An assembled pavement panel bottom void identification method

Abstract

The present application relates to a kind of prefabricated pavement panel bottom void identification method, comprising the following steps: obtaining the vibration signal of each measuring point of reference plate and the pavement panel to be identified, respectively calculating whole board cross power spectral density;Compare the distribution of whole board cross power spectral density of the pavement panel to be identified and reference plate in frequency domain, judge whether the pavement panel to be identified is void;When the judgment result is that there is void, the pavement panel to be identified and reference plate are partitioned, one region corresponds to one perception unit, the corresponding relationship between each measuring point and each region in the plate is established;Respectively calculate the area cross power spectral density of different regions in the pavement panel to be identified and reference plate;The difference of the area cross power spectral density of corresponding region of the pavement panel to be identified and reference plate is calculated, and integration is carried out, obtain the void region identification index, according to the void region identification index, judge the void position of the pavement panel to be identified.Compared with prior art, the present application has the advantages of high identification precision and low cost.

Description

Technical Field

[0001] The present invention relates to the field of intelligent pavement monitoring, and in particular to a method for identifying voids under assembled pavement panels. Background Art

[0002] Cement concrete pavements are the primary form of airport pavement structure due to their long service life and strong bearing capacity. However, with age, the filler in the pavement joints becomes ineffective or absent, and the base material is lost due to the erosion and pumping of surface water, often resulting in voids in the pavement joints. Voids under the slab are a common form of damage in concrete pavements, significantly impacting their structural performance. In practical engineering applications, accurate identification of voids under the slab is crucial to control structural damage and ensure pavement performance and service life.

[0003] In the field of pavement void monitoring and identification, the most commonly used void determination method is based on deflection, which is mainly divided into two types: the falling weight deflectometer (HWD / FWD) test and the Beckman beam test. The falling weight deflectometer (HWD / FWD) is currently the most widely used deflection-based void determination method. Its detection principle is that under the same impact load, the deflection in the void area will increase significantly. However, this method has problems such as lack of timeliness, difficulty in determining the scope, extent, and spatial distribution of the void, low measurement efficiency, and the ability to measure void information at only a single point at a time.

[0004] In response to the problems existing in the deflection detection methods, many new void monitoring technologies have emerged in recent years. Common technologies include ground penetrating radar (GPR), ultrasonic arrays, infrared imaging, etc. This type of technology can measure the scope and degree of voids more accurately, but most of them are point and line measurements. A certain number of sensors need to be installed for each measurement. The array network is complex and needs to be equipped with high-performance detection equipment, which has low engineering economy. For example, CN110487910A relates to a panel void and positioning detection method for a panel dam based on vibration sensing technology. First, it is determined whether there is void in the lower part of the panel; then, the position of the void in the lower part of the panel is determined; when there is a void in the panel, the void position is determined by comparing the differences in the vibration signals of the measuring points in each detection area, and the final detection results are intuitively displayed in the form of images. In this regard, the existing technology has the following defects:

[0005] 1. Limitations of measurement methods: Existing technologies rely on sensors. In actual use, a certain number of sensors need to be installed, which has high installation costs and is difficult to adapt to the long-distance and large-scale detection needs of cement concrete pavements.

[0006] 2. Limitation of measurement accuracy: Existing technologies mainly determine the air gap position by the change of amplitude or acceleration in the time domain, considering a single dimension, resulting in insufficient final recognition accuracy.

[0007] 3. Poor adaptability: The partitioning method of the existing technology is relatively fixed and difficult to adapt to panels of different sizes and shapes. Summary of the Invention

[0008] The purpose of the present invention is to provide a method for identifying voids at the bottom of prefabricated decking panels, thereby realizing economical and accurate identification of voids at the bottom of prefabricated decking panels.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] A method for identifying voids under prefabricated decking panels comprises the following steps:

[0011] S1, obtain the vibration signals of each measuring point of the reference plate and the decking to be identified, and calculate the cross power spectrum density of the whole plate respectively;

[0012] S2, comparing the distribution of the cross power spectrum density of the slab to be identified and the reference slab in the frequency domain to determine whether the slab to be identified has any holes;

[0013] S3, when the result of the judgment is that there is a gap, the slab to be identified and the reference slab are divided into zones, one zone corresponds to one sensing unit, and the corresponding relationship between each measuring point and each zone in the slab is established;

[0014] S4, respectively calculating the regional cross power spectrum density of different regions within the slab to be identified and the reference slab;

[0015] S5, calculating the difference in regional cross-power spectral density between the slab to be identified and the corresponding area of ​​the reference slab, integrating the difference, and obtaining a void area identification index, and determining the void position of the slab to be identified based on the void area identification index.

