A method for post-evaluation of the operation of highway asphalt pavement maintenance technology

Abstract

The present invention proposes a post-evaluation method for the operation of highway asphalt pavement maintenance technology, which relates to the technical field of highway maintenance evaluation, and includes: constructing an evaluation index system; establishing an adaptive weight calculation model based on machine learning to calculate the weight vectors of each index; conducting a quantitative evaluation of economic benefits according to the arctangent decay model to determine the economic benefit-cost ratio; using the success degree method for qualitative evaluation, establishing a hierarchical evaluation system, and forming a standardized evaluation grade table; establishing a comprehensive evaluation system, generating a fusion evaluation matrix; calculating a comprehensive evaluation vector, constructing a comprehensive evaluation matrix, calculating a comprehensive evaluation result based on the comprehensive evaluation matrix, determining the final evaluation grade, and obtaining the final post-maintenance evaluation result. By constructing a multi-dimensional evaluation index system and combining qualitative and quantitative evaluation methods, the present invention establishes a complete post-maintenance evaluation method, realizes the standardization and regularization of the evaluation process, and improves the scientificity and reliability of the evaluation results.

Description

Technical Field

[0001] The present invention relates to the technical field of highway maintenance evaluation, and particularly to a method for post-evaluation of asphalt pavement maintenance technology for operating highways. Background Art

[0002] As an important infrastructure, highways are the lifeblood projects for economic development and an important support for social development. During use, various diseases and damages will inevitably occur on highways, which not only affect driving safety and comfort, but also accelerate the damage of the pavement structure and shorten the service life of highways. Therefore, it is of great significance to use scientific and reasonable maintenance technologies to timely maintain and repair highways to ensure the normal operation of the highway network, extend the service life, and improve the service level. This can not only guide the scientific development of maintenance work, but also provide an important basis for the continuous improvement and innovation of maintenance technologies.

[0003] Currently, the following methods are mainly used for post-evaluation of asphalt pavement maintenance: First, a single evaluation method based on pavement technical condition indicators, mainly evaluating based on the changes of technical indicators such as PCI and RQI; second, a qualitative evaluation method based on expert experience, comprehensively judging through expert scoring or questionnaire surveys; third, a benefit evaluation method based on economic indicators, mainly considering the economic relationship between maintenance input and output. These evaluation methods have the following problems in practical applications: 1. The evaluation index system is imperfect. Existing evaluation methods often overly focus on technical indicators or economic indicators, lacking comprehensive consideration of aspects such as social benefits and environmental benefits, and it is difficult to comprehensively reflect the actual effects of maintenance technologies. 2. The evaluation process is highly subjective. Currently, the evaluation work mainly relies on the experience and technical level of the decision-making team or individuals, lacking scientific quantitative evaluation criteria, and the objectivity and comparability of evaluation results are insufficient. 3. The evaluation methods are not standardized enough. Due to the lack of unified industry standards and specifications, the methods and standards adopted by each region in carrying out post-evaluation of maintenance are inconsistent, resulting in difficulty in making horizontal comparisons of evaluation results. 4. There is insufficient data support. Existing evaluation methods do not make full use of the analysis of historical monitoring data and lack in-depth research on the evolution law of pavement performance, affecting the scientificity and accuracy of evaluation results.

[0004] Therefore, establishing a scientific and systematic post-evaluation method for maintenance has important theoretical significance and practical value. Summary of the Invention

[0005] In view of this, the present invention proposes a method for post-evaluation of asphalt pavement maintenance technology for operating highways. By constructing a comprehensive evaluation index system including technical applicability, social benefits, environmental benefits, and economic benefits, and combining quantitative and qualitative evaluation methods, it solves the problems that existing post-evaluations of maintenance overly rely on personal experience, lack industry standards, and lack scientific basis, thereby realizing the standardization and scientificization of post-evaluations of maintenance.

[0006] The technical solution of the present invention is implemented as follows: The present invention provides a post-evaluation method for maintenance technology of asphalt pavement of an operating highway, comprising:

[0007] S1. Construct a post-maintenance evaluation index system, which includes technical applicability index, social benefit index, environmental benefit index and economic benefit index;

[0008] S2. Establish an adaptive weight calculation model based on machine learning to calculate the weight vector of each indicator;

[0009] S3. Based on the economic benefit index, the economic benefit quantitative evaluation is carried out in combination with the arctangent decay model to determine the economic benefit cost ratio;

[0010] S4. Use the success method to conduct qualitative evaluation of technical applicability indicators, social benefit indicators and environmental benefit indicators, establish a hierarchical evaluation system, and form a standardized evaluation grade table;

[0011] S5, establishing a comprehensive evaluation system integrating subjective and objective factors, unifying the quantitative evaluation results of step S3 and the qualitative evaluation results of step S4 under the same evaluation standard, and generating a fusion evaluation matrix;

[0012] S6. Calculate the comprehensive evaluation vector based on the weight vector of each indicator and the fusion evaluation matrix, and construct a comprehensive evaluation matrix. Calculate the comprehensive evaluation result based on the comprehensive evaluation matrix, determine the final evaluation level according to the maximum membership principle, and obtain the final evaluation result after maintenance.

[0013] Based on the above scheme, preferably, the evaluation index system includes primary index and secondary index, and the primary index includes technical applicability index A 1 、Social Benefit Index A 2 、Environmental benefit index A 3 and economic benefit index A 4 ; Technical applicability index A 1 、Social Benefit Index A 2 、Environmental benefit index A 3 It is a qualitative indicator, and the economic benefit indicator A 4 is a quantitative indicator;

[0014] Among them, each first-level indicator has a corresponding second-level indicator:

[0015] Technical Applicability Index A 1 The road performance improvement rate A 11 and maintenance technology service life A 12 ;

[0016] Social Benefit Index A 2 The local economic development impact 21, Improvement of road traffic capacity A 22 and reduction of traffic accident rate A 23 ;

[0017] Environmental benefit indicators A 3 including noise pollution level A 31 , air pollution level A 32 and solid waste pollution level A 33 ;

[0018] Economic benefit indicators A 4 including maintenance technology benefit-cost ratio A 41 .

