CN113218986A - System and method for detecting compactness after prestressed grouting construction - Google Patents
System and method for detecting compactness after prestressed grouting construction Download PDFInfo
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Abstract
The invention belongs to the technical field of engineering quality detection, and discloses a system and a method for detecting compactness after prestressed grouting construction, wherein the system for detecting the compactness after the prestressed grouting construction comprises the following steps: the device comprises a heating module, an image acquisition module, an image processing module, a central control module, a weighing module, a compactness primary calculation module, a comparison analysis module, an infrared image analysis module, a fluctuation detection module, a compactness determination module and an updating display module. The invention combines numerical calculation, infrared analysis, image comparison and fluctuation signal analysis, can effectively make up for the defects of a single method and the problem of limited use, effectively improves the detection accuracy and enlarges the application range. The characteristics of the mixed fluctuation signal are analyzed, so that whether the grouting defect exists in the pore passage or not is judged; the accuracy of evaluating and judging the grouting quality problem in the pore passage is improved, and the quality detection of the working condition is more reliable.
Description
Technical Field
The invention belongs to the technical field of engineering quality detection, and particularly relates to a system and a method for detecting compactness after prestressed grouting construction.
Background
At present, the prestressed pipeline is also called as a corrugated pipe, the grouting compactness of the prestressed pipeline has important influence on the durability of a bridge, and according to statistics, the actual service life of a building can be shortened to one tenth of the design service life due to the fact that steel strands in the prestressed pipeline are corroded and the prestress is lost in advance because of the uncompacted grouting.
The commonly used detection methods at present include a scattering tracking method and a two-terminal method. The heat dissipation tracking method is a fine detection method, can remove scattering abnormality generated by a structure and only keeps a real grouting defect; the detection mode is that detectors are pasted on the side surface of the prestressed pipeline, defect imaging is carried out by combining signals of all the detectors, and 16 or 32 detectors can be pasted generally and tracked in a segmented mode; the method is suitable for all prestressed bridges including cast-in-place beams and precast beams, and the length of the detected prestressed pipeline is not limited. The two-end method is to paste detectors at two ends of the corrugated pipe, generally two detectors can only receive defect signals reaching two ends of the corrugated pipe; the method is only suitable for prestressed precast beams of about 10 meters. In both the scattering tracking method and the two-end method, the detector is arranged to perform defect imaging, and in actual operation, due to a plurality of factors such as small defects, low precision of images acquired by the detector, serious interference and the like, the detection accuracy is low and the cost is high. Therefore, a new system for detecting the compactness after the prestressed grouting construction is needed.
Through the above analysis, the problems and defects of the prior art are as follows: the existing detection method has inaccurate detection result, high cost and limited application.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a system and a method for detecting compactness after prestressed grouting construction.
The invention is realized in this way, a detecting system for compactness after prestressed grouting construction includes:
the heating module is connected with the central control module and is used for heating the prestressed grouting construction area through a heater;
the image acquisition module is connected with the central control module and is used for acquiring related images of the prestress grouting construction through image acquisition equipment;
the image processing module is connected with the central control module and used for processing the collected related images of the grouting construction through an image processing program;
wherein, the image to the mud jacking construction of gathering is handled, includes:
by adopting a weighting method, an energy function is obtained by balancing PM and TV models, noise is reduced by minimizing the energy function, and the expression of the energy functional is as follows:
k＝k0e-Δt(n-1)，
wherein k is0L is a constant, Δ t is a step length, N is an iteration number, and the corresponding parameter expression is as follows:
the corresponding euler equation is:
according to the gradient downflow method, the mixing model is as follows:
filtering by adopting an anisotropic image smooth diffusion model fusing the curvature of the level set and the gradient characteristics:
wherein k is the curvature of the level set, | k | is the modulus of the curvature of the level set, | div is the divergence operator, | is the gradient operator, I0For the intra-evolution curve image, I is I0Convolving with a Gaussian kernel, wherein l is a gradient threshold;
the central control module is connected with the heating module, the image acquisition module, the image processing module, the weighing module, the compactness primary calculation module, the comparison analysis module, the infrared image analysis module, the fluctuation detection module, the compactness determination module and the updating display module and is used for coordinately controlling the normal work of each module of the detection system for the compactness after the prestressed grouting construction through a single chip microcomputer or a controller;
