A detection device and method for tunnel lining quality

Abstract

The present invention discloses a tunnel lining quality inspection device and method, comprising: a first inspection module for inspecting the tunnel lining's surface quality and obtaining surface inspection results; a second inspection module for inspecting the tunnel lining's single-point quality and obtaining single-point inspection results; and a first determination module for determining the tunnel lining quality inspection result based on the surface inspection results and the single-point inspection results. This method provides both surface inspection and single-point inspection of the tunnel lining, improving the accuracy of tunnel lining quality inspection.

Description

Technical Field

[0001] The present invention relates to the field of detection technology, and in particular to a detection device and method for tunnel lining quality. Background Art

[0002] Tunnel linings are a critical component of tunnel engineering, ensuring the safety, stability, and durability of tunnel structures. They are typically constructed along the tunnel perimeter using materials such as reinforced concrete and shotcrete. As more and more railway tunnels come into operation, some lining quality issues are becoming increasingly apparent, posing a threat to safe tunnel operations. Currently, conventional geological radar methods for inspecting railway tunnel linings, such as the size of voids behind them, the quality of the invert, and the reinforced concrete lining, have limitations. Furthermore, they lack single-point inspection of the tunnel lining, resulting in incomplete and inaccurate test results. Summary of the Invention

[0003] The present invention aims to at least partially address one of the technical problems of the aforementioned technologies. To this end, the present invention aims to provide a tunnel lining quality inspection device and method, which provides both layer-level inspection and single-point inspection of the tunnel lining, thereby improving the accuracy of tunnel lining quality inspection.

[0004] To achieve the above-mentioned purpose, an embodiment of the present invention provides a device for detecting the quality of a tunnel lining, comprising:

[0005] The first detection module is used to detect the surface quality of the tunnel lining and obtain the surface detection results;

[0006] The second detection module is used to detect the single-point quality of the tunnel lining and obtain the single-point detection result;

[0007] The first determination module is used to determine the tunnel lining quality inspection result based on the layer inspection result and the single point inspection result.

[0008] According to some embodiments of the present invention, the first detection module includes:

[0009] The wall-climbing equipment carrying the geological radar is used to enable the geological radar to move horizontally along the survey line and fit closely with the tunnel lining;

[0010] Geological radar, radar data used to determine tunnel linings;

[0011] The second determination module is used to determine the layer detection result based on the radar data.

[0012] According to some embodiments of the present invention, the second determining module includes:

[0013] The classification module is used to perform preliminary classification of radar data and determine each classification set;

[0014] A processing module is used to process each classification set based on a preset processing strategy to obtain a plurality of target classification sets; the preset processing strategy includes a void and void processing strategy, a steel bar processing strategy, an interface processing strategy, and a non-dense area processing strategy;

[0015] A module for building a numerical model of tunnel lining;

[0016] The simulation module is used to input the target classification set into the numerical model for simulation and determine the level detection results.

[0017] According to some embodiments of the present invention, the processing module includes:

[0018] The data editing module is used to perform data editing operations on each classification set; the data editing operations include data merging and splitting, line direction consistency, deletion of obsolete tracks, file header modification, zero point return, normalization and pile number editing;

[0019] The data processing module is used to perform data processing operations on the edited classification set based on a preset processing strategy; the data processing operations include enhancement processing, gain, digital filtering, deconvolution, offset, Hilbert processing, wavelet operation, complex signal, and normalized weight; the digital filtering includes horizontal filtering and vertical filtering; horizontal filtering includes background elimination and inter-channel equalization; vertical filtering includes FIR filtering and IIR filtering.

[0020] According to some embodiments of the present invention, the data processing module performs enhancement processing on the edited classification set, including:

[0021] Perform amplitude recovery on the edited classification set to determine the amplitude of the reflected wave;

[0022] In a homogeneous medium, the amplitude of the electromagnetic wave at a distance r from the transmitting antenna of the geological radar is ;

[0023]

[0024] in, is the amplitude of the reflected wave; is the wavefront spreading factor; is the absorption coefficient; e is a natural constant;

[0025] If the two-way travel time of the received reflected wave is t, then the amplitude of the reflected wave is:

[0026]

[0027] The actual path of the reflected wave r=vt, v is the average speed of the electromagnetic wave, then the above formula becomes:

[0028]

[0029] The amplitude of the reflected wave is equalized within the channel to obtain the enhanced classification set.

