Method for measuring dynamic properties of apparent distress of concrete works

Abstract

The present application belongs to the field of nondestructive testing and metrology of concrete engineering, and discloses a method for measuring dynamic characteristics of apparent diseases of concrete engineering, which comprises the following steps: after a dry-state thermal response pre-scanning is performed on the surface of a structure to be measured to generate pollution compensation parameters, a volatile liquid is applied to the surface; an acoustic excitation is synchronously applied, and a time sequence of thermal images is collected; then, mode switching is performed according to the environmental humidity; when the humidity is low, the compensated and corrected cooling rate, temperature fluctuation and acoustic-thermal correlation are analyzed, or when the humidity is high, the local temperature rise caused by sound is analyzed; finally, a quantitative index representing the dynamic activity of the disease is generated. The present application establishes a dynamic thermal physical response test analysis path under controlled physical excitation, and improves the diagnosis dimension from the traditional static geometric form to the direct quantification of the microscopic dynamic mechanical behavior of the concrete structure, thereby establishing a direct physical causal correlation between the observable thermal signal and the internal structural risk, and providing a decision basis for the safety evaluation of the structure.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for measuring the dynamic characteristics of apparent defects of concrete engineering, belonging to the field of non-destructive testing and metrology of concrete engineering. BACKGROUND

[0002] Currently, identifying and measuring structural apparent defects, especially crack defects, is a basic work to ensure structural safety. In existing technical practices, high-resolution images of the surface to be measured are usually obtained using optical imaging equipment, and then image processing algorithms are used to automatically identify and quantify the geometric parameters such as length, width and orientation of the cracks. Compared with traditional manual contact measurement, this method has improved work efficiency.

[0003] However, when the technology based on accurate geometric parameter measurement is applied to long-term health monitoring of large concrete engineering facilities, the system outputs a large amount of accurate data about the geometric size of the defects in the field engineering practice. However, there is a lack of direct physical correlation between these data and the local mechanical state of the concrete structure where the defects are located. This makes it difficult for technical personnel to effectively classify and sort the massive geometric data in terms of risk level. The entire technical approach is based on the premise that the geometric form of the crack is directly related to its mechanical risk. However, this premise does not hold true in engineering scenarios. Therefore, even if the original path is followed, such as by using more complex image algorithms to pursue higher geometric measurement accuracy, the above technical problems cannot be solved, and instead, more data that is difficult to effectively interpret will be generated. This is because the static geometric form is not a direct representation of the real mechanical state of the structure. The potential risk of the concrete structure depends more on its dynamic mechanical behavior under actual load than on its apparent damage form at a certain static moment.

[0004] Therefore, a long-standing technical bias exists in this field: focusing on obtaining more precise geometric parameters by improving image resolution and algorithm accuracy to indirectly assess structural risk. The inventors recognize that this approach has inherent limitations, as static geometry is not a direct representation of the actual mechanical state of a structure. The potential risk of a concrete structure depends more on its dynamic mechanical behavior under actual loads. Specifically, existing technologies suffer from the following shortcomings: 1. They lack a testing method that can transform the dynamic mechanical behavior of microscopic deformation caused by structural defects under actual loads into a physical signal that can be directly captured by on-site testing equipment; 2. Due to the inability to obtain information on the dynamic mechanical behavior of defects, existing risk assessments largely rely on the empirical correlation between geometric shape and risk level. This correlation lacks direct physical causal support, resulting in limited reliability of the conclusions. Therefore, how to establish a physical process-based testing and analysis method to directly identify and quantify the dynamic mechanical activity of concrete structural defects, thereby providing a more directly physically meaningful decision-making basis for structural safety assessment, is the technical problem this invention aims to solve. Summary of the Invention

[0005] This invention provides a method for measuring the dynamic characteristics of apparent defects in concrete engineering. Its main purpose is to solve the problem that existing technologies cannot obtain the dynamic mechanical behavior of defects through direct physical measurement methods, and can only conduct indirect assessments by analyzing static geometric morphology, resulting in a lack of direct physical basis for risk assessment.

[0006] To achieve the above objectives, the present invention provides a method for measuring the dynamic characteristics of apparent defects in concrete engineering. The method for measuring the dynamic characteristics of apparent defects in concrete engineering includes the following steps:

[0007] S1. Before applying a layer of volatile liquid to the test surface of the concrete structure, a dry surface thermal response pre-scan is performed. A compensation matrix characterizing the contamination distribution on the test surface is generated by applying a sequence of transient thermal excitations to the test surface and acquiring its transient thermal response.

[0008] S2, apply a volatile liquid to the surface to be tested with a preset unit area dose, and during the evaporation of the volatile liquid, use an infrared thermal imaging device to acquire a series of thermal images of the surface to be tested in a continuous time period to form a thermal image time series, and simultaneously apply an acoustic excitation signal with a frequency varying within a preset range to the surface to be tested.

