Method and system for detecting cavity behind wall of subway shield tunnel

Abstract

The invention relates to a method and system for detecting a cavity behind a subway shield tunnel wall, and belongs to the technical field of cavity detection.The method comprises the steps that a point position needing to be detected is marked on a duct piece of a subway tunnel through a marking pen; a rebound instrument is used for hitting the point position, and an acceleration sensor is used for collecting a vibration signal of the point position; carrying out denoising processing on the vibration signal of each point location to obtain a denoised vibration signal; performing time domain conversion on the denoised vibration signal to obtain a frequency domain signal corresponding to the vibration signal; based on the time domain signal of the denoised vibration signal and the frequency domain signal, completing the detection of the subway shield tunnel wall back cavity. Denoising processing and frequency domain analysis are carried out on the collected data, so that the accuracy of a hole detection result can be effectively improved, and the possibility of misjudgment is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of cavity detection, and in particular to a method and system for detecting cavities behind a subway shield tunnel wall. Background Art

[0002] During the service of the tunnel, due to the influence of natural factors such as stratum lithology, geological structure, and groundwater, small cavities gradually form behind the tunnel lining. At the same time, there may be easily dissolved or corroded substances in the surrounding rock or backfill behind the lining. The small cavities gradually connect and expand over time, and gradually evolve into larger cavities. In addition, due to the complex and changeable natural geological conditions during the tunnel construction process, the construction environment is harsh. If the standardization of the construction process and the strictness of the process cannot be guaranteed, such as over-excavation and backfilling, support de-airing, etc., after the composite lining of tunnels of different sizes and locations, de-airing and cavity diseases will also occur easily. The existence of cavity defects has a significant impact on the ultimate bearing capacity and failure mode of the lining structure. Under the influence of factors such as temperature changes, groundwater hydrostatic pressure, and block load in the cavity area, cavities will lead to reduced bearing capacity, cracks and spalling of the lining, which will greatly affect the safety and durability of the tunnel and shorten its service life. As one of the main modes of transportation in the city, the health of the subway structure is very important. Most subways are built in underground tunnels. The cavities exist in the soil behind the lining walls. They are invisible and highly concealed, which puts higher requirements on the detection methods. In the past few decades, a series of non-destructive testing methods have been studied and applied in subway tunnel disease detection, such as ground penetrating radar and infrared thermal imaging.

[0003] 1) Ground Penetrating Radar

[0004] The detection effect of ground penetrating radar is related to the electrical parameters such as the pulse width of the transmitting radar, the dielectric constant of the medium, the conductivity and magnetic permeability. At present, GPR has been widely used to detect voids behind the tunnel lining, the initial thickness of the lining, the lining leakage, and the hidden cracks behind / inside the lining. However, the steel bars buried in the tunnel lining act as strong scatterers, which will generate clutter in the GPR profile, interfere with the target signal, and make the echo of the hidden defects difficult to distinguish. Usually, the spacing between steel bars in the standard tunnel lining is about 30-40 cm. In contrast, the precast shield tunnel section is embedded with a denser double-layer steel mesh, and the spacing between steel bars is only about 10-15 cm. The image is easily interfered by the lining material, resulting in misjudgment.

[0005] 2) Infrared thermal imaging

[0006] As a non-destructive testing method, infrared thermal imaging detection method has received widespread attention in the field of concrete structure detection due to its advantages such as large detection area, non-contact and simple application and operation, and has gradually been applied to on-site inspection of tunnels and bridges. Infrared thermal imaging method is often used to detect reinforced concrete objects. When the depth of the defect exceeds 10mm, heating conditions are required; in addition, after heating, the time of testing the object will also affect the formation of the sampling image, and then affect the detection results; and the shape and size of the defect cannot be accurately reflected in the infrared thermal imaging image, and the detection accuracy still needs to be improved. The subway tunnel is located in an underground environment with small temperature changes. The pipe segments are made of reinforced concrete and are easily disturbed. Summary of the invention

[0007] To solve the above problems, an object of the embodiments of the present invention is to provide a method and system for detecting cavities behind the wall of a subway shield tunnel.