[0030] According to some embodiments of the present invention, the establishing module includes:

[0031] A first acquisition module is used to acquire first parameter information, wherein the first parameter information includes concrete, steel bar specifications and vertical and horizontal spacing, arch frame spacing and specifications, typical disease type, size and location;

[0032] a second acquisition module, configured to acquire second parameter information, wherein the second parameter information includes a dielectric constant of concrete, a dielectric constant of typical defects, and a dielectric constant of steel bars;

[0033] The setting module is used to set the model parameters, including discretized grid, wavelet source, center frequency, antenna spacing, measurement point spacing, antenna-ground spacing, concrete residual strain, friction coefficient between concrete bonding surfaces, concrete conductivity, and electromagnetic wave velocity;

[0034] The generation module is used to generate a numerical model of the tunnel lining according to the first parameter information, the second parameter information and the model parameters.

[0035] According to some embodiments of the present invention, the simulation module includes:

[0036] The recognition module is used to input the target classification set into the numerical model for simulation, perform concrete lining interface recognition processing, steel bar recognition processing in the lining, triangular cavity recognition processing, circular cavity recognition processing and void recognition processing, obtain several recognition results, and determine the layer detection results based on the several recognition results.

[0037] According to some embodiments of the present invention, the method further includes: a third determining module configured to:

[0038] Based on the input of the target classification set into the numerical model for simulation, the typical defects of the tunnel lining non-destructive testing by geological radar are determined and a feature map library is established;

[0039] Add processing measures for each data in the feature map library to build a defect processing library;

[0040] Obtain the level detection results, query the defect handling library based on the level detection results, determine the target handling measures and display them.

[0041] According to some embodiments of the present invention, the second detection module is used to detect the single-point quality of the tunnel lining based on the sounding method and the vibration method to obtain the single-point detection result.

[0042] According to some embodiments of the present invention, the above-mentioned detection method for detecting equipment for tunnel lining quality includes:

[0043] Detect the layer quality of tunnel lining and obtain layer detection results;

[0044] Detect the single-point quality of the tunnel lining and obtain the single-point detection results;

[0045] The tunnel lining quality inspection results are determined based on the layer inspection results and single point inspection results.

[0046] The present invention proposes a detection device and method for tunnel lining quality, which provides layer detection and single-point detection of tunnel lining and improves the accuracy of tunnel lining quality detection.

[0047] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 is a block diagram of a device for detecting tunnel lining quality according to one embodiment of the present invention;

[0051] Figure 2 is a schematic diagram of a geological radar lining interface according to an embodiment of the present invention;

[0052] Figure 3 This is a diagram showing the detection results of a 400 MHz lining steel mesh according to an embodiment of the present invention;

[0053] Figure 4 This is a diagram showing the results of 900 MHz lining steel mesh detection according to one embodiment of the present invention;

[0054] Figure 5 This is a diagram showing the 400 MHz lining arch detection results according to one embodiment of the present invention;

[0055] Figure 6 This is a diagram showing the detection results of a 900 MHz lining triangle cavity according to an embodiment of the present invention;

[0056] Figure 7 This is a diagram showing the results of 900 MHz lining void detection according to one embodiment of the present invention;

[0057] Figure 8 This is a diagram showing the results of 900 MHz lining void detection according to one embodiment of the present invention;

[0058] Figure 9 The present invention is a flowchart of a method for detecting tunnel lining quality according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] like Figure 1 As shown, an embodiment of the present invention provides a device for detecting tunnel lining quality, comprising:

[0061] The first detection module is used to detect the surface quality of the tunnel lining and obtain the surface detection results;

[0062] The second detection module is used to detect the single-point quality of the tunnel lining and obtain the single-point detection result;

[0063] The first determination module is used to determine the tunnel lining quality inspection result based on the layer inspection result and the single point inspection result.

[0064] The working principle of this technical solution is as follows: The first inspection module performs layer-by-layer inspection to evaluate the overall continuity, thickness uniformity, and interlayer bonding of the lining. Inspection metrics include lining thickness deviation, interlayer void distribution, and surface flatness. The second inspection module performs single-point inspection to obtain single-point strength, density, and defect information. Inspection metrics include concrete strength, internal defects, and steel corrosion status. A final quality report is generated using a fuzzy comprehensive evaluation method based on the combined layer-by-layer and single-point data.

[0065] The beneficial effects of the above technical solution are: it provides layer detection and single-point detection of tunnel linings. Layer detection provides macroscopic quality information, and single-point detection supplements local details. The combination of the two can avoid the limitations of a single method (for example, layer detection cannot detect deep voids, and single-point detection is easily affected by local disturbances), realize the comprehensive assessment of tunnel lining quality, and improve the accuracy of tunnel lining quality detection.

[0066] According to some embodiments of the present invention, the first detection module includes:

[0067] The wall-climbing equipment carrying the geological radar is used to enable the geological radar to move horizontally along the survey line and fit closely with the tunnel lining;

[0068] Geological radar, radar data used to determine tunnel linings;

[0069] The second determination module is used to determine the layer detection result based on the radar data.

