Compaction quality detection method based on vibration signal interval cross correlation optimization

Abstract

The invention discloses a compaction quality detection method based on vibration signal interval cross-correlation optimization, which comprises the following steps: firstly, carrying out grid division on a field compaction area and arranging sampling points, collecting vibration signals through an acceleration sensor on a road roller and measuring the compaction degree of each point after compaction, then carrying out area division, preprocessing vibration signals in a specified interval, and carrying out cross-correlation optimization on the vibration signals in the specified interval; and calculating the cross correlation of adjacent intervals. And determining a signal rule degree and a cross correlation peak reference value, removing corresponding ICMV values of the interval lower than the reference value, and establishing a linear regression model of the actual compactness and the ICMV values. And adjusting the interval range and performing iteration, and selecting the model with the highest fitting precision as a final compaction quality analysis model. According to the method, extra detection means are not needed, and the cross correlation of the vibration signals of the road roller is used for detecting the section with large variability; by eliminating variability values, the accuracy of the linear model for evaluating the compactness is improved; meanwhile, the influence of the interval size on the vibration signal variability is also considered.

Description

Technical Field

[0001] The invention belongs to the technical field of road engineering, and in particular relates to a compaction quality detection method based on optimization of vibration signal interval cross-correlation. Background Art

[0002] With the further development of information technology and the needs of the current situation of highway construction in my country, intelligent compaction of roadbed has been further developed and widely used. At present, intelligent compaction mainly evaluates the compaction quality by various ICMVs (compaction quality measurement values, Intelligent Compaction Measure Value, ICMV) extracted from the vibration signal of the roller. Linear fitting with the measured compaction degree can preliminarily obtain the evaluation model of compaction quality. In order to accurately use this model to evaluate the compaction quality of the roadbed, it is necessary to improve the test accuracy of the test section and the reliability of the data as much as possible. However, although the fillers used in the roadbed section within a certain range are of the same type, the roadbed soil is an uneven mixture as a whole, and the filler properties of each section are uncontrollable and will show certain differences from each other. The difference in the properties of the roadbed soil will cause the vibration signal of the roller to show different vibration responses. If this is not distinguished, it will lead to large errors in the compaction quality evaluation using the estimation model, thereby affecting the accuracy of the compaction quality evaluation. Therefore, it is necessary to propose a method to quickly remove the vibration signals in the interval with large differences in filler properties to achieve original data optimization. Summary of the invention

[0003] The purpose of an embodiment of the present invention is to provide a compaction quality detection method based on optimization of vibration signal interval cross-correlation, which measures the regularity of subgrade soil characteristics in the overall subgrade interval through the interval cross-correlation of roller vibration signals, and achieves rapid elimination of signals with large differences in filler properties, thereby improving the accuracy of subgrade compaction quality detection.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is a compaction quality detection method based on vibration signal interval cross-correlation optimization, comprising the following steps: S1. Divide the on-site compaction area into grids and arrange compaction sampling points; S2. Arrange an acceleration sensor on the vibrating wheel of the roller to roll the roadbed and collect vibration signals during the entire compaction process; after compaction is completed, use the ring knife method to measure the compaction degree of each compaction degree sampling point; S3, dividing the compaction strip into regions; S4, preprocessing the vibration signal in the specified interval; after the preprocessing is completed, performing cross-correlation calculation on the vibration signals in adjacent intervals; S5. Determine the ICMV value within the interval according to the vibration signal; S6, determining the degree of regularity of the signals in each section over the entire roadbed; S7, determining a reference control value of the peak value of the correlation, identifying and marking all intervals below the reference control value according to the correlation value distribution trace diagram generated by the roller operation trajectory, and eliminating the corresponding ICMV values; establishing a linear regression relationship model between the actual compaction degree in the interval of the ICMV value not eliminated and the corresponding ICMV value; S8. Repeat the process of S3 to S7, change different interval ranges, determine different linear regression relationship models, and select the relationship model with the highest fitting accuracy as the compaction quality analysis model.

[0005] Furthermore, the side length of the grid divided in S1 is 0.5-0.6 m, and the compaction sampling points are set at the intersections of the grids.

[0006] Furthermore, in S3, the length of the compacted strip is divided into intervals ranging from 2 to 4 m.

[0007] Furthermore, the process of preprocessing the vibration signal in S4 is specifically as follows: firstly, a window function is used to implement a truncation operation to ensure that the vibration signals extracted from each segment have the same length. After the truncation is completed, a normalization process is performed on the obtained vibration signal, specifically as follows: (1); in, is the value of the interval vibration signal at any time, is the minimum value of the interval vibration signal, is the maximum value of the interval vibration signal, is the normalized result.

