A method and system for determining an intelligent compaction evaluation index of a roadbed

Abstract

The present application relates to the technical field of intelligent compaction of roadbed, and provides a method and system for determining evaluation indexes of intelligent compaction of roadbed. The method comprises the following steps: obtaining the representation of the equivalent stiffness of the roadbed based on a vibrating road roller-roadbed coupling model; estimating the dynamic contact area between the roadbed and the vibrating wheel in the process of vibrating compaction according to the relationship between the water content of the roadbed and the dynamic contact area coefficient constructed in advance; obtaining the intelligent compaction vibration modulus of the roadbed representing the compaction quality index according to the relationship among the modulus of the roadbed, the dynamic contact area and the equivalent stiffness of the roadbed; converting the intelligent compaction vibration modulus of the roadbed into the conventional evaluation index of the compaction quality of the roadbed according to the water content interval to which the soil belongs, comparing the conventional evaluation index of the compaction quality of the roadbed with the design requirement, judging whether the compaction quality meets the standard in real time, and adjusting the compaction process accordingly to accurately evaluate the compaction quality of the roadbed.

Description

Technical Field

[0001] This invention relates to the field of intelligent roadbed compaction technology, and in particular to a method and system for determining intelligent roadbed compaction evaluation indicators. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In infrastructure construction such as highways, railways, and airports, the compaction quality of the roadbed is a key indicator for controlling the stability and durability of the project. Traditional compaction quality testing methods, such as the sand cone method, nuclear density meter method, and PFWD, have drawbacks such as low testing efficiency, limited coverage, and destructive effects on the roadbed, making it difficult to achieve real-time, continuous, and comprehensive quality monitoring during construction. Intelligent compaction technology, by installing acceleration sensors on vibratory rollers, collects the dynamic response signals of the vibratory drum in real time and combines them with algorithms to calculate intelligent compaction measurement values, thereby achieving real-time evaluation and feedback control of compaction quality. Currently common intelligent compaction indicators include: compaction measurement value (CMV) and continuous compaction value (CCV) based on harmonic ratio; and stiffness coefficient based on dynamics (…). k s ); energy-based compaction power per unit volume (E), etc.

[0004] The existing intelligent compaction index has the following problems: (1) The CMV and CCV indices are mainly based on frequency domain harmonic analysis and lack clear mechanical constitutive relationships. They are empirical indices and cannot accurately reflect the mechanical properties of the roadbed; (2) The roadbed soil is mostly unsaturated soil. Changes in water content directly affect the properties of matrix suction, soil stiffness, etc., and thus affect the interaction between the vibrating wheel and the roadbed. However, most of the existing indices ignore the influence of water content, resulting in distorted evaluation results under different water content conditions. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method and system for determining intelligent compaction evaluation indicators for roadbeds. By establishing a complete dynamic equation, introducing a dynamic contact area coefficient, and considering the influence of moisture content, it achieves an accurate assessment of the compaction quality of roadbeds.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for determining the evaluation index of intelligent compaction of roadbed.

[0007] In one or more embodiments, a method for determining the evaluation index of intelligent compaction of roadbed is provided, including: Based on the vibratory roller-subgrade coupling model, the equivalent stiffness of the subgrade is characterized. Based on the pre-established relationship between the subgrade moisture content and the dynamic contact area coefficient, the dynamic contact area coefficient is determined and multiplied by the static contact area to estimate the dynamic contact area between the subgrade and the vibratory wheel during the vibration compaction process. Based on the relationship between the modulus of the subgrade, the dynamic contact area, and the equivalent stiffness of the subgrade, and combined with the parameters of the vibratory roller and the vibration acceleration signal, the intelligent compaction vibration modulus of the subgrade, which characterizes the compaction quality index, is obtained. Based on the soil moisture content, determine the moisture content range to which it belongs, match and call the regression model between the intelligent compaction vibration modulus of the subgrade and the conventional evaluation index of subgrade compaction quality, and convert the intelligent compaction vibration modulus of the subgrade into the conventional evaluation index of subgrade compaction quality. The conventional evaluation indicators for the compaction quality of the converted roadbed are compared with the design requirements to determine in real time whether the compaction quality meets the standards and adjust the compaction process accordingly.