the weighing module is connected with the central control module and is used for weighing the slurry required by grouting construction through the weighing equipment, acquiring the pressure of slurry flowing through by using the pressure sensor and acquiring the actually used slurry flow and the slurry amount leaked from the slurry outlet and the exhaust port by using the flowmeter;
the preliminary calculation module of degree of compactness is connected with central control module for carry out the degree of compactness through the preliminary calculation procedure of degree of compactness based on the thick liquids quality of collection, flow and reveal thick liquids volume and other relevant data and calculate, obtain the preliminary calculation result of degree of compactness, include:
Q＝[V1-(V2+V3)]/V×100％；
wherein Q represents the grouting compactness,%; v1Expressing the mud jacking amount of the mud inlet; v2Indicating the external discharge of slurry from the gas vent; v3Indicating the external discharge amount of the slurry at the slurry outlet; v represents the grouting amount of the prestressed channel;
the comparison analysis module is connected with the central control module and used for comparing the collected construction surface image with the compactness defect standard image stored in the database through a comparison analysis program and judging the construction compactness;
infrared image analysis module is connected with central control module for infrared image after through infrared image analysis program to the processing carries out analysis and judges the condition that the mud jacking construction whether has defect or closely knit degree difference, include:
carrying out graying processing on the infrared image spectrum to obtain a gray image, and carrying out histogram transformation on the gray image to improve the contrast of the gray image;
and carrying out brightening treatment on the pixel points of which the gray values are in the corresponding ranges in the gray image, wherein the brightening treatment comprises the following steps:
and calculating the gray value of each pixel point in the gray image according to a formula:
Gray＝(G×77+B×151+R×28)/255；
wherein, gray represents the gray value of the pixel point, R is the brightness value of the red component of the pixel point, G is the brightness value of the green component of the pixel point, and B is the brightness value of the blue component of the pixel point;
carrying out binarization processing on the processed image according to a threshold value to obtain a black-and-white image;
filtering the black and white image to remove noise points, and checking whether a white area exists in the infrared image;
undulant detection module is connected with central control module for carry out the closely knit degree detection in mud jacking construction region through undulant check out test set based on undulant signal, include:
arranging a test line or a test surface in a grouting construction area;
placing a fluctuation signal pickup device on the arranged measuring points, and exciting an incident fluctuation signal by using a signal source generating device;
the wave signal pickup device picks up a mixed wave signal formed by recombining a reflection wave signal and an incidence wave signal and transmits the mixed wave signal to the signal conversion conditioning device;
the signal conversion conditioning device converts the mixed fluctuation signal from an analog signal to a digital signal and conditions the digital signal;
performing characteristic analysis on the picked mixed fluctuation signals, and judging the grouting condition inside the pore passage according to the analyzed characteristics to obtain a compactness fluctuation signal detection result;
the compactness determining module is connected with the central control module and is used for determining the grouting compactness based on the compactness primary calculation result, the comparative analysis result, the infrared analysis result and the fluctuation detection result;
and the updating display module is connected with the central control module and is used for updating and displaying the collected related images and data and the real-time data of the grouting compactness detection result through the display.
Further, the image acquisition module comprises:
the infrared image acquisition unit is used for acquiring an infrared image of the heated construction site by using infrared image acquisition equipment;
and the image acquisition unit is used for acquiring images of the grouting construction area by utilizing the camera equipment.
Further, in the image processing module, when curvature | v |2When + | k | is much larger than l, the diffusion is equivalent to smooth filtering;
at the inflection point, edge, peak and corner of the image, the first and second order differentials of the image approach zero | I |2The + k approaches zero and diffusion stops almost at the edge, protecting the edge and texture information.
Further, in the weighing module, the leakage slurry amount comprises a leakage slurry amount at the air outlet and a leakage slurry amount at the slurry outlet.
Further, in the infrared image analysis module, the brightness value of each color component of each pixel point is calculated according to a formula:
Result 1[i]＝Pic[i]+{(1-Pic[i]/255)×Temp[i]×K2×(255-Mask[i])/255}；
k2 is a preset layer transparency coefficient, and K2 is more than or equal to 0 and less than or equal to 1; pic [ i ] represents the ith pixel of the original image, Temp [ i ] represents the ith pixel of the second temporary image after the darkening processing, and Mask [ i ] represents the ith pixel of the first temporary image after the brightening processing; result 1 represents the image obtained after the layers are overlapped, and Result [ i ] represents the ith pixel of the image.
