CN112730634A - Concrete defect detection method and system - Google Patents
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Abstract
The invention discloses a concrete defect detection method and a system. The method comprises the following steps: acquiring ultrasonic waveforms of a plurality of measuring points of concrete to be measured in a set area under the same test condition to obtain ultrasonic waveforms to be measured of the plurality of measuring points; respectively sampling the reference ultrasonic waveform and the ultrasonic waveform to be measured to obtain a reference wave sampling data set and a wave to be measured sampling data set of a plurality of measuring points; calculating a waveform dissimilarity coefficient of each measuring point according to the reference wave sampling data set and the to-be-measured wave sampling data set; the waveform dissimilarity coefficient is a standardized Euclidean distance of the ultrasonic waveform to be measured relative to the reference ultrasonic waveform; and calculating the statistic of the waveform dissimilarity coefficients of all the measuring points, and determining the position of the defect of the concrete to be measured according to the statistic. The method can effectively judge the concrete defects and improve the accuracy and reliability of the concrete defect detection.
Description
Technical Field
The invention relates to the field of defect detection, in particular to a method and a system for detecting concrete defects.
Background
The ultrasonic method for detecting concrete defects is to adopt an ultrasonic detector with a waveform display function to measure acoustic parameters such as the propagation speed (sound velocity) or sound time, head wave amplitude (wave amplitude), receiving signal main frequency (main frequency) and waveform of ultrasonic pulse waves in concrete, and judge the defect condition of the concrete according to the parameters and relative changes of the parameters.
In the technical regulation for detecting concrete defects by an ultrasonic method, acoustic parameters, such as sound velocity (or sound time), amplitude and main frequency are all measurable, and waveform parameters can be stored in a file format, but the shape change of the waveform parameters can only be visually observed at present, and no quantifiable index exists.
The selection of judgment parameters and the judgment method of abnormal values in the concrete defect detection by the ultrasonic method are very important. In the technical regulation for detecting concrete defects by an ultrasonic method, the sound velocity or sound phase in the existing judgment parameters is accurate but insensitive, so that the judgment is easy to miss; the main frequency is greatly influenced by sampling length, whether the waveform exceeds the screen during sampling and external interference factors, is very unstable and is not generally used as a judgment parameter; the amplitude is the most sensitive parameter for determining the defect among the above parameters, but the amplitude is influenced by the coupling condition to cause misdetermination.
In order to improve the accuracy and reliability of the ultrasonic method for detecting the concrete defects, it is necessary to quantify the waveform with a certain sampling length in the time domain to judge the concrete defects. Because the received waveform must be changed as long as the concrete is defective, but the measuring points are not influenced by the boundary condition of the member and the structural construction of the propagation path non-defective medium is basically consistent.
At present, in the field of detecting concrete defects by an ultrasonic method, a domestic scholars adopts a Pearson correlation coefficient to calculate a correlation coefficient of any waveform relative to a reference waveform, and the absolute value of the correlation coefficient is not more than 1 to measure the correlation degree between waveforms, which is called as a waveform distortion coefficient.
A large amount of engineering detection data analysis and verification prove that the coefficient is not very effective for judging the waveform similarity of the ultrasonic pulse waves similar to sine wave curves. Since the coefficients are between 0 and 1, and are similar or dissimilar in some detail, the threshold value of the outlier (threshold) is not well defined. It is understood that the method takes a complete waveform of 3-5 periods in a time domain, the amount of information extracted from the surface is large, but the method is likely to carry unfavorable information and obscure useful information, so that the calculated coefficient judgment defect is insensitive. According to a number of engineering tests, useful information sensitive to defects is often in the bow wave portion (within a complete waveform of a cycle). In conclusion, the accuracy and reliability of the existing ultrasonic method for detecting the concrete defects still need to be improved.
Disclosure of Invention
Therefore, there is a need for a method and a system for detecting concrete defects to effectively determine concrete defects and fill the gap that waveform parameters cannot be quantified and effectively determined for a long time, so as to improve the accuracy and reliability of concrete defect detection.
