A method and system for tunnel lining concrete assessment
By employing spatial phase locking and adaptive gain factor technology in a multi-channel polarization ground-penetrating radar system, interference from reinforcing bars in tunnel lining is effectively eliminated, improving the accuracy and reliability of tunnel lining defect detection, solving the problem of reinforcing bar interference accidentally damaging defect signals, and achieving effective identification and evaluation of defect signals.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHENGZHOU ENG CO LTD CHINA RAILWAY SEVENTH GRP
- Filing Date
- 2026-04-10
- Publication Date
- 2026-07-14
Smart Images
Figure CN122385643A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of tunnel lining quality testing technology, and in particular to a method and system for evaluating tunnel lining concrete. Background Technology
[0002] As a crucial component of tunnel structure, the quality of tunnel concrete lining directly impacts the tunnel's operational safety and service life. Ground Penetrating Radar (GPR), a non-destructive testing technology, is widely used in tunnel lining quality inspection due to its advantages of high efficiency, speed, and continuous detection. It can effectively identify defects such as lining voids, water accumulation, and lack of compaction.
[0003] However, in actual tunnel lining, a dense steel mesh is typically laid out to enhance structural strength. The steel bars generate strong reflected signals of electromagnetic waves, the energy of which is often far greater than the defect signal, severely masking defect information and posing significant challenges to radar data interpretation and defect identification. Therefore, effectively suppressing steel bar interference while preserving defect signals has become a key technical challenge in GPR (Geometric Reinforcement Processing) inspection of tunnel linings.
[0004] Existing methods for suppressing interference from reinforcing bars mainly include the following categories: The first category is filtering-based methods, such as frequency-wavenumber (FK) filtering, Radon transform filtering, and bandpass filtering. These methods assume that the reflected signal from the rebar has specific characteristics in the frequency or transform domain and remove it by designing filters. However, these methods typically assume that the rebar spacing is fixed and the arrangement is regular, making it difficult to adapt to situations such as uneven rebar spacing and fluctuations in vehicle speed during actual construction. Furthermore, filtering methods often use globally fixed parameters, which cannot adaptively adjust according to local signal characteristics. This can easily lead to the inadvertent removal of defect signals while eliminating rebar interference, especially when defects are close to the rebar, where defect signals are easily filtered out as well.
[0005] The second category is based on signal decomposition methods, such as Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD). These methods attempt to decompose mixed signals into independent components, and then identify and remove the reinforcing steel components. However, these methods typically only utilize single-channel or dual-channel data, failing to fully leverage the polarization information of electromagnetic waves, resulting in limited ability to distinguish between reinforcing steel and defects. Furthermore, signal decomposition methods have high computational complexity, making them difficult to meet the needs of rapid on-site processing in engineering projects.
[0006] Therefore, there is a need to provide an improved technical solution that addresses the shortcomings of the existing technology. Summary of the Invention
[0007] This application aims to provide a steel reinforcement interference suppression scheme that can fully utilize full polarization data, effectively track the spatial periodicity of steel reinforcement, integrate polarization characteristics and spatial information, and achieve adaptive strength adjustment, so as to improve the accuracy and reliability of tunnel lining defect detection.
[0008] To achieve the above objectives, this application provides the following technical solution: Firstly, this application provides a method for evaluating tunnel lining concrete, including: Data acquisition and matrix construction steps: Simultaneously acquire fully polarimetric data using a multi-channel polarimetric ground-penetrating radar system, perform complex analytical transformation on each trace, and establish the complex domain scattering matrix for each sampling point; Spatial phase locking steps: Initialize the spatial frequency of the reinforcing bar based on the total energy distribution of full polarization, construct a spatial phase locking loop, track the spatial phase of the reinforcing bar in real time, determine the locking status based on the phase difference, and determine the high-weight prediction area of the reinforcing bar. Polarization characteristic calculation steps: Within the high-weight prediction area of the reinforcing bars, perform eigenvalue decomposition on the local covariance matrix, and calculate the polarization entropy characterizing the degree of scattering disorder and the average scattering angle characterizing the physical type of the scatterer; Adaptive projection stripping step: Generate an adaptive gain factor based on the polarization entropy and average scattering angle, construct an orthogonal complementary space projection matrix, adjust the projection intensity of the projection matrix using the adaptive gain factor, perform projection transformation on the original polarization vector, and obtain the residual signal after removing the reinforcement interference. Evaluation steps: Reconstruct the defect signal based on the residual signal to evaluate the quality of the tunnel concrete lining.
[0009] Preferably, the data acquisition and matrix construction step involves performing a complex analytical transformation on each trace to establish a complex domain scattering matrix for each sampling point, specifically including: The time-domain signals of the four channels HH, VV, HV, and VH are subjected to Hilbert transforms respectively to construct analytical signals; Scan location and round trip As an index, the analytical signals from the four channels are combined to construct each sampling point. Complex field scattering matrix .