[0008] As one implementation method, the construction of the vibratory roller-subgrade coupling model is based on the following assumptions: The vibrating wheel and the frame are considered rigid bodies, and the two are connected by stiffness and damping elements; Vibration in the vertical direction is considered only, while horizontal and torsional motion are ignored; The subgrade soil is considered as a Kelvin body, which is composed of elastic elements and viscous elements connected in parallel.

[0009] As one implementation method, a quadratic polynomial model is used to construct the relationship between subgrade moisture content and dynamic contact area coefficient based on subgrade compaction test data. ;in, This refers to the dynamic contact area coefficient. denoted as , where is the subgrade moisture content; a, b, and c are fitting parameters.

[0010] As one implementation method, the relationship between the modulus of the subgrade, the dynamic contact area, and the equivalent stiffness of the subgrade is as follows: ; in, The modulus of the roadbed; This refers to the dynamic contact area. The equivalent stiffness of the roadbed; The Poisson's ratio of the roadbed; It is the angle between the tangent at the contact point between the vibratory wheel and the roadbed and the horizontal line.

[0011] As one implementation method, based on the measured data of the intelligent compaction vibration modulus and conventional evaluation indicators of subgrade compaction quality in different moisture content ranges, regression analysis is performed on the intelligent compaction vibration modulus and conventional evaluation indicators of subgrade compaction quality to obtain a regression model between the intelligent compaction vibration modulus and the conventional evaluation indicators of subgrade compaction quality.

[0012] As one implementation method, when the conventional evaluation index for subgrade compaction quality is compaction degree, the regression model between the subgrade intelligent compaction vibration modulus and compaction degree is: ;in, For intelligent compaction vibration modulus of roadbed; Compaction degree; and These are the regression coefficients corresponding to different moisture content ranges.

[0013] As one implementation method, when the conventional evaluation index for subgrade compaction quality is the dynamic resilient modulus, the regression model between the subgrade intelligent compaction vibration modulus and the dynamic resilient modulus is: ;in, For intelligent compaction vibration modulus of roadbed; For dynamic resilience modulus; and These are the regression coefficients corresponding to different moisture content ranges.

[0014] A second aspect of the present invention provides a system for determining the evaluation index of intelligent compaction of roadbed.

[0015] In one or more embodiments, a system for determining intelligent compaction evaluation indicators for roadbeds includes: The roadbed equivalent stiffness characterization module is used to characterize the equivalent stiffness of the roadbed based on the vibratory roller-roadbed coupling model. The dynamic contact area calculation module is used to determine the dynamic contact area coefficient based on the pre-constructed relationship between the subgrade moisture content and the dynamic contact area coefficient, and then multiply it by the static contact area to estimate the dynamic contact area between the subgrade and the vibratory wheel during the vibration compaction process. The intelligent compaction vibration modulus calculation module is used to obtain the intelligent compaction vibration modulus of the roadbed, which characterizes the compaction quality index, based on the relationship between the modulus, dynamic contact area and equivalent stiffness of the roadbed, combined with the parameters of the vibratory roller and the vibration acceleration signal. The compaction quality evaluation index conversion module is used to determine the moisture content range of the soil based on its moisture content, match and call the regression model between the intelligent compaction vibration modulus of the subgrade and the conventional evaluation index of subgrade compaction quality, and convert the intelligent compaction vibration modulus of the subgrade into the conventional evaluation index of subgrade compaction quality. The compaction quality judgment and process adjustment module is used to compare the conventional evaluation indicators of the converted roadbed compaction quality with the design requirements, judge in real time whether the compaction quality meets the standards, and adjust the compaction process accordingly.

[0016] A third aspect of the present invention provides a computer-readable storage medium.

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for determining the intelligent compaction evaluation index of the roadbed as described above.

[0018] A fourth aspect of the present invention provides an electronic device.

[0019] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the method for determining the intelligent compaction evaluation index of the roadbed as described above.

