A method for detecting defects in steel pipe columns used in foundation reinforcement

Abstract

This invention relates to material testing, specifically to a method for detecting defects in steel pipe columns used for foundation reinforcement. The method includes: acquiring echo signals received after emitting acoustic waves onto the steel pipe column; dividing the echo signals into multiple periodic segments; determining the probability of anomalies in each periodic segment based on its own waveform characteristics and the waveform characteristic differences between adjacent periodic signals; determining the multipath difference value between each periodic segment and the first periodic signal based on the multipath characteristics of the signal; determining the degree of distortion in each periodic segment based on the probability of anomalies and the multipath difference value; and determining the defects in the steel pipe column based on the degree of distortion of each periodic signal. This method can improve the accuracy of defect detection in steel pipe columns used for foundation reinforcement.

Description

Technical Field

[0001] This invention relates to material testing, and more specifically to a method for detecting defects in steel pipe columns used for foundation reinforcement. Background Technology

[0002] Steel tubular columns used for foundation reinforcement typically have thick walls to withstand greater loads and pressures, and are therefore widely used in high-rise buildings, large spatial structures, and industrial plants to provide additional support and stability. Consequently, the quality of these steel tubular columns is a crucial factor in determining whether a building poses a safety hazard. During the production process, improper material proportions or inadequate curing conditions can lead to defects such as voids or cracks within the steel tubular columns. If these defects are not detected or identified in a timely manner, they can severely affect the structural load-bearing capacity of the steel tubular column, thereby impacting the overall performance of the building structure and creating safety hazards. Therefore, defect detection of steel tubular columns used for foundation reinforcement is important and necessary.

[0003] Because acoustic wave testing is a non-destructive testing method and can observe the internal structure of steel pipe columns, it is often used for defect detection. Traditionally, sound waves are emitted into the steel pipe column, and the presence of defects is determined based on the amplitude of the echo signal. However, since the wall thickness of steel pipe columns used for foundation reinforcement is greater than that of ordinary steel pipe columns, the acoustic wave signal undergoes significant attenuation. This results in a substantial reduction in the amplitude difference between the echo signals from defective and non-defective steel pipe columns. Therefore, determining the presence of defects based on amplitude is inaccurate and prone to misjudgment. Summary of the Invention

[0004] To address the technical problem of low accuracy in defect detection of steel pipe columns used for foundation reinforcement, the present invention aims to provide a method for defect detection of steel pipe columns used for foundation reinforcement. The specific technical solution adopted is as follows:

[0005] Acquire the echo signal received after emitting sound waves to the steel pipe column;

[0006] The echo signal is periodically divided to obtain multiple periodic signals;

[0007] The probability of an anomaly in each segment of the periodic signal is determined based on its own waveform characteristics and the differences in waveform characteristics between adjacent periodic signals.

[0008] Based on the multipath characteristics of the signal, determine the multipath difference value between each segment of the periodic signal and the first segment of the periodic signal;

[0009] The degree of distortion of each segment of the periodic signal is determined based on the probability of anomaly and the multipath difference value corresponding to each segment of the periodic signal.

[0010] The steel pipe column is defect-determined based on the degree of distortion of each periodic signal.

[0011] In one embodiment, determining the probability of an anomaly in each segment of the periodic signal based on its own waveform characteristics and the waveform characteristic differences between adjacent periodic signals includes:

[0012] Based on the waveform characteristics of each segment of the periodic signal, each segment of the periodic signal is fitted with a sine wave function to obtain the fitting result of each segment of the periodic signal.

[0013] Based on the fitting results of each periodic signal segment, determine the probability that the waveform of each periodic signal segment is normal;

[0014] Each segment of the periodic signal is taken as the periodic signal to be analyzed. Based on the fitting results of the adjacent periodic signals of the periodic signal to be analyzed and the normality of the waveform, the waveform feature differences between the adjacent periodic signals are determined.

[0015] The probability of anomaly in the periodic signal to be analyzed is determined based on the probability of normal waveform of the periodic signal to be analyzed and the difference in waveform characteristics between adjacent periodic signals of the periodic signal to be analyzed.

[0016] In one embodiment, the fitting result includes a correction determination coefficient and a fitting frequency; determining the waveform normality probability of each segment of the periodic signal based on the fitting result for each segment of the periodic signal includes:

[0017] For each segment of the periodic signal, a first difference between the actual frequency of the periodic signal and the corresponding fitted frequency is determined;

[0018] Based on the correction determination coefficient corresponding to the periodic signal and the first difference, the probability that the waveform of the periodic signal is normal is determined.

[0019] In one embodiment, the fitting result includes fitting parameters; the waveform feature differences between adjacent periodic signals include a second difference between the normal probabilities of the waveforms of the adjacent periodic signals and a third difference between the fitting parameters of the adjacent periodic signals.

[0020] The step of determining the probability of an anomaly in the periodic signal to be analyzed based on the probability of its waveform being normal and the waveform characteristic differences between adjacent periodic signals of the periodic signal to be analyzed includes:

[0021] The probability of anomaly in the periodic signal to be analyzed is determined based on the normality of the waveform of the periodic signal to be analyzed, and the second and third differences between adjacent periodic signals of the periodic signal to be analyzed.

[0022] In one embodiment, determining the multipath difference value between each segment of the periodic signal and the first segment of the periodic signal based on the multipath characteristics of the signal includes:

[0023] Each segment of the periodic signal is taken as the periodic signal to be analyzed. The periodic signals preceding the periodic signal to be analyzed, excluding the first periodic signal, are divided into a first set and a second set. The propagation time of the periodic signal in the first set is less than the propagation time of the periodic signal in the second set.

[0024] Based on the differences in the number and characteristics of multipath signals between the first set and the first periodic signal, the energy of multipath signals in the first set and the energy of multipath signals in the second set, the multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined.