[0008] A method for detecting a cavity behind a subway shield tunnel wall, comprising:

[0009] Step 1: Use a marker pen to mark the points to be tested on the tube segment of the subway tunnel;

[0010] Step 2: Hit the point with a rebound tester and use an acceleration sensor to collect the vibration signal of the point;

[0011] Step 3: De-noising the vibration signal of each point to obtain a de-noised vibration signal;

[0012] Step 4: Perform time domain conversion on the denoised vibration signal to obtain a frequency domain signal corresponding to the vibration signal;

[0013] Step 5: Complete the detection of the cavity behind the subway shield tunnel wall based on the time domain signal of the denoised vibration signal and the frequency domain signal.

[0014] Preferably, the step 3: performing denoising processing on the vibration signal of each point to obtain a denoised vibration signal comprises:

[0015] Step 3.1: Set the decomposition scale, and decompose the vibration signal of each point according to the set decomposition scale to obtain the wavelet coefficient;

[0016] Step 3.2: Determine the denoising threshold based on the set decomposition scale;

[0017] Step 3.3: When the wavelet coefficient is greater than or equal to the denoising threshold, the wavelet coefficient is processed using a processing function to generate a processed wavelet coefficient;

[0018] Step 3.4: When the wavelet coefficient is less than the denoising threshold, the corresponding wavelet coefficient is changed to 0;

[0019] Step 3.5: reconstructing the processed wavelet coefficients to form a denoised vibration signal;

[0020] Step 3.6: Use the fusion index to evaluate the denoised vibration signal;

[0021] Step 3.7: When the weighted evaluation value is not within the preset range, reset the decomposition scale until the weighted evaluation value is within the preset range.

[0022] Preferably, the step 3.3: when the wavelet coefficient is greater than or equal to the denoising threshold, using a processing function to process the wavelet coefficient to generate processed wavelet coefficients includes:

[0023] Using the formula:

[0024]

[0025] The wavelet coefficients are processed to generate the processed wavelet coefficients; where w i ' ,k represents the processed wavelet coefficients, sign represents the sign function, and w i,k represents the kth original wavelet coefficient under the i-th decomposition scale, λ represents the denoising threshold, N represents the length of the vibration signal, and σ represents the noise standard deviation.

[0026] Preferably, the step 3.6: using the fusion index to evaluate the denoised vibration signal includes:

[0027] Step 3.6.1: Perform a preliminary evaluation on the denoised vibration signal to obtain the root mean square error index and signal-to-noise ratio index of the signal;

[0028] Step 3.6.2: normalize the root mean square error index and the signal-to-noise ratio index to obtain a normalized root mean square error index and a signal-to-noise ratio index;

[0029] Step 3.6.3: Calculate the weights of the normalized root mean square error index and signal-to-noise ratio index;

[0030] Step 3.6.4: Use the weights to weight the root mean square error index and the signal-to-noise ratio index to obtain the fusion index.

[0031] Preferably, in step 3.6.1, the calculation formulas for the root mean square error index and the signal-to-noise ratio index are:

[0032]

[0033] Wherein, x(i) represents the original vibration signal, y(i) represents the signal-to-noise ratio, n represents the sampling point number, N represents the sampling length, SNR represents the first evaluation value, and RMSE represents the root mean square error.

[0034] Preferably, the step 3.6.3: calculating the weights of the normalized root mean square error index and the signal-to-noise ratio index comprises:

[0035] Calculate the ratio of each indicator to all indicators and determine the weight according to the ratio; the weight calculation formula is:

[0036]

[0037] Among them, P ij represents the ratio of the jth index when the decomposition scale is i, x ij represents the value of the jth indicator when the number of decomposition layers is i after normalization, m represents the number of decomposition scales, and w j Represents the weight of the j-th indicator.

