CN115791803A - Deep-buried tunnel surrounding rock blasting damage test system and test method - Google Patents
Deep-buried tunnel surrounding rock blasting damage test system and test method Download PDFInfo
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Abstract
The invention relates to a test system and a test method for blasting damage of surrounding rocks of a deep-buried tunnel. The method improves the accuracy of the blasting damage test of the surrounding rock, and can quickly judge the damage state of the surrounding rock, thereby taking a series of protective measures on the surrounding rock and ensuring the stability of the tunnel.
Description
Technical Field
The invention belongs to the field of tunnel engineering, and particularly relates to a system and a method for testing blasting damage of surrounding rocks of a deep-buried tunnel.
Background
In recent years, with the higher and higher requirements of society on transportation, tunnel engineering projects are increasing, and the tunnel engineering projects tend to develop in deep burying and growing directions. But the tunnel engineering is complex, the technical aspect is not perfect, and the process has strong uncertainty, wherein the construction safety is threatened due to the inevitable structural deformation in the deep-buried tunnel. Generally, in the blasting process of the tunnel, uncontrollable factors are more, life or property loss is easily caused, and the method which can reduce the influencing factors is lacked, so that the damage to surrounding rocks in the blasting process is reduced to an ideal standard.
The destruction form that deeply buries the tunnel and meet in the work progress is different, but is essentially the hole week deformation that the surrounding rock stress redistribution leads to, and the topography obstacle can be overcome to deeply burying the tunnel, shortens the distance in two places, makes the circuit more smooth-going, improves the security quality of transportation and traffic, consequently, need carry out accurate monitoring to the state of deeply burying surrounding rock in the tunnel.
At present, a plurality of approaches can observe the state of surrounding rocks, but the surrounding rocks generally lack accuracy and persuasiveness, provide guidance for construction by means of scientific technology, and are a great trend of future tunnel engineering and develop towards intellectualization. The mechanical property of the surrounding rock is continuously deteriorated under the continuous blasting action, and the damage is continuously accumulated to cause the loss of the stable state, so that the phenomenon of tunnel collapse occurs. Generally speaking, the damage state of the surrounding rock after blasting can be observed through drilling to observe the internal structure of the surrounding rock, namely, the position of the surrounding rock is determined for punching, and then the data is detected and recorded by utilizing the camera shooting technology; the damage state inside the surrounding rock can be detected by an ultrasonic method, namely the elastic wave speed and the attenuation condition of the rock mass can be effectively measured after the sound waves penetrate the surrounding rock, so that the damage and damage condition of the surrounding rock can be judged; or the quality of the surrounding rock is taken as a reference, such as the joint crack density of the surrounding rock, the quality grade of the surrounding rock and the like, and the blasting damage of the surrounding rock is evaluated or the test result is corrected. The existing damage method for testing surrounding rock blasting is less, has larger deviation with the actual situation, easily causes the problems of tunnel collapse or too large explosive consumption and the like due to misjudgment, easily threatens the life safety of constructors or causes property loss, and simultaneously increases the construction difficulty to a great extent and influences the construction process.
Disclosure of Invention
The invention provides a system and a method for testing blasting damage of surrounding rock of a deep-buried tunnel, aiming at solving the defects of the background technology, improving the accuracy of the blasting damage test of the surrounding rock and quickly judging the damage state of the surrounding rock, so that a series of protective measures are taken for the surrounding rock and the stability of the tunnel is ensured.
In order to achieve the purpose, the invention provides a system and method for testing blasting damage of a deep-buried tunnel surrounding rock, which comprises the following specific steps and technical scheme:
a test system for blasting damage of surrounding rocks of a deep-buried tunnel specifically comprises a surrounding rock recognition module, an ultrasonic detection module, a camera shooting and fracture recognition module and a surrounding rock blasting damage evaluation module, wherein the surrounding rock recognition module, the ultrasonic detection module and the camera shooting and fracture recognition module are all connected with the surrounding rock blasting damage evaluation module;
the surrounding rock recognition module is used for recognizing the surrounding rock by using a convolutional neural network recognition laser radar and then generating a surrounding rock point cloud image, recognizing mesoscopic structure defects existing on the surface of the surrounding rock and outputting a correction coefficient alpha;
the above-mentionedThe ultrasonic detection module is specifically used for acquiring the internal information of the surrounding rock by using a sound wave instrument and judging the macroscopic damage coefficient D of the surrounding rock by using the change characteristics of sound waves before and after blasting 1 ；
The camera shooting and fracture identification module specifically uses a digital panoramic drilling camera shooting technology and an image processing method to judge the mesoscopic damage coefficient D of the surrounding rock by observing the increment of the fracture length width, the increment of the fracture depth and the change of the fracture angle before and after blasting 2 ；
The surrounding rock damage evaluation module inputs the correction coefficient alpha through the surrounding rock recognition module and the macroscopic damage coefficient D through the ultrasonic detection module 1 And microscopic damage coefficient D input by camera shooting and crack identification module 2 Determination of the damage coefficient of the composite index [ D ]]And repeatedly applying damage coefficient [ D ] to the comprehensive indexes of surrounding rock]And (6) correcting.
