Defect detection method based on multiple characteristic information and related product

Abstract

The invention relates to the technical field of defect detection and data processing for a steel lining reinforced concrete structure, in particular to a defect detection method based on multiple characteristic information and a related product, and the method comprises the following steps: collecting original data of a detected work area along a measuring line by using a shock wave echo method; preprocessing the original data to obtain a preprocessed signal; performing time-frequency analysis on the preprocessed data by using a time-frequency analysis method, and drawing a single-frequency profile after extracting a single-frequency component; analyzing primary wave characteristic information and multiple wave characteristic information of the single-frequency profile, and extracting multiple wave signals of the void interface; extracting a dimensionless amplitude value of the multiple signal, setting a threshold value, and determining a defect degree according to a relationship between the dimensionless amplitude value and the threshold value; according to the invention, through combination of double filtering and convolution quotient calculation, primary waves and multiple waves can be accurately separated; on the basis of the extracted multiple signal dimensionless amplitude value, the compactness and void degree of different areas can be visually judged by setting a threshold value.

Description

Technical Field

[0001] The present invention relates to the technical field of defect detection and data processing for steel-lined reinforced concrete structures, and in particular to a defect detection method based on multiple wave characteristic information and related products. Background Art

[0002] Steel-lined reinforced concrete structure is a composite structure that combines the strength of steel and the durability of concrete. It is widely used in water conservancy and hydropower projects, especially in areas such as penstocks, volutes and spillways that are subject to high water pressure. However, due to factors such as design, construction and materials, it is easy to form a hollow interface between the steel lining and reinforced concrete, affecting the overall performance of the structure.

[0003] Impact echo method (IE method) is an acoustic wave detection technology used to detect concrete defects. However, in steel-lined reinforced concrete structures, due to the small thickness of the steel lining and the large concrete structure surface, the signal resolution of the traditional IE method is limited, making it difficult to effectively detect the void interface. Existing detection methods, such as analysis methods based on frequency domain, energy domain and void index, have improved void detection, but they rely on manual experience, are time-consuming and complex to operate, and have a limited scope of application.

[0004] The present invention aims to overcome these shortcomings and provide a high-precision defect detection method based on multiple wave feature information extraction, which can effectively detect the void interface in the steel lining and reinforced concrete structure. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a defect detection method and related products based on multiple wave characteristic information, which solves the problems of relying on manual experience, time-consuming and complicated operation, and limited application scope, and can fully meet the needs of void detection in steel-lined reinforced concrete structures.

[0006] The present invention is achieved through the following technical solutions:

[0007] A defect detection method based on multiple wave characteristic information, comprising:

[0008] The shock wave echo method is used to collect the original data of the measured area along the survey line;

[0009] Preprocessing the original data to obtain preprocessed data;

[0010] The preprocessed data were analyzed by time-frequency analysis method, and the single-frequency components were extracted to draw the single-frequency profile, and then normalized.

[0011] Analyze the primary wave characteristic information and multiple wave characteristic information of the single frequency section, and extract the multiple wave signal of the void interface;

[0012] The dimensionless amplitude value of the multiple wave signal is extracted, a threshold is set, and the defect degree is determined according to the relationship between the dimensionless amplitude value and the threshold.

[0013] Specifically, the methods for determining the degree of defects include:

[0014] Set a first threshold value A1 and a second threshold value A2, A1<A2;

[0015] If the dimensionless amplitude value of the multiple wave signal A≤A1, the corresponding area is judged to be a dense area;

[0016] If the dimensionless amplitude value of the multiple wave signal A>A1 and A≤A2, the corresponding area is judged to be an under-compacted area;

[0017] If the dimensionless amplitude value A of the multiple wave signal is greater than A2, the corresponding area is determined to be a void area.

[0018] Specifically, the methods for preprocessing the raw data include bandpass filtering and gain;

[0019] Transfer function of bandpass filter Where s is a complex frequency variable, ω L is the low frequency cutoff angular frequency, ω H is the high frequency cutoff angular frequency, ω0 is the center frequency, and Q is the quality factor;

[0020] The original data r(t) is bandpass filtered by the transfer function of the bandpass filter to obtain the filtered signal r f (t);

[0021] Perform gain processing on the filtered signal to obtain a signal s(t)=r after gain processing f (t)·e γt , where γ is the gain factor, t is the time, and e is a natural constant.

