CN117077450A - Road void area volume evolution prediction method, electronic equipment and storage medium - Google Patents
Road void area volume evolution prediction method, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN117077450A CN117077450A CN202311336725.3A CN202311336725A CN117077450A CN 117077450 A CN117077450 A CN 117077450A CN 202311336725 A CN202311336725 A CN 202311336725A CN 117077450 A CN117077450 A CN 117077450A
- Authority
- CN
- China
- Prior art keywords
- road
- disease
- image
- void
- void area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000011800 void material Substances 0.000 title claims abstract description 209
- 238000000034 method Methods 0.000 title claims abstract description 74
- 201000010099 disease Diseases 0.000 claims abstract description 182
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 182
- 238000004364 calculation method Methods 0.000 claims abstract description 39
- 230000000149 penetrating effect Effects 0.000 claims abstract description 13
- 230000009467 reduction Effects 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 93
- 238000012545 processing Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 15
- 238000005553 drilling Methods 0.000 claims description 12
- 239000000463 material Substances 0.000 claims description 10
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 9
- 208000037920 primary disease Diseases 0.000 claims description 6
- 238000012887 quadratic function Methods 0.000 claims description 6
- 241000287196 Asthenes Species 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000002708 enhancing effect Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000011426 transformation method Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 5
- 230000008439 repair process Effects 0.000 abstract description 4
- 230000009471 action Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000015556 catabolic process Effects 0.000 description 2
- 238000006731 degradation reaction Methods 0.000 description 2
- 230000001965 increasing effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000013519 translation Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000011229 interlayer Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000017105 transposition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/12—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electromagnetic waves
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2131—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on a transform domain processing, e.g. wavelet transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
- G06V10/422—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation for representing the structure of the pattern or shape of an object therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/176—Urban or other man-made structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30132—Masonry; Concrete
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/06—Recognition of objects for industrial automation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Geometry (AREA)
- Business, Economics & Management (AREA)
- Remote Sensing (AREA)
- Multimedia (AREA)
- General Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Marketing (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Quality & Reliability (AREA)
- Data Mining & Analysis (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Geophysics (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geology (AREA)
- Environmental & Geological Engineering (AREA)
- Computational Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Electromagnetism (AREA)
- Computer Networks & Wireless Communication (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Entrepreneurship & Innovation (AREA)
- Mathematical Analysis (AREA)
- Operations Research (AREA)
- Structural Engineering (AREA)
- Civil Engineering (AREA)
Abstract
A method for predicting the evolution of the volume of a road void area, electronic equipment and a storage medium belong to the technical field of road engineering. In order to solve the problem of accurately mastering the evolution rule of the volume of the road void area. The invention adopts the ground penetrating radar to collect the road internal image, and carries out image noise reduction treatment on the collected road internal image to obtain a reconstructed road internal image; setting a gain coefficient, performing preliminary disease identification on the reconstructed road internal image to obtain a road internal image subjected to preliminary disease identification, and extracting a disease target area to obtain image data of a void area in the disease road; calculating the actual height of the void area of the diseased road; calculating the actual volume of the void area of the disease road; and constructing a disease road void area volume evolution model. The invention provides a void volume evolution law analysis method, which is used for obtaining the correlation between the volume of a void area and the risk degree in a road and guiding the engineering quantity calculation of void repair.
Description
Technical Field
The invention belongs to the technical field of road detection engineering, and particularly relates to a road void area volume evolution prediction method, electronic equipment and a storage medium.
Background
The road is the basis for safe driving of the vehicle. In recent years, due to the influence of urban road underground pipe network leakage, rain wash and surrounding construction disturbance factors, loose materials can appear in the road and gradually evolve into void. The occurrence of void can cause settlement, deformation and structural bearing capacity reduction of the road, further become a cavity, even cause events such as road collapse and the like, and become a serious threat for safe running of road vehicles. And the road collapse event has an increasing trend year by year, seriously threatens the trip safety of people, and becomes a key problem of social concern.
The volume of the void area in the road has important significance for evaluating the potential risk in the road and the damage degree of the road, and when the volume of the void area is larger, the stability of the area is reduced, the collapse risk is increased, precautionary measures are needed to be taken, the occurrence of accidents is reduced, and the driving safety of the road is ensured.
The patent with the application number of 201811534532.8 and the name of a quantitative identification and automatic identification method for the bridge butt strap void based on a ground penetrating radar directly adopts a radar imaging gray value to judge the void height of the bridge butt strap, and the larger the gray value is, the larger the void height is.
The patent with the application number of 201910128568.4 and the invention name of an interlayer void identification method based on a ground penetrating radar image is characterized in that median filtering and binarization processing are sequentially carried out on the ground penetrating radar image, then redundant areas are deleted, and a void area image is obtained through extraction.
The application number is 202210881222.3, the patent is a method, a system and a device for detecting holes and water loss under a ground penetrating radar road surface, the feature image of the void area is extracted in a forward modeling mode, then a data set is established, and intelligent recognition of the void area in the road is carried out by adopting a machine learning algorithm.
In the method, interference of direct waves and noise on echo signals of the cavity area in radar acquired images is less considered, and the cavity identification accuracy is low; meanwhile, in the existing research, the research on the volume of an underground void area is not related, the volume of a cavity area is related to the internal risk degree of a road, and the evolution rule of the volume of the void area characterizes the degradation speed of the material of the void area, so that the higher the degradation speed is, the more collapse is easy to occur on the road, and the evolution rule of the volume of the void area of the road needs to be accurately mastered.
Disclosure of Invention
The invention aims to solve the problem of accurately mastering the evolution rule of the volume of a road void area and provides a road void area volume evolution prediction method, electronic equipment and a storage medium.
In order to achieve the above purpose, the present invention is realized by the following technical scheme:
a method for predicting the volume evolution of a road void area comprises the following steps:
s1, acquiring an internal image of a road by adopting a ground penetrating radar, and performing image noise reduction treatment on the acquired internal image of the road to obtain a reconstructed internal image of the road;
s2, setting a gain coefficient for the reconstructed road internal image obtained in the step S1, and performing preliminary disease identification on the reconstructed road internal image to obtain a road internal image of preliminary disease identification;
s3, extracting a disease target area from the road interior image identified by the preliminary disease obtained in the step S2 to obtain image data of a void area in the disease road;
s4, calculating the actual height of the void area of the disease road based on the image data of the void area inside the disease road obtained in the step S3;
s5, calculating the actual volume of the void region of the disease road based on the image data of the void region inside the disease road obtained in the step S3 and the actual height of the void region of the disease road obtained in the step S4;
The specific implementation method of the step S5 comprises the following steps:
s5.1, drilling a disease road by using a drilling machine, and verifying the image data of the void area inside the disease road obtained in the step S3 on the site of the disease road to obtain the void area of the disease road;
s5.2, injecting water into the disease road void area obtained in the step S5.1 until the water is filled, and recording the volume of the injected water as the actual volume of the disease road void area；
S5.3, collecting the void inside the disease road verified in the step S5.1The total number of pixels corresponding to the plane image of the region image data of the defect road is firstly extracted and recorded asThen counting the total number of pixels corresponding to the void areas in the image data of the void areas in the diseased road, and marking the total number as +.>Then calculate the void area volume inside the diseased road +.>The computational expression is:
；
wherein,for the actual area of the road represented by 1 pixel, < >>The actual height of the void area of the disease road;
s5.4, selecting 10 disease road void areas, and repeating the steps S5.1-S5.3 to sequentially obtain the actual volumes of the disease road void areas，/>Calculating to obtain the volume of the void area in the damaged road>，/>；
S5.5, fitting by adopting a quadratic function based on the 10 disease road void areas selected in the step S5.4 And->The actual volume calculation expression of the void area of the disease road is obtained as follows:
；
wherein,、/>、/>calculating a secondary term parameter, a primary term parameter and a constant term parameter of an expression for the actual volume of the disease road void region respectively;
s5.6, calculating the actual volume of the disease road void region based on the actual volume calculation expression of the disease road void region obtained in the step S5.5;
s6, constructing a disease road void area volume evolution model according to the actual volume of the disease road void area calculated in the step S5.
