CN117078235A - Road network maintenance method, electronic equipment and storage medium for comprehensive evaluation - Google Patents
Road network maintenance method, electronic equipment and storage medium for comprehensive evaluation Download PDFInfo
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Abstract
A road network maintenance method, electronic equipment and storage medium for comprehensive evaluation belong to the technical field of road network maintenance. The road network maintenance method aims at solving the problems of a scientific and reasonable road network maintenance method which comprehensively considers the damage condition and the evolution speed of the interior and the exterior of the road. The invention collects road surface disease images and calculates road surface damage condition indexes; constructing a road internal identification convolutional neural network model based on the ground penetrating radar void disease image, and calculating parameters of a road internal void area; constructing an evaluation index of the service life of the interior of the road; constructing comprehensive evaluation indexes of the service life of the road; performing preliminary road service life assessment; constructing a life distribution function model of the road section by adopting a three-parameter Weibull distribution function, and calculating the average damage occurrence time of the road section; setting the comprehensively estimated road network maintenance factors, constructing a comprehensively estimated road network maintenance scoring matrix, and judging the comprehensively estimated road network maintenance priority. The invention realizes scientific maintenance of road network.
Description
Technical Field
The invention belongs to the technical field of road network maintenance, and particularly relates to a road network maintenance method, electronic equipment and a storage medium for comprehensive evaluation.
Background
The maintenance of the service performance state of the road is an important task of road maintenance. At present, maintenance decision of a road is usually carried out manually according to technical condition information of the road surface or by directly adopting a historical decision scheme, so that the method has strong subjectivity and is difficult to ensure optimization of decision effect. Meanwhile, the important effect of service life on road maintenance is ignored in the decision process.
Because the road mileage is long, the road maintenance department has the problem of lack of maintenance funds, and in the road network maintenance process, road maintenance and maintenance sequencing is usually carried out according to maintenance cost, and the work with low maintenance cost is arranged at a front position; or based on subjective experience of the decision maker, it is difficult to guarantee optimization of decision effect. There is a need to orderly improve the road network maintenance technology level by making a reasonable road network-oriented road maintenance priority scheme.
At present, a large amount of road interior disease detection data and road surface disease detection data are accumulated in the industry, but the data have barriers, the data utilization rate is not high, and the road network maintenance decision is not guided based on the two types of data at the same time.
The invention relates to an urban road lifting and transformation demand comprehensive evaluation method based on driving simulation, which is disclosed by the invention with the application number of 201711226166.5, and is characterized in that two indexes of facility performance and road side landscape of an urban road to be evaluated are evaluated, and the comprehensive lifting and transformation demand score of the urban road to be evaluated is obtained by weighting and summing the scores and weights of the two indexes. However, this method ignores the effects of road internal diseases in the analysis of the performance of the road facilities. Compared with road surface diseases, the road interior diseases have strong concealment and larger destructive power.
The invention patent with the application number of 202111013320.7 and the invention name of a highway network maintenance planning method based on maintenance priority ordering is used for determining the maintenance property of each road section by collecting the basic information of each road section in a highway network, providing a maintenance priority ordering method, and determining the maintenance planning of the next few years by combining maintenance funds and the road surface technical condition prediction information of each road section. However, the method does not consider the influence of diseases in the road, especially the difference between expected service lives of roads, and the road network maintenance decision scheme is high in subjectivity, so that the optimal configuration of economy and benefit is difficult to realize for maintenance funds, and the optimal maintenance decision is difficult to be made.
Disclosure of Invention
The invention aims to solve the problem of comprehensively considering the road internal and external damage condition and the evolution speed, and provides a road network maintenance method, electronic equipment and a storage medium for comprehensive evaluation.
In order to achieve the above purpose, the present invention is realized by the following technical scheme:
a road network maintenance method for comprehensive evaluation comprises the following steps:
s1, collecting pavement damage images, identifying pavement damage and extracting pavement damage size data from the collected pavement damage images, and calculating pavement damage condition indexes based on the extracted pavement damage size data;
s2, constructing a road internal void disease recognition convolutional neural network model based on a ground penetrating radar void disease image, then acquiring a road internal image by adopting the ground penetrating radar, performing disease recognition on the acquired road internal image by utilizing the acquired road internal void disease recognition convolutional neural network model, and calculating road internal void region parameters;
s3, constructing an evaluation index of the service life of the interior of the road based on the parameters of the void area in the interior of the road obtained in the step S2;
s4, carrying out normalization processing on the road internal service life assessment index obtained in the step S3 and the road surface damage condition index obtained in the step S1, and constructing a road service life comprehensive assessment index;
S5, calculating comprehensive evaluation indexes of the service life of the road according to the method of the step S4, and performing preliminary service life evaluation of the road;
s6, constructing a life distribution function model of the road section by adopting a three-parameter Weibull distribution function, and calculating the average damage occurrence time of the road section;
and S7, setting the road section average damage occurrence time, the road technical condition grade, the road width, the road length, the road material, the road grade, the maintenance cost, the traffic volume and the road age obtained in the step S6 as road network maintenance factors for comprehensive evaluation, constructing a road network maintenance scoring matrix for comprehensive evaluation, and judging the road network maintenance priority of the comprehensive evaluation.
Further, the specific implementation method of the step S1 includes the following steps:
s1.1, shooting road images by adopting a vehicle-mounted camera shooting mode, selecting 20000 road surface disease images, and constructing a road surface disease image data set; pavement damage including cracks, blocky cracks, longitudinal cracks, transverse cracks, subsidence, ruts, wave congestion, pit grooves, looseness, oil flooding, and repair;
s1.2, marking images of a pavement disease image data set by adopting marking software labelimg, and dividing the pavement disease image data set into a training set, a verification set and a test set, wherein the training set comprises 12000 images, 4000 images of the verification set and 4000 images of the test set;
S1.3, constructing a UNet deep learning network structure:
s1.3.1 the set UNet deep learning network structure consists of an encoder and a decoder, wherein the encoder comprises 4 downsampling modules, and each downsampling module comprises two 3×3 convolution layers, 1 ReLU activation function and 1 2×2 pooling layer; the decoder comprises 4 up-sampling modules, each up-sampling module comprises 1 up-sampling convolution layer, a feature splicing concat, 2 3×3 convolution layers and 1 ReLU activation function, an image passes through the encoder first and then passes through the decoder, and the image size is 572×572;
s1.3.2, inputting the data set marked in the step S1.2 into a UNet deep learning network structure established in S1.3.1, calculating the weight and bias of a neural network unit through an error back propagation method, and completing training of the UNet deep learning network structure to obtain the UNet deep learning network structure;
s1.3.3, adopting the UNet deep learning network structure of the step S1.3.2 to identify road surface defects and extract road surface defect area data, wherein the road surface defect area data comprises: the position of the pavement defect in the image, the outline of the pavement defect and the solid area of the pavement defect;
s1.4, determining the conversion relation between image pixel data and the actual size of the pavement disease by adopting a Zhang Zhengyou calibration method:
S1.4.1, making black and white checkerboards, and shooting the black and white checkerboards at different angles by using a camera, wherein the shot images are 30 pieces;
s1.4.2 detecting the corner points of the calibration plate in the image shot in the step S1.4.1 to obtain pixel coordinate values of the corner points of the calibration plate, and calculating to obtain physical coordinate values of the corner points of the calibration plate according to the known checkerboard size and the origin of the world coordinate system;
s1.4.3, solving a camera internal parameter matrix and an external parameter matrix corresponding to each image shot in the step S1.4.1:
firstly, establishing a camera imaging model as follows:
；
wherein Z is a scale factor, (U, V) is a pixel coordinate of any point in the image under a pixel coordinate system, (U, V, W) is a world coordinate of any point in the image under a world coordinate system, AA is an internal reference matrix, and BB is an external reference matrix;
establishing a relation between a plurality of groups of pixel coordinates and world coordinates, obtaining an internal reference matrix AA and an external reference matrix AA, and establishing a conversion relation between road image pixel data of road surface diseases and the actual size of the road surface diseases;
s1.5, calculating the pavement damage condition index after obtaining the pavement disease type and size information based on the steps S1.1-S1.4PCIThe computational expression is:
；
；
wherein,for road surface damage rate->Is the first niArea of road surface damage; />For the investigation of the road surface area->Is the firstniWeight of road surface damage class +.>For linear adjustment coefficient>For index adjustment factor, ++>Is the total number of damage types.