[0010] Preferably, in the spatial phase locking step, initializing the spatial frequency of the reinforcing bars based on the total energy distribution of full polarization specifically includes: Select data within a preset length range at the beginning of the survey line as the initialization window; Calculate the total energy of full polarization within the initialization window. The autocorrelation function; Extract the average displacement between energy peaks in the autocorrelation function. According to the average displacement Calculate the initial spatial frequency .
[0011] Preferably, the preset length range is the first 2 meters of the survey line; the spatial phase-locked loop adopts a proportional-integral (PI) controller, wherein the proportional coefficient is... The range of values is Integral coefficient The range of values is .
[0012] Preferably, the specific logic for determining the locking state in the spatial phase locking step is as follows: Calculate the observed phase With predicted phase phase difference ; If the phase difference If the phase value is less than the preset phase threshold and continues for a preset number of cycles, it is determined to be in a locked state. Wherein, the preset phase threshold is The preset number of cycles is 2 cycles.
[0013] Preferably, the polarization characteristic calculation step specifically includes: Within the high-weight prediction region of the reinforcing bars, a local covariance matrix is constructed; The local covariance matrix is decomposed into eigenvalues to obtain eigenvalues; Calculate the polarization entropy based on the eigenvalues. The average scattering angle is calculated based on the eigenvector corresponding to the eigenvalue. ; Wherein, the polarization entropy The average scattering angle is used to characterize the degree of disorder in scattering. It is used to characterize the physical type of a scatterer.
[0014] Preferably, the adaptive projection stripping step, which generates an adaptive gain factor based on the polarization entropy and the average scattering angle, includes: Based on the polarization entropy Calculate the base gain value ; The scattering angle correction factor is calculated based on the average scattering angle. The scattering angle correction coefficient Characterizing the consistency between the physical type of the scatterer and the properties of the reinforced dipole; The weighted confidence score is calculated based on the prediction results of the spatial phase-locked loop. Weighted confidence score It represents the degree of spatial agreement between the current location and the predicted location of the reinforcing bars; The weighted confidence scores are used to spatially augment the base gain value to obtain a preliminary gain value. The initial gain value is physically consistent with the scattering angle correction coefficient to obtain the adaptive gain factor. The expression is as follows: .
[0015] Preferably, the base gain value Based on the polarization entropy Segmented calculation: When the polarization entropy ,set up ; When the polarization entropy ,set up Preset minimum gain , ; when ,set up With polarization entropy The increase of decreases linearly; in, and These are the preset minimum and maximum entropy thresholds.
[0016] Preferably, in the adaptive projection stripping step, the orthogonal complement space projection matrix is: , In the formula, For point Adaptive gain factor at location, For the scan location, For two-way travel, Represents polarization entropy, Point The projection matrix at the location, These are the largest eigenvectors. yes The transpose of .
[0017] Secondly, this application provides a tunnel lining concrete evaluation system, which is used to perform the steps of the tunnel lining concrete evaluation method provided in any of the above embodiments, including: The data acquisition and matrix construction unit is configured to simultaneously acquire fully polarized data using a multi-channel polarimetric ground-penetrating radar system, perform complex analytical transformation on each trace, and establish a complex domain scattering matrix for each sampling point. The spatial phase locking unit is configured to initialize the spatial frequency of the reinforcing bar based on the total energy distribution of the full polarization, construct a spatial phase locking loop, track the spatial phase of the reinforcing bar in real time, determine the locking status based on the phase difference, and determine the high-weight prediction area of the reinforcing bar. The polarization feature calculation unit is configured to perform eigenvalue decomposition on the local covariance matrix within the high-weight prediction area of the reinforcing bars, and calculate the polarization entropy characterizing the degree of scattering disorder and the average scattering angle characterizing the physical type of the scatterer. An adaptive projection stripping unit is configured to generate an adaptive gain factor based on the polarization entropy and the average scattering angle, construct an orthogonal complementary space projection matrix, adjust the projection intensity of the projection matrix using the adaptive gain factor, perform a projection transformation on the original polarization vector, and obtain the residual signal after removing the steel reinforcement interference. The evaluation unit is configured to reconstruct the defect signal based on the residual signal in order to evaluate the quality of the tunnel concrete lining.
[0018] Beneficial effects: The technical solution of this application introduces a Spatial Phase-Locked Loop (SPLL) mechanism. Based on the phase-locked principle in the field of communication, this solution effectively adapts to non-ideal situations in actual tunnel construction, such as uneven rebar spacing, fluctuations in vehicle speed, and local bending of rebars, by tracking the spatial frequency and phase of the rebar reflection signal in real time. Compared with traditional fixed-parameter filtering methods, this solution can dynamically adjust the predicted trajectory to ensure accurate locking even when the rebar position deviates slightly, significantly improving the accuracy of rebar interference location. Simultaneously, by constructing an adaptive gain factor and adjusting the projection intensity of the projection matrix using this factor, the gain factor automatically decreases in defect areas, subtracting only the energy attributed to the rebar characteristics. This fully preserves the high-frequency details and edge information of defects such as voids and water accumulation, maximizing the preservation of defect signals and improving the accuracy of tunnel concrete lining quality assessment.
