Tunnel lining health monitoring system and method based on acoustic emission

Abstract

The invention discloses a tunnel lining health monitoring system and method based on acoustic emission, and relates to the technical field of tunnel engineering health monitoring. The tunnel lining health monitoring system based on acoustic emission comprises a signal capturing system, a signal processing system and a safety risk early warning prompting system. The signal capturing system comprises acoustic emission receiver matrixes which are arranged on a tunnel lining at certain intervals; the signal processing system comprises transmission equipment for receiving signal waveforms and frequency spectrum characteristics, and a signal processor for performing signal processing according to the signal waveforms and the frequency spectrum characteristics; the safety risk early warning prompt system comprises a display device and an alarm, the display device performs positioning according to the signal source point determined by the signal processing system and displays the acoustic emission signal source point in the simplified lining model, and the alarm gives an alarm after the acoustic emission event cumulative number reaches a threshold value. The invention aims to solve the problems of limited monitoring space range, low monitoring sensitivity and untimely monitoring result feedback in current tunnel health monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering health monitoring, and in particular to a tunnel lining health monitoring system and method based on acoustic emission. Background Art

[0002] As mountain tunnels continue to emerge, facing more complex and changeable geological conditions, more and more secondary linings are experiencing defects, resulting in a significant increase in engineering economic costs and safety risks. During tunnel construction and operation, the use of monitoring and measurement methods to timely and comprehensively obtain information on the health status of the secondary lining is a prerequisite for adopting reasonable engineering response measures.

[0003] The current tunnel lining monitoring technology mainly monitors stress and strain, and infers the mechanical state of the entire lining from discrete and isolated measuring points. The monitoring range is limited and it is unable to perceive subtle changes inside the lining concrete. It is not sensitive enough and cannot provide timely feedback on the mechanical state of the lining concrete.

[0004] Acoustic emission monitoring is a type of microseismic monitoring. By monitoring the release and propagation of elastic waves in the medium, the spatial position, energy, magnitude, stress drop and other source parameter information of the acoustic emission event can be inverted and calculated. The mechanical state and damage characteristics of the medium can be inferred based on the activity characteristics of the acoustic emission event, revealing the main damaged areas and potential unstable areas, so as to control and avoid the sudden damage of the medium. Acoustic emission has a good response sensitivity to the medium damage process. The evolution of its parameters and signals can effectively reflect the degree of concrete damage, provide precursor information for concrete damage that is ahead of stress and strain monitoring, and has the ability to predict and identify concrete damage.

[0005] Acoustic emission monitoring is applied to tunnel lining safety monitoring. When microcracks appear and develop inside the concrete, the time, spatial location, and scale of the microcracks can be inferred based on the transient stress waves emitted, and the health status and risk level of the lining concrete can be identified and judged. This provides a new monitoring idea for concrete linings and comprehensively monitors the health status of linings in real time. Summary of the invention

[0006] In order to solve the problems of limited monitoring space, low monitoring sensitivity and untimely feedback of monitoring results in current tunnel health monitoring, the present invention proposes a tunnel lining health monitoring system and method based on acoustic emission.

[0007] The technical solution adopted by the present invention is:

[0008] A tunnel lining health monitoring system based on acoustic emission, the tunnel lining health monitoring system based on acoustic emission includes a signal capture system, a signal processing system, and a safety risk early warning prompt system;

[0009] The signal capture system includes an acoustic emission receiver matrix arranged on the tunnel lining at a certain interval, and a signal transmission device connected to the acoustic emission receivers;

[0010] The signal processing system is connected to the signal capture system, and includes a transmission device for receiving signal waveform and spectrum characteristics, and a signal processor for performing signal processing according to the signal waveform and spectrum characteristics;

[0011] The safety risk early warning prompt system is connected to the signal processing system, which includes a display device and an alarm. The display device locates the signal source point according to the signal processing system, and displays the acoustic emission signal source point in the simplified lining model. The alarm sounds an alarm when the accumulated number of acoustic emission events reaches a threshold.

[0012] Furthermore, the signal processing steps of the signal processing system are as follows:

[0013] Step 1: Conduct a concrete acoustic emission experiment in the laboratory to obtain the waveform and spectrum characteristics of the acoustic emission during the generation and development of microcracks inside the concrete;

[0014] Step 2: According to the acoustic emission waveform and spectrum characteristics of the generation and development of microcracks in concrete materials obtained in the laboratory, the acoustic emission signal received by the transmission equipment is filtered to filter the background noise, and the first arrival time of the acoustic emission signal of different acoustic emission receivers in the acoustic emission receiver matrix is ​​obtained;

[0015] Step 3: According to the first arrival time of the signal, the location of the acoustic emission source point is determined by combining the multi-template fast marching algorithm MSFM and narrowband expansion technology for calculating the first arrival travel time of the stress wave.

[0016] Further, the steps to determine the location of the acoustic emission source point are as follows:

[0017] Step 3.1, simplify the three-dimensional lining into a cylindrical surface without thickness, and consider only the two propagation dimensions of the circumferential and longitudinal directions for the acoustic emission source point;

[0018] Step 3.2, using the two-dimensional stress wave equation to describe the stress wave travel time at different locations in the lining;

[0019] Step 3.3, discretize the two-dimensional Eikonal equation by upwind difference to obtain the Eikonal equation;

[0020] Step 3.4, perform calculations of the circumferential differential operator and the longitudinal differential operator;

[0021] Step 3.5, calculate the preliminary travel time values ​​of 8 points around the node based on the travel time values ​​of adjacent nodes;

[0022] Step 3.6, using narrowband expansion to calculate the stress wave first arrival travel time of the remaining discrete nodes when any discrete node in the region is used as the stress wave source point.

[0023] Furthermore, the two-dimensional stress wave path function equation is:

[0024]

[0025] Right now

[0026] Where: T is the time delay, which represents the propagation time of the sound wave from the transmitting point to the receiving point; l is the circumferential coordinate; z is the longitudinal coordinate; α(l,z) represents the propagation speed of the sound wave at a certain point; s(l,z) represents the phase velocity of the sound wave; Represents the magnitude of the gradient at coordinate (l,z) at time t.

