Method and system for rapidly detecting roadbed compaction quality based on elastic waves

Abstract

The invention discloses a roadbed compaction quality rapid detection method and system based on elastic waves, and the method comprises the steps: building regression equations for a first characteristic value, a second characteristic value and an on-site detected soil-stone mixed filling roadbed compaction quality evaluation index, solving the regression equations according to the collected real-time elastic wave data, and the roadbed compaction quality corresponding to the real-time elastic wave data is obtained. The problems that according to a traditional detection method, the detection cost is high, the rolling face can be damaged, the compaction quality of the whole rolling face cannot be reflected, the detection result cannot be rapidly obtained, and timely feedback control cannot be easily achieved are solved.

Description

Technical Field

[0001] This invention belongs to the field of roadbed compaction quality testing technology, and particularly relates to a rapid testing method and system for roadbed compaction quality based on elastic waves. Background Technology

[0002] Earth-rock mixed-fill roadbeds are commonly found in civil engineering projects such as airports, highways, and railways in mountainous areas. These projects are typically characterized by large fill heights and massive earthwork volumes. For these projects, the quality of roadbed compaction directly affects the roadbed's strength and stability, impacting the service life and operational safety of these large infrastructure projects, thus becoming a key factor in determining construction quality. At highway construction sites, the conventional approach is to control roadbed compaction quality by combining in-process control of compaction parameters (layer thickness, number of passes, roller speed, etc.) with post-construction test pit sampling. However, in-process control only ensures that the compaction process meets requirements and cannot directly obtain compaction quality parameters (compaction degree, porosity, dry density, etc.). Deterministic conclusions about the compaction effect must be obtained through post-construction test pits. Post-construction test pit sampling mainly checks parameters such as dry density, porosity, gradation, and compacted layer thickness at the test pit points. However, this traditional method of test pit sampling is difficult to achieve full site coverage, resulting in a significant lag between project quality control and construction progress. Not only is the cost of testing high, but as a destructive testing method, it is also uneconomical. Furthermore, after completing the test pit, the sampling points need to be backfilled and re-rolled, both of which delay the construction progress. Additionally, due to the limited sample size, it is difficult to comprehensively reflect the compaction quality of the entire construction surface, and test results are often not readily available, making timely feedback and control difficult. In summary, the existing technology has the following problems:

[0003] The traditional test pit method is a destructive direct testing method that requires excavation of the roadbed during testing. This method is time-consuming and can damage the roadbed, requiring subsequent measures to restore the compaction quality of the roadbed, which affects the construction progress.

[0004] Traditional test pit methods are costly and time-consuming, and can only be used to test the compaction quality of the rolling surface by sampling. They cannot reflect the compaction quality of the entire rolling surface, and the test results cannot be obtained quickly, making it difficult to achieve timely feedback and control. Summary of the Invention

[0005] This invention provides a rapid detection method and system for roadbed compaction quality based on elastic waves, which solves the technical problem that damages the roadbed and requires subsequent measures to restore the roadbed compaction quality, thus affecting the construction progress.

[0006] In a first aspect, the present invention provides a rapid detection method for roadbed compaction quality based on elastic waves, comprising:

[0007] Acquire elastic wave data generated when an impact hammer freely falls and strikes the test ground;

[0008] The collected elastic wave data is converted into a frequency domain graph through short-time Fourier transform, and the fundamental frequency peak value in the frequency domain graph is extracted as the first feature value.

[0009] The collected elastic wave data is converted into a time-frequency graph through continuous wavelet transform. A preset frequency range is selected in the time-frequency graph, and the power density ratio is calculated as the second feature value.

[0010] Regression equations are established for the first feature value and the second feature value and the field-detected compaction quality evaluation index of the soil-rock mixed fill roadbed, respectively. The soil-rock mixed fill roadbed compaction quality evaluation index includes dry density and porosity.

[0011] The regression equation is solved based on the collected real-time elastic wave data to obtain the subgrade compaction quality corresponding to the real-time elastic wave data.

