Computer-aided strength testing method for precast concrete components

Abstract

The present invention relates to the technical field of material physical testing, and in particular to a computer-aided precast concrete component strength detection method, comprising: decomposing an actual ultrasonic echo signal of a detection point of the precast concrete component into multiple principal components, and determining a suspected defect component therefrom; determining a suspected defect interval in the suspected defect component, and obtaining a defect degree representation of the suspected defect component based on the duration of the suspected defect interval, the position of the suspected defect component in the suspected defect component, and the defect condition; fusing the characteristic values ​​and defect degree representations of each suspected defect component to obtain the defect degree of the detection point. Compared with the existing method of simply comparing with a normal ultrasonic signal, the precast concrete component strength detection method provided by the present invention accurately identifies the defect degree of the precast concrete component, thereby improving the accuracy of precast concrete component strength detection.

Description

Technical Field

[0001] The present invention relates to the technical field of material physical testing, and in particular to a computer-aided prefabricated concrete component strength detection method. Background Art

[0002] Precast concrete components are prefabricated in factories or on-site. The raw material quality and mix ratios are strictly controlled during the production process, ensuring component precision and quality. These components offer high production efficiency and durability. Precast components only require installation at the construction site, reducing labor intensity and increasing the convenience of on-site construction. They are widely used in construction, water conservancy, and municipal engineering. The quality of precast concrete components is directly related to the structural safety and stability of a building. Component strength is a key indicator of quality and directly impacts the safety of the building structure. Strength testing of precast components can promptly determine whether component strength meets design requirements or is insufficient, thereby preventing safety incidents such as damage and collapse during use due to insufficient component strength.

[0003] There are various methods for strength testing precast concrete components, among which ultrasonic testing is a commonly used non-destructive method. Traditional ultrasonic testing primarily compares the actual ultrasonic signal with a standard ultrasonic signal, identifying anomalies based on the difference between the two and roughly determining whether the concrete component is defective. However, this simple comparison method has low detection accuracy. Summary of the Invention

[0004] In order to solve the technical problem of low accuracy of existing precast concrete component strength testing methods, the present invention aims to provide a computer-aided precast concrete component strength testing method. The technical solution adopted is as follows:

[0005] The present invention provides a computer-aided precast concrete component strength detection method, comprising:

[0006] Decomposing the actual ultrasonic echo signal of the inspection point of the precast concrete component into multiple principal components, and determining the suspected defect component therefrom;

[0007] Determining a suspected defect interval in the suspected defect component, and obtaining a defect degree representation of the suspected defect component based on a duration of the suspected defect interval, a position in the suspected defect component, and a defect condition;

[0008] The characteristic values ​​and defect degree expressions of each of the suspected defect components are integrated to obtain the defect degree of the detection point.

[0009] In an exemplary embodiment, the process of determining the suspected defect component includes:

[0010] Determining the similarity between each of the principal components and a reference ultrasonic echo signal; the reference ultrasonic echo signal represents an ultrasonic echo signal of a non-defective precast concrete component;

[0011] The principal components other than the one with the greatest similarity are determined as suspected defect components.

[0012] In an exemplary embodiment, the process of obtaining the suspected defect interval includes:

[0013] Determining a data difference sequence corresponding to the suspected defect component, the data difference sequence including data differences at each moment between the suspected defect component and the reference ultrasonic echo signal;

[0014] Dividing the data difference sequence into a plurality of sub-segments according to the data difference;

[0015] Compare the data difference mean of each sub-segment with a preset difference threshold, and obtain the time interval of the sub-segment corresponding to the value greater than the preset difference threshold;

[0016] According to the time interval, a suspected defect interval in the suspected defect component is obtained.

[0017] In an exemplary embodiment, the position of the suspected defect interval in the suspected defect component is specifically: the time interval between the suspected defect interval and the middle moment of the suspected defect component;

[0018] The defect condition of the suspected defect interval is specifically: the mean value of the data difference of the suspected defect interval;

[0019] The process of obtaining the defect degree expression includes:

[0020] The duration ratio of each suspected defect interval is determined based on the duration of each suspected defect interval;

[0021] Obtaining a defect severity performance impact index for each suspected defect interval based on the time interval, duration ratio, and data difference mean corresponding to each suspected defect interval; the defect severity performance impact index is inversely proportional to the time interval and directly proportional to the duration ratio and data difference mean;

[0022] The defect degree performance influencing indicators of each suspected defect interval of the suspected defect component are integrated to obtain the defect degree performance of the suspected defect component.

[0023] In an exemplary embodiment, before obtaining the defect degree of the detection point, the precast concrete component strength detection method further includes:

[0024] Determine an average time interval between each two adjacent suspected defect intervals in the suspected defect component;

[0025] The defect degree of the suspected defect component is corrected according to the correction coefficient of the suspected defect component; the correction coefficient is obtained from the average time interval, and the correction coefficient is inversely proportional to the average time interval.

[0026] In an exemplary embodiment, the process of obtaining the defect degree of the inspection point includes:

[0027] According to the eigenvalue of each suspected defect component, the influence weight of each suspected defect component is obtained, wherein the influence weight is proportional to the eigenvalue, and the sum of the influence weights of all suspected defect components is 1;

[0028] According to the influence weight of each suspected defect component, the defect degree performance of each suspected defect component is weightedly summed to obtain the defect degree of the detection point.

