Tunnel lining falling risk block identification and stability assessment method and detection device

Abstract

The present invention relates to the technical field of tunnel engineering and structural engineering, and in particular to a method for identifying risk blocks of tunnel lining falling and evaluating stability, comprising the following steps: S100, inducing tunnel lining vibration by a vibration device; S200, detecting the vibration echo signal of the tunnel lining by a vibration meter; S300, calculating the lining falling block index TSI according to the vibration echo signal of each measuring point i , the vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i and the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7i ; S400, the index TSI of each point of lining block falling under the i-frequency acoustic wave excitation i Normalization processing, using pixel matrix imaging and learning-based super-resolution algorithm to draw a two-dimensional cloud map Cloud i , calculate the correlation between each two-dimensional cloud map, and the TSI of each cloud map point with a correlation coefficient higher than the threshold α i The average value is taken to finally form a two-dimensional cloud map Cloud of the tunnel lining vibration characteristics.

Description

Technical Field

[0001] The invention relates to the technical fields of tunnel engineering and structural engineering, and in particular to a method and a detection device for identifying and evaluating the stability of risk blocks of tunnel lining falling. Background Art

[0002] my country's traffic tunnels have reached an unprecedented scale. By the end of 2022, my country has operated 24,850 highway tunnels with a total length of 26,784.3 km. However, with the continuous increase in service life, tunnel lining block falling accidents have become increasingly prominent, and in recent years, there have been many serious traffic safety accidents caused by lining block falling, causing serious economic losses and adverse social impacts, reflecting that the technical means of identifying and assessing the stability of tunnel lining falling risk blocks cannot meet the current needs.

[0003] The existing methods for identifying and evaluating the stability of falling risk blocks can be divided into three categories:

[0004] First, based on visual inspection, the presence of closed blocks formed by multiple intersecting cracks is determined through on-site observation or digital images obtained by machine vision, and the stability of the risk block is evaluated by combining the crack combination and crack width. However, the correlation between crack width and depth and block stability is difficult to quantify, and it is impossible to identify falling risk blocks formed by combinations of hidden cracks, waterproof board intrusion, and construction joints.

[0005] 2. Based on the numerical analysis method, through on-site investigation of the crack size and roughness of the falling risk blocks, a high-fidelity numerical calculation model is established, and the stable state of the block under external action is determined by calculation. However, in actual engineering, the crack size and roughness have strong variability, which makes investigation difficult, and the load conditions are difficult to determine, making it impossible to make a scientific and accurate assessment, which is inefficient.

[0006] 3. Temperature difference detection: The temperature difference detection equipment uses an irradiation device to heat the lining. It takes advantage of the fact that the temperature of the normal lining drops faster than the lining peeling part. The infrared camera is used to take thermal images of the peeling part and the normal part to judge the abnormal situation such as peeling. The problem with this technology is that the equipment cost is high and the heating energy consumption is large.

[0007] Therefore, those skilled in the art are committed to developing a method and a detection device for identifying and evaluating the risk blocks of falling tunnel linings, so as to improve the detection accuracy of the risk blocks of falling tunnel linings. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a method and a detection device for identifying and evaluating the risk blocks of falling of tunnel linings, so as to improve the detection accuracy of the risk blocks of falling of tunnel linings.

[0009] The technical solution of the present invention to solve the above technical problems is as follows:

[0010] A method for identifying and evaluating the stability of tunnel lining falling risk blocks comprises the following steps:

[0011] S100, inducing vibration of the tunnel lining by means of a vibration device;

[0012] S200, detecting a vibration echo signal of the tunnel lining by using a vibration meter;

[0013] S300, calculate the lining drop index TSI based on the vibration echo signal of each measuring point i , the vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i and the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7i ;

[0014] S400, the index TSI of each point of lining block falling under the i-frequency sound wave excitation i Normalization processing, using pixel matrix imaging and learning-based super-resolution algorithm to draw a two-dimensional cloud map Cloud i , calculate the correlation between each two-dimensional cloud map, and the TSI of each cloud map point with a correlation coefficient higher than the threshold α i Take the average value. When the correlation coefficient is lower than the threshold α, select the next cloud map for calculation and analysis, and finally form a two-dimensional cloud map Cloud of the tunnel lining vibration characteristics.