A test method of a deep-buried tunnel surrounding rock blasting damage test system comprises the steps of (I) establishing a network model by using a surrounding rock recognition module to acquire tunnel face pictures and data, scanning the tunnel face images by using a laser radar to generate point cloud images, carrying out area calculation on target areas in the point cloud images, drawing up a correction coefficient alpha according to the ratio of the areas of cracks or holes to the tunnel face, and inputting the correction coefficient alpha into a surrounding rock blasting damage evaluation module;
step (II), the ultrasonic detection module obtains surrounding rock information by using sound wave test to obtain the macroscopic damage coefficient D of the surrounding rock 1 Inputting the data into a surrounding rock blasting damage evaluation module;
and (C) the camera shooting and fracture identification module obtains a clear visible plane expansion diagram outline image by using a digital panoramic drilling camera shooting technology and an image processing method, processes the image, identifies the fracture in the image, and obtains a microscopic damage coefficient D through the increment of the fracture length width before and after blasting, the increment of the fracture depth and the change of the fracture angle 2 And inputting the data into a surrounding rock blasting damage evaluation module;
step four, correcting the coefficient alpha and the macroscopic damage coefficient D 1 And microscopic Damage coefficient D 2 Input to surrounding rock blasting damage evaluation mouldBlock, determining the damage factor [ D ] of the composite index]And repeatedly applying damage coefficient [ D ] to the comprehensive indexes of the surrounding rock]And (6) correcting.
Further, the macroscopic damage coefficient D in the step (II) 1 The calculation formula of (a) is as follows:
in the formula: d 1 Macroscopic damage coefficient of surrounding rock, E, obtained for the ultrasonic detection module 0 Is the modulus of elasticity of the rock mass before blasting, E is the equivalent modulus of elasticity of the rock mass after blasting, V pm 、V pr Elastic longitudinal wave velocity of an affected rock mass and an intact rock mass after blasting is respectively, eta is sound wave reduction rate, when eta is greater than 10%, the rock mass is judged to be damaged by blasting, and the corresponding smoke platform damage threshold value is D cr ＝0.19。
Further, the microscopic damage coefficient D in the step (III) 2 The calculation formula of (a) is as follows:
in the formula: d 2 Mesoscopic damage factor, θ, obtained for the camera and crack recognition module 1 Is the amount of change in the angle of the crack,. L 1 Increase in fracture Length, b 1 Increase in fracture Width, h 1 Is the increment of the fracture depth, l is the fracture length, b is the fracture width, and h is the fracture depth.
Further, the formula for calculating the fracture angle θ is as follows:
in the formula: theta is the fracture angle, g 2 (y)、g 1 (x) Is the direction of the tangent line of each point,
in the direction of the mean tangent.
Further, the comprehensive index damage coefficient [ D ] correction method comprises the following steps:
the initial formula: [ D ]]＝α(aD 1 +bD 2 )
The primary allocation formula:
proportionality coefficient a:
the proportionality coefficient b:
the secondary distribution formula: [ D ]]＝α(a 1 D 1 +b 1 D 2 )
The formula for n-time allocation: [ D ]]＝α(a n-1 D 1 +b n-1 D 2 )
In the formula: alpha is a correction coefficient, and a is a macroscopic damage coefficient D of the surrounding rock 1 B is the mesoscopic damage coefficient D of the surrounding rock 2 Coefficient of proportionality of D 1 Surrounding rock damage coefficient, D, obtained for an ultrasonic detection module 2 And n is the correction times.
The invention has the following beneficial results:
through the combined work of all the modules, the rock damage is judged more accurately and accords with the actual situation, and the data is processed more intelligently, quickly and efficiently;
through the deep learning method, rock mass details can be accurately captured, and compared with a manual mode, the efficiency is greatly improved and the safety is ensured to a certain extent;
the laser radar is used for replacing the traditional image pickup mode to quantitatively acquire rock mass information, so that the disadvantage of unclear imaging caused by environmental factors is avoided;
the accurate identification of the rock mass damage can greatly weaken the influence of subsequent field blasting or larger errors of the technical scheme, and protect the loss of life and property;
the influence that artifical data acquisition accuracy is low can be effectively overcome, and to a great extent has replaced the work of artifical data acquisition, has improved the efficiency of construction.