[0029] In an exemplary embodiment, after obtaining the defect degree of the detection point, the precast concrete component strength detection method further includes:

[0030] Dividing the inspection surface of the precast concrete component into a plurality of inspection areas, determining a detection point in each inspection area, and determining the degree of defects in each inspection area;

[0031] According to the degree of defects in each inspection area, defective areas are screened from the inspection areas;

[0032] According to the defect degree of each defect area, the defect areas are clustered to obtain several clusters;

[0033] The defect degree of the inspection surface is obtained according to the aggregation degree of each cluster, the mean defect degree of each cluster, the number of clusters and the proportion of the number of defective areas.

[0034] In an exemplary embodiment, obtaining the defect degree of the inspection surface according to the aggregation degree of each cluster, the mean defect degree of each cluster, the number of clusters, and the proportion of defective areas includes:

[0035] The aggregation degree and defect degree mean of each cluster are integrated to obtain the overall performance of cluster defects;

[0036] The defect degree of the inspection surface is obtained according to the overall performance of the cluster defects, the number of clusters, and the proportion of the number of defective areas; the defect degree of the inspection surface is proportional to the overall performance of the cluster defects, the number of clusters, and the proportion of the number of defective areas.

[0037] In an exemplary embodiment, the detection point is the center point of the corresponding detection area;

[0038] The process of obtaining the degree of aggregation includes:

[0039] The distance between the detection points of any two defect areas in the cluster is obtained, and the average value is calculated to obtain the aggregation degree of the cluster, and the aggregation degree is inversely proportional to the average value.

[0040] In an exemplary embodiment, after obtaining the defect degree of the detection surface, the precast concrete component strength detection method further includes:

[0041] All surfaces of the precast concrete component are used as test surfaces, and the average of the defect levels of the test surfaces is calculated as the final defect level of the precast concrete component;

[0042] The strength of the precast concrete component is obtained according to the final defect degree, and the strength is inversely proportional to the final defect degree.

[0043] The present invention has the following beneficial effects: since each principal component in the actual ultrasonic echo signal of the detection point of the precast concrete component can reflect the defect situation from different characteristics, the actual ultrasonic echo signal is decomposed into multiple principal components, and the multiple principal components are analyzed to determine the suspected defect component, which is the principal component that may indicate a defect. Then, the suspected defect component is analyzed in detail to determine the suspected defect interval. The duration of the suspected defect interval, the position in the suspected defect component, and the defect situation are all related to the defect degree performance of the suspected defect component. Therefore, the defect degree performance of the suspected defect component is obtained. Finally, the characteristic values ​​and defect degree performances of each suspected defect component are fused to obtain the defect degree. The strength of the precast concrete component can be obtained according to the defect degree. Compared with the existing method of simply comparing with the standard ultrasonic signal, the precast concrete component strength detection method provided by the present invention can accurately identify the defect degree of the precast concrete component and improve the accuracy of the strength detection of the precast concrete component. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a computer-aided precast concrete component strength detection method provided by one embodiment of the present invention;

[0045] Figure 2 is a flowchart of obtaining suspected defect components provided by one embodiment of the present invention;

[0046] Figure 3 This is a flowchart of obtaining a suspected defect interval provided by an embodiment of the present invention;

[0047] Figure 4 This is a flow chart for obtaining defect degree representation provided by one embodiment of the present invention;

[0048] Figure 5 is a diagram of a correction process for defect degree representation provided by one embodiment of the present invention;

[0049] Figure 6 This is a flow chart for obtaining the defect degree of a detection point provided by one embodiment of the present invention;

[0050] Figure 7 A computer-aided precast concrete component strength detection method provided by one embodiment of the present invention further includes a flowchart of the steps;

[0051] Figure 8 This is a flow chart for obtaining the degree of defects of a detection surface provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To further illustrate the technical means and effects employed by the present invention to achieve its intended objectives, the following detailed description of the specific embodiments, structures, features, and effects of the present invention is provided in conjunction with the accompanying drawings and preferred embodiments. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this invention pertains. The data and information collected in this application were obtained with full consent and authorization.

[0054] This embodiment provides a computer-aided strength testing method for precast concrete components, suitable for performing strength testing on precast concrete components. This embodiment does not specifically limit the structural form of the precast concrete components; they can be rectangular parallelepiped structures, other structures such as cylinders, or other special or irregular shapes. This embodiment uses the most common rectangular parallelepiped structure (a cube is a special case of a rectangular parallelepiped) as an example.

[0055] Select an ultrasonic detector. Instrument selection: Select an appropriate ultrasonic detector based on the inspection requirements and component characteristics to ensure stable instrument performance and accuracy. Before use, inspect the ultrasonic detector, including battery level and instrument parameter settings, to ensure proper operation. Also, check the transducer's performance, such as frequency and sensitivity, to ensure it is in good condition. Ultrasonic testing: Based on the component's shape, select a representative and easily inspectable surface as the inspection surface. The cast side of the component is generally preferred. In this embodiment, any point on the inspection surface is selected as the inspection point. The location of this inspection point on the inspection surface is determined based on actual needs. The center of the inspection surface can be selected as the inspection point for ultrasonic testing. Before testing, clean the surface of the component to be tested to remove slurry, oil, loose layers, and debris. If necessary, polish with a grinding wheel or sandpaper to smooth the surface and ensure good contact between the ultrasonic probe and the component surface. Set relevant testing parameters, such as sampling frequency, gain, and acoustic time reading mode, and then perform ultrasonic testing. Use the ultrasonic instrument's built-in memory or an external USB flash drive to store raw waveform data. In addition, if multiple precast concrete components from the same batch are to be inspected, after ultrasonic testing of one component is completed, the probe is moved to another uninspected component and the same steps are repeated until ultrasonic testing of all components from the same batch is completed.