[0009] S3, based on the comparison result of the ambient humidity and the preset threshold, selecting to perform the first mode analysis or the second mode analysis, specifically comprising: when detecting that the ambient humidity is lower than the preset threshold, applying a compensation matrix to perform normalization processing on the temperature value of each pixel in the thermal image time sequence, and performing the first mode analysis on the dynamic thermal feature of the temperature of the target region in the processed thermal image time sequence over time, the first mode analysis comprising: calculating the slope and variance of the temperature change curve of the target region, and determining the mechanical resonance feature by analyzing the acoustic-thermal correlation degree between the temperature dynamic spectrum of the target region and the change trajectory of the instantaneous frequency of the acoustic excitation signal;

[0010] When detecting that the ambient humidity is not lower than the preset threshold, performing acoustic energy pumping on the target region, and performing the second mode analysis on the thermal image time sequence, the second mode analysis comprising analyzing the local temperature rise feature of the target region caused by the acoustic energy pumping.

[0011] S4, generating a quantitative index representing the dynamic activity of the apparent disease based on the result of the first mode analysis or the second mode analysis.

[0012] Preferably, the first mode analysis further comprises: selecting a region with a heat flux gradient less than a preset gradient threshold as a reference region on the surface to be measured; and comparing the slope and variance of the target region with the slope and variance of the reference region calculated according to the processed thermal image time sequence.

[0013] Preferably, the step of generating a quantitative index representing the dynamic activity of the apparent disease is based on the difference between the slope of the target region and the slope of the reference region, the difference between the variance of the target region and the variance of the reference region, and the analysis result of the mechanical resonance feature.

[0014] Preferably, the step of determining the mechanical resonance feature comprises: performing short-time Fourier transform on the sequence of the temperature change over time of the target region to obtain a temperature dynamic spectrum; and determining whether there is an energy-enhanced response frequency band in the temperature dynamic spectrum, and the acoustic-thermal correlation degree between the center frequency of the response frequency band and the change trajectory of the instantaneous frequency of the acoustic excitation signal is not lower than a preset correlation threshold, if there is, then the acoustic-thermal correlation degree is part of the mechanical resonance feature.

[0015] Preferably, when detecting that the ambient humidity is not lower than the preset threshold, performing acoustic energy pumping on the target region comprises the following steps: if it has been determined that there is a response frequency band, performing acoustic excitation on the target region at the center frequency of the response frequency band; and if it has not been determined that there is a response frequency band, performing acoustic excitation on the target region with a wide-band acoustic signal.

[0016] Preferably, the quantification index, i.e. the disease activity index AI, is calculated by the following rule: AI = w_1 · |Δk| + w_2 · |Δ(σ²)| + w_3 · γ_max, wherein AI is the disease activity index, Δk is the difference of the slope of the target region and the reference region, Δ(σ²) is the difference of the variance of the target region and the reference region, γ_max is the maximum value of the acoustic-thermal correlation degree, and w_1, w_2 and w_3 are preset weighting coefficients.

[0017] Preferably, after generating the quantification index, the method further comprises the following steps: for the identified apparent disease, applying a transient thermal pulse on one side thereof; collecting and analyzing the time delay of the temperature response induced by the transient thermal pulse on the other side of the apparent disease, and determining the depth information of the apparent disease based on the time delay.

[0018] Preferably, the method further comprises the following steps: analyzing the temperature distribution uniformity of the reference region in the processed thermal image time series to determine the material degradation state of the location where the apparent disease is located; the analysis of the temperature distribution uniformity comprises calculating the information entropy of each frame of the thermal image of the reference region within a continuous time period, and generating a curve of the information entropy changing with time, and determining the material degradation state according to the morphological features of the curve of the information entropy changing with time.

[0019] Preferably, the volatile liquid is water.

[0020] Preferably, the method further comprises: after identifying and quantifying the apparent disease, using a non-contact detection method based on electrical principle to measure the conductance or impedance characteristics of the target region; and verifying or arbitrating the quantification index based on the measurement result.

[0021] Compared with the prior art, the present application has the following beneficial effects:

[0022] 1. By applying a volatile liquid to the surface to be measured and continuously acquiring thermal image time series during the evaporation process thereof, a test path directly relating the micro-dynamic behavior of the concrete structure to the change of the surface thermophysical characteristics is established. Under the action of the micro-dynamics of the structure, the liquid evaporation process in the disease region will be disturbed, and this disturbance changes the rate and fluctuation characteristics of the temperature changing with time in this region. By analyzing this dynamic thermal characteristic, the dynamic activity of the disease that cannot be distinguished by geometric observation can be identified, and a physical process-based analysis method for evaluating the structure state is provided.

[0023] 2、In the process of acquiring thermal image time series, the acoustic excitation signal with changing frequency is applied to the surface to be tested. Due to the establishment of the physical index of acoustic-thermal correlation, this method can not only identify whether the disease is active, but also identify its sensitivity to specific frequency excitation, providing direct evidence that traditional methods cannot provide, realizing the promotion from yes / no judgment to mechanism analysis; This testing method not only identifies the dynamic activity of the disease, but also further obtains the mechanical resonance characteristics of the disease, so that the judgment of the cause and sensitivity of the disease has specific physical basis.

