A loess damage degree detection method based on acoustic emission characteristics and waveform signals

Abstract

The application provides a loess damage degree detection method based on acoustic emission characteristics and waveform signals, and belongs to the technical field of loess damage degree detection; in order to solve the technical problems that loess damage detection has poor timeliness, insufficient detection precision, is difficult to comprehensively reflect the damage nature and cannot realize real-time dynamic monitoring, the technical scheme is that a loess acoustic emission signal collection system is constructed, loading tests are carried out on undisturbed loess samples under different loading conditions, and acoustic emission signals of the undisturbed loess samples are collected in real time; the collected original acoustic emission signals are pretreated, a joint denoising method of wavelet threshold and empirical mode decomposition is used to remove interference signals, and the denoising effect is determined; the ringing count, energy and rising time of the denoised acoustic emission signals are extracted as time domain characteristic parameters, frequency and amplitude characteristic extraction is carried out on the waveform signals by using fast Fourier transform, and the frequency range is divided into frequency bands according to the characteristics of loess; the application is applied to loess damage degree detection.

Description

Technical Field

[0001] This invention provides a method for detecting the degree of loess damage based on acoustic emission characteristics and waveform signals, belonging to the field of loess damage detection technology. Background Technology

[0002] Loess is a widely distributed topographic feature. Because it is easily damaged by external loads and environmental changes, it is necessary to detect and assess the degree of damage when carrying out infrastructure construction. It is also an important object of geological disaster research.

[0003] Currently, traditional single-damage detection methods based on physical indicators such as moisture content and void ratio can reflect the basic physical state of loess, but they are difficult to capture subtle damage characteristics such as microcrack propagation and weakening of interparticle cementation. On the other hand, methods that use single signals such as sound waves and electromagnetic waves for detection and analysis often result in large detection errors due to signal interference and complex boundary conditions. These methods generally suffer from poor detection timeliness and insufficient spatial resolution, as well as problems such as insufficient detection accuracy, inability to fully reflect the nature of damage, and inability to monitor in real time. They cannot meet the needs of accurate loess damage assessment during engineering construction in the corresponding areas. Summary of the Invention

[0004] To address the technical problems existing in the background art, the present invention provides a method for detecting the degree of loess damage based on acoustic emission characteristics and waveform signals, comprising the following detection steps:

[0005] Step S1: Construct a loess acoustic emission signal acquisition system, conduct loading tests on undisturbed loess samples under different loading conditions, and acquire the acoustic emission signals of the undisturbed loess samples in real time;

[0006] Step S2: Preprocess the acquired raw acoustic emission signal, use a combined denoising method of wavelet thresholding and empirical mode decomposition to remove interference signals, and determine the denoising effect;

[0007] Step S3: Extract the time-domain characteristic parameters of the ring count, energy, and rise time of the noise-reduced transmitted signal. Use Fast Fourier Transform to extract the frequency and amplitude features of the waveform signal. Divide the frequency range into frequency bands according to the loess characteristics, and count the cumulative count of each frequency band. Analyze the abrupt change law of amplitude with loading time.

[0008] Step S4: Based on ring count, energy, frequency characteristics, correlation dimension D and acoustic emission b-value parameters, a quantitative characterization of the degree of loess damage is achieved, and the detection value of the degree of loess damage is determined based on the characterization.

[0009] The loess acoustic emission signal acquisition system constructed in step S1 includes: multiple acoustic emission sensor probes set on the undisturbed loess sample, each acoustic emission sensor probe being connected to a corresponding signal amplifier via an acoustic emission signal connection line, the output end of each signal amplifier being connected to an acoustic emission acquisition instrument, and the communication end of the acoustic emission acquisition instrument being connected to an acoustic emission analyzer.

[0010] The wavelet threshold denoising method used in step S2 is as follows:

[0011] The minimum risk unbiased estimation threshold rule is selected, and the Coif5 wavelet basis is used for 3-level decomposition to divide the signal into low-frequency approximate component A3 and high-frequency detail components D1, D2, and D3.

