CN114663840B - Tunnel environment inspection equipment monitoring method and system - Google Patents
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Abstract
The invention discloses a method and a system for monitoring inspection equipment in a tunnel environment. Through machine learning, through the fracture contour line of effective discernment crackle and under the mode that section deformation information was as the reward, make the fracture contour line of discernment have certain tunnel road bridge mechanics numerical simulation relevance to reach the technical purpose money of carrying out tunnel road bridge deformation monitoring through machine discernment contour line, it has that machine learning is fast, sparingly calculation power, intelligent monitoring risk, effective identification have the crackle of danger, get rid of the advantage of risk-free crackle.
Description
Technical Field
The invention relates to the technical field of highway tunnel monitoring, in particular to a method and a system for monitoring inspection equipment of a tunnel environment.
Background
In recent years, the bridge construction in China is rapidly developed, and generally, bridge detection is performed once every year on each bridge. Traditional bridge detection needs workers to shoot pictures of bridge diseases on site, and is high in cost and strong in subjectivity. The detection technology of the apparent defects of the bridge can identify the defects of the bridge more quickly and economically, enables the identification result to be more standardized and reduces the subjectivity of measurement. Therefore, more and more researchers in the civil engineering field are trying to identify the damage of the appearance by using CNN, but many researches are only able to identify a specific damage, and still need to perform more complicated preprocessing and post-processing on the image. Compared with the conventional model, the Mask-RCNN neural network model has higher recognition speed and smaller error, so that the method has a good application prospect in the aspect of bridge appearance damage recognition. The prior art, such as the Chinese patent with publication number CN111191714A, discloses and provides an intelligent identification method for bridge appearance damage diseases, which comprises the following steps: s1: finishing apparent defect detection items of the target bridge structure; s2: automatically collecting bridge apparent state image information and establishing a bridge apparent state image information database; s3: training the VGG16 model by using the bridge apparent state image information; s4: binary recognition is carried out on the bridge apparent state image information through the binary classification model obtained in the S3, and image information databases containing apparent defects and image information databases not containing apparent defects are respectively established; s5: for the image information containing the apparent defects, the defect calibration is realized by adopting a trained Mask-RCNN model, and the image information for realizing the defect calibration is imported into an image information database containing the apparent defects in S4 to obtain an updated disease history database; s6: performing model optimization by adopting the updated disease historical database; s7: and obtaining an image for marking the damage disease of the bridge appearance and the category of the damage disease of the bridge appearance. However, in the prior art, research and development directions are mostly focused on accurately identifying non-cracks and cracks, the cracks are not linked with potential risk degrees, overfitting may exist in a model, and generalization capability is difficult to guarantee.
Disclosure of Invention
The invention aims to provide a method and a system for monitoring inspection equipment in a tunnel environment, which can intelligently monitor risks, effectively identify cracks with risks and eliminate non-risk cracks, aiming at the prior art.
The method for monitoring the inspection equipment in the tunnel environment comprises the following steps:
1) an information capturing step: reading tunnel inspection image information;
2) information indexing step: extracting a fracture contour line in the tunnel inspection image information, and storing the fracture contour line into a segmentation image; indexing the segmentation image with the fracture contour line;
3) a machine learning step: converting the indexed segmented image into tensor to serve as a first training set, and using numerical simulation result information of the corresponding section deformation of the indexed segmented image as reward information; training the first training set by using a machine learning model, wherein the reward information is used for adjusting the machine learning model; numerical simulation result information of the corresponding section deformation of the segmentation image with the index is used as reward information, and a tensor of the segmentation image with the index is used as a first training set; until the preset accuracy is met;
4) a detection step: processing and segmenting the inspection photo of the tunnel to be detected according to the step 2), and then processing and identifying each image by using the step 3).
Through machine learning, the fracture contour line of the crack is effectively identified, and the identified fracture contour line has certain tunnel road and bridge mechanical numerical simulation relevance under the mode that the section deformation information is used as reward, so that the technical purpose of monitoring the deformation of the tunnel road and bridge through the machine identification contour line is achieved.
In order to further optimize the technical scheme, the adopted measures further comprise:
the information of the settlement monitoring point needs to be input to obtain the numerical simulation result of the corresponding section deformation. The settlement monitoring point is a common monitoring means, and a settlement monitoring probe or a mark is arranged at a monitoring position as required such as a road bridge, a tunnel, a mountain and the like, so that the mechanical condition of an object to be monitored as required is simulated in a numerical simulation mode, and the effects of predicting and monitoring results to achieve risk monitoring are obtained. Crack images are identified through machine learning and under the action of a reward mechanism, identification of at-risk cracks is improved.
