CN111062583B - Asphalt pavement historical maintenance benefit quantitative evaluation method based on principal component analysis method - Google Patents
Asphalt pavement historical maintenance benefit quantitative evaluation method based on principal component analysis method Download PDFInfo
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Abstract
The invention discloses a quantitative evaluation method for the historical maintenance benefit of an asphalt pavement based on a principal component analysis method, which comprises the following steps: (1) Acquiring road use performance index detection data of at least one year before and after the occurrence of the historical maintenance of the road section, and performing nonlinear curve fitting on the decay of the use performance indexes PCI and RQI by using a Sun's decay model; (2) Solving the decay rate of the service performance index one year after the maintenance intervention; (3) Solving the decay rate of the service performance index of the corresponding year under the state without maintenance intervention; (4) Solving the lifting correction value and the decay rate improvement value of the service performance index under the percent; (5) Constructing a PQII evaluation system of historical maintenance on service performance improvement conditions; (6) And (4) solving each weight coefficient in a PQII evaluation system to finish index evaluation. The invention uses the historical road surface detection data to carry out decay fitting, quantifies the historical maintenance benefit, and establishes a reliable, reasonable and efficient evaluation system.
Description
Technical Field
The invention belongs to the field of road engineering, and relates to a quantitative evaluation method for historical maintenance benefits of an asphalt pavement based on a principal component analysis method.
Background
Most of the high-grade highway pavements in China are asphalt pavements. In the service process of asphalt pavement, the safety performance and the service performance of the highway are reduced due to the comprehensive circulation action of vehicle load and natural environment. The scientific and efficient maintenance decision can play a role in delaying the development of diseases and improving road conditions.
In the existing research on asphalt pavement maintenance decision, a new process and the improvement effect of a new material on related diseases in road maintenance are mainly researched by an outdoor test section and an indoor test mode. However, for the maintenance method adopted in the historical maintenance, whether the maintenance method can be applied to the existing maintenance requirement is often determined only by empirically judging and combining simple mathematical statistics. The defect of doing so is that the improvement of the road surface use performance of the relevant road section by the historical maintenance method is not specifically quantified.
The service performance evaluation system adopted in the current asphalt pavement specification in China is a pavement service performance index PQI evaluation system, and is mainly calculated by four indexes of pavement damage PCI, running quality RQI, pavement rut RDI and pavement skid resistance SRI in a linear weighted summation mode. From the perspective of the structure and the driver, the contribution of the historic maintenance method to the road maintenance can be considered as two contributions to the road damage PCI and the running quality RQI. Maintenance intervention can improve the related service performance index value in the next year and delay the decay rate of the corresponding index, so that quantitative analysis of contribution of historical maintenance to the service performance of the road can be regarded as multi-factor analysis. The principal component analysis method is a method for determining the weight of main factors in a multi-factor analysis method, and the method can be used for well removing the cross overlapping part among each subentry index.
However, this method cannot establish a quantitative evaluation method for evaluating the effect of improving the road use performance by historical maintenance, and cannot fully utilize historical maintenance and detection data.
Therefore, the method adopts a principal component analysis method, and establishes a quantitative evaluation method for evaluating the road use performance improvement effect of historical maintenance on the aspects of road surface damage and driving quality, so that the full utilization of historical maintenance and detection data is realized, and a new cut-in angle is provided for future maintenance decision research.
Disclosure of Invention
In order to solve the technical problems, the invention provides a quantitative evaluation method for the historical maintenance benefit of the asphalt pavement based on a principal component analysis method. The method sets up a quantitative evaluation method for evaluating the improvement effect of historical maintenance on the road use performance from the aspects of road surface damage and running quality, the method realizes the full utilization of historical maintenance and detection data and the quantitative evaluation of historical maintenance benefits, provides a new cut-in angle for future maintenance decision-making research, and can realize the purpose of providing a theoretical basis for scientifically selecting the asphalt pavement maintenance method.
The technical scheme provided by the invention is as follows:
a quantitative evaluation method for the historical maintenance benefit of an asphalt pavement based on a principal component analysis method comprises the following steps:
(1) Obtaining road use performance index detection data of at least one year before and after the historical maintenance of the road section, and carrying out nonlinear curve fitting on the decay of the use performance indexes PCI and RQI by utilizing a Sun's decay model
(2) Performing first-order derivation on the fitted curve in the step (1), and solving the decay rate of service performance indexes PCI and RQI one year after the historical maintenance intervention;
(3) Acquiring road use performance index detection data of at least one year before the historical maintenance of the road section, and simulating and solving the decay rate of use performance indexes PCI and RQI of one year after the historical maintenance intervention of the step (2) in a non-maintenance intervention state by using the method of the steps (1) and (2);
(4) Solving the lifting correction value A, C and the decay rate improvement value B, D of the performance indexes PCI and RQI under the percentage;
(5) PQII evaluation system for building historical maintenance to improve service performance
(6) And solving the weight coefficient by a principal component analysis method, and completing construction of an index system to evaluate the improvement condition of the historical maintenance on the road surface use performance.