[0024] 3、In the process of analyzing the dynamic thermal characteristics of the disease area, the same set of thermal image time series can also be used to analyze the background area around the disease. The differences in material physical state, such as density and porosity, will lead to different surface temperature distribution uniformity during the liquid evaporation process; By analyzing the temperature distribution characteristics of the background area thermal image, the material degradation information of the position where the disease is located can be obtained synchronously, so that the dual evaluation of the mechanical behavior of the disease and the state of the matrix material where it is located is completed at the same time in one test operation. BRIEF DESCRIPTION OF DRAWINGS

[0025] Fig. 1 Flow chart of the dual-mode analysis method of the present application based on environmental humidity determination;

[0026] Fig. 2 Comparison chart of acoustic-thermal correlation response characteristics of dynamic and static diseases of the present application;

[0027] Fig. 3 Interaction diagram of application scenarios for technology implementation and decision support of the present application. DETAILED DESCRIPTION

[0028] In order to make the technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described in detail below, but it should be understood that the following specific embodiments are only explanatory descriptions, and are not used to limit the protection scope of the present application.

[0029] A method for measuring the dynamic characteristics of apparent defects of concrete structures, which comprises a set of dynamic thermal physical response test and analysis procedures under controlled physical excitation, mainly including three stages of dry-state surface thermal physical characteristic pre-scanning, wet-state dynamic response excitation and collection, and double-mode analysis and quantification based on environmental conditions; Through the procedure, the dynamic mechanical behavior of the structure under external load, which is difficult to directly observe, is converted into a set of dynamic thermal characteristic parameters that can be objectively measured and quantified, thereby generating a quantitative index representing the dynamic activity of the defect; In the engineering field, the thermal emissivity and hydrophilicity of the surface of the concrete structure to be tested are often uneven due to the random distribution of pollutants such as dust and oil stains caused by long-term exposure, and this unevenness will produce background thermal signals unrelated to the true state of the defect in subsequent tests, thereby affecting the accuracy of the analysis results; To avoid this physical interference, the method first performs a dry-state surface thermal response pre-scanning step before applying the liquid for the main test procedure, which uses a directional light source such as a laser diode or a high-power light-emitting diode (LED) to scan the surface to be tested at a pre-set grid path, and at each grid point, a standardized, millisecond-level transient thermal excitation is applied, while an industrial infrared thermal imager with a temperature measurement accuracy of not less than 0.1℃ and a sampling frequency of not less than 50Hz synchronously records the transient temperature response curve of each point after the micro-thermal pulse, i.e. the heating and cooling process; Given that different types of surface pollutants have different thermal inertia and thermal conductivity, they will produce identifiable thermal texture characteristics; the system generates a compensation matrix corresponding to each pixel of the main thermal image by analyzing the peak temperature or cooling rate of these transient thermal response curves, and each value of the matrix quantifies the expected thermal signal deviation caused by surface pollution, providing a pixel-by-pixel correction basis for subsequent wet-state measurement data; After completing the dry-state pre-scanning and generating the compensation matrix, enter the wet-state dynamic response excitation and collection stage, which uses a standard spraying device to uniformly apply a layer of volatile liquid to the pre-scanned surface to be tested at a pre-set unit area dose, such as 50mL per square meter, and in a specific embodiment, the volatile liquid is water; Within a pre-set time period after spraying, such as 3min to 5min, the infrared thermal imaging device continuously records videos of the target area to obtain a series of time-continuous thermal images, forming a thermal image time sequence; During the collection by the infrared thermal imaging device, a directional acoustic generator synchronously applies an acoustic excitation signal with a frequency within a pre-set range, such as from 10Hz to 500Hz, which changes linearly over time, to the surface to be tested, actively exciting the dynamic response of the structure defect with a controllable external excitation source.

[0030] After obtaining the time series of thermal images, the system enters the analysis and quantification phase, which adopts a set of adaptive analysis mode switching mechanism based on environmental perception to cope with the changes in dominant physical effects under different humidity conditions; the system monitors the real-time environmental parameters through a temperature and humidity sensor, and uses a preset environmental humidity threshold, such as relative humidity 95%, as the basis for mode switching; when the detected environmental humidity is lower than the preset threshold, liquid evaporative cooling is the dominant thermophysical process, and the system executes the first mode analysis, which first calls the compensation matrix generated in the pre-scanning stage to perform frame-by-frame normalization processing on the original temperature values of each pixel in the collected time series of thermal images, to filter out the background noise caused by surface contamination; then, the system selects an area with a heat flux gradient less than a preset gradient threshold as a reference area in the processed time series of thermal images, and extracts the temperature change curve of each pixel or pixel area of the target area to be analyzed; the first mode analysis specifically includes: calculating the slope of the temperature change curve of the target area, which represents the average cooling rate of the area; calculating the variance of the temperature change curve of the target area, which represents the fluctuation amplitude of the temperature change of the area; determining the mechanical resonance characteristics of the target area by analyzing the acoustic-thermal correlation between the temperature dynamic spectrum of the target area and the change trajectory of the instantaneous frequency of the acoustic excitation signal; the acoustic-thermal correlation is determined by calculating the Pearson correlation coefficient between the center frequency trajectory of the response band and the instantaneous frequency trajectory of the acoustic excitation signal; the implementation of this analysis is: performing short-time Fourier transform (STFT) on the temperature change sequence of the target area to obtain its temperature dynamic spectrum, and then determining whether there is an energy-enhanced response band in the temperature dynamic spectrum, and the acoustic-thermal correlation between the center frequency of the response band and the instantaneous frequency trajectory of the acoustic excitation signal is not less than a preset correlation threshold, such as 0.8; finally, the system compares the slope and variance of the target area with the slope and variance of the reference area calculated from the same data source, respectively, to obtain the difference.