The numerical simulation result of the corresponding section deformation contains the maximum offset distance information.
The maximum offset distance is obtained by detection, namely, the inner surface of the tunnel section is subjected to position calibration by detecting a preset monitoring probe or laser scanning, then the calibration points are fitted, finally, the offset distance between each point and a fitted curve is compared, and the maximum offset distance is searched. A method for monitoring a tunnel section and obtaining a maximum deviation distance belongs to the prior art, and can be researched by referring to a Liu Guanlan and subway tunnel deformation monitoring key technology and an analysis and prediction method, Wuhan university, 2013. According to the technical scheme, the segmentation image is learned by adopting the maximum deviation distance of the section, so that the characteristics of the fracture contour line are associated with the deformation of the tunnel, and the risk is judged and predicted under the conditions of separation from a monitoring probe and laser scanning through certain machine learning.
The numerical simulation result of the corresponding section deformation contains the information of the surrounding rock deformation cloud picture.
The acquisition of the information of the surrounding rock deformation cloud picture is obtained through numerical simulation. Specifically, sensors are arranged on a plurality of sections of a tunnel, and numerical simulation information of each section is obtained after a numerical simulation model is established by taking monitoring information of settlement and deformation as input. For example, in standard texts such as urban rail transit engineering measurement specifications GB50308-2008, there are regulations on monitoring tunnel deformation in both construction and operation stages. The monitoring items in the construction stage comprise supporting structures, the ground surface in a deformation area, buildings, pipelines and other peripheral environments. The monitoring projects in the operation stage comprise tracks, ballast beds, building structures and surrounding environments such as ground surfaces, buildings and pipelines influenced by operation or surrounding construction. The deformation of the subway along with the road in the two stages of construction and operation is comprehensively considered, and the deformation mainly comprises the deformation of the subway along with the road structure during construction and operation, the deformation of a supporting structure during construction, and the deformation of a track and a track bed during operation.
The numerical simulation result of the corresponding section deformation contains deformation settlement numerical information. The deformation settlement numerical information can reflect the deformation state of the tunnel and define risky deformation and risk-free deformation according to the presetting.
The invention also discloses a system for monitoring the inspection equipment in the tunnel environment, which comprises one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, the one or more computer programs including instructions that, when executed by the apparatus, cause the apparatus to perform the above inspection equipment monitoring method of a tunnel environment.
A computer storage medium storing one or more computer programs that, when executed, are capable of performing the above-described inspection equipment monitoring method of a tunnel environment.
The invention adopts the steps of information capture, information indexing, machine learning, detection and the like, and the identified fracture contour line has certain tunnel road and bridge mechanical numerical simulation relevance under the mode that the fracture contour line of the crack is effectively identified and the section deformation information is used as reward through the machine learning, thereby achieving the technical purpose of monitoring the deformation of the tunnel road and bridge through the machine identification contour line, and having the advantages of intelligently monitoring the risk, effectively identifying the crack with the risk and eliminating the crack without the risk.
Drawings
FIG. 1 is a schematic diagram of the method steps of an embodiment of the present invention;
FIG. 2 is a schematic cross-sectional plot of a sensor according to an embodiment of the present invention;
FIG. 3 is a cloud of vertical deformation of surrounding rocks according to an embodiment of the invention;
FIG. 4 is a schematic diagram of deformation and settlement values of different top plate thicknesses according to an embodiment of the present invention;
FIG. 5 is a graph of a vertical interval of surface subsidence measurements according to an embodiment of the present invention;
FIG. 6 is a diagram of the arrangement of the measuring points of the surface subsidence cross section in the embodiment of the invention;
FIG. 7 is a flowchart illustrating a machine learning process according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a tunnel crack recognition result according to an embodiment of the present invention;
FIG. 9 is a comparison of recognition accuracy for different input schemes according to embodiments of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples.
Example (b):
the method for monitoring the inspection equipment in the tunnel environment comprises the following steps:
1) an information capturing step: reading tunnel inspection image information;
2) information indexing step: extracting a fracture contour line in the tunnel inspection image information, and storing the fracture contour line into a segmentation image; indexing the segmentation image with the fracture contour line;
3) a machine learning step: converting the indexed segmented image into tensor to serve as a first training set, and taking numerical simulation result information of section deformation corresponding to the indexed segmented image as reward information; training the first training set by using a machine learning model, wherein the reward information is used for adjusting the machine learning model; taking numerical simulation result information of the section deformation corresponding to the indexed segmented image as reward information, and taking tensor of the indexed segmented image as a first training set; until the preset accuracy is met;
4) a detection step: processing and segmenting the inspection photo of the tunnel to be detected according to the step 2), and then processing and identifying each image by using the step 3). As shown in fig. 1, i.e., the above four steps.