[0012] Secondly, the present invention provides a rapid detection system for roadbed compaction quality based on elastic waves, comprising:

[0013] The acquisition module is configured to acquire elastic wave data generated when an impact hammer falls freely and hits the test ground.

[0014] The first conversion module is configured to convert the collected elastic wave data into a frequency domain graph through short-time Fourier transform, and extract the fundamental frequency peak value in the frequency domain graph as the first feature value.

[0015] The second conversion module is configured to convert the collected elastic wave data into a time-frequency diagram through continuous wavelet transform, select a preset frequency range in the time-frequency diagram, and calculate the power density ratio as a second feature value.

[0016] The construction module is configured to establish regression equations for the first feature value and the second feature value and the field-detected soil-rock mixed fill roadbed compaction quality evaluation index, wherein the soil-rock mixed fill roadbed compaction quality evaluation index includes dry density and porosity.

[0017] The solution module is configured to solve the regression equation based on the collected real-time elastic wave data to obtain the subgrade compaction quality corresponding to the real-time elastic wave data.

[0018] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the rapid detection method for roadbed compaction quality based on elastic waves according to any embodiment of the present invention.

[0019] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the rapid detection method for roadbed compaction quality based on elastic waves according to any embodiment of the present invention.

[0020] This application presents a rapid detection method and system for subgrade compaction quality based on elastic waves. It proposes using elastic wave characteristic values ​​as the feature values ​​for detecting the compaction quality of soil-rock mixed subgrades, and fuses these feature values ​​to improve the reliability and accuracy of the detection results. This solves the problem that the large differences in particle size of soil-rock mixed materials lead to significant variations in detection results, making it unsuitable for detecting the compaction quality of soil-rock mixed subgrades. Furthermore, regression equations are established between the first and second feature values ​​and the field-detected evaluation indicators of the compaction quality of the soil-rock mixed subgrade. The regression equations are solved using real-time elastic wave data to obtain the subgrade compaction quality corresponding to the real-time elastic wave data. This addresses the problems of high detection costs, damage to the compacted surface, inability to reflect the overall compaction quality of the compacted surface, and slow results acquisition, making timely feedback and control difficult with traditional detection methods. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a rapid detection method for roadbed compaction quality based on elastic waves, provided in an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of an elastic wave acquisition device according to a specific embodiment of the present invention;

[0024] Figure 3 This is a structural block diagram of a rapid detection system for roadbed compaction quality based on elastic waves, provided in an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0026] The above figures include the following reference numerals:

[0027] 1 is the power supply; 2 is the lifting motor; 3 is the signal receiver; 4 is the electromagnet; 5 is the sensor; 6 is the impact hammer; 7 is the slider; 8 is the impact hammer's ground contact position; 9, 10, 11, and 12 are the sensor's grounding positions. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figure 1 The diagram shows a flowchart of a rapid detection method for roadbed compaction quality based on elastic waves according to this application.

[0030] like Figure 1 As shown, the rapid detection method for roadbed compaction quality based on elastic waves specifically includes the following steps:

[0031] Step S101: Obtain the elastic wave data generated when the impact hammer falls freely and hits the test ground.

[0032] like Figure 2 As shown, a trolley serves as the main integrated unit. A lifting motor is installed in the middle of the trolley and is powered by a power source. Electromagnets are installed at the lower part of the lifting rod and the tail of the impact hammer, enabling the impact hammer to be stably lifted and freely lowered. Sensors are installed at the spherical crown of the impact hammer and at the grounding position of the slider.

[0033] After the power is turned on, the power supply provides power to the electromagnet, which attracts and fixes the tail of the impact hammer. The lifting motor then lifts the impact hammer to the required height. The power supply to the electromagnet is then cut off, causing it to lose its magnetism, and the impact hammer falls freely to strike the test ground.

[0034] When the impact hammer strikes the test ground, the sensor inside the impact hammer's spherical crown, along with the grounding sensors at points 9, 10, 11, and 12, simultaneously captures elastic wave information, achieving the function of one-point excitation and multi-point data acquisition.

