Pavement maintenance decision-making method based on road element analysis

Abstract

The invention belongs to the technical field of road maintenance, and particularly relates to a road element analysis-based pavement maintenance decision-making method, which comprises the following steps of: acquiring road detection data, and establishing a road element database; establishing a maintenance index of the road metadata, setting a maintenance index standard according to the maintenance index, and generating a maintenance standard road metadata data set; carrying out maintenance demand analysis, and generating a maintenance mileage; carrying out maintenance fund analysis, and generating maintenance fund; performing maintenance benefit analysis, calculating a predicted maintenance index of each pavement unit by adopting the maintenance prediction model, performing comparative analysis on the predicted maintenance index and a maintenance index detected in the current year, and generating a maintenance benefit analysis report; and performing maintenance project optimization, performing ascending sorting on the road element data in the maintenance standard road element data set according to the maintenance indexes, generating a first sorting result, and generating a maintenance optimization data set as a maintenance plan of the next year according to the first sorting result and the user maintenance target, so that scientific and effective maintenance can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of highway maintenance, and particularly relates to a pavement maintenance decision-making method based on road element analysis. Background Art

[0002] With the rapid development of highway construction in China, the highway mileage is increasing continuously. The huge workload of maintenance inspection and management has led to the low efficiency of traditional maintenance management means, and the intelligent maintenance management system is constantly being built and developed. With the rapid development of the expressway network, how to scientifically and efficiently carry out pavement maintenance has become an important issue.

[0003] Traditional maintenance methods often rely on experience and subjective judgment, lacking scientific data support, resulting in waste of maintenance resources and unsatisfactory maintenance effects. And the traditional expressway maintenance system can analyze the appropriate maintenance plan for the current expressway according to the professional data provided by users, but usually it cannot predict the subsequent maintenance of the expressway. In order to improve the scientificity and effectiveness of maintenance work. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a pavement maintenance decision-making method based on road element analysis to solve the problems that existing traditional maintenance methods often rely on experience and subjective judgment, lack scientific data support, resulting in waste of maintenance resources and unsatisfactory maintenance effects. And the traditional expressway maintenance system can analyze the appropriate maintenance plan for the current expressway according to the professional data provided by users, but usually it cannot predict the subsequent maintenance of the expressway. In order to improve the scientificity and effectiveness of maintenance work.

[0005] The technical solution adopted by the present invention is as follows: A pavement maintenance decision-making method based on road element analysis, comprising the following steps:

[0006] S100, collecting highway detection data, establishing a road element database with pavement units as units, and managing the data in the road element database, the road element database including a number of road element data;

[0007] S200, establishing maintenance indicators for road element data, setting maintenance indicator standards according to the maintenance indicators, screening the road element data in the road element database that meet the maintenance indicator standards, and generating a maintenance standard road element data set;

[0008] S300, obtaining the maintenance standard road element data set, conducting maintenance demand analysis, and generating a maintenance mileage;

[0009] S400, obtaining the maintenance standard road element data set, conducting maintenance fund analysis, and generating a maintenance fund;

[0010] S500. Obtain the maintenance standard road element dataset and the maintenance indicators detected in the current year, conduct a maintenance benefit analysis, use the maintenance prediction model to calculate the predicted maintenance indicators for each pavement unit, compare and analyze the predicted maintenance indicators with the maintenance indicators detected in the current year, and generate a maintenance benefit analysis report.

[0011] S600. Obtain the maintenance standard road element dataset and the user's maintenance objectives, conduct a maintenance project optimization. Sort the road element data in the maintenance standard road element dataset in ascending order according to the maintenance indicators, and generate a first sorting result. Generate a maintenance optimization dataset based on the first sorting result and the user's maintenance objectives as the next year's maintenance plan.

[0012] Furthermore, the maintenance indicators include a comprehensive evaluation indicator, a pavement damage indicator, a smoothness indicator, and a rutting indicator; the maintenance indicator standards represent the value ranges of the comprehensive evaluation indicator, the pavement damage indicator, the smoothness indicator, and the rutting indicator.

[0013] Furthermore, in S300, obtain the maintenance standard road element dataset, accumulate the road section lengths of each pavement unit in the maintenance standard road element dataset, and generate the maintenance mileage.

[0014] Furthermore, in S400, obtain the maintenance standard road element dataset, accumulate the maintenance costs of each pavement unit in the maintenance standard road element dataset, and generate the maintenance funds.

[0015] Furthermore, the maintenance prediction model includes a decay model and a promotion model.

