Elastic wave four-wheel array mobile detection system and method for tunnel lining internal cavity

Abstract

The invention discloses an elastic wave four-wheel array mobile detection system and method for an internal cavity of a tunnel lining, and the system comprises a trolley and an electromagnetic positioning base station which are disposed on a tunnel substrate, and the trolley is provided with a supporting arm; the supporting arm comprises an upper supporting arm and a lower supporting arm which are rotationally connected with each other, the lower supporting arm is connected with the trolley, and the top end of the upper supporting arm is connected with a movable elastic wave data acquisition module; the mobile elastic wave data acquisition module, the mobile terminal and the data receiving host are in communication connection through WIFI signals. The invention provides a tunnel lining internal cavity intelligent identification method based on elastic wave data, provides a lining thickness calculation method, an internal cavity real-time determination method, indexes and standards, reduces subjectivity of manual determination, and improves determination accuracy and determination efficiency.

Description

Technical Field

[0001] The invention belongs to the technical field of railway engineering detection, and in particular to a tunnel lining internal cavity elastic wave four-wheel array mobile detection system and method. Background Art

[0002] At present, the tapping method is mainly used to detect the internal voids of tunnel linings. However, the tapping method can only detect shallow voids, and the detection depth can generally only reach 20cm. The thickness of the outermost lining of the tunnel is generally more than 50cm, which is difficult to meet the depth detection requirements. In the existing technology, the tapping method relies on human ears, which is greatly affected by external interference and is prone to missed judgments and wrong judgments; the tapping method positioning relies on manual estimation, the positioning accuracy is low, the results are scattered points, and the accuracy of the results cannot be estimated. Therefore, it is necessary to improve the detection depth, positioning accuracy and continuity of detection results.

[0003] In summary, the application significance of the present invention is:

[0004] The present invention discloses a tunnel lining internal cavity elastic wave four-wheel array mobile detection system and method. The elastic wave four-wheel array mobile detection system is designed and realized, and the mobile coupling of contact sensors and the automated impact of impact sources are realized, thereby solving the problem of low continuity of results and improving the tunnel lining detection efficiency. The problem of autonomous positioning in closed spaces is solved by arranging lightweight electromagnetic base stations. Based on the elastic wave four-wheel array mobile detection data, a tunnel lining thickness and internal cavity real-time determination method and standard are proposed, and the detection depth is increased to 1 meter, thereby effectively solving the problems of shallow detection depth and lack of evaluation standards. Summary of the invention

[0005] In view of the above problems, the present invention proposes a tunnel lining internal cavity elastic wave four-wheel array mobile detection system and method, the specific scheme is as follows:

[0006] A tunnel lining internal cavity elastic wave four-wheel array mobile detection system, comprising a trolley and an electromagnetic positioning base station arranged on a tunnel base, wherein a support arm is installed on the trolley; the support arm comprises an upper support arm and a lower support arm which are rotatably connected to each other, the lower support arm is connected to the trolley, and the top end of the upper support arm is connected to a mobile elastic wave data acquisition module;

[0007] The mobile elastic wave data acquisition module, the mobile terminal and the data receiving host are connected via WIFI signal communication.

[0008] Preferably, the mobile elastic wave data acquisition module includes a wheel sensor, an automated impactor and an electromagnetic positioning tag connected to an acquisition circuit box;

[0009] The wheel sensor comprises a sleeve and a sensor wheel arranged on the upper part of the sleeve, four MEMS sensors are installed on the sensor wheel at equal angles, and a high-precision distance encoder and a 10-core conductive slip ring are installed on the central bearing of the sensor wheel;

[0010] The core conductive slip ring connects the MEMS sensor, the high-precision distance encoder and the acquisition circuit box through a 10-core cable.

[0011] Further preferably, four limiting protrusions are arranged in the sleeve, the bearing spring is limitedly installed in the four limiting protrusions, the lower part of the bearing column is sleeved in the bearing spring, and the upper part of the bearing column is connected to the sensor wheel.

[0012] Further preferably, the wheel sensors are arranged in four numbers, with the automated impactor as the center, and wheel sensor A, wheel sensor B, wheel sensor C, and wheel sensor D are symmetrically distributed.

[0013] Further preferably, the acquisition circuit box includes an acquisition circuit board, a central control circuit board, a boost circuit board, a WIFI module and a power supply battery;

[0014] The central control circuit board is connected to the acquisition circuit board and the electromagnetic positioning tag through a four-core cable, and the WIFI module is connected to the central control circuit board through a two-core cable;

[0015] The central control circuit board is connected to the automated impactor via two single-core cables;

[0016] The 10-core conductive slip rings on the wheel center bearings of the wheel sensor A, the wheel sensor B, the wheel sensor C, and the wheel sensor D are connected to the acquisition circuit board via a 10-core cable.

[0017] Further preferably, the automated impactor comprises an outer sleeve, an electromagnetic coil, a spring and two impact columns;

[0018] Four limiting protrusions are arranged in the middle of the upper section of the impact column, which are combined with the outer sleeve to limit the spring; the bottom of the outer sleeve is connected to the outer shell of the collection circuit box by bolts, and the pins of the electromagnetic coil turns are connected to the central control circuit board in the collection circuit box.