[0031] When the ambient humidity is detected to be not lower than a preset threshold, the liquid evaporation process is inhibited, and the system automatically switches to a second mode analysis; in this mode, the function of the acoustic generator is changed from sweep detection to energy pumping, and the system continuously excites the target area with an acoustic signal at a resonance frequency corresponding to a mechanical resonance characteristic that may have been measured in the first mode, or with a wide-band acoustic signal if the resonance characteristic is not determined; accordingly, the analysis target of the infrared thermal imager is also changed from observing the cooling process to capturing the local temperature rise characteristic caused by acoustic energy dissipation; a dynamically active lesion will produce more intense friction at the internal interface under acoustic excitation, resulting in more acoustic energy being converted into heat energy, which appears as a local temperature rise area on the differential thermal image; the second mode analysis extracts this temperature rise signal, which is the same as the excitation signal, from the background thermal noise by lock-in amplification technology, and quantifies its intensity and range; finally, the system generates a quantitative index representing the apparent lesion dynamic activity based on the results of the first mode analysis or the second mode analysis, and the index is a comprehensive technical indicator whose value reflects the degree of dynamic response of the lesion under external load excitation; in the first mode, the index, i.e., the lesion activity index AI, can be calculated by a weighted summation formula: AI = w_1·|Δk| + w_2·|Δ(σ²)| + w_3·γ_max, wherein AI is the lesion activity index; Δk is the difference between the slopes of the target area and the reference area, with units of ℃ / s; Δ(σ²) is the difference between the variances of the target area and the reference area, with units of ℃²; γ_max is the maximum value of the acoustic-thermal correlation degree, which is a dimensionless parameter; w_1, w_2, and w_3 are a set of preset weighting coefficients calibrated according to experimental data; the method can also include analysis of lesion depth information and material degradation state; after generating the quantitative index, for the identified lesion, a precisely directable light source can be used to apply a transient thermal pulse on one side of the lesion, and a high-frame-rate infrared thermal imager is used to collect and analyze the temperature response time delay caused by the thermal pulse passing through the water medium inside the lesion to the other side, and the lesion depth information is determined based on the time delay; at the same time, the temperature distribution uniformity of the reference area around the lesion can be analyzed using the acquired thermal image time series, specifically by calculating the information entropy of each frame of the thermal image in the reference area within a continuous time period, and generating a curve of information entropy change over time, and the material degradation state of the lesion location is determined according to the morphological characteristics of the curve.To further verify the analysis results, the method can further include an independent verification or arbitration step. After completing the analysis based on the thermophysical principle, a handheld reinforced concrete electric conductivity instrument or other equipment can be used to measure the electric conductivity characteristics of the target area. The specific verification or arbitration rules are as follows: when the measured electric conductivity value of the target area is higher than the preset threshold value, it indicates that the disease has penetrated into the internal structure and may touch the steel bar, forming an electrical path. In this case, if the previously generated disease activity index AI value is already high, the electrical measurement result constitutes a verification of high risk. If the AI value is not high, the electrical result will be used as an arbitration basis to upgrade the risk level of the disease. Although the dynamic activity is not strong, it provides a direct path for medium invasion. On the contrary, if the measured electric conductivity value is lower than the preset threshold value, it indicates that it is a surface disease, and its risk level is mainly determined by the AI value. In this way, independent verification or arbitration of the quantitative index is realized.

[0032] Example 1: To illustrate the application of the present application in the field of precise measurement of concrete engineering, in an application of measuring the structural performance of a large cross-sea bridge T-shaped concrete main beam, there are hundreds of apparent diseases with similar geometric width and length on the surface of the bridge. According to the technical method based on optical image analysis, a list containing the geometric parameters of each disease is obtained. However, the maintenance team cannot determine which diseases are surface material shrinkage textures and which are dynamic active diseases with potential risks caused by structural fatigue or overload, making it difficult to develop a focused maintenance plan. To solve this problem, the method claimed in the present application is used for on-site testing. First, under the condition of environmental humidity of 65%, the integrated system carried by the unmanned aerial vehicle is used to perform dry-state surface thermal response pre-scanning on a key measurement area of the bridge main beam. The system uses a laser diode as a thermal excitation source to perform grid scanning on the surface of the measurement area, and simultaneously collects the transient thermal response of each point by an infrared thermal imager to generate a compensation matrix representing the pollution distribution of the surface of the measurement area. Then, the system uniformly sprays water to the measurement area at a dose of 50 mL per square meter, and immediately collects the time sequence of thermal images while applying an acoustic excitation signal with a frequency varying linearly from 10 Hz to 500 Hz to the measurement area by a directional acoustic generator.

[0033] In the collected data, there are two diseases with width of about 0.2mm in the optical image, which are marked as target area A and target area B respectively. The system detects that the current environmental humidity is lower than the preset threshold of 95%, and thus executes the first mode analysis. The system first applies the compensation matrix generated by the dry-state pre-scan to perform pixel-by-pixel normalization processing on the original thermal image time series, which provides a data basis for the subsequent dynamic thermal feature analysis that eliminates the interference of the non-uniformity of the surface thermal physical properties. Then, the system extracts the temperature change curves of the target area A and target area B after processing, and compares them with the temperature change curve of a reference area extracted from a perfect and flat surface in the measurement area. The analysis results show that the temperature change curve of the target area A has a very small difference in slope and variance from the corresponding values of the reference area, and no peak value of the acoustic-thermal correlation degree is found when the acoustic-thermal correlation degree of the temperature dynamic spectrum and the instantaneous frequency of the acoustic excitation signal is analyzed, indicating that the thermal physical behavior is basically consistent with that of the perfect structure surface. In contrast, the absolute value of the slope of the temperature change curve of the target area B is greater than that of the reference area, and the variance is also increased, indicating that the cooling process is faster and more unstable. Further, by performing short-time Fourier transform on the temperature sequence of the target area B, an energy-enhanced response band is found in its temperature dynamic spectrum, and the center frequency of the band always changes around 88Hz following the instantaneous frequency of the acoustic excitation signal, and the maximum acoustic-thermal correlation degree γ_max reaches 0.92. This high locking relationship between the acoustic excitation frequency and the thermal signal fluctuation frequency, as well as the synchronization of the cooling rate and the temperature fluctuation amplitude, together constitute a physical evidence chain that the target area B not only has dynamic activity, but also is sensitive to the external mechanical vibration excitation of 88Hz. Finally, the system calculates the quantitative index of the target area A and the target area B according to the calculation rule of the disease activity index AI disclosed in the foregoing specific embodiments, wherein the AI value of the target area A is close to zero, and the AI value of the target area B is higher than the preset attention threshold. In this way, the two diseases that cannot be distinguished in risk level based on static geometric dimensions are quantitatively distinguished by one test, which has a clear physical meaning, so that the maintenance team can directly focus on the target area B which has been identified as a high-potential-risk disease point with high dynamic activity and a specific resonance frequency for further investigation.

[0034] Example 2: To objectively verify the effectiveness of the method claimed in the present application in distinguishing different nature of apparent diseases, this example constructs a verification test containing a control group and a test group, aiming to prove that the method can identify diseases with different mechanical properties but similar geometric shapes by quantifying data; the test platform is composed of standard concrete specimens, loading and environmental control equipment, and a set of data acquisition and analysis system that meets the functional specifications of the foregoing detailed embodiments; among them, the test specimens used are two groups of C40 concrete beams, with a size of 400mm x 100mm x 100mm, the first group of specimens is pre-fabricated with a static dry shrinkage crack of 0.2mm in width on its surface by controlling early water loss, this group of specimens is defined as static disease samples; the second group of specimens is embedded with an un-bonded prestressed steel bar during casting, by applying a small tensile stress in the later stage and performing low-cycle reciprocating loading under the action of stress, a crack of 0.2mm in width is also pre-fabricated on its surface, and a servo-hydraulic actuator is used to apply a continuous reciprocating displacement of ±0.005mm at a frequency of 1Hz, to simulate the dynamic active disease that continuously opens and closes under real load, this group of specimens is defined as dynamic disease samples; the entire test process is carried out in an environment chamber with a constant temperature of 25°C and a constant relative humidity of 70%, to exclude the interference of environmental temperature and humidity fluctuations.

[0035] The test is divided into two treatment methods, the first treatment method is the control group, which uses a technical method that only contains thermal imaging analysis without acoustic excitation to test the two disease samples, in this method, the system only collects and analyzes the cooling process of the specimen after wetting, calculates the slope and variance of the temperature change curve; the second treatment method is the test group of the present application, which uses the complete technical solution disclosed in the foregoing detailed embodiments, i.e. the complete process containing dry-state pre-scanning, wet-state thermal response collection, synchronous acoustic excitation and double-mode analysis, to test the two groups of disease samples, among them, the frequency of the acoustic excitation signal is set to linearly scan from 10Hz to 500Hz, and the weighted coefficients of the disease activity index AI are set as w_1=0.4, w_2=0.4, w_3=0.2; during the test process, the above two treatment methods are performed on the static disease samples and the dynamic disease samples respectively, the key physical quantities in each group of tests are collected, and the final disease activity index AI is calculated, the results are recorded in Table 1.

[0036] Table 1: Comparison table of test data of static and dynamic disease samples under different test methods.

[0037]

[0038] The test data show that by introducing a controlled, frequency-varying acoustic excitation signal and analyzing the correlation between the excitation and the thermal physical response of the disease at the time sequence, the dynamic mechanical behavior of the disease can be characterized and amplified; compared with the single passive thermal imaging analysis method, the method claimed in the application can realize the differentiation of the concrete structure diseases with dynamic activity by the cooperative analysis of the thermal and acoustic characteristics.

[0039] To further highlight the necessity and substantial contribution of the technical means of synchronously applying acoustic excitation and analyzing acoustic-thermal correlation in the method of the application, the following comparative examples are provided.

[0040] Comparative Example 1: To further verify the effectiveness of the key technical steps in the method of the application, this comparative example uses a simplified technical solution that omits the acoustic excitation step to test a disease sample with a weaker dynamic characteristic. Except that the acoustic excitation signal is not applied synchronously to the test piece, the test platform, C40 concrete test piece (including a static disease sample with a static dry shrinkage crack with a width of 0.2 mm and a dynamic active disease sample with a width of 0.2 mm under a more weakly sustained reciprocating displacement of ±0.002 mm), environmental control conditions (temperature 25℃, relative humidity 70%), dry pre-scanning procedure, application method and dose of volatile liquid (water), and acquisition parameters of infrared thermal imaging device are all completely consistent with the conditions used in the test group of Example 2 using the method of the application. Under this technical solution, the data analysis process can only analyze the cooling process after wetting, and the final test data are recorded in Table 2.

[0041] Table 2: Test data table of weak dynamic disease sample under simplified technical solution.

[0042]

[0043] The test results show that in the technical path lacking synchronous acoustic excitation and subsequent acoustic-thermal correlation analysis, facing a disease sample with a weaker dynamic characteristic (±0.002 mm displacement), the slope difference and variance difference are almost indistinguishable from those of the static disease sample, and the finally calculated disease activity index AI values of the two are almost identical (0.001 and 0.003, respectively), completely losing the ability to distinguish. This result confirms that relying solely on passive thermal imaging analysis, the design principle cannot separate the microscopic dynamic mechanical behavior of the disease from the background thermal noise, especially when the disease activity is weak, the simplified method is completely ineffective.

[0044] Example 3: This example combines Figs. 1 to 3 the dynamic characteristic measurement method for apparent diseases of concrete engineering, as Fig. 1As shown, first, a dry-state surface thermal response pre-scan is performed to generate a compensation matrix representing surface contamination, and then a wet-state dynamic response excitation and collection phase is entered, in which a volatile liquid and acoustic excitation are applied to the surface under test, and a time series of thermal images is collected synchronously. Based on the series, disease depth information can be determined by analyzing the time delay of the transient thermal pulse response, and material degradation state analysis can be performed by analyzing the temperature distribution uniformity of the reference area. The core of the process is an adaptive analysis switching based on environmental humidity determination. When the environmental humidity is below a preset threshold, the first mode analysis is performed, which involves analyzing the cooling rate, temperature fluctuation, and acoustic-thermal correlation. When the environmental humidity is not below the preset threshold, the second mode analysis is switched to, which involves analyzing the acoustic-induced local temperature rise characteristics. The output results of the two analysis modes are combined to generate a quantitative index, and finally a disease activity index AI is output, and a quantitative evaluation report is formed.

[0045] As shown, Fig. 2 The figure represents the relationship between acoustic-thermal correlation and acoustic excitation frequency Hz. The static disease area identified by the dashed line in the figure has an acoustic-thermal correlation that is always below 0.2 throughout the acoustic excitation frequency scanning range. The dynamic disease area identified by the solid line in the figure has an acoustic-thermal correlation that presents a significant peak at an acoustic excitation frequency of 88 Hz, with a maximum value of 0.92. Fig. 3 As shown, the user as a technical personnel performs a number of operations including disease scanning and data collection, system parameter calibration, material degradation state evaluation, and disease depth information analysis by interacting with the system. The core operation of disease scanning and data collection directly generates a disease activity index, which is the core basis for generating the final quantitative evaluation report. The report is submitted to the structural maintenance team to provide decision support for developing maintenance plans.

[0046] Example 4: To ensure that the output of the disease activity index AI of the claimed method has consistent and traceable physical meaning when applied to concrete structures with different material properties or load environments, a set of standardized offline calibration procedures need to be performed to determine the weighting coefficients w_1, w_2, w_3 in the core calculation formula and the final risk discrimination threshold; The execution of this procedure begins with the preparation of a calibration sample set consisting of at least four test pieces with the same concrete grade and reinforcement ratio as the target structure, and the size of the test pieces is 400mm x 100mm x 100mm; Among them, test piece S0 is a static disease sample, and a static crack is formed on its surface by natural dry shrinkage; Test pieces S1, S2 and S3 are dynamic disease samples, which are respectively subjected to ±0.002mm, ±0.005mm and ±0.010mm sustained reciprocating displacement by servo hydraulic actuators to simulate three different levels of dynamic activity, and are respectively assigned a dimensionless reference activity degree label with values of 0.0, 0.3, 0.6 and 1.0.

[0047] In a controlled laboratory environment, for each test piece in the calibration sample set, the complete test process disclosed in the foregoing detailed embodiments is performed, the time series of thermal images under wetting and acoustic excitation is collected, and the corresponding physical characteristic parameters are extracted; in order to determine the calculation path of the maximum acoustic-thermal correlation degree γ_max, after obtaining the temperature dynamic spectrum through short-time Fourier transform, the system first identifies the time-frequency trajectory of the response frequency band with the strongest energy, then processes the trajectory and the linear frequency scanning trajectory of the known acoustic excitation signal as input as a two-dimensional vector, calculates the Pearson correlation coefficient between the two, and takes the coefficient value as the value of the maximum acoustic-thermal correlation degree γ_max; through the measurement of the four test pieces, a calibration data set containing the reference activity degree label and the corresponding physical characteristic parameters is obtained; next, in order to determine the weighting coefficients w_1, w_2, w_3, the system uses a multiple linear regression analysis method to fit the calibration data set, and the goal of this method is to find a set of coefficients such that the mean square error between the predicted value of the disease activity index AI calculated according to the formula AI = w_1·|Δk| + w_2·|Δ(σ²)| + w_3·γ_max and the reference activity degree label of the sample is minimized; in a calibration test for a certain type of T-beam bridge, after the data obtained is input into the regression model, a set of weighting coefficients w_1 = 0.38, w_2 = 0.41, w_3 = 0.21 is solved; after the weighting coefficients are determined, the AI value of any to-be-measured disease can be calculated, and when a concern threshold is set for the AI value, in order to balance the sensitivity and specificity of detection, the system further uses receiver operating characteristic curve (ROC) analysis to determine the threshold; this analysis requires a validation sample set containing multiple known static and dynamic samples, the system uses the calibrated weighting coefficients to calculate the AI values of all validation samples, by iterating through all possible thresholds, the true positive rate and false positive rate under each threshold are calculated, and an ROC curve is drawn; finally, the AI value corresponding to the point closest to the upper left corner on the curve is selected as the concern threshold in this application scenario, such as 0.25, which represents a working point at which the false negative risk and the false positive risk are balanced under this calibration system.

[0048] Example 5: In an application of detecting a concrete wharf pile eroded by marine salt spray, the initial test is performed in the morning when the environmental humidity is 78%, which is lower than the preset threshold of 95%; the system works using the first mode analysis disclosed in the foregoing detailed embodiments, and by applying acoustic excitation signals with varying frequencies from 10 Hz to 500 Hz, a dynamic active disease is identified, and it is determined that the frequency corresponding to the mechanical resonance characteristics is 75 Hz.

[0049] At afternoon, due to the fog on the sea surface, the humidity of the site environment rises rapidly, when the system's built-in temperature and humidity sensor monitors that the environmental humidity reaches 95%, the system automatically triggers the working mode switching; At this time, the acoustic generator function of the system changes from sweep frequency detection to energy pumping, and the acoustic energy is continuously injected into the disease position at the determined 75Hz resonance frequency; At the same time, the analysis software of the system synchronously switches the analysis target from observing the cooling process to capturing the local temperature rise, through the lock-in amplification processing of the collected thermal image time series, a weak local temperature rise signal with the same frequency as the 75Hz excitation signal is extracted from the background thermal noise, which confirms the dynamic activity of the disease; Through the adaptive switching of this working mode, the method claimed in the present application can still maintain the ability to identify the dynamic activity of the disease through the physical mechanism of acoustic dissipation heat generation under the working condition that the evaporative cooling effect is inhibited.

[0050] Before performing the disease depth quantification detection on a structure made of a specific type of concrete, an offline calibration procedure needs to be performed to establish the mapping relationship between the thermal conduction time delay Δt and the disease depth D of the cross-crack under this specific material; The procedure first prepares a group of calibration specimens made of the same batch of concrete with the same mix proportion as the structure to be tested, and on the surface of these specimens, a series of standard artificial cracks with known geometric depth are made by embedding thin metal sheets of different depths (for example, 5mm, 10mm, 15mm, 20mm and 25mm) and then extracting them after the concrete has been cured.

[0051] Subsequently, all calibration specimens are subjected to wetting treatment, and for each standard artificial crack with known depth D, a depth information determination step is performed, i.e. a standardized transient thermal pulse is applied to one side of the crack, and the time delay Δt of the thermal response transmitted to the other side of the crack is recorded and calculated using a high-frame-rate infrared thermal imager; Through systematic testing of all calibration specimens, a set of (D, Δt) data is obtained; The data points in the data set are plotted in a two-dimensional coordinate system, and a least squares method is used to fit the function to establish a quantitative mathematical relationship that describes the change of disease depth D with cross-crack thermal conduction time delay Δt under this specific concrete material, for example, a mathematical model of the form D equals a times (Δt raised to the power of -b), plus c, where a, b, and c are calibration coefficients suitable for this material obtained by fitting; This mathematical relationship is used in the detection system deployed on site to directly calculate and output the corresponding disease depth estimate for any newly measured time delay.