[0016] Furthermore, in S500, the steps of calculating the predicted maintenance indicators using the decay model are as follows:

[0017] Obtain the maintenance standard road element dataset and input it into the decay model;

[0018] Screen the pavement units with at least one indicator decreasing year by year in the maintenance indicators, and generate a decay dataset;

[0019] Subtract the maintenance indicators of each pavement unit in the decay dataset for two adjacent years to generate a decay value;

[0020] Sort the decay values corresponding to the maintenance indicators of each pavement unit in the decay dataset in descending order according to the decay values corresponding to each maintenance indicator, and generate a second sorting result;

[0021] According to the second sorting result, screen the decay values corresponding to the first quartile, the median, and the third quartile, and generate the predicted maintenance indicators of the decay model.

[0022] Furthermore, in S500, the steps of calculating the predicted maintenance indicators using the promotion model are as follows:

[0023] Obtain the input of the maintenance standard road element dataset to enhance the model;

[0024] Screen the pavement units with at least one maintenance index increasing year by year in the maintenance indexes, and generate an enhanced dataset;

[0025] According to the respective maintenance indexes corresponding to the pavement units, sort the respective maintenance indexes corresponding to the respective pavement units in the enhanced dataset in descending order, and generate a third sorting result;

[0026] According to the third sorting result, screen the corresponding values of the first quartile, median and third quartile, and generate the predicted maintenance indexes of the enhanced model.

[0027] Furthermore, in S600, the user's maintenance goal includes the target maintenance funds and target maintenance indexes. Screen the road element data in the first sorting result in sequence, and analyze whether the maintenance funds or maintenance indexes corresponding to the road element data in the first sorting result reach the target maintenance funds or target maintenance indexes. If so, input the road element data that reaches the target maintenance funds or target maintenance indexes into the maintenance optimization dataset as the next year's maintenance plan.

[0028] The beneficial effects of the present invention: A pavement maintenance decision-making method based on road element analysis provided by this solution, through steps such as collecting highway detection data, establishing a road element database, setting maintenance index standards, conducting demand and funds analysis, using a prediction model for benefit analysis, and engineering optimization according to user goals, helps to improve the scientificity and effectiveness of road maintenance. This solution can not only optimize resource allocation, extend the road life, improve the road quality, but also provide strong support for decision-makers, achieve precise maintenance, promote the sustainable development of road maintenance work, and improve the overall level of road maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flowchart of a pavement maintenance decision-making method based on road element analysis of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0031] In the description of the present invention, unless otherwise specified, "a plurality of" means two or more; the orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation on the present invention. In addition, terms such as "first", "second", "third", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0032] In the description of the present invention, it should be noted that, unless otherwise clearly specified and defined, the terms "connected" and "connected to" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0033] As Figure 1 shown, a pavement maintenance decision-making method based on road element analysis includes the following steps:

[0034] S100, collect highway detection data, establish a road element database with road surface units as the unit, and manage the data in the road element database. The road element database includes a number of road element data; the road element data corresponding to each road element unit includes road element static data and road element dynamic data. The road element static data includes road network route basic data, structural material data, bridge data, and tunnel data; the road element dynamic data includes annual detection and evaluation data, historical maintenance project data, traffic flow data, environmental meteorological data, and traffic safety data. By collecting highway detection data and establishing a road element database with road surface units as the unit, a road element database with road surface units as the unit is established, where each road element is set with a unique identifier and contains detailed data information of the road element, realizing the comprehensive management and analysis of road element data. This database system provides scientific, accurate, and efficient data support for road maintenance, and has application values such as refined management, optimized decision-making support, and improved work efficiency.

[0035] S200, establish maintenance indicators for road element data, set maintenance indicator standards according to the maintenance indicators, and screen the road element data in the road element database that meets the maintenance indicator standards according to the maintenance indicator standards, and generate a maintenance standard road element data set; by setting screening conditions, the system can automatically identify the road elements that meet the maintenance standards, thus greatly improving the efficiency and accuracy of data processing. This automated screening not only reduces manual intervention but also reduces the errors caused by subjective judgment.

[0036] As an optimal solution, the maintenance indicators include comprehensive evaluation indicators, pavement damage indicators, evenness indicators, and rutting indicators. Among them, the comprehensive evaluation indicators comprehensively consider the overall evaluation indicators including pavement damage, evenness, and rutting. The pavement damage indicators are the indicators reflecting the degree of pavement damage, including cracks and potholes. The evenness indicators are the indicators reflecting the evenness degree of the pavement, including the international roughness index. The rutting indicators are the indicators reflecting the rut depth of the pavement. Through the multi-dimensional evaluation of the comprehensive evaluation indicators, pavement damage indicators, evenness indicators, and rutting indicators, the actual condition of the road element can be comprehensively understood, more effective maintenance measures can be formulated, and the maintenance effect can be improved.