In order to achieve the purpose, the invention provides the following scheme:
a method of detecting concrete defects, comprising:
acquiring ultrasonic waveforms of a plurality of measuring points of concrete to be measured in a set area under the same test condition to obtain ultrasonic waveforms to be measured of the plurality of measuring points;
respectively sampling the reference ultrasonic waveform and the ultrasonic waveform to be measured to obtain a reference wave sampling data set and a wave to be measured sampling data set of a plurality of measuring points; the reference ultrasonic waveform is a continuous and complete waveform starting from a head wave starting point in the ultrasonic waveform of the defect-free concrete;
calculating a waveform dissimilarity coefficient of each measuring point according to the reference wave sampling data set and the to-be-measured wave sampling data set; the waveform dissimilarity coefficient is a standardized Euclidean distance of the ultrasonic waveform to be measured relative to the reference ultrasonic waveform;
calculating statistics of waveform dissimilarity coefficients of all measuring points, and determining the defects of the concrete to be measured and positions of the defects according to the statistics; the statistics include mean, standard deviation, and outlier data cutoff for set confidence levels.
Optionally, the calculating a waveform dissimilarity coefficient of each measurement point from the reference wave sampling data set and the to-be-measured wave sampling data set specifically includes:
wherein, XiThe waveform dissimilarity coefficient of the ith measuring point is taken as a coefficient; siThe standard deviation of a sampling data set of the wave to be measured of the ith measuring point is obtained; s0Sampling a standard deviation of the data set for a reference wave; a isi,jJ is the jth acquired data in the to-be-detected wave sampling data set of the ith measuring point, wherein j is 1,2, 3. N is the total number of samples; a is0,jSampling jth acquired data in the data set for the reference wave;
the average value of N sampling data in a sampling data set of the wave to be measured of the ith measuring point is obtained;
the average value of the N sampled data in the set of reference wave sampled data is used.
Optionally, calculating a statistic of waveform dissimilarity coefficients of all the measuring points, and determining a position of the defect of the concrete to be measured according to the statistic, specifically including:
calculating the average value of the waveform dissimilarity coefficients in the waveform dissimilarity coefficient set under the current iteration times and the standard deviation of the waveform dissimilarity coefficients; the waveform dissimilarity coefficient set under the initial iteration times comprises waveform dissimilarity coefficients of all the measuring points;
determining an abnormal data critical value under the current iteration number according to the average value of the waveform dissimilarity coefficient and the standard deviation of the waveform dissimilarity coefficient;
judging whether a waveform dissimilarity coefficient which is larger than or equal to the abnormal data critical value exists in a waveform dissimilarity coefficient set under the current iteration times;
if so, determining the measuring point corresponding to the abnormal waveform dissimilarity coefficient as the position of the defect of the concrete, removing the abnormal waveform dissimilarity coefficient from the waveform dissimilarity coefficient set, updating the waveform dissimilarity coefficient set and the iteration times, and then returning to the step of calculating the average value of the waveform dissimilarity coefficients and the standard deviation of the waveform dissimilarity coefficients in the waveform dissimilarity coefficient set under the current iteration times; the abnormal waveform dissimilarity coefficient is a waveform dissimilarity coefficient which is greater than or equal to the abnormal data critical value in the waveform dissimilarity coefficient set under the current iteration number.
Optionally, the determining an abnormal data critical value under the current iteration number according to the average value of the waveform dissimilarity coefficient and the standard deviation of the waveform dissimilarity coefficient specifically includes:
wherein, X0Is an abnormal data critical value;
the average value of the waveform dissimilarity coefficient is obtained; sxIs the standard deviation of the waveform dissimilarity coefficient; lambda [ alpha ]nAnd determining coefficients for abnormal values corresponding to a waveform dissimilarity coefficient set including n waveform dissimilarity coefficients.
Optionally, before the calculating statistics of the waveform dissimilarity coefficients of all the measuring points, and determining the defect of the concrete to be measured and the position of the defect from the statistics, the method further includes:
and screening the waveform dissimilarity coefficients of all the measuring points by adopting a centroid clustering method.
The invention also provides a concrete defect detection system, which comprises:
the waveform acquisition module is used for acquiring ultrasonic waveforms of a plurality of measuring points of the concrete to be measured in the set area under the same test condition to obtain ultrasonic waveforms to be measured of the plurality of measuring points;
the sampling module is used for respectively sampling the reference ultrasonic waveform and the ultrasonic waveform to be detected to obtain a reference wave sampling data set and a wave to be detected sampling data set of a plurality of measuring points; the reference ultrasonic waveform is a continuous and complete waveform starting from a head wave starting point in the ultrasonic waveform of the defect-free concrete;
the waveform dissimilarity coefficient calculation module is used for calculating the waveform dissimilarity coefficient of each measuring point according to the reference wave sampling data set and the to-be-measured wave sampling data set; the waveform dissimilarity coefficient is a standardized Euclidean distance of the ultrasonic waveform to be measured relative to the reference ultrasonic waveform;
the defect detection module is used for calculating the statistic of the waveform dissimilarity coefficients of all the measuring points and determining the defects of the concrete to be detected and the positions of the defects according to the statistic; the statistics include mean, standard deviation, and outlier data cutoff for set confidence levels.