[0019] This application utilizes orthogonal complementary spatial projection theory for signal stripping, fundamentally eliminating the dominant scattering component of reinforcing bars. Combined with the smooth transition characteristics of the adaptive gain factor, the processed residual signal exhibits better continuity in the spatial domain, avoiding striped artifacts caused by abrupt changes in processing intensity. The reconstructed defect image has a clean background and clear contours, significantly improving the signal-to-noise ratio. This makes subsequent feature extraction and quality assessment more objective and accurate, reducing the difficulty of manual interpretation and subjective errors. Attached Figure Description
[0020] Figure 1 This is a flowchart illustrating the evaluation method for tunnel lining concrete.
[0021] Figure 2 This is a schematic diagram of an electronic device. Detailed Implementation
[0022] The embodiments of this application will now be described with reference to the accompanying drawings.
[0023] In tunnel environments, reinforcing steel is a linearly polarized body, and its scattering characteristics reach their maximum when the electromagnetic wave electric field direction is parallel to the reinforcing steel. Concrete defects (such as voids, cavities, and interfaces) typically exhibit anisotropic or edge diffraction characteristics. This application provides a tunnel lining concrete evaluation scheme that utilizes the polarization characteristics of reinforcing steel to separate the reinforcing steel interference signal from the original ground-penetrating radar signal, obtains the residual signal, and then reconstructs the defect signal to accurately evaluate the quality of tunnel lining concrete under complex reinforcing steel interference, providing a reference for engineering decision-making.
[0024] Example 1 This embodiment provides a method for evaluating tunnel lining concrete, such as... Figure 1 As shown, the method includes: Step S1, Data Acquisition and Matrix Construction: Simultaneously acquire fully polarized data using a multi-channel polarimetric ground-penetrating radar system, perform complex analytical transformation on each trace, and establish the complex domain scattering matrix for each sampling point.
[0025] In a preferred embodiment, step S1, the data acquisition and matrix construction step, specifically includes: performing Hilbert transforms on the acquired time-domain signals of the four channels HH, VV, HV, and VH respectively to construct analytical signals; and using the scan position... and round trip As an index, the analytical signals from the four channels are combined to construct each sampling point. Complex field scattering matrix .
[0026] The data acquisition and matrix construction process is described in detail below. Step S1 specifically includes the following sub-steps: Step S11: A multi-channel polarimetric ground-penetrating radar (GPR) system is used for on-site data acquisition. This system is equipped with a dual-polarized antenna array, capable of simultaneously transmitting and receiving horizontally polarized (H) and vertically polarized (V) electromagnetic waves. The acquisition process simultaneously records the raw time-domain signals of four channels: horizontal transmission / horizontal reception (HH), vertical transmission / vertical reception (VV), horizontal transmission / vertical reception (HV), and vertical transmission / horizontal reception (VH). The data from these four channels are located in spatial positions (…). ) and time sampling points ( Strict alignment is required on the polarization plane to ensure consistency in subsequent polarization analyses.
[0027] Step S12 involves performing a complex analytic transformation on each acquired trace (i.e., the time-series signal at a single scan position) to construct an analytic signal. In this embodiment, the Hilbert transform is preferably used to convert the real-domain signal to the complex-domain. For real signals in any channel... Its analytical signal Represented as: , In the formula, The imaginary unit, for The result of the Hilbert transform. Through this transformation, the data at each sampling point is expanded from real magnitude values to complex numbers containing both magnitude and phase information.
[0028] Step S13: Establish the complex domain scattering matrix for each sampling point.
[0029] The complex domain scattering matrix, also known as the Sinclair matrix, is a mathematical model describing the polarization characteristics of a scattering target. Each element in the matrix is a complex number, with the magnitude representing the scattering intensity and the phase representing the phase delay of the scattered wave relative to the incident wave. For each spatial location... and round trip The sampling points are used to combine the complex analytic signals from the four channels to construct a 2×2 complex scattering matrix. : , In the formula, matrix elements , , , These represent the complex analytical signal values on the corresponding channels of HH, HV, VH, and VV, respectively.
[0030] By acquiring fully polarized four-channel data and constructing a scattering matrix, this scheme can completely preserve the differences between reinforcing bars (usually dipole scatterers) and defects (such as voids and water accumulation, usually diffuse or surface scatterers) in the polarization domain, providing a data foundation for subsequent differentiation between reinforcing bars and defects.
[0031] It should be noted that although this embodiment uses a 2×2 scattering matrix as an example, in actual applications, the dimensions of the scattering matrix can be adjusted according to different antenna configurations, as long as it can characterize the multi-channel polarization scattering relationship, all of which fall within the protection scope of this application.
[0032] Step S2, Spatial Phase Locking Step: Initialize the spatial frequency of the reinforcing bar based on the total energy distribution of full polarization, construct a spatial phase locking loop, track the spatial phase of the reinforcing bar in real time, determine the locking state based on the phase difference, and determine the high-weight prediction area of the reinforcing bar.
[0033] Specifically, based on the total energy distribution of full polarization, the spatial frequency of the reinforcing bars is initialized, and a spatial phase-locked loop is constructed, including: Step S21: Select data within a preset length range at the beginning of the survey line as the initialization window. For example, the preset length range is the first 2 meters of the survey line as the initialization window.
