CN118010849B - Expressway bridge and culvert damage detection method and system - Google Patents
Expressway bridge and culvert damage detection method and system Download PDFInfo
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Abstract
The invention relates to the technical field of material analysis by utilizing ultrasonic waves, in particular to a method and a system for detecting damage to bridges and culverts of expressways. The original ultrasonic data is decomposed to obtain a plurality of independent components, then the cycle characteristic values of the independent components are obtained, and differences of the cycle characteristic values among the independent components are analyzed for evaluating the noise interference degree. Then, a first noise interference regulating coefficient is calculated according to the data change condition of each independent component. And smoothing and constructing each independent component according to the noise interference degree, acquiring updated ultrasonic data, and further acquiring a second noise interference regulating coefficient. And combining two noise interference regulating coefficients and noise interference degree values, carrying out targeted smoothing treatment on independent components, and ensuring the denoising accuracy. The reconstructed independent components are reconstructed into high-quality denoising ultrasonic data, so that accurate basis is provided for bridge and culvert damage detection, and the accuracy of bridge and culvert damage position judgment is ensured.
Description
Technical Field
The invention relates to the technical field of material analysis by utilizing ultrasonic waves, in particular to a method and a system for detecting damage to bridges and culverts of expressways.
Background
Along with the continuous development of traffic infrastructure, the construction of expressways is also increasing, wherein expressway bridges and culverts are taken as components of a road network, not only bear important transportation functions, but also often span rivers, lakes and the like, and become key nodes of traffic channels; however, in natural environment, the bridge structure is slightly damaged due to factors such as long-term natural weathering, traffic load and the like, and the safety and stability of the bridge structure are affected, so that the damage detection of the expressway bridge and culvert is required.
At least one measuring point is arranged on a bridge and culvert to be detected of a highway during detection, then ultrasonic data of the bridge and culvert measuring point are obtained by utilizing an ultrasonic detector and visualized into image data, and therefore relevant staff can observe and analyze damage conditions of the bridge and culvert intuitively. However, due to the influence of electronic elements in equipment, complex environmental factors and the like, more interference information generally exists in ultrasonic data, the prior art generally carries out smoothing treatment on the data according to the numerical characteristics of the data so as to realize a denoising process, but due to the fact that the outdoor environment where a highway is located is complex, the noise intensity has the characteristic of change, the denoising effect is poor only according to the numerical characteristics of the data, and therefore, when the visualization is caused, errors exist between image data and actual conditions, and the accuracy of damage detection is reduced.
Disclosure of Invention
In order to solve the technical problem that the denoising effect is poor only according to the numerical characteristics of data under the condition of noise intensity change, the invention aims to provide a method and a system for detecting the damage of a highway bridge and culvert, and the adopted technical scheme is as follows:
Acquiring original ultrasonic data of measuring points of bridges and culverts;
Independent component analysis is carried out on the original ultrasonic data to obtain independent components; according to the distribution condition and the difference condition of the numerical values in each independent component, obtaining the cycle characteristic value of each independent component; obtaining noise interference degree values of original ultrasonic data according to differences among cycle characteristic values of all independent components and data representation of all independent components;
Obtaining a first noise interference adjustment coefficient according to the data change trend in each independent component; smoothing and reconstructing all independent components according to the noise interference degree value to obtain updated ultrasonic data of the measuring point; obtaining a second noise interference adjustment coefficient according to the numerical difference between the updated ultrasonic data and the original ultrasonic data; and smoothing and reconstructing each independent component of the original ultrasonic data according to the first noise interference regulating coefficient, the second noise interference regulating coefficient and the noise interference degree value to obtain the denoising ultrasonic data.
Further, the method for acquiring the period characteristic value comprises the following steps:
Optionally selecting an independent component as a component to be detected, wherein in the component to be detected, the sum of the values of all data points is used as a value comprehensive index, and the ratio of the value of each data point to the value comprehensive index is used as the value duty ratio of each data point; taking the difference of the numerical value duty ratio of any two data points as a difference factor, and taking the value obtained by carrying out negative correlation mapping and normalization on the average value of all the difference factors in the component to be detected as a first periodic factor of the component to be detected;
Uniformly dividing the component to be detected into a preset first number of data segments according to the time sequence; obtaining a second periodic factor of the component to be detected according to the similarity conditions among all the data segments;
Taking the sum value of the first periodic factor and the second periodic factor of the component to be measured as the periodic characteristic value of the component to be measured.
Further, the formula model of the second periodicity factor includes:
; wherein/> Representing the measured component/>Is a second periodic factor of (2); /(I)Representing the measured component/>The total number of the data segment combinations after the data segments are combined pairwise; representing a total number of data points in each data segment; /(I) Represents the/>In the combination of the data segments, the first data segment is the/>The numerical duty cycle of the data points; /(I)Represents the/>In the combination of the data segments, the/>, of the second data segmentThe numerical duty cycle of the data points; /(I)Expressed as natural constant/>A logarithmic function of the base; /(I)Expressed as natural constant/>An exponential function of the base.