[0016] The vibration signal is collected by winding the optical fiber into a sensing unit, and multiple measuring points are distributed in each sensing unit.

[0017] The calculation method of the cross power spectrum density of the whole board is:

[0018] Get the vibration signal x of each measuring point i (t), i represents the measurement point number. The cross power spectrum is calculated based on the vibration signals of all measurement points in the same decking panel. The obtained data is stored in the form of (measurement point ID, board ID). The cross power spectrum is calculated as follows:

[0019]

[0020] Where: P xy(f) represents the cross-correlation function of signals x(n) and y(n) in the frequency domain, which is used to analyze the resonance characteristics of the signal at different frequencies. x(n) and y(n) represent the vibration signals of two different measuring points. N is the number of samples. y*(n+f) represents the complex conjugate of signal y(n+f). f is the frequency variable, representing different frequency points in the frequency domain. The normalization factor To ensure P xy It represents the average power or energy; the calculated cross power spectrum P xy (f) is a complex value, whose real and imaginary parts respectively reflect the amplitude and phase information of the signal at different frequencies;

[0021] Take the cross power spectrum P xy (f) and the magnitude spectrum of the cross power spectrum |P xy (f)|:

[0022]

[0023] in: and The plural P xy (f) the real and imaginary parts;

[0024] Normalize the magnitude spectrum of the cross power spectrum to obtain the cross power spectrum density:

[0025]

[0026] Where, ∑ f |P xy (f)| is the sum of the amplitudes at all frequency points;

[0027] Calculate the mean cross power spectrum density of different measurement points to obtain the cross power spectrum density P of the entire board f :

[0028]

[0029] Where M represents the number of measurement points in the board. is the cross power spectrum density of the mth measurement point at frequency f.

[0030] The specific steps of establishing the corresponding relationship between each measuring point and each area in the board are as follows:

[0031] The vibration responses of each measuring point, that is, the time domain curves of the vibration signals, are compared. The dynamic time warping algorithm is used to calculate the similarity of the vibration responses of each measuring point, and the Monte Carlo simulation method is used to estimate the mapping relationship between the actual measuring points and the perception units.

[0032] The dynamic time warping algorithm calculates the similarity between vibration signals by optimizing the warping path. The similarity is represented by the DTW distance. The smaller the DTW distance, the higher the signal similarity. The DTW distance of the mapping combination X is calculated as follows:

[0033]

[0034] Where N0 is the number of measuring points of the sensing unit, L0 is the length of the optical fiber contained in the sensing unit, S g is the distance between the measurement points, DTW ij is the DTW distance between the i-th and j-th measuring points in the mapping combination X, where I and J are the measured vibration responses of the i-th and j-th measuring points, respectively, and w k is the kth element in the regular path composed of I and J, and K is the number of elements in the regular path.

[0035] The Monte Carlo simulation method is used to calculate the DTW distance based on the vibration data of the measured measuring points, and the correspondence between the measuring points and the sensing units is estimated according to the DTW distance to achieve accurate positioning of the measuring points in the distributed sensing array.

[0036] The Monte Carlo simulation method performs the following steps:

[0037] Step 1) Input the measured vibration signal data set Data, the number of sensing unit measurement points N0, the number of vibration sensing units M0, and preset the cutoff parameter C, C < 1;

[0038] Step 2) extracting data from the measured vibration signal dataset Data repeatedly without replacement according to Monte Carlo simulation;

[0039] Step 3) The vibration signal dataset extracted for the i-th time is recorded as Data i , vibration signal dataset Data i After preprocessing, it is divided into multiple mapping combinations with N0 window length and 1 step length, and its DTW distance vector is calculated;

[0040] Step 4) Search for the peak of the DTW distance vector based on the minimum peak search algorithm, and extract mapping combinations with a peak-to-peak spacing N0 until the sum of the extracted mapping combinations and the number of converged mapping combinations reaches M0, obtaining a set of mapping combination results corresponding to M0 perception units;

[0041] Step 5) Repeat steps 3) to 4) for a preset first number of times, update the cumulative frequency distribution curve of the non-converged mapping combination corresponding to each perception unit based on the multiple sets of mapping combination results, extract the maximum likelihood estimation result of the cumulative frequency distribution curve using a maximum peak search algorithm, and output the maximum probability mapping combination of the perception unit;

[0042] Step 6) Repeat step 5) and when the same maximum probability mapping combination is output for the second consecutive preset number of times, the mapping combination is considered to have converged;

[0043] Step 7) When the number of newly added converged mapping combinations reaches C*M0, all converged mapping combinations are used as conditional parameters and the process returns to step 3) to remove the corresponding vibration signal data in the converged mapping combinations as the vibration signal dataset Data i , repeat the above cycle until the total number of converged mapping combinations reaches M0;

[0044] Step 8) Output the converged mapping combination X.