[0022] Optionally, the low frequency cutoff angular frequency ω L =2πf L , high frequency cutoff angular frequency ω H =2πf H , center frequency Quality Factors Among them, f L is the low frequency cutoff frequency, f H is the low frequency cutoff frequency.

[0023] Specifically, the method of extracting the single frequency component includes:

[0024] Determine the time spectrum Where, s(t) is the preprocessed data, C is the slope parameter, B is the intercept parameter, η is the time parameter, f is the frequency parameter, i is the imaginary unit, and e is the natural constant;

[0025] Wherein, △t1 and △t2 are the time sampling rates at frequency f1 and frequency f2;

[0026] Select a single frequency and extract the corresponding frequency component to obtain the single frequency component;

[0027] Specifically, the method of drawing a single frequency profile includes:

[0028] The single frequency component is normalized and plotted on the spatial axis to obtain a single frequency profile.

[0029] Specifically, the method for extracting multiple wave signals of the void interface includes:

[0030] The double filtering algorithm is used to filter the single-frequency profile to obtain the filtered signal;

[0031] Calculate the convolution quotient R between the seismic trace and the wavelet d , Where x is the seismic trace extracted from the single-frequency profile, and w is the wavelet corresponding to x;

[0032] The convolution quotient R d The first element of is set to -1, and the entire convolution quotient R d Take the inversion and get the filter vector R′ d ;

[0033] By filtering the vector R′ d Perform inverse filtering on the seismic trace to obtain a primary wave signal x r ={x r (1), x r (2), …, x r (n)}:

[0034]

[0035] Calculate the multiple wave signal x d =xx r .

[0036] Specifically, the method for extracting the dimensionless amplitude value of the multiple wave signal is: Among them, V m is the maximum amplitude of the multiple wave signal, V erf is the maximum amplitude of the normal dense area.

[0037] A defect detection terminal based on multiple wave characteristic information includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a defect detection method based on multiple wave characteristic information as described above is implemented.

[0038] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements a defect detection method based on multiple wave characteristic information as described in any one of the above items.

[0039] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0040] The present invention utilizes a dual filtering algorithm to filter the single-frequency profile, enhances the effective part of the signal, separates the primary wave and multiple wave signals through convolution quotient calculation, effectively removes the interference of the primary wave, extracts the multiple wave signals through inverse filtering operation, further improves the signal-to-noise ratio and accuracy of the signal, and finally obtains the dimensionless amplitude value to judge the density and void degree of different areas.

[0041] The present invention can accurately separate the primary wave from the multiple waves through the combination of double filtering and convolution quotient calculation, thereby avoiding the situation in which the primary wave interferes with the multiple waves in the traditional detection method; based on the dimensionless amplitude value of the extracted multiple wave signal, the density and degree of voids in different areas can be intuitively judged by setting a threshold, thereby avoiding the errors caused by the traditional method's excessive reliance on the operator's experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings illustrate exemplary embodiments of the present invention and, together with the description thereof, are used to explain the principles of the present invention. These drawings are included to provide a further understanding of the present invention, and the accompanying drawings are included in and constitute a part of this specification and do not constitute a limitation of the embodiments of the present invention.

[0043] Figure 1 It is a flow chart of a defect detection method based on multiple wave characteristic information according to the present invention.

[0044] Figure 2 It is part of the original data collected according to the present invention.

[0045] Figure 3 It is a schematic diagram of the result of separation of primary wave and multiple waves according to the present invention.

[0046] Figure 4 is the two-dimensional profile of multiple waves according to the present invention.

[0047] Figure 5 Schematic diagram of dimensionless amplitude value according to the present invention.

[0048] Figure 6 This is a test data diagram of the hollow index method according to the present invention. DETAILED DESCRIPTION

[0049] To make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and implementation methods. It is understood that the specific implementation methods described herein are only used to explain the relevant content, rather than to limit the present invention.

[0050] It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings.