[0025] In one embodiment, determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the differences in the number and characteristics of multipath signals between the first set and the first periodic signal, the multipath signal energy in the first set, and the multipath signal energy in the second set includes:

[0026] Determine the fourth difference between the number of multipath signals in the first set and the number of multipath signals in the first periodic signal, and the fifth difference between the signal characteristics of the periodic signals in the first set and the signal characteristics of the first periodic signal;

[0027] Determine the degree of energy dispersion of the periodic signals in the first set and the energy intensity of the periodic signals in the second set;

[0028] The multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined based on the fourth difference, the fifth difference, the degree of dispersion, and the energy intensity.

[0029] In one embodiment, determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the fourth difference, the fifth difference, the degree of dispersion, and the energy intensity includes:

[0030] The multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined based on the product of the fourth difference, the fifth difference, the degree of dispersion, and the energy intensity.

[0031] In one embodiment, determining the degree of distortion of each segment of the periodic signal based on the anomaly probability corresponding to each segment of the periodic signal and the multipath difference value includes:

[0032] Each segment of the periodic signal is taken as the periodic signal to be analyzed, and the anomalous probability of the first segment of the periodic signal and the sum of the anomalous probabilities of the periodic signal to be analyzed are determined.

[0033] The degree of distortion of the periodic signal to be analyzed is determined by multiplying the sum of the anomaly probabilities with the multipath difference value of the periodic signal to be analyzed.

[0034] In one embodiment, determining the defect of the steel pipe column based on the degree of distortion of each of the periodic signals includes:

[0035] The maximum distortion degree is determined from the distortion degrees of each of the periodic signals;

[0036] Determine the average distortion level after removing the maximum distortion level from the distortion levels of each of the said periodic signals;

[0037] The steel pipe column is judged to have defects based on the difference between the maximum degree of distortion and the average degree of distortion.

[0038] In one embodiment, determining the defect of the steel pipe column based on the difference between the maximum distortion degree and the average distortion degree includes:

[0039] Determine the sixth difference between the maximum distortion degree and the mean distortion degree;

[0040] The defect judgment parameter value is determined based on the product of the maximum distortion degree and the sixth difference.

[0041] If the defect determination parameter value is greater than or equal to the preset value, then the steel pipe column is determined to have a defect;

[0042] If the defect determination parameter value is less than the preset value, then the steel pipe column is determined to have no defects.

[0043] The present invention has the following beneficial effects: by acquiring the echo signal received after emitting sound waves to a steel pipe column, the echo signal is periodically divided to obtain multiple periodic signals. Then, based on the waveform characteristics of each periodic signal and the waveform characteristic differences between adjacent periodic signals, the probability of anomalies in each periodic signal is determined. Furthermore, based on the multipath characteristics of the signal, the multipath difference value between each periodic signal and the first periodic signal is determined. Based on the probability of anomalies and the multipath difference value corresponding to each periodic signal, the degree of distortion of each periodic signal is determined. Finally, based on the degree of distortion of each periodic signal, defects in the steel pipe column are determined. This reduces the dependence on amplitude in the determination of signal distortion, thereby avoiding the problem that the wall thickness of steel pipe columns used for foundation reinforcement is thicker than that of ordinary steel pipe columns, leading to severe attenuation of the sound wave signal and resulting in low accuracy in determining whether there are defects in the steel pipe column based on amplitude. This improves the accuracy of defect detection for steel pipe columns used for foundation reinforcement. Attached Figure Description

[0044] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic flowchart of a method for detecting defects in steel pipe columns used for foundation reinforcement, provided in one embodiment of the present invention.

[0046] Figure 2 This is a waveform diagram of the echo signal provided in one embodiment of the present invention;

[0047] Figure 3 This is a flowchart illustrating the process of determining the probability of anomalies in a periodic signal to be analyzed, according to an embodiment of the present invention.

[0048] Figure 4 This is a schematic diagram of a process for determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal, according to an embodiment of the present invention.

[0049] Figure 5 This is a waveform diagram of an echo signal with distortion in the first segment of a periodic signal, provided as an embodiment of the present invention. Detailed Implementation

[0050] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for detecting defects in steel pipe columns for foundation reinforcement proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0052] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a defect detection method for steel pipe columns used in foundation reinforcement provided by the present invention.

[0053] Please see Figure 1 The document illustrates a flowchart of a method for detecting defects in steel pipe columns used for foundation reinforcement, according to an embodiment of the present invention, including steps 102 to 112. Wherein:

[0054] Step 102: Obtain the echo signal received after emitting sound waves to the steel pipe column.

[0055] Among them, steel pipe columns refer to steel pipe columns used for foundation reinforcement. The wall thickness of steel pipe columns used for foundation reinforcement is thicker than that of other steel pipe columns. The echo signal is a signal emitted from a steel pipe column, which penetrates the internal material of the steel pipe column, propagates and refracts internally, and then returns from the steel pipe column.

[0056] In one embodiment, the acoustic emitting device can emit acoustic waves toward the steel pipe column, and then the acoustic receiving device can receive the echo signal returned by the steel pipe column. The computer device can perform step 102 to acquire the echo signal, and then perform steps 104 to 112 to achieve defect detection of the steel pipe column used for foundation reinforcement.

[0057] In one embodiment, data such as the amplitude, time point, and power of the received echo signal at each moment can be recorded.

[0058] In one embodiment, due to the multipath effect, multiple signals may be received at the same time. Steps 104 to 106 can be performed based on the signal generated by superimposing the multiple signals at the same time.

[0059] Step 104: Divide the echo signal into periods to obtain multiple periodic signals.

[0060] Periodic division refers to dividing the echo signal into multiple segments according to a period, with each segment constituting one period. A periodic signal is a signal that lasts for one period.

[0061] In one embodiment, a computer device can use wavelet transform to periodically divide the echo signal to obtain multiple periodic signals.

[0062] It is understandable that sound waves transmit signals outward in the form of waves. The transmission intensity of sound wave signals in different wavebands will have certain differences. Therefore, it is necessary to divide the received echo signals into periods and analyze the signal distortion within the period to determine whether there are defects in the steel pipe column.

[0063] Figure 2 The waveform of the received echo signal is schematically shown. It can be seen that the echo signal has a period, and the echo signal can be divided into periods.