Optionally, the waveform dissimilarity coefficient calculation module specifically includes:
wherein, XiThe waveform dissimilarity coefficient of the ith measuring point is taken as a coefficient; siThe standard deviation of a sampling data set of the wave to be measured of the ith measuring point is obtained; s0Sampling a standard deviation of the data set for a reference wave; a isi,jJ is the jth acquired data in the to-be-detected wave sampling data set of the ith measuring point, wherein j is 1,2, 3. N is the total number of samples; a is0,jSampling jth acquired data in the data set for the reference wave;
the average value of N sampling data in a sampling data set of the wave to be measured of the ith measuring point is obtained;
the average value of the N sampled data in the set of reference wave sampled data is used.
Optionally, the defect detection module specifically includes:
the statistic calculation unit is used for calculating the average value of the waveform dissimilarity coefficients in the waveform dissimilarity coefficient set under the current iteration number and the standard deviation of the waveform dissimilarity coefficients; the waveform dissimilarity coefficient set under the initial iteration times comprises waveform dissimilarity coefficients of all the measuring points;
the critical value calculating unit is used for determining an abnormal data critical value under the current iteration times according to the average value of the waveform dissimilarity coefficients and the standard deviation of the waveform dissimilarity coefficients;
the judging unit is used for judging whether a waveform dissimilarity coefficient which is larger than or equal to the abnormal data critical value exists in the waveform dissimilarity coefficient set under the current iteration times;
the defect position determining unit is used for determining a measuring point corresponding to the abnormal waveform dissimilarity coefficient as the position of the defect of the concrete if the abnormal waveform dissimilarity coefficient is detected, removing the abnormal waveform dissimilarity coefficient from the waveform dissimilarity coefficient set, updating the waveform dissimilarity coefficient set and the iteration times, and returning to the statistic calculating unit; the abnormal waveform dissimilarity coefficient is a waveform dissimilarity coefficient which is greater than or equal to the abnormal data critical value in the waveform dissimilarity coefficient set under the current iteration number.
Optionally, the critical value calculating unit specifically includes:
wherein, X0Is an abnormal data critical value;
the average value of the waveform dissimilarity coefficient is obtained; sxAre dissimilar in waveformThe standard deviation of the coefficients; lambda [ alpha ]nAnd determining coefficients for abnormal values corresponding to a waveform dissimilarity coefficient set including n waveform dissimilarity coefficients.
Optionally, the concrete defect detecting system further includes:
and the screening module is used for screening the waveform dissimilarity coefficients of all the measuring points by adopting a centroid clustering method.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a concrete defect detection method and system, wherein a waveform dissimilarity coefficient is obtained by calculating a Standardized Euclidean Distance (Standardized Euclidean Distance) of an ultrasonic waveform to be detected relative to a reference ultrasonic waveform, and the waveform dissimilarity coefficient is used as a parameter for waveform quantization. By calculating waveform dissimilarity coefficient statistics (average value, standard deviation and abnormal data critical value of set confidence level), the concrete defect can be effectively judged, and the blank that the waveform parameters cannot be quantified and effectively judged for a long time is filled, so that the accuracy and reliability of concrete defect detection are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for detecting defects in concrete according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a concrete defect detection system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Based on the defects of the prior art, the present embodiment proposes a waveform dissimilarity coefficient, which substantially calculates the distance, similarity, or dissimilarity value (waveform dissimilarity coefficient) of the waveform (data) of each measurement point in the time domain with respect to the reference waveform (data) by using the normalized euclidean distance. The result of the normalized euclidean formula calculation is dimensionless. The waveform dissimilarity coefficient can be used as an important parameter for defect judgment through probability statistical analysis on the basis of considering sampling errors.
Compared with a reference waveform (normal waveform), the waveform dissimilarity coefficient is sensitive to sudden change, dislocation or displacement of waveform data in a time domain, and can reflect the change condition of the waveform to a greater extent. Through a large number of engineering detection analysis verifications over the years, the concrete defects can be effectively judged by adopting the waveform quantization parameter (called as a waveform dissimilarity coefficient), so that the missing judgment is radically reduced.