[0051] In the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0052] Steel-lined reinforced concrete structure is a composite structure that combines the advantages of steel and concrete. It combines the strength, rigidity and corrosion resistance of steel lining with the durability and economy of reinforced concrete, forming a structural form with high bearing capacity, durability and economy. It is mainly composed of two parts: steel lining and reinforced concrete outside the steel lining. The steel lining is a layer of steel plate inside the structure, which directly bears external loads such as water pressure and wind pressure. It usually has high strength and rigidity, can effectively resist external loads, and has good corrosion resistance. The outside of the steel lining is a layer of reinforced concrete, which not only increases the thickness and rigidity of the structure, but also provides additional bearing capacity and seismic resistance. The concrete outside the steel lining can prevent the corrosion of the steel lining and can also bear part of the load.

[0053] Steel-lined reinforced concrete structures are often used in water conservancy and hydropower projects in pressure pipes, volutes, tail pipes, spindles, some spillway areas, ship lock projects and some tunnel areas. They are mainly used to withstand very high water pressure. Steel-lined reinforced concrete structures can effectively resist this water pressure and maintain the stability of the structure. At the same time, the steel lining provides higher strength and rigidity. When combined with reinforced concrete, it can significantly improve the overall bearing capacity and deformation resistance of the structure. On this basis, the steel lining can be used as an impermeable layer to prevent water pressure from causing concrete cracks and leakage, ensure the water tightness of the structure, prevent cracks and leakage, and improve the durability of the structure. When facing areas with complex or unstable geological conditions, steel-lined reinforced concrete structures can better adapt to the deformation and stress changes of the strata. In actual construction, steel-lined reinforced concrete structures can be constructed by prefabrication and on-site assembly, which improves construction efficiency, shortens the construction period, and achieves the required structural performance at a lower cost. For old or damaged water conservancy and hydropower projects, steel-lined reinforced concrete structures can be used for repair and reinforcement to extend the service life of the project.

[0054] However, in the actual construction process, affected by factors such as design, construction, materials, environment and time, a void interface is often easily formed between the steel lining and the reinforced concrete. Taking the typical volute inner corner and pier as an example, the waterproof and anti-seepage requirements of the inner corner may not be fully considered during the design, which may lead to voids due to inadequate waterproof measures; in the construction of the inner corner, due to the difficulty of operation, it is often easy to have uneven concrete pouring, inadequate vibration or improper use of the vibrator, which is also easy to cause in the design; if there are quality problems with the steel, such as insufficient strength or defects, it will also affect the structural stability of the inner corner; long-term operating environment temperature changes, such as freeze-thaw cycles, may cause concrete shrinkage and expansion, which in turn cause voids at the inner corner.

[0055] Generally speaking, the design thickness of the steel lining should fully consider the load that the steel lining needs to bear, different structural forms, underground geological conditions, the expected design life of the steel lining, economic factors, construction difficulty and operability, and relevant industry specifications and standards. In actual projects, the design thickness of the steel lining is usually between a few millimeters and tens of millimeters, such as 10mm to 50mm or thicker. Impact-Echo Method The IE method is an acoustic wave detection technology developed in the mid-1980s. A small steel ball is used to tap the concrete surface, and the elastic wave generated is propagated into the concrete in the form of a spherical wave array. During the propagation of the stress wave, if it encounters a defect or interface (concrete / air), it will be reflected. The sensor placed near the impact point receives the vertical displacement or acceleration of the concrete surface caused by these reflected waves and converts it into an electrical signal to be received. However, in the research of many scholars, it is found that the signal limit resolution of the impact elastic wave method is within the range of 5-10cm. Therefore, it is not suitable for detecting the void area between the steel lining and the reinforced concrete, and the effect is not good. At the same time, because the concrete structure surface of the hydropower station project is generally large, the excitation source energy required by the impact echo method usually cannot meet the requirements, so it is rarely used in water conservancy and hydropower projects. In 2017, Yao Dewu and Shu Liangang and others used three analysis methods, namely energy domain, frequency domain and wavelength oscillation duration, to make some attempts to detect the voiding of the steel lining of the hydropower station. They compared and analyzed the differences between full voiding before grouting and full compaction after grouting, and the interweaving of voiding sections and compaction sections after grouting, and then semi-quantitatively analyzed the difference relationship between voiding and compaction of the steel lining through the difference ratio. However, this method is overly dependent on the experience level of personnel, time-consuming, cumbersome to operate, and requires comparison before and after grouting. In the actual production and construction process, there are few applicable scenarios, poor operability and low efficiency; Tang Ruhua, Gao Yuangui, Huang Botai and others used the fact that the vibration characteristics of the voided part will change, the bending stiffness will be significantly reduced, the outstanding period will increase, and the vibration duration will be prolonged. The normalized voiding index introduced was used for judgment. The larger the voiding index, the greater the possibility of relative voiding within the analysis area. Later, this method was written into the "Technical Code for Detecting Concrete Defects by Impact Elastic Wave Method" (T / CECS 925-2021), which provides a better idea, but the basic theoretical basis is weak and the subsequent scalability is poor.