Drawings
FIG. 1 is a flow chart of a deep-buried tunnel surrounding rock blasting damage testing system;
FIG. 2 is a schematic diagram of a surrounding rock identification module;
FIG. 3 is a schematic flow diagram of an ultrasonic detection module;
FIG. 4 is a schematic view of a camera and flaw identification module;
FIG. 5 is a diagram of the image processing effect in the camera and crack recognition module;
FIG. 6 is a front view panoramic borehole television display;
fig. 7 is a plan development of the structural plane.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following detailed description of the present invention is provided in conjunction with the accompanying drawings and the detailed description. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
The utility model provides a deep tunnel country rock blasting damage test system, specifically includes four parts of country rock identification module, ultrasonic detection module, camera shooting and crack identification module and country rock blasting damage evaluation module. The surrounding rock identification module, the ultrasonic detection module and the camera shooting and crack identification module are all in signal or electric connection with the surrounding rock blasting damage evaluation module.
The surrounding rock identification module is used for identifying the point cloud image of the surrounding rock generated after the laser radar scans the surrounding rock by using the convolutional neural network, identifying the microscopic structure defects such as microcracks or micro-holes on the surface of the surrounding rock, and outputting a correction coefficient alpha. The laser radar is used for solving the problem of light shading in the tunnel, and the trouble that the conventional image is not clear can be effectively avoided by using the method for generating the point cloud to generate the image data. Under the action of various loads, fine cracks or holes are easily expanded into macroscopic cracks and holes until irreversible damage is developed, the rock mass loses part of mechanical properties, microscopic defects on the surrounding rock are detected through a convolutional neural network, feedback is timely made on the actual condition of the surrounding rock after blasting, reference data of the damage degree of the surrounding rock is given, the subsequent blasting work can be adjusted, the surrounding rock is prevented from being irreversibly damaged, the reference data is input into a surrounding rock blasting damage evaluation module, and the surrounding rock damage condition is further tested.
The ultrasonic detection module is specifically used for acquiring internal information of the surrounding rock by utilizing an RSM-SY5 intelligent sound wave instrument and judging the macroscopic damage coefficient D of the surrounding rock by utilizing the change characteristics of sound waves before and after blasting 1 . By adopting the plurality of test holes, all the drill holes in the sound wave test need to be inclined downwards by a certain angle, so that the difference between the interior of the test hole and an exploded rock body is prevented from being far, the measured damage coefficient is small, and all the drill holes are kept parallel. The acoustic wave test is to calculate the acoustic wave velocity of a medium according to different propagation characteristics of acoustic waves in different surrounding rocks, an acoustic wave tester emits high-frequency elastic pulse waves, and according to the Huygens principle, when the acoustic waves reach the structural surface, the acoustic waves generate the effects of reflection, scattering, diffraction and the like, so that the propagation path is prolonged by micro cracks and macro fracture, the acoustic wave velocity is reduced, the reduction degree of the acoustic waves is closely related to the number and width of the cracks, and the cracks are continuously increased and expanded along with the increase of the blasting times, so that the acoustic wave velocity is continuously reduced; the acoustic wave can be divided into longitudinal wave, transverse wave, surface wave and the like, the air medium and the water medium can only transmit the longitudinal wave, and the acoustic wave can be influenced by various factors in the transmission process of the acoustic wave, such as the stress state of a rock body, the integrity of the rock body and the like, and the acoustic wave can be transmitted in a hard and compact rock body more quickly; the propagation is slower in the rock mass with more cracks; when the sound wave speed is attenuated, if the sound wave is attenuated seriously, the damage to the surrounding rock is serious, and otherwise, the damage to the surrounding rock is slight. Therefore, the accurate result can be obtained by utilizing the change characteristics of the sound waves to judge the damage degree of the surrounding rock.
