Intelligent maintenance decision-making auxiliary method and system for expressway network

Abstract

The invention discloses an expressway network intelligent maintenance decision-making auxiliary method and system, and relates to the field of expressway network intelligent maintenance, and the method comprises the steps: obtaining expressway network information of a research region, and judging a road condition based on a performance index; according to the road condition, the maintenance cost and the carbon emission, an optimization decision model of road network maintenance and repair is established; and obtaining a pavement maintenance decision according to the optimized decision model, and completing intelligent maintenance decision assistance of the expressway network. The problems that in the prior art, decision support is not comprehensive, and carbon emission and user cost are not considered are solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent maintenance of expressway networks, and in particular to an intelligent maintenance and repair decision-making assistance method and system for expressway networks. Background Art

[0002] Affected by many factors, the technical condition of highway pavement will continue to decline over time. Effective maintenance and repair can keep the pavement in good condition, prevent pavement deterioration, extend the service life of the road, and improve the quality of road use. However, insufficient funds and continuous pavement deterioration have always been the main challenges faced by pavement management departments around the world.

[0003] Although existing research covers a wide range of fields, there are still some shortcomings. First, in terms of model application, existing research often focuses on the application of a single model, lacks comprehensive and comparative analysis of the results of different models, and may lead to incomplete decision support. Second, research on environmental impacts does not focus on the assessment of carbon emissions. In addition, the consideration of maintenance costs focuses on repair costs, and does not consider user costs. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method and system for assisting decision-making in intelligent maintenance and repair of a highway network, which solves the problem that the prior art leads to incomplete decision support and failure to consider carbon emissions and user costs.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for assisting decision-making in intelligent maintenance and repair of a highway network, comprising the following steps:

[0006] S1: Obtain the highway network information of the study area and judge the road conditions based on the performance index;

[0007] S2: Establish an optimization decision model for road network maintenance based on road conditions, maintenance costs and carbon emissions;

[0008] S3: Obtain pavement maintenance and repair decisions based on the optimization decision-making model, and complete intelligent maintenance and repair decision assistance for the highway network.

[0009] Furthermore, the performance index PPI in S1 is:

[0010] PPI=PPI0+d*Y

[0011] Among them, PPI0 is the initial pavement performance index, d is the average pavement decay coefficient, and Y is the service life.

[0012] Furthermore, judging the road condition based on the performance index in S1 is specifically:

[0013] When the performance index PPI is greater than or equal to 90, the road condition is excellent and no maintenance is required;

[0014] When the performance index PPI is less than 90, maintenance is required.

[0015] Furthermore, the optimization decision model for road network maintenance in S2 is:

[0016]

[0017]

[0018] Among them, Cost is the maintenance cost, CO2E is the maintenance carbon emission, N is the total number of road sections, P is the year of the road section, X ijt T is the maintenance method j adopted on road section i in year t. costj is the maintenance cost corresponding to the jth maintenance method, D costj is the user cost corresponding to the jth maintenance method, T Ej is the carbon emission corresponding to the jth maintenance method, Budget t is the maintenance budget for road maintenance in year t, PPI 0it is the initial pavement performance index of the i-th section of road in the t-th year, Y it is the useful life of the i-th section of road in year t, I j is the optimal protection value of j maintenance methods, M 优 is the proportion of road sections with performance index PPI greater than 90 in the road network, M ≥良 is the proportion of road sections with performance index PPI greater than 80 in the road network, M ≥中 is the proportion of road sections with performance index PPI greater than 70 in the road network, M 良 is the proportion of road sections with performance index PPI greater than 80 and less than 90 in the road network, M 中 is the proportion of road sections with performance index PPI greater than 70 and less than 80 in the road network, M 差 is the proportion of road sections with a performance index (PPI) lower than 70 in the road network. It is the average performance index PPI of all road sections in the road network after maintenance.

[0019] The present invention also adopts the following technical solution: a system for assisting decision-making on intelligent maintenance of a highway network, comprising a road condition judgment module and an optimization decision module for road network maintenance. The road condition judgment module is used to judge the road condition based on the highway network information of the study area and based on the performance index, and the optimization decision module for road network maintenance is used to obtain pavement maintenance decisions based on road conditions, maintenance costs and carbon emissions.

[0020] Beneficial effects of the invention: The invention aims to solve the problem of highway maintenance and repair, use optimized genetic algorithms to study the relationship between pavement technical conditions, maintenance costs, and carbon emissions, establish an optimized decision-making model and method for road network maintenance and repair, and provide support for the sustainable development of highways. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flow chart of a method for assisting decision making in intelligent maintenance and repair of expressway networks. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0023] Embodiment 1, as Figure 1 As shown, a highway network intelligent maintenance and repair decision-making assistance method includes the following steps:

[0024] S1: Obtain the highway network information of the study area and judge the road conditions based on the performance index;

[0025] S2: Establish an optimization decision model for road network maintenance based on road conditions, maintenance costs and carbon emissions;

[0026] S3: Obtain pavement maintenance and repair decisions based on the optimization decision-making model, and complete intelligent maintenance and repair decision assistance for the highway network.