Further, the specific implementation method of the step S2 includes the following steps:
s2.1, constructing a road internal void disease recognition convolutional neural network model;
s2.1.1, establishing a road internal disease data set based on a ground penetrating radar void disease image: marking diseases in the ground penetrating radar void disease images and marking disease categories by using LabelImg software, and storing the names of marking files consistent with the names of the ground penetrating radar void disease images to obtain a road internal disease data set;
s2.1.2, randomly dividing the road internal disease data set obtained in the step S2.1.1 into a training set, a verification set and a test set according to the proportion of 6:2:2;
s2.1.3, inputting the training set, the verification set and the test set obtained in the step S2.1.2 into a convolutional neural network for training, verifying and testing, and outputting model parameters of the convolutional neural network model, including the number of network layers, the number of neuron nodes of each layer, the learning rate, the weight, the bias, the activation function, the loss function and the convolution kernel, so as to obtain the road internal void disease identification convolutional neural network model.
S2.2, acquiring an image of the interior of the road, identifying the disease by using the obtained model of the road interior void disease identification convolutional neural network, and calculating parameters of a void area in the road;
s2.2.1, collecting an internal image of a road by adopting a ground penetrating radar, and carrying out disease identification on the collected internal image of the road by utilizing the internal void disease identification convolutional neural network model of the road, which is obtained in the step S2.1, so as to obtain an internal image of the damaged road;
s2.2.2, obtaining a disease road of an image in the disease road by adopting a drilling machine to drill in the step S2.2.1, and obtaining a road void area;
s2.2.3 the endoscope is put into the road void area, the top plate position and the bottom plate position of the 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 be the height of the road void area；
S2.2.4 then filling water into the road void region obtained in step S2.2.2 until the water is filled, and recording the volume of the water filling as the volume of the road void region；
S2.2.5 calculating the area of the road void regionThe computational expression is:
。
further, the specific implementation method of the step S3 includes the following steps:
s3.1, setting the total number of the road void areas as；
S3.2, constructing an evaluation index of the service life of the interior of the road: the method comprises the steps of total number of road void areas, average height, average volume, average area, height change gradient, volume change gradient, quantity change gradient and area change gradient of the road void areas;
S3.2.1 average height of road void areaThe calculated expression of (2) is:
；
wherein, the road isiThe height of each void area is；
S3.2.2 average volume of road void areaThe calculated expression of (2) is:
；
wherein, the road isiThe volume of each void area is；
S3.2.3 average area of road void areaThe calculated expression of (2) is:
；
wherein, the road isiThe area of each void area is；
S3.2.4 gradient of altitude change in road voidThe calculated expression of (2) is:
；
wherein,、/>respectively the firstiTime and th of data acquisitioni+1 data acquisition time, +_1->Is the firstiAverage height of road void area at individual data acquisition time, +.>Is the firstiAverage height of road void area for +1 data acquisition time;
s3.2.5 gradient of volume change in road void regionThe calculated expression of (2) is:
；
wherein,is the firstiAverage volume of road void area at each data acquisition time +.>Is the firstiAn average volume of road void area of +1 data acquisition time;
s3.2.6 gradient of number change of road void areasThe calculated expression of (2) is:
；
wherein,is the firstiThe number of road void areas at each data acquisition time,/->Is the firstiThe number of road void areas for +1 data acquisition time;
S3.2.7 gradient of area change in road void regionThe calculated expression of (2) is:
；
wherein,is the firstiAverage area of road void area at each data acquisition time,/->Is the firstiAverage area of road void area for +1 data acquisition time.
Further, the specific implementation method of the step S4 includes the following steps:
s4.1, dividing the road into road segments with 50m as road segment lengthNPA segment, wherein,NPthe total number of road segments;
s4.2, taking the time point of the operation starting from the road construction as a time zero point, and respectively detecting the operation of each road section aiming at each road sectiontThe road void area and road surface diseases after the time are calculated, and the road internal service life evaluation index and the road surface damage condition index of each road void area are calculated;
s4.3, traversing the road internal service life evaluation index and the road surface damage condition index of each road void area calculated in the step S4.2 to obtain a maximum value, wherein the maximum value comprises the total number of the road void areasAverage height maximum value of road void area +.>Average volume maximum>Average area maximum>Maximum value of the gradient of the altitude change->Maximum value of volume change gradient- >Maximum value of the number change gradient->Maximum value of area change gradient->Maximum value of road surface damage condition index->；
S4.4, carrying out normalization processing on the road internal service life evaluation index of each road void area obtained in the step S4.2 by using the maximum value obtained in the step S4.3 as a reference, wherein the calculation formula is as follows:
；
wherein,the average height of the road void area is normalized;
；
wherein,the total number of road void areas processed for normalization;
；
wherein,an average volume of the road void area for normalization processing;
；
wherein,an average area of the road void area which is normalized;
；
wherein,a height change gradient of the road void area which is normalized;
；
wherein,a number change gradient of the road void area which is normalized;
；
wherein,a volume change gradient of a road void area which is normalized;
；
wherein,an area change gradient of a road void area which is normalized;
；
wherein,a road surface damage condition index for normalization processing;
then building comprehensive evaluation index of road service lifeTKThe computational expression is:
；
wherein,zk1 is a weight coefficient of the average height of the road void area of the normalization processing, zk2 is the weight coefficient of the total number of the road void areas of the normalization processing,zk3 is a weight coefficient of the average volume of the road void area of the normalization processing,zk4 is a weight coefficient of the average area of the road void area of the normalization processing,zk5 is the weight coefficient of the height change gradient of the road void area subjected to normalization processing,zk6 is a weight coefficient of the number change gradient of the road void area subjected to normalization processing,zk7 is the weight coefficient of the volume change gradient of the road void area subjected to normalization processing,zk8 is a weight coefficient of the area change gradient of the road void area subjected to normalization processing;zk9 is normalizationThe weight coefficient of the pavement damage condition index subjected to chemical treatment;
s4.5, calculating the first according to the method of the step S4.4iOn each road sectiontiComprehensive evaluation index of road service life at moment。
Further, the specific implementation method of the step S5 includes the following steps:
s5.1, counting road detection data of MK roads after 15 years of running, calculating comprehensive evaluation indexes of service life of each road according to the method of the step S4,is the firstjComprehensive evaluation index of road service life after 15 years of running of the MK road, and then calculating average value of comprehensive evaluation index of road service life of MK road +. >Standard deviation of comprehensive evaluation index of road service life>；
S5.2, constructing a threshold value of a comprehensive evaluation index of the service life of the roadThe computational expression is:
；
when (when)If yes, judging that the road is intact, if +.>Judging that the road is damaged;
s5.3, collecting the road section judged to be damaged in the step S5.2iTime when damage occurs to the 1 st time of the road sectionThen building a time matrix of the 1 st occurrence of the damage of the road sectionFT， ；
S5.4, counting road operationt i Number of damaged road segments 1 st time of road segments corresponding in timeThen calculate the road operationt i The number of damaged road sections in the 1 st time of the road sections corresponding to time accounts for the percentage of the total number of the road sections, and the calculation expression is as follows:
；
wherein,for road runningt i The number of damaged road segments 1 st time of the road segments corresponding in time is a percentage of the total number of road segments.