Further, in the infrared image analysis module, the checking whether a white area exists in the infrared image includes:
if yes, determining whether the area or the number of the white areas exceeds a preset threshold value; if so, judging that the construction has defects, and the compactness of the construction area does not meet the requirement; otherwise, the construction is not defective.
Further, in the fluctuation detection module, the test surface is formed by fitting a plurality of measuring lines.
Further, in the fluctuation detection module, the mixed fluctuation signal picked up is subjected to characteristic analysis, and the grouting condition inside the pore passage is judged through the analyzed characteristics, including:
receiving the conditioned mixed fluctuation signal, and analyzing the mixed fluctuation signal; and judging the grouting condition inside the pore channel according to the analyzed characteristic change of the mixed fluctuation signal.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to apply the detection system for post-prestressed grouting compaction, when executed on an electronic device.
Another object of the present invention is to provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to apply the detection system for post-construction compaction of prestressed press grouting.
By combining all the technical schemes, the invention has the advantages and positive effects that: the system for detecting the compactness after the prestressed grouting construction combines numerical calculation, infrared analysis, image contrast and fluctuation signal analysis, can effectively make up for the defects of a single method and the problem of limited use, effectively improves the detection accuracy and enlarges the application range. The characteristics of the mixed fluctuation signal are analyzed, so that whether the grouting defect exists in the pore passage or not is judged; the accuracy of evaluating and judging the grouting quality problem in the pore passage is improved, and the quality detection of the working condition is more reliable.
Meanwhile, the heating module is arranged to rapidly heat the prestressed pipeline to be detected and the surrounding area of the prestressed pipeline to form a large temperature difference, the infrared image spectrum with a large difference can be obtained by using the low-precision camera unit, the hot spots in the infrared image spectrum can be conveniently and accurately extracted, and the hot spots can directly reflect whether the grouting compactness of the prestressed pipeline is qualified or not. According to the method, a signal source generating device is adopted, when the incident wave encounters the phenomenon that a pore channel is not compact in the propagation process, the fluctuation signal is reflected, recombined with the incident fluctuation signal and continuously propagated, and the grouting quality problem inside the prestressed pipeline is judged by analyzing the characteristic difference of the recombined mixed fluctuation signal.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a system for detecting compactness after prestressed grouting construction according to an embodiment of the present invention;
in the figure: 1. a heating module; 2. an image acquisition module; 3. an image processing module; 4. a central control module; 5. a weighing module; 6. a compactness primary calculation module; 7. a comparison analysis module; 8. an infrared image analysis module; 9. a fluctuation detection module; 10. a compactness determining module; 11. and updating the display module.
Fig. 2 is a flowchart of a method for detecting compactness after prestressed grouting construction according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for processing a collected image related to a grouting construction by an image processing module using an image processing program according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for analyzing and determining whether a grouting construction has a defect or a condition with different compactness by using an infrared image analysis program to analyze a processed infrared image through an infrared image analysis module according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for detecting compactness of a grouting construction area based on a wave signal by a wave detection module using a wave detection device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a system and a method for detecting compactness after prestressed grouting construction, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a system for detecting compactness after prestressed grouting construction provided by an embodiment of the present invention includes: the device comprises a heating module 1, an image acquisition module 2, an image processing module 3, a central control module 4, a weighing module 5, a compactness primary calculation module 6, a contrast analysis module 7, an infrared image analysis module 8, a fluctuation detection module 9, a compactness determination module 10 and an updating display module 11.
