Method and system for evaluating the degree of void at the interface between a steel tube and concrete

Abstract

This application relates to the field of detection technology for the degree of voiding at the steel-concrete interface, and provides a method and system for evaluating the degree of voiding at the steel-concrete interface. By establishing an acoustic cross-correlation mapping system between a dense benchmark sample and a preset voiding model, the traditional qualitative judgment relying on human hearing is transformed into a mathematical representation based on waveform similarity. A quantitative relationship between the degree of voiding and the detuning degree of the acoustic signal is constructed through a normalized voiding cross-correlation coefficient, fundamentally eliminating the subjective error of human experience. The acoustic autocorrelation coefficient of the dense steel pipe is introduced as a benchmark, achieving decoupling mapping between the degree of voiding and signal characteristics. A four-level evaluation mechanism of "unqualified - qualified - good - excellent" is established based on the preset voiding model, with the grading threshold derived from the correlation between the actual voiding size and acoustic distortion.

Description

Technical Field

[0001] This invention belongs to the field of detection technology for the degree of voids at the interface between steel pipe and concrete, and specifically relates to a method and system for evaluating the degree of voids at the interface between steel pipe and concrete. Background Technology

[0002] Currently, steel-concrete composite columns for super high-rise buildings and large bridges are characterized by increasing cross-sectional area. Due to the shrinkage of large-volume concrete, the presence of horizontal diaphragms, and improper placement of vents, the bonding condition between the inner wall of the steel tube and the internal concrete has become a common concern. Related research indicates that the bonding performance between the internal concrete and the inner wall of the steel tube after final setting has a significant impact on the load-bearing capacity and ductility, among other mechanical properties, of large-section steel-concrete composite columns.

[0003] The national standard "Code for Acceptance of Construction Quality of Concrete-Concrete Tube Engineering" (GB50628-2010) stipulates that the compactness of the concrete pouring inside the steel tube is the main control item. The "Technical Standard for Concrete-Concrete Tube Structures" (GB / T 51446-2021) specifies in section 10.3.10 that the core concrete inside the steel tube of a concrete-concrete tube should be compacted and should not exceed the void tolerance. When the void tolerance is exceeded, reinforcement should be applied to the voided areas. Furthermore, the concrete inside the steel tube should not exhibit continuous ring-shaped voids along its perimeter. Section 10.5.3 of the national standard "Technical Standard for Concrete-Concrete Tube Structures" (GB / T51446-2021) specifies that the compactness of the concrete pouring inside the steel tube can be tested using the manual tapping method.

[0004] While the manual tapping method for assessing the compactness of concrete-filled steel pipes (PVC-U) has advantages such as convenience and speed, traditional PVC-U tapping tests fall into two categories. One involves manually tapping the steel pipe, with the tapper listening to the sound and making a qualitative judgment based on their experience regarding whether voids have formed. This method is subjective and lacks a test record. The other method uses an air-coupled sensor to record the sound and then performs spectral analysis. This method can only make relative judgments, lacks a unified standard for judging voids, and cannot determine the degree of void formation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for assessing the degree of voiding at the steel-concrete interface, which can determine the degree of voiding without relying on subjective human judgment.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention proposes a method for evaluating the degree of voids at the interface between steel-concrete composite pipes, comprising the following steps:

[0008] Step S1: Tap the cast and compacted steel pipe and record the acoustic data of the cast and compacted steel pipe;

[0009] Step S2: Tap the concrete model of the cast hollow steel pipe with different hollow sizes, record the sound wave data, and obtain multiple sets of sound wave data of hollow steel pipe with different degrees of hollowness.

[0010] Step S3: Perform autocorrelation calculation on the acoustic data of the cast dense steel pipe to obtain the autocorrelation function, and take its maximum value as the autocorrelation coefficient of the cast dense steel pipe. ;

[0011] Step S4: Cross-correlation calculations are performed on the acoustic data of the poured compacted steel pipe with the acoustic data of the vacuolated steel pipes of different degrees to obtain the cross-correlation function. The maximum value of the cross-correlation function is taken as the cross-correlation coefficient. The cross-relationship numbers are respectively Divide by the autocorrelation coefficient The cross-correlation coefficient of voidage between the cast-in-place voided steel pipe and the cast-in-place dense steel pipe was obtained. ;

[0012] Step S5: Tap the steel pipe with unknown void degree, record the acoustic wave data at a certain location with unknown void degree, and perform cross-correlation calculation with the acoustic wave data of the poured and compacted steel pipe to obtain the cross-correlation function. Divide the maximum value of the cross-correlation function by the autocorrelation coefficient. The cross-correlation coefficient of voidage between the steel pipe with unknown voidage degree and the cast-in-place dense steel pipe was obtained. ,Will The degree of cross-correlation between the cross-correlation coefficient obtained in step S4 and the void separation coefficient Segmented comparisons are performed to determine the pouring density level of the location with unknown voiding.