[0019] Based on the above solution, preferably, step S2 includes:

[0020] S21. Based on the evaluation index system, construct the initial judgment matrix in two levels:

[0021] Make pairwise comparisons of the technical applicability indicators A 1 , social benefit indicators A 2 , environmental benefit indicators A 3 and economic benefit indicators A 4 to construct the first-level index judgment matrix;

[0022] Make pairwise comparisons of the secondary indicators under each first-level indicator respectively to construct multiple secondary indicator judgment matrices;

[0023] S22. Collect the evaluation data of historical maintenance projects, including expert scoring data and actual effect data, as the training samples of the BP neural network. Among them, the expert scoring data includes the relative importance judgment values of each level of indicators in historical projects; the actual effect data includes the pavement performance improvement rate, service life, and objective economic benefit indicators of historical projects;

[0024] S23. Construct a three-layer BP neural network, use the expert scoring data as the input and the actual effect data as the output, and train to obtain a weight optimization model;

[0025] S24. Input the index characteristics of the new evaluation project corresponding to each evaluation index into the weight optimization model, and dynamically adjust the first-level index judgment matrix and the secondary index judgment matrix respectively according to the model output results;

[0026] S25. Based on the adjusted judgment matrix, first calculate the largest eigenvalue and its eigenvector of the first-level index judgment matrix; then calculate the largest eigenvalue and its eigenvector of each group of secondary index judgment matrices respectively;

[0027] S26. Normalize the eigenvectors of the first-level indicators to obtain the first-level indicator weight vectors; normalize the eigenvectors of each group of second-level indicators respectively to obtain the corresponding second-level indicator weight vectors.

[0028] Based on the above solution, preferably, in step S23, when training the BP neural network, an adaptive optimization mechanism and an adaptive correction rule are used to update the network weights:

[0029] ,

[0030] ,

[0031] where t represents the current time step, is the learning rate at the current time step, is the basic learning rate, is the time decay coefficient, is the cycle adjustment coefficient, is the length of the training cycle; is the updated network weight at the next time step, is the network weight at the current time step, is the comprehensive correlation weight, is the error gradient, is the adaptive momentum factor, , is the initial momentum coefficient, is the gradient adjustment parameter, is the previous weight change.

[0032] Based on the above solution, preferably, step S3 includes:

[0033] S31. Obtain the pavement performance index data before and after maintenance of the road section to be evaluated. Among them, the pavement performance indexes include pavement condition index PCI, riding quality index RQI, rut depth index RDI, pavement bump index PBI, pavement wear index PWI, skid resistance performance index SRI, pavement structure strength index PSSI;

[0034] S32. Based on the pavement performance index data, establish an arctangent decay model to perform non-linear curve fitting on the pavement performance index PPI:

[0035] ,

[0036] where is the initial performance index value, is the pavement service life, The elastic parameter of the equation is the ratio of the minimum standard value of performance maintenance to the initial value of the performance index; , is the fitting parameter of the equation, the magnitude of which reflects the service life corresponding to the decay of the service performance index to the maintenance standard, and is called the pavement life factor. The magnitude of which can achieve different decay forms, and is called the pavement decay mode factor;

[0037] S33. Calculate the area A formed by the pavement performance change curve before maintenance and the minimum maintenance standard value based on the pavement performance index PPI calculated in step S32 0 , and calculate the increased area A of the curve after maintenance j :

[0038] ,

[0039] ,

[0040] wherein, is the decay curve function of the performance index before maintenance, is the decay curve function of the performance index after maintenance, is the minimum maintenance standard value, is the time for implementing the maintenance technique j, is the time when the PPI curve before maintenance drops to the minimum standard value, is the time when the PPI curve after maintenance drops to the minimum standard value;

[0041] S34. Calculate the standardized benefit SB of the pavement performance index PPI j (PPI):

[0042] ,

[0043] S35. Calculate the pavement maintenance benefit index PBI according to the result value of the standardized benefit SB j (PPI) j :

[0044] ,

[0045] wherein, is the weight coefficient of different pavement performance indexes;

[0046] S36. Calculate the benefit-cost ratio BCR of the pavement maintenance technique according to the result value of the pavement maintenance benefit index PBI j j :

[0047] ,

[0048] wherein, is the average life cycle cost of the maintenance technology.

[0049] Based on the above solution, preferably, step S4 includes:

[0050] S41. Establish a quantification standard for the success degree of the secondary qualitative evaluation indicators, and divide the evaluation values of each secondary qualitative evaluation indicator into five levels: excellent (0.9, 1.0], good (0.8, 0.9], average (0.7, 0.8], poor (0.6, 0.7], and bad (0, 0.6];

[0051] S42. Organize experts to score each secondary qualitative evaluation indicator, collect and collate expert opinions using the Delphi method, and form an expert scoring matrix , where is the score of the x-th expert for the y-th secondary qualitative evaluation indicator, m is the number of experts, and n is the number of secondary qualitative evaluation indicators;

[0052] S43. Calculate the expert consistency coefficient C y :

[0053] ,

[0054] where is the standard deviation of the scores of the y-th secondary qualitative evaluation indicator, is the average score of the y-th secondary qualitative evaluation indicator;

[0055] S44. When the expert consistency coefficient , take the scoring result of this round; otherwise, return to step S42 to continue soliciting expert opinions;

[0056] S45. Calculate the success degree score S y :

[0057] ,

[0058] where is the weight coefficient of the x-th expert;

[0059] S46. Calculate the comprehensive success degree scores of each qualitative indicator A1, A2, and A3 according to the secondary indicator weight values;

[0060] S47. Determine the evaluation grades of each level of indicators according to the success degree scores and the evaluation grade standard established in step S41, and form a standardized evaluation grade table.

[0061] Based on the above solution, preferably, step S5 includes:

[0062] S51. Establish the evaluation grade standards for qualitative and quantitative indicators:

[0063] The qualitative indicators adopt a standardized evaluation grade table established by the success degree method;

[0064] The quantitative indicators are divided into five grades according to the calculated benefit-cost ratio : excellent , good , average , poor , very poor ;

[0065] S52. Construct the qualitative indicator evaluation matrix , where is the membership degree of the th first-level qualitative indicator in the jth evaluation grade, is the number of first-level qualitative evaluation indicators, and 5 is the number of evaluation grades for qualitative indicators;

[0066] S53. Construct the quantitative indicator evaluation matrix , where is the membership degree of the th quantitative indicator in the jth evaluation grade, is the number of quantitative indicators, and 5 is the number of evaluation grades for quantitative indicators;

[0067] S54. Adopt the weighted fusion method to fuse the qualitative indicator evaluation matrix Q and the quantitative indicator evaluation matrix P to generate the fusion evaluation matrix R.

[0068] On the basis of the above scheme, preferably, the expression of the fusion evaluation matrix R is as follows:

[0069] ,

[0070] where:

[0071] ,

[0072] ,

[0073] In the formula, is the qualitative evaluation weight, is the quantitative evaluation weight, and ; The synergy coefficient is used to characterize the synergy effect between qualitative evaluation and quantitative evaluation; i is the serial number of the first-level indicator, and j is the serial number of the evaluation grade.

