A method for acoustic vibration detection and evaluation of debonding at the steel tube concrete interface

Abstract

The present invention discloses an acoustic vibration detection and evaluation method for steel tube concrete interface debonding, which collects the sound signal generated by hammering the steel tube concrete, extracts the effective data, and then performs wavelet transform processing after bandpass filtering and amplitude normalization preprocessing to obtain the wavelet time-frequency diagram, which is defined as below f low The frequency region is the low frequency region, higher than f high The frequency region is the high-frequency region. By calculating the energy ratio R between the low-frequency region and the high-frequency region, the R value is used to determine whether interfacial debonding of the steel tube concrete in that region exists. The proposed acoustic-vibration detection and assessment method for interfacial debonding of steel tube concrete can accurately locate the debonding area and calculate the debonding rate of the steel tube concrete. This eliminates the reliance on operator experience in interpreting data from traditional percussion methods, facilitates integration into the hardware system of the acoustic-vibration method for field applications, and provides scientific data for the detection and assessment of debonding damage in steel tube concrete structures.

Description

Technical Field

[0001] The present invention relates to the field of nondestructive testing of structures, and in particular to an acoustic vibration testing and evaluation method for debonding of a steel tube concrete interface. Background Art

[0002] Concrete-filled steel tube (CFST) is a composite structure combining steel tubes and concrete. Due to its excellent seismic performance, ductility, and rapid construction, it is widely used in high-rise buildings and bridges. However, with increasing service life or due to substandard construction, CFST interfacial debonding can occur. This debonding can lead to a decrease in the stiffness and bearing capacity of CFST components. According to GB50923-2013, "Technical Specification for Concrete-Filled Steel Tube Arch Bridges," when the debonding rate of the CFST arch rib exceeds 20% or the thickness of the debonding gap exceeds 3mm, drilling and grouting reinforcement should be performed at the debonding site. Therefore, regular inspection of CFST structures is necessary. Various nondestructive testing methods have been tested in the laboratory to detect CFST interfacial debonding and have demonstrated some effectiveness. However, in real-world applications, the percussion method is widely used due to its high efficiency and ease of use. However, this method relies on subjective judgment and experience of the operator, and its accuracy is susceptible to auditory fatigue and lacks supporting data. The principle of vibroacoustic testing is based on the different vibration modes excited when striking concrete-filled steel tubes (CFSTs). When the CFST interface is dense, the thickness tensile mode is excited; when the CFST debonds, the bending mode is excited. The energy distribution between these two modes differs in the frequency domain, allowing for rapid identification of debonding at the CFST interface. Therefore, there is an urgent need to develop a rapid method for identifying debonding defects at CFST interfaces based on vibroacoustic testing to address the lack of scientific data support and low accuracy in large-scale testing. Summary of the Invention

[0003] The purpose of the present invention is to provide an acoustic vibration detection and evaluation method for debonding of steel tube concrete interface, which solves the problems of lack of data support and low detection accuracy in large-scale detection in the above background.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for acoustic vibration detection and evaluation of debonding at the steel tube concrete interface, which is specifically implemented according to the following steps:

[0005] Step S1: using a sound sensor to collect sound data of a hammer hitting a steel tube concrete;

[0006] Arrange a grid in the steel tube concrete area to be tested. The grid size is determined according to the on-site conditions. Use a testing hammer to hit the center of the grid and use a microphone to record the sound data of the knocking. The microphone is about 5 cm away from the knocking point to ensure that the collected sound signal is complete.

[0007] Step S2: extracting and preprocessing the sound data in step 1;

[0008] The energy ratio method of sliding time window is used to extract the sound data. The energy ratio is defined as the ratio of the signal energy in two adjacent time windows.

[0009]

[0010] In formula (1), F(a) represents the energy ratio function of the next time window to the previous time window at sample point a; a represents a sampling point in the signal; L1 represents the window length of the time window before sample point a; L2 represents the window length of the time window after sample point a; x(i) represents the sound signal; is the relative energy of the signal; N is the total number of signal samples; and α is a stability coefficient, typically ranging from 0.5 to 2.0. The energy ratio function is sensitive to the onset of the acoustic vibration data. The energy ratio reaches its maximum value when the subsequent time window completely enters the valid signal. Therefore, it can be used to identify the onset of the acoustic vibration data.

[0011] The extracted signal is preprocessed by bandpass filtering and amplitude normalization.