[0012] The minimum risk unbiased estimation threshold rule is automatically calculated based on the Stein unbiased risk estimation principle, and the specific threshold calculation formula is as follows:

[0013] ;

[0014] In the formula, denoted as the detail coefficients after wavelet decomposition at level j, and h as the coefficient length.

[0015] The empirical mode decomposition denoising method used in step S2 is as follows:

[0016] The boundary point k is determined using the continuous mean square error criterion, and the calculation formula is as follows:

[0017] .

[0018] In step S2, the denoising effect is specifically determined using root mean square error and signal-to-noise ratio as evaluation metrics, wherein:

[0019] The formula for calculating the root mean square error (RMSE) evaluation index is as follows:

[0020] ;

[0021] The formula for calculating the signal-to-noise ratio (SNR) evaluation index is:

[0022] ;

[0023] In the formula: y(n) is the pure time series; This is the time series after noise reduction.

[0024] In step S3, the frequency and amplitude features are extracted using Fast Fourier Transform, and the calculation formula is as follows:

[0025] .

[0026] In step S3, the frequency range is specifically divided into low, medium and high frequency bands according to the characteristics of loess, corresponding to 0-50kHz, 50-100kHz and 100-150kHz respectively.

[0027] In step S4, the GP algorithm is specifically used to calculate the correlation dimension D, and the formula for calculating the correlation function is as follows:

[0028] ;

[0029] In the formula: H is the Heaviside function, and its expression is:

[0030] ;

[0031] Where k is the proportionality constant, the expression for r0 is:

[0032] ;

[0033] For a given scale r, the corresponding correlation function C(r) can be obtained by calculation.

[0034] In a log-log coordinate system, plot the data points with lnr as the x-axis and lnC(r) as the y-axis. Perform linear regression fitting on the data points, and the slope of the log-log lnr and lnC(r) lines is the correlation dimension D.

[0035] In step S4, the GP algorithm is specifically used to calculate the acoustic emission b-value, and the calculation formula is as follows:

[0036] ;

[0037] In the formula, A' is the magnitude of the earthquake, A is the number of earthquakes, and a and b are constants;

[0038] In acoustic emission calculation, A' is the acoustic emission amplitude parameter, and A is the number of acoustic emission events with an amplitude greater than A'.

[0039] The energy-event number curve is fitted using the least squares method, and the slope is the acoustic emission b-value. The calculation formula is as follows:

[0040] .

[0041] The specific method for quantitatively characterizing the degree of loess damage in step S4 is as follows:

[0042] Different warning levels are set, and when some or all of the following conditions are met, such as ring count, rapid increase in energy, violent waveform fluctuation, sudden drop in correlation dimension D and acoustic emission b value, disappearance of high frequency and sudden rise of low frequency, different warning levels are output as characterization results.

[0043] The advantages of this invention compared to existing technologies are as follows: This invention provides a method for detecting loess damage based on acoustic emission characteristics and waveform signals. It employs a wavelet thresholding and empirical mode decomposition (EMD) combined denoising algorithm, time-frequency feature quantification analysis, and a multi-parameter joint damage judgment model to overcome the limitations of single analysis methods and achieve accurate quantitative detection of loess damage. This invention constructs an acoustic emission signal acquisition system, applies different loading conditions to loess samples, and acquires acoustic emission signals in real time. It uses an improved soft threshold function and an adaptive boundary point determination method for signal preprocessing, and establishes a damage assessment system by combining correlation dimension and acoustic emission data. Based on indicators such as ring count growth rate and energy surge, it classifies damage levels and accurately assesses the engineering properties of loess. This method is not only applicable to damage research on loess samples under laboratory conditions, but also provides guidance for actual engineering construction in loess areas. Attached Figure Description

[0044] The present invention will be further described below with reference to the accompanying drawings:

[0045] Figure 1 This is a flowchart of the steps in the method for detecting the degree of loess damage of the present invention;

[0046] Figure 2 This is a schematic diagram of the structure of the loess acoustic emission signal acquisition system of the present invention;

[0047] Figure 3 This is a flowchart illustrating the steps of the combined noise reduction method in this embodiment of the invention.