[0045] The calculation method of the regional cross power spectrum density is:

[0046] The cross power spectrum of the vibration signals of each measuring point in the same area is calculated, and the amplitude of the cross power spectrum is taken to obtain the amplitude spectrum of the cross power spectrum; the amplitude spectrum of the cross power spectrum is normalized to obtain the cross power spectrum density; the mean of the cross power spectrum density of different measuring points in the same area is calculated to obtain the regional cross power spectrum density, which is stored as three-dimensional data of (measuring point ID, area ID, plate ID).

[0047] The S5 comprises the following steps:

[0048] S51, calculating the difference in regional cross-power spectrum density between the slab to be identified and the corresponding area of ​​the reference slab:

[0049] ΔP fij (f) = P fij,l (f)-P fij,h (f)

[0050] Where ΔP fij (f) represents the regional cross-power spectral density difference at frequency f, ΔP fij,l (f) and ΔP fij,h (f) Regional cross-power spectral densities of the corresponding area of ​​the slab to be identified and the reference area of ​​the reference slab, respectively;

[0051] S52, integrating the difference in the regional cross-power spectrum density:

[0052]

[0053] Where, DI void It represents the identification index of the gap area, a and b represent the lower and upper limits of the frequency respectively;

[0054] S53, judging the hollow position of the paving panel to be identified according to the hollow area identification index: using the area ID as the horizontal coordinate and the identification index of the hollow area corresponding to the area ID as the vertical coordinate to draw a two-dimensional image, identifying the inflection point of the two-dimensional image, and the area corresponding to the identified inflection point is the location of the hollow area of ​​the paving panel to be identified.

[0055] In S1, before calculating the cross power spectrum density of the entire board, the vibration signal is preprocessed, and the preprocessing method used is one of bandpass filtering, high-pass filtering, and low-pass filtering, or a combination thereof.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. Low cost: The present invention adopts distributed optical fiber, which can realize the collection of vibration information at each point along the pavement panel, thereby realizing distributed measurement, with a higher measurement range and resolution, and low installation cost, which can meet the long-distance and large-scale detection needs of cement concrete pavements.

[0058] 2. High precision: By comparing the distribution differences of the cross-power spectrum density of the entire board in the frequency domain, the present invention can more sensitively capture the changes in vibration characteristics caused by voids, and thus more accurately determine the presence of voids. By calculating the regional cross-power spectrum density and performing difference integration to obtain the void area identification index, the changes in the vibration signal can be more accurately analyzed, thereby improving measurement accuracy.

[0059] 3. Good adaptability: When zoning, this application can accurately establish a clear correspondence between each measuring point and each area in the board. This flexible zoning strategy can better adapt to decking panels of different structures and improve the accuracy of void position judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of the method of the present invention;

[0061] Figure 2 A layout plan for full-size precast concrete pavement panel specimens in one embodiment;

[0062] Figure 3 is a distribution diagram of distributed vibration optical fiber units in one embodiment;

[0063] Figure 4 1 is a comparison diagram of the cross-power spectrum density of P1 and P2 when the plate edge is excited in one embodiment;

[0064] Figure 5 1 is a comparison diagram of the cross power spectrum density when P1 & P2 are excited in the plate in one embodiment;

[0065] Figure 6: is a comparison diagram of the cross power spectrum density of P1 & P2 when the panel corner is excited in one embodiment;

[0066] Figure 7 A diagram showing the vibration response results of two measuring point data within the same mapping combination in one embodiment;

[0067] Figure 8 is the DTW indicator result of the mapping combination under a single load excitation in one embodiment;

[0068] Figure 9 This is a diagram showing the result of identifying a void area in one embodiment. DETAILED DESCRIPTION

[0069] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0070] Example 1

[0071] This embodiment provides a method for identifying a void under an assembled decking panel. Figure 1 As shown, the following steps are included:

[0072] S1: Obtain the vibration signals of each measuring point of the reference plate and the decking to be identified, and calculate the cross-power spectrum density of the entire plate respectively.