The camera shooting and crack identification module is used for shooting by utilizing digital panoramic drillingThe method comprises the steps of observing the increment of the length and width of the fracture before and after blasting, the increment of the depth of the fracture and the change of the angle of the fracture to judge the mesoscopic damage coefficient D of the surrounding rock 2 . The camera shooting and crack identification module comprises a hardware part and a software part, wherein the hardware part comprises a panoramic camera, a depth pulse generator, a computer, a cable and the like, the panoramic camera is a main device, is provided with a light source, carries out real-time illumination and shooting on the hole wall, internally comprises a reflector capable of obtaining a panoramic picture, and is converted into the panoramic picture after being refracted by the reflector; the depth pulse generator consists of a measuring wheel, a photoelectric corner encoder, a depth signal acquisition board and an interface board and mainly has the functions of determining the position of the probe and automatically detecting the system; the software part has the main functions of monitoring and outputting image data in real time, processing the image to form a result image, calculating the structural plane attitude crack width and the like, performing statistical analysis on the detection result and establishing a database. The digital panoramic borehole video technology has a series of advantages that construction work can be efficiently completed, for example, the digital panoramic borehole video technology has the capability of panoramic observation, has the function of real-time monitoring, has the capability of storing output images in a computer, continuously playing the output images, processing and analyzing pore wall images in time, and then displaying the output images in real time, wherein a display image of a front panoramic borehole television and a display image of a structural plane are shown in fig. 6 and 7.
The surrounding rock damage evaluation module is used for analyzing the rock blasting damage degree on the macroscopic measurement and the microscopic measurement. Due to the complexity of rock mass damage properties, the real condition of the rock mass can be more accurately determined by considering the rock mass damage to two different measures, and the surrounding rock blasting damage evaluation module analyzes data on a macroscopic measure and a microscopic measure mainly through an algorithm or a functional relation, and provides a correction method on an integral layer surface to finally determine a module of a result. The ultrasonic detection module is used for emitting sound waves in the test hole in a macroscopic degree, the damage degree of the rock mass is judged according to the change of the sound wave speed, when the sound wave speed is attenuated, if the sound wave is attenuated seriously, the damage of the surrounding rock is serious, otherwise, the damage of the surrounding rock is slight. Outputting the macroscopicity of the surrounding rock through a functional relationCoefficient of damage D 1 (ii) a Judging macroscopical measurement on mesoscopic measurement through factors such as the number and the area of cracks or holes, shooting images of the holes through a camera shooting and crack recognition module, converting the images into panoramic images, restoring the panoramic images into a plane development diagram, recognizing the plane development diagram through a deep learning technology, marking the number and the area of the cracks or holes, and obtaining a mesoscopic damage coefficient D through a functional relation 2 (ii) a Comprehensively analyzing the output results of the ultrasonic detection module and the image pick-up and crack identification module, and determining the comprehensive index damage coefficient [ D ] through the functional relationship]Then according to the area identification of the tunnel face by the surrounding rock identification module, outputting a correction coefficient alpha and a comprehensive index damage coefficient [ D ]]And (5) correcting, and finally determining the concrete numerical value of the rock mass damage variable.
As shown in fig. 1, a testing method of a deep-buried tunnel surrounding rock blasting damage testing system includes the following steps:
establishing a network model for a data set manufactured by labelimg software through a surrounding rock recognition module, wherein the data set specifically comprises a plurality of tunnel face pictures and data files of marked cracks or holes, and the pictures are obtained by shooting and the like; the method comprises the steps of describing a label of an image by selecting a frame characteristic image, storing a label file into an Annotation folder of a VOC2007 folder, and storing a picture file into JPEGImages under the VOC2007 folder before training; scanning the tunnel face image through a laser radar to generate a point cloud image; calculating the area of a target area in the point cloud image, drawing up a correction coefficient alpha according to the ratio of the area of the crack or the hole to the tunnel face of the tunnel, and inputting the correction coefficient alpha into a surrounding rock blasting damage evaluation module;
as shown in fig. 2, the specific steps are as follows:
firstly, a YOLOv5 network model is established, a convolutional neural network mainly comprises an input layer, convolutional layers, a pooling layer, a full-connection layer and an output layer, the convolutional layers are the cores of the convolutional neural network, each convolutional layer is subjected to convolutional operation by a corresponding convolutional kernel, the more the number of the convolutional layers is, the deeper the extracted features are, the convolutional kernels have the two characteristics of local connection and weight sharing, and the weight sharing means that each convolutional kernel in the convolutional layer repeatedly acts in a receptive field to operate an image. The pooling layer reduces the complexity of the feature maps mainly under the condition of large data volume through pooling operation, namely, the number of output feature maps is unchanged, the size of the feature maps is reduced, the calculation complexity can be effectively reduced, and the commonly used pooling method comprises maximum pooling, random pooling and average pooling. The fully-connected layer is generally the last layer of the convolutional neural network, and is mainly used for compressing and flattening multi-dimensional image data, so that the mapping capability of the network is enhanced, and the size of the network scale is limited.
The YOLOv5 network architecture is divided into 3 sections, backbone, neck and Head. The function of the Backbone is mainly characteristic extraction; the role of the Neck is mainly to perform one-wave mixing and combination on the features and transfer the features to a prediction layer; head is used mainly to make the final prediction output.