[0035] Effects of this invention:

[0036] Improve the accuracy of the lifting height of the impact hammer, solve the problem of the impact hammer falling freely without tilt angle during manual operation, eliminate the detection error caused by the lifting height and falling tilt angle, and improve the accuracy of detection.

[0037] The goal of achieving one-point excitation and multi-point information acquisition is realized, enabling the detection area to be transformed from a single-point detection to a regional detection, which greatly increases the area of ​​the detection area and, to a certain extent, frees up manpower, so that the detection personnel no longer need to perform multiple repetitive operations.

[0038] Step S102: The collected elastic wave data is converted into a frequency domain graph through short-time Fourier transform, and the fundamental frequency peak value in the frequency domain graph is extracted as the first feature value.

[0039] In this step, the expression for the short-time Fourier transform is:

[0040] STFT(τ,f)=∫x(t)ω(t-τ)e -jωt dt

[0041] In the formula, x(t) is the original signal, ω(t) is the window function used to limit the locality of the signal in the time domain, and τ is the time offset parameter.

[0042] The fundamental frequency peak value is a key indicator in the frequency domain analysis of elastic waves. It can be used to evaluate the spectral characteristics of a signal, thereby obtaining information about the properties of the medium. Therefore, the leftmost peak value A in the frequency domain diagram, i.e., the fundamental frequency peak value, is selected as a characteristic value for evaluating the compaction quality of soil-rock mixed fill roadbed.

[0043] Step S103: The collected elastic wave data is converted into a time-frequency diagram through continuous wavelet transform, a preset frequency range is selected in the time-frequency diagram, and the power density ratio is calculated as the second feature value.

[0044] In this step, the acquired elastic wave data is converted into a time-frequency diagram using continuous wavelet transform. The frequency range with the richest waveform information is then selected from the elastic wave time-frequency diagram for analysis. The expression for the continuous wavelet transform is:

[0045]

[0046] In the formula, ψ a，b (t) represents the mother wavelet, a is the scaling parameter, and b is the translation parameter. The normalization parameters ensure the consistency of wavelet energy after each stretching transformation.

[0047] The frequency range is divided into two frequency segments, and the squares of the peak values ​​at each point in the two frequency segments are summed and averaged.

[0048] The power density ratio is obtained by calculating the ratio of each test point under different compaction qualities of the soil-rock mixed fill subgrade. The expression is as follows:

[0049]

[0050] In the formula, K is the power density ratio, A1 is the summation and average of the squares of the peak values ​​at each point in one frequency band, and A2 is the summation and average of the squares of the peak values ​​at each point in another frequency band.

[0051] Step S104: Establish regression equations for the first feature value and the second feature value with the field-detected soil-rock mixed-fill roadbed compaction quality evaluation index, wherein the soil-rock mixed-fill roadbed compaction quality evaluation index includes dry density and porosity.

[0052] In this step, the first feature value and the second feature value are fused using a feature fusion method. The first feature value and the second feature value are fused using a feature vector stack to obtain the fused feature X = [A, K].

[0053] A nonlinear relationship was established between the fusion characteristic value X and the evaluation index of the compaction quality of soil-rock mixed fill roadbed, and the regression equation was obtained.

[0054] Step S105: Solve the regression equation based on the collected real-time elastic wave data to obtain the subgrade compaction quality corresponding to the real-time elastic wave data.

[0055] In summary, the method proposed in this application uses elastic wave characteristic values ​​as feature values ​​for detecting the compaction quality of soil-rock mixed fill roadbeds, and fuses these feature values ​​to improve the reliability and accuracy of the detection results. This solves the problem that the large differences in particle size of soil-rock mixed fill materials lead to significant variations in detection results, making it unsuitable for detecting the compaction quality of soil-rock mixed fill roadbeds. Furthermore, regression equations are established between the first and second feature values ​​and the field-detected soil-rock mixed fill roadbed compaction quality evaluation indicators. The regression equations are solved using real-time elastic wave data to obtain the roadbed compaction quality corresponding to the real-time elastic wave data. This addresses the problems of high detection costs, damage to the compacted surface, inability to reflect the overall compaction quality of the compacted surface, and slow acquisition of detection results, making timely feedback and control difficult with traditional methods.