[0038] Preferably, the step 4: performing time domain conversion on the denoised vibration signal to obtain a frequency domain signal corresponding to the vibration signal comprises:

[0039] The denoised vibration signal is transformed using fast Fourier transform to obtain the frequency domain signal of the corresponding vibration signal; the conversion formula is:

[0040]

[0041] Where c ≥ 0, e v (c) = x(2c), o d (c) = x(2c+1), N is the total number of sampling points, k is the frequency index used to represent different frequency components, X k1 Represents the first N / 2 frequency components of the final Fourier transform result, represents the last N / 2 frequency components of the Fourier transform result, k1 represents the frequency index, which determines the position of the frequency component in the frequency domain, and e v (c) represents the even part, that is, the sampling points of all even-indexed positions in signal c, and x(2c) represents extracting the sample values ​​of the signal and rearranging them so that the indexes of these sample values ​​are even-indexed. d (c) represents the odd part, that is, the sampling points of all odd index positions in signal c, DFT represents the discrete Fourier transform function, represents the rotation factor.

[0042] Preferably, the step 5: completing the detection of the cavity behind the subway shield tunnel wall based on the time domain signal of the denoised vibration signal and the frequency domain signal, comprises:

[0043] The amplitude of the periodic fluctuation phase in the time domain signal is extracted. When the amplitude is greater than the set threshold, a hole appears at the corresponding point of the subway tunnel.

[0044] The peak value in the frequency domain signal is extracted. When the peak value exceeds the preset threshold, a hole appears at the corresponding point in the subway tunnel.

[0045] The present invention also provides a system for detecting cavities behind a subway shield tunnel wall, comprising:

[0046] Point marking module, used to mark the points to be inspected on the tube segments of the subway tunnel with a marker pen;

[0047] A vibration signal acquisition module is used to strike a point and collect the vibration signal of the point using an acceleration sensor;

[0048] A denoising module is used to denoise the vibration signal of each point to obtain a denoised vibration signal;

[0049] A time domain conversion module is used to perform time domain conversion on the denoised vibration signal to obtain a frequency domain signal corresponding to the vibration signal;

[0050] The cavity detection module is used to complete the detection of the cavity behind the subway shield tunnel wall based on the time domain signal of the denoised vibration signal and the frequency domain signal.

[0051] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in the above-mentioned method for detecting cavities behind the wall of a subway shield tunnel are implemented.

[0052] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0053] The present invention relates to a method for detecting cavities behind a subway shield tunnel wall. Compared with the prior art, the present invention can effectively improve the accuracy of cavity detection results and reduce the possibility of misjudgment by performing denoising processing and frequency domain analysis on collected data.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A flow chart of a method for detecting cavities behind a subway shield tunnel wall provided by the present invention;

[0057] Figure 2 This is a continuous cavity simulation effect diagram provided by the present invention;

[0058] Figure 3 A schematic diagram of the area division of the lining segments provided by the present invention;

[0059] Figure 4 A schematic diagram of a time domain signal without a hole in area ② provided by the present invention;

[0060] Figure 5 A schematic diagram of time domain signals of corresponding points where holes are placed in area ② provided by the present invention;

[0061] Figure 6 A schematic diagram of a time domain signal without a hole in area ③ provided by the present invention;

[0062] Figure 7 A schematic diagram of a time domain signal in which a hole is placed in area ③ provided by the present invention;

[0063] Figure 8 A schematic diagram of a frequency signal without a hole in area ② provided by the present invention;

[0064] Fig. 9 A schematic diagram of frequency signals of corresponding points where holes are placed in area ② provided by the present invention;

[0065] Fig.10 A schematic diagram of a frequency signal without a hole in area ③ provided by the present invention;

[0066] Fig.11 A schematic diagram of a frequency signal in which a hole is placed in area ③ provided by the present invention. DETAILED DESCRIPTION

[0067] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0068] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0069] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0070] Aiming at the problem that the existing detection methods are easily affected by concrete and steel bars, the present invention proposes a method for detecting voids in surrounding rock behind the wall based on the impact echo method. The acceleration sensor is installed on the test surface near the pulse source to receive vibration data and generate a voltage-time signal, i.e., a waveform, which is recorded by a data acquisition device. The signal describes the instantaneous vibration caused by multiple reflections of stress waves inside the structure between the interfaces of concrete / air, concrete / steel, etc. The dominant frequency in these vibrations is related to the stress waves reflected from different depths inside the structure. The signal processing method of fast Fourier transform is used to obtain frequency domain data. The time domain data is combined with the frequency domain data to better capture the characteristics of the existence of voids. The depth of the void, the size of the void, the relative position of the void and the grouting hole of the lining bolt hole, and the influence of the boundary conditions are comprehensively analyzed, and the detection points and applicable void types based on the impact echo method are clarified. The method of the present invention is of great significance for the application research of the impact echo method in the detection of defects in surrounding rock behind the wall and for improving the detection accuracy and efficiency of voids in surrounding rock behind the wall.