Further, the specific implementation method of the step S6 includes the following steps:
s6.1, constructing a life distribution function model of the road section by adopting a three-parameter Weibull function, wherein the calculation expression is as follows:
；
wherein,for the length of service of road sections,/->For the position parameter of the weibull distribution, +.>Rule for weibull distributionDegree parameter- >Is the shape parameter of the Weibull distribution, +.>The probability of damage occurrence is accumulated for the road section in the service time of the road section;
based on the life distribution function model of the road section, constructing a probability density function model of life distribution of the road section, wherein the calculation expression is as follows:
；
wherein,the probability of damage to the road section corresponding to the moment t in the service time of the road section is provided;
constructing a reliability function model of a road section, wherein the calculation expression is as follows:
；
wherein,the probability that the road section is in good condition in the service time of the road section is provided;
constructing a failure probability function model of a road section, wherein the calculation expression is as follows:
；
wherein,the probability that the damage occurs in the road section unit time after the road section service time t is the probability that the damage does not occur in the road section before the road section service time t;
constructing a time function model of average damage of a road section, wherein the calculation expression is as follows:
；
wherein,the time of average damage of the road section in the service time of the road section is provided;
s6.2, using step S5Andticarrying out a life distribution function model of the road section constructed in the step S6.1, adopting a least square fitting solution, and calculating to obtain +. >、/>、/>。
Further, the specific implementation method of the step S7 includes the following steps:
s7.1, setting the road network maintenance factors which are comprehensively evaluated by the average damage occurrence time, the road width, the road length, the road materials, the road grade, the maintenance cost, the traffic volume, the road age and the environmental conditions of the road section obtained in the step S6, wherein the number of the road network maintenance factors is 9;
s7.2, setting the number of roads in the road network as n, grading the road network maintenance factors comprehensively evaluated by each road in a manual evaluation mode, and constructing a road network maintenance grading matrix comprehensively evaluatedPFThe computational expression is:
；
wherein,the first road network maintenance scoring matrix for comprehensive evaluationiRoad No.jScoring the road network maintenance factors comprehensively evaluated;
s7.3, carrying out standardized processing on indexes in the road network maintenance scoring matrix which is comprehensively evaluated and obtained in the step S7.2, wherein the calculation expression is as follows:
；
wherein,is the firstiRoad No.jStandardized scores of comprehensively evaluated road network maintenance factors;
on the basis, a road network maintenance standardization grading matrix which is comprehensively evaluated is obtainedThe computational expression is:
；
S7.4, constructing road network maintenance scoring entropy, wherein the calculation expression is as follows:
；
wherein,road network maintenance scoring entropy for the j-th comprehensively evaluated road network maintenance factors;
s7.5, constructing road network maintenance influence factors based on the road network maintenance scoring entropy obtained in the step S7.4, wherein the calculation expression is as follows:
；
wherein,road network maintenance influence factors for the j-th comprehensively estimated road network maintenance factors;
s7.6, calculating the final score corresponding to each road based on the road network maintenance influence factors obtained in the step S7.5PFFThe computational expression is:
；
wherein,is thatFwThe final scores corresponding to the roads are arranged in order from small to large, and the maintenance priority is higher as the score is smaller.
An electronic device comprising a memory and a processor, the memory storing a computer program, said processor implementing the steps of said one fully evaluated road network maintenance method when executing said computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of comprehensively evaluating road network maintenance.
The invention has the beneficial effects that:
according to the road network maintenance method for comprehensive evaluation, road damage condition indexes are obtained through calculation by collecting road surface images and adopting the intelligent identification of road surface diseases and the extraction of size information; by fusing the road surface damage condition index and the road internal void size information, considering the surface and internal condition evolution speed and combining the prediction of the average time of the occurrence of different road segment damage, a maintenance sequence determining method oriented to the road network is constructed, the effectiveness of maintenance decision is improved, the optimal maintenance decision scheme can be made under the condition of limited maintenance funds, and the safe operation of the road is further effectively ensured.
According to the road network maintenance method for comprehensive evaluation, when the road network maintenance sequence is carried out, the internal and external states and the service life of the road are comprehensively considered, compared with the traditional road technical condition evaluation, maintenance decision consideration factors are richer and more reasonable, by making a reasonable road maintenance priority scheme facing the road network, the road safety, comfort and durability maintenance targets can be met, the whole technical level of the road network is orderly improved, smooth, coordinated and sustainable development of the road network is realized, the use efficiency and the scientific decision level of maintenance funds can be improved, the maximization of the investment benefit of the maintenance funds is ensured, the transition from passive maintenance to active maintenance and scientific maintenance is realized, the decision level is further improved, the maintenance cost is further reduced, and the service life of the road is prolonged.
The road network maintenance method for comprehensive evaluation can make timely, comprehensive and accurate road condition evaluation based on the consideration of the internal and external states of the road. Because the invention needs the disease data inside the road and the disease data outside the road at the same time, the invention is more suitable for being used under the condition that the road detection data is more sufficient and perfect.