[0056] It should be understood that in the actual production process of precast concrete components, accidental factors such as fluctuations in casting speed and mechanical vibration deviations may cause different types of defects in the components, such as voids, cracks, and material unevenness. The presence of these defects will change the characteristics of the ultrasonic echo signal, resulting in certain differences between the ultrasonic echo signals of different defects. For example, voids will reduce the signal amplitude and distort the waveform, the appearance of cracks will lead to energy attenuation and phase changes, and material unevenness will change the wave velocity and frequency characteristics.

[0057] This embodiment predetermines a detection duration, which is determined by the size of the precast concrete component. Larger precast concrete components require longer detection times. Based on the above process, the actual ultrasonic echo signal at the detection point of the precast concrete component is obtained. The duration of the actual ultrasonic echo signal is the detection duration. It should be understood that the duration of all ultrasonic echo signals referred to in this embodiment refers to the detection duration.

[0058] like Figure 1 As shown, the computer-aided precast concrete component strength detection method provided in this embodiment includes the following steps:

[0059] Step S1: decomposing the actual ultrasonic echo signal of the inspection point of the precast concrete component into multiple principal components, and determining the suspected defect component therefrom;

[0060] Step S2: determining a suspected defect interval in the suspected defect component, and obtaining a defect degree representation of the suspected defect component based on the duration of the suspected defect interval, the position of the suspected defect interval in the suspected defect component, and the defect condition;

[0061] Step S3: The characteristic values ​​and defect degree expressions of each suspected defect component are integrated to obtain the defect degree of the detection point.

[0062] The specific implementation process of each step is described below with reference to the accompanying drawings.

[0063] Step S1: decomposing the actual ultrasonic echo signal of the inspection point of the precast concrete component into multiple principal components, and determining the suspected defect component therefrom.

[0064] The actual ultrasonic echo signal from the inspection point of the precast concrete component is decomposed into multiple principal components. In one exemplary embodiment, principal component analysis is used to decompose the actual ultrasonic echo signal into multiple principal components, and the eigenvalue of each principal component is obtained. The size of the eigenvalue directly reflects the amount of original data information contained in the corresponding principal component. Each principal component is sorted in descending order of eigenvalue.

[0065] After obtaining multiple principal components, the suspected defect component is determined therefrom. In an exemplary embodiment, Figure 2 As shown, a specific process for determining the suspected defect component is given as follows:

[0066] Step S1-1: Determine the similarity between each principal component and a reference ultrasonic echo signal.

[0067] A reference ultrasonic echo signal is preset, which represents the ultrasonic echo signal of a non-defective precast concrete component. That is, the reference ultrasonic echo signal is the ultrasonic echo signal of a non-defective precast concrete component, and is used as a standard for comparison with each principal component.

[0068] In an exemplary embodiment, a prefabricated concrete component without defects is obtained in advance and ultrasonically inspected, and the obtained ultrasonic echo signal is used as the reference ultrasonic echo signal. The duration of the reference ultrasonic echo signal is the above-mentioned inspection duration.

[0069] As another embodiment, since it is usually the most common situation for precast concrete components to be free of defects, the number of precast concrete components with various defects is relatively small compared to the number of precast concrete components without defects. Therefore, within a historical period, ultrasonic echo signals of a large number of precast concrete components are obtained, and the duration of each ultrasonic echo signal is the detection duration. In order to improve the accuracy and reliability of the reference ultrasonic echo signal, the more precast concrete components involved, the better, that is, the more ultrasonic echo signals of precast concrete components obtained, the better. It should be understood that the large number of precast concrete components here and the precast concrete components to be defect-detected are produced under the same conditions, that is, the material ratio, production process and curing conditions are the same, to ensure data reliability.

[0070] The similarity between any two ultrasonic echo signals from a large number of precast concrete components is obtained. In this embodiment, the DTW (Dynamic Time Warping) distance between any two ultrasonic echo signals from the large number of precast concrete components is obtained. Since the DTW distance is inversely proportional to the similarity, the larger the DTW distance, the smaller the similarity. Therefore, negative correlation normalization processing is performed on the DTW distance (the negative correlation normalization in this embodiment can be implemented as: exp(-x), where x is the object to be negatively correlated and exp is an exponential function with the natural constant e as the base). The result obtained is the similarity between the two, thereby obtaining the similarity between any two ultrasonic echo signals.

[0071] Based on the similarity between any two ultrasonic echo signals, a clustering algorithm (such as the K-means clustering algorithm, where the K value can be manually set or obtained using the elbow method) is used to divide the ultrasonic echo signals of the large number of precast concrete components into several clusters. The similarity between any two ultrasonic echo signals within each cluster is high, while the similarity between ultrasonic echo signals in different clusters is low. Since defect-free precast concrete components are generally the most common, the number of ultrasonic echo signals contained in each cluster is counted, and the cluster with the largest number is selected as the representative cluster, which can represent the normal ultrasonic echo signals of precast concrete components, that is, the ultrasonic echo signals of defect-free precast concrete components. Finally, the mean echo signal amplitude of all ultrasonic echo signals in the representative cluster at the same time is calculated as the representative amplitude at that time point, thereby obtaining the representative amplitude at each time point. The representative amplitude at each time point constitutes the reference ultrasonic echo signal.

