Tunnel lining thickness and back cavity sound wave quantitative rapid detection method and system

Abstract

The present application relates to a kind of tunnel lining thickness and back cavity acoustic quantitative fast detection method and system, the method includes the following steps: collecting the tunnel lining vibration response characteristics measured by non-contact, obtains stress vibration time domain curve;Based on the stress vibration time domain curve, signal time-frequency domain conversion is carried out, the frequency domain distribution diagram of response characteristic is obtained, and peak value characteristic extraction is carried out, wherein the peak value characteristic includes radial characteristic peak value and longitudinal characteristic peak value;Based on the peak value characteristic and the frequency domain distribution diagram of response characteristic, the inversion calculation of tunnel lining longitudinal, radial size and back cavity state is carried out, realizes the acoustic fast quantitative detection of tunnel lining thickness and back cavity state.Compared with prior art, the present application has the advantages of non-contact approach, high detection accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of acoustic wave detection, in particular to a tunnel lining thickness and back cavity acoustic wave quantitative rapid detection method and system. BACKGROUND

[0002] As an important part of modern transportation construction, the structure safety and stability of the tunnel is directly related to the safety and reliability of traffic operation. The tunnel lining, as a key part of the tunnel structure, its thickness and whether there is a cavity behind it are directly related to the carrying capacity and durability of the tunnel. Therefore, the detection of the tunnel lining thickness and the cavity behind it is particularly important.

[0003] The traditional tunnel lining thickness and back cavity detection methods mainly include destructive detection methods such as drilling sampling and excavation detection. Although these methods can directly observe and measure the internal structure and material composition of the lining, they have limitations such as destructiveness, high cost and low efficiency. In order to overcome the limitations of traditional detection methods, acoustic wave detection technology has been gradually applied in the detection of tunnel lining thickness and back cavity. Acoustic wave detection technology has the advantages of non-destructiveness, simple operation, fast detection speed and low cost, and has become an important means of tunnel lining quality detection. Patent CN210922500U discloses a tunnel lining thickness detection device. The device uses a ground penetrating radar to emit ultra-high frequency narrow pulse electromagnetic waves to the tunnel lining, and through mathematical calculation and fitting of the returned information, the underground medium spectrum characteristics are displayed to realize tunnel lining thickness measurement. Patent CN208476137U discloses a tunnel lining thickness detection device. This method is based on the scale difference of the measurement components before and after drilling to determine and analyze the support structure parameters. Patent application CN107621231A discloses a tunnel secondary lining thickness detection method, which proposes to use a laser device to scan the point cloud of the hole after the primary support and the secondary lining construction is completed, and then use data fusion technology and fractal technology to obtain the lining thickness. Patent application CN104859672A discloses a railway tunnel lining quality detection vehicle. The equipment uses a radar antenna assembly to realize full-face coverage of the tunnel, and based on the radar image, it can detect the lining thickness and the cavity behind the wall.

[0004] The existing published tunnel thickness and back cavity detection technology faces the following common problems:

[0005] 1) The ground penetrating radar (GPR) uses electromagnetic waves to detect concrete members, and the detection results are greatly affected by internal steel bars and concrete coarse aggregates, and the detection accuracy of internal cavities, wall water, cracks and other diseases is low.

[0006] 2) Drilling type thickness measurement technology uses micro drilling components to penetrate the lining, and the results are greatly affected by the hole structure form and the operation process may damage the lining. The applicability and detection accuracy of this detection method are low.

[0007] 3) The detection method of laser point cloud scanning relies on laser beam scanning and ranging of the chamber. The detection results are easily disturbed by the rough surface of the concrete and the dust environment of the chamber. It is difficult to store and analyze the massive data of the detection results.

[0008] 4) The traditional ultrasonic excitation detection technology often uses the form of contacting the acoustic probe components with the concrete components for excitation. There are limitations in travel speed and detection effect, and the contact components are prone to wear.

[0009] In summary, there is currently a lack of a rapid quantitative detection method and device suitable for tunnel lining thickness and back cavity, especially a detection method that can quantitatively evaluate the state of the chamber support structure in a non-contact and non-destructive manner. SUMMARY

[0010] The purpose of the present application is to provide a tunnel lining thickness and back cavity acoustic quantitative rapid detection method and system that improves the non-contact detection accuracy.

[0011] The purpose of the present application can be achieved by the following technical solutions:

[0012] A tunnel lining thickness and back cavity acoustic quantitative rapid detection method, comprising the following steps:

[0013] Collecting the tunnel lining vibration response characteristics measured by non-contact means to obtain the stress vibration time domain curve;

[0014] Based on the stress vibration time domain curve, signal time-frequency domain conversion is performed to obtain the frequency domain distribution graph of the response characteristics, and peak value characteristics are extracted, wherein the peak value characteristics include radial characteristic peak value and longitudinal characteristic peak value;

[0015] Based on the peak value characteristics and the frequency domain distribution graph of the response characteristics, the tunnel lining longitudinal and radial dimensions and the back cavity state are inversely calculated to realize acoustic rapid quantitative detection of the tunnel lining thickness and the back cavity state.

[0016] Further, the step of performing signal time-frequency domain conversion comprises:

[0017] Based on the mathematical statistics principle and the stress signal sound field distribution characteristics, the stress vibration time domain curve is denoised;

[0018] Based on the denoised stress vibration time domain curve, the signal time-frequency domain conversion is performed using the fast Fourier transform algorithm to obtain the frequency domain distribution graph of the response characteristics.

[0019] Further, the step of performing peak feature extraction comprises:

[0020] obtaining a potential interval of a radial feature frequency and a potential interval of a longitudinal feature frequency of the tunnel lining obtained by prior knowledge;

[0021] Based on the potential interval of the radial feature frequency and the potential interval of the longitudinal feature frequency, a radial feature peak and a longitudinal feature peak are demarcated from the frequency domain distribution of the response feature.

[0022] Further, the step of realizing the fast quantitative detection of the tunnel lining thickness and the state of the cavity behind the tunnel lining by sound waves comprises:

[0023] Comparing the peak feature with the theoretical feature frequency in the prior knowledge, calculating a relative error value, and the relative error value represents the deviation direction and the deviation value;

[0024] Based on the relative error value and the frequency domain distribution of the response feature, the longitudinal and radial dimensions of the tunnel lining and the state of the cavity behind the tunnel lining are inversely calculated, and the fast quantitative detection of the tunnel lining thickness and the state of the cavity behind the tunnel lining by sound waves is realized.

[0025] The application also provides a detection system according to the tunnel lining thickness and the state of the cavity behind the tunnel lining fast quantitative detection method, comprising:

[0026] A signal generator is used to set a sound wave signal containing excitation parameters;

[0027] A remote directional acoustic device is used for sound wave emission, and the sound wave excitation direction is coincided with the normal direction of the vibration measurement point position on the tunnel lining, and then a non-contact acoustic excitation vibration response feature is generated by using the sound wave signal with the pre-set excitation parameters;

[0028] A laser vibration meter is used to emit a laser beam to pick up the vibration response feature of the tunnel lining;

[0029] A processor is used to perform signal time-frequency domain conversion and peak feature extraction based on the dynamic response feature, and inversely calculate the longitudinal and radial dimensions of the tunnel lining and the state of the cavity behind the tunnel lining, and realize the fast quantitative detection of the tunnel lining thickness and the state of the cavity behind the tunnel lining by sound waves.

[0030] Further, the excitation parameters include a transmission frequency band, a frequency modulation interval, a single frequency bandwidth and a sound pressure intensity.

[0031] Further, the sound pressure intensity is calibrated and adjusted by using a sound pressure meter during the non-contact acoustic excitation process.

[0032] Further, when the vibration response feature is collected, a rich-hole sound-absorbing mask is arranged outside the laser vibration meter.

[0033] Further, in the process of picking up the tunnel lining vibration response characteristics by the laser vibration meter, the laser beam is coincident with the normal direction of the vibration point on the tunnel lining.

[0034] Further, in the process of picking up the vibration response characteristics, the vibration point on the tunnel lining is provided with a foil with a reflective coating to assist signal enhancement.

[0035] Compared with the prior art, the present application has the following beneficial effects:

[0036] (1) The present application uses a remote directional acoustic device to remotely excite the lining structure, uses a laser vibration meter to emit a laser beam to pick up the dynamic response characteristics, and the excitation and detection processes are all realized by a non-contact method. In addition, the picked up vibration response characteristics are converted into time and frequency domains, and the peak value characteristics are extracted, so that the precision of non-contact detection is improved through inversion calculation.

[0037] (2) The present application compares the peak value characteristics with prior knowledge, can quickly obtain the offset direction and offset value, and combines the frequency domain distribution diagram of the response characteristics to perform inversion calculation on the longitudinal and radial dimensions of the tunnel lining and the state of the back cavity, thereby improving the detection efficiency of the lining thickness and the back cavity.

[0038] (3) The present application can realize on-the-go excitation detection at a certain driving speed, without the need to close the tunnel road, thereby avoiding the blocking of the main road and the accumulation of traffic flow.

[0039] (4) The present application does not need to transmit excitation detection signals through dry / wet coupling, eliminates the interference and influence of the equipment-sampling interface in the traditional contact detection on the detection results, and improves the detection precision and signal-to-noise ratio. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is a method flowchart of the present application;

[0041] Figure 2 is a schematic diagram of the spatial relationship between the detection system and the lining structure to be detected of the present application;

[0042] Figure 3 is a schematic diagram of the detection results of the present application;

[0043] 1: remote directional acoustic device, 2: sound wave, 3: laser vibration meter, 4: laser beam, 5: signal generator, 6: tunnel segment. DETAILED DESCRIPTION

[0044] The present application will be described in detail below in combination with the drawings and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present application, and detailed implementation modes and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0045] The embodiment provides a tunnel lining thickness and back cavity sound wave quantitative rapid detection method, which is based on a detection system, as shown in the figure. Figure 2 The detection system comprises the following components.

[0046] A signal generator 5 is used for setting a sound wave signal containing excitation parameters.

[0047] A remote directional acoustic device 1 is used for sound wave 2 emission and makes the sound wave excitation direction coincide with the normal direction of the vibration measuring point position on the tunnel lining, and then generates a vibration response feature by using the pre-set sound wave signal containing the excitation parameters for non-contact acoustic excitation.

[0048] A laser vibration meter 3 is used for making the laser beam 4 coincide with the normal direction of the vibration measuring point position on the tunnel lining, picking up the tunnel lining vibration response feature, and obtaining a stress vibration time domain curve.

[0049] A processor is used for signal time-frequency domain conversion and peak value feature extraction based on the stress vibration time domain curve, and inversion calculation of the tunnel lining longitudinal and radial dimensions and the back cavity state, so as to realize the sound wave rapid quantitative detection of the tunnel lining thickness and the back cavity state.

[0050] Based on the above detection system, specifically, as shown in the figure, the detection method comprises the following steps. Figure 1

[0051] S1: The remote directional acoustic device 1 is used for non-contact acoustic excitation of the lining structure to be detected, and the specific steps are as follows.

[0052] S11: The signal generator 5 or the storage medium is set with a sound wave signal containing specific excitation parameters, and the excitation parameters include a transmission frequency band, a frequency modulation interval, a single frequency bandwidth and the like.

[0053] When the sound wave signal is pre-set in step S11, continuous wideband sound wave signals, continuous single frequency / variable frequency sound wave signals, and array single frequency / variable frequency sound wave signals can be used for acoustic excitation of the measuring point according to the needs of the site.

[0054] S12: The sound emission unit of the remote directional acoustic device 1 is aligned with the tunnel lining, and the pre-set sound wave parameters are used for excitation. The excitation position is usually the middle position of the tunnel lining crown to the haunch, and the sound pressure meter is used for sound excitation intensity calibration during excitation.

[0055] When the remote directional acoustic device 1 is used for acoustic excitation in step S12, the sound emission unit of the device needs to be adjusted according to the profile shape of the lining at the site, so that the sound wave 2 emission direction is normal to the measuring point.

[0056] ​Wherein, the remote directional acoustic device 1 is set to 5-8m from the point to be measured; the sound pressure meter is used to calibrate the sound wave 2 excitation intensity, and the lining surface sound pressure intensity can be adjusted according to the field environment.

[0057] S2: The laser vibration meter 3 is used to pick up the vibration response characteristics of the lining under the action of acoustic excitation in a non-contact manner, and the time domain curve of the structure stress vibration is obtained.

[0058] In the non-contact picking, according to the tunnel contour shape, the angle of the laser beam 4 emission and receiving unit is adjusted to make the laser beam 4 coincide with the normal direction of the lining point.

[0059] When collecting the stress characteristics of the lining, if the scattering degree of the vibration laser beam 4 is high due to the influence of the roughness of the lining surface, the foil with a reflective coating is pasted on the vibration measuring point to assist signal enhancement, and the effective collection of vibration information is realized.

[0060] When collecting the stress characteristics of the lining, the rich-hole sound-absorbing mask can be arranged outside the laser vibration meter 3 to weaken the disturbance of the excitation sound wave 2 to the vibration unit itself and improve the signal-to-noise ratio of the detection result.

[0061] S3: The vibration signal obtained by detection is characterized and transformed to realize the interpretation and analysis of the structure dynamics, which includes the following sub-steps:

[0062] S31: Based on the principle of mathematical statistics and the distribution characteristics of the stress signal sound field, the vibration signal characteristics obtained are denoised to realize the removal of abnormal noise points and signal enhancement.

[0063] In order to effectively remove abnormal data noise points, the confidence interval can be determined according to the field test conditions and the space-time distribution characteristics of the sound field, and the mathematical statistical analysis of the collected vibration data is carried out to reduce the interference of background noise and environmental disturbance on the perturbation result.

[0064] S32: The vibration signal time-frequency domain conversion is carried out by using fast Fourier transform algorithm, the frequency domain distribution graph and power spectrum density image of the response characteristics are obtained, and the characteristic peaks appearing in the frequency domain are calibrated and recorded.

[0065] According to the characteristics of the excitation action, the time domain characteristics of the perturbation signal are preliminarily judged, the frequency domain image obtained by fast Fourier transform is used to describe the frequency domain characteristics, and the main frequency is calibrated and collected by peak picking algorithm.

[0066] S4: Based on the evolution law of the acoustic vibration characteristics of the supporting structure under different structural parameters, the thickness of the tunnel lining and the state of the cavity behind the lining are quantitatively evaluated, which includes the following steps:

[0067] S41: Through theoretical analysis and prototype test methods, the self-vibration frequency of the lining with different material properties, profile shapes, and design thicknesses is calculated and analyzed, and the variation law of the radial and longitudinal characteristic frequencies of the support structure under different construction states is established; the control test groups are set by taking the range and height of the cavity behind the lining as variables to find out the variation trend of the dynamic characteristics of the support system under different cavity conditions behind the lining.

[0068] The vibration response characteristics such as the main frequency distribution interval, the main vibration mode, the waveform characteristics, and the side frequency distribution are comprehensively analyzed to perform the forward analysis of the dynamic characteristics of the tunnel support under the coupling action of multiple factors, and to provide a standard data set and prior reference for the potential interval of the radial / longitudinal characteristic frequency of the support and the frequency domain interval of the cavity behind the lining under different construction states.

[0069] S42: The geometric parameters of the tunnel lining and the range and height of the cavity behind the lining are inversely calculated based on the frequency domain characteristic peaks and the dynamic characteristics such as the wave speed v, the period T, and the phase angle φ of the response signal, and are compared with the theoretical calculation results to realize the quantitative evaluation of the construction state of the cavern.

[0070] When the inversion analysis of the lining construction is performed according to the dynamic response, the frequency domain is divided according to the survey data at the design stage and the actual geological conditions revealed during construction, and the low frequency band, the medium frequency band, and the high frequency band are matched with the radial / longitudinal characteristic frequency of the support and the main action interval of the cavity behind the lining.

[0071] Preferably, the inversion analysis of the longitudinal thickness of the lining structure is performed based on the stress characteristics such as the main peak frequency and the vibration amplitude in the medium frequency zone of the perturbation results, and the prior data set obtained from the theoretical analysis and the test results; the inversion analysis of the construction state such as the geometric parameters of the lining structure and the state of the cavity behind the lining is performed based on the waveform distortion characteristics and the phase angle offset values in the medium frequency zone as indicators to realize the quantitative and rapid determination of the construction state of the cavern support.

[0072] Preferably, the inversion analysis of the geometric parameters of the lining structure and the state of the cavity behind the lining is performed based on the stress characteristics such as the main peak frequency in the high frequency zone and the side frequency band distribution characteristics, and the prior theoretical solution of the support under the parameters such as the comprehensive elastic modulus, the Poisson's ratio, and the sound speed to realize the quantitative and rapid determination of the construction state of the cavern support.

[0073] The above-mentioned way is used to perform excitation detection on a certain construction tunnel interval of rail transit, and the measured objects are three selected tunnel rings, which are denoted as H1, H2, and H3 respectively. Each tunnel ring is excited and detected three times, and the results are recorded. The specific implementation steps are as follows:

[0074] Step 1: Set the acoustic wave signal containing specific excitation parameters in the signal generator 5 or the storage medium, and the excitation parameters include the transmission frequency band, the frequency modulation interval, the single frequency bandwidth, and the sound pressure intensity, etc.

[0075] Specifically, the frequency domain of the excitation sound wave, the excitation time of a single frequency band, and the overall excitation duration are adaptively adjusted according to the on-site environment.

[0076] Step 2: Align the acoustic wave emitting component of the remote directional acoustic device 1 with the tunnel pipe ring H1, adjust the acoustic emission unit of the device according to the shape of the lining on site, so that the excitation direction of the acoustic wave 2 coincides with the normal direction of the point to be measured, and then excite it with the preset acoustic emission parameters.

[0077] Specifically, the distance between the remote directional acoustic device 1 and the lining 6 to be tested is 4m. The excitation point is the geometric center of the inner surface of the standard block A1 at the arch foot of the shield tunnel. The sound excitation intensity is calibrated by a sound pressure meter during excitation. The sound pressure intensity on the lining surface can be adjusted autonomously.

[0078] Step 3: Based on the tunnel outline shape, adjust the angle of the laser transmitting and receiving unit so that the laser beam 4 coincides with the normal direction of the lining point, and use the laser vibration meter 3 to non-contactly pick up the vibration response characteristics of the lining under acoustic excitation to obtain the time-domain curve of the stress vibration of the pipe ring H1.

[0079] Step 4: Based on the principles of mathematical statistics and the characteristics of the sound field distribution of the stress signal, the acquired vibration signal characteristics are denoised to remove abnormal noise and enhance the signal.

[0080] Specifically, confidence intervals are established based on the on-site testing conditions of the shield tunnel and the spatiotemporal distribution characteristics of the sound field. Mathematical statistical analysis is performed on the collected vibration data to remove data points with high dispersion, thereby reducing the interference of background noise and environmental disturbances on the perturbation results.

[0081] Step 5: Use algorithms such as Fast Fourier Transform to perform time-frequency domain conversion of the vibration signal and obtain the frequency domain distribution map of the response characteristics. Based on prior knowledge such as finite element modeling and theoretical analysis, within the potential range Σ of the radial characteristic frequency of the shield tunnel... r and the potential interval of longitudinal characteristic frequency Σ l Find the extreme values ​​in the potential interval, and identify the radial characteristic peaks f. r1 and longitudinal characteristic peak f l1 Make calibration records.

[0082] Step 6: Repeat steps 2 to 5 three times to complete the three excitation detection sampling of the pipe ring H1. To reduce the interference of environmental factors and instrument errors on the detection results, the radial characteristic peak f appearing in the three detection results is... r1 f r2 f r3 and longitudinal characteristic peak f l1 f l2 f l3 After averaging, the peak frequency f of the radial response of segment 6 was calculated. rH1 =(fr1 +f r2 +f r3 ) / 3、f lH1 =(f l1 +f l2 +f l3 ) / 3。

[0083] Step 7: According to the survey data in the design stage and the actual geological conditions revealed in the construction, the frequency domain is divided, and the low frequency band, the medium frequency band and the high frequency band are matched with the interval of the supporting radial / longitudinal characteristic frequency and the interval of the back cavity effect.

[0084] Specifically, the elastic modulus of the shield segment 6 measured by the embodiment is 30 GPa, the Poisson's ratio is 0.2, and the density is 2300 kg / m 3 , the elastic modulus of the back wall grouting material is 25 GPa, the Poisson's ratio is 0.2, and the density is 2300 kg / m 3 . According to the theoretical calculation and numerical simulation results, the frequency band of 1.5k-2.5kHz is regarded as the potential interval of the longitudinal characteristic frequency of the shield tunnel segment 6, and the frequency band of 5.5k-6.5kHz is regarded as the potential interval of the radial characteristic frequency of the shield tunnel segment 6.

[0085] Step 8: The radial frequency domain characteristic peak f rH1 , the longitudinal frequency domain characteristic peak f lH1 and the wave speed, period, phase angle and other dynamic characteristics of the response signal are compared with the corresponding theoretical characteristic frequency f gH1 in the prior data set, the relative error values Δf rH1 , Δf lH1 are calculated, the longitudinal and radial sizes of the tunnel lining and the state of the back cavity are inversely calculated according to the offset direction and offset value, and the quantitative evaluation of the structure state of the excitation detection pipe ring is realized.

[0086] Step 9: Move the vibration detection equipment to the shield pipe ring H2, H3 respectively, repeat steps 2-8 to excite and detect the two pipe rings, and obtain and analyze the radial and longitudinal sizes of the segment 6, the back cavity state and other structural parameters.

[0087] The acoustic wave rapid quantitative detection of the geometric parameters of the shield tunnel segment 6, the back cavity state and other supporting system structure states under the field environment is completed. During the detection process, the longitudinal and radial characteristic frequency potential intervals of the three shield pipe rings H1, H2 and H3 are as shown in Figure 3 .