[0056] As mentioned above, the impact echo method utilizes the principle of elastic wave propagation. Multiple waves in elastic waves refer to waves formed during the wave propagation process because some reflection interfaces with large reflection coefficients on the surface or underground cause the primary reflected wave to be reflected back into the underground. These waves are formed through multiple reflections, refractions and scattering in the underground medium. These waves are mixed with the direct waves (or first arrival waves) and interfere with the final data interpretation and imaging. Common multiple waves include interlayer multiple waves, refraction multiple waves, reflection multiple waves, diffraction multiple waves, intralayer multiple waves, scattered multiple waves, etc. In normal signal analysis, this type of waveform often plays a negative role and is usually filtered out as clutter. Multiple waves usually show characteristics such as periodicity, hysteresis and amplitude attenuation.

[0057] Based on the above problems, the present invention provides a high-precision defect detection method for steel-lined reinforced concrete structures based on impact echo extraction of multiple wave feature information, which solves the algorithmic problems of traditional impact echo processing methods such as insufficient limiting resolution and too many boundary parameter settings introducing many dimensionless data, and gets rid of operational problems such as over-reliance on the experience level of on-site personnel, long time consumption, and cumbersome operation. It has interpretability and practical physical significance based on the wave equation propagation theory, and can be implemented at any stage before and after pouring. It is reliable, convenient, and fast, and can fully meet the needs of void detection in steel-lined reinforced concrete structures.

[0058] The present invention is proposed on the basis of studying the following problems: (1) The signal limit resolution of the impact elastic wave method is within the range of 5-10cm, and the design thickness of the steel lining is usually between several millimeters and tens of millimeters. When facing the detection of the void area between the steel lining and the reinforced concrete, its limit resolution is not enough, and the reflected wave information cannot achieve the effect; (2) The traditional hydropower station steel lining void detection mainly uses the reflected wave information, which usually uses the energy, frequency and oscillation duration of the whole wave train, and can only play an overall macroscopic judgment role, especially because the inconsistency of the source energy is greatly affected by the outside world, and it is heavily dependent on the work experience of the operator, resulting in a variety of interpretation results and no fixed standards; (3) The method in the "Technical Code for Detecting Concrete Defects by Impact Elastic Wave Method" (T / CECS 925-2021) provides a better idea, but the basic theoretical basis is weak and the subsequent scalability is poor. The numerical impact of the defect is amplified by the multiple superposition of various parameter quantities, but the actual relative relationship does not actually meet the propagation law, and it also depends on the operator's own processing level. (4) As mentioned earlier, the lack of unified certification standards and standardized interpretations are also important factors that affect actual promotion.

[0059] The present invention needs to conduct a comprehensive analysis of the work area to be tested to understand the actual situation and the detection target. According to the on-site construction characteristics of the steel-lined reinforced concrete structure, the survey line is arranged according to the actual situation to ensure the integrity of information collection, and the key areas are arranged in an encrypted manner. When using the impact echo equipment for collection, because the thickness of the steel lining is very small, it is necessary to improve the sampling resolution, try to stimulate high-frequency source signals when knocking, and select the smallest possible time sampling interval for collection; the collected data is a typical self-excited and self-received single-channel signal, and the main work involves the processes including the elimination of redundant signals, bandpass filtering and gain Adjustment is performed to fully retain the effective signal; when performing time-frequency analysis, time-frequency analysis methods (such as short-time Fourier transform, S transform and other derivative transforms) are used to perform time-frequency analysis to find a common high-frequency component simple harmonic wave (frequency band) that runs through the entire target layer at the same time and extracts it as a single-frequency normalized profile; a double filtering algorithm is used to separate the primary wave characteristic information and the multiple wave characteristic information of the extracted single-frequency profile, and the multiple wave signals of the air interface between the steel lining and the reinforced concrete are extracted; the dimensionless amplitude value A of the multiple wave signal is extracted; and the extracted A value is used to set a threshold and make a defect judgment.