[0070] The working principle of this technical solution is as follows: A wall-climbing device carrying a geological radar moves horizontally along the tunnel lining, covering the entire inspection area. Through magnetic attraction, vacuum adsorption, or mechanical clamping, the radar is tightly attached to the lining surface, reducing signal interference from air gaps. This adapts to tunnel linings of varying curvature and inclination, ensuring detection continuity and improving data accuracy. The geological radar transmits high-frequency electromagnetic waves (typically 10 MHz to 3 GHz), which penetrate the lining and receive reflected signals. Based on the time delay, amplitude, and phase changes of the reflected waves, a radar image of the lining's internal structure is generated. A second determination module determines the layer detection results based on the radar data.

[0071] The beneficial effects of the above technical solution are as follows: the first detection module realizes efficient, accurate and non-destructive detection of the tunnel lining layer through the combination of wall climbing equipment + geological radar, thereby improving the accuracy of the obtained layer detection results.

[0072] According to some embodiments of the present invention, the second determining module includes:

[0073] The classification module is used to perform preliminary classification of radar data and determine each classification set;

[0074] A processing module is used to process each classification set based on a preset processing strategy to obtain a plurality of target classification sets; the preset processing strategy includes a void and void processing strategy, a steel bar processing strategy, an interface processing strategy, and a non-dense area processing strategy;

[0075] A module for building a numerical model of tunnel lining;

[0076] The simulation module is used to input the target classification set into the numerical model for simulation and determine the level detection results.

[0077] The working principle of the above technical solution is as follows: The classification module performs preliminary classification of radar data and determines various classification sets, including void and gap sets, steel bar sets, interface sets, and loose area sets. The void and gap processing strategy is that void and gap sets often exhibit characteristics such as multiple waves, discontinuity, and fuzziness, making it difficult to accurately distinguish their location and scale. Therefore, during the data processing process, digital filtering, deconvolution, and Hilbert transform processing are enhanced according to data quality to achieve the purpose of suppressing noise and highlighting the target. The steel bar processing strategy is that steel bars often exhibit strong diffraction waves and multiple waves in the original data, and the diffraction waves and multiple waves are superimposed on each other, resulting in a complex image and making it difficult to distinguish the actual number and location of steel bars and the defects behind them. During the data processing process, emphasis can be placed on vertical filtering and offset processing to achieve the purpose of returning diffraction waves to their original positions and suppressing multiple waves, thereby improving the ability to identify steel bars and the defects behind them. The interface processing strategy is based on the fact that due to the strong reflection waves at the air-lining interface, the shielding effect of the steel bars, and the influence of multiple waves, the interface reflection waves are severely suppressed, resulting in an unclear or discontinuous interface behind the steel mesh and the defect interface. To highlight the interface, other diffraction waves and reflection waves need to be suppressed and the interface reflection waves enhanced. The strategy for processing the loose area is that the reflection characteristics of the loose area are chaotic phase, and the signal is mainly high-frequency. However, due to the strong shielding effect of the steel bars and the suppression of diffraction waves and multiple waves, it is difficult to distinguish and effectively define its scale. Therefore, during the data processing process, digital filtering and Hilbert transform processing can be strengthened, especially Hilbert processing, to display radar data in multiple aspects such as frequency, amplitude, and phase to achieve the best processing effect.

[0078] A numerical model of tunnel lining is established, and the time-domain finite-difference method is used to conduct numerical simulation research on geological radar detection of tunnel lining, to strengthen the understanding of the propagation characteristics of radar waves in media such as steel mesh, steel arch frame, and cavity, and to more intuitively demonstrate the impact of medium physical property differences on electromagnetic wave propagation and waveform characteristics, effectively guiding engineering applications.

[0079] The beneficial effects of the above technical solution are as follows: the target classification set is input into the numerical model for simulation to determine the layer detection results. Through the numerical model research, the parameter characteristics and influencing factors of the geological radar under various working conditions are deeply studied and summarized; defects such as cavities and voids are classified in detail, and the same defects are arranged at different depths to understand the influence of different types of defects and depths on the geological radar reflection signal; the differences in the geological radar reflection signals under different burial depths and sizes of defects such as cavities and voids are analyzed; the reflection of steel bars in the geological radar received signal is studied, and the impact on detection is mainly determined by the change of the position of steel bars and defects relative to the steel bars, the influence of the reflection signal, the spectrum characteristics and the detection accuracy relationship; the reflection of steel bars in the geological radar received signal is determined, and the impact on detection is mainly determined by the change of the defect position in the absence of steel bars, the influence of the reflection signal, the spectrum characteristics and the detection accuracy relationship; the electromagnetic wave response when the grid arch contains cavities, the grid arch and the I-beam are replaced by iron sheets or bamboo sheets is determined, and the characteristic spectrum is obtained; the geological radar images obtained through numerical simulation research guide the analysis of tunnel lining detection data in applications, and summarize the changing laws and characteristics of identifying typical lining diseases. It is convenient to accurately determine the level detection results.

