggregate motion parameters and microstructure parameters pre-established based on indoor compaction test and scanning test;

[0162] (2) Used to calculate the measured values ​​of the microstructure parameters (mean coordination number and partial texture) of the asphalt mixture under the current compaction surface in real time from the aggregate motion parameter monitoring values ​​uploaded by the smart particles.

[0163] (3) Used to determine whether the measured values ​​of the microstructure parameters of the asphalt mixture have reached the target microstructure parameters. If they have, proceed to the next step of judgment. If they have not, calculate the vibration parameters for the next compaction and output the data transmission module.

[0164] (4) Used to determine whether the rate of change of microstructure parameters per pass is lower than its threshold and whether the measured compaction degree reaches the target compaction degree, and then comprehensively determine whether the current compaction meets the standard. If it meets the standard, it outputs the compaction standard instruction to the interactive module. If it does not meet the standard, it calculates the vibration parameters during the next pass of rolling and outputs them to the data transmission module.

[0165] Those skilled in the art will understand that although the embodiments of the present invention only provide methods and systems, computer program products can also be obtained accordingly based on the methods and systems. The present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0167] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0169] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for controlling the vibration of a compaction wheel considering the load transfer mechanism of a microstructure skeleton, characterized in that: Includes the following steps: Step S1: Conduct indoor compaction tests on asphalt mixtures, collect aggregate motion parameters under different compaction degrees, and construct a fitting model F1 between compaction degree and aggregate motion parameters; perform non-destructive scanning on the compacted specimens obtained from the tests, extract aggregate microstructure parameters, and construct a fitting model F2 between compaction degree and aggregate microstructure parameters. Simultaneously, determine the values ​​of the target microstructure parameters based on the target compaction degree; based on fitting models F1 and F2, derive a fitting model F3 between aggregate motion parameters and microstructure parameters; the motion parameters include acceleration and rotation angle, and the microstructure parameters include average coordination number and partial texture. Step S2: Select a test section at the construction site, set different sets of vibration parameters at different locations to carry out on-site compaction tests, and keep the vibration parameters at the same location constant during the compaction process. Select the set of vibration parameters that requires the fewest compaction passes to achieve the target microstructure parameters from all test results, and use it as the initial vibration parameters of the compaction wheel; the vibration parameters include vibration frequency and amplitude. Step S3: Conduct a monitoring test on the compaction process of asphalt mixture at the construction site. Use the initial vibration parameters obtained in step S2 as the vibration parameters during the first pass of the compaction wheel. Obtain the monitoring values ​​of the motion parameters of the aggregate after each pass of compaction in real time. Substitute the monitoring values ​​of the motion parameters of the aggregate into the fitting model F3 obtained in step S1 to obtain the measured values ​​of the microstructure parameters of the aggregate. Step S4: Determine whether the measured values ​​of the microstructure parameters have reached the target microstructure parameters. If they have, further determine whether the measured compaction degree of the asphalt mixture has reached the target compaction degree and whether the rate of change of the microstructure parameters per pass is lower than the threshold. If so, the compaction is up to standard. Otherwise, adjust the vibration parameters of the compaction wheel and continue the next pass of compaction according to step S3 until the compaction is up to standard. Adjust the vibration parameters of the compaction wheel in real time using the following method: like Then, increase the vibration frequency and amplitude during the next compaction pass; like If so, increase the vibration frequency during the next compaction pass; like Then increase the amplitude during the next compaction pass; in, and These represent the average coordination number and partial texture of the aggregate during the i-th compaction pass, respectively; Z 目标 The value of the target average coordination number of the aggregate; D 目标 The value is determined by the target structure of the aggregate.

2. The compaction wheel vibration control method considering the microscopic skeleton load transfer mechanism according to claim 1, characterized in that: In step S1, the indoor compaction test of asphalt mixture specifically includes the following steps: Asphalt mixture specimens with different compaction degrees were molded under laboratory conditions according to the construction mix proportion. The compaction degree ranged from 86% to 98%. The selected compaction degree covered the key compaction stages in the construction process, and the number of values ​​was no less than 4. Before the compacted specimen is formed, smart particles are embedded in the middle of the compacted specimen. The smart particles are packaged into an aggregate form, and their size and hardness are similar to those of real aggregates. They are equipped with a triaxial accelerometer and a triaxial gyroscope, which are used to test the acceleration and rotation angle of the aggregates, respectively. The smart particles also have a wireless transmission sensing unit. During the specimen compaction process, the acceleration and rotation angle data of the smart particles were collected to characterize the acceleration and rotation angle of the aggregate.

3. The compaction wheel vibration control method considering the microscopic skeleton load transfer mechanism according to claim 1, characterized in that: In step S1, the fitting model F1 between the compaction degree of the asphalt mixture and the aggregate motion parameters is determined according to equations (1.1)-(1.2): （1.1）； （1.2）； In the formula, K is the compaction degree of the asphalt mixture; k1, k2, c1, c2, and c3 are fitting parameters; a is the acceleration of the aggregate; and θ is the rotation angle of the aggregate. The fitting model F2 for the compaction degree of asphalt mixture and the microstructure parameters of aggregates is determined according to equations (1.3)-(1.4): （1.3）； （1.4）； In the formula, α1, α2, β1, and β2 are fitting parameters; Z is the average coordination number of the aggregate; and D is the partial structure of the aggregate.