[0074] On the basis of the above scheme, preferably, step S6 includes:

[0075] S61. The final evaluation grades after maintenance are divided into 5 grades: very successful, relatively successful, basically successful, unsuccessful, and very unsuccessful, and their corresponding evaluation grade characteristic values are 1.0, 0.8, 0.6, 0.4, 0.2;

[0076] S62. Based on the weight vector W of the first-level indicators and the fusion evaluation matrix R, calculate the comprehensive evaluation vector V:

[0077] ,

[0078] where W is the weight vector of the first-level indicators, R is the fusion evaluation matrix, is the comprehensive evaluation vector, and v j represents the evaluation value of the j-th evaluation grade;

[0079] S63. Construct the comprehensive evaluation matrix G:

[0080] ,

[0081] where H is the evaluation grade characteristic matrix, ;

[0082] S64. Calculate the comprehensive evaluation result Z:

[0083] ,

[0084] where is the characteristic value of the j-th evaluation grade;

[0085] S65. Calculate the normalized membership degrees of each evaluation grade:

[0086] ,

[0087] where is the normalized membership degree of the j-th evaluation grade;

[0088] S66. According to the principle of maximum membership degree, select the grade corresponding to the maximum value as the final evaluation grade.

[0089] On the basis of the above scheme, preferably, when the difference in membership degrees between two evaluation grades is less than 0.1, take the lower grade as the final evaluation grade.

[0090] The present invention has the following beneficial effects compared with the prior art:

[0091] (1) The present invention constructs a multi-dimensional evaluation index system including technical applicability, social benefits, environmental benefits and economic benefits, and combines qualitative and quantitative evaluation methods to establish a complete post-maintenance evaluation method, realizing the standardization and regularization of the evaluation process and improving the scientificity and reliability of the evaluation results.

[0092] (2) The present invention adopts a BP neural network adaptive weight calculation model, and through the training of historical maintenance project data and the adaptive optimization mechanism, realizes the dynamic adjustment of the evaluation index weights, overcomes the problem that the determination of weights in the traditional analytic hierarchy process depends too much on the subjective judgment of experts, and makes the weight calculation results more objective.

[0093] (3) The present invention introduces the arctangent decay model to perform non-linear curve fitting on the pavement performance indicators, and quantifies the maintenance effect through the benefit area method, realizing the accurate evaluation of economic benefits and improving the accuracy of quantitative evaluation.

[0094] (4) The present invention establishes a fusion mechanism for qualitative and quantitative indicators. Through the introduction of weighted fusion methods and cooperation coefficients, it realizes the effective integration of different types of evaluation indicators, solves the problem that it is difficult to unify qualitative and quantitative indicators in traditional evaluation methods, and improves the credibility of comprehensive evaluation results.

[0095] (5) The present invention determines the final evaluation grade based on the principle of maximum membership degree and sets the determination rules for similar membership degrees, effectively avoiding the ambiguity of evaluation results and making the evaluation results clearer and more reliable. Description of the Drawings

[0096] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0097] Figure 1 It is the method flow chart of the embodiment of the present invention;

[0098] Figure 2 It is the arctangent decay curve fitting diagram of the PCI of the pre-maintenance evaluation section in the embodiment of the present invention;

[0099] Figure 3 It is the arctangent decay curve fitting diagram of the RQI of the pre-maintenance evaluation section in the embodiment of the present invention;

[0100] Figure 4 It is the arctangent decay curve fitting diagram of the PCI of the post-maintenance evaluation section in the embodiment of the present invention;

[0101] Figure 5 The fitting diagram of the arctangent decay curve of RQI for the evaluation section after maintenance in the embodiment of the present invention;

[0102] Figure 6 The schematic diagram of the change curve of the performance index and the benefit area in the embodiment of the present invention. Specific embodiments

[0103] Next, in combination with the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0104] As Figure 1 shown, the present invention provides a method for post-evaluation of the maintenance technology of an operating highway asphalt pavement, including:

[0105] S1. Construct a post-evaluation index system, and the evaluation index system includes technical applicability indicators, social benefit indicators, environmental benefit indicators, and economic benefit indicators;

[0106] S2. Establish an adaptive weight calculation model based on machine learning to calculate the weight vectors of each index;

[0107] S3. According to the economic benefit indicators, combine with the arctangent decay model to conduct a quantitative evaluation of the economic benefits and determine the economic benefit-cost ratio;

[0108] S4. Use the success degree method to conduct a qualitative evaluation of the technical applicability indicators, social benefit indicators, and environmental benefit indicators, establish a hierarchical evaluation system, and form a standardized evaluation grade table;

[0109] S5. Establish a comprehensive evaluation system that combines subjective and objective factors, unify the quantitative evaluation results in step S3 and the qualitative evaluation results in step S4 under the same evaluation standard, and generate a fusion evaluation matrix;

[0110] S6. Calculate the comprehensive evaluation vector based on the weight vectors of each index and the fusion evaluation matrix, construct a comprehensive evaluation matrix, calculate the comprehensive evaluation result based on the comprehensive evaluation matrix, and determine the final evaluation grade according to the maximum membership degree principle to obtain the final post-evaluation result of the maintenance.

[0111] The post-evaluation method of the maintenance technology referred to in the present invention is to evaluate the operating highway asphalt pavement after the maintenance technology is implemented by using the evaluation method proposed by the present invention.

[0112] Specifically, in one embodiment of the present invention, first, an evaluation index system after maintenance is constructed. The evaluation index system includes primary indexes and secondary indexes. The primary indexes include the technical applicability index A 1 , the social benefit index A 2 , the environmental benefit index A 3 and the economic benefit index A 4 ; the technical applicability index A 1 , the social benefit index A 2 , the environmental benefit index A 3 are qualitative indexes, and the economic benefit index A 4 is a quantitative index;

[0113] Among them, secondary indexes are set corresponding to each primary index:

[0114] The technical applicability index A 1 has the pavement performance improvement rate A 11 and the service life of the maintenance technology A 12 ;

[0115] The social benefit index A 2 has the impact on local economic development A 21 , the improvement of road traffic capacity A 22 and the reduction of the traffic accident incidence rate A 23 ;

[0116] The environmental benefit index A 3 has the noise pollution degree A 31 , the air pollution degree A 32 and the solid waste pollution degree A 33 ;

[0117] The economic benefit index A 4 has the benefit-cost ratio of the maintenance technology A 41 .

[0118] Specifically, the evaluation index system is shown in Table 1:

[0119] Table 1 Evaluation Index System for Highway Asphalt Pavement after Maintenance

[0120]

[0121] Specifically, in one embodiment of the present invention, step S2 includes:

[0122] S21. Based on the evaluation index system, construct the initial judgment matrix in two levels:

[0123] For the technical applicability index A 1 , the social benefit index A 2 , the environmental benefit index A 3 and the economic benefit index A4 Compare the four first-level indicators pairwise to construct a first-level indicator judgment matrix;

[0124] Compare the secondary indicators under each first-level indicator pairwise to construct multiple secondary indicator judgment matrices;

[0125] S22. Collect the evaluation data of historical maintenance projects, including expert scoring data and actual effect data, as the training samples of the BP neural network. Among them, the expert scoring data includes the relative importance judgment values of each level of indicators in historical projects; the actual effect data includes the pavement performance improvement rate, service life, and economic benefit objective indicators of historical projects;

[0126] S23. Construct a three-layer BP neural network, use the expert scoring data as the input and the actual effect data as the output, and train to obtain a weight optimization model;

[0127] S24. Input the index characteristics of the new evaluation project corresponding to each evaluation index into the weight optimization model, and dynamically adjust the first-level indicator judgment matrix and the secondary indicator judgment matrix respectively according to the model output results;

[0128] S25. Based on the adjusted judgment matrix, first calculate the largest eigenvalue and its eigenvector of the first-level indicator judgment matrix; then calculate the largest eigenvalue and its eigenvector of each group of secondary indicator judgment matrices respectively;

[0129] S26. Normalize the first-level indicator eigenvector to obtain the first-level indicator weight vector; normalize each group of secondary indicator eigenvectors respectively to obtain the corresponding secondary indicator weight vectors.

[0130] In this embodiment, when training the BP neural network, an adaptive optimization mechanism and an adaptive correction rule are used to update the network weights:

[0131] ,

[0132] ,

[0133] where t represents the current time step, is the learning rate at the current time step, is the basic learning rate, is the time decay coefficient, is the period adjustment coefficient, is the training period length; is the updated network weight at the next time step, is the network weight at the current time step, is the comprehensive correlation weight, is the error gradient, is the adaptive momentum factor, , is the initial momentum coefficient, is the gradient adjustment parameter, is the weight change amount of the previous time.

[0134] In one example, the implementation process of step S2 is as follows:

[0135] Construction of the first-level index judgment matrix:

[0136] ,

[0137] Construction of the second-level index judgment matrix:

[0138] ,

[0139] Collect training sample data. The expert scoring data collected in this example is:

[0140] Historical project 1: A 1 : A 2 = 3:1; A 1 : A 3 = 2:1; A 1 : A 4 = 4:1; A 2 : A 3 = 1:2; A 2 : A 4 = 2:1; A 3 : A 4 = 3:1; A 11 : A 12 = 2:1; A 21 : A 22 : A 23 = 1:2:3; A 31 : A 32 : A 33 = 3:2:1; Actual effect data: Road surface performance improvement rate: 85%; Service life: 5 years; Traffic capacity improvement: 25%; Accident rate reduction: 30%; Noise reduction: 12 dB; Air quality improvement: 15%; Energy savings: 20%; Benefit-cost ratio: 1.8.

[0141] Historical project 2: A 1 : A 2 = 2.5:1; A 1 : A 3 = 1.5:1; A 1 : A 4 = 3.8:1; A 2 : A 3 = 1:2.1; A 2 : A 4 = 1.9:1; A3 :A 4 =2.8:1; A 11 :A 12 =1.5:1; A 21 :A 22 :A 23 =1:2:2.5; A 31 :A 32 :A 33 =2.5:1.5:1; Actual effect data: Road surface performance improvement rate: 78%; Service life: 4 years; Traffic capacity improvement: 20%; Accident rate reduction: 25%; Noise reduction: 10 dB; Air quality improvement: 12%; Energy savings: 18%; Benefit-cost ratio: 1.5.

[0142] Construct a BP neural network with the network structure as follows: Input layer: Set to 13 nodes according to the actual index numbers (4 first-level index comparison values + 9 second-level index comparison values); Hidden layer 1: 16 nodes; Hidden layer 2: 10 nodes; Output layer: Weight values.

[0143] The training parameters are set as: Basic learning rate η 0 =0.1; Learning rate decay coefficient λ = 0.01; Period adjustment coefficient β = 0.2; Training period length T = 1000.

[0144] According to the output result of the BP network, adjust the first-level index judgment matrix:

[0145] ,

[0146] Calculate the eigenvalue and eigenvector. First-level index judgment matrix: Maximum eigenvalue λ max =4.0123; Eigenvector p = [0.4978, 0.0717, 0.2753, 0.1552].

[0147] Conduct a consistency test: CI = (λ max -n) / (n - 1) = (4.0123 - 4) / (4 - 1) = 0.0041; RI is obtained by referring to Table 2; CR = CI / RI < 0.1. The judgment matrix passes the consistency test.

[0148] Table 2 RI standard values

[0149] Order 1 2 3 4 5 6 7 8 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41

[0150] First-level index weight vector W: W = [0.4978, 0.1717, 0.2753, 0.0552]; Second-level index weight vector w i : Under technical applicability (A 1 ) : w 1=[0.6667,0.3333], Social benefit (A 2 ) Under: w 2 =[0.1634,0.2970,0.5396], Environmental benefit (A 3 ) Under: w 3 =[0.5396,0.2970,0.1634], Economic benefit (A 4 ) Under: w 4 =[1.0000].

[0151] Specifically, in an embodiment of the present invention, step S3 includes:

[0152] S31. Obtain the pavement performance index data before and after maintenance of the road section to be evaluated. Among them, the pavement performance indexes include pavement distress condition index PCI, pavement ride quality index RQI, pavement rut depth index RDI, pavement bump index PBI, pavement abrasion index PWI, pavement skid resistance performance index SRI, and pavement structure strength index PSSI;

[0153] S32. Based on the pavement performance index data, establish an arctangent decay model to perform non-linear curve fitting on the pavement service performance index PPI:

[0154] ,

[0155] In the formula, Is the initial performance index value, Is the pavement service life, Is the elastic parameter of the equation, which is the ratio of the minimum maintenance standard value of the service performance to the initial value of the service performance index; , Is the fitting parameter of the equation, The size of reflects the service life corresponding to when the service performance index decays to the maintenance standard, which is called the pavement life factor, The size of can achieve different decay forms, which is called the pavement decay mode factor;

[0156] S33. According to the pavement service performance index PPI calculated in step S32, calculate the area A formed by the pavement performance change curve before maintenance and the minimum maintenance standard value 0 , and calculate the increased area A of the curve after maintenance j :

[0157] ,

[0158] ,

[0159] In the formula, Is the function of the performance index decay curve before maintenance, is the decay curve function of the performance index after maintenance, is the minimum standard value of maintenance, is the time to implement maintenance technology j, is the time when the PPI curve before maintenance drops to the minimum standard value, is the time when the PPI curve after maintenance drops to the minimum standard value;

[0160] S34. According to the calculated result value in step S33, calculate the standardized benefit SB j (PPI):

[0161] ,

[0162] S35. According to the result value of the standardized benefit SB j (PPI) of the service performance index, calculate the pavement maintenance benefit index PBI j :

[0163] ,

[0164] In the formula, is the weight coefficient of different performance indexes;

[0165] S36. According to the result value of the pavement maintenance benefit index PBI j , calculate the benefit-cost ratio BCR of the pavement maintenance technology j :

[0166] ,

[0167] In the formula, is the average life cycle cost of the maintenance technology.