[0072] It should be understood that this embodiment maps all involved ultrasonic echo signals and principal components to the same time scale. Since all ultrasonic echo signals have the same duration, the start and end times of all ultrasonic echo signals are the same. Therefore, each moment in each ultrasonic echo signal and each principal component is essentially a data point, and the sequence number of each moment is equivalent to the sequence number of each data point. For example, the start time represents the first data point, the second time represents the second data point, and so on, and the end time represents the last data point. This unifies the meaning of each moment and each data point, facilitating understanding.

[0073] The similarity between each principal component and a reference ultrasonic echo signal is obtained. In an exemplary embodiment, the DTW distance between each principal component and the reference ultrasonic echo signal is obtained, and then negative correlation normalization processing is performed to obtain the similarity between each principal component and the reference ultrasonic echo signal.

[0074] Step S1-2: Determine the principal components other than the one with the greatest similarity as suspected defect components.

[0075] The maximum similarity between each principal component and the reference ultrasonic echo signal is determined. The higher the similarity, the more normal the corresponding principal component is, and the less likely it is to contain a defect. Therefore, the maximum similarity and the corresponding principal component are determined. Principal components other than the one with the maximum similarity are then identified as suspected defect components for use in the next step of data processing.

[0076] Step S2: Determine the suspected defect interval in the suspected defect component, and obtain the defect degree representation of the suspected defect component based on the duration of the suspected defect interval, the position in the suspected defect component, and the defect condition.

[0077] The suspected defect interval in the suspected defect component is determined. The suspected defect interval in the suspected defect component indicates the location of the defect in the precast concrete component. Different locations of the suspected defect interval in the suspected defect component indicate different degrees of defect in the precast concrete component. The closer the suspected defect interval is to the middle of the suspected defect component, the more likely it is that the defect is in the middle of the precast concrete component. Furthermore, the duration of the suspected defect interval also reflects different degrees of defect. The longer the suspected defect interval, the higher the degree of defect.

[0078] First, determine the suspected defect interval in the suspected defect component, such as Figure 3 As shown, a specific process of obtaining the suspected defect interval is given as follows:

[0079] Step S2-1: Determine a data difference sequence corresponding to the suspected defect component, where the data difference sequence includes data differences at each moment between the suspected defect component and the reference ultrasonic echo signal.

[0080] For any suspected defect component, obtain the data difference between the suspected defect component and the reference ultrasonic echo signal at each moment. Specifically, calculate the difference between the data at the first moment in the suspected defect component and the data at the first moment in the reference ultrasonic echo signal, where the data difference is the absolute value of the difference between the data. Calculate the difference between the data at the second moment in the suspected defect component and the data at the second moment in the reference ultrasonic echo signal. Calculate the difference between the data at the third moment in the suspected defect component and the data at the third moment in the reference ultrasonic echo signal. And so on, until the difference between the data at the last moment in the suspected defect component and the data at the last moment in the reference ultrasonic echo signal is calculated. Thus, the data differences at each moment are obtained, and the data differences are sorted according to the order of each moment to form a data difference sequence corresponding to the suspected defect component.

[0081] Step S2-2: Divide the data difference sequence into several sub-segments according to the data differences.

[0082] Based on the data differences between each data point, the data difference sequence is segmented to obtain several subsegments. The principle of segmentation is: data differences within the same subsegment are at the same level, with similar values; data differences between different subsegments are at different levels, with larger differences in values. When this segmentation principle is met, implementers can select an appropriate algorithm from existing segmentation algorithms. In one exemplary embodiment, the PELT (Pruned Exact Linear Time) algorithm is used. The PELT algorithm is widely used to identify mutation points in time series data. Its core concept is to decompose time series data into multiple subsequences and identify mutation points by calculating the degree of change in each subsequence. Its advantage is that it can accurately determine the location of mutation points, thereby using mutation points as segmentation points to segment the time series data. Therefore, the PELT algorithm is used to process the data difference sequence to obtain segmentation points, thereby dividing it into several subsegments.

[0083] Step S2-3: Compare the data difference mean of each sub-segment with a preset difference threshold, and obtain the time interval of the sub-segment corresponding to the difference greater than the preset difference threshold.

[0084] Each subsegment contains several data differences. The average of these data differences is calculated to obtain the mean data difference value for each subsegment. To facilitate data processing, the mean data difference values ​​for each subsegment need to be normalized. The normalization method can be a sigmoid function. The mean data difference values ​​for each subsegment in the following text are all normalized results.

[0085] A difference threshold is preset, which is used to determine whether the mean difference of the data of each sub-segment is high. The value range of the preset difference threshold is 0-1, and the specific value is set according to the actual judgment needs. In this embodiment, 0.6 is used as an example.

[0086] Compare the mean data difference of each subsegment with a preset difference threshold to obtain the mean data difference that exceeds the preset difference threshold. A mean data difference that exceeds the preset difference threshold indicates a large mean data difference, that is, a large difference from the reference ultrasonic echo signal, and a high probability of a defect. Therefore, the subsegments corresponding to the mean data difference that exceeds the preset difference threshold are obtained, and the time intervals of these subsegments are obtained. The time interval is essentially the data point interval, that is, the range from the number of data points to the number of data points.

