CN115438547A - Overall evaluation method and system based on pavement service state - Google Patents
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Abstract
The invention discloses an overall evaluation method and system based on a pavement service state, which comprises the steps of obtaining pavement disease data, and evaluating the pavement disease data to obtain the disease severity; evaluating the severity of the diseases to obtain a pavement condition numerical value; and acquiring a road surface condition numerical value, and acquiring a whole evaluation result of the road surface service state based on the road surface service time and the road surface condition numerical value. By the technical scheme, the service pavement can be comprehensively and integrally evaluated.
Description
Technical Field
The invention relates to the technical field of road evaluation, in particular to an overall evaluation method and system based on a pavement service state.
Background
The detection and treatment of the operation pavement diseases become a key problem of pavement management and maintenance, regular and comprehensive health detection is carried out on a serving highway, the overall health condition of a pavement structure is analyzed, the pavement state, cracks, water leakage and other diseases are sensed, timely repair and maintenance work is carried out according to the severity of the diseases, the pavement driving safety coefficient is effectively improved, and economic loss and potential safety hazards are reduced. The research on the aspects of road surface state and disease perception in the operation period is developed, great help is provided for road management, maintenance, treatment and safety, the method is an urgent need for guaranteeing the health and long-term service of a road surface structure, and the method has great theoretical significance and engineering value. Therefore, a method for comprehensively evaluating the service pavement is needed.
Disclosure of Invention
In order to solve the problem that the service pavement can not be comprehensively and comprehensively evaluated in the prior art, the invention provides an overall evaluation method and system based on the service state of the pavement, which can comprehensively and comprehensively evaluate the service pavement.
In order to achieve the technical purpose, the invention provides the following technical scheme: the overall evaluation method based on the service state of the pavement comprises the following steps:
acquiring pavement disease data, and evaluating the pavement disease data to obtain the severity of the diseases; evaluating the severity of the diseases to obtain a pavement condition numerical value; and acquiring a road surface condition numerical value, and acquiring a whole evaluation result of the road surface service state based on the road surface service time and the road surface condition numerical value.
Optionally, the process of acquiring the road surface disease data includes:
processing and analyzing by computer vision and ground penetrating radar signals to obtain pavement disease data; the road surface disease data comprises road surface disease data and road surface internal defect data, wherein the road surface disease comprises cracks and water seepage, and the road surface internal defect comprises holes and water damage.
Optionally, the process of evaluating the road surface disease data includes:
the method comprises the steps of counting pavement disease data, calculating a statistical result to obtain a disease damage score, performing weight distribution on the disease damage score, obtaining a disease severity score based on the weight distribution result, judging the disease severity score to obtain a disease severity degree, wherein the disease severity score comprises a road surface disease severity score and a pavement internal defect severity score, and the disease severity degree comprises a road surface disease severity degree and a pavement disease severity degree.
Optionally, the process of evaluating the severity of the disease includes:
and constructing a fuzzy evaluation library, searching the disease severity through the fuzzy evaluation library to obtain the road surface fuzzy condition, and defuzzifying the road surface fuzzy condition to obtain the road surface condition numerical value.
Optionally, the process of obtaining the overall evaluation result of the service state of the road surface includes:
and constructing a prediction model, predicting the service time of the pavement and the pavement condition value through the prediction model to obtain the residual service life of the pavement, and integrating the residual service life of the pavement, the pavement condition value, the service time of the pavement and the severity of the disease to obtain the overall evaluation result of the service state of the pavement.
In order to better achieve the technical purpose, the invention also provides an overall evaluation system based on the service state of the road surface, which comprises:
the system comprises an acquisition module and an evaluation module; the acquisition module is used for acquiring pavement disease data and evaluating the pavement disease data to obtain the severity of the disease; the evaluation module is used for evaluating the severity of the diseases to obtain a pavement condition numerical value; and acquiring a road surface condition numerical value, and acquiring a whole evaluation result of the road surface service state based on the road surface service time and the road surface condition numerical value.
Optionally, the acquiring module includes a first acquiring module, and the first acquiring module is configured to process and analyze the signal of the ground penetrating radar through computer vision to obtain road surface disease data; the road surface disease data comprises road surface disease data and road surface internal defect data, wherein the road surface disease comprises cracks and water seepage, and the road surface internal defect comprises holes and water damage.
Optionally, the obtaining module includes a second obtaining module, where the second obtaining module is configured to count the road surface disease data, calculate a statistical result, obtain a disease damage score, perform weight distribution on the disease damage score, obtain a disease severity score based on the weight distribution result, and determine the disease severity score to obtain a disease severity degree, where the disease severity score includes a road surface disease severity score and a road surface internal defect severity score, and the disease severity degree includes a road surface disease severity degree and a road surface disease severity degree.
Optionally, the evaluation module includes a first evaluation module, where the first evaluation module is configured to construct a fuzzy evaluation library, retrieve the severity of the disease through the fuzzy evaluation library to obtain a road surface fuzzy condition, and defuzzify the road surface fuzzy condition to obtain a road surface condition value.
Optionally, the evaluation module includes a second evaluation module, where the second evaluation module is configured to construct a prediction model, predict the road service time and the road condition value through the prediction model to obtain the remaining road life, and integrate the remaining road life, the road condition value, the road service time, and the disease severity to obtain an overall evaluation result of the road service state.
The invention has the following technical effects:
the invention provides an overall evaluation method based on the service state of a pavement, which is used for improving the detection efficiency of pavement diseases, comprehensively evaluating the overall service performance of the pavement, facilitating the early establishment of a maintenance scheme and providing data and decision support for pavement management and maintenance. The method has important significance for improving the service quality of the asphalt pavement and prolonging the service life of the asphalt pavement, establishes a platform suitable for comprehensive perception of the pavement state, performs comprehensive and comprehensive evaluation on the pavement disease and damage severity and the overall service state, and provides data and decision support for pavement management and maintenance.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method provided by an embodiment of the present invention;
fig. 2 is a schematic diagram of a road table disease membership function according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an internal defect membership function according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a road surface condition membership function according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Example one
In order to solve the problems that the service road surface cannot be comprehensively and comprehensively evaluated in the prior art, the invention provides the following scheme: as shown in fig. 1, the present invention provides an overall evaluation method based on a service state of a road surface, including:
detecting and collecting pavement disease data on a pavement by a computer vision technology and a ground penetrating radar signal processing and analyzing technology;
the method comprises the steps of collecting road surface disease data by using a computer vision technology, collecting high-definition pictures of road surface cracks and seepage states by using a digital living technology DLSC-RL series image collection system in the process, carrying out digital image enhancement, gray level processing, binarization and other preprocessing on digital images collected at the front end, and enabling the pictures subjected to binary segmentation to only display disease areas by continuously adjusting segmented threshold values through binary segmentation so as to extract image characteristic values required in a deep learning algorithm. The machine learning is carried out on the preprocessed pictures by adopting a YOLO series deep learning algorithm, and the algorithm utilizes the regression idea, has the advantages of high recognition speed, is easy to learn the generalization characteristics of the target, enables the road surface damage recognition speed to be higher, and achieves the aim of accurately recognizing the road surface diseases finally. Through the processing, the relevant data of the road table diseases are finally output through a deep learning algorithm: and (4) crack and water seepage, marking the identified features in the image through a YOLO series deep learning algorithm, and using the marked image under different marks as crack disease data and water seepage disease data.
The method comprises the steps of collecting internal defect data of a road surface by using a ground penetrating radar identification analysis technology, analyzing electromagnetic wave energy attenuation rules of different antenna suspension heights, different road surface structure parameters (porosity, thickness and the like) and different road surface water contents under the coupling state of air and an asphalt layer through FDTD (finite time domain difference method) forward simulation and indoor inversion test, extracting characteristic parameters representing water damage in the process, establishing an asphalt road surface water damage data set, further establishing and optimizing a deep learning identification model based on signal characteristics, and identifying the road surface water damage condition by inputting data collected by a radar into the deep learning identification model related to water damage number.
The method comprises the steps of establishing cavity inflation and water filling cavity models at different structural layer positions based on FDTD, using GprMax and Matlab programming to realize road cavity forward modeling, and analyzing radar wave image characteristics of forward modeling results of the cavity models. And comparing the numerical simulation, the indoor test and the actual measurement result, constructing a radar map database aiming at the cavity diseases, determining map characteristics, automatically identifying the waveform of the interlayer cavity part by adopting a YOLO deep learning algorithm for identifying the road surface structure diseases, and identifying the cavity disease characteristics.
After the road surface disease data and the road surface internal disease data are collected, evaluating the data respectively; the method comprises the steps of counting the shooting positions of road surface damage data, recording the actual positions of road surface damages, recording the number of roadside damages, recording the size of a road surface damage area according to the labeling range of abnormal data in road surface damage images at different positions, counting the size of the road surface damage area by presetting a marker or measuring afterwards, respectively counting and recording the size and the position of a crack and water seepage damage area, calculating the proportion of the counted area size in the whole road surface area by matching the counted area size with the whole road surface area, taking the proportion as a road surface damage score, carrying out weight distribution on the road surface damage score, determining the severity of the road damage through cracks and water seepage, setting the parameter by analyzing and calculating experience knowledge or a previous case according to an entropy value weighting method, carrying out weight distribution on the damage score according to the determined parameter size, carrying out weighted summation on the damage score to obtain the road surface damage score, carrying out weighted summation calculation on a trapezoid-shaped function to judge a triangle and a high-degree road surface damage score, and a high-degree and a low-degree score, wherein the road surface damage score and a high-degree score and a low severity function are obtained by weighting sum.
When internal disease data are counted, a GPS or other positioning equipment is used for recording the real-time position and the actual time of a ground penetrating radar in the measuring process, the actual time corresponds to the time domain of a waveform, the time domain of a signal of the ground penetrating radar corresponds to the position information, the range size of the identified different disease characteristics is calculated according to the position information corresponding to the signal waveform, the range and the position of abnormal characteristics in the detection of the ground penetrating radar are counted, the proportion of different diseases in the whole road section is used as the damage score of the internal defects of the road surface, the weights of the holes and the water damage are distributed in the same method as the weight distribution of the road surface defects, after the distribution, the weighted sum calculation is carried out according to the weights, the severity score of the internal defects of the road surface is obtained, the severity score of the internal defects of the road surface comprises the severity score of the road surface defects and the severity score of the internal defects of the road surface, the severity score of the internal defects of the road surface is judged through a trapezoid and normal distribution type membership function as shown in figure 3, and the severity score of the internal defects is obtained, and the severity score of the internal defects is divided into high, medium and low internal defects.
Establishing a fuzzy evaluation library, wherein the fuzzy evaluation library comprises road surface disease severity, internal disease severity and road surface conditions, the road surface conditions comprise high, medium and low, the two disease severity are converted into fuzzy language values, namely high-TH, high-H, medium-M, low-L and low-TL, after the conversion of the two disease severity is completed, the fuzzy evaluation library is input to carry out fuzzy judgment on the road surface condition to obtain the road surface condition, before the fuzzy evaluation library is input, quantitative factor calculation of the disease severity is carried out through proportion calculation and weighted summation calculation of different disease data and the whole road surface, the disease data are converted into a fuzzy theory domain from an actual theory domain, the disease severity can be effectively corresponded, meanwhile, parameters in the conversion process can also provide certain data support for road surface evaluation and management, and the fuzzy evaluation library is shown in table 1.
TABLE 1
And generating a road surface fuzzy condition through the fuzzy evaluation library, and performing defuzzification processing on the road surface condition through a membership function shown in fig. 4 by using a maximum membership method to obtain a road surface condition numerical value.
In the prediction process, matlab software is adopted to construct a convolutional neural network model as a prediction model, a pavement state value, pavement service time and a disease damage score are used as input values of the prediction model, the prediction model can effectively consider the mutual influence relation among input parameters through optimization training of the prediction model so as to simulate the nonlinear function relation between input and output, the data are input into the prediction model to predict the pavement service residual life, the relevant data are integrated into a storage database, the overall pavement position, the number and the time are used as root elements through an XML data framework, the data are stored as sub-elements under the root elements, and after the storage is finished, the pavement position or the number is subjected to keyword searching and displaying. And integrating and storing the residual service life of the pavement, the pavement condition numerical value, the pavement service time and the severity of the diseases. In the integration process, the positions and the ranges of the diseases are integrated to the same storage position to obtain the overall evaluation result of the service state of the pavement, and the overall evaluation result of the service state of the pavement comprises data stored to the same position, including the residual service life of the pavement, the pavement condition value, the service time of the pavement, the severity of the diseases, and the positions and the ranges of the diseases of different types. And carrying out comprehensive evaluation on each index and overall comprehensive evaluation on the road surface through the integrated data.
And after integration, generating a certain planning scheme according to the road condition corresponding to the road condition numerical value. In the planning process, when the road surface condition is low, the road surface service state is healthy, the disease severity, the damage position and the range are output, relevant personnel are reminded to detect, and the service time and the residual service life are displayed; when the pavement condition is medium, the pavement service state is in sub-health, the disease severity, the damage position and the damage range are output, relevant personnel are reminded to repair the pavement, and the service time and the residual service life are displayed; when the road surface condition is high, indicating that the service state of the road surface is in a deterioration state, outputting the severity of the disease, the damage position and the range, alarming in time to remind relevant personnel to repair, judging the residual service life after repair, judging whether the repair is effective or not by combining the service time of the road surface and the residual service life of the road surface before repair, determining that the repair is effective when a set threshold value is exceeded, determining the threshold value according to manual experience, and continuously detecting the repair until the repair is effective when the repair is invalid; and when the condition of the road surface is very high, indicating that the service state of the road surface is in a damaged state, performing simulation on the damaged position and the damaged range through simulation software according to the disease severity, manually judging the repairability of the road surface, and if the repairability is low, directly re-paving the road, otherwise, repairing the road. And judging whether the repair is effective or not by combining the service time of the pavement and the residual service life of the pavement before repair, determining that the repair is effective when the service time exceeds a set threshold value, determining the threshold value according to manual experience, and continuously detecting and repairing until the repair is effective when the service time is invalid.
The invention relies on an integrated computer vision technology and a ground penetrating radar signal processing and analyzing technology to establish a pavement state evaluation method based on the severity of pavement diseases, comprehensively considers the pavement disease comprehensive sensing and analyzing of damage of a road surface and an internal structure of the pavement, improves the detection efficiency of the pavement diseases, comprehensively evaluates the severity of the pavement diseases and damage and the overall service state, and provides data and decision support for pavement management and maintenance.
Example two
In order to better achieve the technical purpose, the invention also provides an overall evaluation system based on the service state of the road surface, which comprises:
the system comprises an acquisition module and an evaluation module; the acquisition module is used for acquiring pavement disease data and evaluating the pavement disease data to obtain the severity of the disease; the evaluation module is used for evaluating the severity of the diseases to obtain a pavement condition numerical value; and acquiring a road surface condition numerical value, and acquiring a whole evaluation result of the road surface service state based on the road surface service time and the road surface condition numerical value.
Optionally, the acquisition module includes a first acquisition module, and the first acquisition module is configured to obtain the road surface disease data through computer vision and ground penetrating radar signal processing and analysis; the road surface disease data comprises road surface disease data and road surface internal defect data, wherein the road surface disease comprises cracks and water seepage, and the road surface internal defect comprises holes and water damage.
Optionally, the obtaining module includes a second obtaining module, where the second obtaining module is configured to count the road surface disease data, calculate a statistical result, obtain a disease damage score, perform weight distribution on the disease damage score, obtain a disease severity score based on the weight distribution result, and determine the disease severity score to obtain a disease severity.
Optionally, the evaluation module includes a first evaluation module, where the first evaluation module is configured to construct a fuzzy evaluation library, retrieve the severity of the disease through the fuzzy evaluation library to obtain a road surface fuzzy condition, and defuzzify the road surface fuzzy condition to obtain a road surface condition value.
Optionally, the evaluation module includes a second evaluation module, where the second evaluation module is configured to construct a prediction model, predict the road service time and the road condition value through the prediction model to obtain the remaining road life, and integrate the remaining road life, the road condition value, the road service time, and the disease severity to obtain an overall evaluation result of the road service state. The system and the method flow are not described herein again.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. The overall evaluation method based on the service state of the pavement is characterized by comprising the following steps of:
acquiring pavement disease data, and evaluating the pavement disease data to obtain the severity of the diseases; evaluating the severity of the diseases to obtain a pavement condition numerical value; and acquiring a road surface condition numerical value, and acquiring a whole evaluation result of the road surface service state based on the road surface service time and the road surface condition numerical value.
2. The overall evaluation method based on the service state of the pavement according to claim 1, characterized in that:
the process of acquiring pavement disease data comprises:
processing and analyzing by computer vision and ground penetrating radar signals to obtain pavement disease data; the road surface disease data comprises road surface disease data and road surface internal defect data, wherein the road surface disease comprises cracks and water seepage, and the road surface internal defect comprises holes and water damage.
3. The overall evaluation method based on the service state of the pavement according to claim 1, which is characterized in that:
the process of evaluating the pavement damage data includes:
and counting the pavement disease data, calculating a statistical result to obtain a disease damage score, performing weight distribution on the disease damage score, obtaining a disease severity score based on the weight distribution result, and judging the disease severity score to obtain the disease severity.
4. The overall evaluation method based on the service state of the pavement according to claim 1, which is characterized in that:
the process of evaluating the severity of the disease comprises the following steps:
and constructing a fuzzy evaluation library, retrieving the severity of the diseases through the fuzzy evaluation library to obtain a road surface fuzzy condition, and defuzzifying the road surface fuzzy condition to obtain a road surface condition numerical value.
5. The overall evaluation method based on the service state of the pavement according to claim 1, which is characterized in that:
the process for obtaining the overall evaluation result of the service state of the pavement comprises the following steps:
and constructing a prediction model, predicting the service time and the road condition value of the road through the prediction model to obtain the residual service life of the road, and integrating the residual service life of the road, the road condition value, the service time of the road and the severity of the diseases to obtain the overall evaluation result of the service state of the road.
6. Overall evaluation system based on road service state is characterized by comprising:
the system comprises an acquisition module and an evaluation module; the acquisition module is used for acquiring pavement disease data and evaluating the pavement disease data to obtain the severity of the disease; the evaluation module is used for evaluating the severity of the diseases to obtain a pavement condition numerical value; and acquiring a road surface condition numerical value, and acquiring a whole evaluation result of the road surface service state based on the road surface service time and the road surface condition numerical value.
7. The overall evaluation system based on the service condition of the pavement according to claim 6, wherein:
the acquisition module comprises a first acquisition module, and the first acquisition module is used for processing and analyzing by computer vision and ground penetrating radar signals to obtain pavement disease data; the road surface disease data comprises road surface disease data and road surface internal defect data, wherein the road surface disease comprises cracks and water seepage, and the road surface internal defect comprises holes and water damage.
8. The overall evaluation system based on the service state of the pavement according to claim 6, wherein:
the acquisition module comprises a second acquisition module, wherein the second acquisition module is used for counting road surface disease data, calculating a statistical result to obtain a disease damage score, performing weight distribution on the disease damage score, obtaining a disease severity score based on the weight distribution result, and judging the disease severity score to obtain the disease severity.
9. The overall evaluation system based on the service condition of the pavement according to claim 6, wherein:
the evaluation module comprises a first evaluation module, wherein the first evaluation module is used for constructing a fuzzy evaluation library, searching the severity of the disease through the fuzzy evaluation library to obtain the fuzzy condition of the pavement, and defuzzifying the fuzzy condition of the pavement to obtain the numerical value of the pavement condition.
10. The overall evaluation system based on the service condition of the pavement according to claim 6, wherein:
the evaluation module comprises a second evaluation module, wherein the second evaluation module is used for constructing a prediction model, predicting the pavement service time and the pavement condition value through the prediction model to obtain the pavement residual life, and integrating the pavement residual life, the pavement condition value, the pavement service time and the disease severity to obtain the overall evaluation result of the pavement service state.
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| CN117853486A (en) * | 2024-03-07 | 2024-04-09 | 云南省交通规划设计研究院股份有限公司 | Automatic evaluation method for rock mass quality of tunnel working face under condition of data loss |
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| CN117853486A (en) * | 2024-03-07 | 2024-04-09 | 云南省交通规划设计研究院股份有限公司 | Automatic evaluation method for rock mass quality of tunnel working face under condition of data loss |
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