[0027] Furthermore, the two-dimensional Eikonal equation can be discretized by upwind difference to obtain the Eikonal equation at the coordinate (l,z) and point number (i,j):

[0028]

[0029] In the formula, represents the magnitude of the time gradient at the grid point (i, j); D n -l t i,j , D n +l t i,j To simplify the backward and forward n-order difference operators of discrete points (i, j) in the plane; D n -z t i,j , D n +z t i,j It represents the effect of the backward and forward n-order difference operators on time t along the longitudinal direction z at point (i, j); the max() operator is obtained based on the known situation and size of the travel time values ​​of the points around the calculated point;

[0030] The difference operator adopts the first-order difference operator;

[0031] The circumferential difference operator is calculated as follows:

[0032]

[0033] The longitudinal difference operator is calculated as follows:

[0034]

[0035] Where D n +l t,D n -l t represents the first-order difference operator forward and backward along the l direction; ti+1 represents the time value at the grid point (i+1,j); t i represents the time value at the grid point (i, j); Δl represents the grid spacing in the l direction; represents the first-order difference operator forward and backward along the l direction; Δz represents the grid spacing in the z direction; t j+1 represents the time value at the grid point (i, j+1); t j represents the time value at the grid point (i, j); Δz represents the grid spacing in the z direction;

[0036] The first-order specific operation rules are as follows:

[0037]

[0038] Where D1 -l t i,j , D1 +l t i,j represents the backward difference operator from point (i-1, j) to point (i, j) along the l direction, and the forward difference operator from (i, j) to point (i+1, j); t i,j represents the travel time value at the grid point (i, j); t i-1,j represents the travel time value at the grid point (i-1, j); t i+1,j Represents the travel time value at the grid point (i+1,j).

[0039] Furthermore, by rotating the rectangular coordinate system, the diagonal direction is included in the propagation path calculation, namely the multi-template fast marching algorithm MSFM;

[0040] According to the slowness calculation method, the preliminary travel time values ​​of 8 points around the node (i, j) are obtained, namely: the preliminary travel time values ​​of the four directly adjacent points and the four obliquely adjacent points;

[0041] The calculation formula is as follows:

[0042]

[0043] In the formula, t i,j represents the travel time value at the grid point (i, j), that is, the initial travel time of the point; t i,j±1 represents the travel time value at the grid point (i,j+1) or (i,j-1), which represents the upper and lower adjacent points respectively; t i±1,j represents the travel time value at point (i+1,j) or (i-1,j), which respectively represent the adjacent points on the left and right; t i+1,j±1 represents the travel time value at point (i+1,j+1) or (i+1,j-1), which respectively represent the upper right and lower right adjacent points; t i-1,j±1represents the travel time value at point (i-1, j+1) or (i-1, j-1), which respectively represent the upper left and lower left diagonal adjacent points; s i,j represents the slowness at the grid point (i, j); s i±1,j Indicates the slowness of adjacent points in the horizontal and vertical directions; s i±1,j±1 represents the slowness at the oblique adjacent points; h represents the step size used in the calculation, usually the spatial resolution of the grid, which refers to the distance between two adjacent nodes.

[0044] Furthermore, the narrowband extension technology divides the nodes in the area that need to calculate travel time into the following three types of nodes:

[0045] Acceptance point: the point where the initial arrival time has been confirmed;

[0046] Narrowband point: a point that is in direct contact with the receiving point and has its travel time calculated, but will have its travel time updated with a certain probability in subsequent calculations;

[0047] The far point is a far point whose travel time has not been calculated at all;

[0048] All narrowband points constitute a narrowband.

[0049] Furthermore, the signal processing system calculates the first arrival travel time of each discrete node in the tunnel lining space when each acoustic emission receiver position in the acoustic emission receiver matrix is ​​used as a stress wave source point based on the MSFM algorithm combined with narrowband expansion;

[0050] The signal processing system combines the stress wave interchange theorem to obtain the time required for each discrete node to propagate to each acoustic emission receiver. Each acoustic emission receiver forms a travel time matrix T of size m×n. m×n ;

[0051] The element t in the matrix i,j Represents discrete node P i,j When it is the source point, the time required to propagate to the acoustic emission receiver; a total of u×v matrices can be formed, where u and v represent the number of circumferential and longitudinal arrangements in the receiver matrix respectively;

[0052] The acoustic emission signals generated when microcracks in concrete appear and develop are received by the acoustic emission receivers. First, according to the first arrival travel time of the microseismic signals of each receiver, the two columns with the smallest first arrival travel time in the annular direction and the two rows with the smallest first arrival travel time in the longitudinal direction are selected, a total of 4 acoustic emission receivers are used to calculate the source position of the acoustic emission signal;

[0053] The calculation method is as follows:

[0054] First, four acoustic emission receivers are combined in pairs to obtain combinations;

[0055] According to the first arrival travel time measured by the two acoustic emission receivers in each combination, the difference of the first arrival travel time is calculated;

[0056] According to the duration matrix of the acoustic emission receiver, the node set that reaches the measured first arrival travel time difference for each pair of combinations is confirmed, and a total of 6 node sets are obtained;

[0057] Find the intersection of the six node sets, which are regarded as the source points of the acoustic emission signals.

[0058] Furthermore, the layout spacing of the acoustic emission receiver matrix of the signal capture system should be accurately calculated to ensure that the acoustic emission signal positioning error is within the allowable range;

[0059] The specific calculation is done through the following formula:

[0060] ε<ε lim

[0061] Where, ε is the total error of acoustic emission positioning; ε lim Determined based on the specific conditions of the tunnel in the test section and in combination with the specifications;

[0062] The acoustic emission signal positioning error includes the model simplification error and the travel time algorithm error. The travel time algorithm error is the acoustic emission positioning error caused by using the MSFM algorithm to calculate the initial arrival travel time of each point in the area and replacing the actual propagation path of the stress wave with discrete line segments.