[0060] Embodiment 1

[0061] like Figure 1 As shown, a defect detection method based on multiple wave characteristic information is provided, comprising:

[0062] S1. Use the shock wave echo method to collect the original data of the measured area along the survey line; arrange the survey line according to the actual situation based on the on-site construction characteristics of the steel-lined reinforced concrete structure to ensure the integrity of information collection, and at the same time, encrypt the layout of key areas, including but not limited to: for the division of the area, it is advisable to arrange the long-distance survey line for the target object according to the survey area. The scope of the survey area should be larger than the area where the defects are estimated, and there should be normal concrete parts with the same conditions for comparison (or the survey line is long enough to include normal concrete parts with the same conditions), and their respective numbers and positions should be marked. Ensure that the survey line is arranged around the inspected building. At the same time, the length of a single survey line must be long enough to ensure that the single-frequency normalized profile finally extracted has enough sample points to observe the trend of the entire survey line and complete the collection of all data.

[0063] S2. Preprocess the original data to obtain preprocessed data. When preprocessing the collected data, focus on parameters such as sampling interval, recording time, and seismic wavelet. Since the impact echo data is a typical self-excited and self-received single-channel signal, the main work involves the following processes: to ensure the amount of calculation and efficiency, redundant signals should be eliminated, some specific interference frequency signals should be filtered out, and some deep signals should be amplified to ensure that the signals of deep defects are clearly visible and the effective signals are fully retained.

[0064] S3. Use time-frequency analysis methods to perform time-frequency analysis on the preprocessed data, draw the single-frequency profile after extracting the single-frequency component, and normalize it; use time-frequency analysis methods (such as short-time Fourier transform, S transform and other derivative transforms, etc.) to perform time-frequency analysis. The time-frequency spectrum can effectively reveal the frequency range and outliers, track the main frequency signal we want, and achieve the purpose of finding a certain (frequency band) common high-frequency component simple harmonic wave that runs through the entire target layer at the same time. Normalizing each self-excited and self-received single-channel signal can fully reflect the propagation characteristics of the single-channel signal, and at the same time the continuity of the overall profile is displayed.

[0065] S4. Analyze the primary wave characteristic information and multiple wave characteristic information of the single-frequency profile, and extract the multiple wave signals of the void interface; the seismic signals usually collected contain primary waves and multiple waves. The multiple waves have unique application value for defect detection. The separation process can significantly improve the signal-to-noise ratio of the data, making the characteristics of the multiple waves more obvious, which is beneficial to the subsequent defect interpretation. The double filtering algorithm is used to separate the primary wave characteristic information and the multiple wave characteristic information of the self-excited and self-collected single-frequency profile, and extract the multiple wave signals of the void interface between the steel lining and the reinforced concrete.

[0066] S5. Extract the dimensionless amplitude value of the multiple wave signal, set the threshold and determine the degree of defect based on the relationship between the dimensionless amplitude value and the threshold. Extract the dimensionless amplitude value a (value < 1) of the multiple wave signal from each self-excited and self-received single-channel signal: the size of A indicates the degree of voiding in the area, and the larger the value of A, the higher the degree of voiding at its location; after many tests, we have a certain experience in setting the threshold, and this experience is not absolute, but has a certain universality, so the method for judging the degree of defect includes:

[0067] Set a first threshold value A1 and a second threshold value A2, A1<A2;

[0068] If the dimensionless amplitude value of the multiple wave signal A≤A1, the corresponding area is judged to be a dense area;

[0069] If the dimensionless amplitude value of the multiple wave signal A>A1 and A≤A2, the corresponding area is judged to be an under-compacted area;

[0070] If the dimensionless amplitude value A of the multiple wave signal is greater than A2, the corresponding area is determined to be a void area.