[0073] In this embodiment, vibration signals are collected by forming sensing units with optical fibers in loops. Each sensing unit contains multiple measurement points. This increases the number of measurement points per unit area and allows the vibrations measured by the optical fibers to be correlated with actual plane positions.

[0074] Before calculating the cross power spectrum density of the entire board, the vibration signal is preprocessed, and the preprocessing method used is one of bandpass filtering, high-pass filtering, and low-pass filtering, or a combination thereof.

[0075] The calculation process of the cross power spectrum density of the entire board includes the following steps:

[0076] S11, obtain the vibration signal x of each measuring point i (t), i represents the measurement point number. The cross power spectrum is calculated based on the vibration signals of all measurement points in the same decking panel. The obtained data is stored in the form of (measurement point ID, board ID). The cross power spectrum is calculated as follows:

[0077]

[0078] Where: P xy(f) represents the cross-correlation function of signals x(n) and y(n) in the frequency domain, which is used to analyze the resonance characteristics of the signal at different frequencies. x(n) and y(n) represent the vibration signals of two different measuring points. N is the number of samples. y*(n+f) represents the complex conjugate of signal y(n+f). f is the frequency variable, representing different frequency points in the frequency domain. The normalization factor To ensure P xy It represents the average power or energy; the calculated cross power spectrum P xy (f) is a complex value, whose real and imaginary parts respectively reflect the amplitude and phase information of the signal at different frequencies.

[0079] S12, take the cross power spectrum P xy (f) and the magnitude spectrum of the cross power spectrum |P xy (f)|:

[0080]

[0081] Where: and The plural P xy The real and imaginary parts of (f).

[0082] S13, normalize the magnitude spectrum of the cross power spectrum so that the sum is 1, and obtain the cross power spectral density (CPSD):

[0083]

[0084] Where, ∑ f |P xy (f)| is the sum of the amplitudes at all frequency points.

[0085] S14, calculate the mean cross power spectrum density of different measuring points in the whole board, and obtain the cross power spectrum density P of the whole board. f :

[0086]

[0087] Where M represents the number of measurement points in the board. is the cross power spectrum density of the mth measurement point at frequency f. The average normalized value P f The average energy distribution characteristics of the signal at frequency f are reflected by taking the average of the normalized results of all measurement points in the decking panel.

[0088] S2, comparing the distribution of the cross power spectrum density of the slab to be identified and the reference slab in the frequency domain to determine whether the slab to be identified has any holes.

[0089] S3, when the result of the judgment is that there is a gap, the paving board to be identified and the reference board are divided into zones, one zone corresponds to one sensing unit, and the corresponding relationship between each measuring point and each zone in the board is established.

[0090] To facilitate analysis, sensors embedded within concrete paving panels are often zoned, with denser insertion within fixed areas. Analyzing the subsurface conditions within each slab requires mapping fiber optic measurement points to their locations within the slab. The sensor's time-domain curves are used to locate the region within the slab to which the measurement points belong. Vibration data from each measurement point is grouped by location, and cross-power spectral density analysis is performed. This involves calculating the cross-power spectral density (CPSD) for different measurement points within the same slab region.

[0091] The method for establishing the corresponding relationship between each measuring point and each area in the board is:

[0092] Comparing the vibration responses of each measurement point—that is, the time-domain curves of the vibration signal—shows that measurement points within the same vibration sensing unit can be roughly considered to be in the same spatial location, resulting in a high degree of similarity in their vibration responses. The Dynamic Time Warping (DTW) algorithm is used to calculate the similarity of the vibration responses of each measurement point, and Monte Carlo simulation is used to estimate the mapping relationship between the actual measurement points and the sensing unit.

[0093] The dynamic time warping algorithm calculates the similarity between vibration signals by optimizing the warping path. The smaller the DTW distance, the higher the signal similarity. The DTW distance of the mapping combination X is calculated as follows:

[0094]

[0095] Where N0 is the number of measuring points of the sensing unit, L0 is the length of the optical fiber contained in the sensing unit, S g is the distance between the measurement points, DTW ij is the DTW distance between the i-th and j-th measuring points in the mapping combination X, where I and J are the measured vibration responses of the i-th and j-th measuring points, respectively, and w k is the kth element in the regular path composed of I and J, and K is the number of elements in the regular path.