[0087] Step S2-4: Obtain the suspected defect interval in the suspected defect component according to the time interval.

[0088] The obtained time intervals are mapped to suspected defect components, which are then divided to obtain data intervals corresponding to each time interval in the suspected defect component. Each data interval is a suspected defect interval. Each suspected defect interval indicates a significant difference from the reference ultrasonic echo signal and a high probability of a defect.

[0089] For any suspected defect interval, the position of the suspected defect interval in the suspected defect component is obtained, specifically by obtaining the time interval between the suspected defect interval and the middle moment of the suspected defect component. In an exemplary embodiment, the time intervals between each moment in the suspected defect interval and the middle moment of the suspected defect component are obtained, and then the minimum time interval is obtained as the time interval between the suspected defect interval and the middle moment of the suspected defect component. It should be understood that if the middle moment of the suspected defect component is within a suspected defect interval, the time interval between the suspected defect interval and the middle moment of the suspected defect component is 0. The shorter the time interval between the suspected defect interval and the middle moment of the suspected defect component, the closer the suspected defect interval is to the middle position of the suspected defect component, the more likely it is to represent a defect characteristic in the middle of the component, and the higher the degree of defect.

[0090] Based on the duration of the suspected defect interval, the duration ratio of the suspected defect interval is obtained. In one exemplary embodiment, the duration of the suspected defect component is obtained, and the ratio of the duration of the suspected defect interval to the duration of the suspected defect component is calculated as the duration ratio of the suspected defect interval. The larger the duration ratio of the suspected defect interval, the higher the degree of defect.

[0091] Obtain the defect condition of the suspected defect interval, where the defect condition is specifically the mean value of the data difference of the suspected defect interval.

[0092] like Figure 4 As shown, a specific process for obtaining the defect degree of the suspected defect component is given as follows:

[0093] Step S2-5: Obtain the defect degree performance impact index of each suspected defect interval based on the time interval, duration ratio, and data difference mean corresponding to each suspected defect interval.

[0094] For any suspected defect interval, the defect degree performance impact index of the suspected defect interval is obtained based on the time interval between the suspected defect interval and the middle moment of the suspected defect component, the duration ratio of the suspected defect interval and the mean value of the data difference of the suspected defect interval. The shorter the time interval between the suspected defect interval and the middle moment of the suspected defect component, that is, the closer the suspected defect interval is to the center of the precast concrete component, the greater the duration ratio of the suspected defect interval, and the greater the mean value of the data difference of the suspected defect interval, the greater the impact on the strength of the precast concrete component, and the greater the defect degree performance impact index of the suspected defect interval. Therefore, the defect degree performance impact index of the suspected defect interval is inversely proportional to the time interval between the suspected defect interval and the middle moment of the suspected defect component, and is directly proportional to the duration ratio of the suspected defect interval and the mean value of the data difference of the suspected defect interval. In an exemplary embodiment, a specific quantitative method for the defect degree performance impact index is given as follows:

[0095] ;

[0096] in, It represents the defect degree performance impact index of the cth suspected defect interval in the fth suspected defect component, represents the time interval between the cth suspected defect interval in the fth suspected defect component and the middle moment of the fth suspected defect component, represents the duration of the cth suspected defect interval in the fth suspected defect component, represents the duration of the fth suspected defect component, represents the duration ratio of the cth suspected defect interval in the fth suspected defect component, Represents the mean of the data difference of the cth suspected defect interval in the fth suspected defect component.

[0097] Step S2-6: Fusing the defect degree performance influencing indicators of each suspected defect interval of the suspected defect component to obtain the defect degree performance of the suspected defect component.

[0098] The defect degree performance influencing indicators of each suspected defect interval of the f-th suspected defect component are integrated. Specifically, the average value of the defect degree performance influencing indicators of each suspected defect interval of the f-th suspected defect component is calculated. The result obtained is the defect degree performance of the f-th suspected defect component, thereby obtaining the defect degree performance of each suspected defect component.

[0099] For a suspected defect component containing multiple suspected defect intervals, the influence of the continuous distribution of multiple suspected defect intervals on the defect degree performance of the suspected defect component should also be considered. Therefore, after obtaining the defect degree performance of each suspected defect component, that is, before step S3, as shown in FIG. Figure 5 As shown, the strength testing method for precast concrete components also includes the following defect degree expression correction process:

[0100] Step S2-7: Determine the average time interval between every two adjacent suspected defect intervals in the suspected defect component.

[0101] Obtain the time interval between each two adjacent suspected defect intervals in the f-th suspected defect component, and calculate the average of these time intervals as the average time interval corresponding to the f-th suspected defect component. It should be understood that this average time interval is greater than 0. The average time interval corresponding to the f-th suspected defect component represents the overall time intervals between suspected defect intervals in the f-th suspected defect component. The smaller the average time interval, the more continuous the distribution of adjacent suspected defect intervals, and the greater the degree of defect ultimately reflected.

[0102] Step S2-8: Correct the defect degree of the suspected defect component according to the correction coefficient of the suspected defect component.