[0168] In the present invention, the pavement service performance index PPI refers to a comprehensive performance parameter obtained by fitting a non-linear attenuation model based on basic performance indexes such as the pavement condition index (PCI) and the riding quality index (RQI), and is used to evaluate the effect of pavement maintenance technology. Each basic performance index can obtain a corresponding PPI value, and these PPI values are finally weighted and integrated through weight coefficients to calculate the pavement maintenance benefit index.

[0169] Specifically, is obtained by: obtaining pavement performance index data (such as PCI, RQI, etc.) through actual detection. For each performance index, the initial detection value before the implementation of maintenance is taken as the of this index, and this initial value will be used as the reference value for subsequent PPI decay calculation.

[0170] In one example, taking PCI and RQI as the index data of the pavement performance index PPI, the PCI, RQI and detection data for at least two years before and after maintenance of the section to be evaluated are collected. The section to be evaluated is selected as the main lane in the upward direction of the Guangshao Expressway, with the starting and ending stake numbers being K1986+920~K1988+080. The micro-surfacing preventive maintenance technology was adopted on this section in 2017.

[0171] Based on the principles of integrity and validity of the detection data, the pavement PCI and RQI data for several years before and after the maintenance of this section are obtained through investigation, as shown in Table 3 below.

[0172] Table 3 PCI and RQI data of the section to be evaluated over the years

[0173] Year 2014 2015 2016 2017 (Before maintenance) 2017 (After maintenance) 2018 2019 2020 2021 2022 PCI 94.5 92.8 90.0 87.2 98.8 97.6 94.3 92.2 89.5 86.1 RQI 93.2 91.4 89.3 88.1 95.8 93.1 92.1 91.0 88.6 87.9

[0174] Please refer to Figures 2 - 5 , the arctangent decay model is used to fit the pavement PCI and RQI data before and after the maintenance of the evaluation section. Through investigation of the maintenance department of the Guangshao Expressway, the minimum standard values of PCI and RQI for preventive maintenance of the highway are 85 and 82 respectively, that is, λ before maintenance is 0.85 and λ after maintenance is 0.86 in the PCI decay equation; λ before maintenance is 0.82 and λ after maintenance is 0.856 in the RQI decay equation.

[0175] Specifically, according to the decay equation before maintenance, the benefit area A 0 (PCI) of PCI without adopting preventive maintenance technology has the following calculation formula:

[0176] ,

[0177] According to the decay equation before maintenance, the benefit area A 0 (RQI) of RQI without adopting preventive maintenance technology has the following calculation formula:

[0178] ,

[0179] Please refer to Figure 6 , the increased benefit area A j (PCI) of PCI after adopting preventive maintenance has the following calculation formula:

[0180] ,

[0181] The increased benefit area A j (RQI) of RQI after adopting preventive maintenance has the following calculation formula:

[0182] ,

[0183] Specifically, according to the benefit area A of PCI 0 (PCI) when preventive maintenance technology is not adopted and the increased benefit area A of PCI j (PCI) after preventive maintenance is carried out, the standardized benefit SB of the performance index PPI j (PPI) is calculated, and its calculation formula is:

[0184] ,

[0185] According to the benefit area A of RQI 0 (RQI) when preventive maintenance technology is not adopted and the increased benefit area A of RQI j (RQI) after preventive maintenance is carried out, the standardized benefit SB of the performance index PPI j (RQI) is calculated, and its calculation formula is:

[0186] ,

[0187] Construct the judgment matrix of the two indexes of PCI and RQI as follows:

[0188] ,

[0189] Calculate that the maximum eigenvalue of this judgment matrix is λ max = 2, and the corresponding eigenvector is P=(0.89, 0.45) T , after normalization, the weight vector is W=(0.67, 0.33) T , that is, the benefit weight coefficients of PCI and RQI are 0.67 and 0.33 respectively, then the maintenance benefit index PBI j , and its calculation formula is:

[0190] ,

[0191] Through the investigation of the maintenance department of Guangshao Expressway, the average life cycle cost Cost j of the microsurfacing preventive maintenance technology is 155 yuan / m 2 , then the benefit-cost ratio BCR j of this preventive maintenance technology, and its calculation formula is:

[0192] ,

[0193] BCR j reflects the economic benefits of adopting the maintenance technology j.

[0194] Specifically, in an embodiment of the present invention, step S4 includes:

[0195] S41. Establish a quantification standard for the success degree of the secondary qualitative evaluation indicators, and divide the evaluation values of each secondary qualitative evaluation indicator into five levels: excellent (0.9, 1.0], good (0.8, 0.9], average (0.7, 0.8], poor (0.6, 0.7], and bad (0, 0.6].

[0196] S42. Organize experts to score each secondary qualitative evaluation indicator, collect and collate expert opinions using the Delphi method, and form an expert scoring matrix , where is the score of the x-th expert for the y-th secondary qualitative evaluation indicator, m is the number of experts, and n is the number of secondary qualitative evaluation indicators;

[0197] S43. Calculate the expert consistency coefficient C y :

[0198] ,

[0199] where is the standard deviation of the scores of the y-th secondary qualitative evaluation indicator, is the average score of the y-th secondary qualitative evaluation indicator;

[0200] S44. When the expert consistency coefficient , take the scoring result of this round; otherwise, return to step S42 to continue soliciting expert opinions;

[0201] S45. Calculate the success degree score S of each secondary qualitative evaluation indicator y :

[0202] ,

[0203] where is the weight coefficient of the x-th expert;

[0204] S46. Calculate the comprehensive success degree scores of each qualitative indicator A1, A2, and A3 according to the secondary indicator weight values;

[0205] S47. According to the success degree scores, compare with the evaluation grade standard established in step S41 to determine the evaluation grades of each level of indicators, and form a standardized evaluation grade table.