Secondly, training a convolutional neural network, developing a network model based on Microsoft Visual Vterm 2015, adopting Python language, selecting a YOLOv5 model based on a pyrrch framework. The method uses a VOC format for training, utilizes labellimg to make a data set, selects characteristic images through frames, performs label description on the images, stores label files into an Annotation folder of a VOC2007 folder, and stores the image files into JPEGImages under the VOC2007 folder before training.
And processing the manufactured data set through a network model, training the network model to a standard with higher accuracy, identifying the generated training set, testing the identification accuracy of the network model, increasing the number of the data sets if the accuracy is lower, performing network training again, identifying the training set, and repeating the step until the accuracy of the output result reaches the standard and basically meets the real condition. The result is usually output using a Relu function, which is essentially a function taking the maximum value, and outputs 0 if the input is negative, and directly outputs the value if the input is positive; the specific formula is as follows:
f(x)＝max(0,x)(1)
further, the point cloud image is initially processed, in order to accurately and quickly acquire the area of the crack or the hole, the edge of the fine crack or the hole is processed by adopting a median filtering method, the gray value of each pixel point is set as the median of the gray values of all the pixel points in a certain neighborhood window of the point, noise can be effectively eliminated, and meanwhile, the edge information of the crack or the hole is well protected;
further, the area of the target area in the point cloud image is calculated by calculating the total number of pixels and then multiplying the total number by the area of a single pixel, and the specific formula is as follows:
S total area of ＝S Area of single pixel X total number of pixels (2)
Further, a correction coefficient alpha is drawn up according to the ratio of the area of the crack or the hole to the tunnel face, and the specific formula is as follows:
in the formula: alpha is a correction coefficient, n is the number of cracks or holes, S Total area of Area of a single crack or hole, S Tunnel face area Is the total area of the tunnel face.
And finally, inputting the formulated correction coefficient alpha into a surrounding rock blasting damage evaluation module, waiting for the data output by the ultrasonic detection module and the camera and crack identification module, and waiting for cooperative work.
Step (II), the ultrasonic detection module obtains surrounding rock information by using sound wave test to obtain the macroscopic damage coefficient D of the surrounding rock 1 Inputting the data into a surrounding rock blasting damage evaluation module;
as shown in fig. 3, the specific steps are as follows:
firstly, drilling holes in surrounding rock near a tunneling face of a tunnel, wherein the test holes are parallel to the cross section of the tunnel, a plurality of test holes are arranged according to specific conditions, the distance between the test holes can meet the actual requirement, and a certain angle is formed between the test holes and the horizontal direction;
RSM-SY5 intelligent sound wave instrumentAnd (3) preparing, injecting water into the test hole after the test hole is prepared, then inserting the sound wave transmitting probe and the sound wave receiver into the test hole, starting to perform sound wave test from the bottom of the hole and acquiring the speed. Specific acoustic velocity C p Calculated as follows:
in the formula: l is the distance m of the sound wave penetrating the surrounding rock, and t is the time s of the sound wave penetrating the rock mass.
After the underground tunnel surrounding rock is subjected to the action of blasting dynamic load, the rock mass structure can be damaged and destroyed, and the damage of micro-cracks or holes can be generated, so that the subsequent engineering can be greatly influenced, the phenomenon of stress concentration and stress redistribution can be generated, the rock mass damage is caused, the instability of the surrounding rock mass can be caused, the life safety of constructors is seriously threatened, and the underground tunnel surrounding rock can be subjected to the action of the integrity coefficient K of the rock mass v The sum sound wave reduction rate eta can be obtained by using the difference of sound wave speeds before and after blasting and is determined by the integrity coefficient K of the rock mass v The corresponding relation of the damage coefficients of the surrounding rocks can be preliminarily obtained, and the specific formula is as follows:
in the formula: k v Is the coefficient of integrity of the rock mass, V pm 、V pr The elastic longitudinal wave velocities of the affected rock mass and the complete rock mass after blasting is finished are respectively.
In the formula: d 1 Macroscopic damage coefficient of surrounding rock, E, obtained for the ultrasonic detection module 0 Is the modulus of elasticity of the rock mass before blasting, E is the equivalent modulus of elasticity of the rock mass after blasting, V pm 、V pr Elastic longitudinal wave velocity of the affected rock mass and the complete rock mass after blasting is respectively, wherein eta is sound wave reductionWhen eta is larger than 10%, the rock mass is judged to be damaged by blasting damage, and the corresponding damage threshold of the smoke bench is D cr ＝0.19。
The integrity degree of the surrounding rock can be judged according to the damage coefficient of the surrounding rock, the surrounding rock is broken when the damage coefficient is larger, the surrounding rock is complete when the damage coefficient is smaller, the damage coefficient of the surrounding rock can be used for guiding the grading work of the surrounding rock, a certain reference is provided, and the macroscopic damage coefficient D of the surrounding rock is obtained 1 And inputting the data into a surrounding rock blasting damage evaluation module, waiting for the camera shooting and crack identification module to output data, and waiting for cooperative work.