[0034] Step S22: Calculate the total energy of full polarization within the initialization window. The autocorrelation function.
[0035] First, based on the complex domain scattering matrix constructed in the previous steps, the scattering along the survey line direction (scanning position) is calculated. Total energy distribution of full polarization Specifically, the energy of all channels for each trace is accumulated using the following formula: , Because the reflected signal from the reinforcing steel is usually stronger than that from the background medium. It will exhibit periodic peaks at the location of the reinforcing bars.
[0036] Then, within the initialization window, calculate... autocorrelation function : , Step S23: Extract the average displacement between energy peaks in the autocorrelation function. According to the average displacement Calculate the initial spatial frequency .
[0037] Specifically, search for autocorrelation functions The first significant peak position excluding zero hysteresis is the displacement corresponding to the average spacing of the reinforcing bars. The initial spatial frequency is calculated from this. This frequency represents the initial spatial periodicity of the steel reinforcement arrangement.
[0038] Step S24, to initialize the spatial frequency For initial driving, a spatial phase-locked loop is constructed. Furthermore, the spatial phase-locked loop is implemented based on a proportional-integral (PI) controller, mainly comprising: Phase detector (PD): calculates the observed phase With predicted phase phase difference ,in, The instantaneous phase derived from the fully polarized energy at the current scan position (extracted via Hilbert transform). The output originates from the loop integrator.
[0039] Loop filter: Calculate the observed phase With predicted phase phase difference The feedback controller is used to monitor and track the phase difference in real time. That is, a proportional-integral controller is used to process the phase difference error signal. Controller output Used to correct spatial frequency: , in, This is the proportionality coefficient. is the integral coefficient.
[0040] Among them, the proportionality coefficient The range of values is Integral coefficient The range of values is .
[0041] Step S25, lock state determination, the specific logic is: if the phase difference If the phase value is less than a preset phase threshold and persists for a preset number of cycles, it is determined to be in a locked state; where the preset phase threshold is... The preset number of cycles is 2.
[0042] Specifically, phase difference The absolute value is less than the preset phase threshold (e.g. If the loop continues for a preset number of cycles (e.g., 2 spatial cycles), it is determined that the loop has entered a locked state. At this time, the predicted phase trajectory output by the loop is regarded as the high-weight prediction region of the rebar position, which is used to guide the subsequent polarization feature calculation.
[0043] Specifically, when the phase difference If the aforementioned locking conditions are met, such as the absolute value being less than And for two consecutive spatial cycles, the current scanning position will be... The area of the pre-defined spatial window adjacent to it is marked as the high-weight prediction area for the reinforcement.
[0044] In this embodiment, by drawing on phase-locked loop technology in the field of communication, its application domain is extended from the time domain to the spatial domain. Its function is to enable the predicted spatial phase signal to automatically track the spatial phase change of the input signal (reflection of steel bars) through a feedback mechanism, thereby adapting to the non-uniform changes in steel bar spacing and solving the problem of missed or false detection of steel bars caused by the variable steel bar spacing and the fluctuation of detection vehicle speed in actual tunnel construction.
[0045] Step S3, Polarization Feature Calculation Step: Within the high-weight prediction area of the reinforcing bars, perform eigenvalue decomposition on the local covariance matrix to calculate the polarization entropy characterizing the degree of scattering disorder and the average scattering angle characterizing the physical type of the scatterer.
[0046] Furthermore, step S3 specifically includes the following sub-steps: Step S31: Construct a local covariance matrix within the high-weight prediction region for reinforcing bars.
[0047] Specifically, firstly, within the high-weight prediction region, the 2×2 complex scattering matrix established in the aforementioned steps is... Converted to polarization scattering vector In this embodiment, the Pauli basis decomposition method is preferably used to construct a 3D complex vector: , In the formula, Represents odd-order scattering components. Represents even-order scattering components. Representative volume scattering component.
[0048] This embodiment utilizes prior information from high-weight prediction regions to perform complex covariance matrix calculations and eigenvalue decompositions only in areas where reinforcement interference may exist, thereby reducing computational complexity.
[0049] Secondly, the local covariance matrix is calculated. To suppress noise and stabilize the estimation of scattering characteristics, a moving average window is introduced in the spatial domain. For each central sampling point in a high-weight region, the surrounding values are taken. Calculate the local covariance matrix using a neighborhood window (e.g., a 3×3 or 5×5 window). : , In the formula, This indicates a spatial averaging operation. The vector represents the Hermitian conjugate (transpose conjugate), where i represents... The sampling point number within the neighborhood window. (Generated) It is a 3×3 positive semidefinite Hermitian matrix.
[0050] Step S32: Perform eigenvalue decomposition on the local covariance matrix to obtain eigenvalues.
[0051] Specifically, for the local covariance matrix Perform eigenvalue decomposition and solve for its eigenvalues. and the corresponding feature vector : , In the formula, For the characteristic value index, , It is an eigenvalue diagonal matrix. And satisfy ; The eigenvector matrix, The three eigenvalues obtained from the decomposition represent the power distributions of the three independent scattering mechanisms, and are used in subsequent calculations of polarization entropy. and average scattering angle The foundation.
