CN117189239A - Tunnel surrounding rock damage monitoring method - Google Patents
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- CN117189239A CN117189239A CN202311148190.7A CN202311148190A CN117189239A CN 117189239 A CN117189239 A CN 117189239A CN 202311148190 A CN202311148190 A CN 202311148190A CN 117189239 A CN117189239 A CN 117189239A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 61
- 239000011435 rock Substances 0.000 title claims abstract description 57
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Abstract
The invention discloses a method for monitoring tunnel surrounding rock damage, which comprises the steps of arranging a plurality of measuring lines on two side wall surfaces and a vault of a tunnel along the direction of the trend of the tunnel, monitoring natural potential and ultrasonic signals on the surface of the tunnel in real time, and extracting space-time sequences of natural potential and ultrasonic characteristic parameters at all measuring points; obtaining a membership probability map of a monitoring surface by adopting a K-means mean value clustering algorithm, and analyzing an abnormal state dense region in the whole monitoring surface; then, predicting a membership degree maximum time sequence of the whole monitoring surface by adopting a differential autoregressive moving average algorithm, and judging whether the danger of surrounding rock damage and rupture possibly occurs in a plurality of days in the future; if the danger exists, the danger of surrounding rock damage and rupture in the future for several days at all positions in the monitoring surface is further predicted, and the position where the deformation instability of the surrounding rock possibly occurs is accurately positioned. The method and the device realize real-time monitoring and effective prediction of the damage condition of the surrounding rock of the tunnel under the nondestructive condition, and judge the damage and rupture dangerous area of the surrounding rock in time.
Description
Technical Field
The invention relates to the technical field of tunnel surrounding rock stabilization, in particular to a tunnel surrounding rock damage monitoring method.
Background
With the rapid development of infrastructure construction of highways, subways, etc., tunnel mileage includes both established and the rapid growth of the establishment. As underground concealment engineering, tunnels are in a complex geological environment, whose walls and vaults are subjected to compressive stresses inward by the surrounding rock mass. During excavation, artificial disturbance such as blasting, piling and the like can cause deformation of a tunnel structure, and during operation and passing, tunnel surrounding rocks can be displaced and unstably towards the vertical direction or the horizontal direction under the action of continuous ground stress. In addition, environmental factors such as landslide, rainfall and the like may damage the stability structure of the tunnel. Therefore, it is necessary to monitor and predict the safety state of the tunnel engineering.
At present, the combined monitoring of natural potential and ultrasonic signals has less application in tunnel construction and operation stages, and most of the combined monitoring stays in small-scale sample tests in laboratories, and practical application specifications are lacking. Secondly, the conventional sensor for natural potential and ultrasonic signals is fixed on surrounding rock only through glue or tape adhesion, so that loosening or falling off is extremely easy to occur, and a gap exists between the sensor and the wall surface, so that quality of captured signals can be interfered, and missed mining is caused. In addition, the existing natural potential and ultrasonic monitoring mostly directly adopts characteristic parameters at measuring point positions to early warn rock mass fracture, and the effective positioning aspect by adopting the natural potential and the ultrasonic monitoring is not perfect.
In summary, development of a method for monitoring damage to surrounding rock of a tunnel is urgently needed, and effective monitoring and perfecting of stable conditions of the tunnel in construction and operation stages are achieved.