[0056] Please see Figure 3 The diagram shows a structural block diagram of a rapid detection system for roadbed compaction quality based on elastic waves, according to this application.

[0057] like Figure 3 As shown, the rapid detection system 200 for roadbed compaction quality includes an acquisition module 210, a first conversion module 220, a second conversion module 230, a construction module 240, and a solution module 250.

[0058] Among them, the acquisition module 210 is configured to acquire elastic wave data generated when the impact hammer freely falls and hits the test ground;

[0059] The first conversion module 220 is configured to convert the collected elastic wave data into a frequency domain graph through short-time Fourier transform, and extract the fundamental frequency peak value in the frequency domain graph as the first feature value.

[0060] The second conversion module 230 is configured to convert the collected elastic wave data into a time-frequency diagram through continuous wavelet transform, select a preset frequency range in the time-frequency diagram, and calculate the power density ratio as a second feature value.

[0061] The construction module 240 is configured to establish regression equations for the first feature value and the second feature value and the field-detected soil-rock mixed roadbed compaction quality evaluation index, wherein the soil-rock mixed roadbed compaction quality evaluation index includes dry density and porosity.

[0062] The solver module 250 is configured to solve the regression equation based on the collected real-time elastic wave data to obtain the subgrade compaction quality corresponding to the real-time elastic wave data.

[0063] It should be understood that Figure 3 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 3 The various modules in the document will not be described in detail here.

[0064] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the rapid detection method for roadbed compaction quality based on elastic waves in any of the above method embodiments.

[0065] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0066] Acquire elastic wave data generated when an impact hammer freely falls and strikes the test ground;

[0067] The collected elastic wave data is converted into a frequency domain graph through short-time Fourier transform, and the fundamental frequency peak value in the frequency domain graph is extracted as the first feature value.

[0068] The collected elastic wave data is converted into a time-frequency graph through continuous wavelet transform. A preset frequency range is selected in the time-frequency graph, and the power density ratio is calculated as the second feature value.

[0069] Regression equations are established for the first feature value and the second feature value and the field-detected compaction quality evaluation index of the soil-rock mixed fill roadbed, respectively. The soil-rock mixed fill roadbed compaction quality evaluation index includes dry density and porosity.

[0070] The regression equation is solved based on the collected real-time elastic wave data to obtain the subgrade compaction quality corresponding to the real-time elastic wave data.

[0071] Computer-readable storage media may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required for at least one function; the data storage area may store data created based on the use of the elastic wave-based rapid roadbed compaction quality detection system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely disposed relative to a processor, which can be connected to the elastic wave-based rapid roadbed compaction quality detection system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0072] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the rapid detection method for roadbed compaction quality based on elastic waves described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the rapid detection system for roadbed compaction quality based on elastic waves. The output device 340 may include a display screen or other display device.

[0073] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0074] In one implementation, the above-described electronic device is applied to a rapid detection system for roadbed compaction quality based on elastic waves, serving as a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0075] Acquire elastic wave data generated when an impact hammer freely falls and strikes the test ground;

[0076] The collected elastic wave data is converted into a frequency domain graph through short-time Fourier transform, and the fundamental frequency peak value in the frequency domain graph is extracted as the first feature value.

[0077] The collected elastic wave data is converted into a time-frequency graph through continuous wavelet transform. A preset frequency range is selected in the time-frequency graph, and the power density ratio is calculated as the second feature value.

[0078] Regression equations are established for the first feature value and the second feature value and the field-detected compaction quality evaluation index of the soil-rock mixed fill roadbed, respectively. The soil-rock mixed fill roadbed compaction quality evaluation index includes dry density and porosity.