Further, the specific implementation method of the step S1 includes the following steps:
s1.1, acquiring an internal image of a road by adopting a ground penetrating radar, and constructing acquired internal image data of the road into an internal image data matrix of the roadA，
；
Wherein,first of the acquired road interior imageiThe time domain data of the individual waves,mis the total number of channels;
；
wherein,first of the acquired road interior imageiFirst part of the wave>Amplitude data, n is the total number;
s1.2, constructing a background noise frequency matrix of an image in a road:
s1.2.1, establishing a rectangular coordinate system, wherein the vertex of the upper left corner of an internal image of a road is taken as a coordinate origin, the horizontal right is taken as an x-axis positive direction, the vertical downward is taken as a y-axis positive direction, the length of 1 pixel is defined as a unit length, the internal image size of the road is set as a x b, a is the image length along the x-axis direction, and b is the image length along the y-axis direction;
S1.2.2 manually selecting the position of a road area without diseases, randomly selecting a road area without diseases with the size of 10 x b, setting a waveform time domain data matrix of the road area without diseases as R, and comprising waveform time domain data of 10 single-channel waves, wherein the expression is as follows:
；
；
wherein,is the firstiWaveform time domain data of single-channel wave, +.>Is the firstiIn a single wavegThe amplitude data of the plurality of amplitude data,Ttranspose the matrix;
s1.2.3, converting the single-channel waveform time domain data into single-channel waveform frequency domain data by adopting a Fourier transform mode, wherein the calculation expression is as follows:
；
wherein,is the firstiWaveform frequency domain data of single-channel wave, +.>Is imaginary unit, ++>For frequency +.>Time is; obtaining waveform frequency domain data matrix of road area without disease>；
S1.2.4 calculating frequency according to the waveform time domain data of the single-channel wave in the step S1.2.2, and constructing a background noise frequency matrixWherein->Is the firstiThe frequency calculated from the waveform time domain data of the single channel wave,mis the total number of channels;
s1.3, converting the time domain data of the road internal image acquired in the step S1.1 into frequency domain data, wherein the conversion expression is as follows:
；
wherein,first of the acquired road interior imageiFrequency domain data of individual waves to obtain a waveform frequency domain data matrix of the acquired road internal image >；
Then calculating frequency according to the waveform time domain data of the road internal image acquired in the step S1.1, and constructing a frequency matrix of the road internal imageWherein->First of the acquired road interior imageiFrequency calculated by the waveform time domain data;
traversing by taking background noise frequency matrix C as comparison objectLAll elements in (1), deleteLThe same elements as C in the list and then deleteFCorresponding elements in the road interior image are traversed to obtain a waveform frequency domain data matrix of the road interior imageWhereinGpIs the firstpWaveform frequency domain data of the internal images of the road after the traversal;
s1.4, converting frequency domain data in a waveform frequency domain data matrix of the traversed road internal image obtained in the step S1.3 into time domain data, and adopting a calculation expression of Fourier transformation to obtain the following formula:
；
wherein,is the firstiThe time domain data corresponding to the waveform frequency domain data of the road internal image after the traversal is obtained, and the time domain matrix H corresponding to the waveform frequency domain data matrix of the road internal image after the traversal is:
；
；
wherein,is the firstiThe +.f. of the time domain data corresponding to the waveform frequency domain data of the road internal image after the traversal>Amplitude data;
s1.5, reconstructing the road internal image by using a time domain matrix corresponding to the waveform frequency domain data matrix of the traversed road internal image obtained in the step S1.4;
S1.5.1 first, a time domain matrix corresponding to the waveform frequency domain data matrix of the road internal image after traversalHIn (a) and (b)Traversing to obtain maximum->And minimum->；
S1.5.2, pair ofHThe amplitude data in the gray scale image is subjected to standardization processing and mapped into a gray scale value range of 0-255, and the calculation expression is as follows:
；
wherein,and->Normalizing the processed amplitude data;
s1.5.3, constructing a single-channel matrix based on the amplitude data normalized in the step S1.5.2The expression isThen constructing all channel matrixes based on single channel matrixes；
S1.5.4 the entire channel matrix obtained in step S1.5.3KThe element value in the two-dimensional image is used as a gray value, the x-axis direction of the two-dimensional image is used as the motion direction of radar sampling data, the y-axis direction of the two-dimensional image is used as the road depth direction, and a gray map M is drawn to obtain a reconstructed road internal image.
Further, the specific implementation method of the step S2 includes the following steps:
s2.1, dividing the reconstructed road internal image obtained in the step S1 along the vertical direction of the image, wherein the dividing number is set asNhEach divided image has a height ofHhWidth of；
S2.2, setting the image matrix of the ith divided image as Then the average value +.>The expression is:
；
s2.3, constructing a mean matrix based on the average value of the image matrices of the divided images obtained in the step S2.2Then calculate +.>And->Difference matrix->The expression is:
；
s2.4, carrying out absolute value operation on all elements of the difference matrix obtained in the step S2.3 to construct an absolute value matrix of the difference matrixThe expression is:
；
then calculate the standard deviation of the absolute value matrix of the difference matrixThe expression is:
；
s2.5, constructing a standard deviation matrix according to the standard deviation of the absolute value matrix of the difference matrix obtained in the step S4；
S2.6, setting critical standard deviation asStandard deviation of standard deviation matrix +.>And critical standard deviation->Comparison is made when->At the time, the gain factor is set to +.>，/>The value of (2) is determined according to the actual geological condition, the road material type, the radar parameter and the disease type parameter; when->Setting the gain coefficient to be 1;
s2.7, constructing a standard deviation matrix according to the gain coefficient calculated in the step S2.6Corresponding gain coefficient matrix->The method is used for enhancing the characteristics of the echo image of the position of the disease area in the road to obtain the road internal image of the primary disease identification.