Further, the method for acquiring the noise interference level value comprises the following steps:
Combining any two independent components in all independent components to obtain all non-repeated component combinations;
For any one component combination, calculating the difference between the cycle characteristic values of two independent components in the component combination as a component difference index; taking the average value of the component difference indexes of all component combinations as a component difference characteristic value;
obtaining the difference between the kurtosis value and the standard kurtosis value of each independent component as the kurtosis value difference; taking the average value of kurtosis value differences of all independent components as a decomposition characteristic value;
and carrying out negative correlation mapping on the product of the decomposition characteristic value and the component difference characteristic value, and taking the normalized value as a noise interference degree value of the original ultrasonic data.
Further, the method for acquiring the first noise interference regulating coefficient comprises the following steps:
In the component to be measured, in each data segment, taking the data points with the same value as the same type of data points, and taking the ratio of the number of each type of data points to the total number of the data points as the distribution probability of each type of data points; calculating the difference between the distribution probabilities of any two types of data points to be used as a distribution difference value; taking the product of the average value of all the distribution difference values in each data segment and the total number of data point categories as an adjusting factor;
In each data segment, acquiring a slope value of each data point, taking an average value of the slope values of all the data points as an average value slope, calculating a difference between the slope value of each data point and the average value slope as a fluctuation factor corresponding to each data point, and taking the average value of the fluctuation factors corresponding to all the data points as a fluctuation characteristic value of each data segment;
And taking the normalized value of the product of the fluctuation characteristic value of each data segment and the adjustment factor as a first noise interference adjustment coefficient of each data segment.
Further, the smoothing and reconstructing all the independent components according to the noise interference level value to obtain updated ultrasonic data, including:
And carrying out Gaussian filtering on each independent component according to the noise interference degree value, and reconstructing all the filtered independent components based on an independent component analysis algorithm to obtain updated ultrasonic data of the measuring point.
Further, the performing gaussian filtering on each independent component according to the noise interference level value includes:
and taking the noise interference degree value as a filtering coefficient, and carrying out filtering operation on each independent component based on Gaussian filtering to obtain the filtered independent component.
Further, the formula model of the second noise interference regulating coefficient is:
; wherein/> Representing a second noise interference regulating factor; /(I)Representing a total number of data points in the raw ultrasound data; /(I)Representing the/>, in the raw ultrasound dataA value of a data point; /(I)Representing the/>, in updating ultrasound dataA value of a data point; /(I)Representing a hyperbolic tangent function.
Further, the smoothing and reconstructing each independent component of the original ultrasound data according to the first noise interference adjustment coefficient, the second noise interference adjustment coefficient and the noise interference level value to obtain the denoised ultrasound data, including:
In the components to be detected, according to the second noise interference regulating coefficient, the first noise interference regulating coefficient and the noise interference degree value of each data segment, obtaining a filter coefficient of each data segment, wherein the first noise interference regulating coefficient, the second noise interference regulating coefficient and the noise interference degree value are positively correlated with the filter coefficient;
Carrying out Gaussian filtering operation on corresponding data segments in the components to be detected according to the filtering coefficient of each data segment to obtain filtering data corresponding to the components to be detected;
and reconstructing the filtered data corresponding to all the independent components based on the independent component analysis algorithm to obtain the denoising ultrasonic data.
The invention also provides a highway bridge and culvert damage detection system, which comprises:
A memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of any one of the methods when the computer program is executed.
The invention has the following beneficial effects:
The method comprises the steps of obtaining original ultrasonic data at bridge and culvert measuring points, wherein the original ultrasonic data possibly contains various signal components, so that in order to achieve better denoising, the original ultrasonic data are subjected to independent component analysis to obtain all independent components, and the distribution condition of numerical values in each independent component is analyzed due to the fact that a bridge and culvert structure has fixed frequency, vibration modes and the like, so that a period characteristic value of each independent component is obtained. Further, because the influence of noise generated in different independent components may be different, in order to improve the subsequent denoising effect, each independent component is independently subjected to analysis of the data change trend, so as to obtain a first noise interference adjustment coefficient; and on the whole level, smoothing and reconstructing each independent component according to the noise interference degree to obtain updated ultrasonic data, and then analyzing the difference between the updated ultrasonic data and the original ultrasonic data to evaluate the influence condition of noise on the whole. And then, combining the first noise interference regulating coefficient, the second noise interference regulating coefficient and the noise interference degree value to carry out smoothing treatment on the independent components, denoising the independent components in a targeted and more accurate way, and reconstructing the independent components to obtain high-quality denoising ultrasonic data.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for detecting damage to a bridge and culvert of an expressway according to an embodiment of the invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of a method and a system for detecting the damage of a highway bridge according to the present invention, which are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, 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 belongs.