[0103] The correction coefficient for the fth suspected defect component is obtained based on the average time interval corresponding to the fth suspected defect component. The correction coefficient is inversely proportional to the average time interval. Based on the correction coefficient for the fth suspected defect component, the defect degree of the fth suspected defect component is corrected. In an exemplary embodiment, a specific quantitative method for correction is given as follows:

[0104] ;

[0105] in, It represents the defect degree of the fth suspected defect component after correction, represents the defect degree of the f-th suspected defect component, that is, the defect degree of the f-th suspected defect component before correction, represents the average time interval corresponding to the fth suspected defect component, Represents the correction coefficient of the fth suspected defect component.

[0106] The above process is used to obtain the corrected defect degree representation of each suspected defect component. It should be understood that, as other embodiments, the above process of correcting the defect degree representation of the suspected defect component may not be performed, or when the suspected defect component contains only one suspected defect interval, the above process of correcting the defect degree representation of the suspected defect component does not need to be performed.

[0107] Step S3: The characteristic values ​​and defect degree expressions of each suspected defect component are integrated to obtain the defect degree of the detection point.

[0108] Each suspected defect component characterizes the overall characteristics of the actual ultrasonic echo signal of the precast concrete component from different perspectives. The characteristic values ​​of each suspected defect component are different. The larger the characteristic value, the more information the suspected defect component contains in the original data, and the stronger the data summarization ability. Therefore, the characteristic value of each suspected defect component and the defect degree expression are integrated to obtain the defect degree of the detection point. In an exemplary embodiment, Figure 6 As shown, a specific process for obtaining the defect degree of the detection point is given as follows:

[0109] Step S3-1: Obtain the influence weight of each suspected defect component according to the characteristic value of each suspected defect component.

[0110] Based on the eigenvalue of each suspected defect component, an influence weight of each suspected defect component is obtained, where the influence weight is proportional to the eigenvalue, and the sum of the influence weights of all suspected defect components is 1. In an exemplary embodiment, the sum of the eigenvalues ​​of all suspected defect components is calculated, and then the ratio of the eigenvalue of each suspected defect component to the sum is calculated, and the obtained ratio is used as the influence weight of each suspected defect component.

[0111] Step S3-2: Based on the influence weight of each suspected defect component, perform weighted summation on the defect degree performance of each suspected defect component to obtain the defect degree of the detection point.

[0112] The calculation formula for the defect degree of the inspection point is as follows:

[0113] ;

[0114] in, Indicates the degree of defect at the inspection point, and F indicates the number of suspected defect components; Represents the influence weight of the f-th suspected defect component.

[0115] Through the above process, the defect degree of the detection point on the precast concrete component is obtained. The defect degree of the detection point is used to indicate the strength of the precast concrete component. Therefore, the strength of the precast concrete component can be tested based on the defect degree of the detection point. The strength is inversely proportional to the defect degree. The higher the defect degree, the lower the strength.

[0116] The above process only performs ultrasonic testing on one inspection point on one inspection surface of the precast concrete component. However, since the defects at different locations on the same inspection surface may appear differently and the degree of defects may be different, only relying on one inspection point may lead to the omission of some defects. Therefore, determining the degree of defects of the precast concrete component by only using the ultrasonic results of one inspection point has certain limitations and cannot fully represent the overall degree of defects of the precast concrete component. As a better implementation method, after obtaining the degree of defects of the inspection point, such as Figure 7 As shown, the method for testing the strength of precast concrete components further includes the following steps:

[0117] Step S4: Divide the inspection surface of the precast concrete component into multiple inspection areas, determine a detection point in each inspection area, and determine the defect degree of each inspection area.

[0118] The inspection surface of the precast concrete component is divided into multiple inspection areas. The division of the inspection areas is determined based on actual needs. In one exemplary embodiment, if the inspection surface is rectangular, the rectangle is divided into multiple inspection areas with equal areas using a number of horizontal and vertical dividing lines. A detection point is defined in each inspection area. The position of the detection point within the corresponding inspection area is determined based on actual needs. To ensure detection reliability, the detection point is the center point of the corresponding inspection area.

[0119] The defect degree of each detection point is obtained through step S3. Since each detection point corresponds to each detection area one by one, the defect degree of each detection point is also the defect degree of each detection area, and thus the defect degree of each detection area is obtained.

[0120] Step S5: According to the defect degree of each inspection area, the defect area is screened out from the inspection area.

[0121] A defect level threshold is preset and compared with the defect level of each inspection area to determine the defect level with the larger value. The defect level threshold ranges from 0 to 1, and the specific value is set based on actual judgment needs. For example, if the judgment is more stringent, the defect level threshold can be set to a smaller value to allow more inspection areas to meet the screening criteria. In this embodiment, the defect level threshold is set to 0.5 as an example.

[0122] Compare the defect degree of each detection area with the defect degree threshold, obtain the defect degree greater than or equal to the defect degree threshold, and determine the detection area corresponding to the defect degree greater than or equal to the defect degree threshold as the defect area, thereby completing the screening of the defect area from the detection area.

[0123] Step S6: clustering the defective regions according to the defect degree of each defective region to obtain a number of clusters.

[0124] Since the types of defects in different defect areas may be different, different types of defects have different characteristics, reflecting different degrees of defects. For example, some cracks and voids have a certain extension and continuity, while others only appear in a small local area, so the distribution characteristics of the defects also need to be considered.

