Multi-target asphalt pavement maintenance engineering construction plan optimization method and system considering carbon reduction benefit

Abstract

The invention discloses a multi-target asphalt pavement maintenance project construction plan optimization method and system considering carbon reduction benefit, which are applied to the field of asphalt pavement maintenance project construction plan optimization, and comprise the following steps: respectively constructing mathematical calculation functions of maintenance time, maintenance cost, maintenance quality and carbon emission; wherein the carbon emission comprises direct carbon emission and indirect carbon emission generated by traffic delay; the traffic delay is obtained by combining the traffic volume level and performing traffic simulation quantitative analysis according to a maintenance operation road closure management mode; constructing a multi-objective optimization function by taking minimization of maintenance time, maintenance cost and carbon emission and maximization of maintenance quality as objectives, and performing normalization processing: comparing a target value with an extreme value of each function; and solving the multi-objective optimization function by using a genetic algorithm to obtain an optimization result. According to the method, the emission of greenhouse gases can be greatly reduced when the suboptimal solution that the maintenance time and the maintenance cost are minimized and the maintenance quality is maximized is achieved.

Description

Technical Field

[0001] The present invention relates to the field of construction plan optimization of asphalt pavement maintenance projects, and in particular to a multi-objective asphalt pavement maintenance project construction plan optimization method and system taking carbon reduction benefits into consideration. Background Art

[0002] The transportation sector accounts for nearly 14% of global greenhouse gas emissions, of which approximately 72% is attributed to the full lifecycle of pavement construction, maintenance, and use. Existing technologies primarily aim to reduce greenhouse gas emissions through several approaches, including the use of recycled asphalt, optimized construction techniques, and preventive maintenance measures. However, methods for optimizing project-level pavement maintenance schedules that consider carbon reduction benefits have received little research attention. This phase accounts for approximately 10% of greenhouse gas emissions generated throughout the pavement's lifecycle, necessitating the integration of greenhouse gas emission reduction into the optimization framework for project-level maintenance schedules.

[0003] Project-level maintenance project construction plan optimization is a multi-objective optimization problem. Researchers both domestically and internationally began studying this issue in the 1970s, primarily focusing on the trade-off between maintenance time and cost. In 1996, the argument that shortened construction periods would compromise project quality was first proposed. Subsequently, most literature considered maintenance time, cost, and quality as optimization objectives. Models based on these three objectives have been extensively studied, including nonlinear models of cost and time, reliability-based models of quality and cost, and linear, quadratic, and nonlinear models of quality and time. With increasing management requirements, researchers have proposed a variety of optimization objectives, such as minimizing maintenance time, cost, and resources as optimization objectives, with maintenance quality as a constraint; introducing fuzzy set theory to characterize the uncertainty of maintenance projects; considering the safety level of maintenance projects, with the safety index being a normalized value between 0 and 1; and incorporating noise, dust, wastewater, light, and solid waste pollution as a fourth objective in the urban road construction optimization framework. It can be seen that environmental benefits have gradually received attention from researchers, but no study has yet considered greenhouse gas emissions as an optimization target for maintenance project construction plans.

[0004] Incorporating greenhouse gas emissions into the optimization objectives of maintenance project construction plans, thereby forming a multi-objective optimization framework with maintenance time, maintenance cost, maintenance quality, and greenhouse gas emissions as targets, presents two main challenges. First, this multi-objective optimization framework involves a trade-off between cost and non-cost indicators. Due to the different units and scales of these objectives, researchers need to explore appropriate comparison methods. Second, greenhouse gas emissions during the maintenance construction phase are complex, including direct carbon emissions generated by the maintenance process and indirect carbon emissions caused by traffic delays caused by maintenance road closures. Indirect carbon emissions are closely related to traffic volume levels and the management methods of maintenance road closures. Previous studies have shown that carbon emissions from vehicles tend to increase exponentially when traffic volume approaches saturation. Therefore, when calculating indirect carbon emissions, it is necessary to simulate traffic delays caused by maintenance road closures to provide data support.