[0071] Provide a specific reference value, A≤0.2 area is a dense area, 0.2<A≤0.4 area is a poorly dense area, 0.4<A area is a hollow area. On this basis, this method can be used to match each measurement line on site to obtain the hollow situation of each measurement line in the detection area, and finally form a regional situation summary to judge the overall defects.

[0072] Embodiment 2

[0073] This embodiment further describes the first embodiment in detail.

[0074] S1. Before using the impact echo method for data collection, a comprehensive analysis of the work area to be tested is required to understand the actual situation and detection objectives. During on-site testing, the measurement line plan is arranged and designed according to the actual large-volume concrete object, and the on-site work situation is recorded, including but not limited to: 1 Project overview, structural component design and construction status; 2 Detection basis, purpose and client requirements; 3 Detection personnel and instruments and equipment; 4 Measurement area division, measurement line layout; 5 Test methods, steps, quantity, location and progress; 6 Other supporting work. For the division of the area, it is advisable to arrange the long-distance measurement line for the target object according to the measurement area. The measurement area should be larger than the area where defects are estimated, and there should be normal concrete parts with the same conditions for comparison (or the measurement line is long enough to include normal concrete parts with the same conditions), and their respective numbers and positions should be marked to ensure that the measurement lines are arranged around the inspected building. At the same time, the length of a single measurement line must be greater than the target. According to the specific conditions of the work area, the measurement lines are reasonably arranged to cover the entire area to be tested and complete the data collection. Some collected data such as Figure 2 shown.

[0075] S2. Preprocess the collected data, such as removing redundant signals, bandpass filtering and gain adjustment as follows: because the impact echo data is a typical self-excited and self-received single-channel signal, in order to facilitate the operation and subsequent correction of the collected data, when the sampling time is fixed and the sampling interval is small enough, it will lead to a large amount of collected data, which will introduce a large amount of calculation when performing time-frequency analysis. Therefore, redundant signals should be removed, and some specific interference frequency signals should be filtered out, and some deep signals should be amplified to ensure that the signals of deep defects are clearly visible. Bandpass filters should be used to remove high-frequency and low-frequency interference signals, and retain signals within the frequency range useful for analysis.

[0076] Transfer function of bandpass filter Where s is a complex frequency variable, ω L is the low frequency cutoff angular frequency, ω H is the high frequency cutoff angular frequency, ω0 is the center frequency, and Q is the quality factor;

[0077] The original data r(t) is bandpass filtered by the transfer function of the bandpass filter to obtain the filtered signal r f (t);

[0078] Apply gain processing to the deep signal to compensate for the energy attenuation caused by the increase in distance, ensure that the signal of the deep defect is clearly visible, and perform gain processing on the filtered signal to obtain the signal d(t)=r after gain processing. f(t)·e γt , where γ is the gain factor, t is the time, and e is a natural constant.

[0079] Low frequency cutoff frequency ω L =2πf L , high frequency cutoff angular frequency ω H =2πf H , center frequency Quality Factors Among them, f L is the low frequency cutoff frequency, f H is the low frequency cutoff frequency.

[0080] S3. Based on the improved S transform (the following formula), time-frequency analysis is performed on the intercepted data to convert the time domain signal into a time-frequency domain signal, revealing the intensity of each frequency component contained in the signal, thereby helping to identify frequency anomalies caused by material inhomogeneity, cracks or other structural defects, and showing the frequency distribution of the signal at a specific time point, thereby providing a detailed view of the signal changing over time, tracking transient phenomena in the signal (such as scattered waves when cracks are generated), allowing us to quickly and accurately extract specific single-frequency profiles.

[0081] Methods for extracting single frequency components include:

[0082] Determine the time spectrum Where, s(t) is the preprocessed data, C is the slope parameter, B is the intercept parameter, η is the time parameter, f is the frequency parameter, i is the imaginary unit, and e is the natural constant;

[0083] Wherein, △t1 and △t2 are the time sampling rates at frequency f1 and frequency f2;

[0084] Select a single frequency and extract the corresponding frequency component to obtain the single frequency component;

[0085] Methods for drawing single frequency profiles include:

[0086] The single frequency component is normalized and plotted on the spatial axis to obtain a single frequency profile.