[0071] See also Figure 1, a method for detecting a cavity behind a subway shield tunnel wall, comprising:

[0072] Test site layout: Use a crane to place the segments side by side on the ground, and build a T-shaped wall to ensure the stability of the segments;

[0073] One month after the segment was placed, the soil was filled behind the segment. To ensure the density of the soil, a roller was used to compact the soil after each layer of soil was laid, and then the next layer of soil was laid. When the height of the filled soil reached the height where the hole needed to be placed, the empty bottle used to simulate the hole was placed. Use a shovel to carefully spread the soil on the upper surface and around the empty bottle, and compact the soil around the empty bottle. During this period, it is necessary to ensure that the bottle is in a fixed position; finally, the entire area behind the segment is covered.

[0074] The pipe segments used in the present invention are standard pipe segments used in subway shield tunnels, and their parameters are provided by the manufacturer of the pipe segments, with a length of 3.4m, a width of 1.2m, and a thickness of 300mm. The actual pipe segments have an arc and are curved, with an inner diameter of 5.4m and an outer diameter of 6.1m. The concrete strength is C50 and the waterproof grade is P10.

[0075] In order to minimize the influence of the material of the object used to simulate the void on the experiment, the present invention uses water bottles of different capacities to simulate voids of different sizes, as shown in Table 1. In order to simulate the existence of continuous voids, according to Figure 2 As shown, two 1.5-liter mineral water bottles are stacked up and down, and two 5-liter water bottles are placed side by side.

[0076] Table 1 Dimensions of water bottles used to simulate voids

[0077] volume Bottom diameter (cm) Water bottle height (cm) 1.5L 8 16 5L 15 33 12L 23 36 Continuous voids 15-16 82

[0078] The detailed experimental settings are shown in Table 2. In this experiment, the cavity depth and cavity size are used as variables to set up experiments 1-3. Considering that there are bolt holes and grouting holes on the surface of the segment, and because the segment is placed vertically on the ground, the vibration signals between points at different heights on the segment may be different. Therefore, this paper adds a set of variables "placement area (R)" as experiment 1, as follows Figure 3 As shown in the figure, according to the position of the grouting holes on the surface of the segment, the segment is divided into two areas ① and ②. When the cavity is placed behind R-②, it is placed behind the grouting holes. Area ① and area ③ correspond to two heights of the segment, and area ③ is closer to the ground.

[0079] Table 2 Experimental settings

[0080]

[0081] Note: *Burial depth: the distance between the cavity and the segment. 0cm means the cavity is close to the segment wall, and 16.5cm means the distance between the cavity and the segment wall is 16.5cm. **When pre-buried cavities, it was originally planned to control the distance between the cavity and the segment to 15cm in Experiment 3, but the actual operation could not be controlled very accurately, and the actual measured distance was finally 16.5cm.

[0082] In Experiment 1, holes with the same properties were placed behind R-①②③: air hole, burial depth 0, hole size 5 liters, corresponding to B-1, B-3, and C-1 in Table 3; the purpose of Experiment 2 was to obtain hole signals of the same position and different sizes. The four holes were all close to the wall of the segment (ensuring the same burial depth), and the hole sizes were 1.5L, 5L, 12L, and continuous holes, respectively, and the corresponding hole numbers were D-1, B-3, E-1, and E-2; the purpose of Experiment 3 was to obtain hole signals at different burial depths, and the distances between the hole and the segment wall were set to 0cm, 16.5cm, and 30cm, and the corresponding hole numbers were B-3, D-2, and C-2; no holes were placed in the control group (segment A).