[0064] Step 106: Determine the probability of anomalies in each periodic signal based on its own waveform characteristics and the differences in waveform characteristics between adjacent periodic signals.

[0065] Among these, the waveform characteristics refer to the features related to the waveform of the periodic signal itself. Adjacent periodic signals include the preceding and following periodic signals of a given periodic signal. The waveform characteristic difference between adjacent periodic signals refers to the difference in waveform characteristics between the preceding and following periodic signals of a given periodic signal. The probability of an anomaly in a periodic signal refers to the likelihood of an anomaly in the signal's shape. The probability of an anomaly in a periodic signal is positively correlated with the probability of defects in the steel pipe column. That is, the greater the probability of an anomaly in a periodic signal, the greater the probability of defects in the steel pipe column; conversely, the smaller the probability of an anomaly in a periodic signal, the smaller the probability of defects in the steel pipe column.

[0066] In one embodiment, the computer device can determine the probability of normality of each periodic signal based on its own waveform characteristics. Then, each periodic signal is treated as the periodic signal to be analyzed, and the waveform characteristic differences between adjacent periodic signals are determined based on their own waveform characteristics. Finally, the probability of abnormality of the periodic signal to be analyzed is determined based on the probability of normality of the waveform of the periodic signal to be analyzed and the waveform characteristic differences between adjacent periodic signals.

[0067] In one embodiment, the computer device can fit the periodic signal to a sine wave function based on the waveform characteristics of each periodic signal, and determine the probability of normal waveform for each periodic signal based on the fitting results.

[0068] In one embodiment, the probability of an anomaly in the periodic signal being analyzed is positively correlated with the waveform characteristic differences between adjacent periodic signals of the periodic signal being analyzed. The probability of an anomaly in the periodic signal being analyzed is negatively correlated with the probability of a normal waveform in the periodic signal being analyzed.

[0069] In one embodiment, a computer device can determine the probability of an anomaly in a periodic signal to be analyzed based on the ratio between the waveform feature differences between adjacent periodic signals of the periodic signal to be analyzed and the probability that the waveform of the periodic signal to be analyzed is normal.

[0070] In one embodiment, the computer device can fit each periodic signal to a sine wave function based on the waveform characteristics of each periodic signal to obtain the fitting result of each periodic signal, and determine the probability of the waveform of each periodic signal being normal based on the fitting result of each periodic signal.

[0071] In one embodiment, the waveform feature difference between adjacent periodic signals may include at least one of the difference between the fitting results of adjacent periodic signals and the difference between the probability of waveform normality (a second difference).

[0072] In one embodiment, the difference between the fitting results of adjacent periodic signals may include the difference between the fitting parameters of adjacent periodic signals (a third difference). In one embodiment, the fitting parameters may be represented as a vector. That is, the difference between the fitting results of adjacent periodic signals may include the difference between the fitting parameter vectors of adjacent periodic signals.

[0073] Step 108: Determine the multipath difference value between each periodic signal segment and the first periodic signal segment based on the multipath characteristics of the signal.

[0074] The multipath characteristic of a signal refers to the characteristics of signals with multiple paths within a periodic signal. It can be understood that due to the multipath effect, multiple signals may be received at the same time; therefore, multiple paths of signals exist within the same period, and the characteristics of these multiple paths are called the multipath characteristic of the signal. The first periodic signal refers to the first segment of the received echo signal, i.e., the earliest received segment of the periodic signal. The multipath difference value is used to characterize the difference at the multipath level between a certain segment of the periodic signal and the first segment of the periodic signal.

[0075] It is understandable that sound waves will undergo normal reflection even when propagating in a defect-free steel pipe column, resulting in waveform differences between adjacent periodic signals and a higher probability of anomalies in the periodic signal. To avoid inaccurate defect detection due to this situation, further analysis can be conducted by considering the impact of defective steel pipe columns on signal propagation. Specifically, on the one hand, defects such as holes and cracks within the steel pipe column cause significant reflection and refraction of the sound wave signal at the defect location, thus exhibiting a more pronounced multipath effect. In contrast, the multipath effect is less pronounced when the sound wave propagates in a defect-free steel pipe column. On the other hand, the initial periodic signal received is less affected by reflection and refraction, exhibiting a direct wave signal. Subsequent received echo signals are a superposition of the direct wave signal and the multipath signals from reflection and refraction. As the transmission time of the sound wave signal increases, the subsequent multipath signals remain stable. Therefore, the multipath effect of the echo signals received at each moment can be used to determine the signal distortion caused by defects such as cracks and holes in the internal structure of the steel pipe column.

[0076] In one embodiment, the multipath characteristics of a signal may include at least one of the following: the number of multipath signals, signal characteristics, and multipath signal energy.

[0077] In one embodiment, the computer device can treat each periodic signal as a periodic signal to be analyzed, and determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the multipath characteristics of the first periodic signal and the multipath characteristics of each periodic signal before the periodic signal to be analyzed, excluding the first periodic signal.

[0078] In one embodiment, the computer device may treat each periodic signal segment as the periodic signal to be analyzed, and divide the periodic signals preceding the periodic signal to be analyzed (excluding the first periodic signal) into a first set and a second set, wherein the propagation time of the periodic signals in the first set is shorter than the propagation time of the periodic signals in the second set. The multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined based on at least one of the following: the difference in the number of multipath signals between the first set and the first periodic signal, the difference in signal characteristics between the first set and the first periodic signal, and the multipath signal energy in the first set and the second set.

[0079] In one embodiment, the computer device may determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on at least one of the following: a fourth difference between the number of multipath signals between the first set and the first periodic signal; a fifth difference between the signal characteristics between the first set and the first periodic signal; the energy dispersion of the periodic signals in the first set; and the energy intensity of the periodic signals in the second set.

[0080] Step 110: Determine the degree of distortion of each periodic signal based on the probability of anomaly and the multipath difference value corresponding to each periodic signal segment.