[0087] S4. Use the double filtering algorithm to separate the primary wave characteristic information and multiple wave characteristic information of the single frequency section, and extract the multiple wave signal of the air gap interface between the steel lining and the reinforced concrete. Use the filter vector to perform an inverse filtering operation on the seismic trace to obtain the primary wave signal, and finally subtract the primary wave signal from the seismic trace to obtain the multiple wave signal. The results of the double filtering method for separating the primary wave and the multiple wave are shown in the figure below. Figure 3 shown.

[0088] The method of extracting the multiple wave signal of the void interface includes:

[0089] The double filtering algorithm is used to filter the single-frequency profile to obtain the filtered signal;

[0090] Calculate the convolution quotient R between the seismic trace and the wavelet d , Where x is the seismic trace extracted from the single-frequency profile, and w is the wavelet corresponding to x;

[0091] The convolution quotient R d The first element of is set to -1, and the entire convolution quotient R d Take the inversion and get the filter vector R′ d ;

[0092] By filtering the vector R′ d Perform inverse filtering on the seismic trace to obtain a primary wave signal x r ={x r (1), x r (2), …, x r (n)}:

[0093]

[0094] Calculate the multiple wave signal x d =xx r . Figure 4 is the two-dimensional section corresponding to the extracted multiple waves.

[0095] S5. Extract the relative amplitude values ​​A of the multiple waves in the same time series from the separated multiple wave signals (when it is complete enough and there is no void, the value at this time is close to 0) to form a sequence, and then we can get the relative degree of voids in the survey line. After many experiments, we have a certain experience in setting the threshold. This experience is not absolute, but it has a certain universality. We believe that the threshold for the extracted A value should be: A≤0.2 area is a dense area, 0.2<A≤0.4 area is a less dense area, 0.4<A area is a void area, and it can also be adjusted according to the on-site opening conditions. On this basis, this method can be used to match each survey line on site to obtain the void conditions of each survey line in the detection area, and finally form a summary of the regional conditions to judge the overall defects of the building. Figure 5 and Figure 6 shown.

[0096] The method to extract the dimensionless amplitude value of the multiple wave signal is: Among them, V m is the maximum amplitude of the multiple wave signal, V ref is the maximum amplitude of the normal dense area.

[0097] Based on the two-dimensional image generated by the above process ( Figure 5 The dimensionless amplitude value A of the multiple wave signal is related to Figure 6 The effect comparison of the void index method test data can fully demonstrate the superiority of this method. Through this high-precision defect detection based on the extraction of multiple wave feature information, the defects of steel-lined reinforced concrete structures can be well located and the severity of the defects can be estimated.

[0098] Embodiment 3

[0099] A defect detection terminal based on multiple wave characteristic information includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a defect detection method based on multiple wave characteristic information as described above is implemented.

[0100] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing of the terminal by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an execution program required for at least one function, etc.

[0101] The data storage area can store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0102] A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, a defect detection method based on multiple wave characteristic information as described in any one of claims 1 to 8 is implemented. Without loss of generality, the computer-readable medium may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instruction data structures, program modules or other data. Computer storage media include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state storage technology, CD-ROM, DVD or other optical storage, tape cassettes, tapes, disk storage or other magnetic storage devices. Of course, those skilled in the art will know that computer storage media are not limited to the above. The above-mentioned system memory and mass storage devices can be collectively referred to as memory.

[0103] A computer program product comprises a computer program / instruction, wherein when the computer program / instruction is executed by a processor, the steps of any one of the above methods are implemented.

[0104] A computer program product includes a computer program or set of instructions for performing specific tasks or implementing specific functions. These programs or instructions are designed to be executed by a processor to implement a series of predefined steps or operations. The program product may be stored in various forms of computer storage media, such as memory, hard disk, solid-state drive, optical disk or other forms of digital storage devices. It may exist in the form of compiled binary code or in the form of scripts or bytecodes that can be executed by an interpreter. The program product uses carefully designed algorithms and logical instructions to enable the processor to process data in a specific order and manner to complete various functions such as data analysis, user interaction, device control, etc.

[0105] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / method or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments / methods or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments / methods or examples described in this specification and the features of the different embodiments / methods or examples, unless they are contradictory.