Waveform dissimilarity coefficient: the received waveforms of a plurality of measurement points with the same test condition in a certain region are relative to a reference waveform (1 continuous complete waveform is taken from a head wave starting jump point) in a time domain, and the value calculated by normalizing the Euclidean distance is called as a waveform dissimilarity coefficient. The larger the coefficient, the greater the dissimilarity with respect to the reference waveform, i.e., the smaller the degree of similarity.
Fig. 1 is a flowchart of a concrete defect detection method according to an embodiment of the present invention.
Referring to fig. 1, the method for detecting concrete defects of the embodiment includes:
step 101: and acquiring ultrasonic waveforms of a plurality of measuring points of the concrete to be measured in the set area under the same test condition to obtain the ultrasonic waveforms to be measured of the plurality of measuring points.
Step 102: respectively sampling the reference ultrasonic waveform and the ultrasonic waveform to be measured to obtain a reference wave sampling data set and a wave to be measured sampling data set of a plurality of measuring points; the reference ultrasonic waveform is a continuous and complete waveform starting from a head wave starting point in the ultrasonic waveform of the defect-free concrete.
Let W0Sampling a data set for a reference wave, W0＝{a0,1，a0,2,...,a0,NIn which a is0,1，a0,2,...,a0,NThe elements respectively represent the ordered data of N sampling points of the reference ultrasonic waveform in the time domain, a0,1And (3) data representing the head wave jump point, and then sequentially acquiring N pieces of ordered waveform data according to a fixed sampling interval.
Let WiIs a set of waveform data of the ith measuring point of a certain area. I.e. Wi＝{ai,1，ai,2,...,ai,NIt is related to W0Aggregate data correspondence, WiA in the seti,1，ai,2,...,ai,NThe elements respectively represent N orderly collected data of the ultrasonic waveform to be measured of the measuring point (i measuring point), ai,1And representing the data of the head wave jump point of the ultrasonic wave form to be measured of the measuring point.
Step 103: calculating a waveform dissimilarity coefficient of each measuring point according to the reference wave sampling data set and the to-be-measured wave sampling data set; the waveform dissimilarity coefficient is a standardized Euclidean distance between the ultrasonic waveform to be measured and the reference ultrasonic waveform.
The step 103 specifically includes:
wherein，XiIs the waveform dissimilarity coefficient, X, of the ith measurement pointiNot less than 0, is a dimensionless number; siThe standard deviation of a sampling data set of the wave to be measured of the ith measuring point is obtained; s0Sampling a standard deviation of the data set for a reference wave; a isi,jJ is the jth acquired data in the to-be-detected wave sampling data set of the ith measuring point, wherein j is 1,2, 3. N is the total number of samples; a is0,jSampling jth acquired data in the data set for the reference wave;
the average value of N sampling data in a sampling data set of the wave to be measured of the ith measuring point is obtained;
the average value of the N sampled data in the set of reference wave sampled data is used. The waveform dissimilarity coefficient (X) of the measured ultrasonic waveform of the ith measuring point in a certain area relative to the waveform of the reference ultrasonic waveformi) The essence is the distance or similarity between two sets of ordered data, which is a statistical data processing method.
N can be calculated according to the following formula:
wherein, L is the length (mu s) of a continuous complete waveform in one period starting from the head wave starting point in the ultrasonic waveform of the defect-free concrete, and Delta T is the sampling time interval (mu s).
The calculation of the waveform dissimilarity coefficient (Xi) of a plurality of measuring points in a certain area under the same test condition can be completed by adopting the existing software and self-developed programs.
Step 104: calculating statistics of waveform dissimilarity coefficients of all measuring points, and determining the defects of the concrete to be measured and positions of the defects according to the statistics; the statistics include mean, standard deviation, and outlier data cutoff for set confidence levels. The outlier threshold for the set confidence level may be an outlier threshold with a single side confidence level of 90%.
The step 104 specifically includes:
1) calculating the average value of the waveform dissimilarity coefficients in the waveform dissimilarity coefficient set under the current iteration times and the standard deviation of the waveform dissimilarity coefficients; the waveform dissimilarity coefficient set under the initial iteration number comprises waveform dissimilarity coefficients of all the measuring points.