[0052] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0053] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for measuring dynamic properties of apparent defects of concrete works, characterized in that, The method comprises the following steps: S1, before applying a layer of volatile liquid to the surface to be measured of the concrete structure, performing a dry-state surface thermal response pre-scan to generate a compensation matrix representing the contamination distribution of the surface to be measured by applying a sequence of transient thermal excitation to the surface to be measured and collecting the transient thermal response thereof; S2, applying the volatile liquid to the surface to be measured at a preset unit area dose, and during the evaporation of the volatile liquid, using an infrared thermal imaging device to obtain a series of thermal images of the surface to be measured in a continuous time period to form a thermal image time sequence, and simultaneously applying an acoustic excitation signal with a frequency varying within a preset range to the surface to be measured; S3, based on the comparison result of the ambient humidity and the preset threshold, selecting to perform first mode analysis or second mode analysis, comprising: When it is detected that the ambient humidity is lower than the preset threshold, applying the compensation matrix to the temperature value of each pixel in the thermal image time sequence for normalization processing, and performing first mode analysis on the dynamic thermal characteristics of the temperature of the target region in the processed thermal image time sequence over time, the first mode analysis comprising: calculating the slope and variance of the temperature change curve of the target region, and determining the mechanical resonance characteristics by analyzing the acoustic-thermal correlation degree between the temperature dynamic spectrum of the target region and the change trajectory of the instantaneous frequency of the acoustic excitation signal; wherein the step of determining the mechanical resonance characteristics comprises: performing short-time Fourier transform on the sequence of temperature changes over time of the target region to obtain the temperature dynamic spectrum; and determining whether there is an energy-enhanced response frequency band in the temperature dynamic spectrum, and the acoustic-thermal correlation degree between the center frequency of the response frequency band and the change trajectory of the instantaneous frequency of the acoustic excitation signal is not lower than a preset correlation threshold, if so, the acoustic-thermal correlation degree is taken as part of the mechanical resonance characteristics; When it is detected that the ambient humidity is not lower than the preset threshold, performing acoustic energy pumping on the target region, and performing second mode analysis on the thermal image time sequence, the second mode analysis comprising analyzing the local temperature rise characteristics of the target region caused by acoustic energy pumping; S4, generating a quantitative index representing the dynamic activity of the apparent disease based on the results of the first mode analysis or the second mode analysis; And the first mode analysis further comprises: selecting a region with a heat flux gradient less than a preset gradient threshold as a reference region on the surface to be measured; and comparing the slope and variance of the target region with the slope and variance of the reference region calculated according to the processed thermal image time sequence; And the step of generating a quantitative index representing the dynamic activity of the apparent disease is calculated based on the difference between the slope of the target region and the slope of the reference region, the difference between the variance of the target region and the variance of the reference region, and the analysis result of the mechanical resonance characteristics; and a quantification index, i.e., a disease activity index AI, is calculated by the following rule: AI = w 1· |Δk| + w 2· |Δ(σ²)| + w 3· γ max wherein AI is the disease activity index, Δk is a difference in slope of the target region and the reference region, Δ(σ²) is a difference in variance of the target region and the reference region, γ max is a maximum value of the acoustic-thermal correlation degree, and w1, w2, and w3 are preset weighting coefficients.

2. A method of measuring the dynamic properties of apparent defects in concrete structures according to claim 1, characterized in that, When it is detected that the ambient humidity is not lower than the preset threshold, performing acoustic energy pumping on the target region, comprising the following steps: if it is determined that there is a response frequency band, performing acoustic excitation on the target region at the center frequency of the response frequency band; and if it is not determined that there is a response frequency band, performing acoustic excitation on the target region with a wide-band acoustic signal.

3. The method of claim 1, wherein the method is a dynamic property measurement method for apparent distress of concrete structures. After generating the quantification index, the following steps are further included: applying a transient thermal pulse to one side of the apparent lesion that has been identified; collecting and analyzing the time delay of the temperature response induced by the transient thermal pulse on the other side of the apparent lesion, and determining the depth information of the apparent lesion based on the time delay.

4. The method of claim 1, wherein the method is a dynamic property measurement method for apparent distress of concrete structures. The method further includes the following steps: analyzing the temperature distribution uniformity of the reference region in the processed thermal image time series to determine the material degradation state of the location where the apparent lesion is located; the analysis of the temperature distribution uniformity includes calculating the information entropy of each frame of the thermal image of the reference region within a continuous time period, and generating a curve of the information entropy changing with time, and determining the material degradation state according to the morphological characteristics of the curve of the information entropy changing with time.

5. The method of claim 1, wherein the method is a dynamic property measurement method for apparent distress of concrete structures. The volatile liquid is water.

6. The method of claim 1, wherein the method is a dynamic property measurement method for apparent distress of concrete structures. Further comprising: After identifying and quantifying the apparent lesion, a non-contact detection method based on electrical principle is used to measure the conductance or impedance characteristics of the target region; And based on the measurement results, the quantification index is verified or arbitrated.

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