Steel pipe void detection method based on elastic wave attenuation characteristics

Abstract

The invention provides an elastic wave attenuation characteristic-based steel tube void detection method, which comprises the following steps of S1, arranging a single sensor on the surface of a steel tube concrete structure, and performing excitation by adopting an excitation hammer to obtain single hammer knocking data; s2, carrying out absolute value processing on the single-hammer knocking data, then carrying out normalization method processing, processing a waveform curve processed by the normalization method by adopting a rectangular frame selection method to obtain a new waveform curve, and carrying out damping ratio calculation on the obtained new waveform curve; s3, processing the data after the damping ratio calculation by adopting a rectangular frame selection method, and averaging the obtained data to obtain a characteristic value; according to the measured data, the attenuation speed of the impact elastic waves at the detection position can be effectively analyzed, the quality of the concrete filled steel tube can be rapidly detected, the result is stable and reliable, the detection process is simple, the result can be automatically analyzed, and the detection problem existing in a traditional impact echo method is effectively solved.

Description

Technical Field

[0001] The invention relates to the field of nondestructive testing engineering, and in particular to a steel pipe hollowing detection method based on elastic wave attenuation characteristics. Background Art

[0002] Steel tube concrete is widely used in underwater structures, large-span bridges and high-rise buildings due to its advantages such as light weight, strong bearing capacity, outstanding seismic performance, good fire resistance and simple process. However, after long-term use, steel tube concrete will have various defects due to its own material properties, process flow and environmental factors. The most common defect is the hollowing of steel tube concrete. The hollowing defect of steel tube concrete will seriously affect the usability and durability of the structure itself, and even endanger the safety of life and property.

[0003] In the field of non-destructive testing engineering, impact elastic wave is a common method for detecting hollow defects in steel pipes. The impact echo method uses the reflected stress wave signal of the impact elastic wave collected in the detection structure to determine whether the structure has hollows. Due to the differences in internal defects of the structure and the complex on-site environmental factors, the collected reflected stress wave signal is very weak or almost non-existent, which leads to the detection limitations of this method and cannot be applied to a variety of complex on-site conditions; in addition, the results of the impact echo method detection vary greatly, are unstable, and the data lacks accuracy.

[0004] Therefore, there is an urgent need for a steel pipe void detection method based on the elastic wave attenuation characteristics, which can solve the problems of weak or almost no reflected stress wave signal in steel tube concrete detection by the traditional impact echo method and its inability to adapt to complex site conditions. Summary of the invention

[0005] The purpose of the present invention is to provide a steel pipe hollowness detection method based on elastic wave attenuation characteristics to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] The present invention provides a steel pipe hollowness detection method based on elastic wave attenuation characteristics, comprising the following steps:

[0008] S1. Arrange a single sensor on the surface of the steel tube concrete structure, use an exciting hammer to vibrate, and obtain single hammer impact data;

[0009] S2. Performing absolute value processing on the single hammer striking data, and then performing normalization processing, using a rectangular frame selection method to process the waveform curve after the normalization method to obtain a new waveform curve, and calculating the damping ratio of the obtained new waveform curve;

[0010] S3. After processing the data obtained from damping ratio calculation using the rectangular box selection method, the average value of the obtained data is taken to obtain the eigenvalue.

[0011] Preferably, in step S1, the specification of the excitation hammer is selected according to the diameter of the steel pipe in the concrete-filled steel tube structure.

[0012] Preferably, the distance between the excitation point and the sensor is 0.03 - 0.05 m.

[0013] Preferably, in step S2, the single-hammer percussion data is pre-processed by normalization and rectangular box selection data processing.

[0014] Preferably, step S2 includes:

[0015] S21. For the single-hammer percussion data group D 1 (x 1 ,x 2 ,x 3 ,...,x i ) collected at the percussion site, the absolute value is obtained to get D 1 (|x 1 |,|x 2 |,|x 3 |,...,|x i |);

[0016] Where x 1 ,x 2 ,x 3 ,...,x i are all data points of a waveform curve;

[0017] S22. The data after absolute value processing is normalized to obtain D 1 (|x 1 | / (x 1 |+|x 2 |+|x 3 |+...+|x i |),|x 2 | / (x 1 |+|x 2 |+|x 3 |+...+|x i |),|x 3 | / (x 1 |+|x 2 |+|x 3 |+...+|x i |),...,|x i | / (x 1 |+|x 2 |+|x 3 |+...+|x i|));

[0018] S23. Perform rectangular box selection processing on the normalized data to obtain a new waveform curve;

[0019] S24. Calculate the damping ratio for the new waveform curve data, including:

[0020]

[0021] wherein, is the damping ratio corresponding to the i-th data, and Ai is the i-th data.