[0005] To this end, how to provide a multi-objective asphalt pavement maintenance project construction plan optimization method and system that can take greenhouse gas emissions into consideration as the optimization target of the maintenance project construction plan, thereby forming a multi-objective optimization framework with maintenance time, maintenance cost, maintenance quality, and greenhouse gas emissions as the targets, and effectively solve the difficulties existing in the above two aspects and consider carbon reduction benefits is a problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0006] In view of this, the present invention proposes a multi-objective asphalt pavement maintenance project construction plan optimization method and system considering carbon reduction benefits.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits includes:

[0009] Step 1: Construct mathematical calculation functions for maintenance time, maintenance cost, maintenance quality, and carbon emissions. Carbon emissions include direct carbon emissions from maintenance and indirect carbon emissions from traffic delays. Traffic delays are calculated by quantitatively analyzing traffic simulations based on traffic volume levels and road closure management methods for maintenance operations.

[0010] Step 2: With the goal of minimizing maintenance time, maintenance cost, carbon emissions, and maximizing maintenance quality, a multi-objective optimization function is constructed and normalized. Normalization involves comparing the target value with the extreme value of each function.

[0011] Step 3: Use the genetic algorithm to solve the multi-objective optimization function and obtain the multi-objective asphalt pavement maintenance project construction plan optimization result considering carbon reduction benefits.

[0012] Optionally, in step 1, the mathematical calculation function of the curing time is as follows:

[0013]

[0014] Where T is the total curing time; t i is the maintenance time of the i-th sub-project; l is the number of sub-projects on the construction path.

[0015] Optionally, in step 1, the mathematical calculation function of the maintenance cost includes: direct cost, indirect cost and variable cost;

[0016] Direct costs are as follows:

[0017]

[0018] Among them, C d is the total direct cost; l is the number of sub-projects on the construction path; is the direct cost of the i-th sub-project; is the direct cost required for the normal level of the i-th sub-project; p i is the marginal cost factor of the ith sub-project; t i , t ni are the actual maintenance time and normal maintenance time of the i-th sub-project respectively;

[0019] Indirect costs are as follows:

[0020]

[0021]

[0022] Among them, C in is the total indirect cost; is the indirect cost of the i-th sub-project; are the maximum and minimum indirect costs required for the i-th sub-project respectively; are the maximum and minimum maintenance time required for the i-th sub-project respectively;

[0023] Variable costs are as follows:

[0024]

[0025] Among them, C v is the variable cost; F1 and F2 are the reward factor and penalty factor respectively; T, T p The actual completion time of the project and the time agreed in the contract respectively.

[0026] Optionally, in step 1, the mathematical calculation function of the maintenance quality is as follows:

[0027] Q i =ln(a i ×t i +b i );

[0028]

[0029] Among them, Q i is the maintenance quality of the i-th sub-project; a i 、b i is the model parameter; t i is the maintenance time of the i-th sub-project; are the maximum and minimum maintenance time required for the i-th sub-project respectively; is the maintenance quality corresponding to the maximum maintenance time of the i-th sub-project; Q is the total maintenance quality.

[0030] Optionally, in step 1, the mathematical calculation function of carbon emissions is as follows:

[0031] Direct carbon emissions:

[0032]

[0033]

[0034] Among them, E d is the total direct carbon emissions; is the direct carbon emission of the i-th subproject; l is the number of subprojects on the construction path; GWP j is the global warming potential factor of the jth greenhouse gas; r is the amount of greenhouse gas; e i,j is the unit emission of the jth greenhouse gas in the i-th sub-project; is the quantity of materials required for the i-th sub-project;

[0035] Indirect carbon emissions:

[0036]

[0037] F q =F e ×v×d×q t ×T;

[0038] Among them, E in is indirect carbon emissions; F q is the fuel consumption; f j is the amount of the jth greenhouse gas emitted per unit of fuel consumption; F e is the fuel combustion efficiency; v is the average speed of vehicles in the maintenance operation area; d is the average traffic delay; q tis the traffic volume level of vehicles traveling in the maintenance operation area; T is the actual completion time of the project.