[0063] The calculation method of acoustic emission positioning error is as follows:

[0064] ε=ε1+ε2

[0065] Where, ε is the acoustic emission positioning error; ε1 is the simplified error of the acoustic emission positioning model; ε2 is the acoustic emission positioning algorithm error;

[0066] ε1=|l max -l' max |

[0067] In the formula, l max l' is the maximum calculated distance between two points in each simplified unit; max is the actual circumferential distance corresponding to the above two points, which causes the maximum propagation distance error;

[0068] ε2=φl max

[0069] Where φ is the error rate of the positioning algorithm, which is related to the discrete grid size and propagation distance; max It is the maximum calculated circumferential distance between two points in each simplified unit.

[0070] Furthermore, the threshold value is calculated as follows:

[0071] The damage factor of concrete material is proportional to the cumulative number of acoustic emission events and is expressed as:

[0072] D=kN

[0073] Where D is the concrete material damage factor; k is the proportionality coefficient determined based on indoor material tests; N is the cumulative number of acoustic emission events;

[0074] D=1-E / E *

[0075] Where E is the elastic modulus of the material in the damaged state; E * is the initial elastic modulus of the material;

[0076] According to the load size, distribution and cross-sectional dimensions of the lining structure, the minimum elastic modulus required by the concrete material of the lining structure is determined, and then the maximum threshold of the number of acoustic emission events to ensure the safety of the structure is calculated.

[0077] The beneficial effects of the present invention are:

[0078] 1. Lining monitoring range is complete and the results are reliable

[0079] Different from the traditional stress and strain monitoring which infers the stress state of the lining through measuring points on the inner surface of the lining, the acoustic emitter-receiver matrix provided in the present invention can receive the acoustic emission signals of the entire lining, monitor the closure, generation and development of micro-cracks inside it, and can directly monitor the state of the entire lining. The monitoring integrity and reliability are greatly improved compared with the traditional monitoring methods.

[0080] 2. Improve monitoring response sensitivity

[0081] The slight changes in the stress state of concrete materials during loading have little effect on the stress-strain state of the inner surface of the lining, and traditional monitoring is difficult to detect changes in its stress state. The present invention can monitor the acoustic emission signals generated by concrete materials when the stress state changes, and reflect them in the safety risk early warning prompt system.

[0082] 3. The positioning accuracy is adjustable to meet the monitoring accuracy requirements with the lowest material cost.

[0083] According to the engineering measures to be adopted in the later reinforcement and maintenance, the positioning accuracy requirements of potential risk points can be formulated. By adjusting the receiver spacing in the receiver matrix and the stress wave first arrival travel time algorithm, the proposed monitoring accuracy can be achieved while using less monitoring equipment.

[0084] 4. Realize real-time monitoring and early warning

[0085] Dynamic monitoring: The system can record the acoustic emission events of concrete in real time. Once the corresponding threshold is reached, an early warning can be issued immediately to remind relevant personnel of possible safety hazards.

[0086] Instant feedback: Through real-time analysis of acoustic emission signals, the monitoring system can quickly identify the deterioration of the lining structure, thereby facilitating timely repair measures.

[0087] 5. Structural health diagnosis

[0088] Early identification: Acoustic emission technology can detect lining degradation before macro cracks appear, allowing tunnel maintenance management to change from passive to active.

[0089] Traceability: The acoustic emission data recorded by the system can be used as a basis for subsequent analysis to help engineers evaluate the health status and related performance of concrete materials.

[0090] 6. Optimize maintenance strategy

[0091] Information guides decision making: By analyzing AE data, engineers can make a more reasonable assessment of the lining health status, thereby optimizing maintenance and reinforcement strategies, improving structural safety and reducing maintenance costs.

[0092] Material performance monitoring: Combined with the elastic modulus requirement that concrete must maintain after deterioration, the system can ensure that the performance of the material during use will not fall below the predetermined standard, thereby enhancing the safety of the structure.

[0093] 7. Reduce security risks

[0094] Preventive technology application: The acoustic emission monitoring system can take preventive measures before problems occur, reduce the safety risks caused by tunnel collapse or other accidents, and ensure the safety of engineering personnel and passing vehicles.

[0095] Enhanced construction management: By integrating monitoring data in real time, managers can better understand the construction status and formulate targeted measures to improve construction safety and efficiency.

[0096] In summary, the tunnel lining health monitoring system based on acoustic emission not only improves the monitoring accuracy and real-time warning capability, but also provides strong support for the health assessment and maintenance decision-making of concrete structures, contributing important value to ensuring the safety and durability of tunnels. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is a layout diagram of the acoustic emission receiver matrix of the signal capture system of the present invention;

[0098] Figure 2 It is a simplified process diagram of three-dimensional lining of the present invention;

[0099] Figure 3 This is a flow chart of narrowband extension of the present invention;

[0100] Figure 4 This is a lining structure diagram in a verification example of the present invention;

[0101] Figure 5 It is a simplified diagram of three-dimensional lining in the verification example of the present invention;

[0102] In the figure: 1- Acoustic emission receiver matrix. DETAILED DESCRIPTION

[0103] The following will be combined with the accompanying drawings of the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0104] Example 1

[0105] In order to solve the problems of limited monitoring space range, low monitoring sensitivity and untimely feedback of monitoring results in current tunnel health monitoring, the present invention proposes a tunnel lining health monitoring system based on acoustic emission. The tunnel lining health monitoring system based on acoustic emission receives transient elastic waves emitted when internal microcracks appear and expand during concrete loading through an acoustic emission receiver matrix, and reversely infers the appearance and expansion of microcracks inside the concrete based on the transient elastic waves, and further infers the health status of the concrete lining and determines the lining risk points.