[0015] The beneficial effects of adopting the above scheme are: the vibration device induces the tunnel lining vibration and generates vibration echoes, the vibration meter detects the vibration echoes and calculates the lining block drop index, vibration energy and power according to the vibration echo signals of each measuring point and finally forms a two-dimensional cloud map, which solves the problem that the existing lining drop risk block identification and stability assessment results lack scientificity and accuracy;

[0016] Through two-dimensional cloud maps, we can reduce the reliance on personal experience in operations and improve the accuracy of detection.

[0017] Based on the above technical solution, the present invention can also be improved as follows.

[0018] Further, after step S400, the following steps are also included:

[0019] S500, extracting waveforms, pulses, and peak-to-peak values ​​in the time-frequency domain according to the echo signals of each tunnel lining and calculating the cavity index;

[0020] S600, establishing a model and a sample library based on the two-dimensional cloud image Cloud of the tunnel lining vibration characteristics formed in step S400 and the void index in step S500, carrying out model training, and conducting a stability assessment of the falling risk block.

[0021] The beneficial effect of adopting the above further scheme is: detecting the tunnel lining echo signal by the tapping method can cause the tunnel lining vibration data limit, making the detection data more intuitive, and combining it with the two-dimensional cloud map to establish a model library, thereby improving the accuracy of the stability assessment of the tunnel lining falling risk block.

[0022] Further, after step S400, the following steps are also included:

[0023] S700, calculating the vibration amplitude of the tunnel lining according to the echo signal of each tunnel lining, and determining the tunnel cavity threshold according to the vibration amplitude.

[0024] The beneficial effect of adopting the above further scheme is that the vibration amplitude of the tunnel lining can be calculated according to the tunnel lining echo signal, and the size and position of the tunnel lining cavity can be analyzed according to the vibration law.

[0025] Further, in step S300, the lining block index TSI i The calculation formula is

[0026] TSI i =w 1 *I 1i +w 2 *I 2i +w 3 *I 3i +w 4 *I 4i +

[0027] w 5 *I 5i +w 6 *I 6i +w 7 *I 7i +w 8 *I 8i ;

[0028] Among them, w 1 is the variance I of the vibration echo signal under the excitation of the sound wave of frequency i 1i The weight of

[0029] w 2 is the range I of the vibration echo signal under the excitation of the sound wave of frequency i 2i The weight of

[0030] w 3 is the damping I of the vibration echo signal under the excitation of the sound wave of frequency i 3i The weight of

[0031] w 4 is the autocorrelation coefficient I of the vibration echo signal under the excitation of the sound wave of frequency i 4i The weight of

[0032] w 5 The maximum peak of the spectrum of the vibration echo signal under the excitation of the sound wave of frequency i corresponds to the frequency I 5i The weight of

[0033] w 6 is the vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i The weight of

[0034] w 7 is the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7i The weight of

[0035] w 8 is the power spectrum entropy I of the vibration echo signal under the excitation of the sound wave of frequency i 8i The weight of .

[0036] The beneficial effect of adopting the above further scheme is: calculating the lining block index TSI i , which is conducive to the subsequent drawing of two-dimensional cloud map Cloud i .

[0037] Further, in step S300, the vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i The calculation formula is

[0038]

[0039] Where f(t) is the time series function of the vibration echo.

[0040] The beneficial effect of adopting the above further solution is: calculating the vibration energy I 6i , which is conducive to the subsequent drawing of two-dimensional cloud map Cloud i .

[0041] Further, in step S300, the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7i The calculation formula is

[0042]

[0043] The beneficial effect of adopting the above further scheme is: calculating the power I 7i , which is conducive to the subsequent drawing of two-dimensional cloud map Cloud i .