The invention provides a method and a system for detecting the damage of a highway bridge and culvert, which are specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a method flowchart of a method and a system for detecting damage to a highway bridge according to an embodiment of the invention is shown, where the method includes the following steps:
step S1: and obtaining the original ultrasonic data of the measuring points of the bridge and culvert.
Firstly, original ultrasonic data of each measuring point of a bridge and culvert are acquired, ultrasonic detectors are deployed at a plurality of positions on a road bridge and culvert structure for acquiring the original ultrasonic data, and the deployment positions can be specifically: bridge pier, abutment and other structures for supporting bridge and culvert, the ground of the bridge body, the junction of the bridge and the bridge pier, guardrails, wing walls and the like. The two dimensions of the raw ultrasound data are time and acoustic intensity, respectively. It should be noted that, the specific deployment position of the ultrasonic detector depends on the structure and design requirements of the bridge and culvert, and needs to be adjusted according to the actual situation; the specific data acquisition device, sampling frequency, etc. can be adjusted according to the actual situation, and are not limited herein.
Therefore, the original ultrasonic data at each measuring point of the bridge and culvert can be obtained, and can be analyzed in the subsequent process, so that denoising and bridge and culvert damage detection can be realized.
Step S2: independent component analysis is carried out on the original ultrasonic data to obtain independent components; according to the distribution condition and the difference condition of the numerical values in each independent component, obtaining the cycle characteristic value of each independent component; and obtaining the noise interference degree value of the original ultrasonic data according to the differences among the period characteristic values of all the independent components and the data representation of all the independent components.
Because of the plurality of measuring points, in order to facilitate the subsequent explanation and description, the raw ultrasonic data of one measuring point is optionally analyzed in the subsequent process, so as to describe the acquisition process of certain parameters and indexes in the embodiment of the invention.
The original ultrasonic data contains various signal components, such as response of a bridge structure, response waves caused by damage, environmental noise and the like, and the independent component analysis can decompose the mixed signal components into independent signals, so that the analysis of noise influence condition is facilitated, and the independent component analysis is firstly carried out on the original ultrasonic data to obtain the decomposed independent components. It should be noted that the independent component analysis algorithm is a technical means well known to those skilled in the art, and will not be described herein.
When the original ultrasonic data is subjected to noise interference, such as mechanical vibration with tiny bridges or electromagnetic interference in the environment, because the noise interference has certain periodicity, noise exists in a plurality of independent components after the independent component analysis, so that the components which are originally independent of each other have certain similarity, and the similarity is usually represented on the periodic characteristics of the data, so that the periodic characteristics of each independent component need to be acquired, and the periodic characteristics can be characterized according to the distribution condition of the data in the independent components, the fluctuation of differences and the like.
Preferably, in one embodiment of the present invention, the method for acquiring the period characteristic value includes:
Optionally, an independent component is used as a component to be measured, and if the periodicity of the data is strong, more data points with similar values should exist in the data, so that the numerical variation condition of the data points in the component to be measured is analyzed first: in the components to be measured, the values of all data points are accumulated, the accumulated sum is used as a numerical value comprehensive index, then the ratio of the value of each data point to the numerical value comprehensive index is calculated, and the ratio is used as the numerical value duty ratio of each data point; taking the difference of the numerical duty ratio of any two data points in the component to be measured as a difference factor, wherein at the moment, one difference factor exists between any two data points in the component to be measured, and then carrying out negative correlation mapping on the average value of all the difference factors in the component to be measured and normalizing the value to be used as a first periodic factor of the component to be measured.
Then, from the local part, analyzing the similarity condition between the data in the local part, and representing the periodic characteristics of the components to be detected: uniformly dividing the component to be detected into a preset first number of data segments according to the time; and obtaining a second periodic factor of the component to be detected according to the similarity among all the data segments. The component to be measured is an independent componentFor example, the formula model for the second periodicity factor is:
Wherein, Representing the measured component/>Is a second periodic factor of (2); /(I)Representing the measured component/>The total number of the data segment combinations after the data segments are combined pairwise; /(I)Representing a total number of data points in each data segment; /(I)Represents the/>In the combination of the data segments, the first data segment is the/>The numerical duty cycle of the data points; /(I)Represents the/>In the combination of the data segments, the/>, of the second data segmentThe numerical duty cycle of the data points; /(I)Expressed as natural constant/>A logarithmic function of the base; /(I)Expressed as natural constant/>An exponential function of the base.
In the formula model of the second periodic factor, any two data segments in the components to be detected are combined pairwise to obtain all non-repeated data segment combinations, and each data segment combination is calculatedThe value can be regarded as how much information difference is generated when two data segments in the data segment combination are replaced, and the smaller the value is, the more similar the two data segments are represented, the more periodic trend the data in the two data segments can be shown; then/>The sum of information differences generated when the replacement occurs between any two data segments in the component to be measured is represented, the smaller the value is, the higher the periodic characteristic of the component to be measured is, then the value is subjected to negative correlation mapping and normalization processing, logic relation correction is realized, and therefore the second periodic factor of the component to be measured is obtained.