[0080] According to some embodiments of the present invention, the processing module includes:

[0081] The data editing module is used to perform data editing operations on each classification set; the data editing operations include data merging and splitting, line direction consistency, deletion of obsolete tracks, file header modification, zero point return, normalization and pile number editing;

[0082] The data processing module is used to perform data processing operations on the edited classification set based on a preset processing strategy; the data processing operations include enhancement processing, gain, digital filtering, deconvolution, offset, Hilbert processing, wavelet operation, complex signal, and normalized weight; the digital filtering includes horizontal filtering and vertical filtering; horizontal filtering includes background elimination and inter-channel equalization; vertical filtering includes FIR filtering and IIR filtering.

[0083] The working principle of the above technical solution is as follows: data merging and segmentation. Merging combines data from multiple survey lines into a continuous profile, which is suitable for segmented inspection scenarios in long tunnels. Segmentation intercepts data from specific areas by stake number or time range, focusing on key inspection sections. Survey line direction consistency unifies all survey lines (e.g., from left to right) to avoid waveform distortion caused by directional differences. Deletion eliminates invalid survey lines caused by equipment failure or interference. File header modification updates metadata (e.g., acquisition time and equipment parameters) to ensure alignment with actual working conditions. Zero point homing corrects for time delay errors between the transmitting and receiving antennas, ensuring that the zero point of the reflected wave corresponds to the actual lining surface. Normalization unifies the data amplitude range (e.g., 0-1 or -1 to 1) to eliminate gain differences between different acquisition batches. Stake number editing links radar data with the tunnel design stake number, enabling engineering annotation of defect locations. This facilitates standardized data formatting and reduces the complexity of subsequent processing. It also reduces noise interference and improves the data signal-to-noise ratio.

[0084] Gain: Dynamically adjusts the gain of each channel to compensate for energy loss caused by dielectric absorption. Digital filtering: Horizontal filtering (inter-channel processing): Background elimination: Removes common-mode noise between measurement lines (such as environmental electromagnetic interference). Inter-channel equalization: Balances energy differences between adjacent channels to avoid banding artifacts. Vertical filtering (time domain processing): FIR filtering (finite impulse response): Linear phase and high stability, suitable for high-frequency noise suppression. IIR filtering (infinite impulse response): High computational efficiency, suitable for low-frequency trend extraction. Deconvolution: Compresses radar wavelets to improve vertical resolution, making thin layers or small defects easier to identify. Migration: Corrects inclined reflections to vertical reflections to accurately locate defect depth. Hilbert processing: Extracts signal envelopes, enhances instantaneous amplitude characteristics, and assists in defect boundary identification. Wavelet operations: Multi-scale signal decomposition, separating features in different frequency bands (such as high-frequency defects and low-frequency background). Complex signal processing: Converts real signals into complex signals (analytic signals), retains phase information, and improves interpretation accuracy. Normalized Weights: Weighted fusion of multi-line data highlights reliable line information and suppresses interference from anomalous lines. This facilitates multi-dimensional signal quality improvement and adapts to complex geological conditions. Combined with horizontal and vertical filtering, it provides comprehensive noise suppression in both inter-trace and depth domains.

[0085] The beneficial effects of this technical solution include: Through the collaborative design of data editing and data processing, a complete link from raw data to high-quality radar images is established. Editing operations unify data formats, suppressing noise interference. Defect recognition capabilities are enhanced through the integration of multiple technologies.

[0086] According to some embodiments of the present invention, the data processing module performs enhancement processing on the edited classification set, including:

[0087] Perform amplitude recovery on the edited classification set to determine the amplitude of the reflected wave;

[0088] In a homogeneous medium, the amplitude of the electromagnetic wave at a distance r from the transmitting antenna of the geological radar is ;

[0089]

[0090] in, is the amplitude of the reflected wave; is the wavefront spreading factor; is the absorption coefficient; e is a natural constant;

[0091] If the two-way travel time of the received reflected wave is t, then the amplitude of the reflected wave is:

[0092]

[0093] The actual path of the reflected wave r=vt, v is the average speed of the electromagnetic wave, then the above formula becomes:

[0094]

[0095] The amplitude of the reflected wave is equalized within the channel to obtain the enhanced classification set.