[0081] In one embodiment, the computer device can treat each periodic signal segment as the periodic signal to be analyzed, determine the probability of anomaly in the first periodic signal segment and the sum of the probabilities of anomaly in the periodic signal to be analyzed, and then determine the degree of distortion of the periodic signal to be analyzed based on the sum of the probabilities of anomaly and the multipath difference value of the periodic signal to be analyzed.

[0082] Step 112: Defect judgment is made on the steel pipe column based on the degree of distortion of each period signal.

[0083] Among them, defect determination is the process of judging whether there are defects in the steel pipe column.

[0084] In one embodiment, the computer device can determine the defect judgment parameter value based on the degree of distortion of the signal in each cycle, compare the defect judgment parameter value with a preset value, and judge the defect of the steel pipe column based on the comparison result. The defect judgment parameter value is used to measure the maximum difference in the degree of distortion.

[0085] In one embodiment, the computer device can determine the maximum distortion level from the distortion levels of each periodic signal, determine the average distortion level after removing the maximum distortion level from the distortion levels of each periodic signal, and determine the defect judgment parameter value based on the difference between the maximum distortion level and the average distortion level.

[0086] In one embodiment, if the defect determination parameter value is greater than or equal to a preset value, the steel pipe column is determined to have a defect; if the defect determination parameter value is less than the preset value, the steel pipe column is determined to have no defect.

[0087] In one embodiment, the above-described method for detecting defects in steel pipe columns used for foundation reinforcement can be executed by a signal acquisition module, a feature analysis module, and a defect detection model. Specifically, the signal acquisition module executes step 102, the feature analysis module executes steps 104 to 110, and the defect detection model executes step 112.

[0088] In the aforementioned defect detection method for steel pipe columns used for foundation reinforcement, the echo signal received after the sound wave is emitted to the steel pipe column is acquired. The echo signal is then periodically divided into multiple periodic signals. The probability of anomaly in each periodic signal is determined based on its own waveform characteristics and the waveform differences between adjacent periodic signals. Furthermore, the multipath difference value between each periodic signal and the first periodic signal is determined based on the multipath characteristics of the signal. The degree of distortion of each periodic signal is determined based on its probability of anomaly and the multipath difference value. Finally, the steel pipe column is judged for defects based on the degree of distortion of each periodic signal. This method reduces the dependence on amplitude in determining signal distortion, thus avoiding the problem that the wall thickness of steel pipe columns used for foundation reinforcement is thicker than that of ordinary steel pipe columns, leading to severe attenuation of the sound wave signal and resulting in low accuracy in determining the presence of defects based on amplitude. This improves the accuracy of defect detection for steel pipe columns used for foundation reinforcement.

[0089] In one embodiment, such as Figure 3 As shown, step 106 determines the probability of anomalies in each periodic signal based on its own waveform characteristics and the differences in waveform characteristics between adjacent periodic signals, including steps 302 to 308. Wherein:

[0090] Step 302: Based on the waveform characteristics of each periodic signal segment, fit each periodic signal segment to a sine wave function to obtain the fitting result of each periodic signal segment.

[0091] It is understandable that sound waves encountering defects such as cracks and voids during propagation within the steel pipe column will experience reflection or refraction. Furthermore, the reflection or refraction characteristics at these defective locations differ from those within the normal steel pipe column, leading to changes in the received echo signal. Although the amplitude difference of the periodic signal is significantly reduced due to the thickness of the steel pipe column wall, the shape characteristics of the periodic signal can still reflect the signal's abnormality to some extent. For example, defects within the steel pipe column cause a difference in the reception time of the sound wave signal compared to other normal sound waves, causing the periodic signal to no longer exhibit approximate sine wave characteristics. The greater the difference in shape between the periodic signal and the sine wave, the greater the likelihood of an anomaly in that segment of the periodic signal, and the lower the probability of a normal waveform. Therefore, the probability of a normal waveform for the periodic signal can be determined by fitting the periodic signal to a sine wave function.

[0092] In one embodiment, each periodic signal segment can be fitted to a sine wave function of one period.

[0093] In one embodiment, the least squares fitting method can be used to fit each periodic signal to a sine wave function.

[0094] In one embodiment, the fitting result may include a correction determination coefficient R and fitting parameters c. The correction determination coefficient characterizes the goodness of fit. A larger correction determination coefficient indicates a better fit, meaning the periodic signal exhibits more pronounced sinusoidal characteristics and the waveform of the periodic signal is more likely to be normal; a smaller correction determination coefficient indicates a worse fit, meaning the periodic signal exhibits less pronounced sinusoidal characteristics and the waveform of the periodic signal is less likely to be normal. The fitting parameters are the parameters in the sine wave function formula obtained by fitting the periodic signal to a sinusoidal function.

[0095] In one embodiment, the fitting parameters may include a fitting frequency, a fitting amplitude, and a fitting offset. The fitting frequency is the frequency of the fitted sine wave function. The fitting amplitude is the amplitude of the fitted sine wave function. The fitting offset is the vertical axis offset of the fitted sine wave function.

[0096] In one embodiment, it is assumed that the sine wave function is represented as The fitting parameters include the fitting frequency. Fitted amplitude A and fitted offset B.

[0097] Step 304: Based on the fitting results of each periodic signal segment, determine the probability of the waveform being normal for each periodic signal segment.

[0098] Among them, waveform normality probability is used to assess the magnitude of the normality probability of a signal at the waveform level.

[0099] In one embodiment, the computer device can determine the difference between the actual parameters and the fitted parameters of the periodic signal for each periodic signal segment, and determine the probability that the waveform of the periodic signal is normal based on the correction determination coefficient corresponding to the periodic signal and the difference between the actual parameters and the fitted parameters.

[0100] In one embodiment, the probability of a periodic signal having a normal waveform is negatively correlated with the difference between the actual parameters and the fitted parameters. The probability of a periodic signal having a normal waveform is positively correlated with the correction determination coefficient.

[0101] In one embodiment, a computer device can determine the probability that the waveform of a periodic signal is normal based on the ratio of the correction determination coefficient corresponding to the periodic signal to the difference between the actual parameter and the fitted parameter.

[0102] Step 306: Take each periodic signal segment as the periodic signal to be analyzed, and determine the waveform feature differences between adjacent periodic signals based on the fitting results of adjacent periodic signals and the probability of normal waveforms.