[0020] Compared with the prior art, the beneficial effects of the present invention are: The method for determining the evaluation index of intelligent roadbed compaction provided by this invention is based on a vibratory roller-roadbed coupling model. It introduces a dynamic contact area coefficient and considers the influence of moisture content to estimate the dynamic contact area between the roadbed and the vibratory roller during vibratory compaction. Then, based on the relationship between the roadbed modulus, dynamic contact area, and equivalent stiffness of the roadbed, the intelligent compaction vibration modulus of the roadbed, which characterizes the compaction quality index, is derived. Finally, the intelligent compaction vibration modulus of the roadbed is matched and converted into a conventional evaluation index of roadbed compaction quality according to the moisture content range. This index is then compared with the design requirements to determine in real time whether the compaction quality meets the standards and adjust the compaction process accordingly. This method achieves accurate evaluation of the compaction quality of the roadbed and can achieve precise compaction quality evaluation for different moisture content ranges, showing good prospects for engineering applications. Attached Figure Description

[0021] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0022] Figure 1 This is a flowchart of the method for determining the evaluation index of intelligent compaction of roadbed according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the vibratory roller-subgrade coupling model in this invention; Figure 3 This is a diagram showing the relationship between the displacement and peak acceleration of the vibrating wheel in this invention. Figure 4 This is a simplified model of the compaction system in this invention and a schematic diagram of the static contact area between the vibrating wheel and the roadbed; Figure 5 This is a diagram showing the distribution of moisture content control and field test results in an embodiment of the present invention; Figure 6 As described in the embodiments of the present invention E ICV CMV is related to compaction degree K and dynamic resilient modulus E, respectively. p Correlation analysis plot; Figure 7 Three regions in the embodiments of the present invention E ICV The results of linear regression analysis of CMV compaction degree K are shown in the figure. Figure 8 Three regions in the embodiments of the present invention E ICV and CMV dynamic resilience modulus E p The results of the linear regression analysis are shown in the figure. Figure 9 As described in the embodiments of the present invention E ICV Other intelligent compaction indices include compaction degree K and dynamic resilient modulus E. p Correlation comparison chart; Figure 10 This is a schematic diagram of the system structure for determining the intelligent compaction evaluation index of the roadbed according to an embodiment of the present invention; Figure 11 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] Figure 1 A schematic diagram illustrating the principle of the method for determining the intelligent compaction evaluation index of roadbed according to an embodiment of the present invention is provided. Based on... Figure 1The method for determining the evaluation index of intelligent compaction of roadbed in this embodiment may include the following steps S101 to S105.

[0027] The specific implementation process of steps S101 to S105 is as follows: Step S101: Based on the vibration roller-subgrade coupling model, the equivalent stiffness of the subgrade is characterized.

[0028] As one implementation method, the construction of the vibratory roller-subgrade coupling model is based on the following assumptions: The vibrating wheel and the frame are considered rigid bodies, and the two are connected by stiffness and damping elements; Vibration in the vertical direction is considered only, while horizontal and torsional motion are ignored; The subgrade soil is considered as a Kelvin body, which is composed of elastic elements and viscous elements connected in parallel.

[0029] Based on the above assumptions, establish as follows Figure 2 The dynamic equations of the two-degree-of-freedom vibratory roller-subgrade coupled model shown are as follows: (1) (2) in, and These refer to the masses of the vibratory roller frame and the vibratory wheel, respectively. and These represent the displacements of the vibratory roller frame and the vibratory wheel, respectively. and These are the acceleration and velocity of the vibratory roller frame, respectively. and These are the acceleration and velocity of the vibrating wheel, respectively. k t and c t These are the stiffness and damping of the suspension system, respectively. k s The equivalent stiffness of the roadbed. c s For the damping of the roadbed, F s This refers to the dynamic contact force between the vibratory wheel and the subgrade fill material during the vibratory compaction process. P For excitation force, , For the static eccentricity of the eccentric mass, ω Let be the rotational angular frequency, and , f The excitation frequency is the frequency per unit time.

[0030] If we consider the roadbed as a Kelvin body, then we have: (3) Assuming the origin of the coordinate system is set on the uncompacted roadbed surface, such as Figure 3 As shown, the following equation is obtained: (4) By combining equations (3) and (4), we can obtain: (5) The influence of the frame on the dynamic response of the vibrating wheel can be ignored, therefore: (6) The displacement of the vibratory roller frame and vibratory wheel lags behind the excitation force by a phase angle. φ Then it can be expressed as: (7) During the compaction process, the peak negative acceleration is upward and occurs when the vibratory roller is at its lowest point, such as... Figure 3 Point B is shown. And at that point... ,have: (8) The self-weight of the frame and vibrating wheel does not affect the dynamic displacement, and therefore does not affect the stiffness change. Therefore: (9) Step S102: Based on the pre-constructed relationship between the subgrade moisture content and the dynamic contact area coefficient, determine the dynamic contact area coefficient and multiply it by the static contact area to estimate the dynamic contact area between the subgrade and the vibratory wheel during the vibration compaction process.

[0031] quality is M ,radius r The model of a rigid disk under excitation force is as follows: Figure 4 As shown, its vibration equation is: (10) in N = k s fx It is contact force. f It is a complex function related to the disk's mass, size, and roadbed parameters.

[0032] And there are: (11) in μ =0.35 is the Poisson's ratio of the roadbed.

[0033] The contact area between the vibratory roller and the roadbed directly affects stress transfer and energy dissipation. Traditional models often treat the contact area as a constant, but in actual engineering, the contact area is significantly affected by factors such as soil moisture content and compaction degree. This invention defines the static contact area. A s The contact area between the vibratory wheel and the roadbed when the vibratory wheel is at rest can be approximated as a rectangle: (12) in L The width of the vibrating wheel is in meters (m). B The horizontal projected length (m) of the contact arc length.

[0034] Define dynamic contact area coefficient C d This coefficient describes the amplification effect of the contact area during vibration; it is related to the soil moisture content. w Closely related. Based on a large amount of experimental data, the following quadratic polynomial model is used to establish the subgrade moisture content. w and C d The relationship between them: .

[0035] Step S103: Based on the relationship between the modulus of the subgrade, the dynamic contact area, and the equivalent stiffness of the subgrade, and combined with the parameters of the vibratory roller and the vibration acceleration signal, the intelligent compaction vibration modulus of the subgrade, which characterizes the compaction quality index, is obtained.

[0036] If the radius is r If the area of ​​the circle is equivalent, then: (15) Therefore, there is r Approximate value: (16) String length B Available from Figure 4 Conclusion: (17) in R It is the radius of the vibrating wheel. β It is the angle between the tangent at the contact point between the vibratory wheel and the roadbed and the horizontal line.

[0037] By combining equations (16) and (17), we can obtain: (18) Substituting equation (18) into equation (11), we get: (19) The relationship between the modulus of the subgrade, the dynamic contact area, and the equivalent stiffness of the subgrade can then be obtained as follows: ; in, The modulus of the roadbed; This refers to the dynamic contact area. The equivalent stiffness of the roadbed; The Poisson's ratio of the roadbed; It is the angle between the tangent at the contact point between the vibratory wheel and the roadbed and the horizontal line.

[0038] Substituting equation (9) into equation (20) and combining it with equation (13), the vibration modulus of intelligent compaction of the roadbed is calculated. Replace with the modulus of the roadbed The intelligent compaction vibration modulus of the roadbed can be obtained. as follows: (twenty one) Step S104: Determine the moisture content range of the soil based on its moisture content, match and call the regression model between the intelligent compaction vibration modulus of the subgrade and the conventional evaluation index of subgrade compaction quality, and convert the intelligent compaction vibration modulus of the subgrade into the conventional evaluation index of subgrade compaction quality.

[0039] Based on measured data of the vibration modulus of intelligent compaction and conventional evaluation indicators of subgrade compaction quality in different moisture content ranges, regression analysis was conducted on these indicators to obtain a regression model between them. This solves the problem that existing intelligent compaction technologies often use a single control standard and fail to establish differentiated quality assessment thresholds for different moisture content ranges, thus achieving precise control.

[0040] When the conventional evaluation index for subgrade compaction quality is compaction degree, the regression model between the subgrade intelligent compaction vibration modulus and compaction degree is as follows: (twenty two) in, For intelligent compaction vibration modulus of roadbed; Compaction degree; and These are the regression coefficients corresponding to different moisture content ranges.

[0041] When the conventional evaluation index for subgrade compaction quality is the dynamic resilient modulus, the regression model between the subgrade intelligent compaction vibration modulus and the dynamic resilient modulus is: (twenty three) in, For intelligent compaction vibration modulus of roadbed; For dynamic resilience modulus; and are the regression coefficients corresponding to different moisture content intervals.

[0042] Step S105: Compare the converted conventional evaluation index of subgrade compaction quality with the design requirements, and judge in real time whether the compaction quality meets the standard and adjust the compaction process accordingly.

[0043] The design requirements here can be specifically set according to the actual situation and will not be elaborated here.

[0044] The field test was carried out in a road soil subgrade project in 2024. The subgrade soil was classified as CL-ML according to the Unified Soil Classification System (USCS), and its properties are shown in Table 1.

[0045] Table 1 Basic properties of subgrade fillers index result Liquid limit, LL (%) 24.9 Plastic Limit, PL (%) 13.8 Plasticity index, PI 11.1 <![CDATA[Maximum Dry Density, MDD (g / cm 3 )]]> 1.83 Optimal moisture content, OMC (%) 12.7 <![CDATA[Coefficient of non-uniformity, C u > 10.2 <![CDATA[Curvature coefficient, C c > 1.9 USCS Classification CL-ML To study the influence of subgrade moisture content w on E ICV and CMV a 30-meter-long test section was divided into three regions with different moisture contents, as shown in (a)-(b) of Figure 5 . Areas A, B, and C correspond to OMC - 2% < w < OMC, w = OMC, and OMC < w < OMC + 2%. Moisture content data were obtained at 1-meter intervals by the drying method. A total of 30 moisture content tests were carried out before compaction, as shown in (b) of Figure 5 . Subsequently, the moisture content contour map was drawn using Kriging method in MATLAB, as shown in (c) of Figure 5 . It can be seen that the moisture content gradually increases from Area A to Area C.

[0046] Vibratory compaction was carried out using a single-drum vibratory roller SR22M-C5, and its parameters are shown in Table 2. The average rolling speed during the test was 2.8 km / h. The acceleration sensor used in the test was DH 1A111E, with a sensitivity of , a response frequency of 0 - 10000 Hz, and a range of 10 g.

[0047] Table 2 Parameters of SR22M-C5 roller parameter value Total mass 8800kg Vibrating wheel diameter 1.55m Vibrating wheel width 2.14m amplitude 1.8 / 0.9mm Loading frequency 29 / 35Hz Excitation force 410 / 300kN The roadbed was compacted in 25 cm thick layers, with the lower layers composed of the same roadbed material to minimize heterogeneity in support conditions. The compaction process consisted of: one pass of static compaction, two passes of high-frequency, low-amplitude, low-excitation compaction (35 Hz, 0.9 mm, 300 kN), four passes of low-frequency, high-amplitude, high-excitation compaction (29 Hz, 1.8 mm, 419 kN), and a final pass of static compaction, for a total of eight passes, including six passes of vibratory compaction. After the second, fourth, and sixth passes of vibratory compaction, the compaction degree was checked at two-meter intervals using the sand cone method. K The dynamic resilient modulus was obtained at one-meter intervals using a portable falling weight deflectometer (PFWD). E p .

[0048] Due to the complex environment at the construction site, the original acceleration signal contains a large amount of high-frequency noise and low-frequency drift, requiring the following filtering process: (1) A low-pass FIR filter based on Hamming window is used, with a cutoff frequency of 300Hz, a stopband cutoff frequency of 350Hz, a passband ripple of 0.1dB, a minimum stopband attenuation of 60dB, and a filter order of 60, to remove high-frequency noise. (2) A 5th-order Butterworth high-pass filter is used to remove low-frequency drift.

[0049] The filtered acceleration signal is integrated twice to obtain the displacement-time history curve of the vibrating wheel, and the mean value is removed to reduce the integration error.

[0050] Simultaneously, the filtered acceleration signal is subjected to Fast Fourier Transform (FFT) to obtain the acceleration spectrum, and the acceleration is calculated according to formula (22). CMV, For comparison E ICV The ability to characterize the compaction quality of the roadbed.

[0051] (twenty four) in C =300, A 2Ω and A Ω These are the acceleration amplitudes of the first harmonic component and the fundamental component of the vibration, respectively. E ICV and CMV The calculation uses a 0.25-second time window. This decision is made to achieve high spatial resolution for detecting local variations in compaction mass. Given a vibratory roller operating speed of 2.8 km / h, the 0.25-second window corresponds to approximately 0.2 meters of travel distance, effectively providing a measurement range of approximately one-fifth of a meter. E ICV and CMV The measured values ​​allow for a better characterization of the entire compaction process with higher time resolution.

[0052] therefore, E ICV It can be obtained according to formula (21), where P and E ICV Obtained from the parameters of the vibratory roller, through... An acceleration sensor is installed on the vibrating wheel to collect data. x d Through the The result was obtained by quadratic integration. φ =5π / 6 is taken from existing literature. R It is the radius of the vibrating wheel. β The value is taken as 8.836°. L The parameters were obtained from the vibratory roller. For the CL-ML used in the experiment, the fitting formula (13) was obtained. C d =0.0168 w ²-0.402 w +3.706 ( R ² = 0.998).

[0053] During the compaction process, the degree of compaction was tested using the sand cone method after the second, fourth, and sixth rounds of vibration compaction. K The dynamic resilient modulus was measured using a portable falling weight deflectometer. E p The calculated E ICV Linear regression analysis was performed on the test results.

[0054] The results are as follows Figure 6 As shown, E ICV and K correlation coefficient R ² = 0.84, and E p correlation coefficient R ² = 0.73, significantly higher than CMV Correlation between the two ( R (² = 0.77 and 0.53), indicating E ICV It can more accurately reflect the compaction quality of the roadbed.

[0055] The test section was divided into three moisture content intervals: A, B, and C, and separate establishment procedures were performed for each interval. E ICV , CMV and K , E p The regression model. The results are as follows. Figures 7-8As shown, the correlation within each interval is better than that of the overall modeling, which verifies the necessity of the zoning control standard.

[0056] For example, in zone B (where the moisture content is close to the optimal value). E ICV and K The regression equation is: E ICV =12.3 K -8.7, R ² = 0.89 In zone C (moisture content is higher than the optimal value). E ICV and K The regression equation is: E ICV =9.8 K -6.2, R ² = 0.86.

[0057] In an airport expansion project, the following four commonly used smart compaction indices from three categories were selected and compared with... E ICV Comparison: Harmonic ratio index (CMV and continuous compaction value CCV), mechanical index (stiffness) k s Compaction energy index (compaction power per unit volume, E). The alcohol combustion method is used to quickly obtain the subgrade moisture content for calculation. E ICV Settlement tests were conducted using a precision level to calculate the compaction energy index. E After the second, fourth, fifth, and sixth rounds of vibratory compaction, the compaction degree was checked at 3-meter intervals using the sand method. K Five samples were taken each time, for a total of 20 samples.

[0058] The results are as follows Figure 9 As shown, E ICV of R ² = 0.74, which is better than other indicators (CMV: 0.64, CCV: 0.68). k s E: 0.69, E: 0.70), further verifying E ICV Its superiority in reflecting the quality of roadbed compaction.

[0059] The method for determining the evaluation index of intelligent compaction of roadbed provided in this invention achieves accurate evaluation of the compaction quality of roadbed by establishing a complete dynamic equation, introducing a dynamic contact area coefficient, and considering the influence of moisture content. This invention is derived based on a coupled dynamic model of vibratory roller and roadbed, and has clear mechanical significance; the introduction of a dynamic contact area coefficient quantifies the influence of moisture content on the contact area, improving the accuracy of the model; field tests show that… E ICV Its correlation with compaction degree and dynamic rebound modulus is better than that of existing intelligent compaction indices; it proposes a zonal control standard, which can achieve accurate compaction quality assessment for different moisture content ranges and has good engineering application prospects.

[0060] like Figure 10 As shown, the system for determining the intelligent compaction evaluation index of the roadbed provided in this embodiment of the invention can be implemented in software. The system for determining the intelligent compaction evaluation index of the roadbed includes the following software modules: roadbed equivalent stiffness characterization module 1001, dynamic contact area calculation module 1002, roadbed intelligent compaction vibration modulus calculation module 1003, compaction quality evaluation index conversion module 1004, and compaction quality judgment and process adjustment module 1005.

[0061] The functions of each software module in the system for determining intelligent compaction evaluation indicators for roadbeds are described below: The equivalent stiffness characterization module 1001 is used to characterize the equivalent stiffness of the subgrade based on the vibratory roller-subgrade coupling model. The dynamic contact area calculation module 1002 is used to determine the dynamic contact area coefficient based on the pre-constructed relationship between the subgrade moisture content and the dynamic contact area coefficient, and multiply it by the static contact area to estimate the dynamic contact area between the subgrade and the vibratory wheel during the vibration compaction process. The intelligent compaction vibration modulus calculation module 1003 is used to obtain the intelligent compaction vibration modulus of the roadbed, which characterizes the compaction quality index, based on the relationship between the modulus, dynamic contact area and equivalent stiffness of the roadbed, combined with the parameters of the vibratory roller and the vibration acceleration signal. The compaction quality evaluation index conversion module 1004 is used to determine the moisture content range of the soil based on its moisture content, match and call the regression model between the intelligent compaction vibration modulus of the subgrade and the conventional evaluation index of subgrade compaction quality, and convert the intelligent compaction vibration modulus of the subgrade into the conventional evaluation index of subgrade compaction quality. The compaction quality judgment and process adjustment module 1005 is used to compare the conventional evaluation indicators of the converted roadbed compaction quality with the design requirements, judge in real time whether the compaction quality meets the standards, and adjust the compaction process accordingly.

[0062] It should be noted that each module in the system for determining the intelligent compaction evaluation index of the roadbed in this embodiment corresponds one-to-one with each step in the method for determining the intelligent compaction evaluation index of the roadbed in the above embodiment, and their specific implementation processes are the same, so they will not be repeated here.

[0063] The structure of the electronic device according to an embodiment of the present invention will be described in detail below. Figure 11 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of the present invention. It can be understood that... Figure 11 The diagram shows only an exemplary structure of the electronic device, not the entire structure. Some or all of the structures shown may be implemented as needed.

[0064] The electronic device provided in this embodiment of the invention includes: at least one processor 1101, a memory 1102, a user interface 1103, and at least one network interface 1104. The various components in the system for determining the intelligent compaction evaluation index of the roadbed are coupled together through a bus system 1105. It can be understood that the bus system 1105 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 1105 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 11 The general labeled all buses as Bus System 1105.

[0065] The user interface 1103 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0066] It is understood that memory 1102 can be volatile memory or non-volatile memory, or both. In this embodiment of the invention, memory 1102 is capable of storing data to support the operation of the terminal. Examples of this data include any computer programs used to operate on the terminal, such as operating systems and applications. The operating system includes various system programs, such as framework layers, core library layers, driver layers, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0067] In some embodiments, the system for determining intelligent compaction evaluation indicators of roadbed provided by the present invention can be implemented using a combination of hardware and software. For example, the system can be a processor in the form of a hardware decoding processor, programmed to execute the method for determining intelligent compaction evaluation indicators of roadbed provided by the present invention. For instance, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0068] As an example, processor 1101 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., wherein the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0069] As an example of the hardware implementation of the system for determining the intelligent compaction evaluation index of the roadbed provided in this embodiment of the invention, the device provided in this embodiment of the invention can be directly executed by a processor 1101 in the form of a hardware decoding processor. For example, it can be executed by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the method for determining the intelligent compaction evaluation index of the roadbed provided in this embodiment of the invention.

[0070] The memory 1102 in this embodiment of the invention is used to store various types of data to support the operation of the system for determining intelligent compaction evaluation indicators for roadbeds, or to store data for execution. Figure 1The program code for the method shown. Examples of this data include: any executable instructions for operating on a system for determining intelligent compaction evaluation indicators of roadbed, such as executable instructions, and the program for implementing the method for determining intelligent compaction evaluation indicators of roadbed according to embodiments of the present invention may be included in the executable instructions.

[0071] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by the central processing unit, it performs the various functions defined in the apparatus of this application.

[0072] 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, as well as 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. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining intelligent compaction evaluation indicators for roadbeds, characterized in that, include: Based on the vibratory roller-subgrade coupling model, the equivalent stiffness of the subgrade is characterized. Based on the pre-established relationship between the subgrade moisture content and the dynamic contact area coefficient, the dynamic contact area coefficient is determined and multiplied by the static contact area to estimate the dynamic contact area between the subgrade and the vibratory wheel during the vibration compaction process. Based on the relationship between the modulus of the subgrade, the dynamic contact area, and the equivalent stiffness of the subgrade, and combined with the parameters of the vibratory roller and the vibration acceleration signal, the intelligent compaction vibration modulus of the subgrade, which characterizes the compaction quality index, is obtained. Based on the soil moisture content, determine the moisture content range to which it belongs, match and call the regression model between the intelligent compaction vibration modulus of the subgrade and the conventional evaluation index of subgrade compaction quality, and convert the intelligent compaction vibration modulus of the subgrade into the conventional evaluation index of subgrade compaction quality. The conventional evaluation indicators for the compaction quality of the converted roadbed are compared with the design requirements to determine in real time whether the compaction quality meets the standards and adjust the compaction process accordingly.

2. The method for determining the evaluation index of intelligent compaction of roadbed as described in claim 1, characterized in that, The construction of the vibratory roller-subgrade coupling model is based on the following assumptions: The vibrating wheel and the frame are considered rigid bodies, and the two are connected by stiffness and damping elements; Vibration in the vertical direction is considered only, while horizontal and torsional motion are ignored; The subgrade soil is considered as a Kelvin body, which is composed of elastic elements and viscous elements connected in parallel.

3. The method for determining the evaluation index of intelligent compaction of roadbed as described in claim 1, characterized in that, Based on the subgrade compaction test data, a quadratic polynomial model was used to construct the relationship between subgrade moisture content and dynamic contact area coefficient. ;in, This refers to the dynamic contact area coefficient. denoted as , where is the subgrade moisture content; a, b, and c are fitting parameters.

4. The method for determining the evaluation index of intelligent compaction of roadbed as described in claim 1, characterized in that, The relationship between the modulus of the subgrade, the dynamic contact area, and the equivalent stiffness of the subgrade is as follows: ； in, The modulus of the roadbed; This refers to the dynamic contact area. The equivalent stiffness of the roadbed; The Poisson's ratio of the roadbed; It is the angle between the tangent at the contact point between the vibratory wheel and the roadbed and the horizontal line.

5. The method for determining the evaluation index of intelligent compaction of roadbed as described in claim 1, characterized in that, Based on the measured data of the intelligent compaction vibration modulus and conventional evaluation indicators of subgrade compaction quality in different moisture content ranges, regression analysis was conducted on the intelligent compaction vibration modulus and conventional evaluation indicators of subgrade compaction quality to obtain the regression model between the intelligent compaction vibration modulus and the conventional evaluation indicators of subgrade compaction quality.

6. The method for determining the evaluation index of intelligent compaction of roadbed as described in claim 1, characterized in that, When the conventional evaluation index for subgrade compaction quality is compaction degree, the regression model between the subgrade intelligent compaction vibration modulus and compaction degree is as follows: ;in, For intelligent compaction vibration modulus of roadbed; Compaction degree; and These are the regression coefficients corresponding to different moisture content ranges.

7. The method for determining the evaluation index of intelligent compaction of roadbed as described in claim 1, characterized in that, When the conventional evaluation index for subgrade compaction quality is the dynamic resilient modulus, the regression model between the subgrade intelligent compaction vibration modulus and the dynamic resilient modulus is: ;in, For intelligent compaction vibration modulus of roadbed; For dynamic resilience modulus; and These are the regression coefficients corresponding to different moisture content ranges.

8. A system for determining intelligent compaction evaluation indicators for roadbeds, characterized in that, The method for determining the intelligent compaction evaluation index of roadbed as described in any one of claims 1-7 includes: The roadbed equivalent stiffness characterization module is used to characterize the equivalent stiffness of the roadbed based on the vibratory roller-roadbed coupling model. The dynamic contact area calculation module is used to determine the dynamic contact area coefficient based on the pre-constructed relationship between the subgrade moisture content and the dynamic contact area coefficient, and then multiply it by the static contact area to estimate the dynamic contact area between the subgrade and the vibratory wheel during the vibration compaction process. The intelligent compaction vibration modulus calculation module is used to obtain the intelligent compaction vibration modulus of the roadbed, which characterizes the compaction quality index, based on the relationship between the modulus, dynamic contact area and equivalent stiffness of the roadbed, combined with the parameters of the vibratory roller and the vibration acceleration signal. The compaction quality evaluation index conversion module is used to determine the moisture content range of the soil based on its moisture content, match and call the regression model between the intelligent compaction vibration modulus of the subgrade and the conventional evaluation index of subgrade compaction quality, and convert the intelligent compaction vibration modulus of the subgrade into the conventional evaluation index of subgrade compaction quality. The compaction quality judgment and process adjustment module is used to compare the conventional evaluation indicators of the converted roadbed compaction quality with the design requirements, judge in real time whether the compaction quality meets the standards, and adjust the compaction process accordingly.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for determining the intelligent compaction evaluation index of the roadbed as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for determining the intelligent compaction evaluation index of the roadbed as described in any one of claims 1-7.

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