And finally taking the sum value of the first periodic factor and the second periodic factor of the component to be measured as the periodic characteristic value of the component to be measured. The component to be measured is an independent componentFor example, the formula model of the periodic eigenvalue of the component to be measured is:
Wherein, Representing the measured component/>Periodic eigenvalues of (2); /(I)Representing the measured component/>The total number of data combinations after combining every two data points in the data storage unit; /(I)Represents the/>The value of the first data point in the data combination; /(I)Represents the/>The value of the second data point in the data combination; /(I)Representing a numerical comprehensive index; /(I)Representing the measured component/>Is a second periodic factor of (2); /(I)Expressed as natural constant/>An exponential function of the base.
In the formula model of the periodical characteristic value, for all data points in the component to be detected, calculating the numerical value sum value of all data points as a numerical value comprehensive index, wherein the value represents the overall condition of the numerical values of all data points in the component to be detected, and then calculating the ratio of the numerical value of each data point to the numerical value comprehensive index to obtain the numerical value duty ratio of each data pointCombining any two to obtain all non-repeated data combinations, and calculating the difference of numerical duty ratios of two data points as a difference factor/>, in each data combinationThe difference factors represent the similar situation of numerical distribution among data points in the components to be measured, the smaller the value is, the more similar the numerical value among the data points is, the components to be measured are more likely to show a certain periodic rule, then the average value of all the difference factors in the components to be measured is subjected to negative correlation mapping and normalization to realize logic relation correction, the first periodic factor of the components to be measured is obtained, and finally the sum value of the first periodic factor and the second periodic factor is taken as the periodic characteristic value of the components to be measured.
It should be noted that, the preset first number is set to 5, the specific value can be adjusted according to the implementation scene, and the method is not limited again; an example of a method for acquiring a data combination is as follows: for example, if there are 4 data points in the component to be measured, which are respectively denoted as data points 1,2, 3, and 4, the data combinations are (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), and (3, 4), and there are 6 data combinations in total; the data segment combination, component combination and other acquisition methods in the subsequent embodiments of the present invention are the same as the data combination acquisition method, and are not described in detail in the subsequent process.
In other embodiments of the present invention, when the second periodic factor of the component to be measured is obtained, since it is essentially that the data similarity between different data segments in the component to be measured is analyzed, the DTW value between two data segments in each data segment combination may also be calculated, and the smaller the DTW value, the more similar the two data segments are, the more obvious the periodic feature of the component to be measured is, so that the average value of the DTW values of all the data segment combinations is subjected to the negative correlation mapping and the normalization processing, thereby obtaining the second periodic factor of the component to be measured. The method for obtaining the DTW value is a technical means well known to those skilled in the art, and will not be described herein.
By analyzing each independent component in the original ultrasonic data, the cycle characteristic value of each independent component is obtained, and the cycle characteristic value can be used for evaluating the noise influence degree of the original ultrasonic data.
Based on the above analysis, the noise effect may cause relatively consistent variation between the independent components that should be independent of each other, and is embodied on the periodic characteristics, so that the difference between the periodic characteristic values of the different independent components may be analyzed as an index for obtaining the noise interference level value of the original ultrasound data. Meanwhile, since the independent component analysis has a specific decomposition rule, namely, the independent components have the largest non-Gaussian form, at the moment, if the non-Gaussian form represented by the independent components is reduced, the higher the noise influence degree is, and the noise interference degree value of the original ultrasonic data can be evaluated based on the two characteristics.
Preferably, in one embodiment of the present invention, the method for acquiring the noise interference level value includes:
Combining any two independent components in all independent components to obtain all non-repeated component combinations;
For any one component combination, calculating the difference between the cycle characteristic values of two independent components in the component combination as a component difference index; the mean value of the component difference index of all the component combinations is taken as the component difference characteristic value. The component difference feature values may characterize the degree of similarity between periodic features between all individual components.
Then, the difference between the kurtosis value and the standard kurtosis value of each independent component is obtained and used as the kurtosis value difference; and taking the average value of kurtosis value differences of all independent components as a decomposition characteristic value.
And finally, carrying out negative correlation mapping on the multiplied value of the decomposition characteristic value and the component difference characteristic value, and taking the normalized value as a noise interference degree value of the original ultrasonic data. Raw ultrasound data for a survey pointThe original ultrasonic data is taken as an example, and a formula model of the noise interference degree value is as follows:
Wherein, Representing raw ultrasound data/>Noise interference level value of (a); /(I)Representing raw ultrasound data/>The total number of independent components obtained by decomposition; /(I)Represents the/>Kurtosis values of individual components; /(I)Representing a standard kurtosis value; /(I)Representing raw ultrasound data/>The total number of component combinations after the combination of all independent components; /(I)Represents the/>A period characteristic value of a first individual component in the individual component combination; /(I)Represents the/>Cycle characteristic value of the second independent component in the individual component combination.