[0106] The tunnel lining health monitoring system based on acoustic emission includes a signal capture system, a signal processing system, and a safety risk early warning system. The specific composition and functions of each system are as follows:

[0107] like Figure 1 As shown, the signal capture system includes an acoustic emission receiver matrix 1 arranged on the tunnel lining at a certain interval, and a signal transmission device connected to the acoustic emission receiver. The ring and longitudinal spacing of the acoustic emission receiver matrix are controlled according to the error calculation method, and are reasonably arranged according to the internal force distribution of the lining. The acoustic emission receiver matrix is ​​mainly used to receive the acoustic emission signal, record the signal waveform and spectrum characteristics, and transmit it to the signal processing system by the transmission device.

[0108] The signal processing system is connected to the signal capture system, and includes a transmission device for receiving signal waveform and spectrum characteristics, and a signal processor for signal processing according to the signal waveform and spectrum characteristics. The signal processor processes the signal according to the signal waveform and spectrum characteristics to obtain the first arrival travel time of each signal at different receivers in the receiver matrix, and further, combines the multi-template fast marching algorithm MSFM with narrowband expansion to determine the source point of the acoustic emission signal.

[0109] The safety risk early warning prompt system is connected to the signal processing system, which includes a display device and an alarm. The display device locates the signal source point according to the signal processing system, and displays the acoustic emission signal source point in the simplified lining model; the signal processor determines the alarm threshold of the cumulative number of acoustic emission events based on the relationship between the cumulative number of acoustic emission events and the concrete material damage factor. When the cumulative number of acoustic emission events in the same discrete grid reaches the threshold, the signal processor cooperates with the alarm to sound an alarm.

[0110] Furthermore, the signal capture system, signal processing system and safety risk early warning prompt system of the tunnel lining health monitoring system based on acoustic emission are described in detail as follows:

[0111] The specific process of signal processing in the signal processing system is as follows:

[0112] Step 1: Conduct a concrete acoustic emission experiment in the laboratory to obtain the waveform and spectrum characteristics of the acoustic emission during the generation and development of microcracks inside the concrete;

[0113] Step 2: According to the acoustic emission waveform and spectrum characteristics of the generation and development of micro-cracks in concrete materials obtained in the laboratory, the acoustic emission signals received by the transmission equipment are filtered to filter the background noise; the construction noise is mainly filtered during the construction period, and the noise of passing vehicles is mainly filtered during the operation period. After filtering the background noise, the first arrival time of the acoustic emission signals of different acoustic emission receivers in the acoustic emission receiver matrix is ​​obtained.

[0114] Step 3: According to the first arrival time of the signal, the location of the acoustic emission source point is determined by combining the multi-template fast marching algorithm MSFM and narrowband expansion for calculating the first arrival travel time of the stress wave.

[0115] The steps to determine the location of the acoustic emission source point are as follows:

[0116] Step 3.1: The purpose of lining health monitoring is to obtain the longitudinal and circumferential positions of the damage risk points in the lining. The location of the risk points in the thickness direction is of little significance for subsequent engineering treatment. At the same time, considering that the lining thickness is negligible compared to its longitudinal and circumferential lengths. Therefore, if Figure 2 As shown, in this embodiment, the three-dimensional lining is simplified to a cylindrical surface without thickness, and the acoustic emission source point only considers the two propagation dimensions of the circumferential direction and the longitudinal direction.

[0117] Step 3.2, using the two-dimensional stress wave equation to describe the stress wave travel time at different locations in the lining;

[0118] The two-dimensional stress wave path function equation is:

[0119]

[0120] Where: T is the time delay, which represents the propagation time of the sound wave from the transmitting point to the receiving point; l is the circumferential coordinate; z is the longitudinal coordinate; α(l,z) represents the propagation speed of the sound wave at a certain point; s(l,z) represents the phase velocity of the sound wave; Represents the magnitude of the gradient at coordinate (l,z) at time t.

[0121] Step 3.3, discretize the two-dimensional Eikonal equation by upwind difference to obtain the Eikonal equation;

[0122] The two-dimensional Eikonal equation can be discretized by upwind difference to obtain the Eikonal equation at the coordinate (l,z) and point number (i,j):

[0123]

[0124] In the formula, represents the magnitude of the time gradient at the grid point (i, j); D n -l t i,j , D n +l t i,j To simplify the backward and forward n-order difference operators of discrete points (i, j) in the plane; D n -z t i,j , D n +z t i,j It represents the effect of the backward and forward n-order difference operators on time t along the longitudinal direction z at point (i, j); the max() operator is obtained based on the known situation and size of the travel time values ​​of the points around the calculated point.

[0125] Step 3.4, perform calculations of the circumferential differential operator and the longitudinal differential operator;

[0126] The difference operator adopts the first-order difference operator;

[0127] The circumferential difference operator is calculated as follows:

[0128]

[0129] The longitudinal difference operator is calculated as follows:

[0130]

[0131] Where D n +l t,D n -l t represents the first-order difference operator forward and backward along the l direction; t i+1 represents the time value at the grid point (i+1,j); t i represents the time value at the grid point (i, j); Δl represents the grid spacing in the l direction; represents the first-order difference operator forward and backward along the l direction; Δz represents the grid spacing in the z direction; t j+1 represents the time value at the grid point (i, j+1); t j represents the time value at the grid point (i, j); Δz represents the grid spacing in the z direction;

[0132] The first-order specific operation rules are as follows:

[0133]

[0134] Where D1 -l t i,j , D1 +l t i,j represents the backward difference operator from point (i-1, j) to point (i, j) along the l direction, and the forward difference operator from (i, j) to point (i+1, j); t i,j represents the travel time value at the grid point (i, j); t i-1,j represents the travel time value at the grid point (i-1, j); t i+1,j Represents the travel time value at the grid point (i+1,j).