Disclosure of Invention
In order to solve the technical problems, the invention provides a tunnel surrounding rock damage monitoring method for accurately positioning a position where surrounding rock deformation instability possibly occurs. The method has a good monitoring effect, can be recycled, realizes real-time monitoring and effective prediction of the damage condition of the surrounding rock of the tunnel under the nondestructive condition, and judges the damage and rupture dangerous area of the surrounding rock in time.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a tunnel surrounding rock damage monitoring method comprises the following steps:
s1: arranging a plurality of measuring lines on the two side wall surfaces of the tunnel and the vault along the direction of the trend of the tunnel, uniformly distributing a plurality of measuring points on each measuring line, alternately arranging electrode slices and receiving ultrasonic transducers on the measuring points of the same measuring line, arranging a public electrode and an ultrasonic transmitting transducer on the ground surface above the tunnel, wherein the public electrode is connected with the electrode slices through wires, and the ultrasonic transmitting transducer is connected with the receiving ultrasonic transducer through wires;
s2: monitoring natural potential or ultrasonic signals at each measuring point in real time: the method comprises the steps of collecting initial signals at a certain normal state moment in advance as background signals of natural potential and ultrasound of a tunnel in a normal stable state, starting normal monitoring, collecting natural potential or ultrasound signals at each measuring point, transmitting the signals to a collecting station for recording and storing, and transmitting the signals to an analysis microcomputer in a work station by the collecting station;
s3: the analysis microcomputer carries out wavelet filtering treatment on the natural potential and ultrasonic signal data to remove interference noise signals carried in the signals; extending two side wall surfaces and a vault of a tunnel into a plane, namely a monitoring plane, setting a two-dimensional coordinate system, wherein the origin of coordinates is an intersection point of a front end section, a left side wall and a bottom surface of the tunnel, an x-axis is perpendicular to a measuring line direction and points to the right side, and a y-axis is parallel to the measuring line direction and points to the extending direction of the tunnel; the natural potential and ultrasonic signals after wavelet filtering treatment are used for predicting the acousto-electric signals at all positions in the whole monitoring surface of the two side wall surfaces and the vault of the tunnel by a Kriging interpolation method; extracting 4 characteristic parameters of fluctuation amplitude values of natural potential signals at all positions in the whole monitoring surface and wave speed, frequency and power density of ultrasonic signals, and carrying out normalization processing; combining their time evolution sequences with the position coordinates of the two-dimensional coordinate system to obtain a space-time subsequence, and obtaining the g-th coordinate of (xi, yi) i The space-time vector of each position at the kth time is expressed as:
wherein,represents the kth time g i N=4 for the nth characteristic parameter value of the respective position;
s4: carrying out K-means mean value cluster analysis on space-time vectors at all positions in a monitoring surface at the kth moment, setting all membership values in a class cluster corresponding to a minimum cluster center value to be 0, normalizing the membership values in all other clusters according to the K-means mean value cluster analysis to obtain positions and membership values of an abnormal state dense region in the whole monitoring surface, and drawing a membership probability map at the moment to obtain positions and ranges of dangerous deformation regions of a tunnel;
s5: continuously processing a plurality of membership probability graphs of the monitoring surface within a period of time, extracting membership maxima in the membership probability graphs, forming a membership maximum time sequence, predicting the development of dangerous deformation conditions of the tunnel within the time of S days by adopting a differential autoregressive moving average algorithm ARIMA, if the predicted membership maxima continuously exceed an alarm threshold value, indicating that the damage deformation conditions of the surrounding rock of the tunnel are likely to occur, immediately extracting membership at each position in the membership probability graph and obtaining the membership time sequence of each position, and accurately positioning the damage deformation region of the surrounding rock through prediction.
Preferably, in step S1, the electrode plates are adhered by conductive adhesive, the receiving ultrasonic transducers are adhered by ultrasonic coupling agent, they are fixed at the measuring point positions by using a fixing clamp plate, and the surfaces of the electrode plates are covered by hot melt adhesive to isolate the external environment.
Preferably, the fixed fixture plate comprises a connecting plate, the connecting plate is fixed on the rock wall through an expansion screw, two supporting frames with sliding grooves are arranged on one side of the connecting plate, a movable beam is arranged between the two supporting frames, self-locking bolts are respectively arranged on the left side and the right side of the movable beam, the movable beam is fixed between the two supporting frames through the self-locking bolts and the self-locking nuts, a spring in a compressed state is arranged between the movable beam and the connecting plate, and a probe rod is arranged at one end of the movable beam, which is far away from the connecting plate.
Preferably, in step S4, the K-means mean cluster analysis process is specifically:
will g i The space-time vector of the position at the kth moment is taken as an input vector of pattern recognition, the Minkowski distance is selected as a similarity measure between characteristic parameters, and the cluster inner diameter d (a) of the a cluster is calculated by the following formula:
wherein x is i For one sample point in cluster a, C i Is the center of the a cluster, n i The number of sample points in the a-th cluster is the number, and g is a distance parameter;
inter-class distance d between class a and class b ab The calculation formula is as follows:
wherein m is the number of clusters to be solved, C ma And C mb Cluster centers of the a-th class and the b-th class respectively;
according to the similarity R between two classes ij Calculating a DB index E (m), and then enabling the E (m) to obtain the minimum value through iterative operation, so as to obtain the optimal cluster number m, wherein the optimal cluster number m is represented by the following formula:
and carrying out mean value clustering operation according to the determined cluster number m to obtain a cluster center and each cluster range after cluster analysis.