[0125] According to the size of the defect degree of each defect area, the defect areas are clustered to obtain several clusters, thereby determining the number of clusters. Through clustering, the defect degrees of all defect areas contained in the same cluster are close to the same, and the defect degrees between different clusters vary greatly. It should be understood that the clustering method used for clustering here is set according to actual needs, such as: for example, the K-means clustering algorithm, the K value can be set manually or obtained by the elbow method; or, other unsupervised clustering algorithms are used; or, several numerical intervals are preset, and according to the numerical value of the defect degree of each defect area, the defect degree of each defect area is divided into the corresponding numerical interval, and the numerical interval containing the defect degree is determined as each cluster, then the number of numerical intervals containing the defect degree is the number of clusters.

[0126] Step S7: Obtain the defect degree of the inspection surface according to the aggregation degree of each cluster, the mean defect degree of each cluster, the number of clusters, and the proportion of the number of defective areas.

[0127] To determine the degree of clustering for each cluster, in one exemplary embodiment, for any cluster, the distance between the detection points of any two defective regions in the cluster is obtained, and the average of the distances between the detection points of all two defective regions is calculated. It should be understood that if a cluster contains only one defective region, the average distance value for the cluster is 0. A smaller average distance value indicates a smaller distance between the detection points of any two defective regions in the cluster, and a better clustering effect, i.e., a higher degree of clustering. Therefore, the degree of clustering is inversely proportional to the average distance value for the cluster.

[0128] The higher the aggregation degree of the cluster, the more concentrated the distribution of the defect areas in the cluster is, and the more likely it is that there are concentrated and continuously distributed defects in the precast concrete component. Therefore, the higher the defect degree of the detection surface is.

[0129] Therefore, in order to facilitate subsequent calculations, the clustering degree weight of each cluster is obtained based on the average distance value corresponding to each cluster. Among them, the clustering degree weight represents the importance of the clustering degree. The higher the clustering degree, the higher the clustering degree weight, and the sum of the clustering degree weights of each cluster is 1. In an exemplary embodiment, the distance average values ​​corresponding to each cluster are negatively normalized, and then the sum of the negatively normalized distance average values ​​is calculated. Finally, the ratio of the negatively normalized distance average values ​​corresponding to each cluster to the sum is calculated as the clustering degree weight of each cluster, so that not only the distance average values ​​corresponding to each cluster are inversely proportional to the clustering degree weight of each cluster, but also the sum of the clustering degree weights of each cluster is 1.

[0130] According to the degree of clustering of each cluster, the mean value of the defect degree of each cluster, the number of clusters and the proportion of defect areas, the defect degree of the detection surface is obtained. In an exemplary embodiment, Figure 8 As shown, a specific process for obtaining the defect degree of the detection surface is given as follows:

[0131] Step S7-1: The aggregation degree and defect degree mean of each cluster are integrated to obtain the overall performance of cluster defects.

[0132] For any cluster, the defect degree of each defect area in the cluster is obtained, and the average value is calculated to obtain the mean defect degree of the cluster.

[0133] The aggregation degree weight of each cluster is used as the weight of the mean defect degree of each cluster. The mean defect degree of each cluster is weighted and summed. The result is the overall performance of cluster defects, which realizes the fusion analysis of each cluster. The calculation formula is as follows:

[0134] ;

[0135] in, Indicates the overall performance of cluster defects, represents the aggregation weight of the i-th cluster, represents the mean defect degree of the i-th cluster, and I represents the number of clusters.

[0136] Step S7-2: Obtain the defect degree of the inspection surface based on the overall performance of the cluster defects, the number of clusters, and the proportion of defective areas.

[0137] Obtain the number of defective areas on the inspection surface and the total number of inspection areas on the inspection surface. Calculate the ratio of the number of defective areas to the total number of inspection areas as the defective area ratio. A larger defective area ratio indicates a greater number of defective areas with defects and a higher degree of defectivity on the inspection surface. A higher overall cluster defect performance indicates a higher degree of defectivity on the inspection surface. A greater number of clusters indicates a more uneven degree of defectivity on the inspection surface and a higher degree of defectivity on the inspection surface. Therefore, the degree of defectivity on the inspection surface is directly proportional to the overall cluster defect performance, the number of clusters, and the defect area ratio.

[0138] In an exemplary embodiment, the number of clusters is normalized, such as by a sigmoid function. A specific quantitative method for the degree of defects on the inspection surface is given below:

[0139] ;

[0140] in, Indicates the degree of defects on the inspection surface. represents the number of clusters after normalization, and G represents the proportion of defective areas.

[0141] Through the above process, the defect degree of a detection surface on the precast concrete component is obtained, and the strength of the precast concrete component can be tested based on the defect degree of the detection surface. The strength is inversely proportional to the defect degree, and the higher the defect degree, the lower the strength.

[0142] If a precast concrete component has multiple detectable test surfaces, performing ultrasonic testing on only a single test surface to determine the strength of the precast concrete component based on the degree of defects on the test surface still has certain limitations and cannot fully reflect the defect characteristics of the precast concrete component. Therefore, as a more preferred embodiment, after determining the degree of defects on the test surface, the precast concrete component strength testing method further includes: using all surfaces of the precast concrete component as test surfaces, determining the degree of defects on each test surface according to the above process, and then calculating the average value of the defect degrees on each test surface. The obtained average value is used as the final defect degree of the precast concrete component.

[0143] The strength of the precast concrete component is obtained based on the final defect level, and the strength is inversely proportional to the final defect level. In an exemplary embodiment, the difference between the value 1 and the final defect level is used as the strength of the precast concrete component.