[0079] The regression equation is solved based on the collected real-time elastic wave data to obtain the subgrade compaction quality corresponding to the real-time elastic wave data.

[0080] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A rapid detection method for roadbed compaction quality based on elastic waves, characterized in that, include: Acquire elastic wave data generated when an impact hammer freely falls and strikes the test ground; The collected elastic wave data is converted into a frequency domain graph through short-time Fourier transform, and the fundamental frequency peak value in the frequency domain graph is extracted as the first feature value. The collected elastic wave data is converted into a time-frequency graph through continuous wavelet transform. A preset frequency range is selected in the time-frequency graph, and the power density ratio is calculated as the second feature value. Regression equations are established for the first feature value and the second feature value and the field-detected compaction quality evaluation index of the soil-rock mixed fill roadbed, respectively. The soil-rock mixed fill roadbed compaction quality evaluation index includes dry density and porosity. The regression equation is solved based on the collected real-time elastic wave data to obtain the subgrade compaction quality corresponding to the real-time elastic wave data.

2. The rapid detection method for roadbed compaction quality based on elastic waves according to claim 1, characterized in that, The step of selecting a preset frequency range in the time-frequency graph and calculating the power density ratio as the second characteristic value includes: The frequency range is divided into two frequency segments, and the squares of the peak values ​​at each point in the two frequency segments are summed and averaged. The power density ratio is obtained by calculating the ratio of each test point under different compaction qualities of the soil-rock mixed fill subgrade. The expression is as follows: In the formula, K is the power density ratio, A1 is the summation and average of the squares of the peak values ​​at each point in one frequency band, and A2 is the summation and average of the squares of the peak values ​​at each point in another frequency band.

3. The rapid detection method for roadbed compaction quality based on elastic waves according to claim 1, characterized in that, The process of establishing regression equations for the first and second characteristic values ​​and the on-site tested compaction quality evaluation index of soil-rock mixed fill roadbed includes: The first feature value and the second feature value are fused using a feature fusion method. The first feature value and the second feature value are fused using a feature vector stack to obtain the fused feature X = [A, K]. A nonlinear relationship was established between the fusion characteristic value X and the evaluation index of the compaction quality of soil-rock mixed fill roadbed, and the regression equation was obtained.

4. The rapid detection method for roadbed compaction quality based on elastic waves according to claim 1, characterized in that, The expression for the short-time Fourier transform is: STFT(τ,f)=∫x(t)ω(t-τ)e -jωt dt In the formula, x(t) is the original signal, ω(t) is the window function used to limit the locality of the signal in the time domain, and τ is the time offset parameter.

5. The rapid detection method for roadbed compaction quality based on elastic waves according to claim 1, characterized in that, The expression for the continuous wavelet transform is: In the formula, ψ a，b (t) represents the mother wavelet, a is the scaling parameter, and b is the translation parameter. The normalization parameters ensure the consistency of wavelet energy after each stretching transformation.

6. A rapid detection system for roadbed compaction quality based on elastic waves, characterized in that, include: The acquisition module is configured to acquire elastic wave data generated when an impact hammer falls freely and hits the test ground. The first conversion module is configured to convert the collected elastic wave data into a frequency domain graph through short-time Fourier transform, and extract the fundamental frequency peak value in the frequency domain graph as the first feature value. The second conversion module is configured to convert the collected elastic wave data into a time-frequency diagram through continuous wavelet transform, select a preset frequency range in the time-frequency diagram, and calculate the power density ratio as a second feature value. The construction module is configured to establish regression equations for the first feature value and the second feature value and the field-detected soil-rock mixed fill roadbed compaction quality evaluation index, wherein the soil-rock mixed fill roadbed compaction quality evaluation index includes dry density and porosity. The solution module is configured to solve the regression equation based on the collected real-time elastic wave data to obtain the subgrade compaction quality corresponding to the real-time elastic wave data.

7. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. 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 method described in any one of claims 1 to 5.

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