[0206] Specifically, in one example, the evaluation values of each secondary qualitative evaluation index are first divided into five levels: excellent (i.e., very successful) (0.9, 1.0], good (i.e., relatively successful) (0.8, 0.9], average (i.e., basically successful) (0.7, 0.8], poor (i.e., unsuccessful) (0.6, 0.7], and bad (i.e., very unsuccessful) (0, 0.6]. Then, an expert group is established, and 7 - 9 experts with rich maintenance experience are selected, including maintenance engineers, material experts, construction experts, etc. from different fields. The Delphi method is used for multiple rounds of anonymous scoring. The experts score each qualitative index according to the quantitative standard to form an expert scoring matrix D.

[0207] In this example, when the experts score, they score according to the evaluation grade table shown in Tables 4 - 6 as follows:

[0208] Table 4 Evaluation Grade Table of Technical Applicability Evaluation Index after Maintenance

[0209]

[0210] Table 5 Evaluation Grade Table of Social Benefit Evaluation Index after Maintenance

[0211]

[0212] Table 6 Evaluation Grade Table of Environmental Benefit Evaluation Index after Maintenance

[0213]

[0214] After that, the evaluation results of the experts are summarized. According to the evaluation of each expert, it is quantified corresponding to the grade characteristic values in step S41, and combined with the weight of each expert, the success degree score of the secondary index is calculated. Then, according to the weight vector of each secondary index assigned in step S2, the weight value of the specific secondary index is determined to calculate the comprehensive success degree score of the primary index. According to the success degree score, the evaluation grade standard established in step S41 is used as a reference to determine the evaluation grade of each level of index, and a standardized evaluation grade table is formed.

[0215] Specifically, in one embodiment of the present invention, step S5 includes:

[0216] S51. Establish the evaluation grade standards for qualitative and quantitative indicators:

[0217] The qualitative indicators adopt a standardized evaluation grade table established by the success degree method;

[0218] The quantitative indicators are divided into five levels according to the calculated benefit - cost ratio : excellent , good , average , poor , bad ;

[0219] S52. Construct a qualitative index evaluation matrix , where is the membership degree of the th first-level qualitative index in the jth evaluation level, is the number of first-level qualitative evaluation indexes, and 5 is the number of evaluation levels of qualitative indexes;

[0220] S53. Construct a quantitative index evaluation matrix , where is the membership degree of the th quantitative index in the jth evaluation level is the number of quantitative indexes, and 5 is the number of evaluation levels of quantitative indexes;

[0221] S54. Adopt a weighted fusion method to fuse the qualitative index evaluation matrix Q and the quantitative index evaluation matrix P to generate a fusion evaluation matrix R.

[0222] The expression of the fusion evaluation matrix R is as follows:

[0223] ,

[0224] where:

[0225] ,

[0226] ,

[0227] In the formula, is the qualitative evaluation weight, is the quantitative evaluation weight, and ; is the synergy coefficient, used to characterize the synergy effect between qualitative evaluation and quantitative evaluation; i is the serial number of the first-level index, and j is the serial number of the evaluation level.

[0228] Specifically, in an example, the constructed qualitative index evaluation matrix Q is as follows:

[0229] ,

[0230] where the first row of this matrix Q is the membership degree of each evaluation level of the A 1 index, the second row is the membership degree of each evaluation level of the A 2 index, and the third row is the membership degree of each evaluation level of the A 3 index.

[0231] The constructed quantitative index evaluation matrix P is as follows:

[0232] ,

[0233] The matrix P represents the membership degrees of each evaluation level of the A4 index.

[0234] After that, the fused evaluation matrix R is calculated. When calculating, the value is taken, specifically is taken, and is taken. When performing the fused calculation, taking the first element r 11 as an example, its calculation formula is as follows:

[0235]

[0236] The final fused evaluation matrix R is as follows:

[0237]

[0238] Based on the membership degree distribution of the secondary indicators under each primary indicator in the fused evaluation matrix R, it can be analyzed and known that:

[0239] Analysis of the technical applicability indicator (A 1 ): Membership degree distribution: [0.318, 0.424, 0.208, 0.104, 0.000]. The highest membership degree is in "relatively successful" (0.424), and the membership degree of "very successful" is also relatively high (0.318), with the largest weight and the greatest impact on the final result. Conclusion: The technical applicability performs well and meets the expected technical requirements.

[0240] Analysis of the social benefit indicator (A 2 ): Membership degree distribution: [0.212, 0.524, 0.208, 0.104, 0.000]. The membership degree of "relatively successful" is the highest (0.524), with obvious advantages. The membership degrees of other levels are relatively low, with smaller weights and limited influence. Conclusion: The social benefits are outstanding.

[0241] Analysis of the environmental benefit indicator (A 3 ): Membership degree distribution: [0.424, 0.318, 0.208, 0.104, 0.000]. The membership degree of "very successful" is the highest (0.424), and the membership degree of "relatively successful" is the second highest (0.318), with a moderate weight and a certain influence. Conclusion: The environmental benefits perform excellently and reach a relatively high level.

[0242] Analysis of the economic benefit indicator (A 4 ): Membership degree distribution: [0.300, 0.400, 0.200, 0.100, 0.000]. The distributions of each level are relatively balanced. The membership degree of "relatively successful" is the highest (0.400), with the smallest weight and the least influence. Conclusion: The economic benefits perform stably and generally reach a good level.

[0243] Specifically, in one embodiment of the present invention, step S6 includes:

[0244] S61. Divide the final evaluation level after maintenance into 5 levels: very successful, relatively successful, basically successful, unsuccessful, and very unsuccessful, and their corresponding evaluation level characteristic values are 1.0, 0.8, 0.6, 0.4, 0.2;

[0245] S62. Calculate the comprehensive evaluation vector V based on the weight vector W of the first-level indicators and the fusion evaluation matrix R:

[0246] ,

[0247] where W is the weight vector of the first-level indicators, R is the fusion evaluation matrix, is the comprehensive evaluation vector, and v j represents the evaluation value of the j-th evaluation level;

[0248] S63. Construct the comprehensive evaluation matrix G:

[0249] ,

[0250] where H is the evaluation level characteristic matrix, ;

[0251] S64. Calculate the comprehensive evaluation result Z:

[0252] ,

[0253] where is the characteristic value of the j-th evaluation level;

[0254] S65. Calculate the normalized membership degree of each evaluation level:

[0255] ,

[0256] where is the normalized membership degree of the j-th evaluation level;

[0257] S66. According to the principle of maximum membership degree, select the level corresponding to the maximum value as the final evaluation level.

[0258] Among them, when the difference in membership degrees between two evaluation levels is less than 0.1, the lower level is taken as the final evaluation level.

[0259] Specifically, in one example, the established evaluation level system is as follows:

[0260] Very successful: eigenvalue = 1.0; Relatively successful: eigenvalue = 0.8; Basically successful: eigenvalue = 0.6; Unsuccessful: eigenvalue = 0.4; Very unsuccessful: eigenvalue = 0.2.

[0261] Evaluation level feature matrix 。

[0262] The weight vector W of the first-level indicators: W = [0.4978, 0.1717, 0.2753, 0.0552]; The fusion evaluation matrix R is as follows:

[0263] ,

[0264] According to the formula Calculate the comprehensive evaluation vector:

[0265] ,

[0266] Calculation process:

[0267] The first component:

[0268] 0.4978×0.318 + 0.1717×0.212 + 0.2753×0.424 + 0.0552×0.300 = 0.3316;

[0269] The second component:

[0270] 0.4978×0.424 + 0.1717×0.524 + 0.2753×0.318 + 0.0552×0.400 = 0.4089;

[0271] The third component:

[0272] 0.4978×0.208 + 0.1717×0.208 + 0.2753×0.208 + 0.0552×0.200 = 0.2075;

[0273] The fourth component:

[0274] 0.4978×0.104 + 0.1717×0.104 + 0.2753×0.104 + 0.0552×0.100 = 0.1037;

[0275] The fifth component:

[0276] 0.4978×0.000 + 0.1717×0.000 + 0.2753×0.000 + 0.0552×0.000 = 0.0000.

[0277] V = [0.3316, 0.4089, 0.2075, 0.1037, 0.0000].

[0278] Construct the comprehensive evaluation matrix G:

[0279] ,

[0280] Calculate the comprehensive evaluation result Z:

[0281] ,

[0282] Calculate the normalized membership degree:

[0283] ,

[0284] μ 1 = 0.3316 / 1.0517 = 0.3153 (Very successful); μ 2 = 0.4089 / 1.0517 = 0.3888 (Relatively successful); μ 3 = 0.2075 / 1.0517 = 0.1973 (Basically successful); μ 4 = 0.1037 / 1.0517 = 0.0986 (Unsuccessful); μ 5 = 0.0000 / 1.0517 = 0.0000 (Very unsuccessful).

[0285] Membership degree difference check: The difference between μ 1 and μ 2 : , since the difference is less than 0.1, and μ 2 > μ 1 , the lower grade should be taken. Final evaluation grade: Relatively successful.

[0286] It can be seen from the calculation results that:

[0287] The comprehensive evaluation result Z is 0.8247. The membership degree of "Relatively successful" is the highest (0.3888), and the membership degree of "Very successful" is the second highest (0.3153). The difference between the two is less than 0.1. Since the membership degree difference between adjacent grades is less than 0.1, the conservative principle needs to be adopted, and the lower grade "Relatively successful" is taken as the final evaluation grade.

[0288] Conclusion: The final evaluation grade of this maintenance technology is "Relatively successful", and this result reflects that this maintenance technology has good implementation effects.

[0289] The above is only the preferred implementation mode of the present invention, and it is not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A post-evaluation method for maintenance technology of asphalt pavement of an operating highway, characterized in that: include: S1. Construct a post-maintenance evaluation index system, which includes technical applicability index, social benefit index, environmental benefit index and economic benefit index; The evaluation index system includes primary indicators and secondary indicators. The primary indicators include technical applicability index A1, social benefit index A2, environmental benefit index A3 and economic benefit index A4. Technical applicability index A1, social benefit index A2 and environmental benefit index A3 are qualitative indicators, and economic benefit index A4 is a quantitative indicator. Among them, each first-level indicator has a corresponding second-level indicator: Technical applicability index A1 includes pavement performance improvement rate A 11 and maintenance technology service life A 12 ; Social benefit indicator A2 includes local economic development impact A 21 , Road capacity improvement A 22 and traffic accident rate reduction 23 ; Environmental benefit index A3 includes noise pollution level A 31 、Air pollution level A 32 and solid waste pollution level A 33 ; Economic benefit index A4 includes maintenance technology benefit-cost ratio A 41 ; S2. Establish an adaptive weight calculation model based on machine learning to calculate the weight vector of each indicator; Step S2 includes: S21. Based on the evaluation index system, the initial judgment matrix is ​​constructed at two levels: The four first-level indicators of technical applicability index A1, social benefit index A2, environmental benefit index A3 and economic benefit index A4 are compared in pairs to construct a first-level indicator judgment matrix; Compare the secondary indicators under each primary indicator in pairs and construct multiple secondary indicator judgment matrices; S22. Collect evaluation data of historical maintenance projects, including expert scoring data and actual effect data, as training samples for the BP neural network, wherein the expert scoring data includes the relative importance judgment values ​​of indicators at all levels in the historical projects; the actual effect data includes the objective indicators of the road performance improvement rate, service life, and economic benefits of the historical projects; S23, construct a three-layer BP neural network, take the expert scoring data as input and the actual effect data as output, and train to obtain a weight optimization model; S24, input the indicator characteristics of each evaluation indicator corresponding to the new evaluation project into the weight optimization model, and dynamically adjust the primary indicator judgment matrix and the secondary indicator judgment matrix according to the model output results; S25, based on the adjusted judgment matrix, first calculate the maximum characteristic root and eigenvector of the primary indicator judgment matrix; then calculate the maximum characteristic root and eigenvector of each group of secondary indicator judgment matrices respectively; S26, normalizing the first-level indicator feature vector to obtain a first-level indicator weight vector; normalizing each group of second-level indicator feature vectors to obtain a corresponding second-level indicator weight vector; In step S23, when training the BP neural network, the network weights are updated using an adaptive optimization mechanism and an adaptive correction rule: Where t represents the current time step, is the learning rate of the current time step, is the basic learning rate, is the time attenuation coefficient, is the period adjustment coefficient, is the length of the training cycle; Update network weights for the next time step, is the network weight at the current time step, is the comprehensive relevance weight, is the error gradient, is the adaptive momentum factor, , is the initial momentum coefficient, is the gradient adjustment parameter, is the weight change of the last time; S3. Based on the economic benefit index, the economic benefit quantitative evaluation is carried out in combination with the arctangent decay model to determine the economic benefit cost ratio; Step S3 includes: S31, obtaining pavement performance index data of the road section to be evaluated before and after maintenance, wherein the pavement performance index includes pavement damage condition index PCI, pavement ride quality index RQI, pavement rutting depth index RDI, pavement bounce index PBI, pavement wear index PWI, pavement skid resistance index SRI, and pavement structure strength index PSSI; S32. Based on the pavement performance index data, an inverse tangent decay model is established to perform nonlinear curve fitting on the pavement performance index PPI: In the formula, is the initial performance index value, The service life of the road surface. is the elastic parameter of the equation, which is the ratio of the minimum standard value of the performance maintenance to the initial value of the performance index; , are the equation fitting parameters, The size of reflects the corresponding service life when the performance index decays to the maintenance standard, which is called the pavement life factor. The size of can achieve different decay forms, which is called the pavement decay mode factor; S33, according to the pavement performance index PPI calculated in step S32, calculate the area A0 formed by the pavement performance change curve before maintenance and the minimum maintenance standard value, and calculate the increased area A of the curve after maintenance j : In the formula, is the decay curve function of performance index before maintenance, is the decay curve function of performance index after maintenance, The minimum maintenance standard value is The time for implementing maintenance technology j, The time when the PPI curve drops to the lowest standard value before maintenance. It is the time when the PPI curve drops to the lowest standard value after maintenance; S34, according to the calculation result value of step S33, calculate the standardized benefit SB of the performance indicator PPI j (PPI): S35. Standardized benefit SB based on the performance indicator PPI j (PPI) result value, calculate the pavement maintenance benefit index PBI j : In the formula, is the weight coefficient of different pavement performance indicators; S36, according to the pavement maintenance benefit index PBI j The result value is used to calculate the benefit-cost ratio (BCR) of road maintenance technology. j : In the formula, is the average life cycle cost of the maintenance technology; S4. Use the success method to conduct qualitative evaluation of technical applicability indicators, social benefit indicators and environmental benefit indicators, establish a hierarchical evaluation system, and form a standardized evaluation grade table; S5, establishing a comprehensive evaluation system integrating subjective and objective factors, unifying the quantitative evaluation results of step S3 and the qualitative evaluation results of step S4 under the same evaluation standard, and generating a fusion evaluation matrix; S6. Calculate the comprehensive evaluation vector based on the weight vector of each indicator and the fusion evaluation matrix, and construct a comprehensive evaluation matrix. Calculate the comprehensive evaluation result based on the comprehensive evaluation matrix, determine the final evaluation level according to the maximum membership principle, and obtain the final evaluation result after maintenance.