Drawings
Fig. 1 is a flowchart of a road network maintenance method for comprehensive evaluation 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 presented in conjunction with the accompanying drawings 1 to provide a further understanding of the invention in its aspects, features and efficacy:
the first embodiment is as follows:
a road network maintenance method for comprehensive evaluation comprises the following steps:
s1, collecting pavement damage images, identifying pavement damage and extracting pavement damage size data from the collected pavement damage images, and calculating pavement damage condition indexes based on the extracted pavement damage size data;
further, the specific implementation method of the step S1 includes the following steps:
s1.1, shooting road images by adopting a vehicle-mounted camera shooting mode, selecting 20000 road surface disease images, and constructing a road surface disease image data set; pavement damage including cracks, blocky cracks, longitudinal cracks, transverse cracks, subsidence, ruts, wave congestion, pit grooves, looseness, oil flooding, and repair;
s1.2, marking images of a pavement disease image data set by adopting marking software labelimg, and dividing the pavement disease image data set into a training set, a verification set and a test set, wherein the training set comprises 12000 images, 4000 images of the verification set and 4000 images of the test set;
S1.3, constructing a UNet deep learning network structure:
s1.3.1 the set UNet deep learning network structure consists of an encoder and a decoder, wherein the encoder comprises 4 downsampling modules, and each downsampling module comprises two 3×3 convolution layers, 1 ReLU activation function and 1 2×2 pooling layer; the decoder comprises 4 up-sampling modules, each up-sampling module comprises 1 up-sampling convolution layer, a feature splicing concat, 2 3×3 convolution layers and 1 ReLU activation function, an image passes through the encoder first and then passes through the decoder, and the image size is 572×572;
s1.3.2, inputting the data set marked in the step S1.2 into a UNet deep learning network structure established in S1.3.1, calculating the weight and bias of a neural network unit through an error back propagation method, and completing training of the UNet deep learning network structure to obtain the UNet deep learning network structure;
s1.3.3, adopting the UNet deep learning network structure of the step S1.3.2 to identify road surface defects and extract road surface defect area data, wherein the road surface defect area data comprises: the position of the pavement defect in the image, the outline of the pavement defect and the solid area of the pavement defect;
s1.4, determining the conversion relation between image pixel data and the actual size of the pavement disease by adopting a Zhang Zhengyou calibration method:
S1.4.1, making black and white checkerboards, and shooting the black and white checkerboards at different angles by using a camera, wherein the shot images are 30 pieces;
s1.4.2 detecting the corner points of the calibration plate in the image shot in the step S1.4.1 to obtain pixel coordinate values of the corner points of the calibration plate, and calculating to obtain physical coordinate values of the corner points of the calibration plate according to the known checkerboard size and the origin of the world coordinate system;
s1.4.3, solving a camera internal parameter matrix and an external parameter matrix corresponding to each image shot in the step S1.4.1:
firstly, establishing a camera imaging model as follows:
；
wherein Z is a scale factor, (U, V) is a pixel coordinate of any point in the image under a pixel coordinate system, (U, V, W) is a world coordinate of any point in the image under a world coordinate system, AA is an internal reference matrix, and BB is an external reference matrix;
establishing a relation between a plurality of groups of pixel coordinates and world coordinates, obtaining an internal reference matrix AA and an external reference matrix AA, and establishing a conversion relation between road image pixel data of road surface diseases and the actual size of the road surface diseases;
s1.5, calculating the pavement damage condition index after obtaining the pavement disease type and size information based on the steps S1.1-S1.4PCIThe computational expression is:
；
；
wherein,for road surface damage rate->Is the first niArea of road surface damage; />For the investigation of the road surface area->Is the firstniWeight of road surface damage class +.>For linear adjustment coefficient>For index adjustment factor, ++>Is the total number of damage types;
after the road surface disease type and size information is obtained, road surface damage is evaluated by adopting a road surface damage condition index (PCI) according to the highway technical condition evaluation standard (JTG 5210-2018);representing the percentage of the sum of the folded damaged areas of the various damages to the road surface area of investigation; for asphalt pavement->15.00 is adopted; for asphalt pavement->0.412 is employed;
s2, constructing a road internal void disease recognition convolutional neural network model based on a ground penetrating radar void disease image, then acquiring a road internal image by adopting the ground penetrating radar, performing disease recognition on the acquired road internal image by utilizing the acquired road internal void disease recognition convolutional neural network model, and calculating road internal void region parameters;
further, the specific implementation method of the step S2 includes the following steps:
s2.1, constructing a road internal void disease recognition convolutional neural network model;
s2.1.1, establishing a road internal disease data set based on a ground penetrating radar void disease image: marking diseases in the ground penetrating radar void disease images and marking disease categories by using LabelImg software, and storing the names of marking files consistent with the names of the ground penetrating radar void disease images to obtain a road internal disease data set;
S2.1.2, randomly dividing the road internal disease data set obtained in the step S2.1.1 into a training set, a verification set and a test set according to the proportion of 6:2:2;
s2.1.3, inputting the training set, the verification set and the test set obtained in the step S2.1.2 into a convolutional neural network for training, verifying and testing, and outputting model parameters of the convolutional neural network model, including the number of network layers, the number of neuron nodes of each layer, the learning rate, the weight, the bias, the activation function, the loss function and the convolution kernel, so as to obtain the road internal void disease identification convolutional neural network model.
S2.2, acquiring an image of the interior of the road, identifying the disease by using the obtained model of the road interior void disease identification convolutional neural network, and calculating parameters of a void area in the road;
s2.2.1, collecting an internal image of a road by adopting a ground penetrating radar, and carrying out disease identification on the collected internal image of the road by utilizing the internal void disease identification convolutional neural network model of the road, which is obtained in the step S2.1, so as to obtain an internal image of the damaged road;
s2.2.2, obtaining a disease road of an image in the disease road by adopting a drilling machine to drill in the step S2.2.1, and obtaining a road void area;
s2.2.3 the endoscope is put into the road void area, the top plate position and the bottom plate position of the 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 be the height of the road void area ；
S2.2.4 then filling water into the road void region obtained in step S2.2.2 until the water is filled, and recording the volume of the water filling as the volume of the road void region；
S2.2.5 calculating the area of the road void regionThe computational expression is:
；
s3, constructing an evaluation index of the service life of the interior of the road based on the parameters of the void area in the interior of the road obtained in the step S2;
further, the specific implementation method of the step S3 includes the following steps:
s3.1, setting the total number of the road void areas as；
S3.2, constructing an evaluation index of the service life of the interior of the road: the method comprises the steps of total number of road void areas, average height, average volume, average area, height change gradient, volume change gradient, quantity change gradient and area change gradient of the road void areas;
s3.2.1 average height of road void areaThe calculated expression of (2) is:
；
wherein, the road isiThe height of each void area is；
S3.2.2 average volume of road void areaThe calculated expression of (2) is:
；
wherein, the road isiThe volume of each void area is；
S3.2.3 average area of road void areaThe calculated expression of (2) is:
；
wherein, the road isiThe area of each void area is；
S3.2.4 gradient of altitude change in road void The calculated expression of (2) is:
；
wherein,、/>respectively the firstiTime and th of data acquisitioni+1 data acquisition time, +_1->Is the firstiAverage height of road void area at individual data acquisition time, +.>Is the firstiAverage height of road void area for +1 data acquisition time;
s3.2.5 gradient of volume change in road void regionThe calculated expression of (2) is:
；
wherein,is the firstiAverage volume of road void area at each data acquisition time +.>Is the firstiAn average volume of road void area of +1 data acquisition time;
s3.2.6 gradient of number change of road void areasThe calculated expression of (2) is: />
；
Wherein,is the firstiThe number of road void areas at each data acquisition time,/->Is the firstiThe number of road void areas for +1 data acquisition time;
s3.2.7 gradient of area change in road void regionThe calculated expression of (2) is:
；
wherein,is the firstiAverage area of road void area at each data acquisition time,/->Is the firstiAverage area of road void area for +1 data acquisition time;
s4, carrying out normalization processing on the road internal service life assessment index obtained in the step S3 and the road surface damage condition index obtained in the step S1, and constructing a road service life comprehensive assessment index;
Further, the specific implementation method of the step S4 includes the following steps:
s4.1, dividing the road into road segments with 50m as road segment lengthNPA segment, wherein,NPthe total number of road segments;