[0083] Table 3 Cavity properties behind different segments

[0084]

[0085] Step 1: Use a marker pen to mark the points to be tested on the tube segment of the subway tunnel;

[0086] Step 2: Hit the point with a rebound tester and use an acceleration sensor to collect the vibration signal of the point;

[0087] In practical applications, the present invention needs to mark the points to be detected on the pipe segment with a marker pen, place the acceleration sensor close to the point, and hit the point with a rebound tester. At this time, the collector collects the signal of the acceleration sensor and transmits it to the computer synchronously. The signal is then stored on the computer. The signal of each point is named: pipe segment number-horizontal coordinate value-vertical coordinate value. After the data collection of the points near the sensor is completed, the sensor is removed and pasted near the remaining points, and then the strike and signal collection are performed. Repeat the above process until the data collection of all points is completed. Among them, the acceleration sensor model is a single-axis sensor from Langs; the signal collector model is Yangzhou Jingming dynamic acquisition module JM5938; the rebound tester uses the Zhuolin Technology high-strength concrete digital display rebound tester HT450-D.

[0088] Step 3: De-noising the vibration signal of each point to obtain a de-noised vibration signal;

[0089] Furthermore, step 3 includes:

[0090] Step 3.1: Set the decomposition scale, and decompose the vibration signal of each point according to the set decomposition scale to obtain the wavelet coefficient;

[0091] Step 3.2: Determine the denoising threshold based on the set decomposition scale;

[0092] Step 3.3: When the wavelet coefficient is greater than or equal to the denoising threshold, the wavelet coefficient is processed using a processing function to generate a processed wavelet coefficient;

[0093] In step 3.3, the present invention may adopt the formula:

[0094]

[0095] The wavelet coefficients are processed to generate the processed wavelet coefficients; where w i ' ,k represents the processed wavelet coefficients, sign represents the sign function, and w i,k represents the kth original wavelet coefficient under the i-th decomposition scale, λ represents the denoising threshold, N represents the length of the vibration signal, and σ represents the noise standard deviation.

[0096] Step 3.4: When the wavelet coefficient is less than the denoising threshold, the corresponding wavelet coefficient is changed to 0;

[0097] Step 3.5: reconstructing the processed wavelet coefficients to form a denoised vibration signal;

[0098] Step 3.6: Use the fusion index to evaluate the denoised vibration signal;

[0099] Furthermore, step 3.6 includes:

[0100] Step 3.6.1: Perform a preliminary evaluation on the denoised vibration signal to obtain the root mean square error index and signal-to-noise ratio index of the signal;

[0101] In step 3.6.1, the calculation formulas for the root mean square error index and the signal-to-noise ratio index are:

[0102]

[0103] Wherein, x(i) represents the original vibration signal, y(i) represents the signal-to-noise ratio, n represents the sampling point number, N represents the sampling length, SNR represents the first evaluation value, and RMSE represents the root mean square error.

[0104] Step 3.6.2: normalize the root mean square error index and the signal-to-noise ratio index to obtain a normalized root mean square error index and a signal-to-noise ratio index;

[0105] Step 3.6.3: Calculate the weights of the normalized root mean square error index and signal-to-noise ratio index;

[0106] Among them, step 3.6.3 includes:

[0107] Calculate the ratio of each indicator to all indicators and determine the weight according to the ratio; the weight calculation formula is:

[0108]

[0109] Among them, P ij It represents the ratio of the jth index when the decomposition scale is i, x ij represents the value of the jth indicator when the number of decomposition layers is i after normalization, m represents the number of decomposition scales, and w j Represents the weight of the j-th indicator.

[0110] Step 3.6.4: Use the weights to weight the root mean square error index and the signal-to-noise ratio index to obtain the fusion index.

[0111] Step 3.7: When the weighted evaluation value is not within the preset range, reset the decomposition scale until the weighted evaluation value is within the preset range.

[0112] Step 4: Perform time domain conversion on the denoised vibration signal to obtain a frequency domain signal corresponding to the vibration signal;

[0113] In practical applications, the reflected wave is transmitted back to the concrete surface, and its signal will be received by the sensor pre-deployed on the concrete surface. The reflected wave will generate a new reflected wave on the concrete surface and re-transmit to the interior of the concrete lining. Such multiple reflections back and forth will make the waveform have obvious periodicity, which can be decomposed into multiple waves with different frequencies and peaks in the spectrum. Since the signal composed of multiple frequency components will interfere or overlap with each other in the time domain, the waveform shape will be irregular or difficult to understand intuitively. Frequency domain analysis can provide important information about signal characteristics by decomposing the signal into its frequency components, making the energy or amplitude of a specific frequency more clear and distinguishable. This paper uses fast Fourier transform to convert time domain data to obtain frequency domain information of the corresponding vibration signal.