Further, the specific implementation method of the step S3 includes the following steps:
s3.1, performing binarization processing on the road internal image identified by the primary disease obtained in the step S2 by adopting a maximum inter-class variance method;
s3.2, counting the total number of pixels in all the connected areas of the road internal image identified by the preliminary disease after the binarization processing of the step S3.1, deleting the connected areas with the total number of pixels smaller than 500, reserving the connected areas with the total number of pixels larger than or equal to 500, and numbering the connected areas of the road internal image identified by the preliminary disease after the pixel deleting processing as N1, N2 … Ni … Nc;
s3.3, establishing a coordinate system for the road internal image identified by the preliminary disease after the pixel deleting treatment in the step S3.2, taking the top point of the left upper corner of the image as a coordinate origin, taking the horizontal right as the positive x-axis direction and the vertical downward as the positive y-axis direction, extracting coordinate points of pixels in all connected areas of the road internal image identified by the preliminary disease after the pixel deleting treatment, and setting the leftmost pixel point coordinate of the connected area Ni as the coordinate of the left pixel pointThe coordinate of the rightmost pixel point is +.>The coordinate of the topmost pixel point is +.>The coordinate expression is defined as:
；
；
；
wherein,is the leftmost pixel point of the connected region Ni xAxis coordinates->Is the leftmost pixel point of the connected region NiyAn axis coordinate;
s3.4, screening out a region with hyperbolic characteristics by adopting a method for calculating the inclination angle, and respectively obtaining the inclination angles from the top pixel point to the leftmost pixel point and the rightmost pixel pointAnd->The expression is:
；
；
then extracting the maximum inclination angle from the top pixel point to the leftmost pixel point and the rightmost pixel pointThe value is recorded asThe expression is:
；
setting critical angleExtracting +.>Is a hyperbolic region +.>；
S3.5, extracting hyperbolic areas of all the connected areas, and renumbering to beWherein->Is the total number of hyperbolic areas;
s3.6, sequentially extracting single-channel waveform time domain data of all hyperbolic areas passing through the vertex coordinates based on the vertex coordinates of all hyperbolas of all the hyperbolic areas obtained in the step S3.5, and sequentially recording as follows:，
wherein,，/>is the firstiSingle-channel waveform time domain data of single hyperbolic area passing vertex coordinates,/and method for generating the same>Is the firstiSingle-channel waveform time domain data of single hyperbolic region over vertex coordinateslAmplitude data;
s3.7, converting the single-channel waveform time domain data of the hyperbolic region over-vertex coordinates obtained in the step S3.6 into frequency domain data by adopting a wavelet transformation method, wherein the calculation expression is as follows:
；
；
Wherein,is the firstiSingle-channel waveform frequency domain data of peak coordinate crossing of hyperbola region, < ->For the scale of +>For translation amount->Is a basic wavelet;
then calculate the firstiPhase of single-track waveform of crossing vertex coordinates of hyperbolic regionThe computational expression is:
；
wherein,is->Imaginary part of->Is->The real part of (2);
s3.8, setting the electromagnetic wave at the transmitting position as based on the electromagnetic wave theoryElectromagnetic wave at the reflection position is +.>Constructing a direction function->The expression is:
；
calculating a direction function whenWhen the electromagnetic wave is transmitted from the high dielectric constant to the low dielectric constant medium, the dielectric constant of the material at the disease position is smaller than that at the emission position, and the first judgment is madeiThe hyperbolic areas are void areas in the disease road;
and S3.9, calculating the phase of the single-channel waveform of the over-vertex coordinates of all the hyperbolic areas, and judging the road internal void condition of all the hyperbolic areas to obtain image data of the void area in the damaged road.
Further, the specific implementation method of the step S4 includes the following steps:
s4.1, drilling a disease road by adopting a drilling machine, and verifying the image data of the void area inside the disease road obtained in the step S3 on the site of the disease road to obtain the void area of the disease road;
S4.2, the endoscope is deeply penetrated into the disease road void area, the top plate position and the bottom plate position of the disease road void area are determined through the endoscope display, and the distance between the top plate position and the bottom plate position is measured to obtain the actual height of the disease road void area；
S4.3, adopting an image binarization method to the image data of the empty region in the damaged road verified in the step S4.1 to obtain the image data of the empty region in the damaged road after the image binarization treatment, and then extracting the coordinates of the top pixel point of the hyperbola in the image data of the empty region in the damaged road after the binarization treatmentExcessive->Making a straight line parallel to the y-axis and intersecting the hyperbola at +.>And->Then->，/>Obtaining the height of the void area of the damaged road>The calculation formula of (2) is as follows:
；
s4.4, selecting 10 disease road void areas, and repeating the steps S4.1-S4.3 to sequentially obtain the actual height of the disease road void areas，/>Calculating the height of the void area of the damaged road>，/>；
S4.5, fitting by adopting a quadratic function based on the 10 disease road void areas selected in the step S4.4And->The actual height calculation expression of the disease road void area is obtained as follows:
；
wherein,、/>、/>respectively calculating a secondary term parameter, a primary term parameter and a constant term parameter of an expression for the actual height of the disease road void region;
S4.6, calculating the actual height of the disease road void area based on the actual height calculation expression of the disease road void area obtained in the step S4.5.
Further, the specific implementation method of the step S6 includes the following steps:
s6.1, collecting different momentsRadar images of the void area inside the disease road, and calculating the volume of the void area inside the disease road according to the method of the steps S1-S5>Calculating the actual volume of the disease road void region according to the actual volume calculation expression of the disease road void region +.>；
S6.2 unreliable degree function Using Weibull distributionFitting->And (3) withConstructing a disease road void area volume evolution model according to the relation of the two;
weber distribution uncertainty functionThe calculated expression of (2) is:
；
wherein,、/>、/>sequentially an offset parameter, a scale parameter and a shape parameter;
obtaining a disease road void area volume evolution model, wherein the calculation expression is as follows:
。
an electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the road void area volume evolution prediction method when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of predicting the volumetric evolution of a road void area.
The invention has the beneficial effects that:
according to the method for predicting the volume evolution of the road void area, a noise reduction method for a ground penetrating radar image in a road is adopted to carry out noise reduction treatment on the radar image; then, a disease identification method based on a road internal ground penetrating radar image noise reduction method is adopted, so that the accuracy of identifying a void area is improved; then, by establishing a relation model of the image size information and the actual void area size information, accurate calculation of the void area height is realized; then, combining the calculated area of the void area to obtain a relation equation between the calculated volume of the void area and the actual measured volume, so as to realize the accurate calculation of the volume of the void area; finally, based on the void area volume information obtained by calculation at different time, combining with Weibull distribution, a void volume evolution rule analysis method is provided, the volume of the obtained void area is related to the risk degree in the road, and meanwhile, the void area analysis method can be used for guiding engineering quantity calculation of void repair.
The method for predicting the volume evolution of the road void area realizes the analysis of the volume evolution law of the void area in the road, can be used for assisting maintenance personnel in calculating the repair engineering quantity of the void diseases, optimizes the maintenance decision scheme based on the engineering quantity, reasonably arranges manpower and material resources required by the repair engineering, and reduces the maintenance cost.
According to the method for predicting the volume evolution of the road void area, the structural stability and the pavement damage degree of the void area can be indirectly evaluated through analysis of the volume and the volume evolution speed of the void area, the potential possibility of collapse is analyzed, and then a corresponding emergency management and control scheme is formulated to prevent the occurrence of a road collapse event.
Drawings
Fig. 1 is a flowchart of a method for predicting the volumetric evolution of a road void area according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and detailed description. It should be understood that the embodiments described herein are for purposes of illustration only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein can be arranged and designed in a wide variety of different configurations, and the present invention can have other embodiments as well.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
For further understanding of the invention, the following detailed description is to be taken in conjunction with the accompanying drawings 1.