[0096] The Monte Carlo simulation method is used to estimate the correspondence between the measuring points and the various areas within the board using measured data, ultimately achieving accurate positioning of the measuring points in the distributed sensing array. To enhance the robustness of the method and reduce the impact of load distribution uncertainty on the mapping of the measuring points, Monte Carlo simulation is used to obtain the cumulative frequency distribution curve to gradually approximate the actual mapping combination results. To prevent data randomness from causing non-convergence of the results, loop iterations are added to the Monte Carlo simulation process and a cutoff parameter C is set. The converged mapping combination is used as a conditional parameter for subsequent loops. Specifically, the Monte Carlo simulation method performs the following steps:

[0097] Step 1) Input the measured vibration signal data set Data, the number of sensing unit measurement points N0, the number of vibration sensing units M0, and preset the cutoff parameter C, C < 1;

[0098] Step 2) extracting data from the measured vibration signal dataset Data repeatedly without replacement according to Monte Carlo simulation;

[0099] Step 3) The vibration signal dataset extracted for the i-th time is recorded as Data i , vibration signal dataset Data i After preprocessing, it is divided into multiple mapping combinations with N0 window length and 1 step length, and its DTW distance vector is calculated;

[0100] Step 4) Search for the peak of the DTW distance vector based on the minimum peak search algorithm, and extract mapping combinations with a peak-to-peak spacing N0 until the sum of the extracted mapping combinations and the number of converged mapping combinations reaches M0, obtaining a set of mapping combination results corresponding to M0 perception units;

[0101] Step 5) Repeat steps 3)-4) 50 times, update the cumulative frequency distribution curve of the non-converged mapping combination corresponding to each perception unit based on the multiple sets of mapping combination results, extract the maximum likelihood estimation result of the cumulative frequency distribution curve using the maximum peak search algorithm, and output the maximum probability mapping combination of the perception unit;

[0102] Step 6) Repeat step 5) and when the same maximum probability mapping combination is output five times in a row, the mapping combination is considered to have converged;

[0103] Step 7) When the number of newly added converged mapping combinations reaches C*M0, all converged mapping combinations are used as conditional parameters and the process returns to step 3) to remove the corresponding vibration signal data in the converged mapping combinations as the vibration signal dataset Data i , repeat the above cycle until the total number of converged mapping combinations reaches M0;

[0104] Step 8) Output the converged mapping combination X.

[0105] S4, respectively calculating the regional cross-power spectrum density of different regions within the slab to be identified and the reference slab.

[0106] The calculation method of regional cross power spectrum density can refer to the calculation method of the cross power spectrum density of the entire board mentioned above, except that the measurement point data used are only the measurement points within the corresponding area. The specific steps are:

[0107] The cross power spectrum of the vibration signals of each measuring point in the same area is calculated, and the amplitude of the cross power spectrum is taken to obtain the amplitude spectrum of the cross power spectrum; the amplitude spectrum of the cross power spectrum is normalized to obtain the cross power spectrum density; the mean of the cross power spectrum density of different measuring points in the same area is calculated to obtain the regional cross power spectrum density, which is stored as three-dimensional data of (measuring point ID, area ID, plate ID).

[0108] S5, calculating the difference in regional cross-power spectral density between the slab to be identified and the corresponding area of ​​the reference slab, integrating the difference, and obtaining a void area identification index, and determining the void position of the slab to be identified based on the void area identification index.

[0109] Specifically, S5 includes the following steps:

[0110] S51, calculating the difference in regional cross-power spectrum density between the slab to be identified and the corresponding area of ​​the reference slab:

[0111] ΔP fij (f) = P fij,l (f)-P fij,h (f)

[0112] Where ΔP fij (f) represents the regional cross-power spectral density difference at frequency f, ΔP fij,l (f) and ΔP fij,h (f) represents the regional cross-power spectral density of the corresponding area of ​​the slab to be identified and the reference area of ​​the reference slab. By calculating the difference between the two, we can quantify the energy distribution difference between the void area and the reference area at frequency f, providing a basis for subsequent void area identification.

[0113] S52, integrate the difference of regional cross-power spectral densities:

[0114]

[0115] Where, DI void It represents the identification index of the void area, which is calculated by the cross-spectral density difference ΔP in the frequency range [a, b]. fij (f) is integrated, where a and b represent the lower and upper frequency limits, respectively.

[0116] S53, determine the hollow position of the paving panel to be identified based on the hollow area identification index: draw a two-dimensional image with the area ID as the horizontal coordinate and the identification index of the hollow area corresponding to the area ID as the vertical coordinate, identify the inflection point of the two-dimensional image, and the area corresponding to the identified inflection point is the location of the hollow area of ​​the paving panel to be identified.