[0019] The second purpose of the present invention is:

[0020] A method for detecting the internal cavity of a tunnel lining by elastic wave four-wheel array movement, using the above-mentioned tunnel lining internal cavity elastic wave four-wheel array movement detection system, comprises the following steps:

[0021] S1: Tunnel lining elastic wave movement detection system layout;

[0022] After arriving at the tunnel to be tested, the detection section is first determined, the mobile terminal (2) and the data receiving host are turned on, the electromagnetic positioning base station (3) is arranged, an observation coordinate system is established, and the observation coordinate system is entered into the data acquisition software of the mobile terminal (2);

[0023] S2: Health baseline zone marker main frequency and energy acquisition;

[0024] In the healthy benchmark area of ​​the tunnel lining structure, data testing is carried out, and then the main frequency and energy value of the marker are obtained according to the healthy benchmark area marker parameter acquisition process;

[0025] S3: elastic wave, positioning, encoding data acquisition and processing;

[0026] According to the detection system established in step S1, the elastic wave sensor, positioning tag and encoder data are collected through the mobile elastic wave data collection module (1), wherein the positioning tag and encoder are used to determine the position of the sensor, and the elastic wave sensor is used to determine the internal quality of the lining;

[0027] S4: Real-time determination of lining thickness and internal voids;

[0028] According to the data collected by the elastic wave sensor in step S3, waveform screening, bandpass filtering, waveform convolution, time domain integration, and frequency domain peak search are carried out to obtain energy determination index, peak frequency, and structure thickness value, which are then compared and judged with the main frequency of the healthy reference zone obtained in step S2, and the void degree determination index is calculated according to the energy value, so as to determine the lining thickness and internal void situation;

[0029] S5: Output the lining thickness and internal void detection results;

[0030] According to the judgment in step S4 and the positioning information in step S3, a plan view of lining thickness and void distribution is drawn, and the project name, draftsman, and reviewer information are added. At the same time, the lining thickness and internal void detection results are saved in the database.

[0031] Further preferably, the step S1 specifically comprises the following steps:

[0032] S11: Arrange electromagnetic positioning base station;

[0033] After arriving at the tunnel to be tested, first, determine the test section, then arrange two electromagnetic positioning base stations (3) at the starting and ending mileages of the test section, the electromagnetic positioning base stations (3) are respectively located at the left and right side walls of the tunnel, record the mileages of the four electromagnetic positioning base stations (3), and use a ruler to measure the distance between two electromagnetic positioning base stations (3) at the same mileage;

[0034] S12: Establish observation coordinate system;

[0035] An observation coordinate system is established according to the positional relationship of four electromagnetic positioning base stations (3);

[0036] S13: Setting up data acquisition system;

[0037] Turn on the mobile terminal (2), the electromagnetic positioning base station (3), and the data receiving host, set the sampling rate, and adjust the distance L between the wheel sensors on the left and right sides of the mobile detection structure and the center of the impact source. The distance L should be less than the designed thickness of the lining;

[0038] S14: Connect the positioning tag device to the data receiving host, and input the coordinates of the four electromagnetic positioning base stations (3) into the data acquisition software in the mobile terminal (2) according to the observation coordinate system established in step S12. The mobile terminal (2) communicates with the data receiving host via a WIFI signal (4), and the electromagnetic positioning base station (3) is connected to the positioning on the data receiving host via an electromagnetic signal.

[0039] Further preferably, the step S2 specifically comprises the following steps:

[0040] S21: Health benchmark area data test;

[0041] Data testing was performed in the known healthy benchmark area of ​​the tunnel lining to obtain elastic wave detection data at 16 points, and bandpass filtering was performed to eliminate some noise interference;

[0042] S22: Obtain a set of marked main frequencies F 0i ;

[0043] The process of processing the main frequency data of the elastic wave detection data obtained in step S21 includes the following steps:

[0044] S2201: Identify the direct wave time difference ΔT of the sensors on the left and right sides of the earthquake source respectively li , ΔT ri ;

[0045] S2202: Calculation of wave velocity v in concrete structures li =Δs / ΔT li 、v ri =Δs / ΔT ri , where Δs is the distance between the wheel sensors on the left and right sides of the source;

[0046] S2203: Calculation of wave velocity v in concrete structures i =(v li +v ri ) / 2;

[0047] S2204: The concrete structure wave velocity v obtained in step S2203 iSort, remove the 3 maximum values ​​and the 3 minimum values, and get the remaining 10 wave speeds v 0i ;

[0048] S2205: Through the known structure thickness H 0 Calculate the mark frequency F 0i =λ×v 0i / 2 / H 0 , λ is the structural coefficient, ranging from 0.8 to 1.0;

[0049] S23: Obtain a set of marker energies Q0i;

[0050] The elastic wave data of the 16 points obtained in step S21 are processed with the following marker energy data:

[0051] S2301: Perform convolution on the two waveforms received by the sensors on the left and right sides of the earthquake source Generate a new waveform E L 、E R ;

[0052] S2302:E L 、E R Convolution again, Generate a new waveform E;

[0053] S2303: Perform time domain integration on waveform E to obtain energy value Among them, T is the upper limit of the time domain, and its value is within 10000 microseconds;

[0054] S2304: Q i Sort, remove the 3 maximum values ​​and the 3 minimum values, and get the remaining 10 sign energy values ​​Q 0i ;

[0055] S24: Get the main frequency F 0 and the critical value of the energy Q 0 ;

[0056] The 10 marker main frequencies F obtained in step S22 and step S23 are 0 and 10 marker energy thresholds Q 0 Taking the average, we get:

[0057]

[0058] And calculate the energy mean square error: Finally determine the critical value of the air gap abnormality: Get the health zone logo frequency F 0 , marking energy critical value Q 0 .

[0059] Further preferably, step S4 comprises the following steps:

[0060] S41: data preprocessing;

[0061] Import the data of four channels, screen each waveform, remove incorrect waveforms, such as waveforms with amplitudes mostly close to zero and waveforms with large amplitude jumps, and then perform bandpass filtering with the filter band set to 1kHz-10kHz;

[0062] S42: time-frequency dual-domain data processing;

[0063] After data preprocessing, the double waveforms on the left and right sides of the earthquake source are convolved separately to generate a new waveform each, and the two waveforms are convolved again to generate a new waveform, and then time-frequency dual-domain data processing is performed on this waveform:

[0064] S4201: Perform Fourier transform on the waveform, convert the time domain waveform into a frequency domain spectrum, and find the spectrum peak frequency f in the frequency domain;

[0065] S4202: Integrate the waveform in the time domain to obtain an energy value Q, and compare it with the marker energy threshold value Q obtained in step S24. 0 Make the ratio θ=Q / Q 0 , obtain the index θ for judging the degree of voiding;