s4.2, taking the time point of the operation starting from the road construction as a time zero point, and respectively detecting the operation of each road section aiming at each road sectiontThe road void area and road surface diseases after the time are calculated, and the road internal service life evaluation index and the road surface damage condition index of each road void area are calculated;
s4.3, traversing the road internal service life evaluation index and the road surface damage condition index of each road void area calculated in the step S4.2 to obtain a maximum value, wherein the maximum value comprises the total number of the road void areasRoad and trackAverage height maximum value of road void area +.>Average volume maximum>Average area maximum>Maximum value of the gradient of the altitude change->Maximum value of volume change gradient->Maximum value of the number change gradient->Maximum value of area change gradient->Maximum value of road surface damage condition index->；
S4.4, carrying out normalization processing on the road internal service life evaluation index of each road void area obtained in the step S4.2 by using the maximum value obtained in the step S4.3 as a reference, wherein the calculation formula is as follows:
；
Wherein,the average height of the road void area is normalized;
；
wherein,the total number of road void areas processed for normalization; />
；
Wherein,an average volume of the road void area for normalization processing;
；
wherein,an average area of the road void area which is normalized;
；
wherein,a height change gradient of the road void area which is normalized;
；
wherein,a number change gradient of the road void area which is normalized;
；
wherein,a volume change gradient of a road void area which is normalized;
；
wherein,an area change gradient of a road void area which is normalized;
；
wherein,a road surface damage condition index for normalization processing;
then building comprehensive evaluation index of road service lifeTKThe computational expression is:
；
wherein,zk1 is a weight coefficient of the average height of the road void area of the normalization processing,zk2 is the weight coefficient of the total number of the road void areas of the normalization processing,zk3 is a weight coefficient of the average volume of the road void area of the normalization processing,zk4 is a weight coefficient of the average area of the road void area of the normalization processing,zk5 is the weight coefficient of the height change gradient of the road void area subjected to normalization processing, zk6 is a weight coefficient of the number change gradient of the road void area subjected to normalization processing,zk7 is the weight coefficient of the volume change gradient of the road void area subjected to normalization processing,zk8 is a weight coefficient of the area change gradient of the road void area subjected to normalization processing;zk9 is a weight coefficient of the normalized road surface damage condition index;
s4.5, calculating the first according to the method of the step S4.4iOn each road sectiontiComprehensive evaluation index of road service life at moment；
S5, calculating comprehensive evaluation indexes of the service life of the road according to the method of the step S4, and performing preliminary service life evaluation of the road;
further, the specific implementation method of the step S5 includes the following steps:
s5.1, counting road detection data of MK roads after 15 years of running, calculating comprehensive evaluation indexes of service life of each road according to the method of the step S4,is the firstjComprehensive evaluation index of road service life after 15 years of running of the MK road, and then calculating average value of comprehensive evaluation index of road service life of MK road +.>Standard deviation of comprehensive evaluation index of road service life>；
S5.2, constructing a threshold value of a comprehensive evaluation index of the service life of the roadThe computational expression is:
；
when (when)If yes, judging that the road is intact, if +. >Judging that the road is damaged;
s5.3, collecting the road section judged to be damaged in the step S5.2iTime when damage occurs to the 1 st time of the road sectionThen building a time matrix of the 1 st occurrence of the damage of the road sectionFT，/>；
S5.4, counting road operationt i Number of damaged road segments 1 st time of road segments corresponding in timeThen calculate the road operationt i The number of damaged road sections in the 1 st time of the road sections corresponding to time accounts for the percentage of the total number of the road sections, and the calculation expression is as follows:
；
wherein,for road runningt i The number of damaged road sections of the 1 st time of the road sections corresponding to time accounts for the percentage of the total number of the road sections;
s6, constructing a life distribution function model of the road section by adopting a three-parameter Weibull distribution function, and calculating the average damage occurrence time of the road section;
further, the specific implementation method of the step S6 includes the following steps:
s6.1, constructing a life distribution function model of the road section by adopting a three-parameter Weibull function, wherein the calculation expression is as follows:
；/>
wherein,for the length of service of road sections,/->For the position parameter of the weibull distribution, +.>Is a wei clothScale parameters of the molar distribution->Is the shape parameter of the Weibull distribution, +. >The probability of damage occurrence is accumulated for the road section in the service time of the road section;
based on the life distribution function model of the road section, constructing a probability density function model of life distribution of the road section, wherein the calculation expression is as follows:
；
wherein,the probability of damage to the road section corresponding to the moment t in the service time of the road section is provided;
constructing a reliability function model of a road section, wherein the calculation expression is as follows:
；
wherein,the probability that the road section is in good condition in the service time of the road section is provided;
constructing a failure probability function model of a road section, wherein the calculation expression is as follows:
；
wherein,the probability that the damage occurs in the road section unit time after the road section service time t is the probability that the damage does not occur in the road section before the road section service time t;
constructing a time function model of average damage of a road section, wherein the calculation expression is as follows:
；
wherein,the time of average damage of the road section in the service time of the road section is provided;
s6.2, using step S5Andticarrying out a life distribution function model of the road section constructed in the step S6.1, adopting a least square fitting solution, and calculating to obtain +.>、/>、/>；
S7, setting the road section average damage occurrence time, road technical condition grade, road width, road length, road material, road grade, maintenance cost, traffic volume and road age obtained in the step S6 as road network maintenance factors for comprehensive evaluation, constructing a road network maintenance scoring matrix for comprehensive evaluation, and judging the road network maintenance priority of the comprehensive evaluation;
Further, the specific implementation method of the step S7 includes the following steps:
s7.1, setting the road network maintenance factors which are comprehensively evaluated by the average damage occurrence time, the road width, the road length, the road materials, the road grade, the maintenance cost, the traffic volume, the road age and the environmental conditions of the road section obtained in the step S6, wherein the number of the road network maintenance factors is 9;
s7.2, setting the number of the roads in the road network as n, and adopting a manual assessment mode to carry out on each roadScoring the comprehensively evaluated road network maintenance factors, and constructing a comprehensively evaluated road network maintenance scoring matrixPFThe computational expression is:
；
wherein,the first road network maintenance scoring matrix for comprehensive evaluationiRoad No.jScoring the road network maintenance factors comprehensively evaluated;
s7.3, carrying out standardized processing on indexes in the road network maintenance scoring matrix which is comprehensively evaluated and obtained in the step S7.2, wherein the calculation expression is as follows:
；
wherein,is the firstiRoad No.jStandardized scores of comprehensively evaluated road network maintenance factors;
on the basis, a road network maintenance standardization grading matrix which is comprehensively evaluated is obtainedThe computational expression is:
；
S7.4, constructing road network maintenance scoring entropy, wherein the calculation expression is as follows:
；
wherein,road for the jth comprehensively estimated road network maintenance factorGrading entropy of net maintenance;
s7.5, constructing road network maintenance influence factors based on the road network maintenance scoring entropy obtained in the step S7.4, wherein the calculation expression is as follows:
；
wherein,road network maintenance influence factors for the j-th comprehensively estimated road network maintenance factors;
s7.6, calculating the final score corresponding to each road based on the road network maintenance influence factors obtained in the step S7.5PFFThe computational expression is:
；
wherein,is thatFwThe final scores corresponding to the roads are arranged in order from small to large, and the maintenance priority is higher as the score is smaller.
According to the road network maintenance method for comprehensive evaluation, road damage condition indexes are obtained through calculation by collecting road surface images and adopting the intelligent identification of road surface diseases and the extraction of size information; by fusing the road surface damage condition index and the road internal void size information, considering the surface and internal condition evolution speed and combining the prediction of the average time of the occurrence of different road segment damage, a maintenance sequence determining method oriented to the road network is constructed, the effectiveness of maintenance decision is improved, the optimal maintenance decision scheme can be made under the condition of limited maintenance funds, and the safe operation of the road is further effectively ensured.