[0117] Example 2

[0118] This embodiment uses a full-scale prefabricated decking panel void identification experiment as a detailed explanation of the method of Example 1.

[0119] The experiment is based on a 4m×4m full-size precast concrete pavement (PCP) slab specimen, which was cast using C50 cement concrete. According to the JTG3420-2020 specification, the compressive strength of the cast material was measured to be 55MPa, the flexural strength was 5MPa, and the flexural tensile modulus was 34GPa. After the precast specimens were cast and cured for 28 days, they were transported to the indoor full-scale test site. The slabs were placed on a 3cm thick layer of compacted and leveled sand, and below the sand layer was a 20cm thick cement-stabilized gravel base. The two specimens were connected using Conda and named P1 and P2 respectively. The P1 slab was set as the control group, and the bottom of the slab was kept healthy throughout the experiment, which was the reference slab. The P2 slab was the experimental group. During the experiment, different void positions were simulated by lifting the slab and removing the sand from under the designated area. The slab edge voids were set, and the slab corner voids were shaped like this. Figure 2 shown.

[0120] The test used a falling weight deflectometer (FWD) as the excitation load to vibrate the decking at 6 points, which were located at the edge, center, and corner of the two decks. Figure 2 As shown in the figure, a DAS fiber interrogator with a minimum spatial resolution of 4m was used. Considering the ease of construction, each unit was wound with 4 turns, a diameter of 0.4m, and a fiber length of 5m.

[0121] In the subsequent modal analysis, the distance between two adjacent distributed vibration fiber units is set to 0.1m to ensure a spatial resolution of 0.5m. In combination with the modal analysis requirements of this structure, a total of 36 distributed vibration fiber units are arranged in 6 rows and 6 columns. The spacing between rows and columns is 0.1m. Figure 3 shown.

[0122] The measured vibration signal output by the optical fiber demodulator has a total of 144 measurement points. The cross power spectrum density of the entire board is calculated with P1 and P2 as the unit. The CPSD diagrams of the edge, middle and corner of P1 and P2 are compared. The results are as follows: Figure 4 、 5 , 6. The comparison shows that there are obvious differences between the CPSD diagrams of the healthy plate and the hollow plate: the CPSD of the hollow plate is concentrated in the high frequency distribution, while the CPSD of the reference plate is concentrated in the low frequency distribution.

[0123] The concrete panel is partitioned and a corresponding relationship is established between each measuring point and each area within the panel. The vibration responses of measuring points in the same vibration sensing unit are highly similar. Figure 7The vibration responses of consecutive measurement points after data preprocessing are shown. The signal phase and amplitude from measurement points 151 to 153 are relatively similar, but different from those at measurement points 150 and 154. This indicates that measurement points 151 to 153 belong to the same sensing unit, while measurement points 150 and 154 are located in adjacent units or in the connecting segment between units.

[0124] Dynamic time warping (DTW) is used to evaluate the similarity of the P1 & P2 optical fiber measurement point signals, and the Monte Carlo simulation method is used to estimate the correspondence between the measurement points and the various areas in the board using the measured data. This example selects the DWT of different mapping combinations of the measurement points and the sensing units of a 330m length of optical fiber buried in the board under a certain working condition, such as Figure 8 The smaller the DTW, the higher the similarity of the vibration response curves within the mapping combination, and the closer the mapping combination is to the actual layout.

[0125] Figure 8 The DTW performance of the mapping combination within the dashed box fluctuates significantly. The distance between the extreme points identified by the peak-finding algorithm is approximately equal to the array element length L0, indicating significant mapping results in this area. This is likely due to the proximity of this area to the load track and the significant load excitation. The cutoff parameter C was set to 0.2, and the corresponding vibration data was input. The frequency distribution of the mapping combination converged through Monte Carlo simulations was used to assign the coordinates of the sensing elements to the mapping combination using the fiber cascade path, achieving spatial reconstruction from the measurement point to the sensing element and then to the plane position. The resulting P1 and P2 plate measurement point and partition numbers, along with the corresponding region coordinates within the plate, are shown in Table 1.