[0027] In one embodiment of the present invention, a road surface decay study is conducted based on the collection and analysis of information on the highway network conditions in Texas, USA. By exploring the Texas highway network for 12 years, the relevant conditions of the Texas road network are deeply explored, and a ranking of road sections that need maintenance and repair is obtained. Based on the decay data collected from the road sections, the average decay coefficient is calculated to be d=-18.53889.

[0028] The performance index PPI in S1 is:

[0029] PPI=PPI0+d*Y

[0030] Among them, PPI0 is the initial pavement performance index, d is the average pavement decay coefficient, and Y is the service life.

[0031] The road condition is judged based on the performance index in S1, specifically:

[0032] When the performance index PPI is greater than or equal to 90, the road condition is excellent and no maintenance is required;

[0033] When the performance index PPI is less than 90, maintenance is required.

[0034] Road maintenance methods include preventive maintenance, minor maintenance, moderate maintenance and major maintenance. Genetic algorithms can be used for continuous iteration to ultimately find the optimal solution for the maintenance method required for a specific road section under constraints.

[0035] These four maintenance methods can improve road conditions to varying degrees and the costs incurred are shown in Table 1:

[0036] Table 1 Travel delay costs and maintenance and repair costs

[0037]

[0038]

[0039] The carbon emissions generated by different maintenance methods are shown in Figure 2:

[0040] Carbon emissions from different maintenance methods

[0041]

[0042] Maintenance costs include user costs, construction costs, user delay costs and time costs. Time is converted into social and economic value and quantified by the wage level per unit time; the cost of the main materials for pavement maintenance and the costs and maintenance costs of the raw materials and construction stages of the four maintenance methods are comprehensively considered. The present invention mainly considers maintenance costs and user costs, and through a quantitative method, unifies the costs of the two parts into a unified unit for calculation.

[0043] A large amount of resources are consumed during the construction and maintenance of highways, and a large amount of waste emissions are also generated, which has a negative impact on the environment. In order to achieve sustainable development, carbon emissions should be taken into account. Carbon emissions in maintenance projects are divided into three stages: raw material production, raw material transportation and construction. This study mainly considers the carbon emissions generated in the construction stage, and calculates carbon emissions according to different coefficients. The present invention quantifies the carbon emissions per kilometer of different maintenance methods, performs calculations based on the dimensions of the maintenance road, and estimates the carbon emissions generated by its maintenance. The optimization methods for pavement maintenance decisions are mainly divided into mathematical programming and artificial intelligence. The mathematical programming method has the problems of unstable solutions and slow computing speed when the decision space is large. The present invention adopts a hybrid genetic algorithm to optimize the shortcomings. The hybrid genetic algorithm suppresses the premature maturity of the simple genetic algorithm, while ensuring that the optimal solution is not destroyed, and can adaptively adjust parameters to ensure the convergence of the genetic algorithm.

[0044] The optimization decision model for road network maintenance in S2 is:

[0045]

[0046]

[0047] Among them, Cost is the maintenance cost, CO2E is the maintenance carbon emission, N is the total number of road sections, P is the year of the road section, X ijt T is the maintenance method j adopted on road section i in year t. costj is the maintenance cost corresponding to the jth maintenance method, D costj is the user cost corresponding to the jth maintenance method, T Ej is the carbon emission corresponding to the jth maintenance method, Budget t is the maintenance budget for road maintenance in year t, PPI 0it is the initial pavement performance index of the i-th section of road in the t-th year, Y it is the useful life of the i-th section of road in year t, I j is the optimal protection value of j maintenance methods, M 优 is the proportion of road sections with performance index PPI greater than 90 in the road network, M ≥良 is the proportion of road sections with performance index PPI greater than 80 in the road network, M ≥中 is the proportion of road sections with performance index PPI greater than 70 in the road network, M 良 is the proportion of road sections with performance index PPI greater than 80 and less than 90 in the road network, M 中 is the proportion of road sections with performance index PPI greater than 70 and less than 80 in the road network, M 差 is the proportion of road sections with a performance index (PPI) lower than 70 in the road network. It is the average performance index PPI of all road sections in the road network after maintenance.

[0048] Based on the above model, optimization is carried out and the pavement maintenance decision is obtained through genetic algorithm.