[0008] Furthermore, the cross-correlation calculation of the vibration signals in adjacent intervals described in S4 is specifically as follows: (2); Where: is the vibration signal within this interval; is the vibration signal of the adjacent interval on the right side of this interval; is the vibration signal of the adjacent interval on the left side of this interval; t represents the time variable; T is the sampling time; is the signal lag time, is the cross-correlation function between the specified interval and the adjacent interval on the right; It is the cross-correlation function between the specified interval and the adjacent interval on the left.

[0009] Furthermore, the regularity of the signals in each section in S6 over the entire roadbed The specific determination method is: (3); Where: For the The cross-correlation function of an interval and the interval to the right of it Maximum value; For the The cross-correlation function of an interval and the interval to the left of it Maximum value; is the average value of the peak value of the cross-correlation function in each interval; Among them, the first interval and the last interval They are: , ,in, n is the total number of intervals.

[0010] Furthermore, the reference control value of the cross-correlation peak is the average value of the peak value of the cross-correlation function in each interval Take the overall average 0.8 times: (4); (5); Where: is the overall average; is the reference control value.

[0011] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a quality detection method based on the optimization of the cross-correlation of the vibration signal interval of the roller. By analyzing and eliminating the vibration signals in the intervals with large differences in filler properties, the error caused by uneven soil quality is effectively reduced, thereby significantly improving the accuracy of the evaluation of the roadbed compaction quality. This method does not require additional complex detection equipment, simplifies the data processing process and improves work efficiency. At the same time, it takes into account the spatial variability of the roadbed compaction quality, and finds the optimal interval value based on the approximate variability range of the compaction quality (2~4m), ensuring the influence of the interval size on the detection accuracy. The present invention does not require additional detection means, and detects the sections with large variability from the cross-correlation of the vibration signal of the roller; after eliminating the variability value, the evaluation accuracy of the linear model on the compaction degree is improved; at the same time, the influence of the interval size on the variability of the vibration signal is considered. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0013] Figure 1 This is a schematic diagram of the field test interval division of this implementation method; Figure 2 is an abnormal value interval evaluation diagram of the present embodiment; Figure 3 is a linear fitting diagram of ICMV and compaction degree in this embodiment; Figure 4 is the fitting accuracy of CMV and compaction degree in this implementation mode; wherein (a) is the original data, and (b) is the data after removing outliers; Figure 5 is the fitting accuracy of CMV and compaction degree in this implementation mode; wherein (a) is the original data, and (b) is the data after removing outliers; DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] This embodiment provides a compaction quality detection method based on the optimization of the cross-correlation of vibration signal intervals. First, the roadbed is divided into regions in the test section. Each point is set in each interval to measure the compaction of each point by the ring knife method. The overall compaction value of the area is calculated by the average value of the measured compaction of each point in the area. The mutual differences in filler characteristics are evaluated by the cross-correlation of the vibration signals of the rollers in each interval of the roadbed section, because in theory, similar vibration signals will be obtained when the roadbed is rolled under the same working conditions when the filler characteristics are similar. The intervals with large differences in vibration signals from the surrounding intervals are eliminated to achieve data optimization and improve the accuracy of roadbed compaction quality detection. However, since the roadbed compaction quality has spatial variability, the selection of the interval range will affect the compaction effect to a certain extent. Therefore, according to the approximate variability range of the roadbed compaction quality, that is, 2~4m, the optimal interval value is found in this interval.

[0016] This implementation is specifically carried out according to the following steps: S1. Divide the compaction area on site into grids and arrange compaction sampling points. In order to make the test value of interval compaction as accurate as possible, set the grid size to 0.5~0.6m. The intersection of each grid is designated as the sampling point of compaction, which is used as the basic location point for measurement and analysis. Figure 1 shown.

[0017] S2. Arrange an acceleration sensor on the vibrating wheel of the roller, roll the roadbed according to the original working conditions, collect vibration signals during the entire compaction process, and use the ring knife method to measure the compaction degree of each sampling point after compaction. The overall compaction quality of a certain interval is equal to the average compaction degree of each point.

[0018] In some possible implementations, an acceleration sensor of model HCF4000-A1 is selected to collect vibration signals during the compaction process. Depending on specific application requirements, other acceleration sensors with corresponding functions may be selected as alternatives to ensure that vibration characteristic data reflecting the compaction process can be accurately and effectively obtained.