The heating module 1 is connected with the central control module 4 and is used for heating the prestressed grouting construction area through a heater;
the image acquisition module 2 is connected with the central control module 4 and is used for acquiring related images of the prestress grouting construction through image acquisition equipment;
the image processing module 3 is connected with the central control module 4 and is used for processing the collected related images of the grouting construction through an image processing program;
the central control module 4 is connected with the heating module 1, the image acquisition module 2, the image processing module 3, the weighing module 5, the compactness primary calculation module 6, the contrast analysis module 7, the infrared image analysis module 8, the fluctuation detection module 9, the compactness determination module 10 and the update display module 11, and is used for coordinately controlling the normal work of each module of the detection system for the compactness after the prestressed grouting construction through a single chip microcomputer or a controller;
the weighing module 5 is connected with the central control module 4 and is used for weighing the slurry required by grouting construction through a weighing device, collecting the pressure of the slurry flowing through by using a pressure sensor and simultaneously collecting the flow rate of the slurry actually used and the amount of the slurry leaked from a slurry outlet and an air exhaust port by using a flowmeter;
the compactness primary calculation module 6 is connected with the central control module 4 and used for carrying out compactness calculation on the basis of the collected slurry quality, flow, leaked slurry quantity and other related data through a compactness primary calculation program to obtain a compactness primary calculation result;
the contrast analysis module 7 is connected with the central control module 4 and used for comparing the collected construction surface image with the density defect standard image stored in the database through a contrast analysis program and judging the construction density;
the infrared image analysis module 8 is connected with the central control module 4 and is used for analyzing and judging whether the grouting construction has defects or different compactness conditions or not through an infrared image analysis program on the processed infrared image;
the fluctuation detection module 9 is connected with the central control module 4 and is used for detecting the compactness of the grouting construction area based on a fluctuation signal through fluctuation detection equipment,
the compactness determining module 10 is connected with the central control module 4 and used for determining the grouting compactness based on the compactness primary calculation result, the comparative analysis result, the infrared analysis result and the fluctuation detection result;
and the updating display module 11 is connected with the central control module 4 and is used for updating and displaying the collected related images and data and the real-time data of the grouting compactness detection result through a display.
The image acquisition module 2 provided by the embodiment of the invention comprises:
the infrared image acquisition unit 2-1 is used for acquiring an infrared image of the heated construction site by using infrared image acquisition equipment;
and the image acquisition unit 2-2 is used for acquiring images of the grouting construction area by utilizing camera equipment.
As shown in fig. 2, the method for detecting the compactness after the pre-stressed grouting construction provided by the embodiment of the invention comprises the following steps:
s101, heating a prestressed grouting construction area by a heater through a heating module; acquiring related images of the prestress grouting construction by using image acquisition equipment through an image acquisition module;
s102, processing the collected related image of the grouting construction by using an image processing program through an image processing module; the normal work of each module of the detection system for the compactness after the prestressed grouting construction is coordinately controlled by a central control module through a single chip microcomputer or a controller;
s103, weighing the slurry required by grouting construction by using a weighing device through a weighing module, acquiring the pressure of slurry flowing through by using a pressure sensor, and acquiring the actually used slurry flow and the slurry amount leaked from a slurry outlet and an air outlet by using a flowmeter;
s104, performing compactness calculation by using a compactness preliminary calculation module through a compactness preliminary calculation program based on the collected slurry quality, flow, leaked slurry quantity and other related data to obtain a compactness preliminary calculation result;
s105, comparing the collected construction surface image with the density defect standard image stored in the database by using a contrast analysis program through a contrast analysis module, and judging the construction density;
s106, analyzing the processed infrared image by using an infrared image analysis program through an infrared image analysis module to judge whether the grouting construction has defects or different compactness;
s107, detecting the compactness of the grouting construction area by using a fluctuation detection module and a fluctuation detection device based on a fluctuation signal;
s108, determining the grouting compactness based on the compactness primary calculation result, the comparative analysis result, the infrared analysis result and the fluctuation detection result through a compactness determining module;
and S109, updating and displaying the acquired related images and data and the real-time data of the grouting compactness detection result by using the display through the updating and displaying module.
The method for calculating the compactness of the slurry comprises the following steps of:
Q＝[V1-(V2+V3)]/V×100％；
wherein Q represents the press-grouting compactness%; v1Expressing the mud jacking amount of the mud inlet; v2Indicating the external discharge of slurry from the gas vent; v3Indicating the external discharge amount of the slurry at the slurry outlet; v represents the grouting amount of the pre-stressed channel.
The invention is further described with reference to specific examples.
Example 1
The method for detecting the compactness after the pre-stress grouting construction provided by the embodiment of the invention is shown in fig. 2, and as a preferred embodiment, as shown in fig. 3, the method for processing the collected related image of the grouting construction by using the image processing program through the image processing module provided by the embodiment of the invention comprises the following steps:
s201, obtaining an energy function by balancing a PM (particulate matter) model and a TV (television) model by adopting a weighting method;
s202, reducing noise by minimizing an energy function;
and S203, filtering by using an anisotropic image smooth diffusion model fusing the curvature and gradient characteristics of the level set.