[0126] Table 1 Correspondence between some measuring points and partitions in P2

[0127] Measurement point ID Region ID Cir_x Cir_y Measurement point ID Region ID Cir_x Cir_y 228 1 1 1 253 7 2 6 229 1 1 1 254 7 2 6 230 1 1 1 255 8 2 5 232 2 1 2 256 8 2 5 233 2 1 2 257 8 2 5 234 2 1 2 258 8 2 5 236 3 1 3 259 9 2 4 237 3 1 3 260 9 2 4 238 3 1 3 261 9 2 4 240 4 1 4 262 9 2 4 241 4 1 4 263 10 2 3 242 4 1 4 264 10 2 3 244 5 1 5 265 10 2 3 245 5 1 5 266 10 2 3 246 5 1 5 267 11 2 2 248 6 1 6 268 11 2 2 249 6 1 6 269 11 2 2 250 6 1 6 270 11 2 2 251 7 2 6 271 12 2 1 252 7 2 6 272 12 2 1

[0128] Calculate the partition CPSD. Calculate the cross power spectrum density P between different measurement points in the same optical fiber unit for P1 & P2 boards respectively. xy (f), and then calculate the normalized power spectrum density P of each measuring point at each amplitude. f Finally, the average value of the normalized power spectral density of each area in the board is calculated.

[0129] Calculate the difference in the regional cross power spectrum density and integrate it to locate the void. P2 has a void, and P1 is the reference plate. Subtract the CPSD of the P2 and P1 partitions respectively, and integrate the obtained cross spectrum difference. The maximum peak of the integration is the identified void area, such as Figure 9 shown.

[0130] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A method for identifying voids under prefabricated decking panels, characterized in that: The following steps are involved: S1, obtain the vibration signals of each measuring point of the reference plate and the decking to be identified, and calculate the cross power spectrum density of the whole plate respectively; S2, comparing the distribution of the cross power spectrum density of the slab to be identified and the reference slab in the frequency domain to determine whether the slab to be identified has any holes; S3, when the result of the judgment is that there is a gap, the slab to be identified and the reference slab are divided into zones, one zone corresponds to one sensing unit, and the corresponding relationship between each measuring point and each zone in the slab is established; S4, respectively calculating the regional cross power spectrum density of different regions within the slab to be identified and the reference slab; S5, calculating the difference in regional cross-power spectral density between the slab to be identified and the corresponding area of ​​the reference slab, integrating the difference, and obtaining a void area identification index, and determining the void position of the slab to be identified based on the void area identification index.

2. A method for identifying voids under prefabricated decking panels according to claim 1, characterized in that: The vibration signal is collected by winding the optical fiber into a sensing unit, and multiple measuring points are distributed in each sensing unit.

3. The method for identifying voids under prefabricated decking panels according to claim 1, wherein: The calculation method of the cross power spectrum density of the whole board is: Get the vibration signal x of each measuring point i (t), i represents the measurement point number. The cross power spectrum is calculated based on the vibration signals of all measurement points in the same decking panel. The obtained data is stored in the form of (measurement point ID, board ID). The cross power spectrum is calculated as follows: Where: P xy (f) represents the cross-correlation function of signals x(n) and y(n) in the frequency domain, which is used to analyze the resonance characteristics of the signal at different frequencies. x(n) and y(n) represent the vibration signals of two different measuring points. N is the number of samples. y*(n+f) represents the complex conjugate of signal y(n+f). f is the frequency variable, representing different frequency points in the frequency domain. The normalization factor To ensure P xy It represents the average power or energy; the calculated cross power spectrum P xy (f) is a complex value, whose real and imaginary parts respectively reflect the amplitude and phase information of the signal at different frequencies; Take the cross power spectrum P xy (f) and the magnitude spectrum of the cross power spectrum |P xy (f)|: in: and The plural P xy (f) the real and imaginary parts; Normalize the magnitude spectrum of the cross power spectrum to obtain the cross power spectrum density: Where, ∑ f |P xy (f)| is the sum of the amplitudes at all frequency points; Calculate the mean cross power spectrum density of different measurement points to obtain the cross power spectrum density P of the entire board f : Where M represents the number of measurement points in the board. is the cross power spectrum density of the mth measurement point at frequency f.

4. The method for identifying voids under prefabricated decking panels according to claim 1, wherein: The specific steps of establishing the corresponding relationship between each measuring point and each area in the board are as follows: The vibration responses of each measuring point, that is, the time domain curves of the vibration signals, are compared. The dynamic time warping algorithm is used to calculate the similarity of the vibration responses of each measuring point, and the Monte Carlo simulation method is used to estimate the mapping relationship between the actual measuring points and the perception units.