[0066] S43: Calculation of lining thickness;

[0067] First, the first arrival time of the waveform is identified by the amplitude change rate, and then the dual-channel data on the left and right sides of the earthquake source are subtracted to obtain the direct wave time difference Δt from the earthquake source to the left and right dual-channel sensors. 1 , Δt 2 , and take the average of the two time differences Δt=(Δt 1 +Δt 2 ) / 2, and then further calculate the elastic wave velocity v=Δs / Δt of the concrete structure, where Δs is the spacing between the single-side dual-channel sensors, and then combine the peak frequency f obtained in step S42 to calculate the thickness of the structure H=λ×v / 2 / f, where λ is the structural coefficient, ranging from 0.8 to 1.0;

[0068] S44: Lining quality assessment;

[0069] Conduct lining structure quality assessment based on the peak frequency f, structure thickness H, and void level assessment index θ obtained in step S42 and step S43;

[0070] Whether the lining thickness meets the standard: when H is greater than or equal to the lining design thickness, the quality is qualified and the thickness index is qualified; when H is less than the lining design thickness value, the quality is unqualified;

[0071] Internal voids in the lining: When the peak frequency f is greater than or equal to the peak frequency F in the healthy reference zone 0 When the structure is not empty, the quality is qualified;

[0072] When the peak frequency f is less than the peak frequency F in the healthy reference area 0 When θ>3, it is a serious void, and when 1<θ<3, it is a slight void, which means the quality is unqualified.

[0073] The beneficial effects of the present invention are:

[0074] 1. The present invention provides an intelligent identification method for internal voids in tunnel linings based on elastic wave data, proposes a lining thickness calculation method, an internal void real-time determination method, indicators and standards, reduces the subjectivity of human judgment, and improves the accuracy and efficiency of judgment;

[0075] 2. The present invention uses a highly sensitive MEMS sensor to collect elastic wave signals, which has a stronger recognition ability than the human ear and can sense elastic wave signals at a deeper level. Therefore, the detection depth is greater, thereby solving the problem of shallow detection depth of the traditional knocking method. At the same time, a wheel sensor is designed, which can not only ensure signal stability, but also enable the contact sensor to move forward, greatly improving the detection efficiency;

[0076] 3. The present invention also provides a high-energy, automated impactor, which has constant energy each time it strikes, and has good stability and consistency in the detection data, which is convenient for later data processing and analysis;

[0077] 4. The present invention arranges a portable electromagnetic base station in the tunnel to obtain the sensor position in real time. While collecting elastic wave data, the sensor distribution position can be obtained, which improves the positioning accuracy of the measuring points and increases the freedom of data collection. When all the measuring points are completed, a two-dimensional contour map of the internal voids can be generated, which solves the problem of low continuity of the results. The results are continuous, intuitive, and easy to interpret. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments, but it should be understood that these drawings are designed only for explanation purposes and are not intended to limit the scope of the present invention. In addition, unless otherwise specified, these drawings are intended only to conceptually illustrate the structural configurations described herein and are not necessarily drawn to scale.

[0079] Figure 1 is a schematic diagram of the system layout of the present invention;

[0080] Figure 2 is a structural diagram of a mobile elastic wave data acquisition module of the present invention;

[0081] Figure 3 It is the internal structure diagram of the automated impactor of the present invention;

[0082] Figure 4 It is the internal structure diagram of the wheel sensor of the present invention;

[0083] Figure 5 is a flow chart of the method of the present invention;

[0084] Figure 6 It is a flow chart of the main frequency and energy acquisition of the health reference zone mark of the present invention;

[0085] Figure 7 is a flow chart of measured data processing of the present invention;

[0086] Figure 8 It is a schematic plan view of the layout and distribution of the detection points and base stations of the present invention;

[0087] Fig. 9 It is the result of the elastic wave four-wheel array movement detection of the internal cavity of the tunnel lining of the present invention;

[0088] In the figure:

[0089] 1: Mobile elastic wave data acquisition module;

[0090] 1-1: wheel sensor A; 1-2: wheel sensor B; 1-3: wheel sensor C; 1-4: wheel sensor D; 1-5: automated impactor; 1-6: acquisition circuit box; 1-7: electromagnetic positioning tag

[0091] 1-1-1: MEMS sensor;

[0092] 2: mobile terminal; 3: electromagnetic positioning base station; 4: WIFI signal;

[0093] 5: support arm; 5-1: upper support arm; 5-2: lower support arm;

[0094] 6: Trolley; 7: Tunnel lining structure; 8- Hollow. DETAILED DESCRIPTION

[0095] First of all, it should be noted that the specific structure, features and advantages of the present invention will be specifically described below by way of example, but all descriptions are only for illustration and should not be understood as limiting the present invention in any way. In addition, any single technical feature described or implied in the embodiments mentioned herein, or any single technical feature displayed or implied in the drawings, can still be combined or deleted between these technical features to obtain more other embodiments of the present invention that may not be directly mentioned in this document. In addition, in order to simplify the drawings, the same or similar technical features may be marked only in one place in the same drawing.

[0096] In the present invention, unless otherwise clearly stipulated and limited, the terms such as "installation", "setting", "connection", "fixation" and "screw-on" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral one; 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, ordinary technicians in this field can understand the specific meanings of the above terms in the present invention according to the specific circumstances.

[0097] The following is combined with Figure 1 -Attached Fig. 9 The present invention will be described in detail.

[0098] Embodiment 1:

[0099] like Figure 1-Figure 4 As shown, a tunnel lining internal cavity elastic wave four-wheel array mobile detection system includes a trolley 6 and an electromagnetic positioning base station 3 arranged on the tunnel base, and a support arm 5 is installed on the trolley 6; the support arm 5 includes an upper support arm 5-1 and a lower support arm 5-2 that are rotatably connected to each other, the lower support arm 5-2 is connected to the trolley 6, and the top end of the upper support arm 5-1 is connected to a mobile elastic wave data acquisition module 1;

[0100] The mobile elastic wave data acquisition module 1 , the mobile terminal 2 and the data receiving host are connected in communication via a WIFI signal 4 .