The second embodiment is as follows:
an electronic device comprising a memory and a processor, the memory storing a computer program, said processor implementing the steps of said one fully evaluated road network maintenance method when executing said 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 network maintenance method comprehensively evaluated 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.
And a third specific embodiment:
a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of comprehensively evaluating road network maintenance.
The computer readable storage medium of the present invention may be any form of storage medium readable by a processor of a computer device, including but not limited to, nonvolatile memory, volatile memory, ferroelectric memory, etc., having a computer program stored thereon, which when read and executed by the processor of the computer device, implements the steps of a comprehensively evaluated road network maintenance method as described above.
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 (10)
1. The road network maintenance method for comprehensive evaluation is characterized by comprising the following steps of:
s1, collecting pavement damage images, identifying pavement damage and extracting pavement damage size data from the collected pavement damage images, and calculating pavement damage condition indexes based on the extracted pavement damage size data;
s2, constructing a road internal void disease recognition convolutional neural network model based on a ground penetrating radar void disease image, then acquiring a road internal image by adopting the ground penetrating radar, performing disease recognition on the acquired road internal image by utilizing the acquired road internal void disease recognition convolutional neural network model, and calculating road internal void region parameters;
s3, constructing an evaluation index of the service life of the interior of the road based on the parameters of the void area in the interior of the road obtained in the step S2;
s4, carrying out normalization processing on the road internal service life assessment index obtained in the step S3 and the road surface damage condition index obtained in the step S1, and constructing a road service life comprehensive assessment index;
s5, calculating comprehensive evaluation indexes of the service life of the road according to the method of the step S4, and performing preliminary service life evaluation of the road;
s6, constructing a life distribution function model of the road section by adopting a three-parameter Weibull distribution function, and calculating the average damage occurrence time of the road section;
And S7, setting the road section average damage occurrence time, the road technical condition grade, the road width, the road length, the road material, the road grade, the maintenance cost, the traffic volume and the road age obtained in the step S6 as road network maintenance factors for comprehensive evaluation, constructing a road network maintenance scoring matrix for comprehensive evaluation, and judging the road network maintenance priority of the comprehensive evaluation.
2. The comprehensive evaluation road network maintenance method according to claim 1, wherein the specific implementation method of the step S1 comprises the following steps:
s1.1, shooting road images by adopting a vehicle-mounted camera shooting mode, selecting 20000 road surface disease images, and constructing a road surface disease image data set; pavement damage including cracks, blocky cracks, longitudinal cracks, transverse cracks, subsidence, ruts, wave congestion, pit grooves, looseness, oil flooding, and repair;
s1.2, marking images of a pavement disease image data set by adopting marking software labelimg, and dividing the pavement disease image data set into a training set, a verification set and a test set, wherein the training set comprises 12000 images, 4000 images of the verification set and 4000 images of the test set;
s1.3, constructing a UNet deep learning network structure:
S1.3.1 the set UNet deep learning network structure consists of an encoder and a decoder, wherein the encoder comprises 4 downsampling modules, and each downsampling module comprises two 3×3 convolution layers, 1 ReLU activation function and 1 2×2 pooling layer; the decoder comprises 4 up-sampling modules, each up-sampling module comprises 1 up-sampling convolution layer, a feature splicing concat, 2 3×3 convolution layers and 1 ReLU activation function, an image passes through the encoder first and then passes through the decoder, and the image size is 572×572;
s1.3.2, inputting the data set marked in the step S1.2 into a UNet deep learning network structure established in S1.3.1, calculating the weight and bias of a neural network unit through an error back propagation method, and completing training of the UNet deep learning network structure to obtain the UNet deep learning network structure;
s1.3.3, adopting the UNet deep learning network structure of the step S1.3.2 to identify road surface defects and extract road surface defect area data, wherein the road surface defect area data comprises: the position of the pavement defect in the image, the outline of the pavement defect and the solid area of the pavement defect;
s1.4, determining the conversion relation between image pixel data and the actual size of the pavement disease by adopting a Zhang Zhengyou calibration method:
S1.4.1, making black and white checkerboards, and shooting the black and white checkerboards at different angles by using a camera, wherein the shot images are 30 pieces;
s1.4.2 detecting the corner points of the calibration plate in the image shot in the step S1.4.1 to obtain pixel coordinate values of the corner points of the calibration plate, and calculating to obtain physical coordinate values of the corner points of the calibration plate according to the known checkerboard size and the origin of the world coordinate system;
s1.4.3, solving a camera internal parameter matrix and an external parameter matrix corresponding to each image shot in the step S1.4.1:
firstly, establishing a camera imaging model as follows:
；
wherein Z is a scale factor, (U, V) is a pixel coordinate of any point in the image under a pixel coordinate system, (U, V, W) is a world coordinate of any point in the image under a world coordinate system, AA is an internal reference matrix, and BB is an external reference matrix;
establishing a relation between a plurality of groups of pixel coordinates and world coordinates, obtaining an internal reference matrix AA and an external reference matrix AA, and establishing a conversion relation between road image pixel data of road surface diseases and the actual size of the road surface diseases;
s1.5, calculating the pavement damage condition index after obtaining the pavement disease type and size information based on the steps S1.1-S1.4PCIThe computational expression is:
；
；
wherein,for road surface damage rate->Is the first niArea of road surface damage; />In order to investigate the area of the road surface,is the firstniWeight of road surface damage class +.>For linear adjustment coefficient>For index adjustment factor, ++>Is the total number of damage types.