[0039] Optionally, in step 1, traffic delays can be obtained by performing traffic simulation quantitative analysis based on the road closure management method for maintenance operations in combination with the traffic volume level, specifically:

[0040] Traffic organization mode, traffic flow, traffic composition and speed limit are taken as independent variables, and traffic delay is taken as dependent variable, and traffic simulation analysis is carried out using Vissim software.

[0041] Optionally, in step 2, the normalized multi-objective optimization function is as follows:

[0042]

[0043] Among them, T, C, Q, and E are maintenance time, maintenance cost, maintenance quality, and carbon emissions, respectively; t i is the maintenance time of the i-th sub-project; l is the number of sub-projects on the construction path; T max 、T min are the maximum and minimum values ​​of the curing time obtained under the single objective function; C d 、C in 、C v are direct total cost, indirect total cost and variable cost respectively; C max 、C min are the maximum and minimum values ​​of maintenance cost obtained under the single objective function; Q max , Q min are the maximum and minimum values ​​of maintenance quality obtained under the single objective function; E d 、E in are direct total carbon emissions and indirect carbon emissions respectively; E max 、E min are the maximum and minimum carbon emissions obtained under the single objective function; are the maximum and minimum maintenance time required for the i-th sub-project respectively; Q i is the maintenance quality of the i-th sub-project; C p Maintenance contract costs.

[0044] Optionally, in step 3, the genetic algorithm uses NSGA-II.

[0045] Optionally, in step 3, solving the multi-objective optimization function using a genetic algorithm further includes: using a penalty function to ensure the consistency of the constraints of the multi-objective optimization function within the genetic algorithm framework.

[0046] The present invention further discloses a multi-objective asphalt pavement maintenance project construction plan optimization system considering carbon reduction benefits, which utilizes a multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits, comprising:

[0047] Objective function construction module: This module is used to construct mathematical calculation functions for maintenance time, maintenance cost, maintenance quality, and carbon emissions. Carbon emissions include direct carbon emissions from maintenance and indirect carbon emissions from traffic delays. Traffic delays are calculated by quantitatively analyzing traffic simulations based on traffic volume levels and road closure management methods for maintenance operations.

[0048] Multi-objective optimization function construction module: This module is used to construct a multi-objective optimization function with the goals of minimizing maintenance time, maintenance costs, carbon emissions, and maximizing maintenance quality, and perform normalization processing. Normalization involves comparing the target value with the extreme value of each function.

[0049] Multi-objective optimization solution module: used to solve the multi-objective optimization function using genetic algorithms to obtain the multi-objective asphalt pavement maintenance project construction plan optimization results considering carbon reduction benefits.

[0050] As can be seen from the above technical solutions, compared to the prior art, the present invention proposes a multi-objective asphalt pavement maintenance project construction plan optimization method and system that considers carbon reduction benefits. This invention innovatively considers carbon reduction benefits as one of the optimization objectives for asphalt pavement maintenance project construction plans, forming a multi-objective optimization framework with maintenance time, maintenance cost, maintenance quality, and greenhouse gas emissions as its objectives. By combining traffic volume levels and conducting traffic simulation based on the maintenance operation road closure management method, it quantitatively analyzes the indirect carbon emissions caused by traffic delays, providing accurate data support for the multi-objective optimization process. Furthermore, the present invention addresses the issue of the four types of objective units differing when balancing cost and non-cost indicators in the multi-objective optimization framework. By normalizing the objective function by comparing the objective value with the extreme value (minimum or maximum) of each function, the present invention effectively addresses the issue of varying units and scales of objectives in the multi-objective optimization framework. In summary, the present invention achieves a suboptimal solution that minimizes maintenance time, maintenance cost, and maximizes maintenance quality while significantly reducing greenhouse gas emissions, meeting the industry requirements of green and low-carbon development and having significant social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0052] Figure 1 Schematic diagram of the method of the present invention.

[0053] Figure 2 This is a schematic diagram of a simulated road section model in a maintenance area, taking a single lane closure as an example.

[0054] Figure 3 Schematic diagram of the NSGA-II algorithm principle of the present invention.

[0055] Figure 4 This is a schematic diagram of chromosome encoding of the genetic algorithm of the present invention.