Detailed description of the preferred embodiments
A method for predicting the volume evolution of a road void area comprises the following steps:
s1, acquiring an internal image of a road by adopting a ground penetrating radar, and performing image noise reduction treatment on the acquired internal image of the road to obtain a reconstructed internal image of the road;
further, the specific implementation method of the step S1 includes the following steps:
s1.1, acquiring an internal image of a road by adopting a ground penetrating radar, and constructing acquired internal image data of the road into an internal image data matrix of the roadA，
Wherein,first of the acquired road interior imageiThe time domain data of the individual waves,mis the total number of channels;
；
wherein,first of the acquired road interior imageiFirst part of the wave>Amplitude data, n is the total number;
s1.2, constructing noise data of an image inside a road:
s1.2.1, establishing a rectangular coordinate system, wherein the vertex of the upper left corner of an internal image of a road is taken as a coordinate origin, the horizontal right is taken as an x-axis positive direction, the vertical downward is taken as a y-axis positive direction, the length of 1 pixel is defined as a unit length, the internal image size of the road is set as a x b, a is the image length along the x-axis direction, and b is the image length along the y-axis direction;
S1.2.2 manually selecting the position of a road area without diseases, randomly selecting a road area without diseases with the size of 10 x b, setting a waveform time domain data matrix of the road area without diseases as R, and comprising waveform time domain data of 10 single-channel waves, wherein the expression is as follows:
；
；
wherein,is the firstiWaveform time domain data of single-channel wave, +.>Is the firstiThe>Amplitude data, which is matrix transposition;
s1.2.3, converting the single-channel waveform time domain data into single-channel waveform frequency domain data by adopting a Fourier transform mode, wherein the calculation expression is as follows:
；
wherein,is the firstiWaveform frequency domain data of single-channel wave, +.>Is imaginary unit, ++>For frequency +.>Time is; obtaining waveform frequency domain data matrix of road area without disease>；
S1.2.4 calculating frequency according to the waveform time domain data of the single-channel wave in the step S1.2.2, and constructing a background noise frequency matrixWherein->Is the firstiThe frequency calculated from the waveform time domain data of the single channel wave,mis the total number of channels;
s1.3, converting the time domain data of the road internal image acquired in the step S1.1 into frequency domain data, wherein the conversion expression is as follows:
；
wherein,first of the acquired road interior imageiFrequency domain data of individual waves to obtain a waveform frequency domain data matrix of the acquired road internal image >；
Then calculating frequency according to the waveform time domain data of the road internal image acquired in the step S1.1, and constructing a frequency matrix of the road internal imageWherein->First of the acquired road interior imageiFrequency calculated by the waveform time domain data;
traversing by taking background noise frequency matrix C as comparison objectLAll elements in (1), deleteLThe same elements as C in the list and then deleteFCorresponding elements in the road interior image are traversed to obtain a waveform frequency domain data matrix of the road interior imageWhereinGpIs the firstpWaveform frequency domain data of the internal images of the road after the traversal;
s1.4, converting frequency domain data in a waveform frequency domain data matrix of the traversed road internal image obtained in the step S1.3 into time domain data, and adopting a calculation expression of Fourier transformation to obtain the following formula:
；
wherein,is the firstiThe time domain data corresponding to the waveform frequency domain data of the road internal image after the traversal is obtained, and the time domain matrix H corresponding to the waveform frequency domain data matrix of the road internal image after the traversal is:
；
；
wherein,is the firstiThe +.f. of the time domain data corresponding to the waveform frequency domain data of the road internal image after the traversal>Amplitude data;
s1.5, reconstructing the road internal image by using a time domain matrix corresponding to the waveform frequency domain data matrix of the traversed road internal image obtained in the step S1.4;
S1.5.1 first, a time domain matrix corresponding to the waveform frequency domain data matrix of the road internal image after traversalHIn (a) and (b)Traversing to obtain maximum->And minimum->；
S1.5.2, pair ofHThe amplitude data in the gray scale image is subjected to standardization processing and mapped into a gray scale value range of 0-255, and the calculation expression is as follows:
；
wherein,is->Normalizing the processed amplitude data;
s1.5.3, constructing a single-channel matrix based on the amplitude data normalized in the step S1.5.2The expression isThen constructing all channel matrixes based on single channel matrixes；/>
S1.5.4 the entire channel matrix obtained in step S1.5.3KThe element value in the two-dimensional image is used as a gray value, the x-axis direction of the two-dimensional image is used as the motion direction of radar sampling data, the y-axis direction of the two-dimensional image is used as the road depth direction, and a gray map M is drawn to obtain a reconstructed road internal image;
s2, setting a gain coefficient for the reconstructed road internal image obtained in the step S1, and performing preliminary disease identification on the reconstructed road internal image to obtain a road internal image of preliminary disease identification;
further, the specific implementation method of the step S2 includes the following steps:
s2.1, dividing the reconstructed road internal image obtained in the step S1 along the vertical direction of the image, wherein the dividing number is set as NhEach divided image has a height ofHhWidth of；
S2.2, setting the image matrix of the ith divided image asThen the average value +.>The expression is:
；
S2.3、constructing a mean matrix based on the average value of the image matrix of the divided images obtained in step S2.2Then calculate +.>And->Difference matrix->The expression is:
；
s2.4, carrying out absolute value operation on all elements of the difference matrix obtained in the step S2.3 to construct an absolute value matrix of the difference matrixThe expression is:
；
then calculate the standard deviation of the absolute value matrix of the difference matrixThe expression is:
；
s2.5, constructing a standard deviation matrix according to the standard deviation of the absolute value matrix of the difference matrix obtained in the step S4；
S2.6, setting critical standard deviation asStandard of standard deviation matrixDifference (S)>And critical standard deviation->Comparison is made when->At the time, the gain factor is set to +.>，/>The value of (2) is determined according to the actual geological condition, the road material type, the radar parameter and the disease type parameter; when->Setting the gain coefficient to be 1;
s2.7, constructing a standard deviation matrix according to the gain coefficient calculated in the step S2.6Corresponding gain coefficient matrix->The method is used for enhancing the characteristics of the echo image of the position of the disease area in the road to obtain an image of the road in which the primary disease is identified;
S3, extracting a disease target area from the road interior image identified by the preliminary disease obtained in the step S2 to obtain image data of a void area in the disease road;
further, the specific implementation method of the step S3 includes the following steps:
s3.1, performing binarization processing on the road internal image identified by the primary disease obtained in the step S2 by adopting a maximum inter-class variance method;
s3.2, counting the total number of pixels in all the connected areas of the road internal image identified by the preliminary disease after the binarization processing of the step S3.1, deleting the connected areas with the total number of pixels smaller than 500, reserving the connected areas with the total number of pixels larger than or equal to 500, and numbering the connected areas of the road internal image identified by the preliminary disease after the pixel deleting processing as N1, N2 … Ni … Nc;
s3.3, establishing a coordinate system for the road internal image identified by the preliminary disease after the pixel deleting treatment in the step S3.2, taking the top point of the left upper corner of the image as a coordinate origin, taking the horizontal right as the positive x-axis direction and the vertical downward as the positive y-axis direction, extracting coordinate points of pixels in all connected areas of the road internal image identified by the preliminary disease after the pixel deleting treatment, and setting the leftmost pixel point coordinate of the connected area Ni as the coordinate of the left pixel point The coordinate of the rightmost pixel point is +.>The coordinate of the topmost pixel point is +.>The coordinate expression is defined as:
；
；
；
wherein,is the leftmost pixel point of the connected region NixAxis coordinates->Is the leftmost pixel point of the connected region NiyAn axis coordinate;
s3.4, screening out areas with hyperbolic characteristics by adopting a method for calculating dip angles, and respectively solving top pixel points toInclination angle of leftmost pixel point and rightmost pixel pointAnd->The expression is:
；
；
then extracting the maximum value of the inclination angles from the top pixel point to the leftmost pixel point and the rightmost pixel point, and marking the maximum value asThe expression is:
；
setting critical angleExtracting +.>Is a hyperbolic region +.>；
Further, critical angle25 degrees;
s3.5, extracting hyperbolic areas of all the connected areas, and renumbering to beWherein->Is the total number of hyperbolic areas;
s3.6, sequentially extracting single-channel waveform time domain data of all hyperbolic areas passing through the vertex coordinates based on the vertex coordinates of all hyperbolas of all the hyperbolic areas obtained in the step S3.5, and sequentially recording as follows:，
wherein,，/>is the firstiSingle-channel waveform time domain data of single hyperbolic area passing vertex coordinates,/and method for generating the same>Is the first iSingle-channel waveform time domain data of single hyperbolic region over vertex coordinateslAmplitude data;
s3.7, converting the single-channel waveform time domain data of the hyperbolic region over-vertex coordinates obtained in the step S3.6 into frequency domain data by adopting a wavelet transformation method, wherein the calculation expression is as follows:
；
；
wherein,is the firstiSingle-channel waveform frequency domain data of peak coordinate crossing of hyperbola region, < ->For the scale of +>For translation amount->Is a basic wavelet;
then calculate the firstiPhase of single-track waveform of crossing vertex coordinates of hyperbolic regionThe computational expression is:
；
wherein,is->Imaginary part of->Is->The real part of (2);
s3.8, setting the electromagnetic wave at the transmitting position as based on the electromagnetic wave theoryElectromagnetic wave at the reflection position is +.>Constructing a direction function->The expression is:
；
calculating a direction function whenWhen the electromagnetic wave is transmitted from the high dielectric constant to the low dielectric constant medium, the dielectric constant of the material at the disease position is smaller than that at the emission position, and the first judgment is madeiEach pair of pairsThe curve area is a void area in the damaged road;
when (when)When the electromagnetic wave is transmitted from a low dielectric constant to a high dielectric constant medium, the dielectric constant of the material at the disease position is larger than that of the emission position;
s3.9, calculating the phase of a single-channel waveform of the vertex passing coordinates of all hyperbolic areas, and judging the internal void condition of the road of all the hyperbolic areas to obtain image data of the void area inside the damaged road;
S4, calculating the actual height of the void area of the disease road based on the image data of the void area inside the disease road obtained in the step S3;
further, the specific implementation method of the step S4 includes the following steps:
s4.1, drilling a disease road by adopting a drilling machine, and verifying the image data of the void area inside the disease road obtained in the step S3 on the site of the disease road to obtain the void area of the disease road;
s4.2, the endoscope is deeply penetrated into the disease road void area, the top plate position and the bottom plate position of the disease road void area are determined through the endoscope display, and the distance between the top plate position and the bottom plate position is measured to obtain the actual height of the disease road void area；
S4.3, adopting an image binarization method to the image data of the empty region in the damaged road verified in the step S4.1 to obtain the image data of the empty region in the damaged road after the image binarization treatment, and then extracting the coordinates of the top pixel point of the hyperbola in the image data of the empty region in the damaged road after the binarization treatmentExcessive->Making a straight line parallel to the y-axis and intersecting the hyperbola at +.>And->Then->，/>Obtaining the height of the void area of the damaged road>The calculation formula of (2) is as follows:
；
s4.4, selecting 10 disease road void areas, and repeating the steps S4.1-S4.3 to sequentially obtain the actual height of the disease road void areas Calculating the height of the void area of the damaged road；
S4.5, fitting by adopting a quadratic function based on the 10 disease road void areas selected in the step S4.4And->The actual height calculation expression of the disease road void area is obtained as follows:
；
wherein,、/>、/>respectively calculating a secondary term parameter, a primary term parameter and a constant term parameter of an expression for the actual height of the disease road void region;
s4.6, calculating the actual height of the disease road void area based on the actual height calculation expression of the disease road void area obtained in the step S4.5;
s5, calculating the actual volume of the void region of the disease road based on the image data of the void region inside the disease road obtained in the step S3 and the actual height of the void region of the disease road obtained in the step S4;
further, the specific implementation method of the step S5 includes the following steps:
s5.1, drilling a disease road by using a drilling machine, and verifying the image data of the void area inside the disease road obtained in the step S3 on the site of the disease road to obtain the void area of the disease road;
s5.2, injecting water into the disease road void area obtained in the step S5.1 until the water is filled, and recording the volume of the injected water as the actual volume of the disease road void area；
S5.3, collecting the image data of the void area inside the damaged road verified in the step S5.1, firstly extracting the total number of pixels corresponding to the plane image of the image data of the void area inside the damaged road, and recording as Then counting the total number of pixels corresponding to the void areas in the image data of the void areas in the diseased road, and marking the total number as +.>Then calculate the void area volume inside the diseased road +.>The computational expression is:
；
wherein,the actual area of the road represented by 1 pixel;
s5.4, selecting 10 disease road void areas, and repeating the steps S5.1-S5.3 to sequentially obtain the actual volumes of the disease road void areasCalculating to obtain the volume of the void area in the damaged road>；
S5.5, fitting by adopting a quadratic function based on the 10 disease road void areas selected in the step S5.4And->The actual volume calculation expression of the void area of the disease road is obtained as follows:
；
wherein,、/>、/>calculating a secondary term parameter, a primary term parameter and a constant term parameter of an expression for the actual volume of the disease road void region respectively; />
S5.6, calculating the actual volume of the disease road void region based on the actual volume calculation expression of the disease road void region obtained in the step S5.5;
s6, constructing a disease road void area volume evolution model according to the actual volume of the disease road void area calculated in the step S5;
further, the specific implementation method of the step S6 includes the following steps:
s6.1, collecting different moments Radar images of the void area inside the disease road, and calculating the volume of the void area inside the disease road according to the method of the steps S1-S5>Calculating the actual volume of the disease road void region according to the actual volume calculation expression of the disease road void region +.>；
S6.2 unreliable degree function Using Weibull distributionFitting->And (3) withConstructing a disease road void area volume evolution model according to the relation of the two;
weber distribution uncertainty functionThe calculated expression of (2) is:
；
wherein,、/>、/>sequentially an offset parameter, a scale parameter and a shape parameter;
obtaining a disease road void area volume evolution model, wherein the calculation expression is as follows:
。
detailed description of the preferred embodiments
An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the road void area volume evolution prediction method when executing the computer program.
The computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit. And the processor is used for realizing the steps of the road void area volume evolution prediction method when executing the computer program stored in the memory.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Detailed description of the preferred embodiments
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of predicting the volumetric evolution of a road void area.