The camera shooting and crack identification module obtains a clear visible outline image of the plane expansion diagram by using a digital panoramic drilling camera shooting technology and an image processing method and processes the image; identifying the crack in the image, and obtaining a mesoscopic damage coefficient D through the increment of the length and width of the crack before and after blasting, the increase of the depth of the crack and the change of the angle of the crack 2 And inputting the data into a surrounding rock blasting damage evaluation module;
as shown in fig. 4, the specific steps are as follows:
the panoramic camera is placed into the obtained borehole through a cable to obtain an optical image of the rock wall in the borehole, and the obtained image is shown in fig. 6. The panoramic camera is provided with a light source, illuminates and shoots the hole wall, the image is transformed by a reflector to form a panoramic image, the shot image is transmitted to a video distributor by a special cable, one path of the image enters a video recorder to record the whole detection process, the other path of the image enters a computer to be digitalized, a measuring wheel on a winch measures the position of a probe in real time, and the depth value is placed in a special port in the computer through an interface board; the depth value controls the capture mode of the capture card, the panoramic image is quickly restored to a planar expanded view, and a specific image is shown in FIG. 7 and is displayed in real time for on-site monitoring;
the image is grayed, the image is processed by utilizing a color psychology formula, the obtained image is a color image, redundant information interference is easy to generate due to interference of colors, sunlight and the like, in many image processing algorithms, the image is grayed, and then the grayed image is used for designing and improving the algorithm, wherein the specific formula is as follows:
Gray＝0.30×R+0.59×G+0.11×B(7)
the method comprises the steps that the edges of the cracks are detected through an edge detection operator (Canny operator), the outline of the cracks is highlighted, the noise resistance and the detection accuracy are good, actual edges are identified as much as possible and are close to the actual edges in the image as much as possible, and the edges are only identified once; firstly, converting a gray image, then filtering waves and reducing noise, calculating gradient by using first-order finite difference to obtain two matrixes of partial derivatives of the image in x and y directions, using a Sobel operator as a gradient operator in a Canny operator, then inhibiting data of non-maximum values, also understanding that the possibility that the data of the non-maximum values is an edge is eliminated by the data of the non-maximum values, screening the image by using double thresholds, and obtaining an edge image which is closest to the real edge of the image by selecting proper large thresholds and proper small thresholds;
the specific example image is shown in fig. 5: as shown in fig. 5, which is a picture of a crack on a tunnel face, the edge of the crack is detected by an image graying and edge detection operator (Canny operator) to process the image, so as to obtain specific data of image processing: the threshold information is 0.54; the area information is 15454.25; length information is 461.00; maximum width information of 77.00; the minimum width information is 25.00; the shape information is a longitudinal crack.
The crack identification part mainly refers to the angle change of the crack before and after blasting, and expresses the influence caused by the crack change by utilizing a functional relation, and the influence is expressed by a specific damage coefficient expression. The method adopted for the angle change of the fracture is mainly to count the directions of all points on a main framework, establish a histogram of 8 directions, then calculate the dip angle through a gravity center method, solve the tangential direction of all points of the framework, and then calculate the integral average tangential direction of the framework, wherein the specific formula is as follows:
in the formula: g 2 (y)、g 1 (x) Is the direction of the tangent line of each point,
is the direction of the average tangent, λ is the eigenvalue, and m is the number of points.
After solving the direction of the average tangent, the angle of the crack can be obtained through an inverse trigonometric function, and the specific formula is as follows:
in the formula: theta is the angle of the crack, g 2 (y)、g 1 (x) Is the direction of the tangent line of each point,
in the direction of the mean tangent.
According to the digital panoramic drilling and shooting technology in the shooting and crack identification module, the width length of the crack and the depth of the crack are identified and data are output, through observing the increment of the length width of the crack before and after blasting, the increment of the depth of the crack and the change of the angle of the crack, three indexes are integrated to accurately judge the change of the crack before and after blasting, and the specific formula is as follows:
in the formula: d 2 Damage factor, theta, obtained for camera and crack recognition module 1 Is the amount of change in the angle of the crack,. L 1 Increase in fracture Length, b 1 Increase in fracture Width, h 1 Is the increment of the fracture depth, l is the fracture length, b is the fracture width, and h is the fracture depth.