Let XiAnd the waveform dissimilarity coefficients represent the ith measuring point of a certain area. X1,X2,...XnThe samples conform to a normal distribution, the mean value of the sample statistics (mean value of waveform dissimilarity coefficient)
And standard deviation (standard deviation of waveform dissimilarity coefficient) SxThe calculation is performed according to the following formula:
when the statistic is calculated according to the formula, the waveform dissimilarity coefficient is screened. If there are one or more significantly larger suspect data, the data pair sample standard deviation S should be deletedxThe influence of (c). When the data is empirical, normal data can be selected for calculation, and when the data cannot be determined, the original sample data can be divided into two types by adopting a statistical centroid clustering method, and waveform dissimilarity coefficients with smaller values are taken for calculation.
2) And determining an abnormal data critical value under the current iteration number according to the average value of the waveform dissimilarity coefficient and the standard deviation of the waveform dissimilarity coefficient. The method specifically comprises the following steps:
wherein, X0An abnormal data critical value (also called an abnormal value determination value);
the average value of the waveform dissimilarity coefficient is obtained; sxIs the standard deviation of the waveform dissimilarity coefficient; lambda [ alpha ]nAn abnormal value determination coefficient (single-side upper limit confidence interval abnormal value determination coefficient) corresponding to a waveform dissimilarity coefficient set including n waveform dissimilarity coefficients. Lambda [ alpha ]nThe values of (A) can be found in Table 1.
TABLE 1
3) And judging whether the waveform dissimilarity coefficient which is larger than or equal to the abnormal data critical value exists in the waveform dissimilarity coefficient set under the current iteration number.
If yes, determining a measuring point corresponding to the abnormal waveform dissimilarity coefficient as the position of the defect of the concrete, removing the abnormal waveform dissimilarity coefficient from the waveform dissimilarity coefficient set, updating the waveform dissimilarity coefficient set and the iteration times, and returning to the step 1). The abnormal waveform dissimilarity coefficient is a waveform dissimilarity coefficient which is greater than or equal to the abnormal data critical value in the waveform dissimilarity coefficient set under the current iteration number. I.e. first based on initial calculations
SxValue and lambdanCalculating X from the calculation formula of the critical value of the abnormal data0If some data is larger than X0Then these data are outliers. Then, after removing these outliers, the remaining data is recalculated
SxThen calculating X according to the constant data critical value calculation formula0Comparing and removing, and repeating until the residual data are less than X0Until now. Greater than X for the above removal by one or more times0The data of (1) is the abnormal value of the area. According to the parameters and the judgment method, if the waveforms of a certain measuring point are not similar to each other, the coefficient XiThe concrete is judged to be an abnormal value, namely the concrete at the position has defects.
The concrete defect detection method of the embodiment has the following advantages:
1. the waveform dissimilarity coefficient is used as an important parameter for waveform quantification, and the parameter is calculated and determined by statistical analysis to have an abnormal value critical value (a determination value) with 90% unilateral confidence level, so that the concrete defect can be effectively determined, and the blank that the waveform parameter cannot be quantified and effectively determined for a long time is filled.
2. The accuracy of concrete defect judgment can be improved by adopting the waveform dissimilarity coefficient.
3. By adopting the waveform dissimilarity coefficient, the problem that the amplitude parameter cannot be used as a supplementary parameter of the defect judgment parameter due to the fact that the consistency of the coupling condition cannot be guaranteed by human factors can be effectively solved. Because the numerator term and the denominator term in the waveform dissimilarity coefficient formula are waveform data and standard deviation, when the coupling is not good, the waveform shape does not change (namely, the waveform dissimilarity coefficient does not change), and the waveform amplitude is greatly reduced due to the coupling factor. The numerator and the denominator in the waveform dissimilarity coefficient calculation formula can effectively remove amplitude variation, and ensure that the waveform similarity coefficient is unchanged, thereby reducing misjudgment to a certain degree.
The invention also provides a concrete defect detection system, and fig. 2 is a schematic structural diagram of the concrete defect detection system provided by the embodiment of the invention.
Referring to fig. 2, the concrete defect detecting system of the present embodiment includes:
the waveform obtaining module 201 is configured to obtain ultrasonic waveforms of multiple measuring points of the concrete to be measured in the set area under the same test condition, so as to obtain ultrasonic waveforms of the multiple measuring points to be measured.
The sampling module 202 is configured to sample the reference ultrasonic waveform and the to-be-measured ultrasonic waveform respectively to obtain a reference wave sampling data set and a to-be-measured wave sampling data set of a plurality of measurement points; the reference ultrasonic waveform is a continuous and complete waveform starting from a head wave starting point in the ultrasonic waveform of the defect-free concrete.
The waveform dissimilarity coefficient calculation module 203 is configured to calculate a waveform dissimilarity coefficient of each measurement point from the reference wave sampling data set and the to-be-measured wave sampling data set; the waveform dissimilarity coefficient is a standardized Euclidean distance between the ultrasonic waveform to be measured and the reference ultrasonic waveform.
The defect detection module 204 is used for calculating statistics of waveform dissimilarity coefficients of all measuring points, and determining the defects of the concrete to be detected and positions of the defects according to the statistics; the statistics include mean, standard deviation, and outlier data cutoff for set confidence levels.
As an optional implementation manner, the waveform dissimilarity coefficient calculating module 203 specifically includes:
wherein, XiThe waveform dissimilarity coefficient of the ith measuring point is taken as a coefficient; siThe standard deviation of a sampling data set of the wave to be measured of the ith measuring point is obtained; s0Sampling a standard deviation of the data set for a reference wave; a isi,jJ is the jth acquired data in the to-be-detected wave sampling data set of the ith measuring point, wherein j is 1,2, 3. N is the total sampleCounting; a is0,jSampling jth acquired data in the data set for the reference wave;
the average value of N sampling data in a sampling data set of the wave to be measured of the ith measuring point is obtained;
the average value of the N sampled data in the set of reference wave sampled data is used.
As an optional implementation manner, the defect detecting module 204 specifically includes:
the statistic calculation unit is used for calculating the average value of the waveform dissimilarity coefficients in the waveform dissimilarity coefficient set under the current iteration number and the standard deviation of the waveform dissimilarity coefficients; the waveform dissimilarity coefficient set under the initial iteration number comprises waveform dissimilarity coefficients of all the measuring points.
And the critical value calculating unit is used for determining an abnormal data critical value under the current iteration number according to the average value of the waveform dissimilarity coefficient and the standard deviation of the waveform dissimilarity coefficient.
And the judging unit is used for judging whether the waveform dissimilarity coefficient which is greater than or equal to the abnormal data critical value exists in the waveform dissimilarity coefficient set under the current iteration number.
The defect position determining unit is used for determining a measuring point corresponding to the abnormal waveform dissimilarity coefficient as the position of the defect of the concrete if the abnormal waveform dissimilarity coefficient is detected, removing the abnormal waveform dissimilarity coefficient from the waveform dissimilarity coefficient set, updating the waveform dissimilarity coefficient set and the iteration times, and returning to the statistic calculating unit; the abnormal waveform dissimilarity coefficient is a waveform dissimilarity coefficient which is greater than or equal to the abnormal data critical value in the waveform dissimilarity coefficient set under the current iteration number.
As an optional implementation manner, the critical value calculating unit specifically includes:
wherein, X0Is an abnormal data critical value;
the average value of the waveform dissimilarity coefficient is obtained; sxIs the standard deviation of the waveform dissimilarity coefficient; lambda [ alpha ]nAnd determining coefficients for abnormal values corresponding to a waveform dissimilarity coefficient set including n waveform dissimilarity coefficients.
As an optional implementation, the concrete defect detecting system further includes: and the screening module is used for screening the waveform dissimilarity coefficients of all the measuring points by adopting a centroid clustering method.
A specific example is given below.
In this particular example, concrete defects of different sizes and properties (porosity, voids, acceptable small defects) were simulated in the laboratory at a test distance of 1200mm and a grout concrete design strength rating of C40.
The digital ultrasonic detector of Beijing Yutong is adopted, the emission voltage is 500V, the sampling interval is 0.40 mu s, and the instrument is qualified after being identified before detection. Before detection, the instrument is withered. And detecting that the coupling condition is normal and no coupling inconsistency exists.
The 45 groups of the current co-detection data represent 45 measuring points, each 3 points represent one part (15 parts in total), and the data of each measuring point are not influenced by the boundary condition of the component. Specific detection data processing and analysis determination are as follows.
(1) And acoustic parameter data (namely amplitude, sound time or sound velocity) of 45 measuring points acquired by the original specification are shown in a table 2.
TABLE 2
(2) Waveform data of 45 measuring points derived from a TXT text waveform data file stored by an instrument (a reference wave is a complete waveform of a period from a head wave starting point of a 1 st measuring point, and the number of sampling points is 49, specifically:
the sample points at the 1 st to 15 th stations are shown in Table 3-1, the sample points at the 16 th to 30 th stations are shown in Table 3-2, and the sample points at the 31 th to 45 th stations are shown in Table 3-3.
TABLE 3-1
TABLE 3-2
Tables 3 to 3
The calculated waveform dissimilarity coefficients are shown in tables 4-1, 4-2 and 4-3.
TABLE 4-1
TABLE 4-2
Tables 4 to 3
(3) And (3) judging abnormal values by adopting waveform dissimilarity coefficients, wave amplitudes and sound time (or sound velocity).
(a) Using waveform dissimilarity coefficients (X)i) The results of the abnormal values are shown in table 5.
TABLE 5
(b) Using sound time (t)i) The results of determining abnormal values by the parameters are shown in table 6.
TABLE 6
(c) Using amplitude (A)i) The results of determining abnormal values by the parameters are shown in table 7.
TABLE 7
(d) Using the speed of sound (v)i) The results of determining abnormal values by the parameters are shown in table 8.
TABLE 8
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A concrete defect detection method is characterized by comprising the following steps:
acquiring ultrasonic waveforms of a plurality of measuring points of concrete to be measured in a set area under the same test condition to obtain ultrasonic waveforms to be measured of the plurality of measuring points;
respectively sampling the reference ultrasonic waveform and the ultrasonic waveform to be measured to obtain a reference wave sampling data set and a wave to be measured sampling data set of a plurality of measuring points; the reference ultrasonic waveform is a continuous and complete waveform starting from a head wave starting point in the ultrasonic waveform of the defect-free concrete;
calculating a waveform dissimilarity coefficient of each measuring point according to the reference wave sampling data set and the to-be-measured wave sampling data set; the waveform dissimilarity coefficient is a standardized Euclidean distance of the ultrasonic waveform to be measured relative to the reference ultrasonic waveform;
calculating statistics of waveform dissimilarity coefficients of all measuring points, and determining the defects of the concrete to be measured and positions of the defects according to the statistics; the statistics include mean, standard deviation, and outlier data cutoff for set confidence levels.
2. The method for detecting the concrete defect according to claim 1, wherein the calculating the waveform dissimilarity coefficient of each measuring point by the reference wave sampling data set and the wave sampling data set to be measured specifically comprises:
wherein, XiThe waveform dissimilarity coefficient of the ith measuring point is taken as a coefficient; siThe standard deviation of a sampling data set of the wave to be measured of the ith measuring point is obtained; s0Sampling a standard deviation of the data set for a reference wave; a isi,jJ is the jth acquired data in the to-be-detected wave sampling data set of the ith measuring point, wherein j is 1,2, 3. N is the total number of samples; a is0,jSampling jth acquired data in the data set for the reference wave;
the average value of N sampling data in a sampling data set of the wave to be measured of the ith measuring point is obtained;
the average value of the N sampled data in the set of reference wave sampled data is used.
3. The method for detecting the concrete defect according to claim 1, wherein a statistic of waveform dissimilarity coefficients of all measuring points is calculated, and the position of the defect of the concrete to be detected is determined according to the statistic, which specifically comprises the following steps:
calculating the average value of the waveform dissimilarity coefficients in the waveform dissimilarity coefficient set under the current iteration times and the standard deviation of the waveform dissimilarity coefficients; the waveform dissimilarity coefficient set under the initial iteration times comprises waveform dissimilarity coefficients of all the measuring points;
determining an abnormal data critical value under the current iteration number according to the average value of the waveform dissimilarity coefficient and the standard deviation of the waveform dissimilarity coefficient;
judging whether a waveform dissimilarity coefficient which is larger than or equal to the abnormal data critical value exists in a waveform dissimilarity coefficient set under the current iteration times;
if so, determining the measuring point corresponding to the abnormal waveform dissimilarity coefficient as the position of the defect of the concrete, removing the abnormal waveform dissimilarity coefficient from the waveform dissimilarity coefficient set, updating the waveform dissimilarity coefficient set and the iteration times, and then returning to the step of calculating the average value of the waveform dissimilarity coefficients and the standard deviation of the waveform dissimilarity coefficients in the waveform dissimilarity coefficient set under the current iteration times; the abnormal waveform dissimilarity coefficient is a waveform dissimilarity coefficient which is greater than or equal to the abnormal data critical value in the waveform dissimilarity coefficient set under the current iteration number.