[0114] Fourier transform is to decompose the signal into the linear superposition of the fundamental frequency and a series of harmonics, thereby converting the signal from the time domain to the frequency domain. There are two forms of Fourier transform: continuous Fourier transform (CFT) and discrete Fourier transform (DFT). Fast Fourier transform (FFT) is a commonly used signal transformation method and a method for quickly calculating DFT. Its basic idea is to decompose the calculation of DFT into multiple sub-problems, recursively solve these sub-problems, and finally merge the results. That is, by decomposing the DFT matrix into the product of sparse factors to quickly calculate and reduce the computational complexity, the more sampling points are input, the more obvious the speed of FFT is improved. This divide-and-conquer idea greatly reduces the time complexity of the FFT algorithm. The original DFT time complexity is O(n 2 ), while FFT is only O(NlogN), so the fast Fourier transform is used in the calculation. The result of the fast Fourier transform is to transform the signal from the time domain to the frequency domain to highlight the characteristics of the disease. The calculation formula is as follows:

[0115]

[0116] Where c ≥ 0, e v (c) = x(2c), o d (c) = x(2c+1), N is the total number of sampling points, k is the frequency index used to represent different frequency components, X k1 Represents the first N / 2 frequency components of the final Fourier transform result, represents the last N / 2 frequency components of the Fourier transform result, k1 represents the frequency index, which determines the position of the frequency component in the frequency domain, and e v (c) represents the even part, that is, the sampling points of all even-indexed positions in signal c, and x(2c) represents extracting the sample values ​​of the signal and rearranging them so that the indexes of these sample values ​​are even-indexed. d (c) represents the odd part, that is, the sampling points of all odd index positions in signal c, DFT represents the discrete Fourier transform function, represents the rotation factor.

[0117] Step 5: Complete the detection of the cavity behind the subway shield tunnel wall based on the time domain signal of the denoised vibration signal and the frequency domain signal.

[0118] The amplitude of the periodic fluctuation phase in the time domain signal is extracted. When the amplitude is greater than the set threshold, a hole appears at the corresponding point of the subway tunnel.

[0119] The peak value in the frequency domain signal is extracted. When the peak value exceeds the preset threshold, a hole appears at the corresponding point in the subway tunnel.

[0120] The vibration time domain data of each point measured in the test are as follows: Figure 4-7 As shown, the horizontal axis of the signal represents time, and the vertical axis represents acceleration value; Figure 4 , Figure 6 They are the signals without holes in areas ② and ③, Figure 5 , Figure 7 They are the signals of the corresponding points where the holes are placed in areas ② and ③ respectively. From the local waveform, the amplitude in the periodic fluctuation stage is extracted and calculated. Figure 4-7 The amplitude value (periodic vibration in the amplified area, the difference from the highest point to the lowest point in one cycle, and the time of one cycle is 0.021 seconds). The results show that Figure 4-7 The amplitude values ​​are 0.116, 0.25, 0.052, and 0.58, respectively. In a stable fluctuation period, the amplitude value of the area containing voids is significantly greater than the amplitude value of the point without voids. Among them, the value of the 1.5L void is not much different from the value without voids. As the volume of the void increases, the amplitude value increases, and the amplitude of the continuous void is more than 10 times larger. As the burial depth increases, the amplitude gradually decreases. Especially at a burial depth of 30 cm, it is impossible to distinguish whether a void exists.

[0121] Fast Fourier transform is used to transform the time domain data to obtain the frequency domain information of the corresponding vibration signal. The results are as follows: Figure 8-11 shown. Figure 8 The circled part is the peak value of the entire signal. The maximum Y value here is the acceleration value, and the X-axis value corresponding to the maximum value is the frequency value.

[0122] The frequency peaks of the areas with voids are greater than those of the areas without voids, and the peaks all appear at a frequency of 50.8MHz. As the size of the simulated void increases, the amplitude increases successively, and is most obvious at the place where the continuous void is pre-buried, where the amplitude value is one order of magnitude larger than that of the area without voids. The amplitude values ​​of simulated voids of the same size are relatively stable, with the amplitude of the 5L void being around 0.002, the 12L void being around 0.005, the continuous void being around 0.02, and the amplitude values ​​of the two groups without voids being around 0.001. In addition, the smaller value of 1.5L is not much different from the void-free value, so it is difficult to distinguish whether there is a 1.5L void. As the burial depth value increases, the amplitude of the signal becomes smaller, and voids with a burial depth of more than 16.5cm cannot be identified. The amplitude values ​​at the same burial depth are relatively stable, and at 0cm, the average value of the 5L void is 0.00186.

[0123] The present invention can effectively improve the accuracy of cavity detection results and reduce the possibility of misjudgment by performing denoising and frequency domain analysis on the collected data.

[0124] The present invention also provides a system for detecting cavities behind a subway shield tunnel wall, comprising:

[0125] Point marking module, used to mark the points to be inspected on the tube segments of the subway tunnel with a marker pen;

[0126] A vibration signal acquisition module is used to strike a point and collect the vibration signal of the point using an acceleration sensor;

[0127] A denoising module is used to denoise the vibration signal of each point to obtain a denoised vibration signal;

[0128] A time domain conversion module is used to perform time domain conversion on the denoised vibration signal to obtain a frequency domain signal corresponding to the vibration signal;

[0129] The cavity detection module is used to complete the detection of the cavity behind the subway shield tunnel wall based on the time domain signal of the denoised vibration signal and the frequency domain signal.

[0130] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in the above-mentioned method for detecting voids behind the wall of a subway shield tunnel are implemented. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present invention are the same as the beneficial effects of the method for detecting voids behind the wall of a subway shield tunnel described in the above-mentioned technical solution, which will not be repeated here.

[0131] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technical solution that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for detecting cavities behind a subway shield tunnel wall, characterized in that: include: Step 1: Use a marker pen to mark the points to be tested on the tube segment of the subway tunnel; Step 2: Hit the point with a rebound tester and use an acceleration sensor to collect the vibration signal of the point; Step 3: De-noising the vibration signal of each point to obtain a de-noised vibration signal; Step 4: Perform time domain conversion on the denoised vibration signal to obtain a frequency domain signal corresponding to the vibration signal; Step 5: Complete the detection of the cavity behind the subway shield tunnel wall based on the time domain signal of the denoised vibration signal and the frequency domain signal.

2. A method for detecting cavities behind a subway shield tunnel wall according to claim 1, characterized in that: The step 3: performing denoising processing on the vibration signal of each point to obtain a denoised vibration signal, comprises: Step 3.1: Set the decomposition scale, and decompose the vibration signal of each point according to the set decomposition scale to obtain the wavelet coefficient; Step 3.2: Determine the denoising threshold based on the set decomposition scale; Step 3.3: When the wavelet coefficient is greater than or equal to the denoising threshold, the wavelet coefficient is processed using a processing function to generate a processed wavelet coefficient; Step 3.4: When the wavelet coefficient is less than the denoising threshold, the corresponding wavelet coefficient is changed to 0; Step 3.5: reconstructing the processed wavelet coefficients to form a denoised vibration signal; Step 3.6: Use the fusion index to evaluate the denoised vibration signal; Step 3.7: When the weighted evaluation value is not within the preset range, reset the decomposition scale until the weighted evaluation value is within the preset range.

3. A method for detecting cavities behind a subway shield tunnel wall according to claim 2, characterized in that: The step 3.3: when the wavelet coefficient is greater than or equal to the denoising threshold, the wavelet coefficient is processed by a processing function to generate a processed wavelet coefficient, including: Using the formula: The wavelet coefficients are processed to generate the processed wavelet coefficients; where w i ' ,k represents the processed wavelet coefficients, sign represents the sign function, and w i,k represents the kth original wavelet coefficient under the i-th decomposition scale, λ represents the denoising threshold, N represents the length of the vibration signal, and σ represents the noise standard deviation.

4. A method for detecting cavities behind the wall of a subway shield tunnel according to claim 3, characterized in that: The step 3.6: using the fusion index to evaluate the denoised vibration signal, including: Step 3.6.1: Perform a preliminary evaluation on the denoised vibration signal to obtain the root mean square error index and signal-to-noise ratio index of the signal; Step 3.6.2: normalize the root mean square error index and the signal-to-noise ratio index to obtain a normalized root mean square error index and a signal-to-noise ratio index; Step 3.6.3: Calculate the weights of the normalized root mean square error index and signal-to-noise ratio index; Step 3.6.4: Use the weights to weight the root mean square error index and the signal-to-noise ratio index to obtain the fusion index.

5. A method for detecting cavities behind the wall of a subway shield tunnel according to claim 4, characterized in that: In step 3.6.1, the calculation formulas for the root mean square error index and the signal-to-noise ratio index are: Wherein, x(i) represents the original vibration signal, y(i) represents the signal-to-noise ratio, n represents the sampling point number, N represents the sampling length, SNR represents the first evaluation value, and RMSE represents the root mean square error.

6. A method for detecting cavities behind the wall of a subway shield tunnel according to claim 5, characterized in that: The step 3.6.3: calculating the weights of the normalized root mean square error index and the signal-to-noise ratio index includes: Calculate the ratio of each indicator to all indicators and determine the weight according to the ratio; the weight calculation formula is: Among them, P ij It represents the ratio of the jth index when the decomposition scale is i, x ij represents the value of the jth indicator when the number of decomposition layers is i after normalization, m represents the number of decomposition scales, and w j Represents the weight of the j-th indicator.

7. The method for detecting the cavity behind the wall of a subway shield tunnel according to claim 1, characterized in that: The step 4: performing time domain conversion on the denoised vibration signal to obtain a frequency domain signal corresponding to the vibration signal, includes: The denoised vibration signal is transformed using fast Fourier transform to obtain the frequency domain signal of the corresponding vibration signal; the conversion formula is: Where c ≥ 0, e v (c) = x(2c), o d (c) = x(2c+1), N is the total number of sampling points, k is the frequency index used to represent different frequency components, X k1 Represents the first N / 2 frequency components of the final Fourier transform result, represents the last N / 2 frequency components of the Fourier transform result, k1 represents the frequency index, which determines the position of the frequency component in the frequency domain, and e v (c) represents the even part, that is, the sampling points of all even-indexed positions in signal c, and x(2c) represents extracting the sample values ​​of the signal and rearranging them so that the indexes of these sample values ​​are even-indexed. d (c) represents the odd part, that is, the sampling points of all odd index positions in signal c, DFT represents the discrete Fourier transform function, represents the rotation factor.

8. A method for detecting cavities behind a subway shield tunnel wall according to claim 7, characterized in that: The step 5: completing the detection of the cavity behind the subway shield tunnel wall based on the time domain signal of the denoised vibration signal and the frequency domain signal, including: The amplitude of the periodic fluctuation phase in the time domain signal is extracted. When the amplitude is greater than the set threshold, a hole appears at the corresponding point of the subway tunnel. The peak value in the frequency domain signal is extracted. When the peak value exceeds the preset threshold, a hole appears at the corresponding point in the subway tunnel.

9. A system for detecting cavities behind the wall of a subway shield tunnel, characterized in that: include: Point marking module, used to mark the points to be inspected on the tube segments of the subway tunnel with a marker pen; A vibration signal acquisition module is used to strike a point and collect the vibration signal of the point using an acceleration sensor; A denoising module is used to denoise the vibration signal of each point to obtain a denoised vibration signal; A time domain conversion module is used to perform time domain conversion on the denoised vibration signal to obtain a frequency domain signal corresponding to the vibration signal; The cavity detection module is used to complete the detection of the cavity behind the subway shield tunnel wall based on the time domain signal of the denoised vibration signal and the frequency domain signal.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method for detecting cavities behind a subway shield tunnel wall as described in any one of claims 1 to 8 are implemented.

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