[0013] Furthermore, the formula for calculating the autocorrelation function in step S3 is as follows:

[0014]

[0015] in, Acoustic data for pouring dense steel pipes The autocorrelation function, take maximum value As its autocorrelation coefficient, n is the number of points on each set of acoustic data.

[0016] Furthermore, the formula for calculating the cross-correlation function in step S4 is as follows:

[0017]

[0018] in, Acoustic data for pouring dense steel pipes Acoustic data of hollow steel pipe The cross-correlation function, take maximum value As their cross-correlation coefficient, n is the number of points on each set of acoustic data.

[0019] Furthermore, the acoustic data includes waveform data.

[0020] Furthermore, in step S2, at least three locations with different void sizes and one dense location of the steel-concrete composite model are struck, and sound wave data is recorded to obtain at least four sets of waveform data with different void degrees.

[0021] Furthermore, in step S2, the sizes of the three void locations with different void sizes and one dense location, from largest to smallest, are the fourth area, the third area, the second area, and the first area, where the first area is 0.

[0022] Furthermore, in step S4, the correlation coefficient between the positions of the cast hollow steel pipe with hollow areas of the fourth, third, second, and first areas and the hollow area of ​​the cast dense steel pipe is... These are the fourth coefficient, the third coefficient, the second coefficient, and the first coefficient, respectively.

[0023] Furthermore, in step S5, the pouring density level is divided into four levels from low to high: unqualified, qualified, good, and excellent.

[0024] Furthermore, in step S5, the following steps are performed: The degree of cross-correlation between the cross-correlation coefficient obtained in step S4 and the void separation coefficient The steps for determining the pouring density grade at locations with unknown void levels through segmented comparison include:

[0025] The correlation coefficient between the voidage degree of steel pipes with unknown voidage and those of densely cast steel pipes. Compare with the fourth, third, second, and first coefficients respectively;

[0026] like If the density of the pouring at the location with unknown void degree is between the first and second coefficients, then the density is of the best grade.

[0027] like If the density of the pouring at the location with unknown void degree is between the second and third coefficients, then the density is good.

[0028] like If the density of the pouring at the location with unknown void degree is between the third and fourth coefficients, then the density level is qualified.

[0029] like If the density is greater than the fourth coefficient, the pouring density grade at the location with unknown void degree is unqualified.

[0030] Secondly, the present invention also proposes a system for evaluating the degree of voiding at the steel-concrete interface based on the waveform cross-correlation method. This system is used to implement the aforementioned method for evaluating the degree of voiding at the steel-concrete interface. The system includes:

[0031] A percussion hammer is used to strike concrete models of cast-in-place steel pipes, cast-in-place steel pipes with varying degrees of voids, and steel pipes with unknown degrees of voids.

[0032] An air-coupled sensor is used to detect sound waves generated on the surface of a steel pipe when struck.

[0033] The first calculation module is used to perform autocorrelation calculations on the acoustic wave data of the cast dense steel pipe to obtain the autocorrelation function, and take the maximum value as the autocorrelation coefficient of the cast dense steel pipe. ;

[0034] The second calculation module is used to perform cross-correlation calculations between the acoustic wave data of the poured compacted steel pipe and the acoustic wave data of the vacuolated steel pipe with different degrees of voids to obtain the cross-correlation function, and take the maximum value of the cross-correlation function as its cross-correlation coefficient. The cross-relationship numbers are respectively Divide by the autocorrelation coefficient The cross-correlation coefficient of voidage between the cast-in-place voided steel pipe and the cast-in-place dense steel pipe was obtained. ;

[0035] The third calculation module is used to perform cross-correlation calculations on the acoustic wave data of a location with an unknown degree of void in a steel pipe with an unknown degree of void, and the acoustic wave data of a poured and compacted steel pipe to obtain a cross-correlation function. The maximum value of the cross-correlation function is then divided by the autocorrelation coefficient. The cross-correlation coefficient of voidage between the steel pipe with unknown voidage degree and the cast-in-place dense steel pipe was obtained. ;

[0036] The evaluation module is used to evaluate the degree of cross-correlation coefficients after the void period. Cross-correlation coefficient with void Segmented comparisons are performed to determine the pouring density level of the location with unknown voiding.

[0037] The beneficial effects of this invention are:

[0038] 1. Break through reliance on subjective experience and achieve objective quantitative grading.

[0039] By establishing a cross-correlation mapping system between dense benchmark samples and a pre-defined decoupling model, the traditional qualitative judgment relying on human hearing is transformed into a mathematical representation based on waveform similarity. The core lies in utilizing the reflection phase shift and energy attenuation characteristics of sound waves at the decoupling interface (decoupling causes abrupt changes in wave impedance, and the superposition of reflected and incident waves disrupts waveform integrity), using the extreme points of the cross-correlation function to characterize the degree of waveform distortion. A quantitative relationship between the degree of decoupling and the detuning degree of the sound wave signal is constructed through normalized decoupling cross-correlation coefficients, fundamentally eliminating the subjective errors of human experience.

[0040] 2. Addressing the industry pain point of lacking a calibration benchmark for spectrum analysis.

[0041] Unlike traditional spectral analysis methods that only obtain relative characteristics without absolute judgment criteria, this invention uniquely introduces the autocorrelation coefficient of acoustic waves from dense steel pipes as a benchmark, giving the cross-correlation coefficient a clear physical meaning: when it approaches 1, it indicates that the waveform of the measured area matches the dense benchmark closely; however, as the void area increases, waveform distortion intensifies, and the cross-correlation coefficient systematically decreases. This principle achieves decoupling mapping between the void area and signal characteristics, establishing a reproducible and objective standard for grading and evaluation.

[0042] 3. The hierarchical system is closely coupled with structural safety requirements.

[0043] The four-level evaluation mechanism of "unqualified-qualified-good-excellent" based on the pre-defined void model is derived from the correlation between the actual void size and acoustic distortion (the increased void area leads to accelerated attenuation of the peak value of the cross-correlation function). This design allows the grading results to directly correspond to the national standard's control requirements for void tolerance. In particular, by identifying the "unqualified" level, it can accurately intercept dangerous defects such as continuous annular voids, providing a quantitative basis for reinforcement decisions.

[0044] 4. Improve the applicability and reliability of testing large cross-section structures.

[0045] Combining the non-contact detection advantages of air-coupled sensors, this method avoids interference from coupling agents on sound wave propagation. By employing a cross-correlation algorithm to focus on the temporal characteristics of the signal, the influence of ambient noise is significantly suppressed. This method offers higher resolution for localized voids caused by complex structures such as horizontal partitions and vents, meeting the need for rapid, full-range scanning of large-section steel-concrete composite columns. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the method for evaluating the degree of voids at the steel-concrete interface according to the present invention.

[0047] Figure 2 This is a schematic diagram illustrating the impact test for voids at the steel-concrete interface using the system for assessing the degree of voids at the steel-concrete interface according to the present invention.

[0048] Figure 3 This is a cross-sectional schematic diagram of a steel-concrete model of a cast-in-place steel pipe with different void sizes according to the present invention.

[0049] Figure 4 for Figure 3 The waveform diagram of B15 at the fourth area position is shown in the figure.

[0050] Figure 5 for Figure 3 The waveform of B10 at the third area position is shown in the diagram.

[0051] Figure 6 for Figure 3 The B5 waveform diagram shows the size of the void in the middle at the second area position.

[0052] Figure 7 This is a waveform diagram of B0 at the location where the void size is the first area according to the present invention.

[0053] Figure 8 This is a Bx waveform diagram of a location with an unknown degree of voidness when the steel pipe with an unknown degree of voidness is struck in step 5 of the method of the present invention.

[0054] In the diagram: 1-Impact hammer; 2-Air coupling sensor; 3-Steel pipe; 4-Concrete; 5-Interface void; 6-Void size at the fourth area position; 7-Void size at the third area position; 8-Void size at the second area position. Detailed Implementation

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] like Figure 1 The method for assessing the degree of voids at the steel-concrete composite interface, shown below, is based on the waveform cross-correlation method and includes the following steps:

[0057] Step S1: Tap the cast and compacted steel pipe and record the acoustic data of the cast and compacted steel pipe;

[0058] Step S2: Tap the concrete model of the cast hollow steel pipe with different hollow sizes, record the sound wave data, and obtain multiple sets of sound wave data of hollow steel pipe with different degrees of hollowness.

[0059] Step S3: Perform autocorrelation calculation on the acoustic data of the cast dense steel pipe to obtain the autocorrelation function, and take its maximum value as the autocorrelation coefficient of the cast dense steel pipe. ;

[0060] Step S4: Cross-correlation calculations are performed on the acoustic data of the poured compacted steel pipe with the acoustic data of the vacuolated steel pipes of different degrees to obtain the cross-correlation function. The maximum value of the cross-correlation function is taken as the cross-correlation coefficient. The cross-relationship numbers are respectively Divide by the autocorrelation coefficient The cross-correlation coefficient of voidage between the cast-in-place voided steel pipe and the cast-in-place dense steel pipe was obtained. ;

[0061] Step S5: Tap the steel pipe with unknown void degree, record the acoustic wave data at a certain location with unknown void degree, and perform cross-correlation calculation with the acoustic wave data of the poured and compacted steel pipe to obtain the cross-correlation function. Divide the maximum value of the cross-correlation function by the autocorrelation coefficient. The cross-correlation coefficient of voidage between the steel pipe with unknown voidage degree and the cast-in-place dense steel pipe was obtained. ,Will The degree of cross-correlation between the cross-correlation coefficient obtained in step S4 and the void separation coefficient Segmented comparisons are performed to determine the pouring density level of the location with unknown voiding.

[0062] The above acoustic data includes waveform data.

[0063] It should be noted that the impact acoustic method for concrete-filled steel tubes is an effective non-destructive testing method for identifying interfacial debonding defects in concrete-filled steel tubes. Waveform correlation, also known as cross-correlation, is a technique widely used in signal processing and data analysis to quantitatively measure the similarity between two waveforms (signals, time series).

[0064] In step S2, at least three locations with different void sizes and one compacted location on the steel-concrete composite model are tapped, and sound wave data is recorded to obtain at least four sets of waveform data with different degrees of voidness. The void sizes of the three locations with different void sizes and the one compacted location, from largest to smallest, are the fourth area, the third area, the second area, and the first area, where the first area is 0.

[0065] The formula for calculating the autocorrelation function in step S3 is as follows:

[0066]

[0067] in, Acoustic data for pouring dense steel pipes The autocorrelation function, take maximum value As its autocorrelation coefficient, n is the number of points on each set of acoustic data.

[0068] The formula for calculating the cross-correlation function in step S4 is as follows:

[0069]

[0070] in, Acoustic data for pouring dense steel pipes Acoustic data of hollow steel pipe The cross-correlation function, take maximum value As their cross-correlation coefficient, n is the number of points on each set of acoustic data.

[0071] In step S4, the correlation coefficient between the positions of the cast hollow steel pipe with hollow areas of the fourth, third, second, and first areas and the hollow area of ​​the cast dense steel pipe is calculated. These are the fourth coefficient, the third coefficient, the second coefficient, and the first coefficient, respectively.

[0072] In step S5, the density grades of the poured concrete are ranked from low to high as follows: unqualified, qualified, good, and excellent. In step S5, the... The degree of cross-correlation between the cross-correlation coefficient obtained in step S4 and the void separation coefficient The steps for determining the pouring density grade at locations with unknown void levels through segmented comparison include:

[0073] The correlation coefficient between the voidage degree of steel pipes with unknown voidage and those of densely cast steel pipes. Compare with the fourth, third, second, and first coefficients respectively;

[0074] like If the density of the pouring at the location with unknown void degree is between the first and second coefficients, then the density is of the best grade.

[0075] like If the density of the pouring at the location with unknown void degree is between the second and third coefficients, then the density is good.

[0076] like If the density of the pouring at the location with unknown void degree is between the third and fourth coefficients, then the density level is qualified.

[0077] like If the density is greater than the fourth coefficient, the pouring density grade at the location with unknown void degree is unqualified.

[0078] like Figure 2 As shown, based on the same inventive concept, this invention also proposes a system for assessing the degree of voids at the steel-concrete interface. This system is used to implement the aforementioned method for assessing the degree of voids at the steel-concrete interface. The system includes:

[0079] Hammer 1 is used to strike concrete models of cast dense steel pipes, cast hollow steel pipes with different sizes of voids, and steel pipes with unknown degrees of voids.

[0080] Air coupling sensor 2 is used to detect sound waves generated on the surface of the steel pipe due to impact.

[0081] The first calculation module is used to perform autocorrelation calculations on the acoustic wave data of the cast dense steel pipe to obtain the autocorrelation function, and take the maximum value as the autocorrelation coefficient of the cast dense steel pipe. ;

[0082] The second calculation module is used to perform cross-correlation calculations between the acoustic wave data of the poured compacted steel pipe and the acoustic wave data of the vacuolated steel pipe with different degrees of voids to obtain the cross-correlation function, and take the maximum value of the cross-correlation function as its cross-correlation coefficient. The cross-relationship numbers are respectively Divide by the autocorrelation coefficient The cross-correlation coefficient of voidage between the cast-in-place voided steel pipe and the cast-in-place dense steel pipe was obtained. ;

[0083] The third calculation module is used to perform cross-correlation calculations on the acoustic wave data of a location with an unknown degree of void in a steel pipe with an unknown degree of void, and the acoustic wave data of a poured and compacted steel pipe to obtain a cross-correlation function. The maximum value of the cross-correlation function is then divided by the autocorrelation coefficient. The cross-correlation coefficient of voidage between the steel pipe with unknown voidage degree and the cast-in-place dense steel pipe was obtained. ;

[0084] The evaluation module is used to evaluate the degree of cross-correlation coefficients after the void period. Cross-correlation coefficient with void Segmented comparisons are performed to determine the pouring density level of the location with unknown voiding.

[0085] like Figures 3 to 8 As shown, a steel-concrete composite column with a diameter of 1 meter, a height of 1.5 meters, and a steel pipe thickness of 1 cm was constructed. Three voids of 15*15 cm, 10*10 cm, and 5*5 cm were created in the middle. The column was struck, and the sound wave data were recorded. Autocorrelation and cross-correlation calculations were then performed on the data.

[0086] In this embodiment, the fourth, third, second, and first void areas are 15*15 cm, 10*10 cm, 5*5 cm, and 0*0 cm, respectively. The correlation coefficient between the void size of the cast-in-place void steel pipe and the void size of the cast-in-place dense steel pipe at these four void locations is also considered. These are the fourth, third, second, and first coefficients, respectively. The corresponding concrete compaction grades are unqualified, qualified, good, and excellent, respectively.

[0087] In step S5, for the waveform Bx at a location with an unknown degree of voids in the concrete-filled steel tube, cross-correlation is performed with the acoustic waveform B of the poured dense steel tube to obtain the cross-correlation function. The maximum value of this function is divided by the autocorrelation coefficient of the waveform of the poured dense steel tube. The correlation coefficient between the steel pipe with unknown void degree and the cast-in-place dense steel pipe was obtained. ,like exist and Between (inclusive) and (inclusive), the compaction of the concrete at this location is excellent; if exist and Between (inclusive) and (inclusive), the compaction of the concrete at that location is considered good; if exist and Between (inclusive) and (inclusive), the compaction of the concrete at that location is acceptable; if Less than The compaction of the concrete at this location is substandard.

[0088] Table 1. Relationship between void size and cross-correlation coefficient

[0089]

[0090] The cross-correlation function of the waveform of a steel pipe with an unknown degree of voidage is used to obtain its voidage correlation coefficient. =0.45, The corresponding coefficient is 1. The corresponding coefficient is 0.4, and this coefficient exist and Based on this, the pouring quality at this location is determined to be of the excellent grade.

[0091] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating the degree of voids at the interface between steel-concrete composite pipes, characterized in that, Includes the following steps: Step S1: Tap the cast and compacted steel pipe and record the acoustic data of the cast and compacted steel pipe; Step S2: Tap the concrete model of the cast hollow steel pipe with different hollow sizes, record the sound wave data, and obtain multiple sets of sound wave data of hollow steel pipe with different degrees of hollowness. Step S3: Perform autocorrelation calculation on the acoustic data of the cast dense steel pipe to obtain the autocorrelation function, and take its maximum value as the autocorrelation coefficient of the cast dense steel pipe. ; Step S4: Cross-correlation calculations are performed on the acoustic data of the poured compacted steel pipe with the acoustic data of the vacuolated steel pipes of different degrees to obtain the cross-correlation function. The maximum value of the cross-correlation function is taken as the cross-correlation coefficient. The cross-relationship numbers are respectively Divide by the autocorrelation coefficient The cross-correlation coefficient of voidage between the cast-in-place voided steel pipe and the cast-in-place dense steel pipe was obtained. ; Step S5: Tap the steel pipe with unknown void degree, record the acoustic wave data at a certain location with unknown void degree, and perform cross-correlation calculation with the acoustic wave data of the poured and compacted steel pipe to obtain the cross-correlation function. Divide the maximum value of the cross-correlation function by the autocorrelation coefficient. The cross-correlation coefficient of voidage between the steel pipe with unknown voidage degree and the cast-in-place dense steel pipe was obtained. ,Will The degree of cross-correlation between the cross-correlation coefficient obtained in step S4 and the degree of cross-correlation between the cross-correlation coefficient ... Perform segmented comparisons to determine the pouring density level at the location with unknown void degree; The acoustic data includes waveform data; In step S2, at least three locations with different void sizes and one dense location of the steel-concrete composite model are tapped, and sound wave data is recorded to obtain at least four sets of waveform data with different void degrees. In step S2, the sizes of the voids at the three locations with different void sizes and the one dense location are, in descending order, the fourth area, the third area, the second area, and the first area, where the first area is 0. In step S4, the correlation coefficient between the positions of the cast hollow steel pipe with hollow areas of the fourth, third, second, and first areas and the hollow areas of the cast dense steel pipe is determined. These are the fourth coefficient, the third coefficient, the second coefficient, and the first coefficient, respectively. In step S5, the pouring density level is divided into four levels from low to high: unqualified, qualified, good and excellent. In step S5, The degree of cross-correlation between the cross-correlation coefficient obtained in step S4 and the degree of cross-correlation between the cross-correlation coefficient ... The steps for determining the pouring density grade at locations with unknown void levels through segmented comparison include: The correlation coefficient between the voidage degree of steel pipes with unknown voidage and those of densely cast steel pipes. Compare with the fourth, third, second, and first coefficients respectively; like If the density of the pouring at the location with unknown void degree is between the first and second coefficients, then the density is of the best grade. like If the density of the pouring at the location with unknown void degree is between the second and third coefficients, then the density is good. like If the density of the pouring at the location with unknown void degree is between the third and fourth coefficients, then the density level is qualified. like If the density is greater than the fourth coefficient, the pouring density grade at the location with unknown void degree is unqualified; The formula for calculating the autocorrelation function in step S3 is as follows: ； in, Acoustic data for pouring dense steel pipes The autocorrelation function, take maximum value As its autocorrelation coefficient, n is the number of points on each set of acoustic data; The formula for calculating the cross-correlation function in step S4 is as follows: ； in, Acoustic data for pouring dense steel pipes Acoustic data of hollow steel pipe The cross-correlation function, take maximum value As their cross-correlation coefficient, n is the number of points on each set of acoustic data.

2. A system for evaluating the degree of voids at the interface between steel pipe and concrete, characterized in that, The system is used to implement the method for assessing the degree of voids at the steel-concrete interface as described in claim 1; the system comprises: A percussion hammer is used to strike concrete models of cast-in-place steel pipes, cast-in-place steel pipes with varying degrees of voids, and steel pipes with unknown degrees of voids. An air-coupled sensor is used to detect sound waves generated on the surface of a steel pipe when struck. The first calculation module is used to perform autocorrelation calculations on the acoustic wave data of the cast dense steel pipe to obtain the autocorrelation function, and take the maximum value as the autocorrelation coefficient of the cast dense steel pipe. ; The second calculation module is used to perform cross-correlation calculations on the acoustic wave data of the poured compacted steel pipe and the acoustic wave data of the vacuolated steel pipe with different degrees of voids to obtain the cross-correlation function, and take the maximum value of the cross-correlation function as its cross-correlation coefficient. The cross-relationship numbers are respectively Divide by the autocorrelation coefficient The cross-correlation coefficient of voidage between the cast-in-place voided steel pipe and the cast-in-place dense steel pipe was obtained. ; The third calculation module is used to perform cross-correlation calculations on the acoustic wave data of a location with an unknown degree of void in a steel pipe with an unknown degree of void, and the acoustic wave data of a poured and compacted steel pipe to obtain a cross-correlation function. The maximum value of the cross-correlation function is then divided by the autocorrelation coefficient. The cross-correlation coefficient of voidage between the steel pipe with unknown voidage degree and the cast-in-place dense steel pipe was obtained. ; The evaluation module is used to evaluate the degree of cross-correlation coefficients after the void period. Cross-correlation coefficient with void Segmented comparisons are performed to determine the pouring density level of the location with unknown voiding.

Images