Through machine learning, the fracture contour line of the crack is effectively identified, and the identified fracture contour line has certain tunnel road and bridge mechanical numerical simulation relevance under the mode that the section deformation information is used as reward, so that the technical purpose of monitoring the deformation of the tunnel road and bridge through the machine identification contour line is achieved.
The information of the settlement monitoring point needs to be input to obtain the numerical simulation result of the corresponding section deformation. The settlement monitoring point is a common monitoring means, and a settlement monitoring probe or a mark is arranged at a monitoring position as required such as a road bridge, a tunnel, a mountain and the like, so that the mechanical condition of an object to be monitored as required is simulated in a numerical simulation mode, and the effects of predicting and monitoring results to achieve risk monitoring are obtained. Crack images are identified through machine learning and under the action of a reward mechanism, identification of at-risk cracks is improved. By arranging a plurality of measuring points on the cross section as shown in fig. 2, a plurality of cross sections can be tested and obtained through numerical simulation.
The numerical simulation result of the corresponding section deformation contains the maximum offset distance information, the maximum offset distance is obtained through detection, namely, the inner surface of the tunnel section is subjected to position calibration through detecting a preset monitoring probe or laser scanning, then the calibration points are fitted, finally, the offset distance between each point and the fitting curve is compared, and the maximum offset distance is searched. A method for monitoring a tunnel section and obtaining a maximum deviation distance belongs to the prior art, and can be researched by referring to a Liu Guanlan and subway tunnel deformation monitoring key technology and an analysis and prediction method, Wuhan university, 2013. According to the technical scheme, the segmentation image is learned by adopting the maximum deviation distance of the section, so that the characteristics of the fracture contour line are associated with the deformation of the tunnel, and the risk is judged and predicted under the conditions of separation from a monitoring probe and laser scanning through certain machine learning. Fig. 8 shows the result of crack recognition.
The numerical simulation result of the corresponding section deformation contains the information of the surrounding rock deformation cloud chart (namely, contains continuous simulation of the surrounding rock deformation numerical value), as shown in fig. 3. The acquisition of the information of the surrounding rock deformation cloud picture is obtained through numerical simulation. Specifically, sensors are arranged on a plurality of sections of a tunnel, and numerical simulation information of each section is obtained after a numerical simulation model is established by taking monitoring information of settlement and deformation as input. For example, in a standard text such as "urban rail transit engineering measurement specification" GB50308-2008, there are regulations on monitoring tunnel deformation in both construction and operation stages. The monitoring projects in the construction stage comprise supporting structures, the structures, earth surfaces in deformation areas, buildings, pipelines and other peripheral environments. The monitoring projects in the operation stage comprise tracks, ballast beds, building structures and surrounding environments such as ground surfaces, buildings and pipelines which are influenced by operation or surrounding construction. The deformation of the subway along with the road in the two stages of construction and operation is comprehensively considered, and the deformation mainly comprises the deformation of the subway along with the road structure during construction and operation, the deformation of a supporting structure during construction, and the deformation of a track and a track bed during operation.
The numerical simulation result of the corresponding section deformation contains deformation settlement numerical information. As shown in fig. 4, the deformation settlement value information can reflect the deformation state of the tunnel and define the risky deformation and the risk-free deformation according to the preset.
The deformation of the tunnel and the road bridge has obvious space-time effect. According to actual measurement general profiles, a two-dimensional plane strain model is established, according to the Saint-Venn principle, tunnel excavation influences stress strain in a rock mass within 3-5 times of the width around a tunnel, according to the structural size of the tunnel main tunnel, the upper portion calculation range is taken to the ground, the lower portion calculation range is taken from the center of the tunnel to the bottom 30m, the left boundary calculation range and the right boundary calculation range are 150m, a road surface is loaded to be a standard automobile axle load of 100t, in order to simplify calculation, influence of forepoling is not considered, a steel frame and sprayed concrete are equivalent to primary supporting, and full-section excavation is adopted. In order to analyze the influence of the thickness of the tunnel roof on the stability of the tunnel, on the premise that the elevation of the roadbed is not changed, the thickness of the tunnel roof is respectively 5m, 8m, 11m, 14m and 18m according to the actual use thickness or selected, and modeling is respectively carried out on the thicknesses. The model is gridded by 1m in consideration of the accuracy of the simulation.