[0052] In tunnel lining, the steel mesh may exist in multiple layers or cross. The local covariance matrix combined with the eigenvalue distribution can capture information about the complex scattering structure of the steel mesh, so that this scheme can not only handle single-layer steel mesh, but also cope with the interference suppression of multi-layer steel mesh to a certain extent, thus enhancing the robustness of the scheme.
[0053] It should be noted that although this embodiment uses the Pauli basis to construct the 3D scattering vector, in other embodiments, the Lexicographic basis can also be used to construct the 4D vector, or the Coherency matrix can be used instead of the Covariance matrix. As long as the polarization scattering features can be extracted through eigenvalue decomposition, they all fall within the protection scope of this application.
[0054] Step S33: Calculate polarization entropy based on eigenvalues The average scattering angle is calculated based on the eigenvectors corresponding to the eigenvalues. Among them, polarization entropy The average scattering angle is used to characterize the degree of disorder in scattering. This is used to characterize the physical type of a scatterer. Specifically, it includes the following sub-steps: (1) Calculate the probability distribution of eigenvalues: , in, For the first The contribution of each scattering mechanism to the total scattering power. .
[0055] (2) Calculate the polarization entropy Using the above probability distribution, calculate the normalized polarization entropy. This is used to characterize the degree of randomness in the scattering process. The formula is as follows: , The logarithm base is 3, which ensures that for a 3-dimensional scattering vector, the polarization entropy is... The range of values is normalized to .when When, it indicates completely deterministic scattering (dominated by a single mechanism); when When , it represents completely random scattering (each mechanism has equal energy).
[0056] (3) Extracting the scattering angle of a single target .
[0057] The corresponding eigenvectors obtained from eigenvalue decomposition Extract the scattering angle of each scattering mechanism : , In the formula, This represents the magnitude of the first component of the eigenvector.
[0058] (4) Calculate the average scattering angle : , In this embodiment, parameters are used. Identify the dipole characteristics of reinforcing bars.
[0059] Step S4, Adaptive Projection Stripping Step: Generate an adaptive gain factor based on the polarization entropy and average scattering angle, construct an orthogonal complementary space projection matrix, adjust the projection intensity of the projection matrix using the adaptive gain factor, perform a projection transformation on the original polarization vector, and obtain the residual signal after removing the steel reinforcement interference.
[0060] In one embodiment, step S4, generating an adaptive gain factor based on the polarization entropy and the average scattering angle, includes: If polarization entropy And the average scattering angle The region is identified as a pure rebar region, approaching the typical center value of the scattering angle of the reinforcing steel, and an adaptive gain factor is generated. This triggers the full-intensity peeling mode; If polarization entropy The area was identified as a defect, and the adaptive gain factor was reduced. It enters partial stripping mode; If the polarization entropy satisfies This is identified as a transitional region, and a weighted confidence score is introduced. If the current position is at the center of the spatiotemporal window of the spatial phase-locked prediction, the weight is assigned to the rebar stripping.
[0061] and For example, the preset minimum and maximum entropy thresholds are preferably set. , .
[0062] In this embodiment, a three-level decision logic is constructed to generate an adaptive gain factor: determining whether the current sampling point satisfies the pure steel reinforcement feature, i.e., polarization entropy. And the average scattering angle The region is identified as a pure reinforcing steel region, with the scattering angle approaching the typical center value of the reinforcing steel. The resulting adaptive gain factor... This triggers a full-strength stripping mode, which, in subsequent projection steps, almost completely eliminates the signal energy attributed to the rebar feature vector, ensuring that rebar interference is thoroughly removed. Secondly, it determines whether the current sampling point possesses defect characteristics: polarization entropy. If the region is identified as a defect region, the adaptive gain factor is significantly reduced, and a partial stripping mode is entered. Only a very small portion of the energy attributable to the rebar feature vector is subtracted, or the signal is completely preserved. This prevents the defect signal from being mistakenly identified as a rebar signal and mistakenly deleted, ensuring the integrity of the defect information. Furthermore, the transition region determination is weighted by confidence, i.e., handling ambiguous regions with indistinct polarization characteristics: if the polarization entropy satisfies... If the region is identified as a transitional region, it is difficult to accurately classify it using polarization parameters alone. Therefore, it is necessary to introduce spatial prior information, namely, weighted confidence scores. To assist in the processing.
[0063] As a concrete example, the adaptive gain factor Generate using the following steps: Based on polarization entropy Calculate the fundamental gain value, which is to construct the polarization entropy. With base gain The fundamental mapping function between them. Due to polarization entropy Characterizing the randomness of scattering, reinforcing bars, as deterministic scatterers, exhibit low entropy, while defects possess high entropy. Therefore, the gain factor and polarization entropy... They exhibit a negative correlation. In this embodiment, a piecewise linear function is used to construct the mapping: when the polarization entropy ,set up This represents a single rebar signal, i.e., a pure rebar region; when the polarization entropy This indicates that the defect signal is the primary indicator, and the settings are as follows: Preset minimum gain , ;when , as a transition zone, set With polarization entropy The increase of is linearly decreasing. The expression is as follows: , In the formula, and For example, the preset minimum and maximum entropy thresholds are used. 0.4, 0.6. When At that time, the height of the rebar signal is considered fixed, and the foundation gain is 1 (full strength); when At that time, the signal was considered to be highly random, and the fundamental gain tended to be a very small gain. (Weak intensity).
