CN112862279A - Method for evaluating pavement condition of expressway lane - Google Patents

Method for evaluating pavement condition of expressway lane Download PDF

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CN112862279A
CN112862279A CN202110104899.1A CN202110104899A CN112862279A CN 112862279 A CN112862279 A CN 112862279A CN 202110104899 A CN202110104899 A CN 202110104899A CN 112862279 A CN112862279 A CN 112862279A
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肖敏敏
程韦
钱思博
范霖
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Abstract

The invention provides a method for evaluating the road surface condition of a highway lane, which comprises the following steps: standardizing the acquired data, eliminating adverse effects of various indexes on analysis results due to different units, and obtaining a standardized matrix; processing the normalized matrix, eliminating the correlation influence among the original data, and obtaining a correlation coefficient matrix; obtaining characteristic values and contribution rates of all components capable of representing the original data according to the correlation coefficient matrix; extracting components capable of representing most information of the original data as principal components, and solving a feature vector corresponding to a principal component feature value according to a feature equation; and expressing the comprehensive evaluation index by using the principal components and the weight, and establishing the comprehensive index evaluation grade by combining with the corresponding road index grade division standard. The method solves the problem of inaccurate evaluation result caused by no distinguishing of lanes in the conventional highway pavement evaluation process, and achieves more fine evaluation of the highway pavement.

Description

Method for evaluating pavement condition of expressway lane
Technical Field
The invention relates to a method for evaluating the road surface condition of a highway lane.
Background
At present, the infrastructure construction of China tends to be improved gradually, and the highway mileage put into use in China is huge. On the expressway, the design of two-way four lanes is mostly adopted in China. Along with the increase of service life, the damage of the road surface of the expressway is increasingly serious, and the maintenance engineering amount is also increased. The national highway maintenance preventive measures are based on the evaluation results of the whole road section.
The vehicle loads of different lanes of the highway have great difference, the main damage types of the highway also have certain difference, and maintenance according to the traditional evaluation result may cause inaccurate maintenance and waste of resources. In a pavement maintenance evaluation system, damage disease cracks, pits, ruts and the like are mainly expressed in a pavement condition index PCI, the PCI is a comprehensive evaluation index and cannot reflect the type and the severity of typical pavement diseases, the sensitivity is insufficient in the evaluation process, and the evaluation result is disjointed from the actual maintenance requirement. The diseases of the traffic lanes are obviously more serious than the diseases of the overtaking lane, the emergency lane and the like, and the traditional highway pavement evaluation mode can weaken the indexes with difference.
Disclosure of Invention
The invention aims to provide a method for evaluating the road surface condition of a highway lane.
In order to solve the above problems, the present invention provides a method for evaluating a road surface condition of a highway lane, comprising:
step S1, acquiring the acquired data by acquiring single indexes of the road surface of the expressway lane, wherein the single indexes of the road surface comprise severe indexes of road surface damage;
step S2, carrying out standardization processing on the acquired data by using a Z-score method, eliminating the influence of different units on the analysis result of each pavement single index, so as to obtain a standardized matrix;
step S3, processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence between the original data in the normalized matrix, and obtaining a correlation coefficient matrix;
step S4, obtaining characteristic values and contribution rates of all components representing the original data according to the correlation coefficient matrix;
step S5, extracting components which are used for expressing partial information of the original data and are larger than a preset threshold value based on the characteristic values and the contribution rates of all the components of the original data, using the components as principal components, and calculating characteristic vectors corresponding to the characteristic values of the principal components according to a characteristic equation;
and step S6, re-expressing the comprehensive index by using the characteristic vector and the contribution rate corresponding to the characteristic value of the principal component, and establishing the comprehensive index evaluation grade by combining the corresponding road index grade division standard.
Further, in the above method, the extracting of the single road index includes: PCI (pavement damage condition), RQI (running quality index), RDI (track depth), SFC (pavement skid resistance performance), C (longitudinal crack) and D (pavement subsidence).
Further, in the above method, the highway traffic lane is a lane other than a passing lane and an emergency lane.
Further, in the method, in step S2, the normalizing the acquired data includes:
assuming that n data sets are provided, each data array has p indexes participating in evaluation, all the data sets are combined together to obtain a matrix X (X ═ X)ij)n×p，xijFor the ith data set, the jth index, the matrix terms after normalization are
Figure BDA0002916987010000021
Wherein
Figure BDA0002916987010000022
i＝1,2，...，n；j＝1,2，...，p，
Figure BDA0002916987010000023
And σjThe sample mean and standard deviation of the j-th index, respectively.