Preferably, in step S5, the expression of the differential autoregressive moving average algorithm ARIMA (p, d, q) prediction model is:
wherein j is t G represents g i Predicted value of membership degree maximum value of each position at time t, j t-s Represents g i The actual value of the membership maxima of each location at time t-s,is an autocorrelation coefficient, θ s As model coefficient, mu t Interference term representing predicted value and observed value of membership degree maximum at time t, p represents autoregressive model function +.>Q represents the order of the moving average model function +.>D is the number of differences made when the time series of membership maxima becomes stationary;
after the time sequence is subjected to stable treatment, the values of the exact orders p and q of the red pool information criterion AIC and the Bayesian information quantity criterion BIC are adopted, and the expressions are respectively as follows:
AIC＝2k-2ln(L)
BIC＝-2ln(L)+kln(n)
wherein k is the order to be estimated, n is the number of sample points of the time series of the membership maximization, L is the maximum likelihood function of the estimation model, and the iterative calculation is carried out by the following formula until L reaches the maximum value:
wherein h is i For the ith sample point in the membership maximum time sequence, μ is the average of the membership maximum time sequence, σ is the variance of the membership maximum time sequence.
Preferably, in step S5, setting a boundary condition to accurately locate a deformation area where surrounding rock damage may occur is specifically: extracting key characteristic points including maximum value points and gradient vectors at each position according to membership degree prediction cloud pictures obtained at all positions of the whole monitoring surface at a certain moment, and then possibly generating boundary points (x bp ,y bp ) Membership degree predictive value U of (C) bp And gradient vectorThe following relationship needs to be satisfied:
wherein, xi is a constant, and is more than 0.5 and less than 1,is the boundary point (x bp ,y bp ) Corresponding membership degree prediction cloud picture maximum point, U min Predicting the minimum value point of membership in cloud chart for membership, and performing +.>To monitor any position (x pre ,y pre ) And the gradient vector is used for taking the maximum point as the center of the surrounding rock damage deformation area which possibly occurs in the future, and the gradient value needs to meet the increasing trend when moving from the boundary profile to the center, so that the boundary range of the surrounding rock damage deformation area which possibly occurs in the future is determined.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method for monitoring the damage of the surrounding rock of the tunnel, provided by the invention, the state of the surrounding rock of the tunnel can be monitored in real time through arranging the sensors of the natural potential and the ultrasonic signals on the rock wall and the vault of the tunnel, the defect that the damage and the breakage position of the surrounding rock of the tunnel cannot be effectively monitored in the past is overcome, the interference of the complex environment of the tunnel can be resisted, and the damage and the breakage state of the surrounding rock of the tunnel can be monitored in a nondestructive state.
2. According to the invention, characteristic parameters of natural potential and ultrasonic combined monitoring are extracted and form a time sequence, a membership probability map of a monitoring surface is drawn by adopting a K-means mean value clustering algorithm, so that effective positioning of an abnormal state dense area in the whole monitoring surface is realized, and a reliable basis is provided for tunnel surrounding rock damage treatment;
3. according to the invention, the stress state of the surrounding rock of the tunnel is monitored in real time by combining the natural potential and the ultrasonic signal, and the electrode plate and the ultrasonic transducer are firmly fixed on the rock wall through the fixed clamp plate, so that the problem that the sensor on the surface of the surrounding rock of the tunnel is not firmly fixed is solved, the quality of the acquired signal can be ensured, and the occurrence of missed acquisition is avoided.
4. According to the invention, the clustering analysis result is processed through the differential autoregressive moving average algorithm, so that whether the damage and rupture risk of the surrounding rock is about to occur in the whole monitoring surface is predicted, the accurate judgment of whether the damage and rupture of the surrounding rock possibly occur in the future period of the monitoring surface is realized, and the area possibly having the damage risk in the future is positioned.