The computer readable storage medium of the present invention may be any form of storage medium that is read by a processor of a computer device, including but not limited to a nonvolatile memory, a volatile memory, a ferroelectric memory, etc., on which a computer program is stored, and when the processor of the computer device reads and executes the computer program stored in the memory, the steps of a road void area volume evolution prediction method described above may be implemented.
The computer program comprises computer program code which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although the application has been described above with reference to specific embodiments, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the application. In particular, the features of the disclosed embodiments may be combined with each other in any manner so long as there is no structural conflict, and the exhaustive description of these combinations is not given in this specification solely for the sake of brevity and resource saving. Therefore, it is intended that the application not be limited to the particular embodiments disclosed herein, but that the application will include all embodiments falling within the scope of the appended claims.
Claims (8)
1. The method for predicting the volume evolution of the road void area is characterized by comprising the following steps:
s1, acquiring an internal image of a road by adopting a ground penetrating radar, and performing image noise reduction treatment on the acquired internal image of the road to obtain a reconstructed internal image of the road;
s2, setting a gain coefficient for the reconstructed road internal image obtained in the step S1, and performing preliminary disease identification on the reconstructed road internal image to obtain a road internal image of preliminary disease identification;
s3, extracting a disease target area from the road interior image identified by the preliminary disease obtained in the step S2 to obtain image data of a void area in the disease road;
s4, calculating the actual height of the void area of the disease road based on the image data of the void area inside the disease road obtained in the step S3;
s5, calculating the actual volume of the void region of the disease road based on the image data of the void region inside the disease road obtained in the step S3 and the actual height of the void region of the disease road obtained in the step S4;
the specific implementation method of the step S5 comprises the following steps:
s5.1, drilling a disease road by using a drilling machine, and verifying the image data of the void area inside the disease road obtained in the step S3 on the site of the disease road to obtain the void area of the disease road;
S5.2, injecting water into the disease road void area obtained in the step S5.1 until the water is filled, and recording the volume of the injected water as the actual volume of the disease road void area；
S5.3, collecting the image data of the void area inside the damaged road verified in the step S5.1, firstly extracting the total number of pixels corresponding to the plane image of the image data of the void area inside the damaged road, and recording asThen counting the total number of pixels corresponding to the void areas in the image data of the void areas in the diseased road, and marking the total number as +.>Then calculate the volume of the void area in the damaged roadThe computational expression is:
；
wherein,for the actual area of the road represented by 1 pixel, < >>The actual height of the void area of the disease road;
s5.4, selecting 10 disease road void areas, and repeating the steps S5.1-S5.3 to sequentially obtain the actual volumes of the disease road void areas，/>Calculating to obtain the volume of the void area in the damaged road>，/>；
S5.5, fitting by adopting a quadratic function based on the 10 disease road void areas selected in the step S5.4And->The actual volume calculation expression of the void area of the disease road is obtained as follows:
；
wherein,、/>、/>actual volume calculation table for disease road void areasThe secondary term parameter, the primary term parameter and the constant term parameter of the expression;
S5.6, calculating the actual volume of the disease road void region based on the actual volume calculation expression of the disease road void region obtained in the step S5.5;
s6, constructing a disease road void area volume evolution model according to the actual volume of the disease road void area calculated in the step S5.
2. The method for predicting the volumetric evolution of a road void area according to claim 1, wherein the specific implementation method of step S1 comprises the following steps:
s1.1, acquiring an internal image of a road by adopting a ground penetrating radar, and constructing acquired internal image data of the road into an internal image data matrix of the roadA，
；
Wherein,first of the acquired road interior imageiThe time domain data of the individual waves,mis the total number of channels;
；
wherein,first of the acquired road interior imageiFirst part of the wave>Amplitude data, n is the total number;
s1.2, constructing a background noise frequency matrix of an image in a road:
s1.2.1, establishing a rectangular coordinate system, wherein the vertex of the upper left corner of an internal image of a road is taken as a coordinate origin, the horizontal right is taken as an x-axis positive direction, the vertical downward is taken as a y-axis positive direction, the length of 1 pixel is defined as a unit length, the internal image size of the road is set as a x b, a is the image length along the x-axis direction, and b is the image length along the y-axis direction;
S1.2.2 manually selecting the position of a road area without diseases, randomly selecting a road area without diseases with the size of 10 x b, setting a waveform time domain data matrix of the road area without diseases as R, and comprising waveform time domain data of 10 single-channel waves, wherein the expression is as follows:
；
；
wherein,is the firstiWaveform time domain data of single-channel wave, +.>Is the firstiThe>The amplitude data of the plurality of amplitude data,Ttranspose the matrix;
s1.2.3, converting the single-channel waveform time domain data into single-channel waveform frequency domain data by adopting a Fourier transform mode, wherein the calculation expression is as follows:
；
wherein,is the firstiWaveform frequency domain data of single-channel wave, +.>Is imaginary unit, ++>For frequency +.>Time is; obtaining waveform frequency domain data matrix of road area without disease>；
S1.2.4 calculating frequency according to the waveform time domain data of the single-channel wave in the step S1.2.2, and constructing a background noise frequency matrixWherein->Is the firstiThe frequency calculated from the waveform time domain data of the single channel wave,mis the total number of channels;
s1.3, converting the time domain data of the road internal image acquired in the step S1.1 into frequency domain data, wherein the conversion expression is as follows:
；
wherein,first of the acquired road interior imageiFrequency domain data of individual waves to obtain a waveform frequency domain data matrix of the acquired road internal image >；
Then calculating frequency according to the waveform time domain data of the road internal image acquired in the step S1.1, and constructing a frequency matrix of the road internal imageWherein->First of the acquired road interior imageiFrequency calculated by the waveform time domain data;
traversing by taking background noise frequency matrix C as comparison objectLAll elements in (1), deleteLThe same elements as C in the list and then deleteFCorresponding elements in the road interior image are traversed to obtain a waveform frequency domain data matrix of the road interior imageWhereinGpIs the firstpWaveform frequency domain data of the internal images of the road after the traversal;
s1.4, converting frequency domain data in a waveform frequency domain data matrix of the traversed road internal image obtained in the step S1.3 into time domain data, and adopting a calculation expression of Fourier transformation to obtain the following formula:
；
wherein,is the firstiThe time domain data corresponding to the waveform frequency domain data of the road internal image after the traversal is obtained, and the time domain matrix H corresponding to the waveform frequency domain data matrix of the road internal image after the traversal is:
；
；
wherein,is the firstiThe +.f. of the time domain data corresponding to the waveform frequency domain data of the road internal image after the traversal>Amplitude data;
s1.5, reconstructing the road internal image by using a time domain matrix corresponding to the waveform frequency domain data matrix of the traversed road internal image obtained in the step S1.4;
S1.5.1 first, a time domain matrix corresponding to the waveform frequency domain data matrix of the road internal image after traversalHIn (a) and (b)Traversing to obtain maximum->And minimum->；
S1.5.2, pair ofHThe amplitude data in the gray scale image is subjected to standardization processing and mapped into a gray scale value range of 0-255, and the calculation expression is as follows:
；
wherein,is->Normalizing the processed amplitude data;
s1.5.3, constructing a single-channel matrix based on the amplitude data normalized in the step S1.5.2The expression isThen constructing all channel matrixes based on single channel matrixes；
S1.5.4 the entire channel matrix obtained in step S1.5.3KThe element value in the two-dimensional image is used as a gray value, the x-axis direction of the two-dimensional image is used as the motion direction of radar sampling data, the y-axis direction of the two-dimensional image is used as the road depth direction, and a gray map M is drawn to obtain a reconstructed road internal image.