[0048] Figure 4 This is a diagram illustrating the effect of empirical mode decomposition and k-value determination in an embodiment of the present invention.

[0049] Figure 5 This is a diagram showing the effect of denoising the acoustic emission signal of loess before and after in an embodiment of the present invention;

[0050] Figure 6 This is a diagram showing the effect of linear fitting using correlation dimension in an embodiment of the present invention;

[0051] Figure 7 This is a diagram illustrating the effect of acoustic emission characteristic parameter analysis on loess samples in an embodiment of the present invention.

[0052] Figure 8 This is a diagram illustrating the effect of acoustic emission waveform characteristic analysis on loess samples in an embodiment of the present invention.

[0053] Figure 9 This is a diagram illustrating the effect of combined damage determination of acoustic emission characteristic parameters and waveform signals in an embodiment of the present invention.

[0054] Figure labels: 1 is the original loess sample, 2 is the acoustic emission sensor probe, 3 is the acoustic emission signal connection line, 4 is the signal amplifier, 5 is the acoustic emission acquisition instrument, and 6 is the acoustic emission analyzer. Detailed Implementation

[0055] like Figures 1 to 9 As shown, this invention proposes a method for detecting the degree of loess damage based on the joint analysis of acoustic emission characteristic parameters and waveform signals. This method can be used for loess damage detection and engineering property assessment in various engineering construction projects in relevant regions. The detection method mainly includes the following steps:

[0056] Step S1: Construct a dedicated loess acoustic emission signal acquisition system, conduct loading tests on loess samples under different loading conditions, and use the system to acquire acoustic emission signals in real time;

[0057] Step S2: Preprocess the acquired raw acoustic emission signal. Remove interference signals by using a combined wavelet thresholding and empirical mode decomposition (EMD) denoising method. The wavelet decomposition uses the Rigrsure thresholding rule, Coif wavelet basis, and 3-level decomposition. The EMD boundary point k is determined by the continuous mean square error criterion to improve signal quality. The denoising effect is determined by the root mean square error and signal-to-noise ratio evaluation indicators.

[0058] Step S3: Extract key time-domain feature parameters such as ring count, energy, and rise time of the noise-reduced transmitted signal. At the same time, use Fast Fourier Transform (FFT) to extract frequency and amplitude features of the waveform signal, divide the frequency range into high, medium, and low frequency bands according to the loess characteristics, and count the cumulative count and amplitude change of each frequency band.

[0059] Step S4: Based on parameters such as ring count, energy, frequency characteristics, correlation dimension D, and acoustic emission b-value, a quantitative characterization of the degree of loess damage is achieved.

[0060] Furthermore, in step S1, the loess acoustic emission signal acquisition system includes an undisturbed loess sample, an acoustic emission sensor probe, a signal amplifier, an acoustic emission acquisition instrument, and an acoustic emission analyzer, wherein:

[0061] Multiple acoustic emission sensor probes are installed on the undisturbed loess sample. Each acoustic emission sensor probe is connected to a corresponding signal amplifier via an acoustic emission signal connection line. The output of each signal amplifier is connected to an acoustic emission acquisition instrument, and the communication terminal of the acoustic emission acquisition instrument is connected to an acoustic emission analyzer.

[0062] Furthermore, in step S1, the different loading conditions include graded pressure load and variable rate loading mode.

[0063] Furthermore, in step S2, the joint denoising method includes wavelet thresholding denoising and empirical mode decomposition denoising.

[0064] Furthermore, in step S2, wavelet thresholding denoising uses the Rigrsure thresholding rule, Coif5 wavelet basis, and 3-level decomposition to divide the signal into low-frequency approximate components (A3) and high-frequency detail components (D1, D2, D3), where the threshold calculation formula is:

[0065] ;

[0066] In the formula, denoted as the detail coefficients after wavelet decomposition at level j, and h as the coefficient length.