[0044] A device for identifying and detecting blocks at risk of falling of a tunnel lining, comprising the method for identifying and evaluating the stability of blocks at risk of falling of a tunnel lining as described above, comprising a vibration device and a vibrometer, wherein the distance between the vibrometer and the surface of the tunnel lining is greater than the distance between the striking part of the vibration component and the surface of the tunnel lining.

[0045] The beneficial effect of adopting the above further solution is that the vibration device is used to induce vibration of the tunnel lining, and the vibration meter is used to detect the echo signal of the tunnel lining.

[0046] Furthermore, it also includes an interference detection component, the interference detection component includes an induction core, the induction core is fixedly connected to the tunnel lining, an induction coil is arranged outside the induction core, the induction coil is electrically connected to a control device, and the control device is also electrically connected to an interference light generating device;

[0047] The vibration device induces vibration of the tunnel lining, and the tunnel lining transmits the vibration to the induction core. The induction coil is used to sense the vibration change of the induction core and transmit the induction information to the control device. When the induction information received by the control device exceeds a threshold value, the control device sends a signal to the interference light generating device so that the interference light generating device emits interference light to the vibrometer.

[0048] The beneficial effect of adopting the above further scheme is: when the tunnel lining vibration received by the control device exceeds the threshold value, it means that the tunnel lining is subject to external interference. The control device sends a signal to the interference light generating device so that the interference light generating device sends interference light to the vibrometer, so that the laser vibrometer receives the abnormal signal and marks it, which is beneficial to eliminate it in subsequent data processing.

[0049] Furthermore, it also includes a sound receiving device, which is used to receive the echo signal generated by the vibration of the tunnel lining.

[0050] The beneficial effect of adopting the above further solution is that the sound receiving device is used to receive the echo signal of the tunnel lining, and the size and depth of the cavity behind the lining are determined by the sound characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flowchart of a specific embodiment of the present invention;

[0052] Figure 2 A technical roadmap for a specific embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of an application scenario of a device for identifying and detecting risky blocks of falling tunnel linings according to a specific embodiment of the present invention.

[0054] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0055] 1. Vibration device; 2. Vibrometer; 3. Induction core; 4. Induction coil; 5. Interference light generating device; 6. Sound receiving device. DETAILED DESCRIPTION

[0056] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0057] In the description of the present invention, it is necessary to understand that the terms "center", "length", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "inside", "outside", "peripheral", "circumferential" 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, and do not indicate or imply that the referred system or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0058] In the description of the present invention, “plurality” means at least two, for example, two, three, etc., unless otherwise clearly and specifically defined.

[0059] 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 an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0060] like Figure 1 , Figure 2 As shown, a method for identifying and evaluating the stability of tunnel lining falling risk blocks includes the following steps:

[0061] S100, inducing vibration of the tunnel lining by a vibration device, specifically, an energy storage mechanism, a servo mechanism and a percussion hammer can be used, and the energy storage strength of the energy storage mechanism can be adjusted by the servo mechanism, or the vibration device can use a directional sound wave generating device. During detection, energy can be transferred to the percussion hammer through the energy storage mechanism, so that the percussion hammer strikes the tunnel lining surface to induce vibration, or a directional sound wave generating device can be used to emit directional sound waves to the tunnel lining surface to induce tunnel lining vibration. In order to improve the detection accuracy, the vibration frequency range induced on the tunnel lining surface is 50Hz to 6250Hz.

[0062] S200, detecting a vibration echo signal of a tunnel lining by a vibrometer. Specifically, the vibration echo signal of a tunnel lining is detected by a vibrometer by using non-contact methods such as acceleration vibration measurement and laser vibration measurement.