[0019] S3. Divide the compaction strip into regions; the variation range of compaction quality is usually between 2 meters and 4 meters. Selecting a too small range (such as less than 2 meters) is of limited significance for evaluating compaction quality, while selecting a too large range may lead to a decrease in detection accuracy. Therefore, this implementation method selects 2 meters as the initial analysis range, and gradually increases the range size in increments of 0.5 meters to determine the impact of different range sizes on the accuracy of compaction quality evaluation.

[0020] S4. Process the vibration signal in the specified interval. First, use the window function to perform a truncation operation on it to ensure that the vibration signals extracted from each section have the same length. The key to this step is that the selected truncation segment must cover all compaction sampling points in the section to ensure data integrity and representativeness. After the truncation is completed, the obtained vibration signal is normalized to eliminate the impact caused by the difference in signal amplitude and ensure the accuracy of subsequent analysis. Specifically: (1); Subsequently, the vibration signals of adjacent intervals are cross-correlated. Cross-correlation calculation can quantify the similarity or correlation between two vibration signals. Specifically, the peak of the cross-correlation function reflects the highest cross-correlation between the two signals, that is, the maximum matching degree between the two at a certain relative displacement. The cross-correlation function of the vibration signal of a specified interval and the left and right intervals is calculated as follows: (2); Where: is the vibration signal within this interval; is the vibration signal of the adjacent interval on the right side of this interval; is the vibration signal of the adjacent interval on the left side of this interval; t represents the time variable; T is the sampling time; is the signal lag time, is the cross-correlation function between the specified interval and the adjacent interval on the right; It is the cross-correlation function between the specified interval and the adjacent interval on the left.

[0021] S5. Calculate the ICMV value according to the divided intervals. The ICMV value is a traditional continuous compaction quality evaluation value. The calculation methods of various ICMV values ​​are inconsistent. You can select a specific ICMV value for calculation according to your needs.

[0022] The improved compaction quality value (ICMV value) is calculated based on the interval division. As a traditional continuous compaction quality evaluation index, the calculation method and specific type of the ICMV value vary according to the specific application scenario and requirements. In actual application, the appropriate ICMV calculation method can be selected for evaluation according to engineering requirements.

[0023] S6. Calculate the average value of the cross-correlation function peak values ​​of each interval and the adjacent intervals before and after as the cross-correlation representative value to represent the regularity of the signal in the interval in the entire roadbed.

[0024] (3); Where: For the The cross-correlation function of an interval and the interval to the right of it Maximum value; For the The cross-correlation function of an interval and the interval to the left of it Maximum value; the first interval takes the peak value of the cross-correlation function between this interval and the second interval as the representative value of this interval, and the last interval takes the peak value of the cross-correlation function between this interval and the second to last interval as the representative value of this interval: , ,in, n is the total number of intervals.

[0025] S7. Cross-correlation representative values ​​for each interval in the road section Take the overall average 0.8 times As the benchmark control value, the relevant numerical distribution trace diagram (such as Figure 2 ), identify and mark Lower than the above benchmark control value All intervals of: (4); (5); Where: is the overall average; is the reference control value.

[0026] These areas are considered to be areas that do not meet the compaction quality requirements, and the corresponding intelligent compaction measurement values ​​(ICMV values) measured by the vibration signals are removed from the subsequent analysis to avoid adverse effects on the overall evaluation results due to local low-quality compaction points. Finally, the actual compaction degree in the remaining areas that meet the requirements is used for linear regression analysis with the corresponding ICMV values ​​to establish a linear relationship model between the two, such as Figure 3 As shown: ; This implementation method evaluates the interval cross-correlation of the roller vibration signal to eliminate the intervals with large differences in roadbed soil properties, thereby providing a more accurate and reliable means of compaction quality assessment. This process improves the accuracy and reliability of the assessment results and also provides a theoretical basis and technical support for optimizing compaction operations in engineering practice.

[0027] S8. Repeat steps S3 to S6, changing different interval ranges to obtain different linear relationship models, and select the linear regression model with the highest fitting accuracy as the compaction quality analysis model.

[0028] Example The test data of the 40 m long test section of the Leiyi Expressway was analyzed. Single strip compaction was used and the analysis intervals were 2 m, 2.5 m, 3.0 m, 3.5 m, and 4.0 m. The compaction test interval was 0.5 m. The ICMV selected here is the most commonly used CMV value in the compaction system, as shown in the following formula: (6); Where: c is the coefficient, c =200; is the acceleration amplitude corresponding to 2 times the fundamental frequency; is the acceleration amplitude corresponding to the fundamental frequency; the acquired time series signal is transformed into a frequency domain signal by Fourier series transformation to obtain the acceleration amplitude corresponding to the fundamental frequency and twice the fundamental frequency.