Of surrounding rockMicroscopic Damage coefficient D 2 And inputting the data into a surrounding rock blasting damage evaluation module to wait for cooperative work.
Step four, correcting the coefficient alpha and the macroscopic damage coefficient D 1 And microscopic Damage coefficient D 2 Inputting the data into a surrounding rock blasting damage evaluation module to determine a comprehensive index damage coefficient [ D ]]And repeatedly applying damage coefficient [ D ] to the comprehensive indexes of the surrounding rock]And (6) correcting.
The surrounding rock blasting damage evaluation module integrates data output by the surrounding rock recognition module, the ultrasonic detection module and the camera shooting and fracture recognition module, evaluates the tunnel surrounding rock damage, and draws up a functional relation by integrating various indexes according to the correlation of each module on the surrounding rock damage evaluation to complete the final surrounding rock damage test method; according to the correlation of each module on the damage evaluation of the surrounding rock, integrating various indexes such as correction coefficient alpha and macroscopic damage coefficient D of the surrounding rock 1 Fine loss coefficient D 2 And drawing up a functional relation to finish the final surrounding rock damage testing method. The method comprises the following steps of dynamically adjusting the proportion distribution of the surrounding rock damage values of all modules through on-site real-time blasting work, and specifically comprises the following steps:
the method comprises the following steps that a surrounding rock blasting damage evaluation module judges surrounding rock damage by using a functional relation through N times (N = {1,2 \8230; N-1 }), compares independent initial surrounding rock damage coefficients of all modules when the modules are not blasted, calculates and analyzes the initial surrounding rock damage coefficients, and generates proportion distribution data of the surrounding rock damage for the first time; the Nth time data are used as the (N + 1) th time data for judging the surrounding rock damage by utilizing the function relation, the (N + 1) th time data are compared with the independent surrounding rock damage coefficients of the modules during the Nth blasting, the calculation and analysis are carried out again, the (N + 1) th time data are distributed in proportion to the surrounding rock damage, the work is carried out repeatedly for N times, and the surrounding rock damage coefficients can gradually and accurately approach to the real situation.
Specifically, after carrying out the surrounding rock damage judgement through utilizing the functional relation formula for the first time, through the independent initial surrounding rock damage coefficient of each module when the contrast is not blasted, and computational analysis, the proportion distribution data of the first time generation surrounding rock damage, its data judge the data of surrounding rock damage as utilizing the functional relation formula for the second time, again through with the first time blast when each independent surrounding rock damage coefficient of module contrast, and carry out computational analysis again, the proportion distribution data of the second time generation surrounding rock damage, carry out this work repeatedly, the surrounding rock damage coefficient can be accurate gradually and tend to the true condition, concrete formula is as follows:
the initial formula: [ D ]]＝α(aD 1 +bD 2 )(12)
The primary allocation formula:
the proportionality coefficient a:
the proportionality coefficient b:
the secondary distribution formula: [ D ]]＝α(a 1 D 1 +b 1 D 2 )(16)
The n-time allocation formula: [ D ]]＝α(a n-1 D 1 +b n-1 D 2 )(17)
In the formula: alpha is a correction coefficient, and a is a macroscopic damage coefficient D of the surrounding rock 1 B is the mesoscopic damage coefficient D of the surrounding rock 2 Coefficient of proportionality of D 1 Surrounding rock damage coefficient, D, obtained for an ultrasonic detection module 2 And n is the number of times when the difference between the two damage coefficients is small.
Through correction on the surrounding rock damage coefficient for many times, the accuracy rate of the method is closer to the real condition, the tunnel surrounding rock damage degree can be judged dynamically, efficiently and accurately according to the method, and the method has important significance.
Claims (6)
1. The utility model provides a deep tunnel country rock blasting damage test system which characterized in that: the test system specifically comprises a surrounding rock identification module, an ultrasonic detection module, a camera shooting and fracture identification module and a surrounding rock blasting damage evaluation module, wherein the surrounding rock identification module, the ultrasonic detection module and the camera shooting and fracture identification module are all connected with the surrounding rock blasting damage evaluation module;
the surrounding rock identification module is used for identifying surrounding rock scanned by the laser radar through a convolutional neural network to generate a surrounding rock point cloud image, identifying mesoscopic structure defects existing on the surface of the surrounding rock and outputting a correction coefficient alpha;
the ultrasonic detection module is specifically used for acquiring the internal information of the surrounding rock by using a sound wave instrument and judging the macroscopic damage coefficient D of the surrounding rock by using the change characteristics of sound waves before and after blasting 1 ；
The camera shooting and fracture identification module specifically uses a digital panoramic drilling camera shooting technology and an image processing method to judge the mesoscopic damage coefficient D of the surrounding rock by observing the increment of the fracture length width, the increment of the fracture depth and the change of the fracture angle before and after blasting 2 ；
The surrounding rock damage evaluation module inputs the correction coefficient alpha through the surrounding rock recognition module and the macroscopic damage coefficient D through the ultrasonic detection module 1 And microscopic damage coefficient D input by camera shooting and crack identification module 2 Determination of the damage coefficient of the composite index [ D ]]And repeatedly applying damage coefficient [ D ] to the comprehensive indexes of surrounding rock]And (6) correcting.