[0088] Although the preferred embodiments of the present application have been described, those skilled in the art can make further changes and modifications to the embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0089] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for quickly detecting the thickness of a tunnel lining and a cavity behind the lining by sound waves, characterized in that, The method comprises the following steps: Collecting non-contact measured tunnel lining vibration response characteristics, and obtaining stress vibration time domain curves; Based on the stress vibration time domain curves, signal time-frequency domain conversion is performed to obtain frequency domain distribution of the response characteristics, and peak value characteristics are extracted, wherein the peak value characteristics include radial characteristic peak value and longitudinal characteristic peak value; Based on the peak value characteristics and the frequency domain distribution of the response characteristics, inversion calculation is performed on the longitudinal and radial dimensions of the tunnel lining and the state of the cavity behind the lining to realize rapid quantitative detection of the thickness of the tunnel lining and the state of the cavity behind the lining, and the specific implementation steps include the following: The dynamic characteristics of the response signal are comprehensively compared with the corresponding theoretical characteristic frequencies in the prior data set, the relative error value is calculated, the inversion calculation is performed on the longitudinal and radial dimensions of the tunnel lining and the state of the cavity behind the lining according to the deviation direction and deviation value, and quantitative evaluation of the state of the tunnel lining ring structure is realized; Further comprising: Through theoretical analysis and prototype test method, the natural vibration frequency of the lining with different material properties, profile shape and design thickness is calculated and analyzed, and the variation law of the radial and longitudinal characteristic frequencies of the supporting structure under different structural states is established; the range and height of the cavity behind the lining are set as variable control test groups to find out the variation trend of the dynamic characteristics of the supporting system under different cavity working conditions behind the lining; The vibration response characteristics are comprehensively analyzed to perform forward analysis of the tunnel supporting dynamic characteristics under the coupling action of multiple factors, and standard data set and prior reference are provided for the potential interval of the radial and longitudinal characteristic frequencies of the supporting ring under different structural states and the frequency domain interval of the cavity behind the lining.

2. The method according to claim 1, wherein, The step of performing signal time-frequency domain conversion comprises: Based on the mathematical statistics principle and the stress signal sound field distribution characteristics, noise reduction processing is performed on the stress vibration time domain curve; Based on the stress vibration time domain curve after noise reduction processing, fast Fourier transform algorithm is used for signal time-frequency domain conversion to obtain the frequency domain distribution of the response characteristics.

3. The method according to claim 1, wherein, The step of performing peak value characteristic extraction comprises: The potential interval of the radial characteristic frequency and the potential interval of the longitudinal characteristic frequency of the tunnel lining obtained by prior knowledge are obtained; Based on the potential interval of the radial characteristic frequency and the potential interval of the longitudinal characteristic frequency, the radial characteristic peak value and the longitudinal characteristic peak value are marked from the frequency domain distribution of the response characteristics.

4. A detection system for the tunnel lining thickness and back cavity acoustic wave quantitative rapid detection method according to any one of claims 1-3, characterized in that, It comprises: A signal generator (5) is used to set the acoustic signal containing excitation parameters; A remote directional acoustic device (1) is used for acoustic wave (2) emission, and the acoustic excitation direction is coincided with the normal direction of the vibration measurement point on the tunnel lining, and then the non-contact acoustic excitation is generated to produce vibration response characteristics with the acoustic signal containing the pre-set excitation parameters; A laser vibration meter (3) is used to emit a laser beam (4) to pick up the tunnel lining vibration response characteristics; A processor is used to perform signal time-frequency domain conversion and peak value characteristic extraction based on the dynamic response characteristics, and inversion calculation is performed on the longitudinal and radial dimensions of the tunnel lining and the state of the cavity behind the lining to realize rapid quantitative detection of the thickness of the tunnel lining and the state of the cavity behind the lining.

5. The detection system of claim 4, wherein, The excitation parameters include emission frequency band, frequency modulation interval, single frequency bandwidth and sound pressure intensity.

6. The detection system of claim 4, wherein, The sound pressure intensity is calibrated and adjusted by using a sound pressure meter during the non-contact acoustic excitation process.

7. The detection system of claim 4, wherein, When the vibration response characteristics are collected, a rich-hole sound-absorbing cover is arranged outside the laser vibration tester (3).

8. The detection system of claim 4, wherein, During the process of picking up the vibration response characteristics of the tunnel lining by the laser vibration tester (3), the laser beam (4) is coincident with the normal direction of the vibration measuring point on the tunnel lining.

9. The detection system of claim 4, wherein, When the vibration response characteristics are collected, a foil with a reflective coating is arranged at the vibration measuring point on the tunnel lining to assist signal enhancement.

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