Drawings
For a clearer description of embodiments of the invention or of the prior art, the drawings which are used in the description of the embodiments or of the prior art will be briefly described, it being evident that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a tunnel line arrangement and an extended plan view thereof.
Fig. 2 is a schematic view of a structure of a fixing clamp.
In the figure: the device comprises a tunnel, a 2-electrode plate, a 3-public electrode, a 4-receiving ultrasonic transducer, a 5-collecting station, a 6-analysis microcomputer, a 7-self-locking bolt, an 8-connecting plate, a 9-expansion screw, a 10-supporting frame, an 11-sliding chute, a 12-self-locking nut, a 13-spring, a 14-movable beam, a 15-probe rod, a 17-ultrasonic transmitting transducer, an 18-earth surface and a 19-monitoring surface.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings.
As shown in fig. 1 and 2, a method for monitoring damage to surrounding rock of a tunnel comprises the following steps:
s1: 7 measuring lines are arranged on the two side wall surfaces and the vault of the tunnel 1 along the trend direction of the tunnel 1, 9 measuring points are uniformly distributed on each measuring line, electrode plates 2 and ultrasonic receiving transducers 4 are alternately arranged on the measuring points of the same measuring line, a public electrode 3 and an ultrasonic transmitting transducer 17 are arranged on the ground surface 18 above the tunnel, the public electrode 3 is connected with the electrode plates 2 through a wire, and the ultrasonic transmitting transducer 17 is connected with the ultrasonic receiving transducers 4 through a wire; the electrode plate 2 is stuck through conductive adhesive, the receiving ultrasonic transducer 4 is stuck through ultrasonic coupling agent, the electrode plate 2 and the receiving ultrasonic transducer are fixed at the measuring point by adopting a fixed clamp plate, and the surface of the electrode plate 2 is covered by hot melt adhesive to isolate the external environment; the fixed clamp plate comprises a connecting plate 8, the connecting plate 8 is fixed on the wall surface of the rock wall through an expansion screw 9, two supporting frames 10 with sliding grooves 11 are arranged on one side of the connecting plate 8, a movable beam 14 is arranged between the two supporting frames 10, self-locking bolts 7 are respectively arranged on the left side and the right side of the movable beam 14, the self-locking bolts 7 move in the sliding grooves 11 to adjust the distance between the movable beam 14 and the wall surface of the rock wall, the movable beam 14 is fixed between the two supporting frames 10 through the self-locking bolts 7 and self-locking nuts 12, a spring 13 in a compressed state is arranged between the movable beam 14 and the connecting plate 8, and a probe rod 15 is arranged at one end of the movable beam 14 far away from the connecting plate 8; the end of the probe rod 15, which is far away from the connecting plate 8, is abutted against the electrode plate 2 or the ultrasonic emission transducer 17 and applies stress through the spring 13 so as to prevent the electrode plate 2 or the ultrasonic emission transducer 17 from falling off or sliding;
s2: monitoring natural potential or ultrasonic signals at each measuring point in real time: the method comprises the steps of collecting initial signals at a certain normal state moment in advance as background signals of natural potential and ultrasound of a tunnel 1 in a normal stable state, starting normal monitoring, collecting natural potential or ultrasound signals at each measuring point, transmitting the signals to a collecting station 5 for recording and storing, and transmitting the signals to an analysis microcomputer 6 in a work station by the collecting station 5;
s3: the analysis microcomputer 6 carries out wavelet filtering treatment on the natural potential and ultrasonic signal data to remove interference noise signals carried in the signals; extending two side wall surfaces and a vault of the tunnel 1 into a plane, namely a monitoring surface 19, setting a two-dimensional coordinate system, wherein the origin of coordinates is an intersection point of a front end section, a left side wall and a bottom surface of the tunnel 1, an x-axis is perpendicular to a measuring line direction and points to the right side, and a y-axis is parallel to the measuring line direction and points to the extending direction of the tunnel 1; the natural potential and ultrasonic signals after wavelet filtering treatment are used for predicting the acousto-electric signals at all positions in the whole monitoring surface of the two side wall surfaces and the vault of the tunnel 1 by a Kriging interpolation method; extracting all positions in the whole monitoring surfaceCarrying out normalization processing on 4 characteristic parameters, namely the fluctuation amplitude of the natural potential signal and the wave speed, the frequency and the power density of the ultrasonic signal; combining their time evolution sequences with the position coordinates of the two-dimensional coordinate system to obtain a space-time subsequence, and obtaining the g-th coordinate of (xi, yi) i The space-time vector of each position at the kth time is expressed as:
wherein,represents the kth time g i N=4 for the nth characteristic parameter value of the respective position;
s4: carrying out K-means mean value cluster analysis on space-time vectors at all positions in a monitoring surface at the kth moment, setting all membership values in a class cluster corresponding to a minimum cluster center value to be 0, normalizing the membership values in all other clusters according to the K-means mean value cluster analysis to obtain positions and membership values of an abnormal state dense region in the whole monitoring surface, and drawing a membership probability map at the moment to obtain positions and ranges of dangerous deformation regions of a tunnel; the K-means mean cluster analysis process specifically comprises the following steps:
will g i The space-time vector of the position at the kth moment is taken as an input vector of pattern recognition, the Minkowski distance is selected as a similarity measure between characteristic parameters, and the cluster inner diameter d (a) of the a cluster is calculated by the following formula:
wherein x is i For one sample point in cluster a, C i Is the center of the a cluster, n i The number of sample points in the a-th cluster is the number, and g is a distance parameter;
inter-class distance d between class a and class b ab The calculation formula is as follows:
wherein m is the number of clusters to be solved, C ma And C mb Cluster centers of the a-th class and the b-th class respectively;
according to the similarity R between two classes ij Calculating a DB index E (m), and then enabling the E (m) to obtain the minimum value through iterative operation, so as to obtain the optimal cluster number m, wherein the optimal cluster number m is represented by the following formula:
according to the determined cluster number m, carrying out mean value clustering operation to obtain a cluster center and each cluster range after cluster analysis;
s5: continuously processing a plurality of membership probability maps of a monitoring surface within a period of time, extracting membership maxima in the membership probability maps, forming a membership maximum time sequence, predicting the development of dangerous deformation conditions of the tunnel within the time of S (S is more than or equal to 1) day by adopting a differential autoregressive moving average algorithm ARIMA, if the predicted membership maxima continuously exceed an alarm threshold, indicating that the damage deformation condition of the surrounding rock of the tunnel is likely to occur, immediately extracting membership at each position in the membership probability map and obtaining a membership time sequence of each position, and accurately positioning the damage deformation region of the surrounding rock through prediction;
the expression of the differential autoregressive moving average algorithm ARIMA (p, d, q) prediction model is:
wherein j is t G represents g i Predicted value of membership degree maximum value of each position at time t, j t-s Represents g i The actual value of the membership maxima of each location at time t-s,is an autocorrelation coefficient, θ s As model coefficient, mu t Interference term representing predicted value and observed value of membership degree maximum at time t, p represents autoregressive model function +.>Q represents the order of the moving average model function +.>D is the number of differences made when the time series of membership maxima becomes stationary; and satisfies d is more than or equal to 1, and the d value is determined by adopting unit root test;
after the time sequence is subjected to stable treatment, the values of the exact orders p and q of the red pool information criterion AIC and the Bayesian information quantity criterion BIC are adopted, and the expressions are respectively as follows:
AIC＝2k-2ln(L)
BIC＝-2ln(L)+kln(n)
wherein k is the order to be estimated, n is the number of sample points of the time series of the membership maximization, L is the maximum likelihood function of the estimation model, and the iterative calculation is carried out by the following formula until L reaches the maximum value:
wherein h is i The method comprises the steps that (1) mu is an average value of a membership maximum time sequence, sigma is a variance of the membership maximum time sequence, and mu is an ith sample point in the membership maximum time sequence;
setting boundary conditions to accurately position the surrounding rock damage deformation area is specifically as follows: extracting key characteristic points including maximum value points and gradient vectors at each position according to membership degree prediction cloud pictures obtained at all positions of the whole monitoring surface at a certain moment, and then possibly generating boundary points (x bp ,y bp ) Membership degree predictive value U of (C) bp Sum ladderDegree vectorThe following relationship needs to be satisfied:
wherein, xi is a constant, and is more than 0.5 and less than 1,is the boundary point (x bp ,y bp ) Corresponding membership degree prediction cloud picture maximum point, U min Predicting the minimum value point of membership in cloud chart for membership, and performing +.>To monitor any position (x pre ,y pre ) And the gradient vector is used for taking the maximum point as the center of the surrounding rock damage deformation area which possibly occurs in the future, and the gradient value needs to meet the increasing trend when moving from the boundary profile to the center, so that the boundary range of the surrounding rock damage deformation area which possibly occurs in the future is determined.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The tunnel surrounding rock damage monitoring method is characterized by comprising the following steps of:
s1: arranging a plurality of measuring lines on two side wall surfaces and a vault of a tunnel (1) along the direction of the tunnel (1), uniformly distributing a plurality of measuring points on each measuring line, alternately arranging electrode plates (2) and receiving ultrasonic transducers (4) on the measuring points of the same measuring line, arranging a common electrode (3) and an ultrasonic transmitting transducer (17) on the ground surface (18) above the tunnel, wherein the common electrode (3) is connected with the electrode plates (2) through a wire, and the ultrasonic transmitting transducer (17) is connected with the receiving ultrasonic transducers (4) through a wire;
s2: monitoring natural potential or ultrasonic signals at each measuring point in real time: the method comprises the steps of collecting initial signals at a certain normal state moment in advance as background signals of natural potential and ultrasound of a tunnel (1) in a normal stable state, starting normal monitoring, collecting natural potential or ultrasound signals at each measuring point, transmitting the signals to a collecting station (5) for recording and storing, and transmitting the signals to an analysis microcomputer (6) in a work station by the collecting station (5);
s3: the analysis microcomputer (6) carries out wavelet filtering treatment on the natural potential and ultrasonic signal data to remove interference noise signals carried in the signals; extending two side wall surfaces and a vault of the tunnel (1) into a plane, namely a monitoring surface (19), setting a two-dimensional coordinate system, wherein the origin of coordinates is an intersection point of a front end section, a left side wall and a bottom surface of the tunnel (1), an x-axis is perpendicular to a measuring line direction and points to the right side, and a y-axis is parallel to the measuring line direction and points to the extending direction of the tunnel (1); the natural potential and the ultrasonic signal after the wavelet filtering treatment are used for predicting the acousto-electric signals at all positions in the whole monitoring surface of the two side wall surfaces and the vault of the tunnel (1) through a Kriging interpolation method; extracting 4 characteristic parameters of fluctuation amplitude values of natural potential signals at all positions in the whole monitoring surface and wave speed, frequency and power density of ultrasonic signals, and carrying out normalization processing; combining their time evolution sequences with the position coordinates of the two-dimensional coordinate system to obtain a space-time subsequence, and obtaining the g-th coordinate of (xi, yi) i The space-time vector of each position at the kth time is expressed as:
wherein,represents the kth time g i N=4 for the nth characteristic parameter value of the respective position;
s4: carrying out K-means mean value cluster analysis on space-time vectors at all positions in a monitoring surface at the kth moment, setting all membership values in a class cluster corresponding to a minimum cluster center value to be 0, normalizing the membership values in all other clusters according to the K-means mean value cluster analysis to obtain positions and membership values of an abnormal state dense region in the whole monitoring surface, and drawing a membership probability map at the moment to obtain positions and ranges of dangerous deformation regions of a tunnel;
s5: continuously processing a plurality of membership probability maps of the monitoring surface within a period of time, extracting membership maxima in the membership probability maps, forming a membership maximum time sequence, predicting the development of dangerous deformation conditions of the tunnel within the time of S days by adopting a differential autoregressive moving average algorithm ARIMA, if the predicted membership maxima continuously exceed an alarm threshold value, indicating that the damage deformation conditions of surrounding rocks of the tunnel are likely to occur, immediately extracting membership at each position in the membership probability map and obtaining the membership time sequence of each position, and accurately positioning the damage deformation regions of the surrounding rocks which are likely to occur by predicting and setting boundary conditions.
2. The tunnel surrounding rock damage monitoring method according to claim 1, wherein in step S1, the electrode sheet (2) is adhered by conductive adhesive, the receiving ultrasonic transducer (4) is adhered by ultrasonic coupling agent, they are fixed at the measuring point position by using a fixing clamp plate, and the surface of the electrode sheet (2) is covered by hot melt adhesive to isolate the external environment.