5. The method for identifying voids under prefabricated decking panels according to claim 4, characterized in that: The dynamic time warping algorithm calculates the similarity between vibration signals by optimizing the warping path. The similarity is represented by the DTW distance. The smaller the DTW distance, the higher the signal similarity. The DTW distance of the mapping combination X is calculated as follows: Where N0 is the number of measuring points of the sensing unit, L0 is the length of the optical fiber contained in the sensing unit, S g is the distance between the measurement points, DTW ij is the DTW distance between the i-th and j-th measuring points in the mapping combination X, where I and J are the measured vibration responses of the i-th and j-th measuring points, respectively, and w k is the kth element in the regular path composed of I and J, and K is the number of elements in the regular path.

6. The method for identifying voids under prefabricated decking panels according to claim 4, characterized in that: The Monte Carlo simulation method is used to calculate the DTW distance based on the vibration data of the measured measuring points, and the correspondence between the measuring points and the sensing units is estimated according to the DTW distance to achieve accurate positioning of the measuring points in the distributed sensing array.

7. The method for identifying voids under prefabricated decking panels according to claim 4, wherein: The Monte Carlo simulation method performs the following steps: Step 1) Input the measured vibration signal data set Data, the number of sensing unit measurement points N0, the number of vibration sensing units M0, and preset the cutoff parameter C, C < 1; Step 2) extracting data from the measured vibration signal dataset Data repeatedly without replacement according to Monte Carlo simulation; Step 3) The vibration signal dataset extracted for the i-th time is recorded as Data i , vibration signal dataset Data i After preprocessing, it is divided into multiple mapping combinations with N0 window length and 1 step length, and its DTW distance vector is calculated; Step 4) Search for the peak of the DTW distance vector based on the minimum peak search algorithm, and extract mapping combinations with a peak-to-peak spacing N0 until the sum of the extracted mapping combinations and the number of converged mapping combinations reaches M0, obtaining a set of mapping combination results corresponding to M0 perception units; Step 5) Repeat steps 3) to 4) for a preset first number of times, update the cumulative frequency distribution curve of the non-converged mapping combination corresponding to each perception unit based on the multiple sets of mapping combination results, extract the maximum likelihood estimation result of the cumulative frequency distribution curve using a maximum peak search algorithm, and output the maximum probability mapping combination of the perception unit; Step 6) Repeat step 5) and when the same maximum probability mapping combination is output for the second consecutive preset number of times, the mapping combination is considered to have converged; Step 7) When the number of newly added converged mapping combinations reaches C*M0, all converged mapping combinations are used as conditional parameters and the process returns to step 3) to remove the corresponding vibration signal data in the converged mapping combinations as the vibration signal dataset Data i , repeat the above cycle until the total number of converged mapping combinations reaches M0; Step 8) Output the converged mapping combination X.

8. The method for identifying voids under prefabricated decking panels according to claim 1, wherein: The calculation method of the regional cross power spectrum density is: The cross power spectrum of the vibration signals of each measuring point in the same area is calculated, and the amplitude of the cross power spectrum is taken to obtain the amplitude spectrum of the cross power spectrum; the amplitude spectrum of the cross power spectrum is normalized to obtain the cross power spectrum density; the mean of the cross power spectrum density of different measuring points in the same area is calculated to obtain the regional cross power spectrum density, which is stored as three-dimensional data of (measuring point ID, area ID, plate ID).

9. The method for identifying voids under prefabricated decking panels according to claim 1, wherein: The S5 comprises the following steps: S51, calculating the difference in regional cross-power spectrum density between the slab to be identified and the corresponding area of ​​the reference slab: ΔP fij (f)=P fij,l (f)-P fij,h (f) Where ΔP fij (f) represents the regional cross-power spectral density difference at frequency f, ΔP fij,l (f) and ΔP fij,h (f) Regional cross-power spectral densities of the corresponding area of ​​the slab to be identified and the reference area of ​​the reference slab, respectively; S52, integrating the difference in the regional cross-power spectrum density: Where, DI void It represents the identification index of the gap area, a and b represent the lower and upper limits of the frequency respectively; S53, judging the hollow position of the paving panel to be identified according to the hollow area identification index: using the area ID as the horizontal coordinate and the identification index of the hollow area corresponding to the area ID as the vertical coordinate to draw a two-dimensional image, identifying the inflection point of the two-dimensional image, and the area corresponding to the identified inflection point is the location of the hollow area of ​​the paving panel to be identified.

10. The method for identifying voids under prefabricated decking panels according to claim 1, characterized in that: In S1, before calculating the cross power spectrum density of the entire board, the vibration signal is preprocessed, and the preprocessing method used is one of bandpass filtering, high-pass filtering, and low-pass filtering, or a combination thereof.

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