[0144] In an exemplary embodiment, a strength threshold is preset to determine whether the strength of a precast concrete component is qualified. The strength threshold has a numerical range of 0-1, and its specific value is set according to the judgment requirements. If the judgment is more stringent, the strength threshold can be set to a larger value. In this embodiment, 0.6 is used as an example. If the strength of the precast concrete component is greater than the strength threshold, the strength of the precast concrete component is determined to be qualified. Otherwise, the strength of the precast concrete component is determined to be unqualified.

[0145] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0146] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A computer-aided method for detecting the strength of precast concrete components, characterized in that: include: Decomposing the actual ultrasonic echo signal of the inspection point of the precast concrete component into multiple principal components, and determining the suspected defect component therefrom; Determining a suspected defect interval in the suspected defect component, and obtaining a defect degree representation of the suspected defect component based on a duration of the suspected defect interval, a position in the suspected defect component, and a defect condition; The process of obtaining the suspected defect interval includes: determining a data difference sequence corresponding to the suspected defect component, the data difference sequence including data differences at each moment between the suspected defect component and the reference ultrasonic echo signal; dividing the data difference sequence into a plurality of sub-segments based on the data differences; comparing the mean value of the data difference of each sub-segment with a preset difference threshold to obtain a time interval corresponding to a sub-segment greater than the preset difference threshold; and obtaining the suspected defect interval in the suspected defect component based on the time interval; The position of the suspected defect interval in the suspected defect component is specifically: the time interval between the suspected defect interval and the middle moment of the suspected defect component; The defect condition of the suspected defect interval is specifically: the mean value of the data difference of the suspected defect interval; The process of obtaining the defect degree performance includes: determining the duration ratio of each suspected defect interval based on the duration of each suspected defect interval; obtaining a defect degree performance influencing index of each suspected defect interval based on the time interval, duration ratio and data difference mean corresponding to each suspected defect interval; the defect degree performance influencing index is inversely proportional to the time interval and directly proportional to the duration ratio and data difference mean; fusing the defect degree performance influencing index of each suspected defect interval of the suspected defect component to obtain the defect degree performance of the suspected defect component; fusing the characteristic value and defect degree performance of each suspected defect component to obtain the defect degree of the detection point, and the defect degree is used to indicate the strength of the precast concrete component; Among them, the process of obtaining the defect degree of the detection point includes: obtaining the influence weight of each suspected defect component according to the eigenvalue of each suspected defect component, the influence weight is proportional to the eigenvalue, and the sum of the influence weights of all suspected defect components is 1; according to the influence weight of each suspected defect component, the defect degree performance of each suspected defect component is weightedly summed to obtain the defect degree of the detection point.

2. The computer-aided precast concrete component strength detection method according to claim 1, wherein: The process of determining the suspected defect component includes: Determining the similarity between each of the principal components and a reference ultrasonic echo signal; the reference ultrasonic echo signal represents an ultrasonic echo signal of a non-defective precast concrete component; The principal components other than the one with the greatest similarity are determined as suspected defect components.

3. The computer-aided strength testing method for precast concrete components according to claim 1, wherein: Before obtaining the defect degree of the detection point, the precast concrete component strength detection method further includes: Determine an average time interval between each two adjacent suspected defect intervals in the suspected defect component; The defect degree of the suspected defect component is corrected according to the correction coefficient of the suspected defect component; the correction coefficient is obtained from the average time interval, and the correction coefficient is inversely proportional to the average time interval.

4. The computer-aided precast concrete component strength detection method according to claim 1, wherein: After obtaining the defect degree of the detection point, the precast concrete component strength detection method further includes: Dividing the inspection surface of the precast concrete component into a plurality of inspection areas, determining a detection point in each inspection area, and determining the degree of defects in each inspection area; According to the degree of defects in each inspection area, defective areas are screened from the inspection areas; According to the defect degree of each defect area, the defect areas are clustered to obtain several clusters; The defect degree of the inspection surface is obtained according to the aggregation degree of each cluster, the mean defect degree of each cluster, the number of clusters and the proportion of the number of defective areas.

5. The computer-aided precast concrete component strength detection method according to claim 4, wherein: The defect degree of the inspection surface is obtained according to the aggregation degree of each cluster, the mean defect degree of each cluster, the number of clusters, and the proportion of the number of defective areas, including: The aggregation degree and defect degree mean of each cluster are integrated to obtain the overall performance of cluster defects; The defect degree of the inspection surface is obtained according to the overall performance of the cluster defects, the number of clusters, and the proportion of the number of defective areas; the defect degree of the inspection surface is proportional to the overall performance of the cluster defects, the number of clusters, and the proportion of the number of defective areas.

6. The computer-aided precast concrete component strength detection method according to claim 4, wherein: The detection point is the center point of the corresponding detection area; The process of obtaining the degree of aggregation includes: The distance between the detection points of any two defect areas in the cluster is obtained, and the average value is calculated to obtain the aggregation degree of the cluster, and the aggregation degree is inversely proportional to the average value.

7. The computer-aided precast concrete component strength detection method according to claim 4, wherein: After obtaining the defect degree of the detection surface, the precast concrete component strength detection method further includes: All surfaces of the precast concrete component are used as test surfaces, and the average of the defect levels of the test surfaces is calculated as the final defect level of the precast concrete component; The strength of the precast concrete component is obtained according to the final defect degree, and the strength is inversely proportional to the final defect degree.

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