[0067] Furthermore, in step S2, the denoising rule uses a soft threshold function, specifically formulated as follows:

[0068] ;

[0069] Furthermore, in step S2, the EMD denoising uses the continuous mean square error criterion to determine the boundary point k, as shown in the formula:

[0070] ;

[0071] Furthermore, in step S2, the denoising effect is evaluated using root mean square error (RMSE) and signal-to-noise ratio (SNR), as shown in the formula:

[0072] ;

[0073] .

[0074] Furthermore, in step S3, a Fast Fourier Transform (FFT) is used to extract frequency and amplitude features, as shown in the formula:

[0075] .

[0076] Furthermore, in step S3, the frequency range is divided into: low frequency: 0~50kHz, mid frequency: 50~100kHz, and high frequency: 100~150kHz. The cumulative counts for each frequency band are statistically analyzed, and the abrupt change pattern of amplitude with loading time is analyzed.

[0077] Furthermore, in step S4, the GP algorithm is used to calculate the correlation dimension D, and the correlation function is:

[0078] ;

[0079] In the formula: H is the Heaviside function, and the slope of the double logarithmic lnr and lnC(r) lines is the correlation dimension D.

[0080] Furthermore, in step S4, the acoustic emission b-value is calculated based on the GP law:

[0081] ;

[0082] The energy-event number curve is fitted using the least squares method, and the slope is the value of b. The specific formula is as follows:

[0083] ;

[0084] In the formula, A' is the magnitude of the earthquake; A is the number of earthquakes; and a and b are constants. In acoustic emission calculations, A' is the acoustic emission amplitude parameter; and A is the number of acoustic emission events with amplitudes greater than A'.

[0085] Furthermore, in step S4, when the ringing count and energy increase sharply, the waveform fluctuates violently, the correlation dimension D and acoustic emission b value drop suddenly, and high frequencies disappear and low frequencies rise suddenly, the degree of damage to the loess is determined.

[0086] In an embodiment of the present invention, the method for detecting the degree of loess damage specifically includes the following steps:

[0087] On-site soil sampling and sample preparation: At the construction site or research area in the loess region, undisturbed soil samples are obtained using specialized sampling equipment. It is ensured that the original structure and physical properties of the soil samples are preserved during the collection process. The collected undisturbed soil samples are brought back to the laboratory and prepared into loess samples that meet the experimental requirements according to standard sample preparation methods, in preparation for subsequent loading tests.

[0088] Loading Test and Signal Acquisition: The prepared loess sample is placed on the loading device and loaded according to a pre-set loading scheme. The loading scheme can be determined according to the actual research objectives and engineering requirements, such as staged loading or cyclic loading. During the loading process, acoustic emission signals are acquired in real time through an acoustic emission signal acquisition system. The acoustic emission sensor probe is tightly attached to the sample surface to ensure accurate reception of acoustic emission signals. The signals are transmitted to a signal amplifier through an acoustic emission signal connection line, and then the amplified signals are acquired by the acoustic emission acquisition instrument. Finally, the signals are transmitted to an acoustic emission analyzer for storage and preliminary processing.

[0089] Signal preprocessing: The acquired raw acoustic emission signals often contain various noise interferences and require preprocessing. According to... Figure 3The combined denoising method shown first performs wavelet decomposition on the original noisy signal, dividing it into sub-band signals of different frequencies. Then, it performs soft thresholding on the wavelet coefficients to remove small fluctuations caused by noise. After soft thresholding, the signal is reconstructed for the first time to obtain a preliminarily denoised signal. Next, empirical mode decomposition is performed on the reconstructed signal to determine the boundary point K based on the signal characteristics, such as... Figure 4 As shown, the decomposed IMF components are divided into the first K and the last K components. Soft thresholding denoising is applied to the first K IMF components, while the last K IMF components are retained. Finally, a second signal reconstruction is performed to obtain an acoustic emission signal with better denoising (e.g., ...). Figure 5 As shown in the figure, this provides denoised data for subsequent feature extraction and analysis.