[0037] The maintenance indicator standards represent the value ranges of the comprehensive evaluation indicators, pavement damage indicators, evenness indicators, and rutting indicators. Through clear maintenance indicators and standards, the road elements that need maintenance can be scientifically selected, the errors caused by subjective judgment can be avoided, and the accuracy and reliability of maintenance decisions can be ensured.

[0038] S300. Obtain the maintenance standard road element dataset, conduct maintenance demand analysis, and generate the maintenance mileage.

[0039] In S300, obtain the maintenance standard road element dataset, accumulate the section lengths of each pavement unit in the maintenance standard road element dataset, and generate the maintenance mileage. The maintenance mileage refers to the total section length that needs to be maintained determined according to the maintenance demand analysis. It is one of the important bases for measuring the maintenance workload and formulating the maintenance plan. Accurate statistics of the maintenance mileage helps to reasonably allocate maintenance resources and ensure the efficient progress of the maintenance work.

[0040] S400. Obtain the maintenance standard road element dataset, conduct maintenance fund analysis, and generate the maintenance funds.

[0041] As an optimal solution, in S400, obtain the maintenance standard road element dataset, accumulate the maintenance costs of each pavement unit in the maintenance standard road element dataset, and generate the maintenance funds. By summarizing and calculating the screened road element data, the total maintenance funds can be obtained. The statistically obtained maintenance funds are not only used for formulating annual or multi-year maintenance plans, but also provide important references for budget preparation, fund application, etc.

[0042] S500. Obtain the maintenance standard road element dataset and the maintenance indicators detected in the current year, conduct maintenance benefit analysis, calculate the predicted maintenance indicators of each pavement unit using the maintenance prediction model, compare and analyze the predicted maintenance indicators with the maintenance indicators detected in the current year, and generate a maintenance benefit analysis report.

[0043] Specifically, compare and analyze the predicted maintenance indicators with the maintenance indicators detected in the current year, calculate the differences, the difference values of each indicator for each road element, analyze the difference values of each indicator for each road element, evaluate the effect of the maintenance measures, and summarize the overall maintenance benefits through statistical analysis (including mean and standard deviation), and generate a maintenance benefit analysis report, including but not limited to the following content:

[0044] Overall benefit: The average improvement of indicators for all road elements.

[0045] Benefit of specific road element: The improvement of each maintenance indicator for each road element.

[0046] Problematic road element: Road elements with insignificant improvement in maintenance indicators.

[0047] As an optimal solution, the maintenance prediction model includes a decay model and a promotion model. Through the application and calculation of these two models, the maintenance requirements of the road in the future period can be predicted more accurately, and a basis can be provided for formulating a scientific and reasonable maintenance plan. The formulation of the maintenance plan can be guided according to the prediction results of the decay model and the promotion model. For the decay model, give priority to dealing with those sections that are expected to decay most severely, or for the promotion model, give priority to investing in those maintenance measures with the best expected improvement effect.

[0048] As an optimal solution, in S500, calculating the predicted maintenance indicators using the decay model includes the following steps:

[0049] Obtain the maintenance standard road element dataset and input it into the decay model;

[0050] Screen the pavement units with at least one indicator decreasing year by year in the maintenance indicators, and generate a decay dataset; the maintenance indicators include comprehensive evaluation indicators, pavement damage indicators, flatness indicators, and rutting indicators;

[0051] Subtract the maintenance indicators of each pavement unit in the decay dataset for two adjacent years to generate a decay value; quantify the change of the maintenance indicators of each pavement unit through the decay value.

[0052] The calculation formula of the decay value is as follows:

[0053] ΔI i =I t+1 -I t

[0054] In the formula, represents the decay value of the maintenance indicator of the i-th pavement unit, I t+1 represents the maintenance indicator in the (t + 1)-th year, I t represents the maintenance indicator in the t-th year;

[0055] According to the attenuation values corresponding to each maintenance index, sort the attenuation values corresponding to the maintenance indexes of each road surface unit in the attenuation dataset in descending order, and generate a second sorting result;