[0101] In this embodiment, Figure 1 As shown, the detection system includes: a trolley 6 on the tunnel base, an electromagnetic positioning base station 3, and a support arm 5 installed on the trolley 6. The support arm 5 consists of two sections, including an upper support arm 5-1 and a lower support arm 5-2. After adjusting the angle, bolts are used to lock the end position to achieve the connection between the two sections of the support arm. The lower support arm 5-2 is connected to the trolley 6 by bolts.

[0102] The mobile elastic wave data acquisition module 1 is installed on the top of the upper support arm 5 - 1 , and the mobile terminal 2 is held by the staff. The mobile terminal is not limited to a tablet computer, a mobile phone or a mobile notebook;

[0103] For the convenience of description, the following describes various embodiments by taking the mobile terminal 2 as a tablet computer.

[0104] The mobile elastic wave data acquisition module 1 communicates with the tablet computer 2 via the WIFI signal 4 , and the data receiving host 2 communicates with the mobile elastic wave data acquisition module 1 and the data display tablet computer 2 via the WIFI signal 4 to exchange instructions and data.

[0105] During operation, the mobile elastic wave data acquisition module 1 is first installed on the top of the upper support arm 5-1, and four electromagnetic positioning base stations 3 are arranged at the same time. Then the trolley 6 is driven to the work area, and the inspectors hold a tablet computer in their hands. Thus, a detection system is formed for tunnel lining quality inspection.

[0106] After obtaining the detection data of the healthy benchmark area and the measured work area, waveform screening, bandpass filtering, waveform convolution, time domain integration, frequency domain peak search and other data processing work are carried out to obtain the energy judgment index, peak frequency, and structural thickness value. Then, they are compared and judged with the main frequency of the healthy benchmark area mark, and the void degree judgment index is calculated according to the energy value to determine the lining thickness and internal void situation. Finally, based on the judgment result and the measuring point positioning information, the lining thickness and void distribution plan are drawn, and the project name, cartographer, and reviewer information are added. At the same time, the lining thickness and internal void detection results are saved to the database.

[0107] Further, it can also be considered in the embodiment that the mobile elastic wave data acquisition module 1 includes a wheel sensor connected to the acquisition circuit box 1-6, an automated impactor 1-5 and an electromagnetic positioning tag 1-7;

[0108] The wheel sensor comprises a sleeve and a sensor wheel arranged on the upper part of the sleeve, four MEMS sensors 1-1-1 are installed on the sensor wheel at equal angles, and a high-precision distance encoder and a 10-core conductive slip ring are installed on the central bearing of the sensor wheel;

[0109] The 10-core conductive slip ring connects the MEMS sensor 1-1-1, the high-precision distance encoder and the acquisition circuit box 1-6 through a 10-core cable.

[0110] In this embodiment, the wheel sensor is used to receive the elastic wave vibration signal generated by the automated impactor 1-5.

[0111] like Figure 4 As shown, the sensor wheel of the wheel sensor is a circular structure with a diameter of 20 cm. Four MEMS sensors 1-1-1 are set on the edge of the sensor wheel with a distribution angle of 90°. The sensor sensing surface faces outward, and the outer side of the sensor is wrapped with a layer of epoxy resin with a Shore hardness of 80D and a thickness of 2 mm for protection.

[0112] The circumference distance between adjacent MEMS sensors is 15.7 cm. A high-precision distance encoder and a 10-core conductive slip ring are installed on the central bearing of the sensor wheel. The conductive slip ring connects the MEMS sensor, the high-precision distance encoder and the acquisition circuit box 1-6 through a 10-core cable.

[0113] Furthermore, in the embodiment, it can also be considered that four limit protrusions are set in the sleeve, the load-bearing spring is limited and installed in the four limit protrusions, the lower part of the load-bearing column is sleeved in the load-bearing spring, and the upper part of the load-bearing column is connected to the sensor wheel.

[0114] In this embodiment, the limiting protrusions provided in the sleeve of the wheel sensor are used to limit the bearing column, and the bearing column sleeve spring enables the wheel sensor to move up and down to adapt to the change in the distance between the lining and the trolley 6.

[0115] Furthermore, in the embodiment, it can also be considered that the wheel sensors are set to 4, with the automated impactor 1-5 as the center, and the wheel sensor A1-1, wheel sensor B1-2, wheel sensor C1-3, and wheel sensor D1-4 are symmetrically distributed.

[0116] like Figure 2 As shown, in this embodiment, wheel sensor A1-1, wheel sensor B1-2, wheel sensor C1-3, and wheel sensor D1-4 are all used to receive the elastic wave vibration signal generated by the automated impactor 1-5. The five are arranged in a row with the automated impactor as the center, and wheel sensor A1-1, wheel sensor B1-2, wheel sensor C1-3, and wheel sensor D1-4 are symmetrically distributed.

[0117] The contact point between the wheel and the lining of wheel sensor A1-1 is 50 cm away from the hammering center of automated impactor 1-5, and the distance between wheel sensor B1-2 and the hammering center of automated impactor 1-5 is 30 cm. The four wheel sensors have the same structure, but their fixed positions are different.

[0118] Furthermore, it can also be considered in the embodiment that the acquisition circuit box 1-6 includes an acquisition circuit board, a central control circuit board, a boost circuit board, a WIFI module and a power supply battery;

[0119] The central control circuit board is connected to the acquisition circuit board and the electromagnetic positioning tags 1-7 through a four-core cable, and the WIFI module is connected to the central control circuit board through a two-core cable;

[0120] The central control circuit board is connected to the automated impactor 1-5 via two single-core cables;

[0121] The 10-core conductive slip rings on the wheel center bearings of the wheel sensor A1-1, the wheel sensor B1-2, the wheel sensor C1-3, and the wheel sensor D1-4 are connected to the acquisition circuit board via a 10-core cable.

[0122] In this embodiment, Figure 2As shown, the central control circuit board is the core, the acquisition circuit board is connected to the central control circuit board through a four-core cable, the electromagnetic positioning tag 1-7 is connected to the central control circuit board through a four-core cable, the WIFI module is connected to the central control circuit board through a two-core cable, and the central control circuit board is connected to the automated impactor 1-5 through two single-core cables.

[0123] The housing of the collection circuit box 1-6 is fixed to the wheel sensor A1-1, the wheel sensor B1-2, the wheel sensor C1-3, the wheel sensor D1-4, the automated impactor 1-5, the electromagnetic positioning tag 1-7, and the upper support arm 5-1 by bolt connection.

[0124] Furthermore, it can also be considered in the embodiment that the automated impactor 1-5 includes an outer sleeve, an electromagnetic coil, a spring and two impact columns;

[0125] For example, two limiting protrusions are set in the middle part of the upper section of the impact column, which work together with the outer sleeve to limit the spring; the bottom of the outer sleeve is connected to the outer shell of the collection circuit box 1-6 by bolts, and the pins of the electromagnetic coil turns are connected to the central control circuit board in the collection circuit box 1-6.

[0126] In this embodiment, Figure 3 As shown, the impact column is welded from two stainless steel cylinders with a diameter of 1 cm. It can move up and down in the outer sleeve. Two limit bumps are set in the middle of the upper section, which work together with the outer sleeve to limit the spring. The material of the section near the bottom is magnetic, and the other section is 316L stainless steel with weak magnetism. The outer sleeve is also made of 316L stainless steel. The bottom of the outer sleeve is connected and fixed to the shell of the collection circuit box 1-6 with bolts. The number of turns of the electromagnetic coil is 20,000, and its two pins are connected to the central control circuit board in the collection circuit box 1-6.

[0127] Embodiment 2:

[0128] like Figure 5 As shown, a tunnel lining internal cavity elastic wave four-wheel array mobile detection method, using a tunnel lining internal cavity elastic wave four-wheel array mobile detection system, includes the following steps:

[0129] S1: Tunnel lining elastic wave movement detection system layout;

[0130] After arriving at the tunnel to be tested, the detection section is first determined, the mobile terminal 2 and the data receiving host are turned on, the electromagnetic positioning base station 3 is arranged, the observation coordinate system is established, and the observation coordinate system is entered into the data acquisition software of the mobile terminal 2;

[0131] S2: Health baseline zone marker main frequency and energy acquisition;

[0132] In the healthy benchmark area of ​​the tunnel lining structure, data testing is carried out, and then the main frequency and energy value of the marker are obtained according to the healthy benchmark area marker parameter acquisition process;

[0133] S3: elastic wave, positioning, encoding data acquisition and processing;

[0134] According to the detection system established in step S1, the elastic wave sensor, positioning tag, and encoder data are collected through the mobile elastic wave data collection module 1, wherein the positioning tag and the encoder are used to determine the sensor position, and the elastic wave sensor is used to determine the internal quality of the lining;

[0135] S4: Real-time determination of lining thickness and internal voids;

[0136] According to the data collected by the elastic wave sensor in step S3, waveform screening, bandpass filtering, waveform convolution, time domain integration, and frequency domain peak search are carried out to obtain energy determination index, peak frequency, and structure thickness value, which are then compared and judged with the main frequency of the healthy reference zone obtained in step S2, and the void degree determination index is calculated according to the energy value, so as to determine the lining thickness and internal void situation;

[0137] S5: Output the lining thickness and internal void detection results;

[0138] According to the judgment in step S4 and the positioning information in step S3, a plan view of lining thickness and void distribution is drawn, and the project name, draftsman, and reviewer information are added. At the same time, the lining thickness and internal void detection results are saved in the database.

[0139] In this embodiment, a tunnel lining internal cavity elastic wave four-wheel array mobile detection method of the present invention includes: tunnel lining elastic wave mobile detection system layout, health reference zone marker main frequency, energy acquisition, elastic wave, positioning, coding data collection and processing, lining thickness, internal void real-time judgment.

[0140] Furthermore, it can also be considered in the embodiment that the step S1 specifically includes the following steps:

[0141] S11: Arrange electromagnetic positioning base station;

[0142] After arriving at the tunnel to be tested, first determine the test section, then arrange two electromagnetic positioning base stations 3 at the starting and ending mileages of the test area, the electromagnetic positioning base stations 3 are located at the left and right side walls of the tunnel respectively, record the mileages of the four electromagnetic positioning base stations 3, and use a ruler to measure the distance between two electromagnetic positioning base stations 3 at the same mileage; the planar distribution of the measuring points and base stations is shown in the figure below: Figure 8 As shown;

[0143] S12: Establish observation coordinate system;

[0144] An observation coordinate system is established according to the positional relationship of the four electromagnetic positioning base stations 3;

[0145] For example, if the starting mileage is DK100+000, the corresponding base stations A0 and A4 are set to (100 000, 0) and (100000, 15), and the ending mileage is DK100+050, the corresponding base stations A1 and A3 are set to (100 050, 0) and (100 050, 15). A0 is located in the lower left corner, and A0, A1, A2, and A3 are distributed counterclockwise.

[0146] S13: Setting up data acquisition system;

[0147] Turn on the mobile terminal 2, electromagnetic positioning base station 3, and data receiving host, set the sampling rate, and adjust the distance L between the left and right wheel sensors and the center of the impact source in the mobile detection structure. The distance L should be less than the designed thickness of the lining;

[0148] The sampling rate is set to 500kHz, that is, one sample is taken every 2 microseconds; the distance L is related to the lining thickness and should be less than 0.4 times the design lining thickness. The lining thickness of railway tunnels is generally less than 1 meter, so L is generally set to 30cm.

[0149] S14: Connect the positioning tag device to the data receiving host, and according to the observation coordinate system established in step S12, input the coordinates of the four electromagnetic positioning base stations 3 into the data acquisition software in the mobile terminal 2. The mobile terminal 2 communicates with the data receiving host via WIFI signal 4, and the electromagnetic positioning base station 3 is connected to the positioning on the data receiving host via electromagnetic signals.