[0064] Furthermore, a scattering angle correction coefficient is introduced. Characterize the consistency between the physical type of the scatterer and the properties of the reinforcing dipole.
[0065] Using the average scattering angle The base gain value is corrected to exclude low-entropy interference targets that are not reinforced, such as metal pipelines or other dipole foreign objects. A scattering angle correction coefficient is defined. as follows: , in, The typical center value of the scattering angle of the reinforcing steel is 45°. The tolerance range is preferably 5°. When Approaching 45° and within tolerance Within the range, ,when Deviating from this range, Rapid decay.
[0066] Furthermore, a weighted confidence score is introduced. In this embodiment, the instantaneous phase difference is calculated based on spatial phase locking. And then calculate : , The degree of spatial alignment between the current location and the predicted location of the reinforcing bars, when fully locked. , When the phase deviation increases, It rapidly decays to 0. The value is normalized to the [0,1] interval, representing the probability that the current position is at the center of the prediction window for the steel reinforcement.
[0067] Based on the calculation With base gain Based on this, the preliminary gain value is calculated using the following formula: , Finally, using the scattering angle correction factor The initial gain value is corrected for physical consistency to obtain the adaptive gain factor. : , In this embodiment, through avoid Risk of misjudgment of location; ensure correct stripping of reinforcing bars.
[0068] It should be noted that although this embodiment provides specific thresholds (such as...), , , 45° tolerance The threshold is 5°, but in practical applications, these thresholds can be fine-tuned according to the specific radar frequency, the grade of the lining concrete, and the diameter of the reinforcing bars.
[0069] Using adaptive gain factor The projection intensity of the orthogonal complement projection matrix is adjusted by the following expression: , In the formula, For point Adaptive gain factor at location, For the scan location, For two-way travel, Represents polarization entropy, Point The projection matrix at the location, These are the largest eigenvectors. yes The transpose of .
[0070] The original polarization vector The residual signal after removing the reinforcing bars is obtained by transformation using the projection operator. : , According to the above formula, when the signal is dominated by a single reinforcing bar, the polarization entropy is... Lower Full-force projection stripping of rebar signal; when the signal contains complex defects, polarization entropy High, This reduces the scattering detail, preserving more details and preventing the accidental deletion of defect signals during rebar signal processing.
[0071] Step S5, Evaluation Step: Reconstruct the defect signal based on the residual signal to evaluate the quality of the tunnel concrete lining.
[0072] In step S5, the evaluation step involves reconstructing the defect signal based on the residual signal, including: introducing a variational energy function to correct the residual signal, and achieving variational continuity constraint smoothing by minimizing the variational energy function to eliminate artifacts generated by the projection process.
[0073] Due to the adaptive gain factor in the preceding steps Spatial variations can cause discontinuities or artifacts in the residual signal between adjacent channels. Therefore, a variational energy function is first introduced to correct the residual signal. The variational energy function is constructed by including a data fidelity term and a regularization smoothing term: , In the formula, Let be the variational energy function. The corrected signal, For regularization parameters, This is the gradient operator. By minimizing this energy function, for example using the split Bregman algorithm for iterative solution, a smooth residual signal with spatial continuity is obtained, ensuring that the edges of the defect signal are natural and smooth.
[0074] Based on the smoothed residual signal, a radar profile (B-Scan) of the tunnel lining is reconstructed. Since the reinforcement interference signal has been adaptively projected and removed, the reconstructed image mainly retains the defect scattering signal and background noise. A migration imaging algorithm (such as Kirchhoff migration) is used to focus the residual signal, mapping the two-way travel time domain signal to a depth domain image, restoring the true spatial geometry of the defect, and obtaining the reconstructed defect image.
[0075] On the reconstructed defect image, defect feature parameters are automatically extracted, including: Reflection amplitude intensity: characterizes the degree of difference in dielectric constant of defects (such as water accumulation, voids); Spatial distribution range: Characterizing the area or volume of the defect; Morphological continuity: characterizes the extent of crack extension.
[0076] Based on the extracted feature parameters, a quality assessment report is generated by comparing the results with tunnel lining quality assessment standards (such as railway tunnel lining defect judgment standards). For example, if a continuous high-amplitude anomaly is detected and located at the arch crown, it is judged as severe voiding; if a scattered low-amplitude anomaly is detected, it is judged as localized non-compactness.
[0077] In the preliminary steps of this embodiment, the adaptive gain factor The values differ in different regions (pure reinforcement, transition zone, defect zone), and direct projection may cause abrupt changes in the signal at the interface. This scheme uses variational energy function smoothing to ensure the spatial continuity of the final imaging result, avoids false defects or false fractures caused by the algorithm processing itself, and improves the reliability of the evaluation results.