[0103] It is understandable that while fitting a periodic signal to a sine wave function can determine the probability of its normality to some extent, after the sound wave signal has been emitted and sustained for a period of time, the emitted sound waves all exhibit defects such as holes and cracks within the steel pipe column, thus no longer producing sudden and significant signal changes. The signal indicating a defect in the steel pipe column also exhibits strong sine wave characteristics, making it impossible to accurately determine whether a defect actually exists. However, the signal received at the very beginning of the emission does not have significant interference from reflected or refracted waves, leading to a significant difference between the initial waveform and the waveform after a period of sustaining. That is, the sine wave fitted to the periodic signal after a period of emission differs significantly from the sine wave fitted to the periodic signal at the beginning of the emission. Therefore, in sound wave signals indicating a defect in the steel pipe column, there is a significant waveform distortion caused by defect interference between the initial and subsequent periodic signals, increasing the probability of an anomaly in this segment of the sound wave signal. Therefore, by combining the waveform characteristic differences between adjacent periodic signals with the normal probability of the periodic signal itself, the probability of an anomaly in the periodic signal can be determined more accurately.

[0104] In one embodiment, the waveform feature difference between adjacent periodic signals includes at least one of a second difference between the normal waveform probabilities of adjacent periodic signals and a third difference between the fitting parameters of adjacent periodic signals. The computer device can subtract the normal waveform probabilities of adjacent periodic signals of the periodic signal to be analyzed to obtain the second difference, and subtract the fitting parameters of adjacent periodic signals of the periodic signal to be analyzed to obtain the third difference.

[0105] In one embodiment, the fitting parameters can be represented as a vector. The computer device can subtract the fitting parameter vectors corresponding to adjacent periodic signals of the periodic signal to be analyzed to obtain a third difference. The fitting parameter vector may include the fitting frequency, the fitting amplitude, and the fitting offset.

[0106] Step 308: Determine the probability of anomaly in the periodic signal to be analyzed based on the probability of normal waveform of the periodic signal to be analyzed and the waveform feature differences between adjacent periodic signals of the periodic signal to be analyzed.

[0107] In one embodiment, the probability of an anomaly in the periodic signal being analyzed is positively correlated with the waveform characteristic differences between adjacent periodic signals of the periodic signal being analyzed. Conversely, the probability of an anomaly in the periodic signal being analyzed is negatively correlated with the probability of a normal waveform in the periodic signal being analyzed.

[0108] In one embodiment, a computer device can determine the probability of an anomaly in a periodic signal to be analyzed based on the ratio between the waveform feature differences between adjacent periodic signals of the periodic signal to be analyzed and the probability that the waveform of the periodic signal to be analyzed is normal.

[0109] In the above embodiments, each periodic signal is fitted with a sine wave function. Based on the fitting results, the normality probability of the waveform of each periodic signal is determined, thereby enabling the determination of whether the periodic signal is normal at the waveform level. Then, each periodic signal is taken as the periodic signal to be analyzed. Based on the fitting results and the normality probability of the waveform of the adjacent periodic signals to be analyzed, the waveform characteristic differences between adjacent periodic signals are determined. Combining the normality probability of the waveform of the periodic signal to be analyzed and the waveform characteristic differences between adjacent periodic signals, the abnormality probability of the periodic signal to be analyzed is determined. This avoids the problem that when the sound wave signal is emitted and maintained for a period of time, the sound waves emitted before and after will all show that they have passed through defects such as holes and cracks in the steel pipe column, and no longer produce a sudden and obvious change in the signal. The signal of the steel pipe column with defects also shows strong sine wave characteristics, making it impossible to accurately determine whether the steel pipe column actually has defects. By using the waveform characteristic differences between adjacent periodic signals and the normality probability of the periodic signal itself, the abnormality probability of the periodic signal can be determined more accurately, thereby improving the accuracy of defect detection for steel pipe columns used for foundation reinforcement.

[0110] In one embodiment, the fitting result includes a correction determination coefficient and a fitting frequency; determining the normal probability of the waveform of each periodic signal based on the fitting result of each periodic signal includes: determining a first difference between the actual frequency of the periodic signal and the corresponding fitting frequency for each periodic signal; and determining the normal probability of the waveform of the periodic signal based on the correction determination coefficient and the first difference corresponding to the periodic signal.

[0111] In one embodiment, the computer device can determine the actual frequency of the periodic signal based on the reciprocal of the propagation duration of the periodic signal.

[0112] In one embodiment, the probability of a normal waveform for a periodic signal is positively correlated with the correction determination coefficient of the periodic signal. The probability of a normal waveform for a periodic signal is negatively correlated with the corresponding first difference between the periodic signals. That is, the larger the correction determination coefficient, the greater the probability of a normal waveform for the periodic signal; the smaller the correction determination coefficient, the smaller the probability of a normal waveform for the periodic signal. The larger the first difference between the actual frequency and the fitted frequency, the smaller the probability of a normal waveform for the periodic signal; the smaller the first difference between the actual frequency and the fitted frequency, the greater the probability of a normal waveform for the periodic signal.

[0113] In one embodiment, the computer device can determine the probability that the waveform of the periodic signal is normal based on the ratio between the correction determination coefficient corresponding to the periodic signal and the first difference.

[0114] In one embodiment, the probability of a periodic signal having a normal waveform can be determined using the following formula:

[0115]

[0116] in, This indicates the likelihood that the waveform is normal. This represents the correction determination coefficient. This represents the fitting frequency. Indicates the actual frequency. This indicates taking the absolute value. This represents the first difference between the actual frequency and the fitted frequency.

[0117] In the above embodiments, for each periodic signal segment, the first difference between the actual frequency of the periodic signal and the corresponding fitted frequency is determined. Based on the correction determination coefficient corresponding to the periodic signal and the first difference, the normal probability of the waveform of the periodic signal is determined. Thus, the normal probability of the waveform of the periodic signal can be accurately determined based on the correction determination coefficient and fitting parameters in the fitting result, thereby improving the accuracy of defect detection for steel pipe columns used for foundation reinforcement.