[0056] Figure 5 It is a schematic diagram of the sub-projects on the construction path of the present invention.

[0057] Figure 6 Schematic diagram of the Pareto solution set of the maintenance project construction plan under the two optimization strategies of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] Example 1:

[0060] Example 1 of the present invention discloses a multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits, such as Figure 1 Shown, including:

[0061] Step 1: Construct mathematical calculation functions for maintenance time, maintenance cost, maintenance quality, and carbon emissions. Carbon emissions include direct carbon emissions from maintenance and indirect carbon emissions from traffic delays. Traffic delays are calculated by quantitatively analyzing traffic simulations based on traffic volume levels and road closure management methods for maintenance operations.

[0062] Curing time is defined as the sum of the curing times required for all sub-projects on the construction path. The mathematical calculation function for curing time is as follows:

[0063]

[0064] Where T is the total curing time; t i is the maintenance time of the i-th sub-project; l is the number of sub-projects on the construction path.

[0065] Maintenance costs are generally composed of three components: direct costs, indirect costs, and variable costs. Direct costs include expenditures on raw materials, production, transportation, and construction. Indirect costs are related to the management costs required to ensure the smooth operation of the project, while variable costs are related to the reward and punishment mechanism for controlling the construction schedule. The mathematical calculation function of maintenance costs includes: direct costs, indirect costs, and variable costs;

[0066] Generally speaking, as the curing time increases, the direct cost tends to decrease and gradually approaches a stable stage. The direct cost is as follows:

[0067]

[0068] Among them, C d is the total direct cost; l is the number of sub-projects on the construction path; is the direct cost of the i-th sub-project; is the direct cost required for the normal level of the i-th sub-project; p i is the marginal cost factor of the ith sub-project; t i , t ni are the actual maintenance time and normal maintenance time of the i-th sub-project respectively;

[0069] Indirect costs are proportional to the maintenance time. Indirect costs are as follows:

[0070]

[0071] Among them, C in is the total indirect cost; is the indirect cost of the i-th sub-project; are the maximum and minimum indirect costs required for the i-th sub-project respectively; are the maximum and minimum maintenance time required for the i-th sub-project respectively;

[0072] Variable costs are related to the reward-penalty system, where early project completion is rewarded and delays beyond the contract deadline are penalized. Variable costs are as follows:

[0073]

[0074] Among them, C v is the variable cost; F1 and F2 are the reward factor and penalty factor respectively; T, T p The actual completion time of the project and the time agreed in the contract respectively.

[0075] The relationship between curing time and curing quality can be illustrated by the concept of the student syndrome. When construction deadlines are tight, the resulting pressure often forces managers to compromise on quality in order to meet the deadline. Conversely, if there is sufficient time, managers can allocate more resources and attention to improving construction quality. There exists a curing time that maximizes curing quality. The mathematical function for curing quality is as follows:

[0076] Q i =ln(a i ×t i +b i );

[0077]

[0078] Among them, Q i is the maintenance quality of the i-th sub-project; a i 、b i is the model parameter; t i is the maintenance time of the i-th sub-project; are the maximum and minimum maintenance time required for the i-th sub-project respectively; is the maintenance quality corresponding to the maximum maintenance time of the i-th sub-project;

[0079] Pavement maintenance is often a serial process, meaning that the next step is carried out after the previous one is completed. The total maintenance quality based on the serial model is as follows:

[0080]

[0081] Among them, Q is the total maintenance quality.

[0082] Research on carbon emissions generally focuses on the global warming potential of greenhouse gases (CO2, CH4, and N2O). To ensure consistency, CH4 and N2O emissions need to be converted to CO2 equivalents over a 100-year timeframe using conversion factors of 25 and 289, respectively. Greenhouse gas emissions generated during the maintenance phase include both direct carbon emissions from the maintenance phase and indirect carbon emissions from traffic delays caused by road closures. The mathematical calculation function for carbon emissions is as follows:

[0083] Direct carbon emissions:

[0084]

[0085] Among them, E d is the total direct carbon emissions; is the direct carbon emission of the i-th subproject; l is the number of subprojects on the construction path; GWP jis the global warming potential factor of the jth greenhouse gas; r is the amount of greenhouse gas; e i,j is the unit emission of the jth greenhouse gas in the i-th sub-project; is the quantity of materials required for the i-th sub-project;

[0086] Indirect carbon emissions:

[0087]

[0088] F q =F e ×v×d×q t ×T;

[0089] Among them, E in is indirect carbon emissions; F q is the fuel consumption; f j is the amount of the jth greenhouse gas emitted per unit of fuel consumption; F e is the fuel combustion efficiency; v is the average speed of vehicles in the maintenance operation area; d is the average traffic delay; q t is the traffic volume level of vehicles traveling in the maintenance operation area; T is the actual completion time of the project.

[0090] Optionally, in step 1, traffic delays can be obtained by performing traffic simulation quantitative analysis based on the road closure management method for maintenance operations in combination with the traffic volume level, specifically:

[0091] Traffic organization mode, traffic flow, traffic composition and speed limit are taken as independent variables, and traffic delay is taken as dependent variable, and traffic simulation analysis is carried out using Vissim software.

[0092] The parameter settings are explained using a two-lane, 3.75m-wide highway undergoing maintenance as an example. Traffic organization methods include single-lane closures and dual-lane closures. A single-lane closure involves closing one lane for maintenance while the other remains open. Once the first lane is maintained, it is opened and the second lane is closed for maintenance. A dual-lane closure involves closing both lanes simultaneously for maintenance, with traffic redirected to the opposite lane. The traffic flow rate is set to 1000 vehicles per hour, the traffic composition is set to a truck rate of 0.2, and the speed limit is set to 30 km / h.

[0093] Taking a single lane closure as an example, the maintenance area simulation road section model, such as Figure 2 The simulation road section model settings for the maintenance area are shown in Table 1.

[0094] Table 1 Maintenance area simulation section model settings

[0095]

[0096]

[0097] According to traffic simulation results using Vissim software, under traffic conditions with a volume of 1,000 vehicles per hour, a truck rate of 0.2, and a maintenance zone management method with a speed limit of 30 km / h, the average traffic delay for a single-lane closure is 399.09 seconds, and the average traffic delay for a double-lane closure is 471.64 seconds.

[0098] Step 2: With the goal of minimizing maintenance time, maintenance cost, carbon emissions, and maximizing maintenance quality, a multi-objective optimization function is constructed and normalized. The normalization process involves comparing the target value with the extreme value of each function.

[0099] Since the four types of objectives have different units and involve the trade-off between cost indicators and non-cost indicators, the objective function needs to be normalized, that is, the target value is compared with the extreme value (minimum or maximum) of each function.

[0100] The normalized multi-objective optimization function is as follows:

[0101]

[0102] Among them, T, C, Q, and E are maintenance time, maintenance cost, maintenance quality, and carbon emissions, respectively; t i is the maintenance time of the i-th sub-project; l is the number of sub-projects on the construction path; T max 、T min are the maximum and minimum values ​​of the curing time obtained under the single objective function; C d 、C in 、C v are direct total cost, indirect total cost and variable cost respectively; C max 、C min are the maximum and minimum values ​​of maintenance cost obtained under the single objective function; Q max , Q min are the maximum and minimum values ​​of maintenance quality obtained under the single objective function; E d 、E in are direct total carbon emissions and indirect carbon emissions respectively; E max 、E min are the maximum and minimum carbon emissions obtained under the single objective function; are the maximum and minimum maintenance time required for the i-th sub-project respectively; Q i is the maintenance quality of the i-th sub-project; C p Maintenance contract costs.

[0103] Step 3: Use the genetic algorithm to solve the multi-objective optimization function and obtain the multi-objective asphalt pavement maintenance project construction plan optimization result considering carbon reduction benefits.

[0104] The genetic algorithm used is NSGA-II. NSGA-II is an evolutionary genetic algorithm inspired by the principles of natural selection and evolution. Its algorithm principle is as follows: Figure 3 As shown. Figure 4 As shown, in the algorithm, chromosome encoding is in integer form, and its physical meaning is the maintenance time of a single sub-project.