[0049] In one embodiment of the present invention, a road maintenance and repair decision is obtained according to an optimization decision model: first, a part of the individual is randomly changed to introduce new genetic information. Then, some or all of the parent individuals are replaced with offspring to form a new population. Finally, it is continuously iterated to check whether the termination condition is met, so as to output the optimal solution. An initial population is randomly generated, and each individual represents a possible solution, in which a gene represents the maintenance measure selected for a road section in a certain year. Through the objective function, i.e., maintenance cost and carbon emission, the fitness of each individual is calculated as r, and its quality is evaluated. According to the fitness value, an excellent individual is selected and defined as the parent. The genes of two individuals (parents) are crossed to generate a new individual (offspring). Afterwards, each offspring is mutated, i.e., a gene of the individual is randomly changed to introduce new genetic information. Then the offspring is placed in the population set, and the fitness is calculated to select a new parent. Finally, it is continuously iterated to check whether the termination condition is met, so as to output the optimal solution.

[0050] Embodiment 2, a system of a method for assisting decision-making on intelligent maintenance of a highway network, comprising a road condition judgment module and an optimization decision module for road network maintenance, wherein the road condition judgment module is used to judge the road condition according to the highway network information of a study area and based on a performance index, and the optimization decision module for road network maintenance is used to obtain a road surface maintenance decision according to the road condition, maintenance cost and carbon emission.

[0051] The comprehensive model of the present invention integrates multiple key decision factors such as cost, pavement technical status and carbon emissions, and uses genetic algorithms and heuristic multi-objective optimization methods. This not only improves the scientificity and accuracy of the maintenance strategy, but also minimizes the impact on the environment, fully supporting the country's dual carbon goals.

[0052] By introducing a multi-objective optimization algorithm, this study can find the best balance between cost-effectiveness and environmental impact among various possible maintenance activities. This method improves the scientificity and systematicness of decision-making, allowing maintenance plans to maximize the service life of the road while also minimizing carbon emissions.

[0053] The system of the present invention can predict and plan maintenance activities in advance, thus shifting to a preventive maintenance mode. This system not only reduces the need for emergency repairs, but also reduces overall carbon emissions by optimizing maintenance plans, achieving low-carbon management of highway maintenance.

[0054] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A method for assisting decision making in intelligent maintenance of a highway network, characterized in that: The following steps are involved: S1: Obtain the highway network information of the study area and judge the road conditions based on the performance index; S2: Establish an optimization decision model for road network maintenance based on road conditions, maintenance costs and carbon emissions; S3: Obtain pavement maintenance and repair decisions based on the optimization decision-making model, and complete intelligent maintenance and repair decision assistance for the highway network.

2. The method for assisting decision-making in intelligent maintenance of a highway network according to claim 1, characterized in that: The performance index PPI in S1 is: PPI=PPI0+d*Y Among them, PPI0 is the initial pavement performance index, d is the average pavement decay coefficient, and Y is the service life.

3. The method for assisting decision-making in intelligent maintenance of a highway network according to claim 2, characterized in that: The road condition is judged based on the performance index in S1, specifically: When the performance index PPI is greater than or equal to 90, the road condition is excellent and no maintenance is required; When the performance index PPI is less than 90, maintenance is required.

4. The method for assisting decision-making in intelligent maintenance of a highway network according to claim 3, characterized in that: The optimization decision model for road network maintenance in S2 is: Among them, Cost is the maintenance cost, CO2E is the maintenance carbon emission, N is the total number of road sections, P is the year of the road section, X ijt T is the maintenance method j adopted on road section i in year t. costj is the maintenance cost corresponding to the jth maintenance method, D costj is the user cost corresponding to the jth maintenance method, T Ej is the carbon emission corresponding to the jth maintenance method, Budget t is the maintenance budget for road maintenance in year t, PPI 0it is the initial pavement performance index of the i-th section of road in the t-th year, Y it is the useful life of the i-th section of road in year t, I j is the optimal protection value of j maintenance methods, M 优 is the proportion of road sections with performance index PPI greater than 90 in the road network, M ≥良 is the proportion of road sections with performance index PPI greater than 80 in the road network, M ≥中 is the proportion of road sections with performance index PPI greater than 70 in the road network, M 良 is the proportion of road sections with performance index PPI greater than 80 and less than 90 in the road network, M 中 is the proportion of road sections with performance index PPI greater than 70 and less than 80 in the road network, M 差 is the proportion of road sections with a performance index (PPI) lower than 70 in the road network. It is the average performance index PPI of all road sections in the road network after maintenance.

5. A system for the intelligent highway network maintenance decision support method according to any one of claims 1 to 4, characterized in that: It includes a road condition judgment module and a road network maintenance optimization decision module. The road condition judgment module is used to judge the road condition based on the highway network information in the study area and based on the performance index. The road network maintenance optimization decision module is used to obtain road maintenance decisions based on road conditions, maintenance costs and carbon emissions.

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