4. The compaction wheel vibration control method considering the microstructure load transfer mechanism according to claim 1, characterized in that: In step S1, based on the established fitting model F1 between compaction degree and kinematic parameters and the fitting model F2 between compaction degree and microstructure parameters, by using compaction degree as an intermediate variable, a fitting model F3 between aggregate kinematic parameters and aggregate microstructure parameters can be constructed as shown in equations (1.5)-(1.8): （1.5）； （1.6）； （1.7）； （1.8）； In the formula, Z is the average coordination number of the aggregate; D is the partial structure of the aggregate; a is the acceleration of the aggregate; θ is the rotation angle of the aggregate; η1, η2, δ1, δ2, γ1, γ2, γ3, φ1, φ2, φ3 are fitting parameters.

5. The compaction wheel vibration control method considering the microstructure load transfer mechanism according to claim 1, characterized in that: In step S1, the target mesoscopic structure parameters can be determined according to equations (1.9)-(1.10): （1.9）； （1.10）； In the formula, Z 目标 The value of the target average coordination number of the aggregate; D 目标 The value of Z represents the target partial structure of the aggregate; 98% This represents the average distribution number of aggregates when the compaction degree is 98% in the indoor compaction test; D represents the standard deviation of the average coordination number of aggregates in the indoor compaction test. 98% This refers to the partial structure of aggregates when the compaction degree is 98% in an indoor compaction test; This represents the standard deviation of aggregate misalignment in indoor compaction tests.

6. The compaction wheel vibration control method considering the microstructure load transfer mechanism according to claim 1, characterized in that: In step S2, the on-site compaction test is carried out according to the following method: Before formal construction, a test section consistent with the actual engineering conditions is selected at the construction site. Smart particles are laid out at test points at preset intervals within the test section. Different sets of vibration parameters are obtained according to the orthogonal principle. On-site compaction tests are conducted at different test points according to different sets of vibration parameters. The vibration frequency range is 20-60Hz, covering the low-frequency to high-frequency range, and the number of values ​​is no less than 5. The amplitude range is 0.2-2.0mm, covering the small amplitude to large amplitude range, and the number of values ​​is no less than 5.

7. The compaction wheel vibration control method considering the microstructure load transfer mechanism according to claim 1, characterized in that: In step S3, the monitoring test includes the following steps: Several smart particles are pre-laid in the middle of the paved asphalt mixture. The smart particles are laid every 30-100m along the length of the road surface and at the position between the center line of each lane and the wheel track along the width direction. During the compaction process, after each pass of the compaction wheel, the intelligent particle system collects the motion parameter monitoring values ​​during the current pass of compaction.

8. The compaction wheel vibration control method considering the microscopic skeleton load transfer mechanism according to claim 1, characterized in that: In step S4, the rate of change of the aggregate microstructure parameters with each pass is calculated according to equations (4.1)-(4.2): （4.1）； （4.2）； In the formula, and These are the average coordination number and the rate of change of partial texture of the aggregate during the i-th compaction pass, respectively; and These represent the average coordination number and partial texture of the aggregate during the i-th compaction pass, respectively; Z i-1 and D i-1 The compaction of the first i -Average coordination number and partial structure of aggregates at 1 pass.

9. The compaction wheel vibration control method considering the microscopic skeleton load transfer mechanism according to claim 1, characterized in that: In step S4, the vibration frequency during the next compaction pass is determined according to equations (4.3)-(4.4): （4.3）； （4.4）； Among them, f i f i+1 The vibration frequency during the i-th and i+1th compaction passes; This is the adjustment amount for the vibration frequency; This is the preset adjustment coefficient for the vibration frequency; , These are the weighting coefficients; The amplitude of the next compaction pass shall be determined according to formulas (4.5)-(4.6): （4.5）； （4.6）； Among them, A i A i+1 The amplitude of the compaction at the i-th and i+1-th passes; This is the adjustment amount for the amplitude; This is the preset adjustment coefficient for the amplitude; , These are the weighting coefficients.

10. A compaction wheel vibration control system considering the microscopic skeleton load transfer mechanism, comprising: The intelligent particles, embedded in the asphalt mixture, are shaped to match the aggregate and have built-in triaxial accelerometers and triaxial gyroscopes to monitor the motion parameters of the aggregate. Motion parameters include acceleration and rotation angle; The data acquisition module is used to collect the motion parameters of smart particles in real time during the actual compaction of asphalt mixtures, so as to characterize the motion parameter monitoring values ​​of aggregates; The data processing module is used to substitute the monitored values ​​of the aggregate's motion parameters into the fitting model F3, solve for the measured values ​​of the aggregate's microstructure parameters, and then determine whether the target microstructure parameters, the target compaction degree, and the threshold of the rate of change of microstructure parameters are reached. If so, the module outputs that the compaction meets the standard. Otherwise, calculate and output the vibration parameters for the next compaction pass; the microstructural parameters include the average coordination number and the partial fabric, and the vibration parameters include the vibration frequency and amplitude. The interactive module is used to input the initial vibration parameters of the compaction wheel and the measured compaction degree of the asphalt mixture during the actual compaction process. It is used to display whether the target microstructure parameters have been reached, whether the target compaction degree has been reached, whether it is lower than the threshold of the rate of change of microstructure parameters per pass, and whether the compaction meets the standards. The data transmission module is used to transmit the vibration parameters for the next compaction cycle to the vibration controller of the compaction wheel; To achieve the compaction wheel vibration control method considering the microstructure load transfer mechanism as described in any one of claims 1-9.

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