[0078] Since the interference from the reinforcing bars has been specifically removed, the main energy in the residual signal comes from the defects. The reconstructed image has a clean background and clear defect outlines, which enables subsequent feature extraction algorithms to more accurately identify minute defects and reduce the false negative rate.
[0079] Example 2 This embodiment provides a tunnel lining concrete evaluation system, which is used to perform the tunnel lining concrete evaluation method provided in any of the above embodiments, including: The data acquisition and matrix construction unit is configured to simultaneously acquire fully polarized data using a multi-channel polarimetric ground-penetrating radar system, perform complex analytical transformation on each trace, and establish a complex domain scattering matrix for each sampling point.
[0080] The spatial phase locking unit is configured to initialize the spatial frequency of the reinforcing bar based on the total energy distribution of the full polarization, construct a spatial phase locking loop, track the spatial phase of the reinforcing bar in real time, determine the locking status based on the phase difference, and determine the high-weight prediction area of the reinforcing bar. The polarization feature calculation unit is configured to perform eigenvalue decomposition on the local covariance matrix within the high-weight prediction area of the reinforcing bars, and calculate the polarization entropy characterizing the degree of scattering disorder and the average scattering angle characterizing the physical type of the scatterer. An adaptive projection stripping unit is configured to generate an adaptive gain factor based on the polarization entropy and the average scattering angle, construct an orthogonal complementary space projection matrix, adjust the projection intensity of the projection matrix using the adaptive gain factor, perform a projection transformation on the original polarization vector, and obtain the residual signal after removing the steel reinforcement interference. The evaluation unit is configured to reconstruct the defect signal based on the residual signal in order to evaluate the quality of the tunnel concrete lining.
[0081] The tunnel lining concrete evaluation system provided in this embodiment can realize the steps and processes of the tunnel lining concrete evaluation method provided in any of the above embodiments and achieve the same technical effect, which will not be described in detail here.
[0082] The embodiments of this application can be applied to Figure 2 Among the electronic devices shown, the electronic devices may be, but are not limited to, mobile terminals such as mobile phones, tablets, handheld computers, and personal digital assistants (PDAs), smart home devices such as smart TVs and smart cameras, wearable devices such as smart bracelets, smartwatches, and smart glasses, or other computer devices such as desktop, laptop, notebook, ultra-mobile personal computer (UMPC), netbook, and smart screen.
[0083] like Figure 2 As shown, the electronic device 200 may include one or more of the following components: a processor 201, a memory 203, a communication interface 202, and a communication bus 204. The memory 203 can be connected to the processor 201 via the bus 204. The bus can transfer data between the processor 201 and the memory 203. The bus can be divided into an address bus, a data bus, a control bus, etc.
[0084] Processor 201 may include one or more processing cores. Processor 201 can connect to various parts within the electronic device 200 using various interfaces and lines. It performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 203, and by calling data stored in memory 203. For example, processor 201 may include an application processor (AP), a modem processor, a CPU, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA), and / or a neural network processing unit (NPU). The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed; the NPU implements artificial intelligence (AI) functions; and the modem handles wireless communication. Different processing units can be independent devices or integrated into one or more processors. For example, the multiple processing units shown above are all integrated into a single SoC, or the AP is a separate semiconductor chip, while other processing units are integrated into a single SoC. This application does not limit this to any particular type.
[0085] Memory 203 may include random access memory (RAM), read-only memory (ROM), or non-transitory computer-readable storage medium. Memory 203 can be used to store instructions, programs, code, code sets, or instruction sets. Memory 203 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system or instructions for at least one function, such as a method for evaluating tunnel lining concrete. The data storage area may store data created based on the use of electronic device 200, such as input data for numerical solutions.
[0086] In addition, those skilled in the art will understand that the structure of the electronic device 200 shown in the above figures does not constitute a limitation on the electronic device 200. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the electronic device 200 may also include components such as a microphone, speaker, radio frequency circuit, sensor, audio circuit, power supply, and Bluetooth module, which will not be described in detail here.
[0087] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for evaluating tunnel lining concrete, characterized in that, include: Data acquisition and matrix construction steps: Simultaneously acquire fully polarimetric data using a multi-channel polarimetric ground-penetrating radar system, perform complex analytical transformation on each trace, and establish the complex domain scattering matrix for each sampling point; Spatial phase locking steps: Initialize the spatial frequency of the reinforcing bar based on the total energy distribution of full polarization, construct a spatial phase locking loop, track the spatial phase of the reinforcing bar in real time, determine the locking status based on the phase difference, and determine the high-weight prediction area of the reinforcing bar. Polarization characteristic calculation steps: Within the high-weight prediction area of the reinforcing bars, perform eigenvalue decomposition on the local covariance matrix, and calculate the polarization entropy characterizing the degree of scattering disorder and the average scattering angle characterizing the physical type of the scatterer; Adaptive projection stripping step: Generate an adaptive gain factor based on the polarization entropy and average scattering angle, construct an orthogonal complementary space projection matrix, adjust the projection intensity of the projection matrix using the adaptive gain factor, perform projection transformation on the original polarization vector, and obtain the residual signal after removing the reinforcement interference. Evaluation steps: Reconstruct the defect signal based on the residual signal to evaluate the quality of the tunnel concrete lining.