[0090] Feature Extraction and Analysis: Feature extraction and analysis were performed on the denoised acoustic emission signal. On one hand, key feature parameters such as ring count, energy, and rise time were extracted. At different loading stages, the changes in ring count over time were recorded, and the accumulation and release patterns of energy, as well as the fluctuation characteristics of rise time, were analyzed. For example, in the initial loading stage, the ring count may be low and increase slowly; as the load increases, when microcracks begin to appear inside the loess, the ring count gradually increases; energy is significantly released during crack propagation, manifested as a sudden increase in energy value. On the other hand, time-frequency analysis techniques were used to process the waveform signal. The frequency range of the waveform signal was divided into high, medium, and low frequency ranges, and the cumulative counts of different frequency ranges were statistically analyzed. The changing trends of high-frequency, medium-frequency, and low-frequency signals during the damage process were observed. For example, in the microcrack initiation stage, high-frequency signals may increase; as the crack propagates, low-frequency signals may gradually become dominant. Simultaneously, the amplitude changes of the waveform signal were analyzed; the magnitude of the amplitude reflects the intensity of the acoustic emission event, and sudden changes in amplitude may indicate the occurrence of a larger damage event.

[0091] Damage assessment and early warning: Based on the extracted feature parameters and waveform characteristics, combined with... Figure 9 The diagram illustrating the combined damage assessment quantitatively characterizes and evaluates the degree of damage to loess. Different warning levels are set; for example, different warning levels correspond to situations where characteristic parameters and waveform characteristics meet some or all of the following conditions: "ring count, sharp increase in energy, violent waveform fluctuations, sudden drop in correlation dimension D and acoustic emission b-value, disappearance of high frequencies, and sudden increase in low frequencies." When a warning threshold is reached, a timely warning message is issued, prompting engineers to take appropriate measures, such as adjusting the construction plan or reinforcing the loess, to ensure the safety and stability of the project. In practical engineering applications, the warning levels and thresholds can be reasonably adjusted according to the importance of the project and the risk tolerance to ensure the effectiveness and reliability of the warning system.

[0092] During loess damage, different types of damage (such as microcrack initiation, propagation, and penetration) generate acoustic emission signals with different characteristics. This invention, through joint analysis of acoustic emission characteristic parameters and waveform signals, can capture these subtle changes more meticulously. For example, in the microcrack initiation stage, the acoustic emission signal may appear as a low-energy, short-rise-time high-frequency signal. By jointly analyzing parameters such as ring count and frequency, the initiation of cracks can be detected in a timely manner. As the crack propagates, the signal energy, amplitude, and other characteristics will change significantly. Combining the time-frequency characteristics of the waveform signal, the direction and speed of crack propagation can be accurately determined, thereby achieving precise quantification of the degree of loess damage and significantly improving the accuracy and reliability of loess damage characterization. This invention captures loess damage information from multiple dimensions, overcoming the one-sidedness and limitations of single analysis methods in characterizing loess damage states, and providing more reliable data support for engineering practice.