4. The method according to claim 3, wherein the abnormal data critical value at the current iteration number is determined by the average value of the waveform dissimilarity coefficient and the standard deviation of the waveform dissimilarity coefficient, and specifically comprises:
wherein, X0Is an abnormal data critical value;
the average value of the waveform dissimilarity coefficient is obtained; sxIs the standard deviation of the waveform dissimilarity coefficient; lambda [ alpha ]nAnd determining coefficients for abnormal values corresponding to a waveform dissimilarity coefficient set including n waveform dissimilarity coefficients.
5. The method as claimed in claim 1, wherein before calculating statistics of waveform dissimilarity coefficients of all the measuring points and determining the defect of the concrete to be measured and the position of the defect from the statistics, the method further comprises:
and screening the waveform dissimilarity coefficients of all the measuring points by adopting a centroid clustering method.
6. A concrete defect detection system, comprising:
the waveform acquisition module is used for acquiring ultrasonic waveforms of a plurality of measuring points of the concrete to be measured in the set area under the same test condition to obtain ultrasonic waveforms to be measured of the plurality of measuring points;
the sampling module is used for respectively sampling the reference ultrasonic waveform and the ultrasonic waveform to be detected to obtain a reference wave sampling data set and a wave to be detected sampling data set of a plurality of measuring points; the reference ultrasonic waveform is a continuous and complete waveform starting from a head wave starting point in the ultrasonic waveform of the defect-free concrete;
the waveform dissimilarity coefficient calculation module is used for calculating the waveform dissimilarity coefficient of each measuring point according to the reference wave sampling data set and the to-be-measured wave sampling data set; the waveform dissimilarity coefficient is a standardized Euclidean distance of the ultrasonic waveform to be measured relative to the reference ultrasonic waveform;
the defect detection module is used for calculating the statistic of the waveform dissimilarity coefficients of all the measuring points and determining the defects of the concrete to be detected and the positions of the defects according to the statistic; the statistics include mean, standard deviation, and outlier data cutoff for set confidence levels.
7. The concrete defect detecting system of claim 6, wherein the waveform dissimilarity coefficient calculating module is specifically:
wherein, XiThe waveform dissimilarity coefficient of the ith measuring point is taken as a coefficient; siThe standard deviation of a sampling data set of the wave to be measured of the ith measuring point is obtained; s0Sampling a standard deviation of the data set for a reference wave; a isi,jJ is the jth acquired data in the to-be-detected wave sampling data set of the ith measuring point, wherein j is 1,2, 3. N is the total number of samples; a is0,jSampling jth acquired data in the data set for the reference wave;
the average value of N sampling data in a sampling data set of the wave to be measured of the ith measuring point is obtained;
the average value of the N sampled data in the set of reference wave sampled data is used.
8. The concrete defect detecting system of claim 6, wherein the defect detecting module specifically comprises:
the statistic calculation unit is used for calculating the average value of the waveform dissimilarity coefficients in the waveform dissimilarity coefficient set under the current iteration number and the standard deviation of the waveform dissimilarity coefficients; the waveform dissimilarity coefficient set under the initial iteration times comprises waveform dissimilarity coefficients of all the measuring points;
the critical value calculating unit is used for determining an abnormal data critical value under the current iteration times according to the average value of the waveform dissimilarity coefficients and the standard deviation of the waveform dissimilarity coefficients;
the judging unit is used for judging whether a waveform dissimilarity coefficient which is larger than or equal to the abnormal data critical value exists in the waveform dissimilarity coefficient set under the current iteration times;
the defect position determining unit is used for determining a measuring point corresponding to the abnormal waveform dissimilarity coefficient as the position of the defect of the concrete if the abnormal waveform dissimilarity coefficient is detected, removing the abnormal waveform dissimilarity coefficient from the waveform dissimilarity coefficient set, updating the waveform dissimilarity coefficient set and the iteration times, and returning to the statistic calculating unit; the abnormal waveform dissimilarity coefficient is a waveform dissimilarity coefficient which is greater than or equal to the abnormal data critical value in the waveform dissimilarity coefficient set under the current iteration number.
9. The system of claim 8, wherein the threshold calculation unit is specifically:
wherein, X0Is an abnormal data critical value;
the average value of the waveform dissimilarity coefficient is obtained; sxIs the standard deviation of the waveform dissimilarity coefficient; lambda [ alpha ]nAnd determining coefficients for abnormal values corresponding to a waveform dissimilarity coefficient set including n waveform dissimilarity coefficients.
10. The concrete defect detection system of claim 6, further comprising:
and the screening module is used for screening the waveform dissimilarity coefficients of all the measuring points by adopting a centroid clustering method.
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