The model adopts a two-dimensional plane strain model, the rock constitutive model adopts a Mokolun model, and the primary support and the secondary lining are simulated by beam units.
The highway automobile load grade adopts a highway-I grade, according to the highway engineering technical standard, the vehicle load is adopted in the calculation of the numerical value, the gravity standard value of each vehicle is 550kN, the vehicle is uniformly distributed on each lane, the width of each lane is 3.75m, the model automobile is calculated to be loaded into uniformly distributed load, and the size q =550/3.75=146.67 kN/m.
And (3) combining the actual condition of the supporting and monitoring project, carrying out simulation by using finite element software, wherein the surrounding rock and tunnel supporting parameters are shown in a table 1. Wherein the primary support is equivalent parameters of a steel frame and sprayed concrete.
TABLE 1 computational model mechanics parameter Table
Can see out by the deformation cloud picture, when the difference of tunnel roof thickness, tunnel country rock is different with cutting slope deformation trend, and when tunnel roof thickness was 14m and 18m, tunnel country rock warp and cutting slope deformation trend is comparatively independent, for the deformation condition of the tunnel country rock under each operating mode of clearer contrast, arrange 6 characteristic points in left hole, arrange 5 characteristic points on the road surface. And obtaining the deformation settlement value of any point of the actual measured tunnel section according to the simulation.
Surface subsidence
(1) Test method
And arranging observation points at positions where surface subsidence is likely to occur in the construction process, burying 3 temporary leveling base points in an area which is 3 times of the hole diameter and is outside the expected section by referring to a standard leveling point burying method, and calculating the subsidence of each observation point as a standard for the elevation measurement of each observation point. And retesting the datum points every 3 months, and judging the stability of the datum points.
(2) Laying of cross sections
The surface subsidence measuring point section is arranged at the shallow buried section of the opening and is a bias section which has influence on the tunnel construction.
The surface subsidence measuring points are preferably arranged on the cross section where the net width convergence measuring points are arranged in the tunnel, the longitudinal spacing can be adopted according to the specification of the following table, and 4 longitudinal measuring sections are arranged in each tunnel.
TABLE 2 vertical spacing of surface subsidence fracture
Note:Din order to excavate the width of the tunnel,hthe tunnel is buried deeply.
The vertical measurement interval of the surface subsidence is shown in fig. 5.
The advance distance of the measuring points arranged on the longitudinal section ish+h1The longitudinal measurement interval is (h+h1)+h’+(2~5)D(ii) a The surface subsidence measurement can arrange 5-10 measuring points on the cross section, the number of the measuring points can be properly increased particularly for the weak surrounding rock section, and the distance between the two measuring pointsIs 2 to 5 m. The measuring points near the tunnel center line should be arranged densely, and the measuring points far away from the tunnel center line should be sparse. The surface subsidence cross-sectional test point arrangement is shown in figure 6.
(3) Station burying
The measuring line on each section is vertical to the central line of the tunnel, the central monitoring point is arranged at the ground surface position of the axis of the tunnel when the measuring points are buried, other monitoring points are symmetrically arranged along the central line, and the distance between the measuring points is from the central point to the farthest point away from the axis of the tunnel from the ground surface from the central point to the sparse point.
And 2 temporary leveling base points are buried in the areas outside 3 times of the hole diameter in the longitudinal and transverse directions of the tunnel excavation according to a standard leveling point burying method. The datum point requires good visibility, the measurement is convenient, the foundation is firm, and the protection is easy. One datum point serves as a test station and the other as a check point.
The monitoring net point observation mark adopts a reinforced concrete observation mark pier. The standard pier foundation is strived to be stable, or a surface weathered layer is removed to pour the standard pier on the fresh bedrock; or when the surface covering layer is thick, a foundation pit is dug, the depth is not less than 1m, and the mark pier is cast in place.
Digging a pit with the length, width and depth of 200mm at the position of a measuring point, then placing the pit into a self-made embedded part, adopting flat round-head reinforcing steel bars with the diameter of 20-30 mm and the length of 1m at the measuring point, welding a steel plate with the side length of about 5cm square at one end of the reinforcing steel bar, placing the other end of the reinforcing steel bar into the pit, using a hammer to knock the patch to be 30cm away from the ground surface, adjusting the direction of the measuring point, and using concrete to fill the pit until the concrete is solidified, thus measuring.