[0150] After the above steps, the detection system is deployed.

[0151] Furthermore, it can also be considered in the embodiment that the step S2 specifically includes the following steps: Figure 6 As shown:

[0152] S21: Health Base Area Data Test

[0153] Data testing was performed in the known healthy benchmark area of ​​the tunnel lining to obtain elastic wave detection data at 16 points, and bandpass filtering was performed to eliminate some noise interference;

[0154] In the reference area, the number of elastic wave detection data is fixed, generally 16, and in the subsequent steps, the 3 maximum values ​​and 3 minimum values ​​will be removed, and the remaining 10 will be averaged;

[0155] If the elastic wave detection data does not have 16 points in some cases, the maximum and minimum values ​​removed are generally set to 20% of the number of elastic wave detection data, and only integers are taken without decimal points.

[0156] S22: Obtain a set of marked main frequencies F 0i

[0157] The process of processing the main frequency data of the elastic wave detection data obtained in step S21 includes the following steps:

[0158] S2201: Identify the direct wave time difference ΔT of the sensors on the left and right sides of the earthquake source respectively li , ΔT ri ;

[0159] S2202: Calculation of wave velocity v in concrete structures li =Δs / ΔT li 、v ri =Δs / ΔT ri , where Δs is the distance between the wheel sensors on the left and right sides of the source;

[0160] S2203: Calculation of wave velocity v in concrete structures i =(v li +v ri ) / 2;

[0161] S2204: The concrete structure wave velocity v obtained in step S22 i Sort, remove the 3 maximum values ​​and the 3 minimum values, and get the remaining 10 wave speeds v 0i ;

[0162] In this embodiment, 16 concrete structure wave velocities v i Sort, remove the maximum and minimum values ​​​​each 3, and get 10 wave speed values ​​​​v i ;

[0163] S2205: Through the known structure thickness H 0 Calculate the mark frequency F 0i =λ×v 0i / 2 / H 0 , λ is the structural coefficient, ranging from 0.8 to 1.0;

[0164] In this embodiment, for the tunnel lining structure, the value of 0.96 is generally more accurate, that is, using the formula F 0i =0.96×v 0i / 2 / H 0 ;

[0165] S23: Obtain a set of marker energies Q0i;

[0166] The elastic wave data of the 16 points obtained in step S21 are processed with the following marker energy data:

[0167] S2301: Perform convolution on the two waveforms received by the sensors on the left and right sides of the earthquake source Generate a new waveform E L 、E R ;

[0168] S2302:E L 、E R Convolution again, Generate a new waveform E;

[0169] S2303: Perform time domain integration on waveform E to obtain energy value Among them, T is the upper limit of the time domain, and its value is within 10000 microseconds;

[0170] In this embodiment, for the tunnel lining structure, the size is generally within 1 meter, and the T value is generally 8192 microseconds;

[0171] S2304: Q i Sort, remove the 3 maximum values ​​and the 3 minimum values, and get the remaining 10 sign energy values ​​Q 0i ;

[0172] S24: Get the main frequency F 0 and the critical value of the energy Q 0 ;

[0173] The 10 marker main frequencies F obtained in step S22 and step S23 are 0 and 10 marker energy thresholds Q 0 Taking the average, we get:

[0174]

[0175] And calculate the energy mean square error: Finally determine the critical value of the air gap abnormality: Get the health zone logo frequency F 0 , marking energy critical value Q 0 .

[0176] In this formula The value 3 is a value summarized based on previous measurement experience.

[0177] Furthermore, it can also be considered in the embodiment that step S4 includes the following steps: Figure 7 As shown:

[0178] S41: data preprocessing;

[0179] Import the data of four channels, screen each waveform, remove incorrect waveforms, such as waveforms with amplitudes mostly close to zero and waveforms with large amplitude jumps, and then perform bandpass filtering with the filter band set to 1kHz-10kHz;

[0180] S42: time-frequency dual-domain data processing;

[0181] After data preprocessing, the double waveforms on the left and right sides of the earthquake source are convolved separately to generate a new waveform each, and the two waveforms are convolved again to generate a new waveform, and then time-frequency dual-domain data processing is performed on this waveform:

[0182] S4201: Perform Fourier transform on the waveform, convert the time domain waveform into a frequency domain spectrum, and find the spectrum peak frequency f in the frequency domain;

[0183] S4202: Integrate the waveform in the time domain to obtain an energy value Q, and compare it with the marker energy threshold value Q obtained in step S24. 0 Make the ratio θ=Q / Q 0 , obtain the index θ for judging the degree of voiding;

[0184] S43: Calculation of lining thickness;

[0185] First, the first arrival time of the waveform is identified by the amplitude change rate, and then the dual-channel data on the left and right sides of the earthquake source are subtracted to obtain the direct wave time difference Δt from the earthquake source to the left and right dual-channel sensors. 1 , Δt 2 , and take the average of the two time differences Δt=(Δt 1 +Δt 2 ) / 2, and then further calculate the elastic wave velocity v=Δs / Δt of the concrete structure, where Δs is the spacing between the single-side dual-channel sensors, and then calculate the structure thickness H=λ×v / 2 / f in combination with the peak frequency f obtained in step S42. For the tunnel lining structure, λ is generally taken as 0.96;

[0186] S44: Lining quality assessment;

[0187] Conduct lining structure quality assessment based on the peak frequency f, structure thickness H, and void level assessment index θ obtained in step S42 and step S43;

[0188] Whether the lining thickness meets the standard: when H is greater than or equal to the lining design thickness, the quality is qualified and the thickness index is qualified; when H is less than the lining design thickness value, the quality is unqualified;

[0189] Internal voids in the lining: When the peak frequency f is greater than or equal to the peak frequency F in the healthy reference zone 0 When the structure is not empty, the quality is qualified;

[0190] When the peak frequency f is less than the peak frequency F in the healthy reference area 0 When θ>3, it is a serious void, and when 1<θ<3, it is a slight void, which means the quality is unqualified.

[0191] Fig. 9 It is a plane diagram of the results of the elastic wave four-wheel array mobile detection of the tunnel lining quality of the present invention. The horizontal direction is the tunnel extension direction, the vertical direction is the direction perpendicular to the tunnel, the horizontal and vertical coordinates are both in centimeters, and the values ​​in the figure are frequency values. The lining inside the white area in the image contains voids, and the dark area is a high-quality area without void development. At the same time, from an overall observation, the frequency of the 0-220 cm section is lower, and the lining thickness of this section is larger, and the frequency of the 220-400 cm section is higher, and the lining thickness of this section becomes smaller.

[0192] The above embodiments describe the present invention in detail, but the contents are only preferred embodiments of the present invention and cannot be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A tunnel lining internal cavity elastic wave four-wheel array mobile detection system, characterized by: It comprises a trolley (6) and an electromagnetic positioning base station (3) arranged on a tunnel base, wherein a support arm (5) is installed on the trolley (6); the support arm (5) comprises an upper support arm (5-1) and a lower support arm (5-2) which are rotatably connected to each other, the lower support arm (5-2) is connected to the trolley (6), and the top end of the upper support arm (5-1) is connected to a mobile elastic wave data acquisition module (1); The mobile elastic wave data acquisition module (1), the mobile terminal (2) and the data receiving host are connected in communication via a WIFI signal (4).

2. The tunnel lining internal cavity elastic wave four-wheel array mobile detection system according to claim 1 is characterized by: The mobile elastic wave data acquisition module (1) comprises a wheel sensor connected to an acquisition circuit box (1-6), an automated impactor (1-5) and an electromagnetic positioning tag (1-7); The wheel sensor comprises a sleeve and a sensor wheel arranged on the upper part of the sleeve, four MEMS sensors (1-1-1) are installed on the sensor wheel at equal angles, and a high-precision distance encoder and a 10-core conductive slip ring are installed on the central bearing of the sensor wheel; The 10-core conductive slip ring connects the MEMS sensor (1-1-1), the high-precision distance encoder and the acquisition circuit box (1-6) via a 10-core cable.

3. The tunnel lining internal cavity elastic wave four-wheel array mobile detection system according to claim 2 is characterized by: Four limiting protrusions are arranged in the sleeve, and the bearing spring is limitedly installed in the four limiting protrusions. The lower part of the bearing column is sleeved in the bearing spring, and the upper part of the bearing column is connected to the sensor wheel.

4. The tunnel lining internal cavity elastic wave four-wheel array mobile detection system according to claim 3 is characterized by: The wheel sensors are arranged in four numbers, with the automated impactor (1-5) as the center, and wheel sensor A (1-1), wheel sensor B (1-2), wheel sensor C (1-3), and wheel sensor D (1-4) symmetrically distributed.

5. The tunnel lining internal cavity elastic wave four-wheel array mobile detection system according to claim 4 is characterized by: The acquisition circuit box (1-6) comprises an acquisition circuit board, a central control circuit board, a boost circuit board, a WIFI module and a power supply battery; The central control circuit board is connected to the acquisition circuit board and the electromagnetic positioning tag (1-7) via a four-core cable, and the WIFI module is connected to the central control circuit board via a two-core cable; The central control circuit board is connected to the automated impactor (1-5) via two single-core cables; The 10-core conductive slip rings on the wheel center bearings of the wheel sensor A (1-1), the wheel sensor B (1-2), the wheel sensor C (1-3), and the wheel sensor D (1-4) are connected to the acquisition circuit board via a 10-core cable.

6. The tunnel lining internal cavity elastic wave four-wheel array mobile detection system according to claim 5 is characterized by: The automated impactor (1-5) comprises an outer sleeve, an electromagnetic coil, a spring and two impact columns; Two limiting protrusions are arranged in the middle of the upper section of the impact column, and are combined with the outer sleeve to limit the spring; the bottom of the outer sleeve is connected to the outer shell of the collection circuit box (1-6) by bolts, and the pins of the electromagnetic coil turns are connected to the central control circuit board in the collection circuit box (1-6).

7. A method for detecting the internal cavity of a tunnel lining by using elastic waves of a four-wheel array, using the system for detecting the internal cavity of a tunnel lining by using elastic waves of a four-wheel array as claimed in claim 6, characterized in that: The following steps are involved: S1: Tunnel lining elastic wave movement detection system layout; After arriving at the tunnel to be tested, the detection section is first determined, the mobile terminal (2) and the data receiving host are turned on, the electromagnetic positioning base station (3) is arranged, an observation coordinate system is established, and the observation coordinate system is entered into the data acquisition software of the mobile terminal (2); S2: Health baseline zone marker main frequency and energy acquisition; In the healthy benchmark area of ​​the tunnel lining structure, data testing is carried out, and then the main frequency and energy value of the marker are obtained according to the healthy benchmark area marker parameter acquisition process; S3: elastic wave, positioning, encoding data acquisition and processing; According to the detection system established in step S1, the elastic wave sensor, positioning tag and encoder data are collected through the mobile elastic wave data collection module (1), wherein the positioning tag and encoder are used to determine the position of the sensor, and the elastic wave sensor is used to determine the internal quality of the lining; S4: Real-time determination of lining thickness and internal voids; According to the data collected by the elastic wave sensor in step S3, waveform screening, bandpass filtering, waveform convolution, time domain integration, and frequency domain peak search are carried out to obtain energy determination index, peak frequency, and structure thickness value, which are then compared and judged with the main frequency of the healthy reference zone obtained in step S2, and the void degree determination index is calculated according to the energy value, so as to determine the lining thickness and internal void situation; S5: Output the lining thickness and internal void detection results; According to the judgment in step S4 and the positioning information in step S3, a plan view of lining thickness and void distribution is drawn, and the project name, draftsman, and reviewer information are added. At the same time, the lining thickness and internal void detection results are saved in the database.