[0105] The method utilizes a genetic algorithm to solve a multi-objective optimization function, and further includes: using a penalty function to ensure the consistency of the constraint conditions of the multi-objective optimization function within the framework of the genetic algorithm.

[0106] Given that the required maintenance times for each subproject differ between single-lane and dual-lane closures, and the required minimum and maximum maintenance times are also different, a penalty function is employed to ensure consistency within the algorithm framework. Specifically, the maintenance time constraints for each subproject in the NSGA-II algorithm are defined as the union of the maintenance time constraints for the two lane closures. If a selected construction plan satisfies the algorithm's union of maintenance time constraints but not the maintenance time constraints for the lane closure, the penalty function will disqualify that solution. Similarly, the penalty function incorporates maintenance cost and quality constraints to ensure that solutions that violate these constraints are not adopted during the optimization process.

[0107] Example 2:

[0108] Example 2 of the present invention discloses a practical engineering case application of a multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits, specifically:

[0109] A highway has a pavement structure composed of 4cm AC-13, 6cm AC-20, 8cm AC-25, and 34cm 5% cement-stabilized crushed stone. Due to performance degradation, a minor repair of 25km is planned for two lanes in one direction. This involves resurfacing with a 4cm AC-13 top layer. The required project time (Tp) is set at 80 days, with a maintenance quality target exceeding 0.8. The reward and penalty coefficients are set at 20,000 and 30,000 yuan per day, respectively, for a total maintenance budget of 21.5 million yuan. Subprojects along the construction path, such as Figure 5 shown.

[0110] For single-lane closures and double-lane closures, the normal maintenance time and maintenance time constraints of sub-projects on the construction path are shown in Table 2 and Table 3 respectively.

[0111] Table 2 Maintenance and construction plan under single lane closure

[0112]

[0113] Table 3 Maintenance and construction plan under dual lane closure

[0114]

[0115] The parameters of the genetic algorithm are set as follows:

[0116] Algebraic quantity = 600;

[0117] Population size = 200;

[0118] Intermediate crossover rate = 0.8;

[0119] Gaussian mutation rate = 0.1.

[0120] A genetic algorithm was used to evaluate the proposed multi-objective maintenance project construction plan optimization method (TCQE) that considers carbon reduction benefits, and the traditional multi-objective maintenance project construction plan optimization method (TCQ), which only considers maintenance time, maintenance cost, and maintenance quality, in a real-world application. The Pareto solution set obtained using the proposed optimization method is shown in Table 4.

[0121] Table 4 Optimization solution set for maintenance project construction plan

[0122]

[0123]

[0124] The genetic algorithm was used to apply the multi-objective maintenance project construction plan optimization method (TCQE) that considers carbon reduction benefits proposed in this paper and the traditional multi-objective maintenance project construction plan optimization method (TCQ), which only considers maintenance time, maintenance cost, and maintenance quality. The Pareto solution sets of the maintenance project construction plan under the two optimization strategies are as follows: Figure 6 shown.

[0125] The objective function values ​​for the two optimization strategies were calculated. Table 5 compares the mean objective function values ​​for the Pareto solution set, and Table 6 compares the mean objective function values ​​for the optimal solution. The optimal solution was selected based on the shortest standard Euclidean distance.

[0126] Table 5 Comparison of mean values ​​of objective functions of Pareto solution sets

[0127]

[0128] Table 6 Comparison of the mean values ​​of the objective functions of the optimal solutions

[0129]

[0130]

[0131] The numerical comparison in the above table shows (target value TCQE -Target value TCQ ) / target value TCQ. According to the comparison results, the strategy based on the TCQE optimization method can significantly reduce greenhouse gas emissions by slightly increasing maintenance time and maintenance costs, while helping to improve maintenance quality. Specifically, by adopting the TCQE-based optimization method instead of the TCQ optimization method, greenhouse gas emissions can be effectively reduced by more than 30%. Therefore, the multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits proposed in the present invention can significantly reduce greenhouse gas emissions, meet the strategic requirements of green development, and have significant social benefits.