Further, in the above method, in step S3, the correlation coefficient matrix is a diagonal matrix with 1 on the diagonal.
Further, in the above method, the step S3 includes:
step S31, processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence among the original data in the normalized matrix, and obtaining the normalized result data;
and step S32, processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence among the original data, and obtaining a correlation coefficient matrix. The correlation coefficient is obtained by using the normalized result data, and the matrix of the correlation coefficient is recorded as R ═ R (R)jk)p×p，rjkThe correlation coefficients of j and k indexes are obtained, wherein the calculation principle of a correlation coefficient matrix R is as follows:
Figure BDA0002916987010000024
wherein r isij＝1，rkj＝rjk。i＝1,2，...，n；j＝1,2，...，p。
Further, in the above method, in step S4, the contribution rate is used to reflect how much information of the representative component is, that is, the weight of each component.
Further, in the above method, step S4 includes:
according to the characteristic equation R-Lambda E0, the characteristic value Lambda of all the components representing the original data is obtainedgAnd a contribution vg. Wherein the contribution rate
Figure BDA0002916987010000031
And (3) processing the correlation coefficient matrix by adopting SPSS software for data calculation, calculating to obtain the characteristic values and the contribution rates of all the components, wherein the corresponding contribution rates are the weights of all the components。
Further, in the method, in step S5, the component of the partial information greater than the preset threshold is a component having a characteristic value greater than 1 or having an accumulated contribution rate of 80%.
Further, in the above method, in step S6, the synthetic index is a linear equation.
Compared with the prior art, the method provided by the invention objectively reflects the actual situation of the expressway lane by extracting indexes with different disease types and severity degrees of the expressway lane, the overtaking lane road, the emergency lane and other roads, and provides a new direction for accurate maintenance. The invention provides a scientific, feasible and accurate evaluation method for evaluating the road surface condition of the highway lane aiming at the condition that the evaluation results of different lanes are inaccurate due to the fact that comprehensive evaluation indexes of the whole road section are adopted for evaluation on the highway. The method solves the problem of inaccurate evaluation results caused by no distinguishing of lanes in the conventional highway pavement evaluation process, achieves more fine evaluation of the highway pavement, and provides a theoretical basis for accurate maintenance of the pavement.
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Fig. 1 is a flowchart of a method for evaluating a road surface condition of a highway lane according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the present invention provides a method for evaluating a road surface condition of a highway lane, comprising:
step S1, acquiring the acquired data by acquiring single indexes of the road surface of the expressway lane, wherein the single indexes of the road surface comprise severe indexes of road surface damage;
preferably, the extraction of the single road index comprises: PCI (pavement damage condition), RQI (running quality index), RDI (track depth), SFC (pavement skid resistance), C (longitudinal crack) and D (pavement subsidence);
the two indexes of cracks and ruts can be separated from the PCI, the weight of the indexes is calculated, and a comprehensive evaluation index of the highway lane pavement is established;
preferably, the highway traffic lane is a lane except for a passing lane and an emergency lane, generally, the highway traffic lane is close to the right, heavy-load vehicles are arranged on the lane, and the damage to the road surface is relatively serious.
Step S2, carrying out standardization processing on the acquired data by using a Z-score method, eliminating the influence of different units on the analysis result of each pavement single index, so as to obtain a standardized matrix;
preferably, step S2, the normalizing the collected data includes: assuming that n data sets are provided, each data array has p indexes participating in evaluation, all the data sets are combined together to obtain a matrix X (X ═ X)ij)n×p，xijFor the ith data set, the jth index, the matrix terms after normalization are
Figure BDA0002916987010000041
Wherein
Figure BDA0002916987010000042
Figure BDA0002916987010000043
i＝1,2，...，n；j＝1,2，...，p，
Figure BDA0002916987010000044
And σjThe sample mean and standard deviation of the j-th index, respectively.
Step S3, processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence between the original data in the normalized matrix, and obtaining a correlation coefficient matrix;
preferably, in step S3, the correlation coefficient matrix is a diagonal matrix with 1 on the diagonal, and may reflect the correlation of each index;
preferably, step S3 may include:
step S31, processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence among the original data in the normalized matrix, and obtaining the normalized result data;
and step S32, processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence among the original data, and obtaining a correlation coefficient matrix. The correlation coefficient is obtained by using the normalized result data, and the matrix of the correlation coefficient is recorded as R ═ R (R)jk)p×p，rjkThe correlation coefficients of j and k indexes are obtained, wherein the calculation principle of a correlation coefficient matrix R is as follows:
Figure BDA0002916987010000045
wherein r isij＝1，rkj＝rjk。i＝1,2，...，n；j＝1,2，...，p。
Step S4, obtaining characteristic values and contribution rates of all components representing the original data according to the correlation coefficient matrix;
preferably, in step S4, the contribution rate is used to reflect the amount of information representing the component, that is, the weight of each component;
preferably, step S4 may include:
according to the characteristic equation R-Lambda E0, the characteristic value Lambda of all the components representing the original data is obtainedgAnd a contribution vg. Wherein the contribution rate
Figure BDA0002916987010000051
And (3) processing the correlation coefficient matrix by adopting SPSS software for data calculation, and calculating to obtain the characteristic values and the contribution rates of all the components, wherein the corresponding contribution rates are the weights of all the components.
Step S5, extracting components which are used for expressing the partial information (most of information) of the original data and are larger than a preset threshold value based on the characteristic values and the contribution rates of all the components of the original data, taking the components as principal components, and calculating characteristic vectors corresponding to the characteristic values of the principal components according to a characteristic equation;
preferably, in step S5, the component of the partial information greater than the preset threshold is a component having a characteristic value greater than 1 or having an accumulated contribution rate of 80%.
When extracting the main component, two ways are usually adopted, one is that the characteristic value is greater than 1, and the other is that the cumulative contribution rate reaches 80%. The contribution rate can be adjusted according to the actual situation. The characteristic value of each principal component corresponds to a characteristic vector, and SPSS software is adopted to directly calculate.
And step S6, re-expressing the comprehensive index by using the characteristic vector and the contribution rate corresponding to the characteristic value of the principal component, and establishing the comprehensive index evaluation grade by combining the corresponding road index grade division standard.
Preferably, in step S6, the comprehensive index is a linear equation, and may be consistent with the original evaluation method in terms of equation form.
In this case, the linear equation may be substituted into the single index rating set to obtain the comprehensive index rating criterion.
In conclusion, the method and the device objectively reflect the actual situation of the expressway lane by extracting indexes with different disease types and severity degrees of the expressway lane, the overtaking lane, the emergency lane and other roads, and provide a new direction for accurate maintenance. The invention provides a scientific, feasible and accurate evaluation method for evaluating the road surface condition of the highway lane aiming at the condition that the evaluation results of different lanes are inaccurate due to the fact that comprehensive evaluation indexes of the whole road section are adopted for evaluation on the highway. The method solves the problem of inaccurate evaluation results caused by no distinguishing of lanes in the conventional highway pavement evaluation process, achieves more fine evaluation of the highway pavement, and provides a theoretical basis for accurate maintenance of the pavement.
To illustrate the features and specific implementation steps of the present application, the following examples are given:
a certain high-speed road section K4+ 350-K14 +350 is designed to have a speed per hour of 100km/h and four bidirectional lanes. The road surface condition of the motor vehicle lane is evaluated.
Step 1: the method comprises the steps of carrying out pavement single index collection on the highway lane pavement and independently extracting pavement damage severe indexes.
And taking 1km as a monitoring road section measurement basic data. The road surface basic index is shown in table 1.
TABLE 1 road surface inspection data
Figure BDA0002916987010000052
Figure BDA0002916987010000061
Step 2: and carrying out standardization processing on the acquired data by using a Z-score method, eliminating adverse effects of various indexes on an analysis result due to different units, and obtaining a matrix after standardization.
In the evaluation and analysis process, the data indexes are more and the units are not uniform. If the raw data is directly used for analysis, the final evaluation result will have large errors, so that the data is generally normalized by the method of Z-score before the data analysis. The data calculation is performed by using SPSS software to perform description analysis on the data, and the normalized value is stored as data, which is the normalized result, as shown in table 2.
TABLE 2 data normalization Process
Figure BDA0002916987010000062
Figure BDA0002916987010000071
And step 3: and processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence among the original data and obtaining a correlation coefficient matrix.
The normalized data results were used to find a correlation coefficient matrix, which is a diagonal matrix with a diagonal of 1, as shown in table 3.
TABLE 3 correlation coefficient matrix
PCI RQI RDI SFC C D
PCI 1 0.056 0.125 0.154 0.363 0.344
RQI 0.056 1 0.136 0.444 0.28 -0.578
RDI 0.125 0.136 1 0.082 0.636 0.221
SFC 0.154 0.444 0.082 1 0.127 0.268
C 0.363 0.28 0.636 0.127 1 0.196
D 0.344 -0.578 0.221 0.268 0.196 1
And 4, step 4: and obtaining characteristic values and contribution rates of all components capable of representing the original data according to the correlation coefficient matrix.
P eigenvalues and eigenvectors of the correlation coefficient matrix R can be found from the eigen equation. And after determining the characteristic values corresponding to all the variables, sequencing according to the magnitude of the values, and finally obtaining a specific principal component expression through linear combination. And carrying out dimension reduction analysis on the standardized data through SPSS, and extracting a total variance explanation and a component matrix, so that a characteristic value and a contribution rate can be obtained. Table 4 shows the eigenvalues, contribution rates, cumulative contribution rate table.
TABLE 4 eigenvalue, contribution rate, cumulative contribution rate table
Composition (I) Initial characteristic value Contribution rate% Cumulative contribution rate%
F1 2.08 34.673 34.673
F2 1.647 27.443 62.116
F3 1.113 18.55 80.666
F4 0.801 13.353 94.019
F5 0.301 5.017 99.036
F6 0.058 0.964 100
And 5: and extracting components capable of representing most information of the original data, extracting the components as principal components, and solving a feature vector corresponding to the feature value of the principal component according to a feature equation.
Conventionally, a component having a cumulative contribution rate of more than 80% is generally determined as a main component, that is, a portion exceeding 80% may be reduced in specific gravity or may not be considered. As can be seen from table 4 above, among the 6 components, the components having a characteristic value greater than 1 include F1, F2, and F3, and the cumulative contribution ratio of the three components is greater than 80%, which is very representative. That is, the first two components can express most of the information before, so the main components extracted are F1, F2, and F3. The feature vectors of the 3 principal components were then directly calculated using SPSS, as shown in table 5.
TABLE 5 eigenvectors corresponding to eigenvalues
Composition (I) PCI RQI RDI SFC C D
F1 0.590 0.291 0.725 0.468 0.837 0.448
F2 -0.222 0.932 0.011 0.274 0.100 -0.803
F3 0.248 0.128 -0.501 0.756 -0.362 0.286
Step 6: and (4) representing the comprehensive index by using the principal components and the weight, and establishing the evaluation level of the comprehensive index by combining the corresponding road index level division standard.
The principal components can be represented as:
F1＝0.590 PCI+0.291 RQI+0.725 RDI+0.468 SFC-0.837 C+0.448 D (1)
F2＝-0.222 PCI+0.932 RQI-0.011 RDI+0.274 SFC-0.100 C-0.803 D (2)
F3＝0.248 PCI+0.128 RQI-0.501 RDI+0.756 SFC-0.362 C+0.286 D (3)
the road surface comprehensive evaluation index is expressed by the extracted main components as follows:
PQI＝0.34673 F1+0.27443 F2+0.1855 F3 (4)
by substituting the above expressions (1), (2) and (3) into (4), an expression of the road surface comprehensive evaluation index PQI, that is, the road surface comprehensive evaluation index PQI, can be obtained
PQI＝0.19 PCI-0.38 RQI+0.16 RDI+0.38 SFC-0.38 C-0.01 D (5)
The evaluation scheme of each single index is shown in table 6 according to technical specification JTG5142-2019 of highway asphalt pavement maintenance, and the evaluation range is the same as the value of the PCI because the longitudinal crack C and the subsidence D are single indexes extracted from the PCI.
TABLE 6 evaluation criteria for individual indices
Evaluation index Superior food Good wine In Next time Difference (D)
PCI x≥92 80≤x＜92 70≤x＜80 60≤x＜70 x＜60
RQI x≥90 80≤x＜90 70≤x＜80 60≤x＜70 x＜60
RDI x≥90 80≤x＜90 70≤x＜80 60≤x＜70 x＜60
SFC x≥50 40≤x＜50 30≤x＜40 20≤x＜30 x＜20
C x≥92 80≤x＜92 70≤x＜80 60≤x＜70 x＜60
D x≥92 80≤x＜92 70≤x＜80 60≤x＜70 x＜60
The data in table 6 was substituted into formula (5) to obtain an evaluation scheme of the lane combination index, which is shown in table 7.
TABLE 7 New evaluation criteria for PQI
Comprehensive evaluation index Superior food Good wine In Next time Difference (D)
PQI ＞51 44-51 37-44 29-37 x＜29
The evaluation indexes of the highway pavement are analyzed by adopting a PCA method, the weight of each index is calculated, the actual pavement condition of the highway pavement is objectively reflected, and a theoretical direction is provided for the accurate maintenance of the highway pavement.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A method for evaluating the road surface condition of a highway lane is characterized by comprising the following steps:
step S1, acquiring the acquired data by acquiring single indexes of the road surface of the expressway lane, wherein the single indexes of the road surface comprise severe indexes of road surface damage;
step S2, carrying out standardization processing on the acquired data by using a Z-score method, eliminating the influence of different units on the analysis result of each pavement single index, so as to obtain a standardized matrix;
step S3, processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence between the original data in the normalized matrix, and obtaining a correlation coefficient matrix;
step S4, obtaining characteristic values and contribution rates of all components representing the original data according to the correlation coefficient matrix;
step S5, extracting components which are used for expressing partial information of the original data and are larger than a preset threshold value based on the characteristic values and the contribution rates of all the components of the original data, using the components as principal components, and calculating characteristic vectors corresponding to the characteristic values of the principal components according to a characteristic equation;
and step S6, re-expressing the comprehensive index by using the characteristic vector and the contribution rate corresponding to the characteristic value of the principal component, and establishing the comprehensive index evaluation grade by combining the corresponding road index grade division standard.
2. The method for evaluating a road surface condition of a highway traffic lane according to claim 1, wherein the extracting of the road surface singles includes: PCI (pavement damage condition), RQI (running quality index), RDI (track depth), SFC (pavement skid resistance performance), C (longitudinal crack) and D (pavement subsidence).
3. The method for evaluating a road surface condition of an expressway traffic lane according to claim 1, wherein the expressway traffic lane is a lane other than a passing lane and an emergency lane.
4. The method for evaluating a road surface condition of a highway traffic lane according to claim 1, wherein the step S2 of normalizing the collected data comprises:
assuming that n data sets are provided, each data array has p indexes participating in evaluation, all the data sets are combined together to obtain a matrix X (X ═ X)ij)n×p，xijFor the ith data set, the jth index, the matrix terms after normalization are
Figure FDA0002916986000000011
Wherein
Figure FDA0002916986000000012
Figure FDA0002916986000000013
And σjThe sample mean and standard deviation of the j-th index, respectively.
5. The method for evaluating a road surface condition of a highway traffic lane according to claim 1, wherein in step S3, the correlation coefficient matrix is a diagonal matrix in which each of the diagonals is 1.
6. The method for evaluating a road surface condition of a highway traffic lane according to claim 1, wherein the step S3 comprises:
step S31, processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence among the original data in the normalized matrix, and obtaining the normalized result data;
and step S32, processing the normalized matrix by adopting a principal component analysis method, eliminating the correlation influence among the original data, and obtaining a correlation coefficient matrix. The correlation coefficient is obtained by using the normalized result data, and the matrix of the correlation coefficient is recorded as R ═ R (R)jk)p×p，rjkThe correlation coefficients of j and k indexes are obtained, wherein the calculation principle of a correlation coefficient matrix R is as follows:
Figure FDA0002916986000000021
wherein r isij＝1，rkj＝rjk。i＝1,2，...，n；j＝1,2，...，p。
7. The method for evaluating a road surface condition of an expressway traffic lane according to claim 1, wherein in step S4, the contribution ratio is used to reflect the amount of information of the representative component, that is, the weight of each component.
8. The method for evaluating a road surface condition of a highway traffic lane according to claim 1, wherein the step S4 comprises:
according to the characteristic equation R-Lambda E0, the characteristic value Lambda of all the components representing the original data is obtainedgAnd a contribution vg. Wherein the contribution rate
Figure FDA0002916986000000022
And (3) processing the correlation coefficient matrix by adopting SPSS software for data calculation, and calculating to obtain the characteristic values and the contribution rates of all the components, wherein the corresponding contribution rates are the weights of all the components.
9. The method for evaluating a road surface condition of an expressway traffic lane according to claim 1, wherein in step S5, the component of the partial information greater than the preset threshold value is a component having a characteristic value greater than 1 or an accumulated contribution rate of 80%.
10. The method for evaluating a road surface condition of a highway traffic lane according to claim 1, wherein in step S6, the comprehensive index is a linear equation.
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