[0096] The working principle of the above technical solution is that due to external interference and complex media, geological radar data requires image enhancement processing to improve image quality and facilitate identification. The amplitude of the reflected wave recorded by the radar gradually decays over time due to wavefront diffusion and the medium's absorption of electromagnetic waves. To ensure that the reflected wave amplitude is related only to the reflecting layer, amplitude recovery is required. After radar data processing, shallow layers are typically very powerful, while deep layers are very weak, which is not conducive to information identification and output. To ensure a clear display of shallow, medium, and deep layers, intra-channel equalization is crucial. Intra-channel equalization compresses the high-energy waves in each channel by a certain ratio and amplifies the relatively weak waves by a certain ratio, so that the amplitudes of strong and weak waves are controlled within a certain dynamic range. Accordingly, the amplitude value of a recorded channel is multiplied by different weighting coefficients in different reflection segments.

[0097] Perform intra-channel equalization on the amplitude of the reflected wave:

[0098] set up is the amplitude value after equalization, is the amplitude value to be equalized, is the weight coefficient (j is the sampling number, N is the number of samples), then

[0099] .

[0100] The beneficial effects of the above technical solution are: improving image quality to facilitate recognition and obtaining an accurate classification set after enhancement processing.

[0101] According to some embodiments of the present invention, the establishing module includes:

[0102] A first acquisition module is used to acquire first parameter information, wherein the first parameter information includes concrete, steel bar specifications and vertical and horizontal spacing, arch frame spacing and specifications, typical disease type, size and location;

[0103] a second acquisition module, configured to acquire second parameter information, wherein the second parameter information includes a dielectric constant of concrete, a dielectric constant of typical defects, and a dielectric constant of steel bars;

[0104] The setting module is used to set the model parameters, including discretized grid, wavelet source, center frequency, antenna spacing, measurement point spacing, antenna-ground spacing, concrete residual strain, friction coefficient between concrete bonding surfaces, concrete conductivity, and electromagnetic wave velocity;

[0105] The generation module is used to generate a numerical model of the tunnel lining according to the first parameter information, the second parameter information and the model parameters.

[0106] The working principle of the above technical solution: The first parameter information and the second parameter information are as follows: the numerical model mainly studies the representative lining structures of Class III and Class IV surrounding rock types, with lining thicknesses of 35 cm (excluding steel bars) and 45 cm (including steel bars); the steel bar specification is 20 mm, the horizontal spacing is 200 mm, the vertical spacing is 350 mm, the relative dielectric constant is 300, and the conductivity is 1×1010 S·m-1; the primary support thickness is 180 mm, the relative dielectric constant is 7.3, and the conductivity is 0.02 S·m-1; the secondary lining relative dielectric constant is 6.5, and the conductivity is 0.005 S·m-1; the grid arch steel bar specification is 22 mm, the center spacing between steel bars is 150 mm, the spacing between supports is 1 m, the relative dielectric constant is 300, and the conductivity is 1×1010S·m-1; the I-beam specification is 180×94 mm, the spacing is 0.8 m, the relative dielectric constant is 300, and the conductivity is 1×1010S·m-1; Table 1 shows the medium parameters used in the numerical simulation; Table 2 shows the model parameters used in the numerical simulation.

[0107] Table 1 Medium parameters used in numerical simulation

[0108]

[0109] Table 2 Model parameters used in numerical simulation

[0110]

[0111] The beneficial effects of this technical solution include generating a 3D CAD model based on the first parameter and mapping the second parameter to the geometric region to achieve material assignment. By setting model parameters and corresponding boundary conditions, the electromagnetic boundary includes a PML absorbing boundary (to reduce reflections) and a mechanical boundary that fixes the model bottom (to simulate surrounding rock constraints). Electromagnetic wave propagation is solved using the finite-difference time-domain method, generating a numerical model file, and thus facilitating the creation of an accurate numerical model of the tunnel lining.

[0112] According to some embodiments of the present invention, the simulation module includes:

[0113] The recognition module is used to input the target classification set into the numerical model for simulation, perform concrete lining interface recognition processing, steel bar recognition processing in the lining, triangular cavity recognition processing, circular cavity recognition processing and void recognition processing, obtain several recognition results, and determine the layer detection results based on the several recognition results.