Specifically, the fitting formula of the grand-son decay model in the step (1) is as follows:
wherein, y is PCI or RQI, and x is service life; PPI 0 The initial value of the service performance index when a road is newly built is generally 100; t, U is a model fitting parameter, the size of T is related to the service life of a pavement, and the size of U reflects the difference of curve shapes.
Specifically, the formula of step (2) is as follows:
wherein y' is the decay rate of PCI or RQI; PPI 0 The initial value of the service performance index when a road is newly built is generally 100; t, U is a model fitting parameter, the size of T is related to the service life of a pavement, and the size of U reflects the difference of curve shapes.
Specifically, the calculation formula of the PCI lift correction value a in step (4) is as follows:
in the formula: a is PCI lifting correction value,%; delta PCI is PCI lifting amplitude,%; delta PCI max The maximum amplitude can be increased for PCI;
the same method is used to calculate the RQI lifting correction value C.
Further, the Δ PCI max The maximum range which can be promoted is determined by the value range of PCI classification in the technical Specification of road asphalt pavement, as shown in the following table:
TABLE 1 "technical Specification for maintaining asphalt road surface" PCI classification boundary condition table
Specifically, the decay rate improvement value B in the step (4) is calculated by the following formula:
in the formula: Δ PCI' is the rate of decay of PCI after a maintenance intervention.
D was calculated in the same manner.
Specifically, the PQII evaluation system formula in step (5) is as follows:
PQII＝A·w A +B·w B +C·w C +D·w D
in the formula: PQII is road surface usability improvement index (road surface quality improved index); a, w A Lifting the correction value (%) and the weight coefficient thereof for the PCI; b, w B The PCI decay rate improvement value (%) and the weight coefficient thereof; c, w C Lifting the correction value (%) and the weight coefficient thereof for the RQI; d, w D RQI decay rate improvement (%) and its weight coefficient.
Specifically, the method for calculating the weighting coefficients in the steps (5) and (6) is as follows: the four index calculation values of A, B, C, D which are solved are used as original data, a 3 x 4 original data matrix is constructed and substituted into factor analysis of SPSS data analysis software, and each subentry index weight coefficient is solved through a principal component analysis method.
Further, the principal component analysis method is as follows: outputting a component matrix containing initial factor load by adopting a factor analysis algorithm; converting the initial factor load into a characteristic vector value; then multiplying and summing the characteristic vector numerical value and the corresponding original data under each type of indexes to obtain each subentry index; calculating the proportion of each subentry index in the total index, and taking the value as the weight of each subentry index; and sorting the corresponding characteristic values of the components from large to small, and taking the components with the cumulative variance contribution rate of more than 80% as principal components, wherein the component with the largest variance is the first principal component, the second principal component and so on.
The invention has the beneficial effects that:
according to the method, firstly, nonlinear curve fitting is carried out on road surface service performance indexes of relevant road sections through a Sun's decay model, model parameters are solved, the service performance index decay rate under the condition of considering maintenance intervention and assuming maintenance without intervention is solved by using a first-order derivative expression of the model, and the hundred differentiation of service performance index decay rate change conditions is completed. And then, finishing the hundred differentiation of the service performance index change condition according to the rating limit value of the corresponding index of the specification. And finally, constructing a PQII evaluation system of historical maintenance on the service performance improvement condition by using four-item subentry indexes based on a principal component analysis method. The invention can quantify the benefits of the existing maintenance means, so that the highway maintenance manager can select the road combination actual situation of the existing method when developing the maintenance work, the utilization rate of the detection and maintenance data is improved, and the asphalt pavement maintenance decision is more reasonable and efficient, and the method specifically comprises the following steps:
(1) Decay fitting is carried out by using historical road surface detection data, so that the result is more reliable
The evaluation method adopts continuous year pavement service performance detection data, and carries out decay fitting of corresponding indexes along with service years by using data results of PCI and RQI for many years. The calculation process is simple and easy to understand, and the data comes from engineering practice and serves the engineering practice, so that the reliability of the calculation result is high.
(2) The historical maintenance benefits are quantized, so that the maintenance decision is more reasonable and efficient
The evaluation method realizes the percentage of the subentry index by comparing and considering the change conditions of the values and the decay change rate of the pavement damage index PCI and the driving quality index RQI under the condition of maintenance intervention and assumed maintenance without intervention, establishes a quantitative evaluation PQII evaluation system for improving the service performance by historical maintenance, provides a quantity basis for selecting the existing maintenance means and technology for maintenance workers, and improves the rationality and the high efficiency of maintenance decision.
(3) The established historical maintenance quantitative evaluation method lays a foundation for subsequent research
The evaluation method opens a new research angle on the basis of the traditional new technology and new material research and maintenance, considers the evaluation and utilization of historical maintenance and lays a foundation for subsequent research.
Drawings
FIG. 1 is a fitting graph of Sunwer decay curves of PCI data points in three years from road section No. 1 to road section No. 1 in 2017;
FIG. 2 is a fitting graph of Sunwer decay curves of PCI data points for three years from road section No. 2 to road section No. 2 in 2017;
FIG. 3 is a fitting graph of Sunwer decay curves of three-year PCI data points in road section No. 3 from 2015 to 2017;
wherein, the year 2001, namely the year of building a universal vehicle in the north section of the high-speed lake of hong Kong, beijing, is taken as a scale point 0 on the abscissa.
Detailed Description
In order to clarify a quantitative evaluation method for the historical maintenance benefit of asphalt pavement based on principal component analysis, the technical solution of the present invention is further illustrated by the following typical examples, which should not be construed as a limitation to the scope of the present invention.
Examples
(1) Selecting example road segments
The example road section is located in the north section of the high-speed lake of hong Kong, jing Kong, australia section (G4) and Shang Yu section (G50), and is an important economic traffic artery in the south and north directions of China. The calculation example takes the most prominent transverse crack diseases of the riser section as an example, and selects example road sections in the table 2. The middle repair of the three road sections is carried out in 2016.
Table 2 example road segment selection results
(2) Decay curve fitting
And fitting the PCI and RQI data in the road surface detection from 2015 to 2017 of the three road sections by adopting a SunShelter decay model to obtain model parameters considering 2016 middle-repair maintenance intervention and assuming 2016 middle-repair maintenance non-intervention.
For the case of considering the intervention of maintenance in 2016, curve fitting is carried out through performance indexes in three years 2015 to 2017, and model parameters are solved. The 2017 decay rate is then solved according to a first derivative model. For the case of supposing that the intermediate repair maintenance in 2016 is not intervened, in data points from 2015 to 2017, point locations in 2017 are abandoned, and the model parameters are obtained by performing Sunsliak decay model fitting on the data only in 2015 and 2016. The 2017-year decay rate under the condition that 2016-year maintenance is simulated without intervention is obtained through solving of a first-order derivative model. Fig. 1 to 3 are fitting graphs. Calculation of the improved value of the decay rate after the maintenance intervention can then be completed. The calculation results are shown in tables 3 to 4.
Table 3 to 3. Road section PCI correlation index calculation result%
RQI related index calculation results of No. 4 to No. 3 road sections in Table 4%
(3) Method for solving PQII (quality of service) subentry index weight by principal component analysis method
Taking four index calculation values of No. 1 to No. 3 road sections A, B, C, D as original data, constructing a 3 x 4 original data matrix, substituting the original data matrix into factor analysis of SPSS data analysis software, and solving each sub index weight coefficient by a principal component analysis method. And sorting the corresponding characteristic values of the components from large to small, and taking the components with the accumulated variance contribution rate of more than 80% as principal components, wherein the component with the largest variance is the first principal component, the second principal component and so on. The total variance calculation results are in table 5.
TABLE 5 Total variance case
As seen from the results in Table 5, there was only one major component, which had a characteristic value of 2.916. When the SPSS carries out principal component analysis, a factor analysis algorithm is adopted, so that the numerical value l in the characteristic vector cannot be directly calculated ij But instead outputs a component matrix containing data as the initial factor load f ij . But l ij And f ij There is a conversion relationship as shown below.
In the formula: lambda [ alpha ] j Is the eigenvalue found in the principal component analysis.
The composition matrix results are output in table 6.
TABLE 6 component matrix case
The results of solving for the values of the elements of the eigenvectors are shown in table 7.
TABLE 7 feature vector element case
Finding the eigenvector element l from the component matrix ij Then, multiplying and summing the value of the partial indexes by corresponding original data of each road section under each index type to obtain each subentry index V ij . Then calculating each subentry index V ij Account for the total V ij And the ratio of the sum is used as the index weight of each subentry. The results of the index weight calculations are shown in table 8.
TABLE 8 subentry index weight calculation
(3) Calculating PQII value of each road section and verifying the PQII value according to historical maintenance actual conditions
The results of the PQII calculation for each link are shown in table 9.
Table 9 to 3 link PQII calculation results
From the calculation results in table 9, it can be seen that for the road surfaces of the road segments 1 to 3 under the transverse crack damage, the improvement effect of the 2016 maintenance measure on the road surface service performance is ranked from good to bad as the road segment 3, the road segment 2 and the road segment 1.
The disease characteristics and 2016 repair and maintenance measures for road segments 1 through 3 are summarized in Table 10. The results well demonstrate that the PQII score is higher when disease characteristics are treated more specifically, thus verifying the applicability of the system.
Table 10-3 road segment disease characteristics and 2016 medium maintenance means
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention should be included in the scope of the present invention.
Claims (6)
1. A quantitative evaluation method for the historical maintenance benefit of an asphalt pavement based on a principal component analysis method is characterized by comprising the following steps:
(1) Obtaining road surface use performance index detection data of at least one year before and after the historical maintenance of the road section, and utilizing a Sun's decay model to carry out the detection on the use performance indexesPCIAndRQIdecay to non-linearityFitting a curve;
(2) Performing first-order derivation on the fitting curve in the step (1), and solving the service performance index of the past maintenance intervention in the next yearPCIAndRQIthe rate of decay of (a);
(3) Obtaining road surface use performance index detection data of at least one year before the historical maintenance occurrence year of the road section, simulating and solving the use performance index of the same year after the historical maintenance intervention of the step (2) in the state without maintenance intervention by using the method of the steps (1) and (2)PCIAndRQIthe rate of decay of;
(4) Solving service performance index under hundred differentiationPCI、RQILift correction value of
、
And decay Rate improvement value
、
；
in the formula:
is composed of
Lifting the correction value,%;
is composed of
Amplitude of lift,%;
is composed of
The maximum amplitude can be improved;
calculated by the same methodRQILifting correction valueC；
The decay rate improvement value
The calculation formula is as follows:
in the formula:
the difference in PCI decay rates before and after a maintenance intervention;
calculated by the same methodD；
(5) For improving service performance by building historical maintenance
Evaluating the system;
the above-mentioned
The evaluation system formula is as follows:
in the formula:
an index is improved for the road surface usability;
is composed of
Lifting the correction value and the weight coefficient thereof;
is composed of
Decay rate improvement values and their weighting coefficients;
is composed of
Lifting the correction value and the weight coefficient thereof;
is composed of
Decay rate improvement values and their weighting coefficients;
(6) Solving by principal component analysis
All the weight coefficients in the system are evaluatedAnd constructing an index system to evaluate the improvement condition of the historical maintenance on the service performance of the pavement.
2. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 1, wherein the method comprises the following steps: the fitting formula of the Sun's decay model in the step (1) is as follows:
wherein, the first and the second end of the pipe are connected with each other,yis composed ofPCIOrRQI，xService life;
the initial value of the service performance index when the road is newly built is 100;
、
for the parameters of the fit of the model,
the size of the road surface is related to the service life of the road surface,
the size of (d) reflects the difference in curve shape.
3. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 1, wherein the method comprises the following steps: the formula of the step (2) is as follows:
wherein the content of the first and second substances,y’is composed ofPCIOrRQIThe rate of decay of (a);
the initial value of the service performance index when the road is newly built is 100;
、
for the parameters of the fit of the model,
the size of the road surface is related to the service life of the road surface,
the size of (d) reflects the difference in curve shape.
4. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 1, wherein the method comprises the following steps: the above-mentioned
Is a Chinese traditional medicine in the technical Specification for asphalt road surfaces of highwaysPCIThe range of values of the hierarchy determines the maximum range that it can boost.
5. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 1, wherein the method for calculating the weighting coefficient in the step (6) is as follows: the four index calculation values of A, B, C, D which are solved are used as original data, a 3 x 4 original data matrix is constructed and substituted into factor analysis of SPSS data analysis software, and each subentry index weight coefficient is solved through a principal component analysis method.
6. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 5, wherein the principal component analysis method is as follows: outputting a component matrix containing initial factor load by adopting a factor analysis algorithm; converting the initial factor load into a characteristic vector value; then multiplying and summing the characteristic vector numerical value and the corresponding original data under each type of indexes to obtain each subentry index; calculating the proportion of each subentry index in the total index, and taking the value as the weight of each subentry index; and sorting the corresponding characteristic values of the components from large to small, and taking the components with the cumulative variance contribution rate of more than 80% as principal components, wherein the component with the largest variance is the first principal component, the second principal component and so on.
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