The embodiment of the invention provides an energy function obtained by balancing PM and TV models, noise is reduced by minimizing the energy function, and the expression of the energy functional is as follows:
k＝k0e-Δt(n-1)，
wherein k is0L is a constant, Δ t is a step length, N is an iteration number, and the corresponding parameter expression is as follows:
the corresponding euler equation is:
according to the gradient downflow method, the mixing model is as follows:
the filtering processing by adopting the anisotropic image smooth diffusion model fusing the level set curvature and the gradient characteristics provided by the embodiment of the invention comprises the following steps:
wherein k is the curvature of the level set, | k | is the modulus of the curvature of the level set, | div is the divergence operator, | is the gradient operator, I0For the intra-evolution curve image, I is I0Convolved with a gaussian kernel, where l is the gradient threshold.
The method for processing the collected related images of the grouting construction by using the image processing program through the image processing module further comprises the following steps:
when curvature |. I |)2When + | k | is much larger than l, the diffusion is equivalent to smooth filtering;
at the inflection point, edge, peak and corner of the image, the first and second order differentials of the image approach zero | I |2The + k approaches zero and diffusion stops almost at the edge, protecting the edge and texture information.
Example 2
The method for detecting the compactness after the pre-stress grouting construction provided by the embodiment of the invention is shown in fig. 2, and as a preferred embodiment, as shown in fig. 4, the method for analyzing the processed infrared image by using the infrared image analysis program through the infrared image analysis module to judge whether the grouting construction has defects or the compactness is different comprises the following steps:
s301, graying the infrared image spectrum to obtain a gray image, and performing histogram transformation on the gray image to improve the contrast of the gray image;
s302, carrying out brightening treatment on pixel points of which the gray values are in the corresponding range in the gray image;
s303, performing binarization processing on the processed image according to a threshold value to obtain a black-and-white image;
s304, filtering the black-and-white image to remove noise, and checking whether a white area exists in the infrared image.
The embodiment of the present invention provides a method for brightening a pixel point in a gray image, where the gray value of the pixel point is within a corresponding range, the method includes:
and calculating the gray value of each pixel point in the gray image according to a formula:
Gray＝(G×77+B×151+R×28)/255；
wherein, gray represents the gray value of the pixel point, R is the brightness value of the red component of the pixel point, G is the brightness value of the green component of the pixel point, and B is the brightness value of the blue component of the pixel point.
The embodiment of the invention provides a method for calculating the brightness value of each color component of each pixel point according to a formula:
Result 1[i]＝Pic[i]+{(1-Pic[i]/255)×Temp[i]×K2×(255-Mask[i])/255}；
k2 is a preset layer transparency coefficient, and K2 is more than or equal to 0 and less than or equal to 1; pic [ i ] represents the ith pixel of the original image, Temp [ i ] represents the ith pixel of the second temporary image after the darkening processing, and Mask [ i ] represents the ith pixel of the first temporary image after the brightening processing; result 1 represents the image obtained after the layers are overlapped, and Result [ i ] represents the ith pixel of the image.
The checking whether the white area exists in the infrared image provided by the embodiment of the invention comprises the following steps:
if yes, determining whether the area or the number of the white areas exceeds a preset threshold value; if so, judging that the construction has defects, and the compactness of the construction area does not meet the requirement; otherwise, the construction is not defective.
Example 3
The method for detecting the compactness after the prestressed grouting construction provided by the embodiment of the invention is shown in fig. 2, and as a preferred embodiment, as shown in fig. 5, the method for detecting the compactness of the grouting construction area based on a fluctuation signal by using a fluctuation detection device through a fluctuation detection module provided by the embodiment of the invention comprises the following steps:
s401, arranging a test line or a test surface in a grouting construction area; the test surface is formed by fitting a plurality of measuring lines;
s402, placing a fluctuation signal pickup device on the arranged measuring points, and exciting an incident fluctuation signal by using a signal source generating device;
s403, the fluctuation signal pickup device picks up a mixed fluctuation signal formed by recombining the reflected fluctuation signal and the incident fluctuation signal and transmits the mixed fluctuation signal to the signal conversion conditioning device;
s404, converting the mixed fluctuation signal from an analog signal to a digital signal by the signal conversion conditioning device, and conditioning;
s405, performing characteristic analysis on the picked mixed fluctuation signals, and judging grouting conditions inside the pore passage according to the analyzed characteristics to obtain a compactness fluctuation signal detection result.