[0132] Example 3:

[0133] Embodiment 3 of the present invention further discloses a multi-objective asphalt pavement maintenance project construction plan optimization system considering carbon reduction benefits, which utilizes a multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits, comprising:

[0134] Objective function construction module: This module is used to construct mathematical calculation functions for maintenance time, maintenance cost, maintenance quality, and carbon emissions. Carbon emissions include direct carbon emissions from maintenance and indirect carbon emissions from traffic delays. Traffic delays are calculated by quantitatively analyzing traffic simulations based on traffic volume levels and road closure management methods for maintenance operations.

[0135] Multi-objective optimization function construction module: This module is used to construct a multi-objective optimization function with the goals of minimizing maintenance time, maintenance costs, carbon emissions, and maximizing maintenance quality, and perform normalization processing. Normalization involves comparing the target value with the extreme value of each function.

[0136] Multi-objective optimization solution module: used to solve the multi-objective optimization function using genetic algorithms to obtain the multi-objective asphalt pavement maintenance project construction plan optimization results considering carbon reduction benefits.

[0137] The embodiments of the present invention disclose a multi-objective asphalt pavement maintenance project construction plan optimization method and system that considers carbon reduction benefits. The present invention innovatively considers carbon reduction benefits as one of the optimization goals of the asphalt pavement maintenance project construction plan, forming a multi-objective optimization framework with maintenance time, maintenance cost, maintenance quality, and greenhouse gas emissions as goals. By combining traffic volume levels and conducting traffic simulation based on the maintenance operation road closure management method, the indirect carbon emissions caused by traffic delays are quantitatively analyzed, providing accurate data support for the multi-objective optimization process. At the same time, the present invention addresses the problem of different units of four types of objectives in the multi-objective optimization framework involving the trade-off between cost indicators and non-cost indicators. By normalizing the objective function by comparing the objective value with the extreme value (minimum or maximum value) of each function, the problem of different units and scales of the objectives in the multi-objective optimization framework is effectively solved. In summary, the present invention achieves a suboptimal solution of minimizing maintenance time, maintenance cost, and maximizing maintenance quality while significantly reducing greenhouse gas emissions, meeting the industry requirements of green and low-carbon development and having significant social benefits.

[0138] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0139] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits, characterized in that: include: Step 1: Construct mathematical calculation functions for maintenance time, maintenance cost, maintenance quality, and carbon emissions. Carbon emissions include direct carbon emissions from maintenance and indirect carbon emissions from traffic delays. Traffic delays are calculated by quantitatively analyzing traffic simulations based on traffic volume levels and road closure management methods for maintenance operations. Step 2: With the goal of minimizing maintenance time, maintenance cost, carbon emissions, and maximizing maintenance quality, a multi-objective optimization function is constructed and normalized. The normalization process involves comparing the target value with the extreme value of each function. Step 3: Use a genetic algorithm to solve the multi-objective optimization function to obtain a multi-objective asphalt pavement maintenance project construction plan optimization result that takes carbon reduction benefits into consideration.

2. The multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits according to claim 1 is characterized in that: In step 1, the mathematical calculation function of the curing time is as follows: Where T is the total curing time; t i is the maintenance time of the i-th sub-project; l is the number of sub-projects on the construction path.

3. The multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits according to claim 1 is characterized in that: In step 1, the mathematical calculation function of the maintenance cost includes: direct cost, indirect cost and variable cost; The direct costs are as follows: Among them, C d is the total direct cost; l is the number of sub-projects on the construction path; is the direct cost of the i-th sub-project; is the direct cost required for the normal level of the i-th sub-project; p i is the marginal cost factor of the ith sub-project; t i , t ni are the actual maintenance time and normal maintenance time of the i-th sub-project respectively; The indirect costs are as follows: Among them, C in is the total indirect cost; is the indirect cost of the i-th sub-project; are the maximum and minimum indirect costs required for the i-th sub-project respectively; are the maximum and minimum curing time required for the i-th sub-project respectively; The variable costs are as follows: Among them, C v is the variable cost; F1 and F2 are the reward factor and penalty factor respectively; T, T p The actual completion time of the project and the time agreed in the contract respectively.