3. The road network maintenance method for comprehensive evaluation according to claim 1 or 2, wherein the specific implementation method of step S2 comprises the following steps:
s2.1, constructing a road internal void disease recognition convolutional neural network model;
s2.1.1, establishing a road internal disease data set based on a ground penetrating radar void disease image: marking diseases in the ground penetrating radar void disease images and marking disease categories by using LabelImg software, and storing the names of marking files consistent with the names of the ground penetrating radar void disease images to obtain a road internal disease data set;
s2.1.2, randomly dividing the road internal disease data set obtained in the step S2.1.1 into a training set, a verification set and a test set according to the proportion of 6:2:2;
s2.1.3, inputting the training set, the verification set and the test set obtained in the step S2.1.2 into a convolutional neural network for training, verifying and testing, and outputting model parameters of a convolutional neural network model, including the number of network layers, the number of neuron nodes of each layer, the learning rate, the weight, the bias, the activation function, the loss function and the convolution kernel, so as to obtain a road internal void disease identification convolutional neural network model;
S2.2, acquiring an image of the interior of the road, identifying the disease by using the obtained model of the road interior void disease identification convolutional neural network, and calculating parameters of a void area in the road;
s2.2.1, collecting an internal image of a road by adopting a ground penetrating radar, and carrying out disease identification on the collected internal image of the road by utilizing the internal void disease identification convolutional neural network model of the road, which is obtained in the step S2.1, so as to obtain an internal image of the damaged road;
s2.2.2, obtaining a disease road of an image in the disease road by adopting a drilling machine to drill in the step S2.2.1, and obtaining a road void area;
s2.2.3 the endoscope is put into the road void area, the top plate position and the bottom plate position of the 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 be the height of the road void area；
S2.2.4 then filling water into the road void region obtained in step S2.2.2 until the water is filled, and recording the volume of the water filling as the volume of the road void region；
S2.2.5 calculating the area of the road void regionThe computational expression is:
。
4. the road network maintenance method for comprehensive evaluation according to claim 3, wherein the specific implementation method of step S3 comprises the following steps:
S3.1, setting the total number of the road void areas as；
S3.2, constructing an evaluation index of the service life of the interior of the road: the method comprises the steps of total number of road void areas, average height, average volume, average area, height change gradient, volume change gradient, quantity change gradient and area change gradient of the road void areas;
s3.2.1 average height of road void areaThe calculated expression of (2) is:
；
wherein, the road isiThe height of each void area is；
S3.2.2 average volume of road void areaThe calculated expression of (2) is:
；
wherein, the road isiThe volume of each void area is；
S3.2.3 average area of road void areaThe calculated expression of (2) is:
；
wherein, the road isiThe area of each void area is；
S3.2.4 gradient of altitude change in road voidThe calculated expression of (2) is:
；
wherein,、/>respectively the firstiTime and th of data acquisitioni+1 data acquisition time, +_1->Is the firstiAverage height of road void area at individual data acquisition time, +.>Is the firstiAverage height of road void area for +1 data acquisition time;
s3.2.5 gradient of volume change in road void regionThe calculated expression of (2) is:
；
wherein,is the firstiAverage volume of road void area at each data acquisition time +. >Is the firstiAn average volume of road void area of +1 data acquisition time;
s3.2.6 gradient of number change of road void areasThe calculated expression of (2) is:
；
wherein,is the firstiThe number of road void areas at each data acquisition time,/->Is the firstiThe number of road void areas for +1 data acquisition time;
s3.2.7 gradient of area change in road void regionThe calculated expression of (2) is:
；
wherein,is the firstiAverage area of road void area at each data acquisition time,/->Is the firstiAverage area of road void area for +1 data acquisition time.
5. The comprehensive evaluation road network maintenance method according to claim 4, wherein the specific implementation method of step S4 comprises the following steps:
s4.1, dividing the road into road segments with 50m as road segment lengthNPA segment, wherein,NPthe total number of road segments;
s4.2, taking the time point of the operation starting from the road construction as a time zero point, and respectively detecting the operation of each road section aiming at each road sectiontThe road void area and road surface diseases after the time are calculated, and the road internal service life evaluation index and the road surface damage condition index of each road void area are calculated;
S4.3, traversing the road internal service life evaluation index and the road surface damage condition index of each road void area calculated in the step S4.2 to obtain a maximum value, wherein the maximum value comprises the total number of the road void areasAverage height maximum value of road void area +.>Average volume maximum>Average area maximum>Maximum value of height variation gradientMaximum value of volume change gradient->Maximum value of the number change gradient->Maximum value of area change gradientMaximum value of road surface damage condition index->；
S4.4, carrying out normalization processing on the road internal service life evaluation index of each road void area obtained in the step S4.2 by using the maximum value obtained in the step S4.3 as a reference, wherein the calculation formula is as follows:
；
wherein,the average height of the road void area is normalized;
；
wherein,the total number of road void areas processed for normalization;
；
wherein,an average volume of the road void area for normalization processing;
；
wherein,an average area of the road void area which is normalized;
；
wherein,a height change gradient of the road void area which is normalized;
；
wherein,a number change gradient of the road void area which is normalized;
；
Wherein,a volume change gradient of a road void area which is normalized;
；
wherein,an area change gradient of a road void area which is normalized;
；
wherein,a road surface damage condition index for normalization processing;
then building comprehensive evaluation index of road service lifeTKThe computational expression is:
；
wherein,zk1 is a weight coefficient of the average height of the road void area of the normalization processing,zk2 is the weight coefficient of the total number of the road void areas of the normalization processing,zk3 is a weight coefficient of the average volume of the road void area of the normalization processing,zk4 is a weight coefficient of the average area of the road void area of the normalization processing,zk5 is the weight coefficient of the height change gradient of the road void area subjected to normalization processing,zk6 is a weight coefficient of the number change gradient of the road void area subjected to normalization processing,zk7 is the weight coefficient of the volume change gradient of the road void area subjected to normalization processing,zk8 is a weight coefficient of the area change gradient of the road void area subjected to normalization processing;zk9 is a weight coefficient of the normalized road surface damage condition index;
s4.5, calculating the first according to the method of the step S4.4iOn each road sectiontiComprehensive evaluation index of road service life at moment 。
6. The comprehensive evaluation road network maintenance method according to claim 5, wherein the specific implementation method of step S5 comprises the following steps:
s5.1, counting road detection data of MK roads after 15 years of running, calculating comprehensive evaluation indexes of service life of each road according to the method of the step S4,is the firstjComprehensive evaluation index of road service life after 15 years of running of the MK road, and then calculating average value of comprehensive evaluation index of road service life of MK road +.>Standard deviation of comprehensive evaluation index of road service life>；
S5.2, constructing a threshold value of a comprehensive evaluation index of the service life of the roadThe computational expression is:
；
when (when)If yes, judging that the road is intact, if +.>Judging that the road is damaged;
s5.3, collecting the road section judged to be damaged in the step S5.2iTime when damage occurs to the 1 st time of the road sectionThen building a time matrix of the 1 st occurrence of the damage of the road sectionFT， ；
S5.4, counting road operationt i Number of damaged road segments 1 st time of road segments corresponding in timeThen calculate the road operationt i The number of damaged road sections in the 1 st time of the road sections corresponding to time accounts for the percentage of the total number of the road sections, and the calculation expression is as follows:
；
Wherein,for road runningt i The number of damaged road segments 1 st time of the road segments corresponding in time is a percentage of the total number of road segments.
7. The comprehensive evaluation road network maintenance method according to claim 6, wherein the specific implementation method of step S6 comprises the following steps:
s6.1, constructing a life distribution function model of the road section by adopting a three-parameter Weibull function, wherein the calculation expression is as follows:
；
wherein,for the length of service of road sections,/->For the position parameter of the weibull distribution, +.>Is the scale parameter of Weibull distribution, +.>Is the shape parameter of the Weibull distribution, +.>The probability of damage occurrence is accumulated for the road section in the service time of the road section;
based on the life distribution function model of the road section, constructing a probability density function model of life distribution of the road section, wherein the calculation expression is as follows:
；
wherein,the probability of damage to the road section corresponding to the moment t in the service time of the road section is provided;
constructing a reliability function model of a road section, wherein the calculation expression is as follows:
；
wherein,the probability that the road section is in good condition in the service time of the road section is provided;
constructing a failure probability function model of a road section, wherein the calculation expression is as follows:
；
Wherein,the probability that the damage occurs in the road section unit time after the road section service time t is the probability that the damage does not occur in the road section before the road section service time t;
constructing a time function model of average damage of a road section, wherein the calculation expression is as follows:
；
wherein,the time of average damage of the road section in the service time of the road section is provided;
s6.2, using step S5Andticarrying out a life distribution function model of the road section constructed in the step S6.1, adopting a least square fitting solution, and calculating to obtain +.>、/>、/>。
8. The comprehensive evaluation of maintenance and repair method for road network according to claim 7, wherein the specific implementation method of step S7 comprises the following steps:
s7.1, setting the road network maintenance factors which are comprehensively evaluated by the average damage occurrence time, the road width, the road length, the road materials, the road grade, the maintenance cost, the traffic volume, the road age and the environmental conditions of the road section obtained in the step S6, wherein the number of the road network maintenance factors is 9;
s7.2, set upThe number of roads in the road network is n, the road network maintenance factors which are comprehensively evaluated for each road are scored by adopting a manual evaluation mode, and a road network maintenance scoring matrix which is comprehensively evaluated is constructed PFThe computational expression is:
；
wherein,the first road network maintenance scoring matrix for comprehensive evaluationiRoad No.jScoring the road network maintenance factors comprehensively evaluated;
s7.3, carrying out standardized processing on indexes in the road network maintenance scoring matrix which is comprehensively evaluated and obtained in the step S7.2, wherein the calculation expression is as follows:
；
wherein,is the firstiRoad No.jStandardized scores of comprehensively evaluated road network maintenance factors;
on the basis, a road network maintenance standardization grading matrix which is comprehensively evaluated is obtainedThe computational expression is:
；
s7.4, constructing road network maintenance scoring entropy, wherein the calculation expression is as follows:
；
wherein,road network maintenance scoring entropy for the j-th comprehensively evaluated road network maintenance factors;
s7.5, constructing road network maintenance influence factors based on the road network maintenance scoring entropy obtained in the step S7.4, wherein the calculation expression is as follows:
；
wherein,road network maintenance influence factors for the j-th comprehensively estimated road network maintenance factors;
s7.6, calculating the final score corresponding to each road based on the road network maintenance influence factors obtained in the step S7.5PFFThe computational expression is:
；
wherein,is thatFwThe final scores corresponding to the roads are arranged in order from small to large, and the maintenance priority is higher as the score is smaller.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a fully evaluated road network maintenance method according to any of claims 1-8 when the computer program is executed.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements a fully evaluated road network maintenance method according to any of claims 1-8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117522175A (en) * | 2024-01-08 | 2024-02-06 | 中国公路工程咨询集团有限公司 | Road maintenance decision method and system |
| CN117518281A (en) * | 2023-12-29 | 2024-02-06 | 山东高速集团有限公司 | Method for quantitatively evaluating pavement hidden diseases based on three-dimensional ground penetrating radar |
| CN117726324A (en) * | 2024-02-07 | 2024-03-19 | 中国水利水电第九工程局有限公司 | Road traffic construction inspection method and system based on data identification |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110189046A (en) * | 2019-06-04 | 2019-08-30 | 江苏中路工程技术研究院有限公司 | A kind of asphalt pavement structure inside disease evaluation method |
| CN113424055A (en) * | 2019-10-09 | 2021-09-21 | 山东大学 | Multi-scale tunnel structure disease detection and intelligent diagnosis system and method |
| CN113962960A (en) * | 2021-10-22 | 2022-01-21 | 西安电子科技大学 | Pavement disease detection method based on deep learning |
| CN114355339A (en) * | 2022-01-12 | 2022-04-15 | 华南理工大学 | Method and system for identifying radar spectrum of road surface void disease |
| CN114937033A (en) * | 2022-06-27 | 2022-08-23 | 辽宁工程技术大学 | Rural highway pavement disease intelligent detection method based on deep convolutional neural network |
| CN114998852A (en) * | 2021-08-05 | 2022-09-02 | 浙江杉工智能科技有限公司 | Intelligent detection method for road pavement diseases based on deep learning |
| CN115062881A (en) * | 2022-08-19 | 2022-09-16 | 深圳市城市交通规划设计研究中心股份有限公司 | Tunnel structure property prediction method, device, computer equipment and storage medium |
| CN115063053A (en) * | 2022-08-17 | 2022-09-16 | 湖北交投智能检测股份有限公司 | Decision-making method for digital construction and preventive maintenance of asphalt pavement |
| CN115390033A (en) * | 2022-07-26 | 2022-11-25 | 广西北投交通养护科技集团有限公司 | Method, system and device for detecting cavity and water loss under road surface based on ground penetrating radar |
| 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 |
| CN115439750A (en) * | 2022-09-22 | 2022-12-06 | 中煤(西安)地下空间科技发展有限公司 | Road disease detection method and device, electronic equipment and storage medium |
| CN115841225A (en) * | 2022-11-17 | 2023-03-24 | 华中科技大学 | Road maintenance priority ranking method in road network |
-
2023
- 2023-10-17 CN CN202311336933.3A patent/CN117078235B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110189046A (en) * | 2019-06-04 | 2019-08-30 | 江苏中路工程技术研究院有限公司 | A kind of asphalt pavement structure inside disease evaluation method |
| CN113424055A (en) * | 2019-10-09 | 2021-09-21 | 山东大学 | Multi-scale tunnel structure disease detection and intelligent diagnosis system and method |
| CN114998852A (en) * | 2021-08-05 | 2022-09-02 | 浙江杉工智能科技有限公司 | Intelligent detection method for road pavement diseases based on deep learning |
| CN113962960A (en) * | 2021-10-22 | 2022-01-21 | 西安电子科技大学 | Pavement disease detection method based on deep learning |
| CN114355339A (en) * | 2022-01-12 | 2022-04-15 | 华南理工大学 | Method and system for identifying radar spectrum of road surface void disease |
| CN114937033A (en) * | 2022-06-27 | 2022-08-23 | 辽宁工程技术大学 | Rural highway pavement disease intelligent detection method based on deep convolutional neural network |
| CN115390033A (en) * | 2022-07-26 | 2022-11-25 | 广西北投交通养护科技集团有限公司 | Method, system and device for detecting cavity and water loss under road surface based on ground penetrating radar |
| CN115063053A (en) * | 2022-08-17 | 2022-09-16 | 湖北交投智能检测股份有限公司 | Decision-making method for digital construction and preventive maintenance of asphalt pavement |
| CN115062881A (en) * | 2022-08-19 | 2022-09-16 | 深圳市城市交通规划设计研究中心股份有限公司 | Tunnel structure property prediction method, device, computer equipment and storage medium |
| CN115439750A (en) * | 2022-09-22 | 2022-12-06 | 中煤(西安)地下空间科技发展有限公司 | Road disease detection method and device, electronic equipment and storage medium |
| 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 |
| CN115841225A (en) * | 2022-11-17 | 2023-03-24 | 华中科技大学 | Road maintenance priority ranking method in road network |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117518281A (en) * | 2023-12-29 | 2024-02-06 | 山东高速集团有限公司 | Method for quantitatively evaluating pavement hidden diseases based on three-dimensional ground penetrating radar |
| CN117522175A (en) * | 2024-01-08 | 2024-02-06 | 中国公路工程咨询集团有限公司 | Road maintenance decision method and system |
| CN117522175B (en) * | 2024-01-08 | 2024-04-02 | 中国公路工程咨询集团有限公司 | Road maintenance decision method and system |
| CN117726324A (en) * | 2024-02-07 | 2024-03-19 | 中国水利水电第九工程局有限公司 | Road traffic construction inspection method and system based on data identification |
| CN117726324B (en) * | 2024-02-07 | 2024-04-30 | 中国水利水电第九工程局有限公司 | Road traffic construction inspection method and system based on data identification |
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