[0063] S300, calculate the lining drop index TSI based on the vibration echo signal of each measuring point i, the vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i and the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7i ;

[0064] Lining Falling Index TSI i The calculation formula is

[0065] TSI i =w 1 *I 1i +w 2 *I 2i +w 3 *I 3i +w 4 *I 4i +

[0066] w 5 *I 5i +w 6 *I 6i +w 7 *I 7i +w 8 *I 8i ;

[0067] Among them, w 1 is the variance I of the vibration echo signal under the excitation of the sound wave of frequency i 1i The weight of

[0068] w 2 is the range I of the vibration echo signal under the excitation of the sound wave of frequency i 2i The weight of

[0069] w 3 is the damping I of the vibration echo signal under the excitation of the sound wave of frequency i 3i The weight of

[0070] w 4 is the autocorrelation coefficient I of the vibration echo signal under the excitation of the sound wave of frequency i 4i The weight of

[0071] w 5 The frequency corresponding to the maximum peak of the spectrum of the vibration echo signal under the excitation of the sound wave of frequency i

[0072] I 5i The weight of

[0073] w 6 is the vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i The weight of

[0074] w 7 is the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7iThe weight of

[0075] w 8 is the power spectrum entropy I of the vibration echo signal under the excitation of the sound wave of frequency i 8i The weight of .

[0076] The vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i The calculation formula is

[0077]

[0078] Where f(t) is the time series function of the vibration echo.

[0079] In step S300, the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7i The calculation formula is

[0080]

[0081] In order to further improve the detection accuracy, after the sound wave generating device sends the i-frequency sound wave detection, the sound wave frequency is switched. Different frequencies are used for detection in the same detection area. Sound waves of different frequencies cause the tunnel lining to resonate with different amplitudes, which is beneficial for the vibration meter to capture echo signals of different frequencies.

[0082] S400, the index TSI of each point of lining block falling under the i-frequency sound wave excitation i Normalization processing, using pixel matrix imaging and learning-based super-resolution algorithm to draw a two-dimensional cloud map Cloud i , calculate the correlation between each two-dimensional cloud map, and the TSI of each cloud map point with a correlation coefficient higher than the threshold α i Take the average value. When the correlation coefficient is lower than the threshold α, select the next cloud map for calculation and analysis, and finally form a two-dimensional cloud map Cloud of the tunnel lining vibration characteristics.

[0083] After step S400, the method also includes detecting the tunnel lining by tapping. The tunnel lining echo signal is detected by tapping method, which can cause the tunnel lining vibration data limit, making the detection data more intuitive, and combining it with the two-dimensional cloud map to establish a model library to improve the accuracy of stability assessment of tunnel lining falling risk blocks.

[0084] S500, extracting waveforms, pulses, and peak-to-peak values ​​in the time-frequency domain according to the echo signals of each tunnel lining and calculating the cavity index;

[0085] S600, establishing a model and a sample library based on the two-dimensional cloud image Cloud of the tunnel lining vibration characteristics formed in step S400 and the void index in step S500, carrying out model training, and conducting a stability assessment of the falling risk block.

[0086] Specifically, the knock echo signal is cleaned and filtered to extract pure sound signal; secondly, the signal is processed and analyzed, and according to the original waveform, ringing, octave spectrum, impulse response spectrum, wavelet packet transform and other data information, the void feature vectors such as waveform, pulse, kurtosis, margin, peak-to-peak value, mean square frequency, wavelet energy spectrum are extracted in the time domain, frequency domain and time-frequency domain, and the void index is calculated.

[0087] S700, calculating the vibration amplitude of the tunnel lining according to the echo signal of each tunnel lining, and determining the tunnel cavity threshold according to the vibration amplitude.

[0088] Specifically, when there is a cavity in the tunnel lining, the vibration signal will be reflected and refracted on the defect surface, causing the vibration signal to be attenuated. The higher the frequency, the faster the attenuation, which significantly reduces the amplitude of the received signal. Then, the size and position of the cavity in the tunnel lining can be judged based on the amplitude of the echo signal and the tunnel cavity threshold.