(4) Monitoring instrument
The surface subsidence adopts high precision Chelidica LS10 level gauge and indium steel ruler.
(5) Monitoring frequency
When the excavation face distance measuring section is less than 2B, 1-2 times/day, when the excavation face distance measuring section is less than 5B, 1 time/2 day, when the excavation face distance measuring section is greater than 5B, 1 time/week.
Machine learning:
the algorithm adopts a parting recognition algorithm of an opportunistic CNN-SVM (convolutional neural network-support vector machine) to perform blocking, feature extraction and classification on the image. The basic flow of the learning algorithm is shown in fig. 7.
And (3) rolling layers:
firstly, selecting a position (x, y) on the graph A to be convolved, then calculating the coiling machine by taking the position as the center, and outputting the value of the position (x, y) on the graph H by the coiling machine. The J-th channel 4 of the l-layer convolution output is calculated. The convolution kernel is represented by a heart, and the X of the l-1 layer and the convolution kernel are calculated each time l-1 The parts are connected. The input in the field of view is denoted x i Output quantity z of jth channel before nonlinear mapping j . Wherein z is j Is a scalar quantity, representing a matrix Z j A point of (a), b j Is a neuron bias. X is to be j And stretching the vector to obtain an inner product with the convolution kernel vector, and obtaining a convolution result.
The discrete convolution can be converted to a matrix multiplication. And multiplying the convolution kernel matrix and the transformed characteristic diagram matrix to realize one characteristic diagram convolution.
A pooling layer:
pooling is performed using, for example, maxporoling.
Full connection layer:
at this level, the neurons of the l-th level and each neuron of the l-1 th level are linked, and the connection weight matrix is W l 。
Activation function:
the activation function is the reward. In the model, the response relationship is determined by the activation function. In the present embodiment, a sigmoid function is used, and a tanh function may also be used. As an optimization scheme, a corrective linear unit (ReLUs) may be used to accelerate training.
Softmax layer:
the fully connected layer is followed by the softmax layer, the sum of the outputs of which is 1. The technical scheme adopts probability distribution. For example:
loss function:
the error J and the sample number of the technical scheme are m, and the superscript (i) represents the sample serial number, and the relation is as follows:
the back propagation phase is in a classical way and will not be described in further detail here.
The technical scheme is further improved on the softmax layer by adjusting the probability function so as to improve the corresponding learning effect. The specific adjustment is as follows:
in order to enable neuron learning to have better risk prediction capability, the maximum offset distance information of the settlement monitoring points, the surrounding rock deformation cloud picture information and the maximum value and the minimum value of the deformation settlement numerical value information after traversal of all the points are normalized and then are brought into x and y.
Through comparison of model forming modes after the maximum offset distance information, the surrounding rock deformation cloud picture information and the deformation settlement value information are learned and rewarded by three machines, as shown in fig. 9, the recognition accuracy rate is best that the learning model is adjusted by the maximum offset distance, and the risk cracks can be recognized accurately to the maximum extent.
While the invention has been described in connection with a preferred embodiment, it is not intended to limit the invention, and it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention.
Claims (5)
1. The monitoring method of the inspection equipment of the tunnel environment is characterized by comprising the following steps: the method comprises the following steps:
1) an information capturing step: reading tunnel inspection image information;
2) information indexing step: extracting a fracture contour line in the tunnel inspection image information, and storing the fracture contour line into a segmentation image; indexing the segmentation image with the fracture contour line;
3) a machine learning step: converting the indexed segmented image into tensor to serve as a first training set, and taking numerical simulation result information of section deformation corresponding to the indexed segmented image as reward information; training a first training set by using a machine learning model, wherein the reward information is used for adjusting the machine learning model;
taking the tensor of the segmentation image with the index as a first training set; until the preset accuracy is met;
a detection step: processing and segmenting the inspection photo of the tunnel to be detected according to the step 2), and then processing and identifying each image by using the step 3).
2. The inspection equipment monitoring method according to the tunnel environment, characterized in that: and inputting the information of the settlement monitoring point when obtaining the numerical simulation result of the corresponding section deformation.
3. The inspection equipment monitoring method according to the tunnel environment, characterized in that: the numerical simulation result of the corresponding section deformation contains the maximum offset distance information.
4. The inspection equipment monitoring method according to the tunnel environment, characterized in that: the numerical simulation result of the corresponding section deformation contains the information of the surrounding rock deformation cloud picture.
5. The inspection equipment monitoring method for the tunnel environment according to claim 2, wherein: the numerical simulation result of the corresponding section deformation contains deformation settlement numerical information.
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