[0135] Step 3.5, based on the travel time values ​​of the adjacent nodes, calculate the preliminary travel time values ​​of the eight points around the node; in this embodiment, by rotating the rectangular coordinate system, the diagonal direction is included in the propagation path calculation, that is, the multi-template fast marching algorithm MSFM;

[0136] According to the slowness calculation method, the preliminary travel time values ​​of 8 points around the node (i, j) are obtained, namely: the preliminary travel time values ​​of the four directly adjacent points and the four obliquely adjacent points;

[0137] The calculation formula is as follows:

[0138]

[0139] In the formula, t i,j represents the travel time value at the grid point (i, j), that is, the initial travel time of the point; t i,j±1represents the travel time value at the grid point (i,j+1) or (i,j-1), which represents the upper and lower adjacent points respectively; t i±1,j represents the travel time value at point (i+1,j) or (i-1,j), which respectively represent the adjacent points on the left and right; t i+1,j±1 represents the travel time value at point (i+1,j+1) or (i+1,j-1), which respectively represent the upper right and lower right adjacent points; t i-1,j±1 represents the travel time value at point (i-1, j+1) or (i-1, j-1), which respectively represent the upper left and lower left diagonal adjacent points; s i,j represents the slowness at the grid point (i, j); s i±1,j Indicates the slowness of adjacent points in the horizontal and vertical directions; s i±1,j±1 represents the slowness at the oblique adjacent points; h represents the step size used in the calculation, usually the spatial resolution of the grid, which refers to the distance between two adjacent nodes.

[0140] Step 3.6, using narrowband expansion to calculate the stress wave first arrival travel time of the remaining discrete nodes when any discrete node in the region is used as the stress wave source point.

[0141] Narrowband extension technology divides the nodes that need to calculate travel time in the area into the following three types of nodes:

[0142] Acceptance point: the point where the initial arrival time has been confirmed;

[0143] Narrowband point: a point that is in direct contact with the receiving point and has its travel time calculated, but will have its travel time updated with a certain probability in subsequent calculations;

[0144] The far point is a far point whose travel time has not been calculated at all;

[0145] The narrowband extension process is as follows Figure 3 As shown, all narrowband points constitute a narrowband.

[0146] The signal processing system is based on the MSFM algorithm combined with narrowband expansion to calculate the first arrival travel time of each discrete node in the tunnel lining space when each acoustic emission receiver position in the acoustic emission receiver matrix is ​​used as the stress wave source point;

[0147] The signal processing system combines the stress wave interchange theorem to obtain the time required for each discrete node to propagate to each acoustic emission receiver. Each acoustic emission receiver forms a travel time matrix T of size m×n. m×n ;

[0148] The element t in the matrix i,j Represents discrete node P i,j When it is the source point, the time required to propagate to the acoustic emission receiver; a total of u×v matrices can be formed, where u and v represent the number of circumferential and longitudinal arrangements in the receiver matrix respectively;

[0149] The acoustic emission signals generated when microcracks in concrete appear and develop are received by the acoustic emission receivers. First, according to the first arrival travel time of the microseismic signals of each receiver, the two columns with the smallest first arrival travel time in the annular direction and the two rows with the smallest first arrival travel time in the longitudinal direction are selected, a total of 4 acoustic emission receivers are used to calculate the source position of the acoustic emission signal;

[0150] The calculation method is as follows:

[0151] First, four acoustic emission receivers are combined in pairs to obtain combinations;

[0152] According to the first arrival travel time measured by the two acoustic emission receivers in each combination, the difference of the first arrival travel time is calculated;

[0153] According to the duration matrix of the acoustic emission receiver, the node set that reaches the measured first arrival travel time difference for each pair of combinations is confirmed, and a total of 6 node sets are obtained;

[0154] Find the intersection of the six node sets, which are regarded as the source points of the acoustic emission signals.

[0155] Signal capture system layout:

[0156] The signal capture system is arranged based on the signal processing of the signal processing system.

[0157] First, the positioning accuracy required for monitoring should be determined based on the specific conditions of the tunnel project, including the stress characteristics of the lining and the reinforcement measures to be adopted in the later stage. The size of the discrete grid should be preliminarily determined based on the required positioning accuracy. Taking the positioning accuracy requirement of 1m as an example, to determine the positioning accuracy within 1m, when calculating its travel time matrix, the discrete grid size should not be larger than 1m. Based on the preliminarily proposed discrete grid size, Figure 2 As shown, the discrete simplified model.

[0158] In order to facilitate the calculation of the travel time from each node to the receiver, the receiver should be arranged on the nodes of the discrete grid. According to the stress characteristics of the lining structure, the receiver should be arranged as much as possible in the lining part with larger internal force to improve the positioning accuracy of the acoustic emission signal.

[0159] The spacing of the receivers in the acoustic emission receiver matrix directly affects the positioning accuracy of the acoustic emission event. The layout spacing of the receiver matrix should make the positioning error meet the error requirements. After the acoustic emission receiver matrix is ​​laid out according to the stress characteristics of the lining, its positioning accuracy should be verified according to the following formula:

[0160] ε<ε lim

[0161] Where, ε is the total error of acoustic emission positioning; ε limIt is determined based on the specific conditions of the tunnel in the inspection section and in accordance with the specifications, and is generally 0.25m to 3m.

[0162] There are two main sources of acoustic emission signal positioning errors:

[0163] ① Model simplification error: When simplifying the lining model, the lining mid-surface is used instead of the lining three-dimensional structure, and the actual three-dimensional propagation path of the acoustic emission signal is replaced by a two-dimensional curve in the surface, resulting in positioning error.

[0164] ② Travel time algorithm error: The MSFM algorithm is used to calculate the initial arrival travel time of each point in the area, and the positioning error caused by using discrete line segments instead of the actual propagation path of the stress wave.

[0165] The calculation method of acoustic emission positioning error is as follows:

[0166] ε=ε1+ε2

[0167] Where, ε is the acoustic emission positioning error; ε1 is the simplified error of the acoustic emission positioning model; ε2 is the acoustic emission positioning algorithm error.