[0056] According to the second sorting result, screen the attenuation values corresponding to the first quartile, median, and third quartile, and generate the predicted maintenance indexes of the attenuation model. Select the maintenance indexes corresponding to the first quartile, median, and third quartile. These positions usually represent the extreme values, middle values, and another extreme value in the data of the third sorting result, which is of great significance for understanding the data distribution. Specifically, by identifying the maintenance indexes corresponding to the first quartile, it is possible to clarify which sections are in the greatest need of priority maintenance, which helps decision-makers concentrate limited resources where they are most needed, thereby improving the efficiency and effectiveness of maintenance work; the median, as the center point of the data, represents the medium-level road conditions. By paying attention to the maintenance indexes corresponding to the median, the overall road conditions can be comprehensively understood, providing a basis for formulating a reasonable maintenance plan; although the third quartile represents relatively good road conditions, there may still be certain potential problems. By paying attention to the maintenance indexes corresponding to the third quartile, these problems can be discovered and solved in a timely manner to prevent potential safety hazards.

[0057] Specifically, if there are attenuation values corresponding to the maintenance indexes of n road surface units in the attenuation dataset, the first quartile is expressed as The median is expressed as The third quartile is expressed as

[0058] Through the attenuation model, it is possible to scientifically predict which road elements' maintenance indexes may continue to decline in the future, so as to identify in advance the road elements that need to be focused on and take timely measures to prevent further deterioration. By generating the predicted maintenance indexes of the attenuation model, it is possible to preferentially select road elements with larger attenuation values for maintenance, ensuring the effective utilization of limited maintenance resources and avoiding waste of resources.

[0059] As an optimal solution, in S500, calculating the predicted maintenance indexes using the improvement model includes the following steps:

[0060] Obtain the dataset of maintenance standard road elements and input it into the improvement model;

[0061] Screen the road surface units whose at least one index among the maintenance indexes rises year by year, and generate an improvement dataset; the maintenance indexes include comprehensive evaluation indexes, road surface damage indexes, flatness indexes, and rutting indexes; by screening out the road surface units whose these indexes rise year by year, it is possible to more accurately locate the sections and parts that need key maintenance.

[0062] According to the maintenance indicators corresponding to each road surface unit, sort the maintenance indicators corresponding to each road surface unit in the improvement dataset in descending order, and generate a third sorting result; the third sorting result represents the arrangement of all maintenance indicators of each road surface unit in descending order, which can more intuitively understand the degree of maintenance demand and urgency of each road surface unit.

[0063] According to the third sorting result, select the corresponding first quartile, median, and third quartile, and generate the predicted maintenance indicators of the improvement model. Select the maintenance indicators corresponding to the first quartile, median, and third quartile. These positions usually represent the extreme values, median values, and another extreme value in the third sorting result data, which is of great significance for understanding the data distribution. Specifically, if there are m road surface units in the improvement dataset corresponding to maintenance indicators, the first quartile is denoted as The median is denoted as The third quartile is denoted as

[0064] By generating the predicted maintenance indicators of the improvement model, road elements with larger improvement values can be preferentially selected for maintenance, ensuring the effective use of limited maintenance resources and avoiding resource waste. Using the improvement model to calculate the predicted maintenance indicators can more accurately predict future road maintenance needs. This helps to make preparations for maintenance in advance and avoid problems such as road damage and traffic accidents caused by untimely maintenance.

[0065] S600, obtain the maintenance standard road element dataset and the user's maintenance goal, optimize the maintenance project, sort the road element data in the maintenance standard road element dataset in ascending order according to the maintenance indicators, and generate a first sorting result. According to the first sorting result and the user's maintenance goal, generate a maintenance optimization dataset as the next year's maintenance plan. Sort the road element data in the maintenance standard road element dataset in ascending order. The road elements that need maintenance the most (i.e., the road elements with the worst maintenance indicators) are ranked at the front. This can ensure that under limited resource conditions, the sections that are most urgently in need of maintenance are processed first, avoiding waste of resources. Prioritizing the processing of the road elements that need maintenance the most can quickly improve the road conditions and enhance the overall road usage experience. At the same time, since these sections usually have the most serious problems, solving these problems can significantly improve the overall maintenance effect.

[0066] As a preferred solution, in S600, the user's maintenance objectives include the target maintenance funds and target maintenance indicators. The road element data in the first sorting result is screened in sequence, and it is analyzed whether the maintenance funds or maintenance indicators corresponding to the road element data in the first sorting result reach the target maintenance funds or target maintenance indicators. If so, the road element data that reaches the target maintenance funds or target maintenance indicators is input into the maintenance optimization dataset as the next year's maintenance plan. By combining the user's specific maintenance objectives (including target maintenance funds and target maintenance indicators), it is ensured that the maintenance plan can accurately match the user's needs and improve the satisfaction of the maintenance work.