8. The tunnel lining internal cavity elastic wave four-wheel array mobile detection method according to claim 7 is characterized by: The step S1 specifically includes the following steps: S11: Arrange electromagnetic positioning base station; After arriving at the tunnel to be tested, first, determine the test section, then arrange two electromagnetic positioning base stations (3) at the starting and ending mileages of the test section, the electromagnetic positioning base stations (3) are respectively located at the left and right side walls of the tunnel, record the mileages of the four electromagnetic positioning base stations (3), and use a ruler to measure the distance between two electromagnetic positioning base stations (3) at the same mileage; S12: Establish observation coordinate system; An observation coordinate system is established according to the positional relationship of four electromagnetic positioning base stations (3); S13: Setting up data acquisition system; Turn on the mobile terminal (2), the electromagnetic positioning base station (3), and the data receiving host, set the sampling rate, and adjust the distance L between the wheel sensors on the left and right sides of the mobile detection structure and the center of the impact source. The distance L should be less than the designed thickness of the lining; S14: Connect the positioning tag device to the data receiving host, and input the coordinates of the four electromagnetic positioning base stations (3) into the data acquisition software in the mobile terminal (2) according to the observation coordinate system established in step S12. The mobile terminal (2) communicates with the data receiving host via a WIFI signal (4), and the electromagnetic positioning base station (3) is connected to the positioning on the data receiving host via an electromagnetic signal.

9. The tunnel lining internal cavity elastic wave four-wheel array mobile detection method according to claim 8, characterized in that: The step S2 specifically includes the following steps: S21: Health benchmark area data test; Data testing was performed in the known healthy benchmark area of ​​the tunnel lining to obtain elastic wave detection data at 16 points, and bandpass filtering was performed to eliminate some noise interference; S22: Obtain a set of marked main frequencies F 0i ; The process of processing the main frequency data of the elastic wave detection data obtained in step S21 includes the following steps: S2201: Identify the direct wave time difference ΔT of the sensors on the left and right sides of the earthquake source respectively li , ΔT ri ; S2202: Calculation of wave velocity v in concrete structures li =Δs / ΔT li 、v ri =Δs / ΔT ri , where Δs is the distance between the wheel sensors on the left and right sides of the source; S2203: Calculation of wave velocity v in concrete structures i =(v li +v ri ) / 2; S2204: The concrete structure wave velocity v obtained in step S2203 i Sort, remove the 3 maximum values ​​and the 3 minimum values, and get the remaining 10 wave speeds v 0i ; S2205: Calculate the main frequency F of the marker by using the known thickness H0 of the structure 0i =λ×v 0i / 2 / H0, λ is the structural coefficient, ranging from 0.8 to 1.0; S23: Obtain a set of marker energies Q0i; The elastic wave data of the 16 points obtained in step S21 are processed with the following marker energy data: S2301: Perform convolution on the two waveforms received by the sensors on the left and right sides of the earthquake source Generate a new waveform E L 、E R ; S2302:E L 、E R Convolution again, Generate a new waveform E; S2303: Perform time domain integration on waveform E to obtain energy value Among them, T is the upper limit of the time domain, and its value is within 10000 microseconds; S2304: Q i Sort, remove the 3 maximum values ​​and the 3 minimum values, and get the remaining 10 sign energy values ​​Q 0i ; S24: Obtaining the mark main frequency F0 and the mark energy critical value Q0; The 10 marker main frequencies F0 and the 10 marker energy critical values ​​Q0 obtained in step S22 and step S23 are averaged to obtain: And calculate the energy mean square error: Finally determine the critical value of the air gap abnormality: Obtain the health zone mark main frequency F0 and mark energy critical value Q0.

10. The tunnel lining internal cavity elastic wave four-wheel array mobile detection method according to claim 9, characterized in that: Step S4 includes the following steps: S41: data preprocessing; Import the data of four channels, screen each waveform, remove incorrect waveforms, such as waveforms with amplitudes mostly close to zero and waveforms with large amplitude jumps, and then perform bandpass filtering with the filter band set to 1kHz-10kHz; S42: time-frequency dual-domain data processing; After data preprocessing, the double waveforms on the left and right sides of the earthquake source are convolved separately to generate a new waveform each, and the two waveforms are convolved again to generate a new waveform, and then time-frequency dual-domain data processing is performed on this waveform: S4201: Perform Fourier transform on the waveform, convert the time domain waveform into a frequency domain spectrum, and find the spectrum peak frequency f in the frequency domain; S4202: Integrate the waveform in the time domain to obtain an energy value Q, and make a ratio θ=Q / Q0 with the marker energy critical value Q0 obtained in step S24 to obtain a hollowness determination index θ; S43: Calculation of lining thickness; First, the waveform first arrival time is identified through the amplitude change rate, and then the dual-channel data on the left and right sides of the earthquake source are subtracted to obtain the direct wave time difference Δt1 and Δt2 from the earthquake source to the left and right dual-channel sensors, and the two time differences are averaged Δt=(Δt1+Δt2) / 2, and then the elastic wave velocity v=Δs / Δt of the concrete structure is further calculated, where Δs is the spacing between the dual-channel sensors on one side, and then the peak frequency f obtained in step S42 is combined to calculate the thickness of the structure H=λ×v / 2 / f, where λ is the structural coefficient, and the value range is 0.8~1.0; S44: Lining quality assessment; Conduct lining structure quality assessment based on the peak frequency f, structure thickness H, and void level assessment index θ obtained in step S42 and step S43; Whether the lining thickness meets the standard: when H is greater than or equal to the lining design thickness, the quality is qualified and the thickness index is qualified; when H is less than the lining design thickness value, the quality is unqualified; Internal voids in the lining: When the peak frequency f is greater than or equal to the peak frequency F0 in the healthy reference zone, there is no void inside the structure and the quality is qualified; When the peak frequency f is less than the peak frequency F0 of the healthy reference zone, the lining contains voids. Furthermore, when θ>3, it is a serious void, and when 1<θ<3, it is a slight void, which means the quality is unqualified.

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