[0168] According to the sources of ε1 and ε2, the calculation formula of ε1 is given as follows:

[0169] ε1=|l max -l' max |

[0170] In the formula, l max l' is the maximum calculated distance between two points in each simplified unit; max is the actual circumferential distance corresponding to the above two points, which causes the maximum propagation distance error.

[0171] The calculation formula of ε2 is given as follows:

[0172] ε2=φl max

[0173] Where φ is the error rate of the positioning algorithm, which is related to the discrete grid size and propagation distance; max It is the maximum calculated circumferential distance between two points in each simplified unit.

[0174] Alarms of the security risk early warning system:

[0175] The signal processor determines the alarm threshold of the cumulative number of acoustic emission events based on the relationship between the cumulative number of acoustic emission events and the concrete material damage factor. When the cumulative number of acoustic emission events in the same discrete grid reaches the threshold, the signal processor cooperates with the alarm to sound an alarm.

[0176] Therefore, calculation is required for setting the threshold, and the threshold calculation process is as follows:

[0177] The damage factor of concrete material is proportional to the cumulative number of acoustic emission events and is expressed as:

[0178] D=kN

[0179] Where D is the concrete material damage factor; k is the proportionality coefficient determined based on indoor material tests; N is the cumulative number of acoustic emission events;

[0180] D=1-E / E *

[0181] Where E is the elastic modulus of the material in the damaged state; E * is the initial elastic modulus of the material;

[0182] Thus we get:

[0183] 1-E / E * =kN

[0184] According to the load size, distribution and cross-sectional dimensions of the lining structure, the minimum elastic modulus required by the concrete material of the lining structure is determined, and then the maximum acoustic emission event number threshold to ensure the safety of the structure is calculated. Once the number of acoustic emissions located in the same grid reaches the threshold, the signal processor cooperates with the alarm to sound an alarm.

[0185] Example 2

[0186] Based on the tunnel lining health monitoring system based on acoustic emission provided in Example 1, this embodiment further provides a tunnel lining health monitoring method based on acoustic emission, and the tunnel lining health monitoring method based on acoustic emission includes the following steps:

[0187] Step 1, design and layout:

[0188] Determine tunnel lining structure: Define the lining geometry inside the tunnel, identify critical areas (such as crowns, spandrels, abutments) and determine lining thickness and material properties.

[0189] Simplified model: Simplify the lining structure into a two-dimensional surface to facilitate subsequent numerical calculation and analysis.

[0190] Discretized grid: According to the design requirements, the two-dimensional surface is discretized according to the grid size of 1m to facilitate the processing and analysis of acoustic emission signals.

[0191] Step 2, Receiver Arrangement:

[0192] Risk point identification: Based on the stress characteristics, a total of five acoustic emission receivers are set at the arch crown, arch span and arch foot. These locations are potential risk points.

[0193] Arrangement of acoustic emission receivers: arrange acoustic emission receivers in a circumferential direction according to the error calculation method. After forming an acoustic emission receiver matrix, control the longitudinal spacing of the acoustic emission receiver matrix according to the error calculation method; arrange the acoustic emission receiver matrix to ensure coverage of the entire tunnel.

[0194] Step 3: Signal collection and processing:

[0195] Acquisition of acoustic emission signals: The signal processor collects acoustic emission events through the transmission device and analyzes them through position and time data.

[0196] Calculate travel time: The signal processor uses the second-order MSFM algorithm to calculate the first arrival travel time of each node signal. According to the known acoustic emission signal propagation speed of 4000m / s and distance, the signal propagation time and error are evaluated.

[0197] Step 4: Positioning accuracy analysis:

[0198] Error calculation: Calculate the positioning error according to the formula.

[0199] Verify error requirements: Check whether the overall error meets the following requirements.

[0200] Step 5, concrete degradation monitoring:

[0201] Concrete material properties: Determine the elastic modulus of concrete to ensure that it is not less than 75% of the initial modulus after deterioration.

[0202] Acoustic emission events trigger alarms: The data obtained through acoustic emission monitoring is used to calculate the number of acoustic emission events N in the same discrete grid. If it reaches N times, an alarm is issued and the location of the risk point is indicated.

[0203] Step 6, Data Analysis and Reporting:

[0204] Risk point location: Analyze the collected acoustic emission data and locate specific risk points, and provide them to project managers to take necessary maintenance or reinforcement measures.

[0205] Regular monitoring and evaluation: Develop a regular monitoring plan to conduct long-term health monitoring and evaluation of the lining structure in order to respond to potential risk points in a timely manner.

[0206] Furthermore, in order to verify the feasibility of the tunnel lining health monitoring system based on acoustic emission provided in Example 1 and the tunnel lining health monitoring method based on acoustic emission provided in Example 2, this embodiment provides the following actual engineering verification:

[0207] Take a tunnel in Yunnan Province as an example. Its lining structure is as follows: Figure 4 As shown, the inner contour is a single-center circle with a radius of 550 cm and a lining thickness of 75 cm.

[0208] According to the specific engineering conditions of the tunnel, it is necessary to control the positioning accuracy of risk points with crack risk within 1m. Figure 5 The method shown is simplified to a two-dimensional surface structure, and the two-dimensional surface is discretized according to a grid size of 1m.

[0209] According to the stress characteristics of the lining, the internal forces at the crown, spandrel and arch foot are relatively large, which are relatively large risk points. Accordingly, a total of five acoustic emission receivers are arranged in the crown, spandrel and arch foot in the circumferential direction. The five acoustic emission receivers form an acoustic emission receiver matrix 1.

[0210] According to the reinforcement measures to be adopted later, the acoustic emission signal positioning error ε should meet the following requirements:

[0211] ε<ε lim =1m

[0212] According to the simplified discrete model and the preliminary circumferential arrangement of acoustic emission receivers, the spacing between two adjacent circumferential acoustic emission receivers should meet the following requirements:

[0213] l max =7m

[0214] l′ max =6.554m

[0215] ε1=|l max -l′ max |=0.446m

[0216] The second-order MSFM algorithm is used to calculate the first arrival travel time of each node signal. Previous studies have shown that when the propagation rate of the acoustic emission signal is 4000m / s, the propagation speed of the stress wave in the concrete is 3000-5000m / s. When the grid size is 1m, the error rate of the second-order MSFM algorithm in calculating the travel time is 0.14%.