The embodiment of the invention provides a method for analyzing the characteristics of the picked mixed fluctuation signals and judging the grouting condition inside the pore passage through the analyzed characteristics, which comprises the following steps:
receiving the conditioned mixed fluctuation signal, and analyzing the mixed fluctuation signal; and judging the grouting condition inside the pore channel according to the analyzed characteristic change of the mixed fluctuation signal.
In the description of the present invention, "a plurality" means two or more unless otherwise specified; the terms "upper", "lower", "left", "right", "inner", "outer", "front", "rear", "head", "tail", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention disclosed herein, which is within the spirit and principle of the present invention, should be covered by the present invention.
Claims (10)
1. A detecting system for compactness after prestressed grouting construction is characterized by comprising the following components:
the heating module is connected with the central control module and is used for heating the prestressed grouting construction area through a heater;
the image acquisition module is connected with the central control module and is used for acquiring related images of the prestress grouting construction through image acquisition equipment;
the image processing module is connected with the central control module and used for processing the collected related images of the grouting construction through an image processing program;
wherein, the image to the mud jacking construction of gathering is handled, includes:
by adopting a weighting method, an energy function is obtained by balancing PM and TV models, noise is reduced by minimizing the energy function, and the expression of the energy functional is as follows:
k＝k0e-Δt(n-1)，
wherein k is0L is a constant, Δ t is a step length, N is an iteration number, and the corresponding parameter expression is as follows:
the corresponding euler equation is:
according to the gradient downflow method, the mixing model is as follows:
filtering by adopting an anisotropic image smooth diffusion model fusing the curvature of the level set and the gradient characteristics:
wherein k is the curvature of the level set, | k | is the module value of the curvature of the level set, div is the divergence operator,
as gradient operator, I0For the intra-evolution curve image, I is I0Convolving with a Gaussian kernel, wherein l is a gradient threshold;
the central control module is connected with the heating module, the image acquisition module, the image processing module, the weighing module, the compactness primary calculation module, the comparison analysis module, the infrared image analysis module, the fluctuation detection module, the compactness determination module and the updating display module and is used for coordinately controlling the normal work of each module of the detection system for the compactness after the prestressed grouting construction through a single chip microcomputer or a controller;
the weighing module is connected with the central control module and is used for weighing the slurry required by grouting construction through the weighing equipment, acquiring the pressure of slurry flowing through by using the pressure sensor and acquiring the actually used slurry flow and the slurry amount leaked from the slurry outlet and the exhaust port by using the flowmeter;
the preliminary calculation module of degree of compactness is connected with central control module for carry out the degree of compactness through the preliminary calculation procedure of degree of compactness based on the thick liquids quality of collection, flow and reveal thick liquids volume and other relevant data and calculate, obtain the preliminary calculation result of degree of compactness, include:
Q＝[V1-(V2+V3)]/V×100％；
wherein Q represents the grouting compactness,%; v1Expressing the mud jacking amount of the mud inlet; v2Indicating the external discharge of slurry from the gas vent; v3Indicating the external discharge amount of the slurry at the slurry outlet; v represents the grouting amount of the prestressed channel;
the comparison analysis module is connected with the central control module and used for comparing the collected construction surface image with the compactness defect standard image stored in the database through a comparison analysis program and judging the construction compactness;
infrared image analysis module is connected with central control module for infrared image after through infrared image analysis program to the processing carries out analysis and judges the condition that the mud jacking construction whether has defect or closely knit degree difference, include:
carrying out graying processing on the infrared image spectrum to obtain a gray image, and carrying out histogram transformation on the gray image to improve the contrast of the gray image;
and carrying out brightening treatment on the pixel points of which the gray values are in the corresponding ranges in the gray image, wherein the brightening treatment comprises the following steps:
and calculating the gray value of each pixel point in the gray image according to a formula:
Gray＝(G×77+B×151+R×28)/255；
wherein, gray represents the gray value of the pixel point, R is the brightness value of the red component of the pixel point, G is the brightness value of the green component of the pixel point, and B is the brightness value of the blue component of the pixel point;