3. The method for predicting the volumetric evolution of a road void area according to claim 2, wherein the specific implementation method of step S2 comprises the following steps:
s2.1, dividing the reconstructed road internal image obtained in the step S1 along the vertical direction of the image, wherein the dividing number is set asNhEach divided image has a height of HhWidth of；
S2.2, setting the image matrix of the ith divided image asThen the average value +.>The expression is:
；
s2.3, constructing a mean matrix based on the average value of the image matrices of the divided images obtained in the step S2.2Then calculate +.>And->Difference matrix->The expression is:
；
s2.4, carrying out absolute value operation on all elements of the difference matrix obtained in the step S2.3 to construct an absolute value matrix of the difference matrixThe expression is:
；
then calculate the standard deviation of the absolute value matrix of the difference matrixThe expression is:
；
s2.5, constructing a standard deviation matrix according to the standard deviation of the absolute value matrix of the difference matrix obtained in the step S4；
S2.6, setting critical standard deviation asStandard deviation of standard deviation matrix +.>And critical standard deviation->Comparison is made when->At the time, the gain factor is set to +.>，/>The value of (2) is determined according to the actual geological condition, the road material type, the radar parameter and the disease type parameter; when->Setting the gain coefficient to be 1;
s2.7, constructing a standard deviation matrix according to the gain coefficient calculated in the step S2.6Corresponding gain coefficient matrixThe method is used for enhancing the characteristics of the echo image of the position of the disease area in the road to obtain the road internal image of the primary disease identification.
4. The method for predicting the volumetric evolution of a road void area according to claim 3, wherein the specific implementation method of step S3 comprises the following steps:
s3.1, performing binarization processing on the road internal image identified by the primary disease obtained in the step S2 by adopting a maximum inter-class variance method;
s3.2, counting the total number of pixels in all the connected areas of the road internal image identified by the preliminary disease after the binarization processing of the step S3.1, deleting the connected areas with the total number of pixels smaller than 500, reserving the connected areas with the total number of pixels larger than or equal to 500, and numbering the connected areas of the road internal image identified by the preliminary disease after the pixel deleting processing as N1, N2 … Ni … Nc;
s3.3, establishing a coordinate system for the road internal image identified by the preliminary disease after the pixel deleting treatment in the step S3.2, taking the top point of the left upper corner of the image as a coordinate origin, taking the horizontal right as the positive x-axis direction and the vertical downward as the positive y-axis direction, extracting coordinate points of pixels in all connected areas of the road internal image identified by the preliminary disease after the pixel deleting treatment, and setting the leftmost pixel point coordinate of the connected area Ni as the coordinate of the left pixel pointThe coordinate of the rightmost pixel point is +.>The coordinate of the topmost pixel point is +. >The coordinate expression is defined as:
；
；
；
wherein,is the leftmost pixel point of the connected region NixAxis coordinates->Is the leftmost pixel point of the connected region NiyAn axis coordinate;
s3.4, screening out a region with hyperbolic characteristics by adopting a method for calculating the inclination angle, and respectively obtaining the inclination angles from the top pixel point to the leftmost pixel point and the rightmost pixel pointAnd->The expression is:
；
；
then extracting the maximum value of the inclination angles from the top pixel point to the leftmost pixel point and the rightmost pixel point, and marking the maximum value asThe expression is:
；
setting critical angleExtracting +.>Is a hyperbolic region +.>；
S3.5, extracting hyperbolic areas of all the connected areas, and renumbering to beWherein->Is the total number of hyperbolic areas;
s3.6, sequentially extracting single-channel waveform time domain data of all hyperbolic areas passing through the vertex coordinates based on the vertex coordinates of all hyperbolas of all the hyperbolic areas obtained in the step S3.5, and sequentially recording as follows:，
wherein,，/>is the firstiSingle-channel waveform time domain data of single hyperbolic area passing vertex coordinates,/and method for generating the same>Is the firstiNo. I in single-channel waveform time domain data of single hyperbolic region over vertex coordinates>Amplitude data;
s3.7, converting the single-channel waveform time domain data of the hyperbolic region over-vertex coordinates obtained in the step S3.6 into frequency domain data by adopting a wavelet transformation method, wherein the calculation expression is as follows:
；
；
Wherein,is the firstiSingle-pass waveform with single hyperbolic region passing vertex coordinatesFrequency domain data,/->For the scale of +>In order to be able to translate the quantity,is a basic wavelet;
then calculate the firstiPhase of single-track waveform of crossing vertex coordinates of hyperbolic regionThe computational expression is:
；
wherein,is->Imaginary part of->Is->The real part of (2);
s3.8, setting the electromagnetic wave at the transmitting position as based on the electromagnetic wave theoryElectromagnetic wave at the reflection position is +.>Constructing a direction function->The expression is:
；
calculating a direction function whenWhen the electromagnetic wave is transmitted from the high dielectric constant to the low dielectric constant medium, the dielectric constant of the material at the disease position is smaller than that at the emission position, and the first judgment is madeiThe hyperbolic areas are void areas in the disease road;
and S3.9, calculating the phase of the single-channel waveform of the over-vertex coordinates of all the hyperbolic areas, and judging the road internal void condition of all the hyperbolic areas to obtain image data of the void area in the damaged road.
5. The method for predicting the volumetric evolution of a road void area according to claim 4, wherein the specific implementation method of step S4 comprises the following steps:
s4.1, drilling a disease road by adopting a drilling machine, and verifying the image data of the void area inside the disease road obtained in the step S3 on the site of the disease road to obtain the void area of the disease road;
S4.2, the endoscope is deeply penetrated into the disease road void area, the top plate position and the bottom plate position of the disease road void area are determined through the endoscope display, and the distance between the top plate position and the bottom plate position is measured to obtain the actual height of the disease road void area；
S4.3, adopting an image binarization method to the image data of the empty region in the damaged road verified in the step S4.1 to obtain the image data of the empty region in the damaged road after the image binarization treatment, and then extracting the coordinates of the top pixel point of the hyperbola in the image data of the empty region in the damaged road after the binarization treatmentExcessive->Making a straight line parallel to the y-axis and intersecting the hyperbola at +.>And->Then->，/>Obtaining the height of the void area of the damaged road>The calculation formula of (2) is as follows:
；
s4.4, selecting 10 disease road void areas, and repeating the steps S4.1-S4.3 to sequentially obtain the actual height of the disease road void areasCalculating the height of the void area of the damaged road>；
S4.5, fitting by adopting a quadratic function based on the 10 disease road void areas selected in the step S4.4And->The actual height calculation expression of the disease road void area is obtained as follows:
；
wherein,、/>、/>respectively calculating a secondary term parameter, a primary term parameter and a constant term parameter of an expression for the actual height of the disease road void region;
S4.6, calculating the actual height of the disease road void area based on the actual height calculation expression of the disease road void area obtained in the step S4.5.