3. The tunnel surrounding rock damage monitoring method according to claim 2, wherein the fixing clamp plate comprises a connecting plate (8), the connecting plate (8) is fixed on a rock wall through an expansion screw (9), two supporting frames (10) with sliding grooves (11) are arranged on one side of the connecting plate (8) positioned on the expansion screw (9), a moving beam (14) is arranged between the two supporting frames (10), self-locking bolts (7) are respectively arranged on the left side and the right side of the moving beam (14), the moving beam (14) is fixed between the two supporting frames (10) through the self-locking bolts (7) and the self-locking nuts (12), a spring (13) in a compressed state is arranged between the moving beam (14) and the connecting plate (8), and a probe rod (15) is arranged at one end of the moving beam (14) far away from the connecting plate (8).
4. The tunnel surrounding rock damage monitoring method according to claim 1, wherein in step S4, the process of K-means mean cluster analysis is specifically as follows:
will g i The space-time vector of the position at the kth moment is taken as an input vector of pattern recognition, the Minkowski distance is selected as a similarity measure between characteristic parameters, and the cluster inner diameter d (a) of the a cluster is calculated by the following formula:
wherein x is i For one sample point in cluster a, C i Is the center of the a cluster, n i The number of sample points in the a-th cluster is the number, and g is a distance parameter;
inter-class distance d between class a and class b ab The calculation formula is as follows:
wherein m is the number of clusters to be solved, C ma And C mb Cluster centers of the a-th class and the b-th class respectively;
according to the similarity R between two classes ij Calculating a DB index E (m), and then enabling the E (m) to obtain the minimum value through iterative operation, so as to obtain the optimal cluster number m, wherein the optimal cluster number m is represented by the following formula:
and carrying out mean value clustering operation according to the determined cluster number m to obtain a cluster center and each cluster range after cluster analysis.
5. The method for monitoring tunnel surrounding rock damage according to claim 1, wherein in step S5, the expression of the predictive model of the differential autoregressive moving average algorithm ARIMA (p, d, q) is:
wherein j is t G represents g i Predicted value of membership degree maximum value of each position at time t, j t-s Represents g i The actual value of the membership maxima of each location at time t-s,is an autocorrelation coefficient, θ s As model coefficient, mu t Interference term representing predicted value and observed value of membership degree maximum at time t, p represents autoregressive model function +.>Q represents the moving average model functionD is the number of differences made when the time series of membership maxima becomes stationary;
after the time sequence is subjected to stable treatment, the values of the exact orders p and q of the red pool information criterion AIC or the Bayesian information quantity criterion BIC are adopted, and the expressions are respectively as follows:
AIC＝2k-2ln(L)
BIC＝-2ln(L)+kln(n)
wherein k is the order to be estimated, n is the number of sample points of the time series of the membership maximization, L is the maximum likelihood function of the estimation model, and the iterative calculation is carried out by the following formula until L reaches the maximum value:
wherein h is i For the ith sample point in the membership maximum time sequence, μ is the average of the membership maximum time sequence, σ is the variance of the membership maximum time sequence.
6. The method for monitoring tunnel surrounding rock damage according to claim 1, wherein in step S5, setting a boundary condition to accurately locate a deformation area where surrounding rock damage is likely to occur is specifically: extracting key characteristic points including maximum value points and gradient vectors at each position according to membership degree prediction cloud pictures obtained at all positions of the whole monitoring surface at a certain moment, and then possibly generating boundary points (x bp ,y bp ) Membership degree predictive value U of (C) bp And gradient vectorThe following relationship needs to be satisfied:
wherein, xi is a constant, and is more than 0.5 and less than 1,is the boundary point (x bp ,y bp ) Corresponding membership degree prediction cloud picture maximum point, U min Predicting the minimum value point of membership in cloud chart for membership, and performing +.>To monitor any position (x pre ,y pre ) And the gradient vector is used for taking the maximum point as the center of the surrounding rock damage deformation area which possibly occurs in the future, and the gradient value needs to meet the increasing trend when moving from the boundary profile to the center, so that the boundary range of the surrounding rock damage deformation area which possibly occurs in the future is determined.
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