[0022] Preferably, step S3 includes:

[0023] S31. Perform rectangular box selection processing on the obtained damping ratio values to obtain a series of processed data, including:

[0024] A 0 = mean(D 0 (m,n)), A 1 = mean(D 0 (i + m, i + n)),..., A j = mean(D 0 (i*(j - 1)+m, i*(j - 1)+

[0025] n));

[0026] wherein, (A 0 , A 1 ,..., A j ) are the data after box selection processing, mean is to calculate the average value of the data, m is the starting point of the box selection, n is the ending point of the box selection, i is the moving distance of the box selection, and j is the j-th rectangular box;

[0027] S32. Calculate the average value of the obtained series of processed data to obtain the characteristic value.

[0028] Preferably, the range of the characteristic value is 0 - 1.

[0029] The present invention has achieved the following beneficial technical effects compared with the prior art:

[0030] 1. A steel pipe void detection method based on the elastic wave attenuation characteristics provided by the present invention can effectively analyze the speed of attenuation of the impact elastic wave at the detection position according to the measured data.

[0031] 2. A steel pipe void detection method based on the elastic wave attenuation characteristics provided by the present invention can quickly detect the quality of steel pipe concrete, and the results are stable and reliable.

[0032] 3. The steel pipe void detection method provided by the present invention based on the elastic wave attenuation characteristic has a simple detection process and can realize automatic result analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is a flowchart of a steel pipe void detection method provided by the present invention based on the elastic wave attenuation characteristic;

[0035] Figure 2 It is a schematic diagram of the excitation method in the steel pipe void detection method provided by the present invention based on the elastic wave attenuation characteristic. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0037] The purpose of the present invention is to provide a steel pipe void detection method based on the elastic wave attenuation characteristic to solve the problems existing in the prior art.

[0038] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0039] Embodiment 1:

[0040] This embodiment provides a steel pipe void detection method based on the elastic wave attenuation characteristic. As Figure 1 shown, a self-compacting steel pipe concrete model is constructed. The model is 80 cm high, the wall thickness of the steel pipe 1 is 3 mm, the diameter is 50 mm, and the inside is filled with self-compacting concrete 2. A void defect is pre-set inside the model, and then the following method is used for steel pipe void detection, as Figure 2 shown:

[0041] S1. Arrange a single sensor 3 on the surface of the concrete-filled steel tube structure, and use a vibration hammer 4 for vibration excitation. The specification of the vibration hammer 4 is selected according to the diameter of the steel tube 1 in the concrete-filled steel tube structure. In this embodiment, a D50 vibration hammer is used, and the models of the vibration hammer 4 include but are not limited to D10, D17, D30, and D50; the distance between the vibration excitation point and the sensor 3 is 0.04 m to obtain single-hammer percussion data;

[0042] S2. Perform absolute value processing on the single-hammer percussion data, then perform normalization processing, and use the rectangular box selection method to process the waveform curve after normalization processing to obtain a new waveform curve, and calculate the damping ratio of the obtained new waveform curve;

[0043] It should be noted that the single-hammer percussion data is pre-processed by normalization and rectangular box selection data processing;

[0044] This step is specifically as follows:

[0045] S21. For the single-hammer percussion data group D 1 (x 1 ,x 2 ,x 3 ,...,x i ) collected at the percussion part, take the absolute value to get D 1 (|x 1 |,|x 2 |,|x 3 |,...,|x i |);

[0046] Among them, x 1 ,x 2 ,x 3 ,...,x i are all data points of a waveform curve;

[0047] S22. Perform normalization processing on the data after absolute value processing to obtain

[0048] D 1 (|x 1 | / (x 1 |+|x 2 |+|x 3 |+...+|x i |),|x 2 | / (x 1 |+|x 2 |+|x 3 |+...+|x i |),|x 3 | / (x 1 |+|x 2 |+|x 3 |+...+|xi |),...,|x i | / (x 1 |+|x 2 |+|x 3 |+...+|x i |));

[0049] S23. Perform rectangular box selection processing on the normalized data to obtain a new waveform curve;

[0050] S24. Calculate the damping ratio for the new waveform curve data, including:

[0051]

[0052] wherein, is the damping ratio value corresponding to the i-th data, and Ai is the i-th data;

[0053] S3. After processing the data after damping ratio calculation using the rectangular box selection method, take the average value of the obtained data to obtain the characteristic value; specifically, this step is as follows:

[0054] S31. Perform rectangular box selection processing on the obtained damping ratio values to obtain a series of processed data, including:

[0055] A 0 = mean(D 0 (m,n)), A 1 = mean(D 0 (i + m, i + n)),..., A j = mean(D 0 (i*(j - 1)+m, i*(j - 1)+n));

[0056] wherein, (A 0 , A 1 ,..., A j ) are the data after box selection processing, mean is to calculate the average value of the data, m is the starting point of the box selection, n is the ending point of the box selection, i is the moving distance of the box selection, and j is the j-th rectangular box;

[0057] S32. Take the average value of the obtained series of processed data to obtain the characteristic value, and the range of the characteristic value is 0 - 1.

[0058] Perform percussion detection on the self-compacting steel tube concrete model through the above method, and the results are shown in Table 1. From this result, it can be seen that when using the steel tube void detection method based on the elastic wave attenuation characteristics provided by the present invention for detection, the detection structure coincides with the actual defect position.

[0059] Table 1: Detection Results

[0060]

[0061]

[0062] The present invention uses specific examples to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A steel pipe hollowing detection method based on elastic wave attenuation characteristics, characterized in that: The following steps are involved: S1. Arrange a single sensor on the surface of the steel tube concrete structure, use an exciting hammer to vibrate, and obtain single hammer impact data; S2. Performing absolute value processing on the single hammer striking data, and then performing normalization processing, using a rectangular frame selection method to process the waveform curve after the normalization method to obtain a new waveform curve, and calculating the damping ratio of the obtained new waveform curve; S3. The data after the damping ratio is calculated is processed by the rectangular frame selection method, and the average value of the obtained data is taken to obtain the characteristic value.

2. The steel pipe hollowing detection method based on elastic wave attenuation characteristics according to claim 1 is characterized in that: In step S1, the specification of the exciting hammer is selected according to the diameter of the steel tube in the steel tube concrete structure.

3. The steel pipe hollowing detection method based on elastic wave attenuation characteristics according to claim 2 is characterized in that: The distance between the excitation point and the sensor is 0.03-0.05m.

4. The steel pipe hollowness detection method based on elastic wave attenuation characteristics according to claim 1 is characterized in that: In step S2, the single hammer striking data is pre-normalized and rectangular frame selected.

5. The steel pipe hollowing detection method based on elastic wave attenuation characteristics according to claim 4 is characterized in that: Step S2 includes: S21. The single hammer striking data set D1 (x1, x2, x3, ..., x i ) to find the absolute value of D1(|x1|,|x2|,|x3|,...,|x i |); Among them, x1,x2,x3,...,x i are all the data points of a waveform curve; S22. Normalize the absolute value to obtain D1(|x1| / (x1|+|x2|+|x3|+...+|x i |),|x2| / (x1|+|x2|+|x3|+...+|x i |),|x3| / (x1|+|x2|+|x3|+...+|x i |),...,|x i | / (x1|+|x2|+|x3|+...+|x i |)); S23. Performing rectangular frame selection on the normalized data to obtain a new waveform curve; S24. Calculate the damping ratio of the new waveform curve data, including: in, is the damping ratio corresponding to the i-th data, and Ai is the i-th data.

6. The steel pipe hollowing detection method based on elastic wave attenuation characteristics according to claim 5 is characterized in that: Step S3 includes: S31. The obtained damping ratio is processed by a rectangular frame selection method to obtain a series of processed data, including: A0=mean(D0(m,n)),A1=mean(D0(i+m,i+n)),...,A j =mean(D0(i*(j-1)+m,i*(j-1)+n)); Among them, (A0, A1, ..., A j ) is the data after the box selection, mean is the average value of the data, m is the starting point of the box selection, n is the end point of the box selection, i is the distance moved by the box selection, and j is the jth rectangular box; S32. Calculate the average value of a series of processed data to obtain a characteristic value.

7. The steel pipe hollowness detection method based on elastic wave attenuation characteristics according to claim 6 is characterized in that: The eigenvalues ​​range from 0 to 1.

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