[0067] For the multi-year plan, repeat the above steps to formulate the maintenance standards for multiple years and calculate the multi-year maintenance plan.

[0068] The above has introduced the present invention in detail. The description of the specific embodiments is only used to help understand the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and modifications can still be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A road maintenance decision-making method based on road element analysis, characterized by: The following steps are involved: S100, collecting highway detection data, and establishing a road element database with road surface units as units, and performing data management on the road element database, wherein the road element database includes a plurality of road element data; S200, establishing maintenance indicators for road metadata, setting maintenance indicator standards according to the maintenance indicators, screening road metadata in a road metadata database that meet the maintenance indicator standards according to the maintenance indicator standards, and generating a maintenance standard road metadata dataset; S300, obtaining a maintenance standard road element data set, performing maintenance demand analysis, and generating maintenance mileage; S400, obtaining a maintenance standard road metadata dataset, performing maintenance fund analysis, and generating maintenance funds; S500, obtaining a maintenance standard road element data set and maintenance indicators detected in the current year, performing maintenance benefit analysis, using a maintenance prediction model to calculate the predicted maintenance indicators of each pavement unit, performing a comparative analysis between the predicted maintenance indicators and the maintenance indicators detected in the current year, and generating a maintenance benefit analysis report; S600, obtaining a maintenance standard road metadata data set and a user maintenance target, optimizing the maintenance project, sorting the road metadata in the maintenance standard road metadata set in ascending order according to the maintenance index, and generating a first sorting result, and generating a maintenance optimization data set according to the first sorting result and the user maintenance target as a maintenance plan for the next year.

2. The pavement maintenance decision-making method based on road element analysis according to claim 1 is characterized by: The maintenance index includes a comprehensive evaluation index, a road surface damage index, a flatness index and a rutting index; the maintenance index standard represents the value range of the comprehensive evaluation index, the road surface damage index, the flatness index and the rutting index.

3. The pavement maintenance decision-making method based on road element analysis according to claim 1 is characterized by: In S300, a maintenance standard road element data set is obtained, and the section lengths of each pavement unit in the maintenance standard road element data set are accumulated to generate maintenance mileage.

4. The pavement maintenance decision-making method based on road element analysis according to claim 1 is characterized by: In S400, a maintenance standard road metadata data set is obtained, and the maintenance costs of each pavement unit in the maintenance standard road metadata set are accumulated to generate maintenance funds.

5. The pavement maintenance decision-making method based on road element analysis according to claim 1 is characterized by: The maintenance prediction model includes a decay model and an improvement model.

6. A road maintenance decision-making method based on road element analysis according to claim 5, characterized in that: In S500, using the attenuation model to calculate the predicted maintenance index includes the following steps: Obtain maintenance standard road metadata dataset to input attenuation model; Screen the pavement units whose at least one maintenance index decreases year by year, and generate a decay data set; Subtract the maintenance index of each pavement unit in the attenuation data set in two consecutive years to generate the attenuation value; According to the attenuation value corresponding to each maintenance index, the attenuation value corresponding to the maintenance index of each pavement unit in the attenuation data set is sorted in descending order, and a second sorting result is generated; According to the second sorting result, the attenuation values ​​corresponding to the first quartile, the median and the second quartile are screened, and the predictive maintenance index of the attenuation model is generated.

7. The pavement maintenance decision-making method based on road element analysis according to claim 5 is characterized by: In S500, the calculation and prediction of maintenance indicators using the lifting model includes the following steps: Obtain maintenance standard road metadata dataset to input into the improvement model; Screen the pavement units with at least one maintenance index increasing year by year, and generate an improvement data set; According to the maintenance indicators corresponding to the pavement units, the maintenance indicators corresponding to the pavement units in the boosted data set are sorted in descending order, and a third sorting result is generated; According to the third sorting result, the corresponding first quartile, median and last quartile are screened, and the prediction maintenance index of the improvement model is generated.

8. The pavement maintenance decision-making method based on road element analysis according to claim 1 is characterized by: In S600, the user maintenance target includes target maintenance funds and target maintenance indicators. The road data in the first sorting result are filtered in order, and it is analyzed whether the maintenance funds or maintenance indicators corresponding to the road data in the first sorting result reach the target maintenance funds or target maintenance indicators. If so, the road data that reaches the target maintenance funds or target maintenance indicators is input into the maintenance optimization data set as the maintenance plan for the next year.

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