[0029] Table 1 Test results with a range of 2m ; Depend on Figure 4 (a) and (b) show that after removing outliers, the fitting accuracy is higher than the original linear model.

[0030] Table 2 Test results for interval 2.5m ; Depend on Figure 5 (a) and (b) show that after removing the outliers, the improved model fitting accuracy is higher than the original linear model. The effect of using 2.0m as the division interval is better than 2.5m, but due to limited test data, further expanding the division area will result in too little data. However, the above test results fully demonstrate that the invention can effectively remove the outlier value section of the vibration signal in the roadbed area to improve the model accuracy.

[0031] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0032] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A compaction quality detection method based on optimization of vibration signal interval cross-correlation, characterized in that: The following steps are involved: S1. Divide the on-site compaction area into grids and arrange compaction sampling points; S2. Arrange an acceleration sensor on the vibrating wheel of the roller to roll the roadbed and collect vibration signals during the entire compaction process; after compaction is completed, use the ring knife method to measure the compaction degree of each compaction degree sampling point; S3, dividing the compaction strip into regions; S4, preprocessing the vibration signal in the specified interval; after the preprocessing is completed, performing cross-correlation calculation on the vibration signals in adjacent intervals; S5. Determine the ICMV value within the interval according to the vibration signal; S6, determining the degree of regularity of the signals in each section over the entire roadbed; S7, determining a reference control value of the cross-correlation peak value, identifying and marking all intervals below the reference control value according to the correlation value distribution trace diagram generated by the roller operation trajectory, and eliminating the corresponding ICMV values; Establish a linear regression relationship model between the actual compaction degree and the corresponding ICMV value within the range of ICMV values ​​that have not been eliminated; S8. Repeat the process of S3 to S7, change different interval ranges, determine different linear regression relationship models, and select the relationship model with the highest fitting accuracy as the compaction quality analysis model.

2. A compaction quality detection method based on vibration signal interval cross-correlation optimization according to claim 1, characterized in that: The side length of the grid divided in S1 is 0.5-0.6 m, and the compaction sampling points are set at the intersections of the grids.

3. The compaction quality detection method based on vibration signal interval cross-correlation optimization according to claim 1 is characterized in that: In S3, the length of the compacted strip is divided into intervals ranging from 2 to 4 m.

4. The compaction quality detection method based on vibration signal interval cross-correlation optimization according to claim 1 is characterized in that: The process of preprocessing the vibration signal in the specified interval in S4 is specifically as follows: firstly, a window function is used to implement a truncation operation to ensure that the vibration signals extracted from each section have the same length. After the truncation is completed, a normalization process is performed on the obtained vibration signal, specifically as follows: （1）； in, is the value of the interval vibration signal at any time, is the minimum value of the interval vibration signal, is the maximum value of the interval vibration signal, is the normalized result.

5. The compaction quality detection method based on vibration signal interval cross-correlation optimization according to claim 1 is characterized in that: The cross-correlation calculation of the vibration signals of adjacent intervals described in S4 is specifically as follows: （2）； Where: is the vibration signal within this interval; is the vibration signal of the adjacent interval on the right side of this interval; is the vibration signal of the adjacent interval on the left side of this interval; t represents the time variable; T is the sampling time; is the signal lag time, is the cross-correlation function between the specified interval and the adjacent interval on the right; It is the cross-correlation function between the specified interval and the adjacent interval on the left.

6. The compaction quality detection method based on vibration signal interval cross-correlation optimization according to claim 1 is characterized in that: The regularity of the signals in each section described in S6 over the entire roadbed The specific determination method is: （3）； Where: For the The cross-correlation function of an interval and the interval to the right of it Maximum value; For the The cross-correlation function of an interval and the interval to the left of it Maximum value; is the average value of the peak value of the cross-correlation function in each interval; Among them, the first interval and the last interval They are: , ,in, n is the total number of intervals.

7. The compaction quality detection method based on vibration signal interval cross-correlation optimization according to claim 1 is characterized in that: The reference control value of the cross-correlation peak is the average value of the peak value of the cross-correlation function in each interval Take the overall average 0.8 times: （4）； （5）； Where: is the overall average; is the reference control value.

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