In the formula model of the noise interference level value, the difference between the period characteristic values of two independent components in each component combination is calculated as a component difference indexWhen the value is smaller, the period characteristics between two independent components are similar, the noise interference intensity of the original ultrasonic data is larger, the component difference indexes of all component combinations are integrated, and the average value of the accumulated sums is taken as the component difference characteristic valueThe smaller the value, the more consistent the periodic characteristics between every two of all independent components of the original ultrasonic data are, and the more serious the noise interference is considered; similarly, since the decomposition rule of the independent component analysis is that the independent components have the largest non-gaussian shape, but since the original ultrasonic data is affected by noise, the largest non-gaussian shape between the independent components is reduced, that is, a certain gaussian characteristic is obtained, the difference between the kurtosis value of each independent component and the standard kurtosis value is calculated as kurtosis value difference/>The smaller the value, the smaller the deviation between the kurtosis value of the independent component and the standard kurtosis value, namely the smaller the deviation between the kurtosis value and the standard Gaussian distribution, the more serious the noise influence on the original ultrasonic data is; taking the average value of kurtosis value differences of all independent components as decomposition characteristic value/>The smaller the value, the more noise-affected the raw ultrasound data. Finally, the component difference characteristic value/>And decompose eigenvalues/>And carrying out negative correlation mapping and normalization processing on the product of the ultrasonic data to realize logic relation correction, thereby obtaining the noise interference degree value of the original ultrasonic data.
The standard kurtosis value is obtained from a standard normal distribution, and is herein defined as 3Set to 3.
Based on the above process, the noise interference intensity of the original ultrasonic data can be estimated to obtain a noise interference degree value, and in the subsequent process, filtering and other processes can be performed by using the noise interference degree value to obtain the denoising ultrasonic data corresponding to the original ultrasonic data.
Step S3: obtaining a first noise interference adjustment coefficient according to the data change trend in each independent component; smoothing and reconstructing all independent components according to the noise interference degree value to obtain updated ultrasonic data of the measuring point; obtaining a second noise interference adjustment coefficient according to the numerical difference between the updated ultrasonic data and the original ultrasonic data; and smoothing and reconstructing each independent component of the original ultrasonic data according to the first noise interference regulating coefficient, the second noise interference regulating coefficient and the noise interference degree value to obtain the denoising ultrasonic data.
In the foregoing step, the ultrasonic data is subjected to independent component decomposition according to the independent component analysis, and the influence degree of noise is evaluated according to the rule of the independent component analysis and the differences among all the independent components, so as to obtain the noise interference degree value suffered by the whole data to be measured. However, since the interference amounts of the noise in the single independent component are not necessarily completely equal, in this embodiment of the present invention, the estimated noise interference level value is adjusted by two adjustment coefficients for respectively obtaining the noise interference level value through the overall level and the local level, which is more favorable for distributing the influence of the noise interference in different independent components, and is favorable for obtaining a more denoising result.
Firstly, starting from a local hierarchy, quantifying a first noise interference adjustment coefficient by analyzing the change trend of data in each independent component corresponding to original ultrasonic data.
Preferably, in one embodiment of the present invention, the method for acquiring the first noise interference regulation coefficient includes:
The data change trend can be mainly characterized by the numerical distribution and the slope of the data points, so that an independent component is selected as a component to be measured, in the component to be measured, from the local part, the data points with the same numerical value are used as the same class of data points in each data segment, and the ratio of the number of each class of data points to the total number of the data points is used as the distribution probability of each class of data points; calculating the difference between the distribution probabilities of any two types of data points to be used as a distribution difference value; at this time, any two kinds of data points in the data segment correspond to one distribution difference value, and then the average value of all the distribution difference values in each data segment is used as an adjusting factor.
And then, in each data segment, acquiring a slope value of each data point, taking the average value of the slope values of all the data points as an average slope, calculating the difference between the slope value of each data point and the average slope as a fluctuation factor corresponding to each data point, and taking the average value of the fluctuation factors corresponding to all the data points as a fluctuation characteristic value of each data segment. The slope acquisition method is a process well known to those skilled in the art, and will not be described in detail herein, and the slope value of the last data point in each data segment is set equal to the slope value of the previous data point.
And finally, taking the normalized value of the product of the fluctuation characteristic value of each data segment and the adjustment factor as a first noise interference adjustment coefficient of each data segment. The component to be measured is an independent componentFor example, the formula model of the first noise interference regulating coefficient may specifically be, for example:
Wherein, Representing the measured component/>Middle/>A first noise interference regulating factor for each data segment; /(I)Representing the measured component/>Middle/>The total number of categories of data points in the data segments; /(I)Representing the measured component/>Middle/>The total number of class combinations after the combination of any two classes of data points in the data segments; /(I)Represents the/>The number of class 1 data points in the class combination; Represents the/> The number of class 2 data points in the class combination; /(I)Representing the measured component/>Middle/>Total number of data points in the data segments; /(I)Represents the/>Slope values of the data points; /(I)Representing the mean slope; /(I)Representing a hyperbolic tangent function.