2. The post-evaluation method for maintenance technology of asphalt pavement of an operating highway as claimed in claim 1, characterized in that: Step S4 includes: S41. Establish the success quantification standard of the qualitative evaluation secondary indicators, and divide the evaluation values ​​of each qualitative evaluation secondary indicator into five levels: excellent (0.9, 1.0], good (0.8, 0.9], average (0.7, 0.8], poor (0.6, 0.7], and bad (0, 0.6]; S42. Organize experts to score each secondary indicator of qualitative evaluation, use the Delphi method to collect and organize expert opinions, and form an expert scoring matrix ,in, is the score of the xth expert on the yth qualitative evaluation secondary indicator, m is the number of experts, The number of secondary indicators for qualitative evaluation; S43. Calculate the expert consistency coefficient C of each qualitative evaluation secondary indicator y : in, is the scoring standard deviation of the yth qualitative evaluation secondary indicator, is the average score of the yth qualitative evaluation secondary indicator; S44, when the expert consistency coefficient If yes, take the result of this round of evaluation; otherwise, return to step S42 to continue to seek expert opinions; S45. Calculate the success score S of each qualitative evaluation secondary indicator y : in, is the weight coefficient of the x-th expert; S46. Calculate the comprehensive success scores of the qualitative indicators A1, A2, and A3 according to the weight values ​​of the secondary indicators; S47, determining the evaluation grades of indicators at all levels according to the success score and the evaluation grade standard established in step S41, and forming a standardized evaluation grade table.

3. A post-evaluation method for maintenance technology of asphalt pavement of an operating highway as claimed in claim 2, characterized in that: Step S5 includes: S51. Establish evaluation standards for qualitative and quantitative indicators: Qualitative indicators use a standardized rating scale established using the success method; Quantitative indicators are based on the calculated benefit-cost ratio Divided into five levels: Excellent ,good ,generally , poor ,Difference ; S52. Constructing a qualitative indicator evaluation matrix ,in, For the The membership degree of a first-level qualitative indicator in the jth evaluation level, is the number of first-level qualitative evaluation indicators, and 5 is the number of evaluation levels of qualitative indicators; S53. Constructing a quantitative indicator evaluation matrix ,in, For the The membership degree of a quantitative indicator in the jth evaluation level, is the number of quantitative indicators, and 5 is the number of evaluation levels of the quantitative indicators; S54. Using a weighted fusion method, the qualitative indicator evaluation matrix Q and the quantitative indicator evaluation matrix P are fused to generate a fused evaluation matrix R.

4. A post-evaluation method for maintenance technology of asphalt pavement of an operating highway as claimed in claim 3, characterized in that: The expression of the fusion evaluation matrix R is as follows: in: In the formula, is the qualitative evaluation weight, is the quantitative evaluation weight, and ; is the synergy coefficient, which is used to characterize the synergy effect of qualitative evaluation and quantitative evaluation; i is the first-level indicator number, and j is the evaluation level number.

5. The post-evaluation method for maintenance technology of asphalt pavement of an operating highway as claimed in claim 1, characterized in that: Step S6 includes: S61. The final evaluation level after maintenance is divided into five levels: very successful, relatively successful, basically successful, unsuccessful and very unsuccessful, and the corresponding evaluation level characteristic values ​​are 1.0, 0.8, 0.6, 0.4 and 0.2; S62. Based on the weight vector W of the primary index and the fusion evaluation matrix R, calculate the comprehensive evaluation vector V: In the formula, W is the weight vector of the first-level index, R is the fusion evaluation matrix, is the comprehensive evaluation vector, v j represents the evaluation value of the jth evaluation level; S63. Construct a comprehensive evaluation matrix G: In the formula, H is the evaluation level feature matrix, ; S64. Calculate the comprehensive evaluation result Z: In the formula, is the characteristic value of the jth evaluation level; S65. Calculate the normalized membership of each evaluation level: In the formula, is the normalized membership of the jth evaluation level; S66. According to the maximum membership principle, select The grade corresponding to the maximum value is taken as the final evaluation grade.

6. A post-evaluation method for maintenance technology of asphalt pavement of an operating highway as claimed in claim 5, characterized in that: When the difference in membership between two evaluation levels is less than 0.1, the lower level is taken as the final evaluation level.

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