[0114] The working principle of the above technical solution: concrete lining interface identification and processing, including: the order of arrival of received radar waves is air direct wave, surface reflected wave, secondary lining and primary lining interface reflected wave, primary lining and surrounding rock interface reflected wave. Due to the small difference in dielectric constant between secondary lining-primary lining and primary lining-surrounding rock, and due to the high intensity of air direct wave and surface reflected wave, strong shielding of steel bars and the influence of diffraction wave, the reflected energy of lining-primary lining and lining-surrounding rock is relatively weak. The uniformly vibrated concrete lining is densely combined with the surrounding rock, and the radar image shows a continuous and horizontal phase axis. The waveform of poor quality concrete is messy, and the phase axis is disturbed, but basically horizontal. The most obvious characteristics of radar reflected waves between surrounding rock and lining are mainly manifested as follows: First, the frequency characteristics of the wave are greatly different, from Figure 2 It can be seen that the frequency of the upper half of the single-channel waveform is smaller than that of the lower half, and there are intermittent strong reflections along the interfaces. The figure shows the geological radar results of the lining. The reflection surfaces of each layer are complete and clear, such as Interfaces 1 and 2, with few random reflections, indicating that the concrete is dense and uniform. Interface 1 is the air-concrete interface, and Interface 2 is the concrete-surrounding rock interface. However, below Interface 2, the attenuation of the radar wave during propagation causes the reflection wave intensity to be significantly weakened, while many random reflections are present, indicating that the rock mass structure is complex and fragmented. The two-way travel time at the interface locations in the radar results, combined with the dielectric constant of the lining, can accurately determine the lining concrete thickness.

[0115] Rebar identification in the lining is as follows: a single-layer steel mesh made of Φ22 round steel bars with a grid size of 20cm x 20cm. Continuous measurements were performed using a 900M antenna at the tunnel haunch. As can be seen from the figure, the diffraction waves from the rebar are hyperbolic, arranged sequentially, and have high signal strength. The specific depth of the rebar is determined by combining the selection of surface waveform points. The maximum amplitude point of the transmitted wave is used as the reflection point in this paper, so the rebar position is also selected based on the maximum value of the reflected signal. As shown in the figure, the specific depth of the rebar can be calculated based on the time of the left-hand reflection signal corresponding to the waveform vertex position on the right. The distance between the rebars is calculated based on the corresponding mileage distance of the rebars and the number of rebars in the figure. Furthermore, the continuity of the rebar mesh and the changes in the buried depth are used to determine quality issues such as whether the rebar mesh is effectively overlapped and whether the rebar mesh is flat. Figure 3 In the measurement, the diffraction waves of the steel bars form a downward-opening hyperbola, which is arranged in order. The apex of the hyperbola is the position of the steel bar. The number of hyperbolas can be used to accurately determine the number of steel bars. Combined with the mileage number, the density of the steel mesh within the measurement range can be calculated. Figure 4 This is the lining radar result detected by 900Mhz antenna. It can be seen from the figure that the steel mesh is densely arranged, but the steel mesh is discontinuous at the 527m position, the front and rear steel meshes are offset, and the surface steel meshes are not effectively overlapped.

[0116] Figure 5 This is the result of arch radar testing using a 400 MHz antenna. For metal, the reflection coefficient is -1, resulting in a high signal strength and a phase reversal relative to the reflection from the air-concrete surface. This characteristic can be used to determine whether an anomalous reflection is metal. Furthermore, the physical properties of the anomaly can be inferred based on the amplitude and phase variations of the reflected waves. The figure shows that the four arches are evenly arranged in a dispersed crescent shape. The arch spacing can be effectively calculated based on the measured distance and number of arches. This spacing can be used to determine whether the arches are missing or meet design requirements. A continuous weakly reflecting interface exists above the arches, suggesting that this interface is the primary-to-secondary lining interface. In practice, due to the good adhesion between the secondary lining and the primary lining, resulting in a small difference in dielectric constant, the reflecting interface is very weak. Furthermore, the poor adhesion between the secondary lining and the primary lining results in an intermittent and variable reflection interface, making it difficult to interpret the secondary-to-primary lining interface. Although the secondary lining-primary lining reflection interface can be highlighted to a certain extent through data processing, the effect is still not ideal due to the shielding effect of the steel mesh inside the lining. In this case, the secondary lining-primary lining surface can be judged with the help of the position of the arch.

[0117] Triangle hole identification and processing: Figure 6This is a diagram of the lining measured using a 900 MHz antenna. The image shows a reflection anomaly at a horizontal distance of 4.5 meters and a depth of 0.2 meters. This anomaly is phase-correlated and tilted, with regular multiple waves below it. The single-channel waveform on the right shows the waveform at this location. The reflected wave amplitude increases significantly and the wavelength lengthens at this location, indicating that the anomaly is a cavity filled with air. The shape of the anomaly wave indicates that it is a triangular cavity.

[0118] Circular cavity recognition and processing: Figure 7 This is a diagram of the lining measured using a 900 MHz antenna. As can be seen from the figure, a downward-pointing reflected wave appears near 2.8 meters, forming a hyperbola with a regular, continuous phase pattern. Below this, regular multiple waves exist. Analysis suggests this anomaly is a circular cavity.