In the formula model of the first noise interference regulating coefficient, the ratio of the number of data points of each type to the total number of data points is taken as the distribution probability of the data points of each typeCombining any two types of data points in the data segment to obtain all non-repeated class combinations, and calculating the difference between the distribution probabilities of the two types of data points in each class combination to obtain a distribution difference value/>The value characterizes the distribution difference condition of data points of different categories in the data segment, and after the distribution difference values corresponding to all category combinations are comprehensively averaged, the value is multiplied by the category total number of the data points in the data segment to obtain the regulating factor/>The larger the value of the adjusting factor, the more data point categories in the data segment are indicated, namely, the data points with different values are distributed, the types are richer, and the more complex the data in the data segment is, the greater the influence degree of noise can be possibly increased; similarly, the difference between the slope value and the mean slope of each data point in the data segment is calculated as the fluctuation factor/>And taking the mean value of fluctuation factors of all data points as fluctuation characteristic values/>, of the data segmentThe average slope represents the average level of all the data point change trends in the data segment, so that if the fluctuation factor of the data point is larger, the fluctuation characteristic value of the data segment is larger, the more complex and unstable the data change condition in the data segment is, and the higher the degree of influence of noise is. Therefore, the adjusting factor is combined with the fluctuation characteristic value, multiplied by the fluctuation characteristic value and normalized to obtain a first noise interference adjusting coefficient of the data segment.
To this end, the first noise floor is derived for each data segment in each individual component, and then the second noise floor is derived from the overall floor.
Since the noise interference level value of the original ultrasonic data is estimated according to the difference between the independent components, in order to better estimate the noise removal effect of the noise interference level value at this time, each independent component can be smoothed and reconstructed by using the noise interference level value of the original ultrasonic data, then updated ultrasonic data of the original ultrasonic data is obtained, and the difference between the updated ultrasonic data and the original ultrasonic data is analyzed, so that the amount to be adjusted is determined, namely, the second noise interference adjustment coefficient is obtained.
Preferably, in one embodiment of the present invention, the method for acquiring updated ultrasound data includes: and taking the noise interference degree value as a filtering coefficient, so as to carry out Gaussian filtering on each independent component, and reconstructing all the filtered independent components based on an independent component analysis algorithm, thereby obtaining updated ultrasonic data of the measuring point. It should be noted that, the gaussian filtering and the independent component analysis algorithm are all technical means well known to those skilled in the art, and are not described herein.
Preferably, in one embodiment of the present invention, the formula model of the second noise interference regulating coefficient is:
Wherein, Representing a second noise interference regulating factor; /(I)Representing a total number of data points in the raw ultrasound data; /(I)Representing the/>, in the raw ultrasound dataA value of a data point; /(I)Representing the/>, in updating ultrasound dataA value of a data point; representing a hyperbolic tangent function.
In a formula model of the second noise interference regulating coefficient, calculating the square of the difference between the data points in the original ultrasonic data and the updated ultrasonic dataWhen the value is larger, it is indicated that after each independent component is filtered and denoised by using the noise interference level value, a larger deviation exists between the updated ultrasonic data obtained by reconstruction and the original ultrasonic data, that is, the noise interference level of the original ultrasonic data is higher, so that the uncertainty of the estimated noise interference level value obtained in the process is higher, and therefore, the estimated noise interference level value needs to be positively adjusted, and a formula model of a second interference adjustment coefficient is constructed based on the logic to obtain the second noise interference adjustment coefficient.
After the first noise interference regulating coefficient and the second noise interference regulating coefficient are obtained, the noise interference degree value can be regulated and distributed according to the first noise interference regulating coefficient and the second noise interference regulating coefficient, so that each independent component can be smoothed and reconstructed more accurately in a targeted manner, and high-quality denoising ultrasonic data can be obtained.
Preferably, in one embodiment of the present invention, the reconstructing after smoothing each independent component of the original ultrasound data according to the first noise interference adjustment coefficient, the second noise interference adjustment coefficient and the noise interference level value to obtain the denoised ultrasound data includes:
And obtaining a filter coefficient of each data segment according to the second noise interference regulating coefficient, the first noise interference regulating coefficient and the noise interference degree value of each data segment in the component to be detected, wherein the first noise interference regulating coefficient, the second noise interference regulating coefficient and the noise interference degree value are positively correlated with the filter coefficient. The component to be measured is an independent component For example, the formula model of the filter coefficient may specifically be, for example:
Wherein, Representing the measured component/>Middle/>Filtering coefficients of the individual data segments; /(I)Representing the measured component/>Middle/>A first noise interference regulating factor for each data segment; /(I)Representing a second noise interference regulating factor; /(I)Representing raw ultrasound data/>Is a noise interference level value of (a).
