CN116151522B - DEA-based expressway pavement maintenance auxiliary decision-making method and system - Google Patents
DEA-based expressway pavement maintenance auxiliary decision-making method and system Download PDFInfo
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Abstract
The invention discloses a DEA-based expressway pavement maintenance auxiliary decision-making method and system, wherein the method comprises the following steps: acquiring historical and current pavement use performance data of the expressway in the target area; acquiring historical road surface maintenance data and extracting to obtain a target road section; based on the fuzzy C-means clustering model, according to historical road surface use performance data, gathering the target road sections into C classes; obtaining repair cost of unit area of each road section of C classes, and calculating to obtain short-term change value and long-term change value of PCI according to the current road surface service performance data; obtaining technical benefits, scale benefits and comprehensive benefits of various road section repairs according to analysis of the DEA evaluation model; and taking the maximization of the total benefit of road section repair as a target condition, taking the improvement condition of PCI and the investment of repair cost as constraint conditions, and establishing a road surface maintenance strategy optimization model to obtain the maintenance repair strategy of the current road section. The repairing benefit of the expressway is effectively improved, and the maintenance cost is reduced.
Description
Technical Field
The invention belongs to the technical field of traffic, and particularly relates to a DEA-based expressway pavement maintenance auxiliary decision-making method and system.
Background
At present, for the maintenance of the expressway, the expressway management department generally makes a maintenance management plan statically according to the current technical specification of disease repair, and the consideration of the comprehensive benefit of repairing various diseases is lacking. This can result in excessive highway pavement damage repair costs and less improvement in pavement performance. Therefore, how to formulate a scientific and reasonable pavement maintenance strategy is a research problem of great concern. A part of scholars build a comprehensive evaluation model, can evaluate the application condition and the repair effect of various preventive repair measures, but cannot provide an optimization scheme for repairing the highway pavement diseases. Some scholars perform a certain preventive repair measure decision research, and a certain decision optimization is realized by generating a preventive repair decision table. But this is only applicable in cases of substantial funds and not in cases of funding constraints. Part of scholars develop an optimization model, and can optimize the repairing strategy with the best economic benefit. However, these models evaluate only a few repair measures, so the noise immunity of the model is weak and poor in the case of small sample sets.
In summary, the existing model and method cannot adapt to the actual situation that a small sample set exists and a large amount of noise data exists, and it is difficult to provide an optimization scheme of maintenance decision under the condition that actual funds are limited, namely, the priority of pavement maintenance repair.
Disclosure of Invention
The invention provides a DEA-based expressway pavement maintenance auxiliary decision-making method and system, which can provide scientific decision basis and decision support for management decision makers, build a scientific maintenance decision-making system, effectively improve expressway repair benefits and reduce maintenance cost.
In order to solve the technical problems, the invention adopts the following technical scheme:
in a first aspect, a method for assisting in decision making of maintenance of a highway pavement based on DEA is provided, including:
acquiring historical road surface use performance data and current road surface use performance data of the expressway in the target area;
acquiring historical road surface maintenance data of the expressway in the target area, and extracting a target road section subjected to historical road surface maintenance according to the historical road surface maintenance data;
based on the fuzzy C-means clustering model, according to historical road surface use performance data, gathering the target road sections into C classes;
obtaining repair cost of unit area of each road section of C classes, and calculating to obtain a short-term change value and a long-term change value of the road surface damage state index PCI according to the current road surface use performance data;
establishing and obtaining a data envelope analysis DEA evaluation model according to the repair cost, the short-term change value and the long-term change value of the unit area;
analyzing DEA evaluation model according to data envelope analysis to obtain technical benefit, scale benefit and comprehensive benefit of various road section repairs;
and taking the maximization of the total benefit of road section repair as a target condition, taking the improvement condition of the road surface damage state index PCI and the repair cost investment as constraint conditions, establishing a road surface maintenance strategy optimization model, and obtaining the maintenance repair strategy of the current road section according to the road surface maintenance strategy optimization model.
Optionally, the historical road surface usage performance data and the current road surface usage performance data include road surface damage condition indexes PCI, road surface running quality indexes RQI, road surface track depth indexes RDI and lengths or areas of road surface diseases of each road section on the target area expressway.
Optionally, the historical road surface maintenance data includes a historical road surface maintenance record, and the historical road surface maintenance record includes a maintenance engineering company, a maintenance engineering name, a maintenance measure, a maintenance cost, a maintenance road section number, a maintenance road section name, a start pile number and a stop pile number of the maintenance road section.
Optionally, obtaining historical road surface maintenance data of the expressway in the target area, and extracting the target road section subjected to the historical road surface maintenance according to the historical road surface maintenance data includes:
acquiring historical road surface maintenance data of the expressway in the target area through a traffic maintenance record table;
obtaining historical road surface maintenance records of the expressway in the target area according to the historical road surface maintenance data;
and extracting the initial pile number and the final pile number of the maintenance road section in the historical road surface maintenance record, and matching to obtain the target road section subjected to historical road surface maintenance in the expressway in the target area.
Optionally, the clustering of the target road segments into C classes based on the fuzzy C-means clustering model according to the historical road surface usage performance data includes:
extracting a road surface damage condition index PCI, a road surface running quality index RQI and a road surface rut depth index RDI from historical road surface use performance data;
taking a road surface damage condition index PCI, a road surface running quality index RQI and a road surface rut depth index RDI as inputs of a fuzzy C-means clustering model;
sequentially clustering all road sections into N classes based on a fuzzy C-means clustering model, wherein N is a positive integer greater than 3;
according to the fuzzy classification coefficient FPC, evaluating the clustering effect, determining the most suitable clustering number C, wherein C is a positive integer not more than N;
the target segments are grouped into C classes.
In a second aspect, there is provided a DEA-based highway pavement maintenance aid decision system, comprising:
the utilization performance data acquisition module is used for acquiring historical road surface utilization performance data and current road surface utilization performance data of the expressway in the target area;
the maintenance data acquisition module is used for acquiring historical road surface maintenance data of the expressway in the target area, and extracting a target road section subjected to historical road surface maintenance according to the historical road surface maintenance data;
the clustering module is used for clustering the target road sections into C classes according to the historical road surface use performance data based on the fuzzy C-means clustering model;
the road section data acquisition module is used for acquiring the repair cost of unit area of each road section of C classes, and calculating to obtain a short-term change value and a long-term change value of the road surface damage state index PCI according to the current road surface use performance data;
the DEA evaluation model construction module is used for constructing and obtaining a data envelope analysis DEA evaluation model according to the repair cost, the short-term change value and the long-term change value of the unit area;
the benefit analysis module is used for analyzing DEA evaluation model according to the data envelope to obtain the technical benefit, scale benefit and comprehensive benefit of repairing various road sections;
the road surface maintenance strategy optimization model construction module is used for constructing a road surface maintenance strategy optimization model by taking the maximization of the total benefit of road section repair as a target condition and taking the improvement condition of the road surface damage state index PCI and the repair cost investment as constraint conditions, and obtaining the maintenance repair strategy of the current road section according to the road surface maintenance strategy optimization model.
Optionally, the historical road surface usage performance data and the current road surface usage performance data include road surface damage condition indexes PCI, road surface running quality indexes RQI, road surface track depth indexes RDI and lengths or areas of road surface diseases of each road section on the target area expressway.
Optionally, the historical road surface maintenance data includes a historical road surface maintenance record, and the historical road surface maintenance record includes a maintenance engineering company, a maintenance engineering name, a maintenance measure, a maintenance cost, a maintenance road section number, a maintenance road section name, a start pile number and a stop pile number of the maintenance road section.
Optionally, the maintenance data acquisition module is specifically configured to acquire historical road surface maintenance data of the expressway in the target area through a traffic maintenance record table; obtaining historical road surface maintenance records of the expressway in the target area according to the historical road surface maintenance data; and extracting the initial pile number and the final pile number of the maintenance road section in the historical road surface maintenance record, and matching to obtain the target road section subjected to historical road surface maintenance in the expressway in the target area.
Optionally, the clustering module is specifically configured to extract a road surface damage condition index PCI, a road surface running quality index RQI, and a road surface rut depth index RDI from the historical road surface usage performance data; taking a road surface damage condition index PCI, a road surface running quality index RQI and a road surface rut depth index RDI as inputs of a fuzzy C-means clustering model; sequentially clustering all road sections into N classes based on a fuzzy C-means clustering model, wherein N is a positive integer greater than 3; according to the fuzzy classification coefficient FPC, evaluating the clustering effect, determining the most suitable clustering number C, wherein C is a positive integer not more than N; the target segments are grouped into C classes.