2. The method according to claim 1, characterized in that, The data acquisition and matrix construction steps involve performing a complex analytical transformation on each trace to establish a complex domain scattering matrix for each sampling point, specifically including: The time-domain signals of the four channels HH, VV, HV, and VH are subjected to Hilbert transforms respectively to construct analytical signals; Scan location and round trip As an index, the analytical signals from the four channels are combined to construct each sampling point. Complex field scattering matrix .
3. The method according to claim 1, characterized in that, In the spatial phase locking step, the spatial frequency of the reinforcing bars is initialized based on the total energy distribution of full polarization, specifically including: Select data within a preset length range at the beginning of the survey line as the initialization window; Calculate the total energy of full polarization within the initialization window. The autocorrelation function; Extract the average displacement between energy peaks in the autocorrelation function. According to the average displacement Calculate the initial spatial frequency .
4. The method according to claim 3, characterized in that, The preset length range is the first 2 meters of the measuring line; The spatial phase-locked loop employs a proportional-integral (PI) controller, wherein the proportional coefficient is... The range of values for is Integral coefficient The range of values for is .
5. The method according to claim 1, characterized in that, The specific logic for determining the locking state in the spatial phase locking step is as follows: Calculate the observed phase With predicted phase phase difference ; If the phase difference If the phase value is less than the preset phase threshold and continues for a preset number of cycles, it is determined to be in a locked state. Wherein, the preset phase threshold is The preset number of cycles is 2 cycles.
6. The method according to claim 1, characterized in that, The polarization characteristic calculation steps specifically include: Within the high-weight prediction region of the reinforcing bars, a local covariance matrix is constructed; The local covariance matrix is decomposed into eigenvalues to obtain eigenvalues; Calculate the polarization entropy based on the eigenvalues. The average scattering angle is calculated based on the eigenvector corresponding to the eigenvalue. ; Wherein, the polarization entropy The average scattering angle is used to characterize the degree of disorder in scattering. It is used to characterize the physical type of a scatterer.
7. The method according to claim 6, characterized in that, The adaptive projection stripping step, which generates an adaptive gain factor based on the polarization entropy and the average scattering angle, includes: Based on the polarization entropy Calculate the base gain value ; The scattering angle correction factor is calculated based on the average scattering angle. The scattering angle correction coefficient Characterizing the consistency between the physical type of the scatterer and the properties of the reinforced dipole; The weighted confidence score is calculated based on the prediction results of the spatial phase-locked loop. Weighted confidence score It represents the degree of spatial agreement between the current location and the predicted location of the reinforcing bar; The weighted confidence scores are used to spatially augment the base gain value to obtain a preliminary gain value. The initial gain value is physically consistent with the scattering angle correction coefficient to obtain the adaptive gain factor. The expression is as follows: 。 8. The method according to claim 7, characterized in that, Base gain value Based on the polarization entropy Segmented calculation: When the polarization entropy ,set up ; When the polarization entropy ,set up Preset minimum gain , ; when ,set up With polarization entropy The increase of decreases linearly; in, and These are the preset minimum and maximum entropy thresholds.
9. The method according to claim 8, characterized in that, In the adaptive projection stripping step, the orthogonal complement space projection matrix is: ， In the formula, For point Adaptive gain factor at location For the scan location, For two-way travel, Represents polarization entropy, Point The projection matrix at the location, These are the largest eigenvectors. yes The transpose of .
10. A tunnel lining concrete evaluation system, characterized in that, The system is used to perform the steps of the method according to any one of claims 1 to 9, including: The data acquisition and matrix construction unit is configured to simultaneously acquire fully polarized data using a multi-channel polarimetric ground-penetrating radar system, perform complex analytical transformation on each trace, and establish a complex domain scattering matrix for each sampling point. The spatial phase locking unit is configured to initialize the spatial frequency of the reinforcing bar based on the total energy distribution of the full polarization, construct a spatial phase locking loop, track the spatial phase of the reinforcing bar in real time, determine the locking status based on the phase difference, and determine the high-weight prediction area of the reinforcing bar. The polarization feature calculation unit is configured to perform eigenvalue decomposition on the local covariance matrix within the high-weight prediction area of the reinforcing bars, and calculate the polarization entropy characterizing the degree of scattering disorder and the average scattering angle characterizing the physical type of the scatterer. An adaptive projection stripping unit is configured to generate an adaptive gain factor based on the polarization entropy and the average scattering angle, construct an orthogonal complementary space projection matrix, adjust the projection intensity of the projection matrix using the adaptive gain factor, perform a projection transformation on the original polarization vector, and obtain the residual signal after removing the steel reinforcement interference. The evaluation unit is configured to reconstruct the defect signal based on the residual signal in order to evaluate the quality of the tunnel concrete lining.