6. The method for predicting the volumetric evolution of a road void area according to claim 5, wherein the specific implementation method of step S6 comprises the following steps:
s6.1, collecting different momentsRadar images of the void area inside the disease road, and calculating the volume of the void area inside the disease road according to the method of the steps S1-S5>Calculating the actual volume of the disease road void region according to the actual volume calculation expression of the disease road void region +.>；
S6.2 unreliable degree function Using Weibull distributionFitting->And->Constructing a disease road void area volume evolution model according to the relation of the two;
weber distribution uncertainty functionThe calculated expression of (2) is:
；
wherein,、/>、/>sequentially an offset parameter, a scale parameter and a shape parameter;
obtaining a disease road void area volume evolution model, wherein the calculation expression is as follows:
。
7. an electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a method for predicting the volumetric evolution of a road void area according to any one of claims 1-6 when executing the computer program.
8. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a road void area volume evolution prediction method according to any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311336725.3A CN117077450B (en) | 2023-10-17 | 2023-10-17 | Road void area volume evolution prediction method, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311336725.3A CN117077450B (en) | 2023-10-17 | 2023-10-17 | Road void area volume evolution prediction method, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117077450A true CN117077450A (en) | 2023-11-17 |
| CN117077450B CN117077450B (en) | 2024-03-26 |
Family
ID=88719835
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311336725.3A Active CN117077450B (en) | 2023-10-17 | 2023-10-17 | Road void area volume evolution prediction method, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117077450B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015161552A (en) * | 2014-02-26 | 2015-09-07 | ジオ・サーチ株式会社 | Cavity survey method and risk evaluation method of quay or levee |
| CN114076943A (en) * | 2021-11-17 | 2022-02-22 | 广州市市政工程试验检测有限公司 | Rapid automatic comprehensive detection method for urban road underground cavity |
| WO2022095536A1 (en) * | 2020-11-04 | 2022-05-12 | 武汉理工大学 | Method and apparatus for calculating pavement cavity moisture content based on radar nondestructive testing |
| CN114821296A (en) * | 2022-03-14 | 2022-07-29 | 西安电子科技大学 | Underground disease ground penetrating radar image identification method and system, storage medium and terminal |
| CN115424232A (en) * | 2022-11-04 | 2022-12-02 | 深圳市城市交通规划设计研究中心股份有限公司 | Method for identifying and evaluating pavement pit, electronic equipment and storage medium |
| CN115439643A (en) * | 2022-08-31 | 2022-12-06 | 中远海运科技股份有限公司 | Road disease size analysis and management method based on monocular measurement |
| CN116266215A (en) * | 2022-08-01 | 2023-06-20 | 广西北投公路建设投资集团有限公司 | Geometric feature extraction method for pavement recessive disease area |
-
2023
- 2023-10-17 CN CN202311336725.3A patent/CN117077450B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015161552A (en) * | 2014-02-26 | 2015-09-07 | ジオ・サーチ株式会社 | Cavity survey method and risk evaluation method of quay or levee |
| WO2022095536A1 (en) * | 2020-11-04 | 2022-05-12 | 武汉理工大学 | Method and apparatus for calculating pavement cavity moisture content based on radar nondestructive testing |
| CN114076943A (en) * | 2021-11-17 | 2022-02-22 | 广州市市政工程试验检测有限公司 | Rapid automatic comprehensive detection method for urban road underground cavity |
| CN114821296A (en) * | 2022-03-14 | 2022-07-29 | 西安电子科技大学 | Underground disease ground penetrating radar image identification method and system, storage medium and terminal |
| CN116266215A (en) * | 2022-08-01 | 2023-06-20 | 广西北投公路建设投资集团有限公司 | Geometric feature extraction method for pavement recessive disease area |
| CN115439643A (en) * | 2022-08-31 | 2022-12-06 | 中远海运科技股份有限公司 | Road disease size analysis and management method based on monocular measurement |
| CN115424232A (en) * | 2022-11-04 | 2022-12-02 | 深圳市城市交通规划设计研究中心股份有限公司 | Method for identifying and evaluating pavement pit, electronic equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| JONGEUN BAEK 等: "A Case Study on Detection of Subsurface Cavities of Urban Roads Using Ground-coupled GPR", 17TH INTERNATIONAL CONFERENCE ON GROUND PENETRATING RADAR, pages 1 - 5 * |
| 王子彬;刘海军;蔡迎春;: "基于路面雷达的水泥路面脱空检测正演模拟与试验分析", 交通科技, no. 02, pages 12 - 15 * |
| 王端宜;李思源;: "基于ABAQUS的水泥路面脱空形态神经网络识别方法", 筑路机械与施工机械化, no. 05, pages 102 - 106 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117077450B (en) | 2024-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kalfarisi et al. | Crack detection and segmentation using deep learning with 3D reality mesh model for quantitative assessment and integrated visualization | |
| Liu et al. | Novel YOLOv3 model with structure and hyperparameter optimization for detection of pavement concealed cracks in GPR images | |
| CN115424232B (en) | Method for identifying and evaluating pavement pit, electronic equipment and storage medium | |
| CN110619258B (en) | Road track checking method based on high-resolution remote sensing image | |
| CN111798411B (en) | Concrete inner steel bar intelligent positioning method based on ground penetrating radar and deep learning | |
| CN113009447B (en) | Road underground cavity detection and early warning method based on deep learning and ground penetrating radar | |
| CN117078235B (en) | Road network maintenance method, electronic equipment and storage medium for comprehensive evaluation | |
| CN107727658A (en) | Pantograph crackle on-line measuring device and method based on image procossing | |
| CN115063525B (en) | Three-dimensional mapping method and device for urban road subgrade and pipeline | |
| CN109034073B (en) | Method, system, equipment and medium for predicting generation amount of building demolition waste | |
| CN114895302A (en) | Method and device for rapidly detecting roadbed diseases of urban roads | |
| CN114742957B (en) | Building facade extraction method based on point cloud data | |
| CN117077450B (en) | Road void area volume evolution prediction method, electronic equipment and storage medium | |
| CN117077452B (en) | Comprehensive evaluation method for service life of road, electronic equipment and storage medium | |
| CN117079145B (en) | Comprehensive road condition evaluation method, electronic equipment and storage medium | |
| CN106324708B (en) | Digitizing solution, the device of rainfall record drawing | |
| CN106022217A (en) | Civil airport runway area detection method free from supervision multistage classification | |
| CN117077448B (en) | Road void area evolution prediction method, electronic equipment and storage medium | |
| CN117079147B (en) | Road interior disease identification method, electronic equipment and storage medium | |
| CN115630336A (en) | Non-sample-library-area landslide susceptibility prediction method based on domain adaptive transfer learning | |
| CN117077500B (en) | Numerical simulation modeling method for road internal void, electronic equipment and storage medium | |
| CN113962968B (en) | Multi-source mixed interference radar image target detection system oriented to complex electromagnetic environment | |
| CN117077449B (en) | Road void area height evolution prediction method, electronic equipment and storage medium | |
| CN114723699A (en) | Intelligent identification method and system for detecting underground hidden danger of road by geological radar | |
| CN117115049B (en) | Noise reduction method for road interior image of ground penetrating radar, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |