CN115375010A - Decision-making method and electronic equipment suitable for long-term maintenance planning of airport pavement - Google Patents
Decision-making method and electronic equipment suitable for long-term maintenance planning of airport pavement Download PDFInfo
- Publication number
- CN115375010A CN115375010A CN202210906506.3A CN202210906506A CN115375010A CN 115375010 A CN115375010 A CN 115375010A CN 202210906506 A CN202210906506 A CN 202210906506A CN 115375010 A CN115375010 A CN 115375010A
- Authority
- CN
- China
- Prior art keywords
- maintenance
- decision
- pavement
- airport
- airport pavement
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012423 maintenance Methods 0.000 title claims abstract description 210
- 238000013439 planning Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000007774 longterm Effects 0.000 title claims abstract description 35
- 238000005457 optimization Methods 0.000 claims abstract description 60
- 238000010276 construction Methods 0.000 claims abstract description 36
- 238000001514 detection method Methods 0.000 claims abstract description 7
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 6
- 230000006870 function Effects 0.000 claims description 10
- 238000012216 screening Methods 0.000 claims description 7
- 230000003449 preventive effect Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 230000035508 accumulation Effects 0.000 claims description 3
- 238000009825 accumulation Methods 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 2
- 239000011800 void material Substances 0.000 claims description 2
- 238000007726 management method Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 238000007728 cost analysis Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06314—Calendaring for a resource
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Genetics & Genomics (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Physiology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a decision-making method and electronic equipment suitable for long-term maintenance planning of an airport pavement, wherein the method comprises the following steps: the method comprises the steps of establishing a pavement maintenance decision model, a pavement performance prediction model and an airport pavement long-term maintenance decision multi-objective optimization model under non-navigation construction by combining airport pavement period detection and maintenance historical data, generating a pavement maintenance alternative scheme by repeating decision-prediction steps in a decision planning period, and solving the optimization model by using a multi-objective evolutionary algorithm to obtain an optimal solution of the airport pavement long-term maintenance planning. The method can carry out decision optimization according to the operation requirement of non-navigation-stop construction of the airport pavement under the condition of considering the influence of specific maintenance measures on the performance decay of the pavement, realizes the optimal service condition of the pavement and the minimum maintenance cost in a decision period, and provides practical reference for the long-term maintenance planning of the airport pavement.
Description
Technical Field
The invention belongs to the field of airport pavement maintenance decision optimization, and particularly relates to a decision method and electronic equipment suitable for long-term maintenance planning of an airport pavement.
Background
The high-speed development of the economic society leads to the increasing demand of air transportation, the number of large and medium-sized airplanes in air transportation is increased year by year, the airport pavement bears unprecedented pressure due to high-load operation, the diseases and potential safety hazards of the airport pavement built at an early stage are increased, and the maintenance demand is increased continuously. However, unlike highways, maintenance of airport pavement is often limited by the task of operating the pavement, and therefore non-stop construction has been the focus of attention in maintaining the airport pavement. Based on the method, the operation requirement of non-navigation-stop construction of the airport pavement is combined to optimize the pavement maintenance decision, which is the primary task of current airport pavement maintenance management.
At present, airport pavement maintenance work mainly depends on expert decision, the decision mode is limited by the technical level of decision makers, decision subjectivity is high, meanwhile, post maintenance aiming at existing pavement diseases not only can delay the best maintenance opportunity to increase maintenance cost, but also can cause the risk of difficult prediction of airplane operation. Therefore, in combination with the decay law of airport pavement performance, timely preventive maintenance is the key to maintaining long-term high-quality operation of airport pavements.
Although some scholars try to explore the performance decay rule of the airport pavement and provide some preventive maintenance indexes in the airport pavement management research, the scholars mainly give maintenance opportunities, and the case of performing long-term maintenance decision on the pavement is always carried out by shallow time on maintenance decision optimization and less comprehensive benefit cost analysis. In highway maintenance management, chinese patent application No. CN 201911280981.9 proposes to use a planning method to make a pavement maintenance decision, but the method does not take long-term performance decay of the pavement into consideration, neglects the influence caused by specific maintenance measures in the decision and is difficult to realize refined pavement long-term maintenance planning; the chinese patent application No. CN202111330804.4 proposes a road maintenance decision method considering the prediction of the performance of the microscopic unit, but only optimizes the performance of the road in the decision, and does not consider the cost, which is an important constraint factor in the maintenance decision.
In summary, currently, there is no systematic decision optimization method for airport pavement maintenance management, and although some decision methods exist in highway pavement maintenance, the consideration factors are single, and the decision methods are limited by different emphasis points in highway and airport pavement maintenance work, and are also difficult to apply to airport pavement maintenance decisions.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the invention provides a decision-making method and electronic equipment suitable for long-term maintenance planning of an airport pavement.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a decision-making method suitable for long-term maintenance planning of airport pavement comprises the following steps:
s1, establishing an airport pavement maintenance decision model: inputting airport pavement performance indexes and outputting applicable maintenance measures under the condition of the indexes; the airport pavement maintenance decision model comprises an airport pavement maintenance strategy library and an airport pavement maintenance decision logic;
s2, collecting airport pavement periodic detection and maintenance historical data, and establishing an airport pavement performance index prediction model: different prediction models of each index under different maintenance measures are included; inputting the pavement performance index and the maintenance measure into an airport pavement performance index prediction model, and outputting a pavement performance index prediction value of the next quarter;
s3, carrying out single maintenance decision of the airport pavement, setting a decision planning period T by taking the quarter as the shortest decision period of the airport pavement, and repeating the steps of S1 and S2 in each quarter in the decision planning period to generate a maintenance plan of each unit of the airport pavement;
s4, establishing an airport pavement long-term maintenance decision multi-objective optimization model under non-navigation-stop construction, wherein the model comprises an objective function and constraint conditions;
and S5, taking each unit maintenance plan generated in the step S3 as a multi-objective optimization feasible region, and performing optimization solution by adopting the multi-objective optimization model established in the step S4 to obtain an optimal solution set of the airport pavement long-term maintenance plan.
Preferably, the performance index of the airport pavement in step S1 includes: the road surface condition index PCI, the road surface grade number PCN, the road surface panel void coefficient T, the international flatness index IRI and the road surface friction coefficient mu.
Preferably, the step S1 of establishing the airport pavement maintenance strategy library specifically refers to: the method comprises the following steps of combining the civil aviation administration 'civil airport pavement flight area site maintenance technical guideline' and actual airport pavement maintenance historical data to determine the types of airport pavement maintenance measures and the application conditions, unit price estimation and construction time of various maintenance measures, wherein the maintenance measures are divided into 10 items in total of 3 types: preventive maintenance, targeted maintenance and structural maintenance, wherein the preventive maintenance comprises daily maintenance and grooving; the targeted maintenance comprises crack filling, plate grinding, basic grouting, shallow layer repairing, partial thickness repairing and full thickness repairing; the structural maintenance comprises plate replacement and additional paving.
Preferably, the step S1 of establishing the airport pavement maintenance decision logic specifically refers to: after the airport pavement maintenance strategy library is established, maintenance measures suitable for various index levels are determined by combining airport pavement period detection and maintenance historical data.
Preferably, the expression of the airport pavement performance index prediction model in step S2 is as follows:
wherein y (t) is a performance index prediction curve, t represents time, y 0 The initial value of the performance is, a and b are model parameters, and the calculation formula is as follows:
in the formula (II)
The index value of the pavement performance for n years is screened from historical data of the pavement performance,
is a weighted moving average sequence consisting of
A sequence of accumulations of
And the weighted moving average is generated, alpha is a weighting coefficient, and the value range is 0-1.
Preferably, the step S3 of making a single maintenance decision of the airport pavement specifically includes the following steps:
s31, setting a decision planning period T;
s32, inputting airport pavement performance index data, and making a decision by using the pavement maintenance decision logic established in the step S1 to generate an applicable maintenance measure;
s33, based on the decision result of the step S32, respectively selecting a corresponding performance prediction model for each maintenance measure to calculate and output performance indexes of the airport pavement in the next quarter;
and S34, inputting the predicted performance index of the next quarter, and repeating the steps S32-S33 until the decision of each quarter in the decision planning period T is completed and all maintenance plans in the decision planning period are output.
Preferably, the airport pavement maintenance decision multi-objective optimization model in the step S4 takes the maximization of the average PCI of the pavement in the decision planning period and the minimization of the maintenance cost as a model objective function, and the maintenance construction window period B of each quarter is preset j The maintenance scheme is restricted to ensure the operation requirement of the airport pavement non-stop construction, and the formula is expressed as follows:
wherein, maxF 1 Representing the maximum of the average PCI of the pavement in the decision planning period, wherein M is the total number of decision units of the pavement of the airport, T is the decision planning period and the PCI it Area, the predicted value of the pavement PCI for quarter t of Unit i i The Area of the unit i, area is the total Area of the runway surface of the decision airport; minF 2 Indicates that the maintenance cost is minimized, N is the total number of maintenance measures in the pavement maintenance countermeasure library, x ijt Whether the unit i uses the kth maintenance measure or not in the tth quarter is represented as a binary variable, the value 1 represents using, the value 0 represents not using, and the value P represents k Unit price for kth care; s.t. constraint of model, C k Construction time for kth maintenance measures, B j And D, determining the maintenance construction window period of the airport in the jth quarter, and determining the lower limit of the pavement PCI in the decision planning period.
Preferably, step S5 specifically includes the steps of:
s51, establishing an objective function equation of an optimization model, selecting two optimization targets of pavement average PCI maximization and maintenance cost minimization in a decision planning period based on airport pavement maintenance requirements, and determining an objective function according to a quantitative relation between the optimization targets and a maintenance scheme;
s52, determining constraint conditions of an optimization model, setting a pavement PCI lower limit to constrain an optimization scheme, and also taking a pavement maintenance construction window period as a constraint condition according to the operation requirement of non-stop construction of an airport pavement, and constraining a decision optimization process according to the maintenance construction window period of each quarter in the airport operation practice; according to the steps S51-S52, finally establishing an airport pavement long-term maintenance decision multi-objective optimization model under non-navigation construction;
s53, solving the multi-objective optimization model obtained in the step S52 to obtain a non-dominated optimal solution set in a decision planning period;
and S54, determining a corresponding road surface maintenance scheme by combining the current-year operation of the airport and the maintenance plan according to the optimal solution set of the decision optimization problem obtained by the solution in the step S53.
Preferably, step S53 specifically refers to: the method comprises the steps of solving a constructed multi-target optimization model by adopting a non-dominated sorting genetic algorithm with an elite strategy, randomly generating an initial generation population with a certain number of individuals in a decision space, wherein each individual represents a group of solutions of the optimization problem, screening the individuals in the population by fast non-dominated sorting and congestion calculation, so that the individuals are uniformly distributed in the decision space while ensuring population fitness, obtaining a new generation population by utilizing selection, crossing and variation operations, and reserving elite individuals in a parent population by combining the parent population and an offspring population during population iteration so as to improve optimization efficiency.
An electronic device, comprising: the system comprises a memory and a processor, wherein the memory stores a computer program executable by the processor, and the processor realizes the decision-making method suitable for the airport pavement long-term maintenance planning when executing the computer program.
Adopt the beneficial effect that above-mentioned technical scheme brought:
the invention provides a decision-making method and electronic equipment suitable for long-term maintenance planning of an airport pavement, wherein the method brings performance decay rules of the airport pavement under different maintenance measures into a decision-making process, simultaneously establishes a multi-objective optimization model aiming at the operation requirement of non-navigation construction of the airport pavement in the decision-making process, solves the optimal solution set of maximizing the performance level of the pavement and minimizing the maintenance cost in the decision-making period through an intelligent algorithm, answers the problems of whether to maintain, when to maintain and what maintenance measures to adopt in the maintenance decision of the airport pavement, and has positive significance for realizing the operation and maintenance management of the airport pavement in the whole life cycle.
Drawings
FIG. 1 is a schematic flow chart of a decision-making method suitable for long-term maintenance planning of an airport pavement according to the present invention;
FIG. 2 is a schematic view of a road maintenance decision logic;
FIG. 3 is a schematic view of a single maintenance decision flow;
FIG. 4 is a schematic diagram of a decision-making optimization non-dominated solution set according to an embodiment of the invention;
FIG. 5 is a schematic diagram of maintenance workload of each scheme in the optimization result in the embodiment of the present invention.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
Referring to fig. 1, taking the airport cement concrete pavement maintenance decision optimization as an example, the steps of the embodiment of the present application are as follows:
s1, establishing an airport pavement maintenance decision model:
step S1.1, establishing an airport pavement maintenance strategy library: the method comprises the steps of combining the technical guideline for maintaining civil airport pavement flight area fields (AC-140-CA-2010-3) of China civil aviation administration and airport pavement maintenance historical data, building a pavement maintenance strategy library, and determining the types of airport pavement maintenance measures, application conditions of various maintenance measures, unit price estimation and construction time, wherein the application conditions, unit price estimation and construction time are shown in the table 1.
TABLE 1 pavement maintenance strategy warehouse
S1.2, establishing airport pavement maintenance decision logic: and establishing an airport pavement maintenance decision tree by combining the airport pavement period detection and maintenance historical data. The civil airport pavement evaluation management technical specification (MH/T5024-2019) respectively grades the pavement evaluation indexes (the index grading result in the embodiment is shown in table 2), and then determines applicable maintenance measures for all the conditions (5 × 3 × 4 × 3=720 in total) in different grade permutation and combination of the indexes by combining airport pavement period detection and maintenance historical data, so as to serve as airport pavement maintenance decision logic. When a specific pavement unit is decided, only the performance index of the unit needs to be input, and the maintenance measure applicable to the unit can be obtained by judging which condition the unit belongs to, as shown in fig. 2.
TABLE 2 road surface rating index grading results
| Index (I) | |
Class 2 | Class 3 | Class 4 | Grade 5 |
| PCI | PCI≥85 | 70≤PCI<85 | 55≤PCI<70 | 40≤PCI<55 | PCI<40 |
| PCN | PCN≥80 | 50≤PCN<80 | PCN<50 | / | / |
| T | T≤1.5 | 1.5<T≤2.5 | 2.0<T≤3.0 | T>3.0 | / |
| IRI | IRI<2.5 | 2.5≤IRI<3.5 | 3.5≤IRI<4.5 | IRI≥4.5 | / |
| μ | μ≥0.60 | 0.50≤μ<0.60 | μ<0.50 | / | / |
S2, establishing an airport pavement performance prediction model:
s2.1, screening airport pavement performance and maintenance historical data:classifying the airport pavement performance and maintenance history data according to used maintenance measures, and screening out the time sequence of each performance index of the airport pavement after each maintenance measure is used
S2.2, establishing an airport pavement performance prediction system: respectively establishing performance prediction models by using the results of data screening, and sequentially calculating the primary accumulation sequence
Weighted moving average sequence
And model parameters a, b:
wherein, the sequence
Is the index value of the pavement performance for n years,
in order to accumulate the sequence once,
for the weighted moving average sequence, α is the weighting factor, in this example, the values α =0.5, y (t) is the performance index prediction curve, t represents time (quarterly), y 0 The initial values of the performance are a and b are model parameters.
And step S3: the flow of the single maintenance decision of the airport pavement is shown in figure 3.
Step S3.1: setting a decision planning period T: in the embodiment, a total of 12 quarters in 3 years is taken as a decision planning period;
step S3.2: and (3) airport pavement maintenance decision: inputting airport pavement performance index data (current value or predicted value), and making a decision by using the pavement maintenance decision logic established in the step S1.2 to generate an applicable maintenance measure;
step S3.3: predicting the performance of the airport pavement: for each maintenance measure generated in the step S3.2, selecting a corresponding performance prediction model to calculate the performance index of the next quarter;
step S3.4: and inputting the performance index of the next quarter obtained by prediction, and repeating the steps of decision-prediction (S3.2 and S3.3) until the decision of each quarter in the decision planning period is completed and all maintenance plans in the decision planning period are output.
Taking a single pavement unit as an example, assuming that 2 available measures can be generated according to current index data, respectively predicting the index data according to the 2 measures to obtain 2 possible situations of pavement performance in the next quarter, then respectively making decisions on the 2 situations, generating a plurality of available measures in each situation, predicting again to obtain a plurality of possible situations in the 3 rd quarter, repeating the process to finally generate a plurality of maintenance plans (which maintenance measures are adopted in each quarter) of the pavement unit in a decision planning period, and taking the maintenance plans as alternatives of the pavement unit in long-term decision optimization. In this embodiment, a total of 13730 maintenance plans are generated for 100 units of airport pavement through the above steps, and pavement long-term decision optimization is preferred from a permutation and combination of the plans.
And step S4: airport pavement long-term decision optimization:
step S4.1: establishing an objective function equation of an optimization model: based on the airport pavement maintenance requirements, selecting two optimization targets of pavement average PCI maximization and maintenance cost minimization in a decision planning period, and determining an objective function according to a quantitative relation between the optimization targets and a maintenance scheme;
step S4.2: determining the constraint conditions of the optimization model: in the invention, besides setting the PCI lower limit of the pavement to constrain the optimization scheme, the decision optimization process is constrained according to the maintenance construction window period of each quarter in the practical operation of the airport by taking the pavement maintenance construction window period as a constraint condition according to the operation requirement of the airport pavement non-stop construction.
According to the steps S4.1-S4.2, finally establishing an airport pavement long-term maintenance decision multi-objective optimization model under non-navigation construction:
wherein M is the total number of decision units of the airport pavement, T is the decision planning period (quarter), PCI it Area, the predicted value of the pavement PCI for quarter t of Unit i i The Area of the unit i, area, total Area of the runway surface of the decision airport, N, the total number of maintenance measures in the runway surface maintenance strategy library, x ijt Whether the unit i uses the kth maintenance measure or not in the tth quarter is represented as a binary variable, the value 1 represents use, the value 0 represents non-use, and the value P represents k Unit price for kth maintenance measure, C k Construction time for maintenance measures in kth, B j In order to decide the maintenance construction window period (day) of the airport in the jth quarter, the sampling conditions in this embodiment are shown in table 3, D is the lower limit of the pavement PCI in the decision planning period, in this embodimentTaking D =80;
TABLE 3 decision planning period for each quarter construction window period of the airport
Step S4.3: solving the multi-objective optimization model to obtain a non-dominated optimal solution set in a decision planning period: in this embodiment, the constructed multi-objective optimization model is solved by using a non-dominated sorting genetic algorithm with elite strategy (NSGA-ii algorithm). During solving, firstly, randomly generating an initial generation population with a certain number of individuals in a decision space, wherein each individual represents a group of solutions of the optimization problem, secondly, screening the individuals in the population through rapid non-dominated sorting and congestion calculation, so that the individuals are uniformly distributed in the decision space while the population fitness is ensured, and finally, obtaining a new generation population by utilizing selection, intersection and variation operations, and reserving elite individuals in the parent population through combination of the parent population and an offspring population during population iteration so as to improve the optimization efficiency;
step S4.4: obtaining a decision optimization result: the solution obtained in step S4.3 is a series of non-dominated solutions, that is, the optimal solution set of the decision optimization problem, including the specific maintenance scheme, performance index, cost estimation, and construction time consumption of each unit in each quarter, and the road manager may refer to the above solutions, determine the corresponding road maintenance scheme by combining the current-year operation and maintenance plan of the airport, and in this embodiment, obtain 18 non-dominated solutions, as shown in table 4, the maintenance cost and PCI prediction result of each scheme is as shown in fig. 4, and the maintenance work amount of each scheme is as shown in fig. 5.
TABLE 4 alternative construction time, cost and Performance prediction statistics
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.
Claims (10)
1. A decision method suitable for airport pavement long-term maintenance planning is characterized by comprising the following steps:
s1, establishing an airport pavement maintenance decision model: inputting airport pavement performance indexes and outputting applicable maintenance measures under the condition of the indexes; the airport pavement maintenance decision model comprises an airport pavement maintenance strategy library and an airport pavement maintenance decision logic;
s2, collecting airport pavement periodic detection and maintenance historical data, and establishing an airport pavement performance index prediction model: different prediction models of each index under different maintenance measures are included; inputting the pavement performance index and the maintenance measure into an airport pavement performance index prediction model, and outputting a pavement performance index prediction value of the next quarter;
s3, carrying out single maintenance decision of the airport pavement, setting a decision planning period T by taking the quarter as the shortest decision period of the airport pavement, and repeating the steps of S1 and S2 in each quarter in the decision planning period to generate a maintenance plan of each unit of the airport pavement;
s4, establishing an airport pavement long-term maintenance decision multi-objective optimization model under non-navigation construction, wherein the model comprises a target function and constraint conditions;
and S5, taking each unit maintenance plan generated in the step S3 as a multi-objective optimization feasible domain, and performing optimization solution by adopting the multi-objective optimization model established in the step S4 to obtain an optimal solution set of the long-term maintenance plan of the airport pavement.
2. The method according to claim 1, wherein the performance index of the airport pavement in step S1 comprises: the road surface condition index PCI, the road surface grade number PCN, the road surface panel void coefficient T, the international flatness index IRI and the road surface friction coefficient mu.
3. The decision method suitable for airport pavement long-term maintenance planning as claimed in claim 1, wherein the step S1 of establishing an airport pavement maintenance strategy library specifically refers to: the method comprises the following steps of combining the civil aviation administration 'civil airport pavement flight area site maintenance technical guideline' and actual airport pavement maintenance historical data to determine the types of airport pavement maintenance measures and the application conditions, unit price estimation and construction time of various maintenance measures, wherein the maintenance measures are divided into 10 items in total of 3 types: preventive maintenance, targeted maintenance and structural maintenance, wherein the preventive maintenance comprises daily maintenance and grooving; the targeted maintenance comprises crack filling, plate grinding, basic grouting, shallow layer repairing, partial thickness repairing and full thickness repairing; the structural maintenance comprises plate replacement and additional paving.
4. The decision method suitable for airport pavement long-term maintenance planning according to claim 1, wherein the step S1 of establishing airport pavement maintenance decision logic specifically refers to: after the airport pavement maintenance strategy library is established, maintenance measures suitable for various index levels are determined by combining airport pavement period detection and maintenance historical data.
5. The decision method suitable for airport pavement long-term maintenance planning as claimed in claim 1, wherein the expression of the airport pavement performance index prediction model in step S2 is:
wherein y (t) is a performance index prediction curve, t represents time, y 0 The initial value of the performance is, a and b are model parameters, and the calculation formula is as follows:
in the formula (II)
Screening the index values of the pavement performance for continuous n years from historical data of the pavement performance,
is a weighted moving average sequence consisting of
A sequence of accumulations of
And generating the weighted moving average, wherein alpha is a weighting coefficient and has a value range of 0-1.
6. The decision method suitable for airport pavement long-term maintenance planning as claimed in claim 1, wherein the step S3 of making the airport pavement single maintenance decision specifically comprises the following steps:
s31, setting a decision planning period T;
s32, inputting airport pavement performance index data, and making a decision by using the pavement maintenance decision logic established in the step S1 to generate an applicable maintenance measure;
s33, based on the decision result of the step S32, respectively selecting a corresponding performance prediction model for each maintenance measure to calculate and output performance indexes of the airport pavement in the next quarter;
and S34, inputting the performance index of the next quarter obtained by prediction, and repeating the steps S32-S33 until the decision of each quarter in the decision planning period T is completed and all maintenance plans in the decision planning period are output.
7. The decision method for airport pavement long-term maintenance planning as claimed in claim 1, wherein the airport pavement maintenance decision-making multi-objective optimization model in step S4 uses the maximization of the average PCI of the pavement and the minimization of the maintenance cost as model objective functions in the decision planning period, and the maintenance construction window period B of each quarter is preset j The maintenance scheme is restricted to ensure the operation requirement of the airport pavement non-stop construction, and the formula is expressed as follows:
wherein, maxF 1 Representing the maximum of the average PCI of the pavement in the decision planning period, wherein M is the total number of decision units of the airport pavement, T is the decision planning period and the PCI it Area, the predicted value of the pavement PCI for quarter t of Unit i i The Area of the unit i, area is the total Area of the runway surface of the decision airport; minF 2 Representing the minimum maintenance cost, N is the total number of maintenance measures in the pavement maintenance strategy library, x ijt Whether the unit i uses the kth maintenance measure or not in the tth quarter is represented as a binary variable, the value 1 represents using, the value 0 represents not using, and the value P represents k Unit price for kth care; s.t. constraint of model, C k Construction time for kth maintenance measures, B j D is a decision rule for deciding the maintenance construction window period of the airport in the jth quarterThe lower limit of the PCI of the pavement in the planning period.
8. The decision method for airport pavement long-term maintenance planning as claimed in claim 1, wherein step S5 specifically comprises the steps of:
s51, establishing an objective function equation of an optimization model, selecting two optimization targets of pavement average PCI maximization and maintenance cost minimization in a decision planning period based on airport pavement maintenance requirements, and determining an objective function according to a quantitative relation between the optimization targets and a maintenance scheme;
s52, determining constraint conditions of an optimization model, setting a pavement PCI lower limit to constrain an optimization scheme, and further taking a pavement maintenance construction window period as a constraint condition according to the operation requirement of non-navigation construction of an airport pavement, and constraining a decision optimization process according to the maintenance construction window period of each quarter in airport operation practice; according to the steps S51-S52, finally establishing an airport pavement long-term maintenance decision multi-objective optimization model under non-navigation construction;
s53, solving the multi-objective optimization model obtained in the step S52 to obtain a non-dominated optimal solution set in a decision planning period;
and S54, determining a corresponding road surface maintenance scheme by combining the current-year operation of the airport and the maintenance plan according to the optimal solution set of the decision optimization problem obtained by the solution in the step S53.
9. The decision method for airport pavement long-term maintenance planning as claimed in claim 8, wherein step S53 specifically refers to: the method comprises the steps of solving a constructed multi-target optimization model by adopting a non-dominated sorting genetic algorithm with an elite strategy, randomly generating an initial generation population with a certain number of individuals in a decision space, wherein each individual represents a group of solutions of the optimization problem, screening the individuals in the population by fast non-dominated sorting and congestion calculation, so that the individuals are uniformly distributed in the decision space while ensuring population fitness, obtaining a new generation population by utilizing selection, crossing and variation operations, and reserving elite individuals in a parent population by combining the parent population and an offspring population during population iteration so as to improve optimization efficiency.
10. An electronic device, comprising: a memory storing a computer program executable by the processor, and a processor implementing the decision-making method for long-term maintenance planning of an airport pavement according to any one of claims 1 to 9 when the computer program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210906506.3A CN115375010A (en) | 2022-07-29 | 2022-07-29 | Decision-making method and electronic equipment suitable for long-term maintenance planning of airport pavement |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210906506.3A CN115375010A (en) | 2022-07-29 | 2022-07-29 | Decision-making method and electronic equipment suitable for long-term maintenance planning of airport pavement |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115375010A true CN115375010A (en) | 2022-11-22 |
Family
ID=84062775
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210906506.3A Pending CN115375010A (en) | 2022-07-29 | 2022-07-29 | Decision-making method and electronic equipment suitable for long-term maintenance planning of airport pavement |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115375010A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116151522A (en) * | 2023-04-24 | 2023-05-23 | 中南大学 | DEA-based expressway pavement maintenance auxiliary decision-making method and system |
-
2022
- 2022-07-29 CN CN202210906506.3A patent/CN115375010A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116151522A (en) * | 2023-04-24 | 2023-05-23 | 中南大学 | DEA-based expressway pavement maintenance auxiliary decision-making method and system |
| CN116151522B (en) * | 2023-04-24 | 2023-07-14 | 中南大学 | DEA-based expressway pavement maintenance auxiliary decision-making method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111737916B (en) | Road and bridge disease analysis and maintenance decision method based on big data | |
| Wu et al. | An intuitionistic fuzzy multi-criteria framework for large-scale rooftop PV project portfolio selection: Case study in Zhejiang, China | |
| CN110288210A (en) | Project level road infrastructure comprehensive maintenance decision-making technique and system | |
| Abdelkader et al. | An exponential chaotic differential evolution algorithm for optimizing bridge maintenance plans | |
| CN108846526A (en) | A kind of CO2 emissions prediction technique | |
| CN110288137A (en) | Road network grade road infrastructure structure composition Maintenance Decision making method and system | |
| CN109872061A (en) | Power grid infrastructure improvement and promotion decision-making method | |
| CN115860357B (en) | Multi-objective optimization scheduling method for running water | |
| Wang et al. | Integrated model framework for the evaluation and prediction of the water environmental carrying capacity in the Guangdong-Hong Kong-Macao Greater Bay Area | |
| CN113344449B (en) | Method for predicting monthly industrial water demand | |
| CN113918538B (en) | New road maintenance data migration system based on artificial neural network | |
| CN110852560A (en) | Comprehensive evaluation method for green construction of subway in limited space | |
| CN110458722A (en) | Flood interval prediction method based on multiple target random vector function connection network | |
| CN107748940A (en) | A kind of energy conservation potential Quantitative prediction methods | |
| HERABAT et al. | Multi-objective optimization model using constraint-based genetic algorithms for Thailand pavement management | |
| Shang et al. | The sorted-waste capacitated location routing problem with queuing time: A cross-entropy and simulated-annealing-based hyper-heuristic algorithm | |
| CN115375010A (en) | Decision-making method and electronic equipment suitable for long-term maintenance planning of airport pavement | |
| Ji et al. | An integrated multi-objectives optimization approach on modelling pavement maintenance strategies for pavement sustainability | |
| Liang et al. | Evaluation of urban sustainability based on GO-SRA: Case study of Ha-Chang and Mid-southern Liaoning urban agglomerations in northeastern China | |
| CN114298538A (en) | Investment scheme evaluation method, system and storage medium for power grid retail project | |
| Ekhtiari et al. | Optimizing the dam site selection problem considering sustainability indicators and uncertainty: An integrated decision-making approach | |
| Li et al. | Minimizing passenger waiting time in the multi-route bus fleet allocation problem through distributionally robust optimization and reinforcement learning | |
| CN111160719A (en) | Two-network fused multi-index evaluation method | |
| Li et al. | Predicting and assessing greenhouse gas emissions during the construction of monorail systems using artificial intelligence | |
| CN112785022B (en) | Method and system for excavating electric energy substitution potential |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |