A method for calibrating a continuous compaction detection device for a roadbed

Abstract

This invention relates to the field of continuous compaction testing technology for roadbeds, and discloses a calibration method for a continuous compaction testing device for roadbeds. The method includes: S2, combining multiple rubber pads of different hardness and thickness to obtain a simulated test system; S3, testing the dynamic elastic modulus of multiple rubber pad layers to verify the uniformity and continuous variability of the rubber pad layer stiffness; S4, conducting in-situ single-point vibration tests on the simulated test system, collecting steel wheel vibration acceleration signals using the continuous compaction testing device, and calculating the continuous compaction testing index to be calibrated after preprocessing the collected signals; S5, using the average dynamic elastic modulus of the rubber pad layers as an equivalent stiffness reference value, and using regression analysis to fit the correlation between the index to be calibrated and the dynamic elastic modulus, thereby obtaining a calibration curve. This invention provides more accurate and universal calibration results for roadbed compaction quality testing equipment, improves calibration efficiency, and reduces calibration costs.

Description

Technical Field

[0001] This invention relates to the field of continuous compaction testing technology for roadbeds, and more specifically to a calibration method for continuous compaction testing equipment for roadbeds. Background Technology

[0002] As an advanced construction quality control device, the continuous compaction testing equipment for roadbeds integrates high-precision accelerometers, GNSS positioning modules, and other sensing devices on the road roller. It collects dynamic response data in real time and calculates continuous testing indicators reflecting the compaction quality through professional algorithms. These indicators provide a real-time view of the roadbed compaction status, offering a basis for construction control and quality assessment. In current technical specifications and practical engineering applications, establishing the correlation between indicators through field experiments is the core quality control principle: First, typical test sites are selected, and traditional point measurement methods such as nuclear density meter for compaction degree, bearing plate for resilient modulus, and falling weight deflectometer (FWD) are used to obtain key roadbed quality parameters. Second, correlation analysis and regression modeling are performed between these point measurement results and the continuous compaction testing indicators to establish mathematical mapping relationships. Finally, quality control threshold standards applicable to specific projects are determined to achieve scientific guidance and effective management of large-scale construction areas.

[0003] However, research on calibration based on actual engineering field experiments is constrained by numerous complex factors. Key influencing factors include soil moisture content, ambient temperature, base stiffness, material layer thickness, and testing equipment parameters. The coupling of these factors leads to significant differences in experimental data under different sites and test conditions, making it difficult to construct a universally applicable model. This results in insufficient data transfer and comparability across sites and working conditions, limiting the application value of experimental results. Furthermore, objective factors such as positioning accuracy, sensor installation, measuring equipment drift, sample size, and test schemes exacerbate measurement noise, increase data processing difficulty, and easily lead to model overfitting. Moreover, the accumulation of systematic errors makes it difficult to guarantee the measurement accuracy and reliability of field experimental results, increasing experimental costs and time. Given the differences in field working conditions and the variability of fill material parameters, the calibration of continuous compaction testing equipment for roadbeds relies heavily on field experiments, making it difficult to ensure the reliable stability of compaction indicators. This results in difficulties in horizontal comparison and mutual verification of experimental data under different working conditions, hindering the construction of a widely accepted reference index and standard system.

[0004] Therefore, the industry urgently needs a calibration method for roadbed continuous compaction testing equipment to improve the accuracy and versatility of calibration results, reduce calibration costs, and provide a more reliable basis for roadbed compaction quality testing. Summary of the Invention

[0005] This invention aims to overcome the shortcomings of existing technologies and provide a calibration method for roadbed continuous compaction testing equipment. By simulating the roadbed continuous compaction testing process under controllable experimental conditions, it effectively eliminates the interference of complex external factors and achieves more accurate and efficient equipment calibration. This method improves the accuracy and versatility of calibration results, reduces calibration costs, and provides a more reliable basis for roadbed compaction quality testing. This method is particularly suitable for compaction quality control scenarios with high requirements for testing consistency and has significant advantages under standardized construction conditions.

[0006] To achieve the above objectives, the present invention provides a calibration method for a roadbed continuous compaction testing device, comprising the following steps:

[0007] S1. Constructing an equivalent stiffness system for rubber: By combining several rubber pads with different hardness and thickness, multiple rubber pad layers are obtained, and the multiple rubber pad layers constitute a simulation test system with equivalent uniform stiffness.

[0008] S2. Stiffness characteristic verification: The dynamic elastic modulus (Evd) of the multiple rubber pads is tested respectively, and based on the test results, the uniformity and continuous variability of the stiffness of each rubber pad are verified to verify that its stiffness has good continuous variability while meeting the uniformity requirements.

[0009] S3. Vibration test and signal preprocessing: In-situ single-point vibration test is carried out on the simulated test system. Vibration acceleration signal of steel wheel is collected with the help of roadbed continuous compaction test equipment. After preprocessing the collected signal, the continuous compaction test index to be calibrated is calculated.

[0010] S4. Fitting Calculation and Calibration Curve: The average dynamic elastic modulus of the measured rubber pad layer is used as a reference value for the equivalent stiffness. Regression analysis is employed to fit the correlation between the continuous compaction test index to be calibrated and the dynamic elastic modulus, thereby obtaining the calibration curve. This provides more accurate and universal calibration results for roadbed compaction quality testing equipment, improving calibration efficiency and reducing calibration costs.

[0011] Furthermore, in step S1, the hardness of the rubber pad is 65~75°, and the thickness of the rubber pad is 10~30mm.

[0012] Furthermore, in step S2, dynamic elastic modulus is collected at the four corners and the center of each rubber pad layer; the average of the five collected data is taken, and then standard deviation analysis is performed. The average dynamic elastic modulus of all rubber pad layers is compared to verify the uniformity and variability of stiffness.

[0013] Furthermore, for multiple dynamic elastic modulus values ​​of the simulation test system, if the standard deviation of each dynamic elastic modulus value is less than 10% of the mean, the data is considered to be relatively uniform.

[0014] Furthermore, in step S3, a small road roller with a steel wheel width of 50~80cm is used, and a roadbed continuous compaction detection device is installed on the small road roller to collect the steel wheel vibration acceleration signal. The sampling frequency of the signal is set to 500~1000HZ.

[0015] Furthermore, the compaction process of the rubber pad layer is as follows: first, pre-stabilize for 5~10s, then collect vibration acceleration signals with a steady-state effective window time of 20~30s for index calculation, and after the end, relax for 20~30s before conducting the compaction test of the next rubber pad layer.

[0016] Furthermore, the preprocessing steps for the acquired signal are as follows: First, the vibration acceleration signal is optimized in the frequency domain using an FIR bandpass filter, while retaining the vibration characteristic frequency band; at the same time, low-frequency drift and high-frequency noise are filtered out, and the frequency range includes the fundamental frequency and the second harmonic ±5Hz; then, the signal is dynamically smoothed using a moving average filter to suppress glitch noise.

[0017] Furthermore, the continuous compaction test index to be calibrated is CMV, with the following calculation parameters: amplification factor of 300, sampling frequency of 1000Hz, and time step of 1s, so as to ensure that the calculation results can effectively cover multiple cycles of the signal.

[0018] Furthermore, in step S4, the least squares method is used to perform data fitting calculations and correlation analysis; if a relationship is to be fitted... To describe the data points ( , First, the sum of squared errors is defined as:

[0019] (1);

[0020] According to the principles of calculus, in order to make Minimum, parameter and The following differential equations must be satisfied, and the formula for the optimal parameters can be obtained by solving the following system of equations:

[0021] (2);

[0022] (3);

[0023] (4);

[0024] Where n is the number of data points, The mean of the Evd data points; This represents the mean of the CMV data points.

[0025] Ultimately, this minimizes the sum of squared errors between the model's predicted values ​​and the actual observed values.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] This invention provides a calibration method for a roadbed continuous compaction testing device. It constructs a uniform and variable equivalent stiffness system, upon which in-situ single-point vibration tests of a road roller are conducted. The collected vibration acceleration signals are then preprocessed to calculate the continuous compaction testing indicators to be calibrated. The average Evd value of each rubber pad layer is used as the equivalent stiffness reference value, and the average CMV value of the rubber pad layers is fitted to the average Evd value to obtain the final calibration curve. This provides a method for calibrating roadbed continuous compaction testing devices. This invention provides more accurate and universal calibration results for roadbed compaction quality testing devices, improves calibration efficiency, and reduces calibration costs.

[0028] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0029] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0030] Figure 1 This is a flowchart of a calibration method for a roadbed continuous compaction testing device according to the present invention;

[0031] Figure 2 This is a graph showing the mean and maximum deviation of CMV in a preferred embodiment of the present invention.

[0032] Figure 3 This is a fitted curve of CMV and Evd in a preferred embodiment of the present invention. Detailed Implementation

[0033] The present invention will now be described in detail with reference to the embodiments shown in the accompanying drawings. However, it should be noted that these embodiments are not intended to limit the present invention. Equivalent transformations or substitutions in function, method, or structure made by those skilled in the art based on these embodiments are all within the scope of protection of the present invention.

[0034] Please see Figure 1This embodiment provides a calibration method for a roadbed continuous compaction testing device, including the following steps:

[0035] S1. Constructing an equivalent stiffness system for rubber: By combining several rubber pads with different hardness and thickness, multiple rubber pad layers are obtained; the multiple rubber pad layers constitute a simulation test system with equivalent uniform stiffness.

[0036] Table 1 Equivalent Stiffness System

[0037]

[0038] In this invention, the rubber pads are 1 meter in length and 1 meter in width. Then, by rationally combining rubber pad layers of different hardness and thickness, a series of simulation test systems with equivalent uniform stiffness characteristics are constructed. Two assumptions are made here: (1) the stiffness of the rubber pads is uniform, meaning the stiffness at any position is approximately the same, and fluctuations within a certain numerical range are permissible; (2) the stiffness of rubber pad layers with different hardness and thickness is variable and exhibits a numerical variation law. In this invention, the equivalent stiffness system shown in Table 1 above is constructed.

[0039] S2. Stiffness Characteristic Verification: The dynamic elastic modulus (Evd) of multiple rubber pad layers was tested separately, and based on the test results, the uniformity and continuous variability of the stiffness of each rubber pad layer were verified. This section verifies the uniformity and variability of stiffness in the rubber equivalent stiffness system. Based on the dynamic elastic modulus of the rubber pad layers, dynamic elastic modulus data were collected for each lower rubber pad layer near its four corners and at its center. The mean of these five data points was then calculated, followed by standard deviation analysis. The mean Evd values ​​for each rubber pad layer were compared to verify the uniformity and variability of stiffness. The calculation results are shown in Table 2 below:

[0040] Table 2 Numerical Analysis of Evd

[0041]

[0042] As shown in Table 2, for the Evd values ​​of the 15 rubber pads, the standard deviation of all rubber pads is less than 10% of the mean, indicating that the data is relatively uniform. Furthermore, the mean Evd values ​​for each rubber pad are arranged from smallest to largest: R4-R1-R7-R5-R10-R8-R6-R11-R2-R9-R12-R3-R13-R14-R15. The variation in these values ​​conforms to objective laws: when the thickness is the same, greater hardness results in greater stiffness; when the hardness is the same, greater thickness results in lower stiffness. Therefore, by combining rubber pads with different hardnesses and thicknesses, an equivalent stiffness system with uniform and variable stiffness can be effectively constructed.

[0043] S3. Vibration testing and signal preprocessing process: In-situ single-point vibration test is conducted on the simulation test system. Vibration acceleration signal of steel wheel is collected with the help of roadbed continuous compaction testing equipment. After preprocessing the collected signal, the continuous compaction test index to be calibrated is calculated.

[0044] In this invention, a small road roller with a steel wheel width of 60cm is used, and a continuous compaction testing device for the roadbed is installed on the roller to collect the vibration acceleration signal of the steel wheel. The sampling frequency of the signal is set to 500~1000HZ. Furthermore, as verified above, the stiffness of the rubber pad layer is uniform; therefore, vibration testing can be performed using in-situ single-point vibration. The compaction process for each rubber pad layer is as follows: first, pre-stabilize for 5~10s (not included in the calculation), then collect the vibration acceleration signal for a steady-state effective window time of 20~30s for index calculation, and finally relax for 20~30s before performing the next combination of compaction tests. This eliminates signal errors caused by the start and stop of the road roller vibration.

[0045] For the vibration acceleration signal acquired from vibration testing, the continuous compaction detection index used in this invention is CMV, and the fundamental frequency of the road roller's vibration is 46 Hz, with its second harmonic frequency at 92 Hz. Based on these characteristics, the signal was preprocessed: First, an FIR bandpass filter (passband range 40~100Hz) was used to optimize the signal in the frequency domain, preserving the vibration characteristic frequency band while filtering out low-frequency drift and high-frequency noise, and ensuring that the frequency range includes the fundamental frequency and the second harmonic ±5 Hz. Then, a moving average filter was used to dynamically smooth the signal, suppressing glitch noise, preserving the main vibration characteristics, and improving the smoothness and analyzability of the signal.

[0046] Step S3 involves the calculation and numerical analysis of the calibrated index: In this invention, the continuous compaction test index to be calibrated is CMV. Therefore, during the calculation process, the amplification factor is set to 300. Given the sampling frequency of 1000Hz, a time step of 1s is selected to ensure that the calculation results effectively cover multiple cycles of the signal. Based on the CMV calculation results, numerical analysis is performed. Based on the verified validity of the above assumptions of uniform and variable stiffness, the CMV calculated for each rubber pad layer (i.e., each permutation and combination) should also numerically satisfy the laws of stability and mean variation. The numerical analysis results are shown in Table 3.

[0047] Table 3. CMV numerical analysis in the embodiments of the invention

[0048]

[0049] In the numerical analysis, 20 CMV calculations were performed at 15 steady-state times for the rubber pads. The statistical parameters obtained from the calculations were compared with... Figure 2 The results lead to the following conclusions: (1) The trend of CMV mean change is consistent with EVD. The calculation results are arranged from smallest to largest as follows: R4-R1-R7-R5-R10-R8-R6-R11-R2-R9-R12-R3-R13-R14-R15; (2) Their standard deviations are all less than 10% of the mean, which can be considered as relatively uniform data; (3) Deviation refers to the degree to which the data at a point deviates from the average value, i.e. (data at that point - average value) / average value. The average absolute deviation is the average of the deviations of the data at 20 points. The average absolute deviations of the above results are all less than 10%, which can be considered as the data fluctuating slightly within the mean range. This also verifies the uniformity and variability of the stiffness of the rubber equivalent stiffness system.

[0050] S4. Fitting calculation and calibration curve:

[0051] The least squares method is used for data fitting calculation and correlation analysis. The specific calculation steps are as follows: Assume that a relationship is to be fitted. To describe the data points ( , First, the sum of squared errors is defined as:

[0052] (1);

[0053] According to the principles of calculus, in order to make Minimum, parameter and The following differential equations must be satisfied, and the formula for the optimal parameters can be obtained by solving the following system of equations:

[0054] (2);

[0055] (3);

[0056] (4);

[0057] Where n is the number of data points, The mean of the Evd data points; This represents the mean of the CMV data points.

[0058] The fundamental goal is to find a set of parameters that minimizes the sum of squared errors between model predictions and actual observations. In an equivalent uniform stiffness system, Evd is considered an equivalent stiffness reference value. Since there is a certain correlation between CMV and Evd, the least squares method is used to fit CMV and Evd:

[0059] Depend on Figure 3The fitting curves of CMV and Evd lead to the following conclusions: In the constructed rubber equivalent stiffness system, CMV and Evd exhibit a good correlation, and can be obtained... The calibration curve can be calculated through fitting. =1.23036±0.0344, =-29.10505±6.67629, where R 2 =0.98992, indicating a high degree of correlation.

[0060] In summary, this invention constructs a uniform and variable equivalent stiffness system, upon which in-situ single-point vibration tests of a road roller are conducted. The collected vibration acceleration signals are then preprocessed to calculate the continuous compaction test index to be calibrated (CMV is used as an example in this invention). The mean Evd value for each permutation and combination is used as the equivalent stiffness reference value, and the mean CMV value for each permutation and combination is fitted with the mean Evd value to obtain the final calibration curve. This provides an effective method for calibrating continuous compaction testing equipment for roadbeds.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the 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 calibration method for a roadbed continuous compaction testing device, characterized in that, Includes the following steps: S1. Constructing an equivalent stiffness system for rubber: By combining several rubber pads with different hardness and thickness, multiple rubber pad layers are obtained, and the multiple rubber pad layers constitute a simulation test system with equivalent uniform stiffness. S2. Stiffness Characteristic Verification: The dynamic elastic modulus of the multiple rubber pads is tested separately, and based on the test results, the uniformity and continuous variability of the stiffness of each rubber pad are verified. Specifically, dynamic elastic modulus data are collected at the four corners and the center of each rubber pad. The average of the five collected data points is taken, and then standard deviation analysis is performed. The average dynamic elastic modulus values ​​of all rubber pads are compared to verify the uniformity and variability of stiffness. For the multiple dynamic elastic modulus values ​​of the simulated test system, if the standard deviation of each dynamic elastic modulus value is less than 10% of the mean, the data is considered relatively uniform. S3. Vibration Testing and Signal Preprocessing: In-situ single-point vibration testing is conducted on the simulated testing system. Vibration acceleration signals of the steel wheel are collected using a roadbed continuous compaction testing device. After preprocessing the collected signals, the continuous compaction testing indicators to be calibrated are calculated. A small road roller with a steel wheel width of 50-80cm is used, and the roadbed continuous compaction testing device is installed on the small road roller to collect the steel wheel vibration acceleration signals. The sampling frequency of the signal is set to 500-1000Hz. The compaction process of the rubber pad layer is as follows: first, pre-stabilize for 5-10s, then collect vibration acceleration signals with a steady-state effective window time of 20-30s for indicator calculation. After the calculation, relax for 20-30s before conducting the compaction test of the next rubber pad layer. S4. Fitting Calculation and Calibration Curve: The average dynamic elastic modulus of the measured rubber pad layer is used as the reference value of the equivalent stiffness. The correlation between the continuous compaction test index to be calibrated and the dynamic elastic modulus is fitted by regression analysis to obtain the calibration curve.

2. The calibration method of claim 1, wherein, In step S1, the hardness of the rubber pad is 65~75° and the thickness of the rubber pad is 10~30mm.

3. The calibration method of claim 1, wherein, The preprocessing steps for the acquired signal are as follows: First, the vibration acceleration signal is optimized in the frequency domain using an FIR bandpass filter, while retaining the vibration characteristic frequency band; at the same time, low-frequency drift and high-frequency noise are filtered out, and the frequency range includes the fundamental frequency and the second harmonic ±5Hz; then, the signal is dynamically smoothed using a moving average filter to suppress glitch noise.

4. The calibration method of claim 1, wherein The continuous compaction test index that needs to be calibrated is CMV, and its calculation parameters are: amplification factor of 300, sampling frequency of 1000Hz, and time step of 1s, so as to ensure that the calculation results can effectively cover multiple cycles of the signal.

5. The calibration method of claim 1, wherein In step S4, the least squares method is used to perform data fitting calculations and correlation analysis.

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