4. The multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits according to claim 1 is characterized in that: In step 1, the mathematical calculation function of the maintenance quality is as follows: Q i =ln((a i ×t i +b i ); Among them, Q i is the maintenance quality of the i-th sub-project; a i 、b i is the model parameter; t i is the maintenance time of the i-th sub-project; are the maximum and minimum curing time required for the i-th sub-project respectively; is the maintenance quality corresponding to the maximum maintenance time of the i-th sub-project; Q is the total maintenance quality.

5. The multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits according to claim 1 is characterized in that: In step 1, the mathematical calculation function of carbon emissions is as follows: Direct carbon emissions: Among them, E d is the total direct carbon emissions; is the direct carbon emission of the i-th subproject; l is the number of subprojects on the construction path; GWP j is the global warming potential factor of the jth greenhouse gas; r is the amount of greenhouse gas; e i,j is the unit emission of the jth greenhouse gas in the i-th sub-project; is the quantity of materials required for the i-th sub-project; The indirect carbon emissions: F q =F e ×v×d×q t ×T; Among them, E in is indirect carbon emissions; F q is the fuel consumption; f j is the amount of the jth greenhouse gas emitted per unit of fuel consumption; F e is the fuel combustion efficiency; v is the average speed of vehicles in the maintenance operation area; d is the average traffic delay; q t is the traffic volume level of vehicles traveling in the maintenance operation area; T is the actual completion time of the project.

6. The multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits according to claim 1 is characterized in that: In step 1, the traffic delay is obtained by conducting a traffic simulation quantitative analysis based on the traffic volume level and the road closure management mode for maintenance operations, specifically: Traffic organization mode, traffic flow, traffic composition and speed limit are taken as independent variables, and traffic delay is taken as dependent variable, and traffic simulation analysis is carried out using Vissim software.

7. The multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits according to claim 1 is characterized in that: In step 2, the normalized multi-objective optimization function is as follows: Among them, T, C, Q, and E are maintenance time, maintenance cost, maintenance quality, and carbon emissions, respectively; t i is the maintenance time of the i-th sub-project; l is the number of sub-projects on the construction path; T max 、T min are the maximum and minimum values ​​of the curing time obtained under the single objective function; C d 、C in 、C v are direct total cost, indirect total cost and variable cost respectively; C max 、C min are the maximum and minimum values ​​of maintenance cost obtained under the single objective function; Q max , Q min are the maximum and minimum values ​​of maintenance quality obtained under the single objective function; E d 、E in are direct total carbon emissions and indirect carbon emissions respectively; E max 、E min are the maximum and minimum carbon emissions obtained under the single objective function; are the maximum and minimum maintenance time required for the i-th sub-project respectively; Q i is the maintenance quality of the i-th sub-project; C p Maintenance contract costs.

8. The multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits according to claim 1 is characterized in that: In step 3, the genetic algorithm is NSGA-II.

9. The multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits according to claim 1 is characterized in that: In step 3, solving the multi-objective optimization function using a genetic algorithm also includes: using a penalty function to ensure the consistency of the constraints of the multi-objective optimization function within the genetic algorithm framework.

10. A multi-objective asphalt pavement maintenance project construction plan optimization system considering carbon reduction benefits, utilizing the multi-objective asphalt pavement maintenance project construction plan optimization method considering carbon reduction benefits according to claims 1-9, characterized in that: include: Objective function construction module: used to construct mathematical calculation functions for maintenance time, maintenance cost, maintenance quality, and carbon emissions. Carbon emissions include direct carbon emissions from maintenance and indirect carbon emissions from traffic delays. Traffic delays are calculated by quantitatively analyzing traffic simulations based on traffic volume levels and road closure management methods for maintenance operations. Multi-objective optimization function construction module: used to construct a multi-objective optimization function with the goals of minimizing maintenance time, maintenance cost, carbon emissions, and maximizing maintenance quality, and perform normalization processing; wherein the normalization processing is to compare the target value with the extreme value of each function; Multi-objective optimization solution module: used to solve the multi-objective optimization function using a genetic algorithm to obtain a multi-objective asphalt pavement maintenance project construction plan optimization result that takes into account carbon reduction benefits.

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