[0118] In one embodiment, the fitting result includes fitting parameters; the waveform feature differences between adjacent periodic signals include a second difference between the normal waveform probabilities of adjacent periodic signals and a third difference between the fitting parameters of adjacent periodic signals; determining the abnormal probability of the periodic signal to be analyzed based on the normal waveform probability of the periodic signal to be analyzed and the waveform feature differences between adjacent periodic signals of the periodic signal to be analyzed includes: determining the abnormal probability of the periodic signal to be analyzed based on the normal waveform probability of the periodic signal to be analyzed, and the second and third differences between adjacent periodic signals of the periodic signal to be analyzed.

[0119] In one embodiment, the third difference can be the difference between the fitting parameter vectors of adjacent periodic signals.

[0120] In one embodiment, the probability of anomaly in the periodic signal to be analyzed is positively correlated with the second and third differences. The probability of anomaly in the periodic signal to be analyzed is negatively correlated with the probability of a normal waveform. That is, the larger the second or third difference corresponding to the periodic signal to be analyzed, the greater the probability of anomaly; the smaller the second or third difference, the smaller the probability of anomaly. Similarly, the greater the probability of a normal waveform in the periodic signal to be analyzed, the smaller the probability of anomaly; and vice versa.

[0121] In one embodiment, the computer device can take the modulo of the product of the second difference and the third difference to obtain the waveform characteristic difference value of adjacent periodic signals. The probability of an anomaly in the periodic signal to be analyzed is determined based on the ratio of the waveform characteristic difference value of adjacent periodic signals to the probability of the waveform being normal in the periodic signal to be analyzed.

[0122] In one embodiment, the probability of an anomaly in the periodic signal to be analyzed can be determined according to the following formula:

[0123]

[0124] Where 'a' represents the probability of an anomaly in the periodic signal to be analyzed. This indicates the probability that the waveform of the signal to be analyzed is normal. These represent the probability that the waveforms of the previous adjacent periodic signal and the next adjacent periodic signal of the periodic signal to be analyzed are normal, respectively. The second difference, representing the probability of normal waveforms between adjacent periodic signals, is used to characterize the magnitude of abnormal changes between adjacent periodic signals of the periodic signal to be analyzed. These represent the fitting parameter vectors for the previous and next adjacent periodic signals of the periodic signal to be analyzed, respectively, reflecting the shape characteristics of the fit. The third difference between the fitted parameter vectors of adjacent periodic signals reflects the difference in shape between adjacent periodic signals, that is, the distortion of the waveform before and after the periodic signal to be analyzed. This indicates taking the modulus.

[0125] In the above embodiments, by combining the normal probability of the waveform of the periodic signal to be analyzed, as well as the second and third differences between adjacent periodic signals of the periodic signal to be analyzed, the abnormal probability of the periodic signal to be analyzed can be determined together. This can avoid the problem that when the sound wave signal is emitted and maintained for a period of time, the sound waves emitted before and after will all show that they have passed through defects such as holes and cracks in the steel pipe column, and no longer produce a sudden and obvious change in the signal. The signal of the steel pipe column with defects also shows a strong sine wave characteristic, which makes it impossible to accurately determine whether the steel pipe column actually has defects. This can more accurately determine the abnormal probability of the periodic signal, thereby improving the accuracy of defect detection of steel pipe columns used for foundation reinforcement.

[0126] In one embodiment, such as Figure 4 As shown, step 108 determines the multipath difference value between each periodic signal segment and the first periodic signal segment based on the multipath characteristics of the signal, including steps 402 to 404. Wherein:

[0127] Step 402: Each periodic signal segment is taken as the periodic signal to be analyzed. The periodic signals before the periodic signal to be analyzed, except for the first periodic signal segment, are divided into a first set and a second set. The propagation time of the periodic signal in the first set is less than the propagation time of the periodic signal in the second set.

[0128] It is understood that the periodic signals selected for analysis in this embodiment are all periodic signals except for the first periodic signal.

[0129] It is understandable that the first periodic signal is a signal that has not undergone significant refraction and reflection interference within the steel pipe column, representing a direct wave signal. When there are no defects inside the steel pipe column, the multipath effect of subsequent propagating signals after the first periodic signal is not significant, and the signal energy is low, so it will not interfere with the main direct wave signal. Therefore, the periodic signals in the first set are highly similar to the first periodic signal, both exhibiting direct wave signals. The energy of the periodic signals in the second set will be relatively low. When there are defects inside the steel pipe column, there will be a large amount of reflection and refraction of sound waves at the defect location, interfering with both the first and second sets of signals. The signals will exhibit a significant multipath effect, the number of signals in the first set will increase significantly, the propagation time of signals in the first set will be significantly longer than that of the first periodic signal, and the energy differences between different signals in the first set will be larger. The energy of the signals in the second set will be significantly enhanced. Therefore, the periodic signals before the periodic signal to be analyzed, excluding the first periodic signal, can be divided into a first set and a second set according to the length of their propagation time. Then, the multipath characteristics of the signals in the first set and the second set can be analyzed to accurately determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal.

[0130] Step 404: Based on the differences in the number and characteristics of multipath signals between the first set and the first periodic signal, the energy of multipath signals in the first set and the energy of multipath signals in the second set, determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal.

[0131] The number of multipath signals refers to the number of signals received from different paths within the same time period. This can be understood as the reception of multiple signals simultaneously under the multipath effect. Signal characteristics are the overall characteristics of the combined signals received within the same time period. Multipath signal energy refers to the energy of signals received from different paths within a given time period.

[0132] In one embodiment, signal characteristics may include signal power and propagation duration.

[0133] In one embodiment, the signal characteristics can be the average of the characteristics of the signals of each path within the same time period.

[0134] In one embodiment, the multipath signal energy can be characterized by the power of the signal in each path.

[0135] In one embodiment, the computer device may determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on at least one of the following: the difference between the number of multipath signals between the first set and the first periodic signal, the difference between the signal characteristics between the first set and the first periodic signal, the energy dispersion of the periodic signals in the first set, and the energy intensity of the periodic signals in the second set.