The invention has the beneficial effects that:
obtaining historical road surface use performance data and current road surface use performance data of a target area expressway, obtaining historical road surface maintenance data of the target area expressway, extracting a target road section subjected to historical road surface maintenance according to the historical road surface maintenance data, classifying the target road section into C classes according to the historical road surface use performance data based on a fuzzy C-means clustering model, obtaining repair cost of unit areas of all road sections of the C classes, calculating according to the current road surface use performance data to obtain a short-term change value and a long-term change value of a road surface damage state index PCI, establishing a data envelope analysis DEA evaluation model according to the repair cost of the unit areas, the short-term change value and the long-term change value, analyzing the DEA evaluation model according to the data envelope to obtain technical benefits, scale benefits and comprehensive benefits of repairing all road sections, establishing a road surface maintenance strategy optimization model according to a road surface maintenance strategy optimization model, and obtaining a current repair strategy according to the road surface maintenance strategy optimization model, wherein the improvement condition and the repair cost investment of the road surface damage state index PCI are taken as target conditions. The method can assist the expressway management maintenance department to make a scientific and reasonable repair decision scheme, and determine which road sections are mainly repaired at present so as to effectively improve the overall repair effect of the expressway pavement technical condition and reduce the total repair cost.
Drawings
FIG. 1 is a flow chart of the DEA-based highway pavement maintenance aid decision-making method of the present invention;
fig. 2 is a block diagram of the DEA-based highway pavement maintenance aid decision-making system of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in fig. 1, the embodiment of the invention provides a method for assisting decision-making of highway pavement maintenance based on DEA, which comprises the following steps:
101, acquiring historical road surface use performance data and current road surface use performance data of a highway in a target area;
the road surface service performance conditions of the expressway in the target area to be researched comprise road surface damage condition indexes PCI, road surface running quality indexes RQI, road surface track depth indexes RDI and the length or area of road surface diseases of each road section of the expressway.
102, acquiring historical road surface maintenance data of the expressway in the target area, and extracting a target road section subjected to historical road surface maintenance according to the historical road surface maintenance data;
the historical road surface maintenance data comprise historical road surface maintenance records, wherein the historical road surface maintenance records comprise maintenance engineering companies, maintenance engineering names, maintenance measures, maintenance cost, maintenance road section numbers, maintenance road section names, starting pile numbers and ending pile numbers of maintenance road sections. The maintenance and repair modes of the expressway pavement can be divided into three types, namely minor repair, medium repair and major repair. The minor repair works are outsourced by the expressway group according to a certain amount, and follow-up daily minor repair works, such as stone removal and filling of pavement pits, are directly maintained by outsourcing companies. The major repair works take extensive road resurfacing measures, and the works generally have longer periods and do not need to be planned prematurely. Therefore, the key point of the embodiment of the invention is to assist the highway management department to better make a middle maintenance plan of the highway section.
The specific implementation process is as follows: the historical road surface maintenance data of the expressway in the target area is obtained through the traffic maintenance record table, for example, the expressway in the Hunan province area is selected as a research object in the embodiment, and 848 historical road surface middle maintenance records in 2019 are obtained. For example: the road surface middle repair (temporary growth) project in 2019, which is executed by Changsha division company, takes a 'cover-added' measure for a road section with a starting point pile number of 1444.3 and an end point pile number of 1444.77 on a temporary long-speed Chang sand section in the Hunan section of the Highway in Beijing Kong, and takes 234,775.3 yuan;
obtaining historical road surface maintenance records of the expressway in the target area according to the historical road surface maintenance data;
and extracting the initial pile number and the final pile number of the maintenance road section in the historical road surface maintenance record, matching to obtain a target road section subjected to historical road surface maintenance in the expressway in the target area, and extracting the road section subjected to road surface maintenance in 2019 through pile number matching. For example: in the Hunan section of the Highway in the Beijing Kong Australian, the road section with the starting point pile number 1444.3 and the end point pile number 1444.77 on the temporary long-speed long-sand section is essentially a small section of the road section with the starting point pile number 1444.0 and the end point pile number 1445.0 belonging to the Hunan section of the Highway in the Beijing Kong Australian, so that the repair cost of the former is accumulated in the latter. Finally, we reserve 121 road segments for which the repair cost is not 0.
103, based on the fuzzy C-means clustering model, according to historical road surface use performance data, gathering the target road sections into C classes;
the maintenance measures are carried out on road sections with different road conditions, and the obtained benefits are different. To achieve classification of road segments by different road condition classes, we use a fuzzy C-means (FCM) clustering model. The method comprises the following specific steps:
the embodiment realizes the application of the FCM clustering model based on the python programming language, calls the cluster.cmeans method of the skfuzzy toolkit, takes PCI, RQI and RDI of the road section 2019 as input, and sequentially gathers 121 road sections mentioned in the step 102 into 1, 2 and 3 … classes. Then, the fuzzy classification coefficient FPC was used to measure the clustering effect when 1, 2, 3, … classes were clustered. The FPC reflects the similarity of road segments in the class and the difference of different classes, so that the effect of clustering can be effectively evaluated;
finally, FPC was largest when the number of clusters was found to be c=3. Thus, 121 road segments are grouped into 3 classes. The central positions of the 3 classes are (90.3959, 91.7012, 95.2871), (95.6749, 93.6055, 95.1526) and (99.8828, 94.8446, 97.6577), the values in the coordinates correspond to PCI, RQI and RDI respectively, and the names of the 3 classes are sequentially defined as poor road condition, medium road condition and excellent road condition, and the number of road sections in the classes is 30, 54 and 37 respectively.
104, obtaining repair cost of unit area of each road section of C classes, and calculating to obtain a short-term change value and a long-term change value of the road surface damage state index PCI according to the current road surface use performance data;
firstly, counting the total area and the total repair cost of various road sections, and calculating the repair cost of unit area of various road sections:
for example: the short-term change value of the PCI of the road section with the class name of 'road condition medium' is 4.7377265;
the calculation formula of the long-term change value of the PCI of various road sections is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
for repairing the long-term change value of PCI after class i road section, < >>
To repair PCI of road segment k in class i road segment, +.>
To repair PCI of road segment k in the preceding class i road segment, +.>
The total number of road segments included for the i-type road segments.
105, establishing and obtaining a data envelope analysis DEA evaluation model according to the repair cost, the short-term change value and the long-term change value of the unit area;
wherein, the calculation in the step 104 is performed
As input index, ->
And->
As a yield index, a data envelope analysis DEA evaluation model is established.
106, analyzing according to a data envelope analysis DEA evaluation model to obtain technical benefits, scale benefits and comprehensive benefits of repairing various road sections;
the DEA evaluation model analyzes technical benefits, scale benefits and comprehensive benefits of repairing various road sections. Wherein, the technical benefit is an index for measuring whether the performance of the highway pavement can be improved in terms of the technical level; the scale benefit is an index for measuring whether the repairing scale can be enlarged to improve the performance of the expressway pavement; the comprehensive benefit is a comprehensive consideration index for scale benefit and technical benefit. The repair effect for various road sections is considered by technical benefit, scale benefit and comprehensive benefit. For example: the road sections with the name of road condition medium are repaired, the technical benefit is 1, the scale benefit is 0.811, and the condition that the road sections are repaired is not needed to be considered from the technical aspect, but the number of the road sections to be repaired is increased so that the scale benefit reaches a higher state (namely 1); meanwhile, the road section with the repair class name of road condition medium has the comprehensive benefit of 0.003; the comprehensive benefit of the road section with the repair class name of road condition medium is 0.811; the road section with the repair class name of 'poor road condition' has the comprehensive benefit of 1. Therefore, the road section with the repair class of 'poor road condition' is better than other road sections, and the road section with the repair class of 'poor road condition' should be maintained preferentially.
107, taking the maximization of the total benefit of road section repair as a target condition, taking the improvement condition of the road surface damage state index PCI and the repair cost investment as constraint conditions, establishing a road surface maintenance strategy optimization model, and obtaining the maintenance repair strategy of the current road section according to the road surface maintenance strategy optimization model.
In step 106, after the comprehensive benefits of repairing various road segments are evaluated, a road surface maintenance strategy optimization model can be constructed to determine the maintenance and repair strategy of the current road segment. The method comprises the following specific steps:
firstly, calling a cluster.cmeans_prediction method of a skfuzzy tool kit, and classifying 121 road sections into 3 categories of poor road conditions, medium road conditions and excellent road conditions according to PCI, RQI and RDI and the central positions of 3 categories of poor road conditions, medium road conditions and excellent road conditions of the road section 2020. Under reclassification, the number of road segments in 3 classes is 25, 55 and 41, respectively.
Then, constructing an objective function by taking the total benefit maximization of road segment repair as an objective condition:
wherein Z is the objective function value,
for decision variables, it is indicated whether road segment k in class i road segment is repaired, < >>
The comprehensive benefit for repairing the i-type road section is achieved;
starting from improvement condition of road section PCI and repair cost investment, constructing constraint conditions:
n is the total number of road segments,
the average long-term rise value of the PCI of the road section after repair is used before optimization; />And
the number of road sections with the PCI more than 92 after repair is respectively determined after optimization and before optimization; />
An area of road segment k in the i-type road segment; />
To the total repair costs of the pavement before optimization.
And finally, solving the established pavement maintenance strategy optimization model by utilizing Gurobi software. And obtaining a road surface maintenance strategy of the road surface level, and determining the road surface needing to be repaired currently. For example: based on the result of the optimization model, 84 road segments (i.e. the road segments needing to be repaired currently) are decided to be repaired, and compared with the 2019 maintenance repair scheme, the average long-term improvement value of the PCI of the road segments is increased by 0.31 under the condition that the total repair cost is reduced by 0.27 hundred million yuan. The effectiveness of the highway maintenance aid decision making system provided by the present embodiment is verified.
The invention has the beneficial effects that:
obtaining historical road surface use performance data and current road surface use performance data of a target area expressway, obtaining historical road surface maintenance data of the target area expressway, extracting a target road section subjected to historical road surface maintenance according to the historical road surface maintenance data, classifying the target road section into C classes according to the historical road surface use performance data based on a fuzzy C-means clustering model, obtaining repair cost of unit areas of all road sections of the C classes, calculating according to the current road surface use performance data to obtain a short-term change value and a long-term change value of a road surface damage state index PCI, establishing a data envelope analysis DEA evaluation model according to the repair cost of the unit areas, the short-term change value and the long-term change value, analyzing the DEA evaluation model according to the data envelope to obtain technical benefits, scale benefits and comprehensive benefits of repairing all road sections, establishing a road surface maintenance strategy optimization model according to a road surface maintenance strategy optimization model, and obtaining a current repair strategy according to the road surface maintenance strategy optimization model, wherein the improvement condition and the repair cost investment of the road surface damage state index PCI are taken as target conditions. The method can assist the expressway management maintenance department to make a scientific and reasonable repair decision scheme, and determine which road sections are mainly repaired at present so as to effectively improve the overall repair effect of the expressway pavement technical condition and reduce the total repair cost.
Based on the above DEA-based expressway road surface maintenance aid decision-making method in the embodiment shown in FIG. 1, the DEA-based expressway road surface maintenance aid decision-making system is described below by way of an embodiment, and as shown in FIG. 2, the DEA-based expressway road surface maintenance aid decision-making system according to the embodiment of the invention includes:
a usage performance data acquisition module 201, configured to acquire historical road usage performance data and current road usage performance data of the expressway in the target area;
a maintenance data obtaining module 202, configured to obtain historical road surface maintenance data of the expressway in the target area, and extract a target road section subjected to historical road surface maintenance according to the historical road surface maintenance data;
the clustering module 203 is configured to group the target road segments into C classes according to the historical road surface usage performance data based on the fuzzy C-means clustering model;
the road section data acquisition module 204 is used for acquiring repair cost of unit area of each road section of C classes, and calculating to obtain a short-term change value and a long-term change value of the road surface damage state index PCI according to the current road surface use performance data;
the DEA evaluation model construction module 205 is configured to build a data envelope analysis DEA evaluation model according to the repair cost, the short-term change value, and the long-term change value of the unit area;
the benefit analysis module 206 is used for analyzing the technical benefit, the scale benefit and the comprehensive benefit of the repair of various road sections according to the data envelope analysis DEA evaluation model;
the road surface maintenance strategy optimization model construction module 207 is configured to establish a road surface maintenance strategy optimization model with the overall benefit maximization of road segment repair as a target condition and the improvement condition of the road surface damage state index PCI and the repair cost investment as constraint conditions, and obtain the maintenance repair strategy of the current road segment according to the road surface maintenance strategy optimization model.
The implementation principle of the embodiment of the invention is as follows:
the method comprises the steps of obtaining historical road surface use performance data and current road surface use performance data of a target area expressway by using a performance data obtaining module 201, obtaining historical road surface maintenance data of the target area expressway by a maintenance data obtaining module 202, extracting target road segments subjected to historical road surface maintenance according to the historical road surface maintenance data, clustering the target road segments into C classes according to the historical road surface use performance data by a clustering module 203 based on a fuzzy C-means clustering model, obtaining repair costs of unit areas of all road segments of the C classes by a road segment data obtaining module 204, calculating short-term change values and long-term change values of a road surface damage state index PCI according to the current road surface use performance data by a DEA evaluation model constructing module 205, establishing a data envelope analysis DEA evaluation model according to the repair costs, the short-term change values and the long-term change values of the unit areas, analyzing the DEA evaluation model according to the data envelope analysis to obtain technical benefits, scale benefits and comprehensive benefits of all road segments, and establishing a road surface maintenance policy optimization model constructing a road surface maintenance policy optimization model 207 by taking the total benefit maximization of the road segments as target conditions and taking the improvement condition of road surface damage state index PCI and the cost investment as constraint conditions, and establishing a road surface maintenance policy optimization model to obtain a road surface maintenance policy optimization model. The method can assist the expressway management maintenance department to make a scientific and reasonable repair decision scheme, and determine which road sections are mainly repaired at present so as to effectively improve the overall repair effect of the expressway pavement technical condition and reduce the total repair cost.
Preferably, in some embodiments of the present invention, the historical road surface usage performance data and the current road surface usage performance data include road surface damage condition index PCI, road surface running quality index RQI, road surface rut depth index RDI, and length or area of road surface damage for each road segment on the target area highway.
Preferably, in some embodiments of the present invention, the historical road surface maintenance data comprises a historical road surface maintenance record comprising a maintenance engineering company, a maintenance engineering name, a maintenance measure, a maintenance cost, a maintenance road segment number, a maintenance road segment name, a start stake number and a stop stake number of the maintenance road segment.
Preferably, in combination with the embodiment shown in fig. 2, in some embodiments of the present invention, the maintenance data obtaining module 202 is specifically configured to obtain, through a traffic maintenance record table, historical road surface maintenance data of the expressway in the target area; obtaining historical road surface maintenance records of the expressway in the target area according to the historical road surface maintenance data; and extracting the initial pile number and the final pile number of the maintenance road section in the historical road surface maintenance record, and matching to obtain the target road section subjected to historical road surface maintenance in the expressway in the target area.
Preferably, in combination with the embodiment shown in fig. 2, in some embodiments of the present invention, the clustering module 203 is specifically configured to extract the road surface damage condition index PCI, the road surface driving quality index RQI, and the road rut depth index RDI from the historical road surface usage performance data; taking a road surface damage condition index PCI, a road surface running quality index RQI and a road surface rut depth index RDI as inputs of a fuzzy C-means clustering model; sequentially clustering all road sections into N classes based on a fuzzy C-means clustering model, wherein N is a positive integer greater than 3; according to the fuzzy classification coefficient FPC, evaluating the clustering effect, determining the most suitable clustering number C, wherein C is a positive integer not more than N; the target segments are grouped into C classes.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (10)
1. The DEA-based expressway pavement maintenance auxiliary decision-making method is characterized by comprising the following steps of:
acquiring historical road surface use performance data and current road surface use performance data of the expressway in the target area;
acquiring historical road surface maintenance data of the expressway in the target area, and extracting a target road section subjected to historical road surface maintenance according to the historical road surface maintenance data;
based on a fuzzy C-means clustering model, according to the historical road surface use performance data, the target road sections are clustered into C classes;
obtaining repair cost of unit area of each road section of the C classes, and calculating to obtain a short-term change value and a long-term change value of a road surface damage state index PCI according to the current road surface use performance data;
establishing and obtaining a data envelope analysis DEA evaluation model according to the repair cost of the unit area, the short-term change value and the long-term change value;
analyzing DEA evaluation model according to the data envelope to obtain technical benefits, scale benefits and comprehensive benefits of repairing various road sections; the technical benefit is an index for measuring whether the performance of the highway pavement can be improved in the aspect of technical level; the scale benefit is an index for measuring whether the repairing scale can be enlarged to improve the performance of the expressway pavement; the comprehensive benefit is a comprehensive consideration index for the scale benefit and the technical benefit;
taking the total benefit maximization of road section repair as a target condition, and constructing an objective function:
wherein Z is an objective function value, the
To determine variables, indicate whether to patchiRoad section in class road sectionkThe said
To repair theiComprehensive benefits of road class, then i Representing the saidiThe class road section comprises the total number of road sections;
taking improvement condition of road surface damage state index PCI and repair cost investment as constraint conditions, wherein the constraint conditions comprise a first constraint condition
Second constraint->
Third constraint conditionFourth constraint->
The method comprises the steps of carrying out a first treatment on the surface of the Said->
To repair theiLong-term change value of PCI after class section, said +.>
The average long-term rise value of PCI of the road section after repair before using the maintenance repair strategy; said->
And said->
The number of road sections with PCI greater than 92 after repair is respectively after the maintenance repair strategy and before the maintenance repair strategy is used; said->
Is saidiThe road segments in the class road segmentskIs a part of the area of (2); said->
The total repair cost for the pavement prior to the use of the maintenance repair strategy; said->
Is saidiRepair cost per unit area of the road-like section; the saidnRepresenting the total road section number;
and establishing a pavement maintenance strategy optimization model according to the objective function and the constraint condition, and obtaining a maintenance and repair strategy of the current road section according to the pavement maintenance strategy optimization model.
2. The DEA-based highway pavement maintenance aid decision-making method according to claim 1, wherein,
the historical road surface use performance data and the current road surface use performance data comprise road surface damage condition indexes PCI, road surface running quality indexes RQI, road surface track depth indexes RDI and the length or the area of road surface diseases of each road section on the target area expressway.
3. The DEA-based highway pavement maintenance aid decision-making method according to claim 1, wherein,
the historical road surface maintenance data comprises a historical road surface maintenance record, wherein the historical road surface maintenance record comprises a maintenance engineering company, a maintenance engineering name, maintenance measures, maintenance cost, a maintenance road section number, a maintenance road section name, a start pile number and a stop pile number of a maintenance road section.
4. The DEA-based highway pavement maintenance aid decision-making method according to claim 3, wherein the obtaining the historical pavement maintenance data of the highway in the target area, and extracting the target road section subjected to the historical pavement maintenance according to the historical pavement maintenance data, comprises:
acquiring historical road surface maintenance data of the expressway in the target area through a traffic maintenance record table;
obtaining historical road surface maintenance records of the expressway in the target area according to the historical road surface maintenance data;
and extracting the initial pile number and the final pile number of the maintenance road section in the historical road surface maintenance record, and matching to obtain the target road section subjected to historical road surface maintenance in the target area expressway.
5. The DEA-based expressway surface maintenance aid decision-making method according to claim 2, wherein the fuzzy C-means clustering model groups the target segments into C classes according to the historical road surface use performance data, comprising:
extracting a road surface damage condition index PCI, a road surface running quality index RQI and a road surface rut depth index RDI from the historical road surface use performance data;
taking the road surface damage condition index PCI, the road surface running quality index RQI and the road surface rutting depth index RDI as inputs of a fuzzy C-means clustering model;
sequentially clustering all road sections into N classes based on the fuzzy C-means clustering model, wherein N is a positive integer greater than 3;
according to the fuzzy classification coefficient FPC, evaluating the clustering effect, determining the most suitable clustering number C, wherein C is a positive integer not more than N;
and gathering the target road sections into C classes.
6. DEA-based expressway pavement maintenance auxiliary decision-making system is characterized by comprising:
the utilization performance data acquisition module is used for acquiring historical road surface utilization performance data and current road surface utilization performance data of the expressway in the target area;
the maintenance data acquisition module is used for acquiring historical road surface maintenance data of the expressway in the target area and extracting a target road section subjected to historical road surface maintenance according to the historical road surface maintenance data;
the clustering module is used for clustering the target road sections into C classes according to the historical road surface use performance data based on a fuzzy C-means clustering model;
the road section data acquisition module is used for acquiring the repair cost of the unit area of each road section of the C classes, and calculating to obtain a short-term change value and a long-term change value of the road surface damage state index PCI according to the current road surface use performance data;
the DEA evaluation model construction module is used for constructing and obtaining a data envelope analysis DEA evaluation model according to the repair cost of the unit area, the short-term change value and the long-term change value;
the benefit analysis module is used for analyzing DEA evaluation model analysis according to the data envelope to obtain technical benefit, scale benefit and comprehensive benefit of various road section repairs; the technical benefit is an index for measuring whether the performance of the highway pavement can be improved in the aspect of technical level; the scale benefit is an index for measuring whether the repairing scale can be enlarged to improve the performance of the expressway pavement; the comprehensive benefit is a comprehensive consideration index for the scale benefit and the technical benefit;
the road maintenance strategy optimization model construction module is used for constructing an objective function by taking the total benefit maximization of road section repair as a target condition:
wherein Z is an objective function value, the
To determine variables, indicate whether to patchiRoad section in class road sectionkThe said
To repair theiComprehensive benefits of road class, then i Representing the saidiThe class road section comprises the total number of road sections;
taking improvement condition of road surface damage state index PCI and repair cost investment as constraint conditions, wherein the constraint conditions comprise a first constraint condition
Second constraint->
Third constraint condition
Fourth constraint->
The method comprises the steps of carrying out a first treatment on the surface of the Said->
To repair theiLong-term change value of PCI after class section, said +.>
The average long-term rise value of PCI of the road section after repair before using the maintenance repair strategy; said->
And said->
The number of road sections with PCI greater than 92 after repair is respectively after the maintenance repair strategy and before the maintenance repair strategy is used; said->
Is saidiThe road segments in the class road segmentskIs a part of the area of (2); said->
The total repair cost for the pavement prior to the use of the maintenance repair strategy; said->
Is saidiRepair cost per unit area of the road-like section; the saidnRepresenting the total road section number;
and establishing a pavement maintenance strategy optimization model according to the objective function and the constraint condition, and obtaining a maintenance and repair strategy of the current road section according to the pavement maintenance strategy optimization model.
7. The DEA-based highway pavement maintenance aid decision-making system according to claim 6, wherein,
the historical road surface use performance data and the current road surface use performance data comprise road surface damage condition indexes PCI, road surface running quality indexes RQI, road surface track depth indexes RDI and the length or the area of road surface diseases of each road section on the target area expressway.
8. The DEA-based highway pavement maintenance aid decision-making system according to claim 6, wherein,
the historical road surface maintenance data comprises a historical road surface maintenance record, wherein the historical road surface maintenance record comprises a maintenance engineering company, a maintenance engineering name, maintenance measures, maintenance cost, a maintenance road section number, a maintenance road section name, a start pile number and a stop pile number of a maintenance road section.
9. The DEA-based highway pavement maintenance aid decision-making system according to claim 8, wherein,
the maintenance data acquisition module is specifically used for acquiring historical road surface maintenance data of the expressway in the target area through a traffic maintenance record table; obtaining historical road surface maintenance records of the expressway in the target area according to the historical road surface maintenance data; and extracting the initial pile number and the final pile number of the maintenance road section in the historical road surface maintenance record, and matching to obtain the target road section subjected to historical road surface maintenance in the target area expressway.
10. The DEA-based highway pavement maintenance aid decision-making system according to claim 7, wherein,
the clustering module is specifically configured to extract a road surface damage condition index PCI, a road surface running quality index RQI, and a road surface rut depth index RDI from the historical road surface usage performance data; taking the road surface damage condition index PCI, the road surface running quality index RQI and the road surface rutting depth index RDI as inputs of a fuzzy C-means clustering model; sequentially clustering all road sections into N classes based on the fuzzy C-means clustering model, wherein N is a positive integer greater than 3; according to the fuzzy classification coefficient FPC, evaluating the clustering effect, determining the most suitable clustering number C, wherein C is a positive integer not more than N; and gathering the target road sections into C classes.
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