2. The testing method of the deep-buried tunnel surrounding rock blasting damage testing system according to claim 1, characterized in that:
establishing a network model for the collected tunnel face picture and data through a surrounding rock recognition module, scanning the tunnel face picture through a laser radar to generate a point cloud image, carrying out area calculation on a target area in the point cloud image, drawing up a correction coefficient alpha according to the ratio of the area of a crack or a hole to the tunnel face, and inputting the correction coefficient alpha into a surrounding rock blasting damage evaluation module;
step (II), the ultrasonic detection module obtains surrounding rock information by using sound wave test to obtain the macroscopic damage coefficient D of the surrounding rock 1 And inputting the data into a surrounding rock blasting damage evaluation module;
step (three) shooting and crack identification module digital panoramic drilling shooting technologyAnd the image processing method obtains a clear visible plane expansion image outline image, processes the image, identifies the fracture in the image, and obtains a mesoscopic damage coefficient D through the increment of the fracture length width, the increment of the fracture depth and the change of the fracture angle before and after blasting 2 And inputting the data into a surrounding rock blasting damage evaluation module;
step four, correcting the coefficient alpha and the macroscopic damage coefficient D 1 And microscopic Damage coefficient D 2 Inputting the data into a surrounding rock blasting damage evaluation module to determine a comprehensive index damage coefficient [ D ]]And repeatedly applying damage coefficient [ D ] to the comprehensive indexes of the surrounding rock]And (6) correcting.
3. The testing method of the deep-buried tunnel surrounding rock blasting damage testing system according to claim 2, characterized in that: macroscopic damage coefficient D in step (II) 1 The calculation formula of (c) is as follows:
in the formula: d 1 Macroscopic damage coefficient of surrounding rock, E, obtained for the ultrasonic detection module 0 Is the modulus of elasticity of the rock mass before blasting, E is the equivalent modulus of elasticity of the rock mass after blasting, V pm 、V pr Elastic longitudinal wave velocity of an affected rock mass and an intact rock mass after blasting is respectively, eta is sound wave reduction rate, when eta is greater than 10%, the rock mass is judged to be damaged by blasting, and the corresponding smoke platform damage threshold value is D cr ＝0.19。
4. The testing method of the deep-buried tunnel surrounding rock blasting damage testing system according to claim 2, characterized in that: microscopic damage coefficient D in step (III) 2 The calculation formula of (c) is as follows:
in the formula: d 2 For taking picturesAnd the microscopic damage coefficient theta obtained by the crack identification module 1 Is the amount of change in the angle of the crack,. L 1 Increase in fracture Length, b 1 Increase in fracture Width, h 1 Is the increment of the fracture depth, l is the fracture length, b is the fracture width, and h is the fracture depth.
5. The testing method of the deep-buried tunnel surrounding rock blasting damage testing system according to claim 4, characterized in that: the calculated fracture angle θ is formulated as follows:
in the formula: theta is the angle of the crack, g 2 (y)、g 1 (x) Is the direction of the tangent line of each point,
in the direction of the mean tangent.
6. The testing method of the deep-buried tunnel surrounding rock blasting damage testing system according to claim 2, characterized in that: the comprehensive index damage coefficient [ D ] correction method comprises the following steps:
the initial formula: [ D ]]＝α(aD 1 +bD 2 )
The primary allocation formula:
proportionality coefficient a:
the proportionality coefficient b:
the secondary distribution formula: [ D ]]＝α(a 1 D 1 +b 1 D 2 )
The n-time allocation formula: [ D ]]＝α(a n-1 D 1 +b n-1 D 2 )
In the formula: alpha is a correction coefficient, and a is a macroscopic damage coefficient D of the surrounding rock 1 B is the mesoscopic damage coefficient D of the surrounding rock 2 Coefficient of proportionality of D 1 Surrounding rock damage coefficient, D, obtained for an ultrasonic detection module 2 And n is the correction times.
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