Multiplying the second noise interference regulating coefficient with the noise interference level value of the original ultrasonic data in the formula model of the filter coefficient to obtain a productIt can be considered that the noise interference level value obtained in step S2 is corrected so that/>The condition that the original ultrasonic data is interfered by noise can be accurately represented; then for each independent component, when the first noise interference regulating coefficient of the data segment in the independent component is larger, the data change condition in the data segment is more complex and unstable, the degree of influence of the noise is higher, so/>Further characterization requires a greater degree of smoothing, and then correlating this value with/>As the filter coefficients required for the data segment.
And then carrying out Gaussian filtering operation on the corresponding data segment in the components to be detected according to the filtering coefficient of each data segment to obtain filtering data. And finally, reconstructing all the filtered data based on an independent component analysis algorithm to obtain the denoising ultrasonic data. It should be noted that, the positive correlation relationship indicates that the dependent variable increases with the increase of the independent variable, and the dependent variable decreases with the decrease of the independent variable, and the specific relationship may be a multiplication relationship, an addition relationship, an idempotent of an exponential function, and is determined by practical application; the negative correlation indicates that the dependent variable decreases with increasing independent variable, and the dependent variable increases with decreasing independent variable, which may be a subtraction relationship, a division relationship, or the like, and is determined by the actual application.
Based on the steps, high-quality denoising ultrasonic data at each measuring point of the bridge and culvert can be obtained, and then bridge and culvert damage detection can be carried out according to the denoising ultrasonic data, so that damage positions and the like can be determined.
The specific method comprises the following steps: the denoising ultrasonic data of all the measuring points of the bridge and culvert are converted into analog electric signals, and then visual characteristic recognition is carried out, wherein special software or tools can be used for visualizing the analog electric signals; and features are identified through the features such as echo intensity, echo duration and the like. And then, generating a real-time state image of the bridge and culvert, wherein the image displays structural characteristics of the bridge and culvert, potential damage positions and other information, and finally, a worker can perform damage positioning according to the image to obtain the damage positions.
The embodiment also provides a highway bridge and culvert damage detection system, which comprises a memory, a processor and a computer program, wherein the memory is used for storing the corresponding computer program, the processor is used for running the corresponding computer program, and the computer program can realize any one of the steps of the highway bridge and culvert damage detection method when running on the processor.
In summary, in the embodiment of the present invention, the original ultrasonic data at each measuring point of the bridge is obtained, and because the original ultrasonic data may contain multiple signal components, in order to implement better denoising, the invention performs independent component analysis on the original ultrasonic data to obtain all independent components, and because the bridge structure has the characteristics of fixed frequency, vibration mode, and the like, the distribution condition of the numerical values in each independent component is analyzed to obtain the periodic characteristic value of each independent component, further, because the independent component analysis has a specific decomposition rule, and if the noise influence is greater, the periodicity represented between the independent components is similar, so the difference between the periodic characteristic values of the independent components is analyzed, and the noise interference degree value suffered by the original ultrasonic data is evaluated in combination with the data representation of the independent components. Further, because the influence of noise generated in different independent components may be different, in order to improve the subsequent denoising effect, each independent component is independently subjected to analysis of the data change trend, so as to obtain a first noise interference adjustment coefficient; and on the whole level, smoothing and reconstructing each independent component according to the noise interference degree to obtain updated ultrasonic data, and then analyzing the difference between the updated ultrasonic data and the original ultrasonic data to evaluate the influence condition of noise on the whole. And then, combining the first noise interference regulating coefficient, the second noise interference regulating coefficient and the noise interference degree value to carry out smoothing treatment on the independent components, denoising the independent components in a targeted and more accurate way, and reconstructing the independent components to obtain high-quality denoising ultrasonic data. And finally, bridge and culvert damage detection is carried out according to high-quality denoising ultrasonic data at all measuring points, and more accurate detection results can be obtained when the damage positions are determined.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (6)
1. The method for detecting the damage of the bridge and culvert of the expressway is characterized by comprising the following steps of:
Acquiring original ultrasonic data of measuring points of bridges and culverts;
Independent component analysis is carried out on the original ultrasonic data to obtain independent components; according to the distribution condition and the difference condition of the numerical values in each independent component, obtaining the cycle characteristic value of each independent component; obtaining noise interference degree values of original ultrasonic data according to differences among cycle characteristic values of all independent components and data representation of all independent components;
Obtaining a first noise interference adjustment coefficient according to the data change trend in each independent component; smoothing and reconstructing all independent components according to the noise interference degree value to obtain updated ultrasonic data of the measuring point; obtaining a second noise interference adjustment coefficient according to the numerical difference between the updated ultrasonic data and the original ultrasonic data; smoothing and reconstructing each independent component of the original ultrasonic data according to the first noise interference regulating coefficient, the second noise interference regulating coefficient and the noise interference degree value to obtain denoising ultrasonic data;
The method for acquiring the period characteristic value comprises the following steps:
Optionally selecting an independent component as a component to be detected, wherein in the component to be detected, the sum of the values of all data points is used as a value comprehensive index, and the ratio of the value of each data point to the value comprehensive index is used as the value duty ratio of each data point; taking the difference of the numerical value duty ratio of any two data points as a difference factor, and taking the value obtained by carrying out negative correlation mapping and normalization on the average value of all the difference factors in the component to be detected as a first periodic factor of the component to be detected;
Uniformly dividing the component to be detected into a preset first number of data segments according to the time sequence; obtaining a second periodic factor of the component to be detected according to the similarity conditions among all the data segments;
taking the sum of the first periodic factor and the second periodic factor of the component to be measured as the periodic characteristic value of the component to be measured;
The method for acquiring the noise interference degree value comprises the following steps:
Combining any two independent components in all independent components to obtain all non-repeated component combinations;
For any one component combination, calculating the difference between the cycle characteristic values of two independent components in the component combination as a component difference index; taking the average value of the component difference indexes of all component combinations as a component difference characteristic value;
obtaining the difference between the kurtosis value and the standard kurtosis value of each independent component as the kurtosis value difference; taking the average value of kurtosis value differences of all independent components as a decomposition characteristic value;
carrying out negative correlation mapping on the product of the decomposition characteristic value and the component difference characteristic value and taking the normalized value as a noise interference degree value of the original ultrasonic data;
the method for acquiring the first noise interference regulating coefficient comprises the following steps:
In the component to be measured, in each data segment, taking the data points with the same value as the same type of data points, and taking the ratio of the number of each type of data points to the total number of the data points as the distribution probability of each type of data points; calculating the difference between the distribution probabilities of any two types of data points to be used as a distribution difference value; taking the product of the average value of all the distribution difference values in each data segment and the total number of data point categories as an adjusting factor;
In each data segment, acquiring a slope value of each data point, taking an average value of the slope values of all the data points as an average value slope, calculating a difference between the slope value of each data point and the average value slope as a fluctuation factor corresponding to each data point, and taking the average value of the fluctuation factors corresponding to all the data points as a fluctuation characteristic value of each data segment;
taking the normalized value of the product of the fluctuation characteristic value of each data segment and the adjustment factor as a first noise interference adjustment coefficient of each data segment;
The formula model of the second noise interference regulating coefficient is as follows:
; wherein/> Representing a second noise interference regulating factor; /(I)Representing a total number of data points in the raw ultrasound data; /(I)Representing the/>, in the raw ultrasound dataA value of a data point; /(I)Representing the/>, in updating ultrasound dataA value of a data point; /(I)Representing a hyperbolic tangent function.
2. The method for detecting highway bridge damage according to claim 1, wherein said formula model of the second periodicity factor comprises:
; wherein/> Representing the measured component/>Is a second periodic factor of (2); /(I)Representing the measured component/>The total number of the data segment combinations after the data segments are combined pairwise; representing a total number of data points in each data segment; /(I) Represents the/>In the combination of the data segments, the first data segment is the/>The numerical duty cycle of the data points; /(I)Represents the/>In the combination of the data segments, the/>, of the second data segmentThe numerical duty cycle of the data points; /(I)Expressed as natural constant/>A logarithmic function of the base; /(I)Expressed as natural constant/>An exponential function of the base.
3. The method for detecting highway bridge and culvert damage according to claim 1, wherein said smoothing and reconstructing all independent components according to said noise interference level value to obtain updated ultrasonic data comprises:
And carrying out Gaussian filtering on each independent component according to the noise interference degree value, and reconstructing all the filtered independent components based on an independent component analysis algorithm to obtain updated ultrasonic data of the measuring point.
4. A method for detecting highway bridge damage according to claim 3, wherein said gaussian filtering of each individual component according to said noise interference level value comprises:
and taking the noise interference degree value as a filtering coefficient, and carrying out filtering operation on each independent component based on Gaussian filtering to obtain the filtered independent component.
5. The method for detecting the damage to the bridge and culvert on the highway according to claim 1, wherein the smoothing and reconstructing each independent component of the original ultrasonic data according to the first noise interference adjustment coefficient, the second noise interference adjustment coefficient and the noise interference level value to obtain the denoising ultrasonic data comprises the following steps:
In the components to be detected, according to the second noise interference regulating coefficient, the first noise interference regulating coefficient and the noise interference degree value of each data segment, obtaining a filter coefficient of each data segment, wherein the first noise interference regulating coefficient, the second noise interference regulating coefficient and the noise interference degree value are positively correlated with the filter coefficient;
Carrying out Gaussian filtering operation on corresponding data segments in the components to be detected according to the filtering coefficient of each data segment to obtain filtering data corresponding to the components to be detected;
and reconstructing the filtered data corresponding to all the independent components based on the independent component analysis algorithm to obtain the denoising ultrasonic data.
6. A highway bridge damage detection system comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor when executing the computer program implements the steps of the method according to any one of claims 1 to 5.
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