[0106] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0107] It should be understood by those skilled in the art that the above embodiments are only for the purpose of clearly illustrating the present invention, and are not intended to limit the scope of the present invention. For those skilled in the art, other changes or modifications may be made based on the above invention, and these changes or modifications are still within the scope of the present invention.

Claims

1. A defect detection method based on multiple wave characteristic information, characterized in that: include: The shock wave echo method is used to collect the original data of the measured area along the survey line; Preprocessing the original data to obtain preprocessed data; The preprocessed data is analyzed using the time-frequency analysis method, the single-frequency component is extracted, the single-frequency profile is drawn, and normalization is performed. Analyze the primary wave characteristic information and multiple wave characteristic information of the single frequency section, and extract the multiple wave signal of the void interface; The dimensionless amplitude value of the multiple wave signal is extracted, a threshold is set, and the defect degree is determined according to the relationship between the dimensionless amplitude value and the threshold.

2. A defect detection method based on multiple wave characteristic information according to claim 1, characterized in that: Methods for determining the degree of defects include: Set a first threshold value A1 and a second threshold value A2, A1>A2; If the dimensionless amplitude value of the multiple wave signal A≤A1, the corresponding area is judged to be a dense area; If the dimensionless amplitude value of the multiple wave signal A>A1 and A≤A2, the corresponding area is judged to be an under-compacted area; If the dimensionless amplitude value A of the multiple wave signal is greater than A2, the corresponding area is determined to be a void area.

3. The defect detection method based on multiple wave characteristic information according to claim 1 is characterized in that: Methods for preprocessing raw data include bandpass filtering and gain; Transfer function of bandpass filter Where s is a complex frequency variable, ω L is the low frequency cutoff angular frequency, ω H is the high frequency cutoff angular frequency, ω0 is the center frequency, and Q is the quality factor; The original data r(t) is bandpass filtered by the transfer function of the bandpass filter to obtain the filtered signal r f (t); Perform gain processing on the filtered signal to obtain a signal s(t)=r after gain processing f (t)·e γt , where γ is the gain factor, t is the time, and e is a natural constant.

4. A defect detection method based on multiple wave characteristic information according to claim 3, characterized in that: Low frequency cutoff frequency ω L =2πf L , high frequency cutoff angular frequency ω H =2πf H , center frequency Quality Factors Among them, f L is the low frequency cutoff frequency, f H is the low frequency cutoff frequency.

5. The defect detection method based on multiple wave characteristic information according to claim 1 is characterized in that: Methods for extracting single frequency components include: Determine the time spectrum Where, s(t) is the preprocessed data, C is the slope parameter, B is the intercept parameter, η is the time parameter, f is the frequency parameter, i is the imaginary unit, and e is the natural constant; Wherein, △t1 and △t2 are the time sampling rates at frequency f1 and frequency f2; Select a single frequency and extract the corresponding frequency component to obtain the single frequency component.

6. A defect detection method based on multiple wave characteristic information according to claim 5, characterized in that: Methods for drawing single frequency profiles include: The single frequency component is normalized and plotted on the spatial axis to obtain a single frequency profile.

7. The defect detection method based on multiple wave characteristic information according to claim 1 is characterized in that: The method of extracting the multiple wave signal of the void interface includes: The double filtering algorithm is used to filter the single-frequency profile to obtain the filtered signal; Calculate the convolution quotient R between the seismic trace and the wavelet d , Where x is the seismic trace extracted from the single-frequency profile, and w is the wavelet corresponding to x; The convolution quotient R d The first element of is set to -1, and the entire convolution quotient R d Take the inversion and get the filter vector R′ d ; By filtering the vector R′ d Perform an inverse filtering operation on the seismic trace to obtain a primary wave signal xr = {x r (1), x r (2), …, x r (n)}: Calculate the multiple wave signal x d =xx r .

8. The defect detection method based on multiple wave characteristic information according to claim 1 is characterized in that: The method to extract the dimensionless amplitude value of the multiple wave signal is: Among them, V m is the maximum amplitude of the multiple wave signal, V ref is the maximum amplitude of the normal dense area.

9. A defect detection terminal based on multiple wave characteristic information, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, a defect detection method based on multiple wave characteristic information as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a defect detection method based on multiple wave characteristic information as described in any one of claims 1 to 8.

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