[0012] Step S3: performing wavelet transform on the pre-processed signal;

[0013] Traditional Fourier transform is a common method for stationary signals, but sound signals are non-stationary signals and cannot be directly processed by Fourier transform. In time-frequency analysis, wavelet transform has better time-frequency resolution and can be decomposed at multiple scales, making it easier to observe the different time characteristics of the signal at different scales. For the signal x(t)∈L 2 (R), whose continuous wavelet transform is defined as the inner product:

[0014]

[0015] In formula (2), x(t) is the sound signal; f and τ are frequency and time; f s is the signal sampling frequency; f c is the center frequency of the wavelet; ψ * Is the conjugate complex number of the wavelet function ψ(τ). The wavelet function is a complex Morlet function, and its formula is:

[0016]

[0017] In formula (3), f bis the bandwidth of the wavelet; j is an imaginary number.

[0018] Step S4: Calculate the energy ratio R;

[0019] The time-frequency data of the signal is obtained by wavelet transform, and the calculation frequency is lower than f low Frequency region and frequencies above f high The sum of the energy in the frequency region is used to determine whether the steel tube concrete interface has debonded by the ratio R of the two. The calculation formula is as follows:

[0020]

[0021]

[0022] R=log(E L / E H ) (6)

[0023] When the R value is greater than or equal to zero, it is defined as the steel tube concrete interface is debonded, otherwise it is defined as the steel tube concrete interface is dense.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] (1) The present invention calculates the energy ratio of the signal after wavelet transformation, quantitatively analyzes the energy distribution of the signal in the time-frequency domain, determines the size of the energy ratio, makes the judgment of suspected debonding more objective, and reduces the professional knowledge level requirements of practitioners;

[0026] (2) The signal acquisition method required by the present invention is simple and the signal processing speed is fast, which greatly reduces the detection cost, reduces the workload of practitioners in signal processing and reduces the difficulty of data interpretation;

[0027] (3) It can accurately locate the debonding area and provide data support for the acoustic vibration method. The algorithm has low complexity and is easy to integrate into other systems for practical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of the method for identifying debonding defects at the steel tube concrete interface based on the acoustic vibration method and wavelet transform according to the present invention;

[0029] Figure 2 Schematic diagram of acoustic vibration signal extraction of the steel tube concrete interface debonding defect identification method based on acoustic vibration method and wavelet transform of the present invention;

[0030] Figure 3 This is the time-frequency result of the wavelet transform of the typical steel tube concrete interface debonding and compaction signal in the present invention;

[0031] Figure 4The present invention uses R value to judge the detection results of the debonding and compaction positions of the steel tube concrete column in the indoor model test;

[0032] Figure 5 The mean and standard deviation of the R value are statistically analyzed for three groups of 50 samples in each group. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions of the present invention in the embodiments of the present invention in conjunction with the drawings of the present invention in the embodiments of the present invention. Obviously, the embodiments described 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 making creative efforts are within the scope of protection of the present invention.

[0034] like Figure 1-4 As shown, the present invention provides a method for acoustic vibration detection and evaluation of debonding at the steel tube concrete interface, which is specifically implemented according to the following steps:

[0035] Step S1: using a sound sensor to collect sound data of a hammer hitting a steel tube concrete;

[0036] Arrange a grid in the steel tube concrete area to be tested. The grid size is determined according to the on-site conditions. Use a testing hammer to hit the center of the grid and use a microphone to record the sound data of the knocking. The microphone is about 5 cm away from the knocking point to ensure that the collected sound signal is complete.

[0037] Step S2: extracting and preprocessing the sound data in step 1;

[0038] The energy ratio method of sliding time window is used to extract the sound data. The energy ratio is defined as the ratio of the signal energy in two adjacent time windows.

[0039]

[0040] Where: F(a) represents the energy ratio function of the next time window to the previous time window at sample point a; a represents a sampling point in the signal; L1 represents the window length of the time window before sample point a; L2 represents the window length of the time window after sample point a; x(i) represents the sound signal; is the relative energy of the signal; N is the total number of signal samples; and α is a stability coefficient, typically ranging from 0.5 to 2.0. The energy ratio function is sensitive to the onset of the acoustic vibration data. The energy ratio reaches its maximum value when the subsequent time window completely enters the valid signal. Therefore, it can be used to identify the onset of the acoustic vibration data. The extracted signal is preprocessed by bandpass filtering and amplitude normalization.

[0041] Step S3: performing wavelet transform on the pre-processed signal;

[0042] Traditional Fourier transform is a common method for stationary signals, but sound signals are non-stationary signals and cannot be directly processed by Fourier transform. In time-frequency analysis, wavelet transform has better time-frequency resolution and can be decomposed at multiple scales, making it easier to observe the different time characteristics of the signal at different scales. For the signal x(t)∈L 2 (R), whose continuous wavelet transform is defined as the inner product:

[0043]

[0044] Where x(t) is the sound signal; f and τ are frequency and time; f s is the signal sampling frequency; f c is the center frequency of the wavelet; ψ * Is the conjugate complex number of the wavelet function ψ(τ). The wavelet function is a complex Morlet function, and its formula is:

[0045]

[0046] Where f b is the bandwidth of the wavelet; j is an imaginary number.

[0047] Step S4: Calculate the energy ratio R;

[0048] The time-frequency data of the signal is obtained by wavelet transform, and the calculation frequency is lower than f low Frequency region and frequencies above f high The sum of the energy in the frequency region is used to determine whether the steel tube concrete interface has debonded by the ratio R of the two. The calculation formula is as follows:

[0049]

[0050]

[0051] R=log(E L / E H ) (6)

[0052] When the R value is greater than or equal to zero, it is defined as the steel tube concrete interface debonding, otherwise it is defined as the steel tube concrete interface dense. When the steel tube concrete debonds, the overall stiffness of the steel tube concrete component decreases. When impacted, the vibration wave produces reflection, refraction and transmission phenomena at the debonding point, generating bending vibration mode, the vibration spectrum changes from a single frequency band to multiple frequency bands, and energy is transferred from high frequency to low frequency, such as Figure 3 shown.

[0053] The steel tube concrete interface debonding identification method of the present invention is based on acoustic vibration signals, which can be collected by using a microphone when a hammer is used to hit the steel tube concrete.

[0054] The sound signal of hammering steel tube concrete is collected, and the signal is preprocessed by signal extraction, bandpass filtering and amplitude normalization. The processed signal is processed by wavelet transform to obtain the wavelet time-frequency diagram, which is defined as below f low The frequency region is the low frequency region, higher than f high The frequency region is the high-frequency region. By calculating the energy ratio R between the low-frequency region and the high-frequency region, the presence of interfacial debonding in the steel tube concrete in that region can be determined using the R value. The proposed method for acoustic vibration detection and assessment of interfacial debonding in steel tube concrete can accurately locate debonding areas, reducing the signal processing workload and the difficulty of data interpretation. The algorithm also has a low complexity, making it easy to integrate into acoustic vibration acquisition software for field applications.

[0055] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for acoustic vibration detection and evaluation of debonding at the steel tube concrete interface, characterized in that: Please follow the steps below to implement it: Step S1: using a sound sensor to collect sound data of a hammer hitting a steel tube concrete; Arrange a grid in the steel tube concrete area to be tested. The grid size is determined according to the on-site conditions. Use a testing hammer to hit the center of the grid and use a microphone to record the sound data of the knocking. The microphone is about 5 cm away from the knocking point to ensure that the collected sound signal is complete. Step S2: extracting and preprocessing the sound data in step 1; The energy ratio method of sliding window is used to extract the sound data; The energy ratio is defined as the ratio of the signal energies in two adjacent time windows; In formula (1), F(a) represents the energy ratio function of the next time window to the previous time window at sample point a; a represents a sampling point in the signal; L1 represents the window length of the time window before sample point a; L2 represents the window length of the time window after sample point a; x(i) represents the sound signal; is the relative energy of the signal; N is the total number of sample points of the signal; α is the stability coefficient, usually ranging from 0.5 to 2.0; the energy ratio function is more sensitive to the starting point of the acoustic vibration data. When the latter time window just completely enters the effective signal, the energy ratio reaches its maximum value; therefore, it can be used to pick up the starting point of the acoustic vibration data; the extracted signal is preprocessed by bandpass filtering and amplitude normalization; Step S3: performing wavelet transform on the pre-processed signal; The traditional Fourier transform is a common method for stationary signals, but the sound signal is a non-stationary signal and cannot be directly processed by the Fourier transform. In time-frequency analysis, wavelet transform has better time-frequency resolution and can be decomposed at multiple scales, making it easier to observe the different time characteristics of the signal at different scales. 2 (R), the continuous wavelet transform is defined as the inner product: In formula (2), x(t) is the sound signal; f and τ are frequency and time; f s is the signal sampling frequency; f c is the center frequency of the wavelet; ψ * is the conjugate complex number of the wavelet function ψ(τ); the wavelet function is a complex Morlet function, and the formula is: Where f b is the bandwidth of the wavelet; j is an imaginary number; Step S4: Calculate the energy ratio R; The time-frequency data of the signal is obtained by wavelet transform, and the calculation frequency is lower than f low Frequency region and frequencies above f high The sum of the energy in the frequency region is used to determine whether the steel tube concrete interface has debonded by the ratio R of the two. The calculation formula is as follows: R(log(E). L / E H ) (6) When the R value is greater than or equal to zero, it is defined as the steel tube concrete interface is debonded, otherwise it is defined as the steel tube concrete interface is dense.

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