[0119] Void identification and processing: The void area between the lining and the surrounding rock is air, which has a very different wave impedance from the concrete and surrounding rock. The reflected waves are alternating between positive and negative, and the wave phases change between red and blue. The reflection is very strong, the void area is intermittent and winding, the position is clear and obvious, and it is very easy to identify. Figure 8 This is the detection result of the 900Mhz antenna lining. It can be seen from the figure that at a horizontal distance of 1-2m and a depth of 0.5-0.6m, there is a continuous reflection wave anomaly with continuous phase and enhanced amplitude, indicating that there is a gap in this area.

[0120] The beneficial effect of the above technical solution is that it facilitates accurate determination of layer detection results.

[0121] According to some embodiments of the present invention, the method further includes: a third determining module configured to:

[0122] Based on the input of the target classification set into the numerical model for simulation, the typical defects of the tunnel lining non-destructive testing by geological radar are determined and a feature map library is established;

[0123] Add processing measures for each data in the feature map library to build a defect processing library;

[0124] Obtain the level detection results, query the defect handling library based on the level detection results, determine the target handling measures and display them.

[0125] The working principle of this technical solution is as follows: A target classification set (such as radar measurement data or pre-set defect scenarios) is input into a numerical model to simulate electromagnetic wave propagation and identify typical defects. The simulation results are converted into structured feature data to establish a defect feature map library. Defect types include cavities, cracks, voids, and rebar corrosion. Treatment measures are added to each data point in the feature map library to form a defect-measure mapping relationship. Based on the layer detection results (such as on-site radar measurement data), the defect treatment library is queried and the target treatment measures are output. The defect location, type, and treatment plan are displayed in an overlay on the interface.

[0126] The beneficial effects of this technical solution include the establishment of an intelligent diagnosis and decision-making support system for tunnel lining defects through a closed-loop design consisting of simulation analysis, feature mapping, processing library, and real-time query. Standardized defect signatures are generated through numerical simulation, eliminating reliance on measured data and improving the completeness of the mapping library. The integration of electromagnetic, geometric, and material features enables precise defect classification and location. Based on a rules engine or machine learning model, treatment measures are automatically recommended, reducing manual intervention.

[0127] According to some embodiments of the present invention, the second detection module is used to detect the single-point quality of the tunnel lining based on the sounding method and the vibration method to obtain the single-point detection result.

[0128] The working principle of the above technical solution is as follows: The sound-beating method uses a sound acquisition instrument to collect sound data collected by a striking device after impact. This data is then input into an analysis program, which automatically generates a spectrum and dominant frequency range. The corresponding concrete grade is assigned based on the definition of the degree of scattering of the sound-beating spectrum. The characteristics of the spectrum for thickness classifications (10-60 cm) have been determined. The vibration testing instrumentation includes 1) an accelerometer; 2) an accelerometer; 3) a data acquisition device; and 4) a computer and software system. The natural frequency of the structure is determined. The natural frequency of the structure is determined by the mass and rigidity of the structure. The amplitude of the Fourier amplitude vector is expressed as the dominant vibration frequency (i.e., the amplitude vector at the point of maximum amplitude) and the vibration frequency at 90 degrees in the phase difference vector. The specific method involves superimposing the test waveforms at each measuring point 5-10 times, calculating the amplitude vector and phase difference vector of the superimposed Fourier transform, and analyzing the amplitude and phase difference vectors to determine the natural frequency of the structure. The peak method is used. This means that at the structural natural frequency, the displacement signal has a maximum amplitude spectrum at a phase angle of 90° or 270°; the velocity signal has a maximum amplitude spectrum at a phase angle of 180° or 360°; and the acceleration signal has a maximum amplitude spectrum at a phase angle of 90° or 270°. The structural soundness index is the ratio of the measured structural natural frequency to the reference value of the structural natural frequency.

[0129] The beneficial effects of the above technical solution are: the single-point quality of the tunnel lining is detected based on the sounding method and the vibration method, which facilitates accurate single-point detection results.

[0130] like Figure 9 As shown, according to some embodiments of the present invention, the detection method of the detection equipment applied to the tunnel lining quality as described above includes steps S1-S3:

[0131] S1. Detect the quality of the tunnel lining and obtain the surface inspection results;

[0132] S2. Detect the single-point quality of the tunnel lining and obtain the single-point detection result;

[0133] S3. Determine the tunnel lining quality inspection results based on the layer inspection results and single point inspection results.

[0134] The beneficial effects of the above technical solution are: it provides layer detection and single-point detection of tunnel linings. Layer detection provides macroscopic quality information, and single-point detection supplements local details. The combination of the two can avoid the limitations of a single method (for example, layer detection cannot detect deep voids, and single-point detection is easily affected by local disturbances), realize the comprehensive assessment of tunnel lining quality, and improve the accuracy of tunnel lining quality detection.