[0136] In the above embodiments, the periodic signals before the periodic signal to be analyzed, excluding the first periodic signal, are divided into a first set and a second set according to their propagation duration. Then, based on the differences in the number and characteristics of multipath signals between the first set and the first periodic signal, as well as the energy of multipath signals in the first set and the second set, the multipath difference value between the periodic signal to be analyzed and the first periodic signal can be accurately determined based on the multipath effect, thereby improving the accuracy of defect detection for steel pipe columns used for foundation reinforcement.

[0137] In one embodiment, determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the differences between the number and signal characteristics of multipath signals in the first set and the first periodic signal, the energy of multipath signals in the first set and the energy of multipath signals in the second set, includes: determining a fourth difference between the number of multipath signals in the first set and the number of multipath signals in the first periodic signal, and a fifth difference between the signal characteristics of the periodic signals in the first set and the signal characteristics of the first periodic signal; determining the dispersion of the energy of the periodic signals in the first set and the energy intensity of the periodic signals in the second set; and determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the fourth difference, the fifth difference, the dispersion, and the energy intensity.

[0138] In one embodiment, the dispersion of the energy of the periodic signals in the first set can be measured by any one of the standard deviation, variance, or coefficient of variation of the energy of the periodic signals in the first set.

[0139] In one embodiment, the energy intensity of the periodic signals in the second set can be measured by any one of the mean, median, or sum of the energy of the periodic signals in the second set.

[0140] In one embodiment, the multipath difference value is positively correlated with the fourth difference, the fifth difference, the degree of dispersion, and the energy intensity. Specifically, a larger fourth difference value results in a larger multipath difference value, and a smaller fourth difference value results in a smaller multipath difference value. Similarly, a larger fifth difference value results in a larger multipath difference value, and a smaller fifth difference value results in a smaller multipath difference value. A greater degree of dispersion results in a larger multipath difference value, and a smaller degree of dispersion results in a smaller multipath difference value. Finally, a greater energy intensity results in a larger multipath difference value, and a smaller energy intensity results in a smaller multipath difference value.

[0141] In the above embodiments, based on the fourth difference between the number of multipath signals in the first set and the number of multipath signals in the first periodic signal, the fifth difference between the signal characteristics of the periodic signals in the first set and the signal characteristics of the first periodic signal, the energy dispersion of the periodic signals in the first set, and the energy intensity of the periodic signals in the second set, the multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined. This enables accurate determination of the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the multipath effect of the signal, thereby improving the accuracy of defect detection for steel pipe columns used for foundation reinforcement.

[0142] In one embodiment, determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the fourth difference, the fifth difference, the degree of dispersion, and the energy intensity includes: determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the product of the fourth difference, the fifth difference, the degree of dispersion, and the energy intensity.

[0143] In one embodiment, the computer device can determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the product of the fourth difference, the fifth difference, the standard deviation of the energy of the periodic signals in the first set, and the mean of the energy of the periodic signals in the second set.

[0144] In one embodiment, the multipath difference value between the periodic signal to be analyzed and the first periodic signal can be determined according to the following formula:

[0145]

[0146] in, This represents the multipath difference value between the periodic signal to be analyzed and the first periodic signal. Represents the first set, This represents the second set. The energy of any signal in the first set can be represented by power. The standard deviation of the energy of the periodic signals in the first set. The energy of any signal in the second set can be represented by power. This represents the average energy of the periodic signals in the second set. Represents the first set The number of multipath signals in the signal. This indicates the number of multipath signals in the first periodic signal segment. This represents the fourth difference between the number of multipath signals in the first set and the number of multipath signals in the first periodic signal. This indicates taking the absolute value. The signal characteristics representing the first periodic signal can be a two-dimensional vector. This represents the signal characteristics of periodic signals in the first set. This represents the fifth difference between the signal characteristics of the periodic signals in the first set and the signal characteristics of the first periodic signal. This indicates taking the modulus.

[0147] In the above embodiments, based on the product of the fourth difference, the fifth difference, the degree of dispersion, and the energy intensity, the multipath difference value between the periodic signal to be analyzed and the first periodic signal can be accurately determined through multi-faceted multipath characteristics, thereby improving the accuracy of defect detection for steel pipe columns used for foundation reinforcement.

[0148] In one embodiment, the degree of distortion of each periodic signal is determined based on the probability of anomaly and the multipath difference value corresponding to each periodic signal segment, including: taking each periodic signal segment as the periodic signal to be analyzed, determining the probability of anomaly of the first periodic signal segment and the sum of the probabilities of anomaly of the periodic signal to be analyzed; and determining the degree of distortion of the periodic signal to be analyzed based on the product of the sum of the probabilities of anomaly and the multipath difference value of the periodic signal to be analyzed.

[0149] In one embodiment, the degree of distortion of the periodic signal to be analyzed can be determined according to the following formula:

[0150]

[0151] in, This indicates the degree of distortion of the periodic signal to be analyzed. This indicates the possibility of an anomaly in the first segment of the periodic signal. This indicates the probability of an anomaly in the periodic signal to be analyzed. This represents the multipath difference value of the periodic signal to be analyzed.

[0152] It is understandable that the signal distortion may have already occurred in the first period due to defects within the steel pipe column, for example, Figure 5 The acoustic signal in the image has a problem of distortion in the first periodic signal. Therefore, it is easy to make it inaccurate to directly determine the degree of distortion based on the multipath difference between the periodic signal to be analyzed and the first periodic signal and the probability of anomaly in the periodic signal to be analyzed. Therefore, the degree of distortion of the signal can be determined by combining the probability of anomaly in the first periodic signal, the probability of anomaly in the periodic signal to be analyzed, and the multipath difference between the periodic signal to be analyzed and the first periodic signal.

[0153] In the above embodiments, the distortion of the first periodic signal is taken into account, which can more accurately determine the degree of distortion, thereby improving the accuracy of defect detection for steel pipe columns used for foundation reinforcement.

[0154] In one embodiment, the defect determination of the steel pipe column based on the distortion degree of each period signal includes: determining the maximum distortion degree from the distortion degrees of each period signal; determining the average distortion degree after removing the maximum distortion degree from the distortion degrees of each period signal; and determining the defect of the steel pipe column based on the difference between the maximum distortion degree and the average distortion degree.

[0155] The maximum distortion level refers to the maximum value among the distortion levels of each periodic signal. This can be understood as obtaining the corresponding distortion level for each periodic signal, resulting in a distortion level sequence, and then determining the maximum value from this sequence as the maximum distortion level.

[0156] In one embodiment, the greater the difference between the maximum distortion degree and the average distortion degree, the more significant the signal distortion is within a certain period, and the distortion within that period is significantly different from the distortion in other periods.

[0157] In the above embodiments, the maximum distortion degree is determined from the distortion degree of each period signal, and the average distortion degree after removing the maximum distortion degree from the distortion degree of each period signal is determined. Then, based on the difference between the maximum distortion degree and the average distortion degree, the steel pipe column is judged for defects, which can accurately detect defects in steel pipe columns used for foundation reinforcement.

[0158] In one embodiment, a defect determination is made for the steel pipe column based on the difference between the maximum distortion degree and the average distortion degree, including: determining a sixth difference between the maximum distortion degree and the average distortion degree; determining a defect determination parameter value based on the product between the maximum distortion degree and the sixth difference; if the defect determination parameter value is greater than or equal to a preset value, the steel pipe column is determined to have a defect; if the defect determination parameter value is less than the preset value, the steel pipe column is determined to have no defect.

[0159] In one embodiment, the product between the maximum distortion degree and the sixth difference can be normalized to obtain the defect judgment parameter value, and then the defect judgment parameter value can be compared with a preset value.

[0160] In one embodiment, the defect determination parameter value can be determined according to the following formula:

[0161]

[0162] in, This indicates the value of the defect determination parameter. This represents the normalization function. This indicates the maximum distortion level. I represents the index of the maximum distortion level within the distortion level sequence. This represents the average distortion level after removing the maximum distortion level from the distortion levels of each periodic signal. This indicates taking the absolute value. This represents the sixth difference between the maximum distortion and the mean distortion.

[0163] In one embodiment, the preset value can be set according to actual needs. For example, the preset value could be 0.9.

[0164] In the above embodiments, a defect determination parameter value is determined. If the defect determination parameter value is greater than or equal to a preset value, the steel pipe column is determined to have a defect. If the defect determination parameter value is less than the preset value, the steel pipe column is determined to have no defect. This enables efficient and accurate defect determination of steel pipe columns used for foundation reinforcement.

[0165] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0166] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0167] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0168] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A method for detecting defects in steel pipe columns used in foundation reinforcement, characterized in that, The method includes: Acquire the echo signal received after emitting sound waves to the steel pipe column; The echo signal is periodically divided using wavelet transform to obtain multiple periodic signals; The probability of anomalies in each segment of the periodic signal is determined based on its own waveform characteristics and the differences in waveform characteristics between adjacent periodic signals. Specifically, this includes: fitting each segment of the periodic signal to a sine wave function based on its own waveform characteristics to obtain a fitting result for each segment, the fitting result including a correction determination coefficient, a fitting frequency, and fitting parameters; determining a first difference between the actual frequency of each periodic signal and the corresponding fitting frequency for each segment; and determining the probability of anomalies in each segment based on the correction determination coefficient and the first difference. The normal probability of the waveform of the periodic signal; taking each segment of the periodic signal as the periodic signal to be analyzed, and determining the waveform feature difference between adjacent periodic signals based on the fitting results of adjacent periodic signals and the normal probability of the waveform, the waveform feature difference includes a second difference between the normal probabilities of the waveforms of adjacent periodic signals and a third difference between the fitting parameters of adjacent periodic signals; and determining the abnormal probability of the periodic signal to be analyzed based on the normal probability of the waveform of the periodic signal to be analyzed, and the second difference and the third difference between adjacent periodic signals of the periodic signal to be analyzed. Based on the multipath characteristics of the signal, the multipath difference value between each segment of the periodic signal and the first segment of the periodic signal is determined. Specifically, this includes: taking each segment of the periodic signal as the periodic signal to be analyzed; dividing the periodic signals preceding the periodic signal to be analyzed (excluding the first segment of the periodic signal) into a first set and a second set; the propagation time of the periodic signals in the first set is less than the propagation time of the periodic signals in the second set; determining a fourth difference between the number of multipath signals in the first set and the number of multipath signals in the first segment of the periodic signal, and a fifth difference between the signal characteristics of the periodic signals in the first set and the signal characteristics of the first segment of the periodic signal; determining the energy dispersion of the periodic signals in the first set and the energy intensity of the periodic signals in the second set; and determining the multipath difference value between the periodic signal to be analyzed and the first segment of the periodic signal based on the product of the fourth difference, the fifth difference, the dispersion, and the energy intensity. The degree of distortion of each segment of the periodic signal is determined based on the probability of anomaly and the multipath difference value corresponding to each segment of the periodic signal. Specifically, this includes: taking each segment of the periodic signal as the periodic signal to be analyzed, determining the probability of anomaly of the first segment of the periodic signal and the sum of the probabilities of anomaly of the periodic signal to be analyzed; and determining the degree of distortion of the periodic signal to be analyzed based on the product of the sum of the probabilities of anomaly and the multipath difference value of the periodic signal to be analyzed. The steel pipe column is defect-determined based on the distortion degree of each of the periodic signals, specifically including: determining the maximum distortion degree from the distortion degrees of each of the periodic signals; determining the average distortion degree after removing the maximum distortion degree from the distortion degrees of each of the periodic signals; determining a sixth difference between the maximum distortion degree and the average distortion degree; determining a defect determination parameter value based on the product of the maximum distortion degree and the sixth difference; if the defect determination parameter value is greater than or equal to a preset value, the steel pipe column is determined to have a defect; if the defect determination parameter value is less than the preset value, the steel pipe column is determined to have no defect.

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