[0093] 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting the degree of loess damage based on acoustic emission characteristics and waveform signals, characterized in that: The testing steps include the following: Step S1: Construct a loess acoustic emission signal acquisition system, conduct loading tests on undisturbed loess samples under different loading conditions, and acquire the acoustic emission signals of the undisturbed loess samples in real time; Step S2: Preprocess the acquired raw acoustic emission signal, use a combined denoising method of wavelet thresholding and empirical mode decomposition to remove interference signals, and determine the denoising effect; The specific wavelet thresholding denoising method used is as follows: The minimum risk unbiased estimation threshold rule is selected, and the Coif5 wavelet basis is used for 3-level decomposition to divide the signal into low-frequency approximate component A3 and high-frequency detail components D1, D2, and D3. The minimum risk unbiased estimation threshold rule is automatically calculated based on the Stein unbiased risk estimation principle, and the specific threshold calculation formula is as follows: ； In the formula, represents the detail coefficients after wavelet decomposition at level j, and h is the coefficient length; The empirical mode decomposition denoising method used is as follows: Determining the boundary point using the continuous mean square error criterion The calculation formula is: ； The denoising effect was determined using root mean square error and signal-to-noise ratio as evaluation metrics, where: The formula for calculating the root mean square error (RMSE) evaluation index is as follows: ； The formula for calculating the signal-to-noise ratio (SNR) evaluation index is: ； In the formula: y(n) is the pure time series; The time series after noise reduction; Step S3: Extract the time-domain characteristic parameters of the ring count, energy, and rise time of the noise-reduced transmitted signal. Use Fast Fourier Transform to extract the frequency and amplitude features of the waveform signal. Divide the frequency range into frequency bands according to the loess characteristics, and count the cumulative count of each frequency band. Analyze the abrupt change law of amplitude with loading time. Step S4: Based on ring count, energy, frequency characteristics, correlation dimension D and acoustic emission b-value parameters, a quantitative characterization of the degree of loess damage is achieved, and the detection value of the degree of loess damage is determined based on the characterization.

2. The method for detecting loess damage degree based on acoustic emission characteristics and waveform signals according to claim 1, characterized in that: The loess acoustic emission signal acquisition system constructed in step S1 includes: multiple acoustic emission sensor probes set on the undisturbed loess sample, each acoustic emission sensor probe being connected to a corresponding signal amplifier via an acoustic emission signal connection line, the output end of each signal amplifier being connected to an acoustic emission acquisition instrument, and the communication end of the acoustic emission acquisition instrument being connected to an acoustic emission analyzer.

3. The method for detecting loess damage degree based on acoustic emission characteristics and waveform signals according to claim 1, characterized in that: In step S3, the frequency and amplitude features are extracted using Fast Fourier Transform, and the calculation formula is as follows: 。 4. The method for detecting loess damage degree based on acoustic emission characteristics and waveform signals according to claim 1, characterized in that: In step S3, the frequency range is specifically divided into low, medium and high frequency bands according to the characteristics of loess, corresponding to 0-50kHz, 50-100kHz and 100-150kHz respectively.

5. The method for detecting loess damage degree based on acoustic emission characteristics and waveform signals according to claim 1, characterized in that: In step S4, the GP algorithm is specifically used to calculate the correlation dimension D, and the formula for calculating the correlation function is as follows: ； In the formula: H is the Heaviside function, and its expression is: ； scale The expression is , As the proportionality constant, r0 is expressed as: ； For a given scale r, the corresponding correlation function C(r) can be obtained by calculation. In a log-log coordinate system, plot the data points with lnr as the x-axis and lnC(r) as the y-axis. Perform linear regression fitting on the data points, and the slope of the log-log lnr and lnC(r) lines is the correlation dimension D.

6. The method for detecting loess damage degree based on acoustic emission characteristics and waveform signals according to claim 1, characterized in that: In step S4, the GP algorithm is specifically used to calculate the acoustic emission b-value, and the calculation formula is as follows: ； In the formula, A' is the magnitude of the earthquake, A is the number of earthquakes, and a and b are constants; In acoustic emission calculation, A' is the acoustic emission amplitude parameter, and A is the number of acoustic emission events with an amplitude greater than A'. The slope of the energy-event number curve fitted by the least squares method represents the acoustic emission. The value is calculated using the following formula: 。 7. The method for detecting loess damage degree based on acoustic emission characteristics and waveform signals according to claim 1, characterized in that: The specific method for quantitatively characterizing the degree of loess damage in step S4 is as follows: Different warning levels are set, and when some or all of the following conditions are met, such as ring count, rapid increase in energy, violent waveform fluctuation, sudden drop in correlation dimension D and acoustic emission b value, disappearance of high frequency and sudden rise of low frequency, different warning levels are output as characterization results.

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