[0089] Embodiment 2

[0090] like Figure 3 As shown, the present invention also provides a tunnel lining falling risk block identification and detection device, comprising applying the tunnel lining falling risk block identification and stability assessment method as described above,

[0091] The invention comprises a vibration device 1 and a vibration meter 2. The vibration device 1 can adopt a knocking impact or sound wave generating device. The vibration device 1 can adopt a long-distance directional sound wave generating device or a knocking component. The knocking component can adopt a knocking detection device as disclosed in the publication number: CN217084805U (an automatic knocking detection device for railway tunnel lining). During the detection and installation, the distance between the vibration meter 2 and the surface of the tunnel lining is greater than the distance between the vibration part of the vibration device 1 and the surface of the tunnel lining.

[0092] It also includes an interference detection component, which includes an induction core 3, which is fixedly connected to the tunnel lining, an induction coil 4 is arranged outside the induction core 3, and the induction coil 4 is electrically connected to a control device (not shown in the figure), and the control device is also electrically connected to an interference light generating device 5;

[0093] The vibration device 1 induces vibration of the tunnel lining, and the tunnel lining transmits the vibration to the induction core 3. The induction coil 4 is used to sense the vibration changes of the induction core 3 and transmit the induction information to the control device. When the induction information received by the control device exceeds the threshold, the control device sends a signal to the interference light generating device 5 so that the interference light generating device emits interference light to the vibrometer 2.

[0094] It also includes a sound receiving device 6, which is used to receive the echo signal generated by the vibration of the tunnel lining. The echo signal is mainly a low-frequency echo signal. When there is a cavity in the tunnel lining, the vibration signal will be reflected and refracted on the defect surface, causing the vibration signal to be attenuated, so that the amplitude of the received echo signal is significantly reduced, and then the size, position and depth of the cavity in the tunnel lining are judged according to the amplitude of the echo signal and the tunnel cavity threshold.

[0095] In the present invention, a vibration device is used to strike the tunnel lining to cause the tunnel lining to resonate, and the vibrometer 2 detects the echo signal to calculate the lining block drop index, vibration energy and power to form a two-dimensional cloud map. At the same time, the waveform, pulse and peak-to-peak value are extracted in the time-frequency domain according to the echo signal to calculate the void index. The tunnel lining drop risk blocks are identified and the stability is evaluated based on the two-dimensional cloud map and the void index, which solves the problem that the existing lining drop risk block identification and stability evaluation results lack scientificity and accuracy.

[0096] At the same time, the vibration meter detects the echo signal, extracts the amplitude from the echo signal, and then judges the size and position of the cavity in the lining of the hollow tunnel according to the amplitude of the echo signal and the tunnel cavity threshold. In order to further improve the detection accuracy, the hammer head can be replaced or the energy storage mechanism can be adjusted, or the vibration frequency and energy of the directional sound wave can be increased, so that the tunnel lining has a larger amplitude and the vibration limit of the tunnel lining can be measured, effectively eliminating the dependence on the personal experience of the operators in the traditional detection technology, and the detection results are more accurate, true and scientific. The lining that is about to fall can also be knocked off, so as to avoid them falling at an inappropriate time or when traffic is flowing, causing safety accidents and reducing the risk of accidents. At the same time, knocking off loose stones or linings can prevent the problem from expanding, thereby avoiding more expensive repairs that may occur in the future, ensuring the smooth flow of the tunnel and improving its efficiency.

[0097] Furthermore, the lining vibration echo signal mentioned in the present invention can be obtained in a variety of ways. When significant development of lining diseases has been observed, the lining vibration echo signal can be monitored for a long time by pre-setting an accelerometer on the lining. When it is necessary to detect diseases in a tunnel without sensors, non-contact laser vibration measurement technology can be used for measurement. Therefore, the present invention has a wide range of application scenarios and can fully adapt to existing engineering needs.

[0098] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying and evaluating the stability of tunnel lining falling risk blocks, characterized in that: The following steps are involved: S100, inducing vibration of the tunnel lining by means of a vibration device; S200, detecting a vibration echo signal of the tunnel lining by using a vibration meter; S300, calculate the lining drop index TSI based on the vibration echo signal of each measuring point i , the vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i and the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7i ; Lining Falling Index TSI i The calculation formula is TSI i =w1*I 1i +w2*I 2i +w3*I 3i +w4*I 4i + w5*I 5i +w6*I 6i +w7*I 7i +w8*I 8i ; Where w1 is the variance I of the vibration echo signal under the excitation of the sound wave of frequency i 1i The weight of w2 is the range of the vibration echo signal under the excitation of the sound wave of frequency i 2i The weight of w3 is the damping i of the vibration echo signal under the excitation of the sound wave of frequency i 3i The weight of w4 is the autocorrelation coefficient I of the vibration echo signal under the excitation of the sound wave of frequency i 4i The weight of w5 is the maximum peak of the spectrum of the vibration echo signal under the excitation of the sound wave of frequency i, corresponding to the frequency I 5i The weight of w6 is the vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i The weight of w7 is the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7i The weight of w8 is the power spectrum entropy I of the vibration echo signal under the excitation of the sound wave of frequency i 8i The weight of S400, the index TSI of each point of lining block falling under the i-frequency sound wave excitation i Normalization processing, using pixel matrix imaging and learning-based super-resolution algorithm to draw a two-dimensional cloud map Cloud i , calculate the correlation between each two-dimensional cloud map, and the TSI of each cloud map point with a correlation coefficient higher than the threshold α i Take the average value. When the correlation coefficient is lower than the threshold α, select the next cloud map for calculation and analysis, and finally form a two-dimensional cloud map Cloud of the tunnel lining vibration characteristics; S500, extracting waveforms, pulses, and peak-to-peak values ​​in the time-frequency domain according to the echo signals of each tunnel lining and calculating the cavity index; S600, establishing a model and a sample library based on the two-dimensional cloud image Cloud of the tunnel lining vibration characteristics formed in step S400 and the void index in step S500, carrying out model training, and conducting stability assessment of the falling risk block; S700, calculating the vibration amplitude of the tunnel lining according to the echo signal of each tunnel lining, and determining the tunnel cavity threshold according to the vibration amplitude.

2. The method for identifying and assessing the stability of tunnel lining falling risk blocks according to claim 1 is characterized by: In step S300, the vibration energy I of the vibration echo signal under the excitation of the sound wave of frequency i 6i The calculation formula is Where f(t) is the time series function of the vibration echo.

3. The method for identifying and assessing the stability of tunnel lining falling risk blocks according to claim 1 is characterized by: In step S300, the power I of the vibration echo signal under the excitation of the sound wave of frequency i 7i The calculation formula is 4. A tunnel lining falling risk block identification and detection device, comprising the tunnel lining falling risk block identification and stability assessment method according to any one of claims 1 to 3, characterized in that: The invention comprises a vibration device and a vibration meter, wherein the distance between the vibration meter and the tunnel lining surface is greater than the distance between the striking part of the vibration device and the tunnel lining surface.

5. The tunnel lining falling risk block identification and detection device according to claim 4 is characterized in that: It also includes an interference detection component, which includes an induction core, which is fixedly connected to the tunnel lining, an induction coil is arranged outside the induction core, the induction coil is electrically connected to a control device, and the control device is also electrically connected to an interference light generating device; The vibration device induces vibration of the tunnel lining, and the tunnel lining transmits the vibration to the induction core. The induction coil is used to sense the vibration change of the induction core and transmit the induction information to the control device. When the induction information received by the control device exceeds a threshold value, the control device sends a signal to the interference light generating device so that the interference light generating device emits interference light to the vibrometer.

6. The tunnel lining falling risk block identification and detection device according to claim 4 is characterized in that: It also includes a sound receiving device, which is used to receive echo signals generated by the vibration of the tunnel lining.

Images