[0135] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A device for detecting the quality of tunnel lining, characterized in that: include: The first detection module is used to detect the surface quality of the tunnel lining and obtain the surface detection results; The second detection module is used to detect the single-point quality of the tunnel lining and obtain the single-point detection result; The first determination module is used to determine the tunnel lining quality inspection result based on the layer inspection result and the single point inspection result; The first detection module includes: The wall-climbing equipment carrying the geological radar is used to enable the geological radar to move horizontally along the survey line and fit closely with the tunnel lining; Geological radar, radar data used to determine tunnel linings; A second determination module is used to determine the layer detection result based on the radar data; The second determining module includes: The classification module is used to perform preliminary classification of radar data and determine various classification sets; the classification sets include void and void sets, steel bar sets, interface sets, and loose area sets; A processing module is used to process each classification set based on a preset processing strategy to obtain a plurality of target classification sets; the preset processing strategy includes a void and void processing strategy, a steel bar processing strategy, an interface processing strategy, and a non-dense area processing strategy; A module for building a numerical model of tunnel lining; The simulation module is used to input the target classification set into the numerical model for simulation and determine the level detection results; The establishment module includes: A first acquisition module is used to acquire first parameter information, wherein the first parameter information includes concrete, steel bar specifications and vertical and horizontal spacing, arch frame spacing and specifications, typical disease type, size and location; a second acquisition module, configured to acquire second parameter information, wherein the second parameter information includes a dielectric constant of concrete, a dielectric constant of typical defects, and a dielectric constant of steel bars; The setting module is used to set the model parameters, including discretized grid, wavelet source, center frequency, antenna spacing, measurement point spacing, antenna-ground spacing, concrete residual strain, friction coefficient between concrete bonding surfaces, concrete conductivity, and electromagnetic wave velocity; A generation module is used to generate a numerical model of the tunnel lining based on the first parameter information, the second parameter information and the model parameters; generate a three-dimensional CAD model based on the first parameter information, map the second parameter information to the geometric area, and implement material assignment; set the model parameters and corresponding boundary conditions; the boundary conditions include: electromagnetic boundary: set the PML absorption boundary; mechanical boundary: fix the bottom of the model; The simulation module includes: The recognition module is used to input the target classification set into the numerical model for simulation, perform concrete lining interface recognition processing, steel bar recognition processing in the lining, triangular cavity recognition processing, circular cavity recognition processing and void recognition processing, obtain several recognition results, and determine the layer detection results based on the several recognition results.

2. The tunnel lining quality detection device according to claim 1, characterized in that: The processing module includes: The data editing module is used to perform data editing operations on each classification set; the data editing operations include data merging and splitting, line direction consistency, deletion of obsolete tracks, file header modification, zero point return, normalization and pile number editing; The data processing module is used to perform data processing operations on the edited classification set based on a preset processing strategy; the data processing operations include enhancement processing, gain, digital filtering, deconvolution, offset, Hilbert processing, wavelet operation, complex signal, and normalized weight; the digital filtering includes horizontal filtering and vertical filtering; horizontal filtering includes background elimination and inter-channel equalization; vertical filtering includes FIR filtering and IIR filtering.

3. The tunnel lining quality detection device according to claim 2, characterized in that: The data processing module performs enhanced processing on the edited classification set, including: Perform amplitude recovery on the edited classification set to determine the amplitude of the reflected wave; In a homogeneous medium, the amplitude of the electromagnetic wave at a distance r from the transmitting antenna of the geological radar is ; in, is the amplitude of the reflected wave; is the wavefront spreading factor; is the absorption coefficient; e is a natural constant; If the two-way travel time of the received reflected wave is t, then the amplitude of the reflected wave is: The actual path of the reflected wave r=vt, v is the average speed of the electromagnetic wave, then the above formula becomes: The amplitude of the reflected wave is equalized within the channel to obtain the enhanced classification set.

4. The tunnel lining quality detection device according to claim 1, characterized in that: Also includes: The third determining module is configured to: Based on the input of the target classification set into the numerical model for simulation, the typical defects of the tunnel lining non-destructive testing by geological radar are determined and a feature map library is established; Add processing measures for each data in the feature map library to build a defect processing library; Obtain the level detection results, query the defect handling library based on the level detection results, determine the target handling measures and display them.

5. The tunnel lining quality detection device according to claim 1, characterized in that: The second detection module is used to detect the single-point quality of the tunnel lining based on the sounding method and the vibration method to obtain the single-point detection result.

6. The detection method for tunnel lining quality detection equipment according to any one of claims 1 to 5, characterized in that: include: Detect the layer quality of tunnel lining and obtain layer detection results; Detect the single-point quality of the tunnel lining and obtain the single-point detection results; The tunnel lining quality inspection results are determined based on the layer inspection results and single point inspection results.

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