[0217] but

[0218]

[0219] d represents the longitudinal spacing of the acoustic emission receiver matrix. When d = 100m, it can be calculated that ε = 0.586m < ε lim =1m, meeting the error requirement.

[0220] In order to ensure that the acoustic emission signal meets the later positioning accuracy requirements, the acoustic emission receivers are arranged in the annular direction according to the above method, and the acoustic emission receiver matrix is ​​arranged at equal intervals of 100m in the longitudinal direction.

[0221] According to the specific conditions of this project, the elastic modulus of concrete material after deterioration shall not be less than 75% of the initial elastic modulus, that is, E≥0.75E * According to indoor experiments, concrete material k = 1.84 × 10-4, substitute into the following formula:

[0222] 1-E / E * =kN

[0223] It can be obtained that N = 1358, that is, when the number of acoustic emission events located in the same discrete grid reaches 1358 times, an alarm is issued and the corresponding risk point position is given.

[0224] Verified by actual engineering projects, the acoustic emission-based tunnel lining health monitoring system and method receives transient elastic waves emitted when internal microcracks appear and expand in the concrete during loading through an acoustic emission receiver matrix, and reversely infers the appearance and expansion of microcracks inside the concrete. It can further infer the health status of the concrete lining and determine the risk points of the lining.

[0225] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A tunnel lining health monitoring system based on acoustic emission, characterized by: The tunnel lining health monitoring system based on acoustic emission includes a signal capture system, a signal processing system, and a safety risk early warning prompt system; The signal capture system includes an acoustic emission receiver matrix arranged on the tunnel lining at a certain interval, and a signal transmission device connected to the acoustic emission receivers; The signal processing system is connected to the signal capture system, and includes a transmission device for receiving signal waveform and spectrum characteristics, and a signal processor for performing signal processing according to the signal waveform and spectrum characteristics; The safety risk early warning prompt system is connected to the signal processing system, which includes a display device and an alarm. The display device locates the signal source point according to the signal processing system, and displays the acoustic emission signal source point in the simplified lining model. The alarm sounds an alarm when the accumulated number of acoustic emission events reaches a threshold.

2. The tunnel lining health monitoring system based on acoustic emission according to claim 1 is characterized by: The steps of signal processing of the signal processing system are as follows: Step 1: Conduct a concrete acoustic emission experiment in the laboratory to obtain the waveform and spectrum characteristics of the acoustic emission during the generation and development of microcracks inside the concrete; Step 2: According to the acoustic emission waveform and spectrum characteristics of the generation and development of microcracks in concrete materials obtained in the laboratory, the acoustic emission signal received by the transmission equipment is filtered to filter the background noise, and the first arrival time of the acoustic emission signal of different acoustic emission receivers in the acoustic emission receiver matrix is ​​obtained; Step 3: According to the first arrival time of the signal, the location of the acoustic emission source point is determined by combining the multi-template fast marching algorithm MSFM and narrowband expansion for calculating the first arrival travel time of the stress wave.

3. The tunnel lining health monitoring system based on acoustic emission according to claim 2 is characterized in that: The steps to determine the location of the acoustic emission source point are as follows: Step 3.1, simplify the three-dimensional lining into a cylindrical surface without thickness, and consider only the two propagation dimensions of the circumferential and longitudinal directions for the acoustic emission source point; Step 3.2, using the two-dimensional stress wave equation to describe the stress wave travel time at different locations in the lining; Step 3.3, discretize the two-dimensional Eikonal equation by upwind difference to obtain the Eikonal equation; Step 3.4, perform calculations of the circumferential differential operator and the longitudinal differential operator; Step 3.5, calculate the preliminary travel time values ​​of 8 points around the node based on the travel time values ​​of adjacent nodes; Step 3.6, using narrowband expansion to calculate the stress wave first arrival travel time of the remaining discrete nodes when any discrete node in the region is used as the stress wave source point.

4. The tunnel lining health monitoring system based on acoustic emission according to claim 3 is characterized in that: The two-dimensional stress wave path function equation is: That is, |▽t(l,z)|=s(l,z) In the formula: T is the time delay, which represents the propagation time of the sound wave from the transmitting point to the receiving point; l is the circumferential coordinate; z is the longitudinal coordinate; α(l,z) represents the propagation speed of the sound wave at a certain point; s(l,z) represents the phase velocity of the sound wave; ▽t(l,z) represents the modulus of the gradient at the coordinate (l,z) at time t.

5. The tunnel lining health monitoring system based on acoustic emission according to claim 4 is characterized in that: The two-dimensional Eikonal equation can be discretized by upwind difference to obtain the Eikonal equation at the coordinate (l,z) and point number (i,j): In the formula, |▽t| i,j represents the magnitude of the time gradient at the grid point (i, j); D n -l t i,j , D n +l t i,j To simplify the backward and forward n-order difference operators of discrete points (i, j) in the plane; D n -z t i,j , D n +z t i,j It represents the effect of the backward and forward n-order difference operator on time t along the longitudinal direction z at point (i, j); the max() operator is obtained based on the known situation and size of the travel time values ​​of the points around the calculated point; The difference operator adopts the first-order difference operator; The circumferential difference operator is calculated as follows: The longitudinal difference operator is calculated as follows: Where D n +l t,D n -l t represents the first-order difference operator forward and backward along the l direction; t i+1 represents the time value at the grid point (i+1,j); t i Represents the time value at the grid point (i, j); Δl represents the grid spacing in the l direction; represents the first-order difference operator forward and backward along the l direction; Δz represents the grid spacing in the z direction; t j+1 represents the time value at the grid point (i, j+1); t j represents the time value at the grid point (i, j); Δz represents the grid spacing in the z direction; The first-order specific operation rules are as follows: Where D1 -l t i,j , D1 +l t i,j represents the backward difference operator from point (i-1, j) to point (i, j) along the l direction, and the forward difference operator from (i, j) to point (i+1, j); t i,j represents the travel time value at the grid point (i, j); t i-1,j represents the travel time value at the grid point (i-1, j); t i+1,j Represents the travel time value at the grid point (i+1,j).