carrying out binarization processing on the processed image according to a threshold value to obtain a black-and-white image;
filtering the black and white image to remove noise points, and checking whether a white area exists in the infrared image;
undulant detection module is connected with central control module for carry out the closely knit degree detection in mud jacking construction region through undulant check out test set based on undulant signal, include:
arranging a test line or a test surface in a grouting construction area;
placing a fluctuation signal pickup device on the arranged measuring points, and exciting an incident fluctuation signal by using a signal source generating device;
the wave signal pickup device picks up a mixed wave signal formed by recombining a reflection wave signal and an incidence wave signal and transmits the mixed wave signal to the signal conversion conditioning device;
the signal conversion conditioning device converts the mixed fluctuation signal from an analog signal to a digital signal and conditions the digital signal;
performing characteristic analysis on the picked mixed fluctuation signals, and judging the grouting condition inside the pore passage according to the analyzed characteristics to obtain a compactness fluctuation signal detection result;
the compactness determining module is connected with the central control module and is used for determining the grouting compactness based on the compactness primary calculation result, the comparative analysis result, the infrared analysis result and the fluctuation detection result;
and the updating display module is connected with the central control module and is used for updating and displaying the collected related images and data and the real-time data of the grouting compactness detection result through the display.
2. The system for detecting the compactness after prestressed grouting construction according to claim 1, wherein the image acquisition module comprises:
the infrared image acquisition unit is used for acquiring an infrared image of the heated construction site by using infrared image acquisition equipment;
and the image acquisition unit is used for acquiring images of the grouting construction area by utilizing the camera equipment.
3. The system for detecting compactness after prestressed grouting construction according to claim 1, wherein in the image processing module, when curvature occurs
When the value is far larger than l, the diffusion is equivalent to smooth filtering;
at the inflection point, edge, peak and corner point of the image, the first order differential amount and the second order differential amount of the image approach to zero,
the approach is zero and the diffusion stops almost at the edge, protecting the edge and texture information.
4. The system for detecting the compactness after prestressed grouting construction according to claim 1, wherein the leaked slurry amount comprises a gas outlet leaked slurry amount and a slurry outlet leaked slurry amount in the weighing module.
5. The system for detecting the compactness after the prestressed grouting construction according to claim 1, wherein in the infrared image analysis module, the brightness value of each color component of each pixel point is calculated according to a formula:
Result 1[i]＝Pic[i]+{(1-Pic[i]/255)×Temp[i]×K2×(255-Mask[i])/255}；
k2 is a preset layer transparency coefficient, and K2 is more than or equal to 0 and less than or equal to 1; pic [ i ] represents the ith pixel of the original image, Temp [ i ] represents the ith pixel of the second temporary image after the darkening processing, and Mask [ i ] represents the ith pixel of the first temporary image after the brightening processing; result 1 represents the image obtained after the layers are overlapped, and Result [ i ] represents the ith pixel of the image.
6. The system for detecting the compactness degree of the prestressed grouting construction as claimed in claim 1, wherein the checking whether the white area exists in the infrared image analysis module comprises:
if yes, determining whether the area or the number of the white areas exceeds a preset threshold value; if so, judging that the construction has defects, and the compactness of the construction area does not meet the requirement; otherwise, the construction is not defective.
7. The system for detecting the compactness after the prestressed grouting construction according to claim 1, wherein in the fluctuation detection module, the test surface is formed by fitting a plurality of measuring lines.
8. The system for detecting compactness after prestressed grouting construction according to claim 1, wherein in the fluctuation detection module, the performing feature analysis on the picked mixed fluctuation signal and determining the grouting condition inside the duct according to the analyzed features comprises:
receiving the conditioned mixed fluctuation signal, and analyzing the mixed fluctuation signal; and judging the grouting condition inside the pore channel according to the analyzed characteristic change of the mixed fluctuation signal.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for applying the detection system for post-construction compaction of pre-stressed grouting of any one of claims 1-8 when executed on an electronic device.
10. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to apply the detection system for post-construction compaction of prestressed grouting according to any one of claims 1 to 8.
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