6. The tunnel lining health monitoring system based on acoustic emission according to claim 5 is characterized in that: By rotating the rectangular coordinate system, the diagonal direction is included in the propagation path calculation, namely the multi-template fast marching algorithm MSFM; According to the slowness calculation method, the preliminary travel time values ​​of 8 points around the node (i, j) are obtained, namely: the preliminary travel time values ​​of the four directly adjacent points and the four obliquely adjacent points; The calculation formula is as follows: Where, t i,j represents the travel time value at the grid point (i, j), that is, the initial travel time of the point; t i,j±1 represents the travel time value at the grid point (i, j+1) or (i, j-1), which represents the upper and lower adjacent points respectively; t i±1,j represents the travel time value at point (i+1,j) or (i-1,j), which respectively represent the adjacent points on the left and right; t i+1,j±1 represents the travel time value at point (i+1,j+1) or (i+1,j-1), which represents the upper right and lower right adjacent points respectively; t i-1,j±1 represents the travel time value at point (i-1, j+1) or (i-1, j-1), which represents the upper left and lower left diagonal adjacent points respectively; s i,j represents the slowness at the grid point (i, j); s i±1,j Indicates the slowness of adjacent points in the horizontal and vertical directions; s i±1,j±1 represents the slowness at the oblique adjacent points; h represents the step size used in the calculation, usually the spatial resolution of the grid, which refers to the distance between two adjacent nodes.

7. The tunnel lining health monitoring system based on acoustic emission according to claim 3 is characterized by: Narrowband extension technology divides the nodes that need to calculate travel time in the area into the following three types of nodes: Acceptance point: the point where the initial arrival time has been confirmed; Narrowband point: a point that is in direct contact with the receiving point and has its travel time calculated, but will have its travel time updated with a certain probability in subsequent calculations; The far point is a far point whose travel time has not been calculated at all; All narrowband points constitute a narrowband.

8. The tunnel lining health monitoring system based on acoustic emission according to claim 2, characterized in that: The signal processing system calculates the first arrival travel time of each discrete node in the tunnel lining space when each acoustic emission receiver position in the acoustic emission receiver matrix is ​​used as the stress wave source point based on the MSFM algorithm combined with narrowband expansion; The signal processing system combines the stress wave interchange theorem to obtain the time required for each discrete node to propagate to each acoustic emission receiver. Each acoustic emission receiver forms a travel time matrix T of size m×n. m×n ; The element t in the matrix i,j Represents discrete node P i,j When it is the source point, the time required to propagate to the acoustic emission receiver; a total of u×v matrices can be formed, where u and v represent the number of circumferential and longitudinal arrangements in the receiver matrix respectively; The acoustic emission signals generated when microcracks in concrete appear and develop are received by the acoustic emission receivers. First, according to the first arrival travel time of the microseismic signals of each receiver, the two columns with the smallest first arrival travel time in the annular direction and the two rows with the smallest first arrival travel time in the longitudinal direction are selected, a total of 4 acoustic emission receivers are used to calculate the source position of the acoustic emission signal; The calculation method is as follows: First, four acoustic emission receivers are combined in pairs to obtain combinations; According to the first arrival travel time measured by the two acoustic emission receivers in each combination, the difference of the first arrival travel time is calculated; According to the duration matrix of the acoustic emission receiver, the node set that reaches the measured first arrival travel time difference for each pair of combinations is confirmed, and a total of 6 node sets are obtained; Find the intersection of the six node sets, which are regarded as the source points of the acoustic emission signals.

9. The tunnel lining health monitoring system based on acoustic emission according to claim 1, characterized in that: The layout spacing of the acoustic emission receiver matrix of the signal capture system should be accurately calculated to ensure that the acoustic emission signal positioning error is within the allowable range; The specific calculation is done through the following formula: e<e lim Where, ε is the total error of acoustic emission positioning; lim Determined based on the specific conditions of the tunnel in the test section and in combination with the specifications; The acoustic emission signal positioning error includes the model simplification error and the travel time algorithm error. The travel time algorithm error is the acoustic emission positioning error caused by using the MSFM algorithm to calculate the initial arrival travel time of each point in the area and replacing the actual propagation path of the stress wave with discrete line segments. The calculation method of acoustic emission positioning error is as follows: ε=ε1+ε2 Where, ε is the acoustic emission positioning error; ε1 is the simplified error of the acoustic emission positioning model; ε2 is the acoustic emission positioning algorithm error; ε1=|l max -l' max | In the formula, l max l' is the maximum calculated distance between two points in each simplified unit; max is the actual circumferential distance corresponding to the above two points, which causes the maximum propagation distance error; ε2=φl max Where φ is the error rate of the positioning algorithm, which is related to the discrete grid size and propagation distance; max It is the maximum calculated circumferential distance between two points in each simplified unit.

10. The tunnel lining health monitoring system based on acoustic emission according to claim 1, characterized in that: The threshold value is calculated as follows: The damage factor of concrete material is proportional to the cumulative number of acoustic emission events and is expressed as: D=kN Where D is the concrete material damage factor; k is the proportionality coefficient determined based on indoor material tests; N is the cumulative number of acoustic emission events; D=1-E / E * Where E is the elastic modulus of the material in the damaged state; E * is the initial elastic modulus of the material; According to the load size, distribution and cross-sectional dimensions of the lining structure, the minimum elastic modulus required by the concrete material of the lining structure is determined, and then the maximum threshold of the number of acoustic emission events to ensure the safety of the structure is calculated.

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