# Expressway Asphalt Pavement Technical Status Cluster Dimensional Reduction Evaluation Method

Authors: Yanli Li
Journal: Advances in Materials Science and Engineering
 (2022)

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## Abstract

In the process of asphalt pavement maintenance management, the technical condition of the pavement is accurately predicted, its performance trend is predicted, and the right time is chosen to take targeted maintenance measures that will play a multiplier role, so predicting the technical condition of the pavement is important significance. This paper mainly studies the cluster dimensionality reduction evaluation method of the technical condition of expressway asphalt pavement. This paper summarizes the experience of regular highway inspection data inspection and analysis, and mainly studies the standardized analysis method of highway pavement inspection data, aiming to meet the data requirements at all levels through effective data analysis indicators. The experimental data in this paper show that the driving quality of the two-way pavement is good, the total average SRI is 91.2, and the evaluation is “excellent,” and the left width is slightly better than the right width; the transverse crack penetration of most reconstructed road sections is greater than 0.4, indicating that the transverse crack penetrates the road surface more serious. The experimental results of this paper show that the technical status of asphalt pavement based on cluster analysis has practical significance, and the analysis results show that the treatment effect is ideal.


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## Body

## 1. Introduction

In order to strengthen the management of highway maintenance, scientifically evaluate the technical status and service level of highways, and promote the scientific, standardized, and institutionalized work of highway technical status detection and evaluation, it is stipulated that highways must regularly check the technical status of pavement and clear the minimum frequency. However, how to standardize and scientifically analyze the results of road surface regular inspection results to better provide basic support for road surface maintenance decision-making, large and medium-scale repair plan formulation, and maintenance activity evaluation is a technology that still needs to be improved in China’s current standard system weak points. Traditional pavement maintenance requires traditional machines and a lot of manpower. In this paper, through the application of various analysis technologies and achievements, this paper summarizes the cluster dimensionality reduction evaluation method of regular inspection data of highway asphalt pavement and aims to promote the data in the top-level design, assessment and evaluation, design and construction of maintenance management. The deep application of the link will improve the value of data application and enhance the scientific level of highway pavement maintenance.Karimi Zhang et al. research found that when the amount of self-healing capsules is 0.50%, the indirect tensile strength, water stability, and fatigue performance of asphalt mixture will increase. When the dose of capsules continues to increase, the mechanical properties will be seriously reduced, even unable to meet the relevant specifications [1, 2]. Lotfi and research found that the fatigue life of modified asphalt mixture is greatly improved compared with conventional mixture [3, 4]. Kar believes that among the different mixtures, the mixture containing 50% recycled asphalt pavement shows the best results in the elastic modulus and moisture damage resistance tests. Therefore, high percentages of recycled asphalt pavement can be used to design high-quality asphalt mixtures. These percentages can meet the required volume and required performance standards [5, 6]. In addition to asphalt, concrete can also be used as pavement materials.


## 2. Proposed Method

### 2.1. Data Dimensionality Reduction

#### 2.1.1. PCA

Data dimensionality reduction is to reduce the dimensionality of data so that people can better understand the data. The dimensionality reduction result of principal component analysis is a linear combination of the original data features and the maximum sample variance, and try to ensure that the new features are not related to each other, that is, take the first few linear combinations of features that contribute a lot to the deviation [7, 8]. From a mathematical point of view, for a sample dataset, the PCA method attempts to find a linear map M such that covT (x) M reaches the maximum, where cov (X) is the covariance matrix of the data et X [9, 10]. Because cov (X) is a symmetric matrix, the singular value of cov (X) is equal to the cov (X) eigenvalue and the M matrix is orthogonal, that is, the singular value decomposition of cov (X) can get M, and the M matrix. The vectors are not related to each other [11, 12]. The map M can be obtained in four steps. The first is to centralize the matrix X features. The second is to find the covariance matrix V of the dataset after feature centralization. Again, find the eigenvalues and eigenvectors of V [13, 14]. Finally, the feature vector with the largest previous d feature value is selected as M [15]. In practical applications, the basis for taking the first d feature values is generally determined according to formula (1), and the general recommendation rate is greater than 80%, and when the data of different features of the dataset are relatively correlated, it should be standardized, that is, using the standard deviation, except for the difference between feature and mean [16, 17].(1)r=∑​i=1dλi/∑​i=1nλi.After obtainingM, the data Y in the low-dimensional space can be expressed as(2)Y=x−x¯M.PCA method has been applied in many fields, such as face recognition, coin classification, and seismic sequence analysis. Its advantage is that it can remove part of the noise and find part of the data structure in the data [14, 15]. PCA also has some shortcomings. First, PCA is based on the assumption that the sample points are linear. For nonlinear data, it cannot find the hidden information in the data. Second, it can find the direction that represents the sample points well, but this direction may not help to classify the data. Later, some improved methods of PCA, such as Simple PCA and probabilistic PCA, will produce better results than the original PCA [16, 17].


#### 2.1.2. LDA

The linear discriminant analysis method is a supervised data dimensionality reduction method. Dimensionality reduction means mapping each multidimensional vector to a low-dimensional vector. In other words, each multidimensional vector is represented (replaced) by a low-dimensional vector. Its main idea is to use the classification information to project high-dimensional data to an optimal low-dimensional linear discriminant subspace so that the intraclass aggregation is high and the interclass coupling is low [18, 19]. Its main purpose is to extract classification information and reduce the dimension of the data [20]. LDA defines interclass and intraclass dispersion matrices Sb and Sw to represent the relationship between the data points in each class and the overall dataset and the relationship between each data point and the class to which they belong. These are defined as follows:(3)Sb=∑i=1cnimi−m¯mi−m¯T,JFw=wTsbwwTsww.In order to make the dataset achieve a small intraclass distance and a large interclass distance in a low-dimensional space, the LDA method defines the following Fisher criterion function:(4)JFw=wTsbwwTsww.Maximizing this criterion function can achieve the goal of the method. The final dimensionality reduction result is to use this projection vector to project high-dimensional data into low-dimensional space [21].


### 2.2. Nonlinear Dimensionality Reduction

The main idea of the SNE (Stochastic Neighbor Embedding) method is to use conditional probabilities to represent the structural information in the data and then to minimize the difference in conditional probabilities between high-dimensional and low-dimensional data [22, 23]. That is, when embedding similar samples in high-dimensional data when embedding into low-dimensional space, try to keep these sample points as close as possible, while in high-dimensional data, samples with low similarity try to keep these sample points when embedding in low-dimensional space [24, 25].The specific SNE algorithm is as follows: in a high-dimensional space, for any target point xi, the similarity of other arbitrary pointsxj and xi is defined as the asymmetry probability pij [26, 27].(5)pij=exp−dij2∑k≠iexp−dik2,.where dij2 is the difference between xi and xj, which can be defined as(6)dij2=‖xi−xj‖22σi2.


## 2.1. Data Dimensionality Reduction

### 2.1.1. PCA

Data dimensionality reduction is to reduce the dimensionality of data so that people can better understand the data. The dimensionality reduction result of principal component analysis is a linear combination of the original data features and the maximum sample variance, and try to ensure that the new features are not related to each other, that is, take the first few linear combinations of features that contribute a lot to the deviation [7, 8]. From a mathematical point of view, for a sample dataset, the PCA method attempts to find a linear map M such that covT (x) M reaches the maximum, where cov (X) is the covariance matrix of the data et X [9, 10]. Because cov (X) is a symmetric matrix, the singular value of cov (X) is equal to the cov (X) eigenvalue and the M matrix is orthogonal, that is, the singular value decomposition of cov (X) can get M, and the M matrix. The vectors are not related to each other [11, 12]. The map M can be obtained in four steps. The first is to centralize the matrix X features. The second is to find the covariance matrix V of the dataset after feature centralization. Again, find the eigenvalues and eigenvectors of V [13, 14]. Finally, the feature vector with the largest previous d feature value is selected as M [15]. In practical applications, the basis for taking the first d feature values is generally determined according to formula (1), and the general recommendation rate is greater than 80%, and when the data of different features of the dataset are relatively correlated, it should be standardized, that is, using the standard deviation, except for the difference between feature and mean [16, 17].(1)r=∑​i=1dλi/∑​i=1nλi.After obtainingM, the data Y in the low-dimensional space can be expressed as(2)Y=x−x¯M.PCA method has been applied in many fields, such as face recognition, coin classification, and seismic sequence analysis. Its advantage is that it can remove part of the noise and find part of the data structure in the data [14, 15]. PCA also has some shortcomings. First, PCA is based on the assumption that the sample points are linear. For nonlinear data, it cannot find the hidden information in the data. Second, it can find the direction that represents the sample points well, but this direction may not help to classify the data. Later, some improved methods of PCA, such as Simple PCA and probabilistic PCA, will produce better results than the original PCA [16, 17].


### 2.1.2. LDA

The linear discriminant analysis method is a supervised data dimensionality reduction method. Dimensionality reduction means mapping each multidimensional vector to a low-dimensional vector. In other words, each multidimensional vector is represented (replaced) by a low-dimensional vector. Its main idea is to use the classification information to project high-dimensional data to an optimal low-dimensional linear discriminant subspace so that the intraclass aggregation is high and the interclass coupling is low [18, 19]. Its main purpose is to extract classification information and reduce the dimension of the data [20]. LDA defines interclass and intraclass dispersion matrices Sb and Sw to represent the relationship between the data points in each class and the overall dataset and the relationship between each data point and the class to which they belong. These are defined as follows:(3)Sb=∑i=1cnimi−m¯mi−m¯T,JFw=wTsbwwTsww.In order to make the dataset achieve a small intraclass distance and a large interclass distance in a low-dimensional space, the LDA method defines the following Fisher criterion function:(4)JFw=wTsbwwTsww.Maximizing this criterion function can achieve the goal of the method. The final dimensionality reduction result is to use this projection vector to project high-dimensional data into low-dimensional space [21].


## 2.1.1. PCA

Data dimensionality reduction is to reduce the dimensionality of data so that people can better understand the data. The dimensionality reduction result of principal component analysis is a linear combination of the original data features and the maximum sample variance, and try to ensure that the new features are not related to each other, that is, take the first few linear combinations of features that contribute a lot to the deviation [7, 8]. From a mathematical point of view, for a sample dataset, the PCA method attempts to find a linear map M such that covT (x) M reaches the maximum, where cov (X) is the covariance matrix of the data et X [9, 10]. Because cov (X) is a symmetric matrix, the singular value of cov (X) is equal to the cov (X) eigenvalue and the M matrix is orthogonal, that is, the singular value decomposition of cov (X) can get M, and the M matrix. The vectors are not related to each other [11, 12]. The map M can be obtained in four steps. The first is to centralize the matrix X features. The second is to find the covariance matrix V of the dataset after feature centralization. Again, find the eigenvalues and eigenvectors of V [13, 14]. Finally, the feature vector with the largest previous d feature value is selected as M [15]. In practical applications, the basis for taking the first d feature values is generally determined according to formula (1), and the general recommendation rate is greater than 80%, and when the data of different features of the dataset are relatively correlated, it should be standardized, that is, using the standard deviation, except for the difference between feature and mean [16, 17].(1)r=∑​i=1dλi/∑​i=1nλi.After obtainingM, the data Y in the low-dimensional space can be expressed as(2)Y=x−x¯M.PCA method has been applied in many fields, such as face recognition, coin classification, and seismic sequence analysis. Its advantage is that it can remove part of the noise and find part of the data structure in the data [14, 15]. PCA also has some shortcomings. First, PCA is based on the assumption that the sample points are linear. For nonlinear data, it cannot find the hidden information in the data. Second, it can find the direction that represents the sample points well, but this direction may not help to classify the data. Later, some improved methods of PCA, such as Simple PCA and probabilistic PCA, will produce better results than the original PCA [16, 17].


## 2.1.2. LDA

The linear discriminant analysis method is a supervised data dimensionality reduction method. Dimensionality reduction means mapping each multidimensional vector to a low-dimensional vector. In other words, each multidimensional vector is represented (replaced) by a low-dimensional vector. Its main idea is to use the classification information to project high-dimensional data to an optimal low-dimensional linear discriminant subspace so that the intraclass aggregation is high and the interclass coupling is low [18, 19]. Its main purpose is to extract classification information and reduce the dimension of the data [20]. LDA defines interclass and intraclass dispersion matrices Sb and Sw to represent the relationship between the data points in each class and the overall dataset and the relationship between each data point and the class to which they belong. These are defined as follows:(3)Sb=∑i=1cnimi−m¯mi−m¯T,JFw=wTsbwwTsww.In order to make the dataset achieve a small intraclass distance and a large interclass distance in a low-dimensional space, the LDA method defines the following Fisher criterion function:(4)JFw=wTsbwwTsww.Maximizing this criterion function can achieve the goal of the method. The final dimensionality reduction result is to use this projection vector to project high-dimensional data into low-dimensional space [21].


## 2.2. Nonlinear Dimensionality Reduction

The main idea of the SNE (Stochastic Neighbor Embedding) method is to use conditional probabilities to represent the structural information in the data and then to minimize the difference in conditional probabilities between high-dimensional and low-dimensional data [22, 23]. That is, when embedding similar samples in high-dimensional data when embedding into low-dimensional space, try to keep these sample points as close as possible, while in high-dimensional data, samples with low similarity try to keep these sample points when embedding in low-dimensional space [24, 25].The specific SNE algorithm is as follows: in a high-dimensional space, for any target point xi, the similarity of other arbitrary pointsxj and xi is defined as the asymmetry probability pij [26, 27].(5)pij=exp−dij2∑k≠iexp−dik2,.where dij2 is the difference between xi and xj, which can be defined as(6)dij2=‖xi−xj‖22σi2.


## 3. Experiments

### 3.1. Experimental Design

This article uses the split evaluation database ACS500 database, statistical methods, unsupervised evaluation methods, and supervised evaluation methods to be consistent with the subjective evaluation results, to verify the effectiveness of this method, and compared with unsupervised evaluation methods and supervised methods, the competitive advantage of this method in segmentation quality evaluation performance [28, 29].The ACS500 database contains two algorithm segmentation results for each original image and several reference segmentations [30, 31]. The content contained in this database is very suitable for the study of asphalt pavement carried out in this paper. Among them, the segmentation quality between each pair of algorithm segmentation results is high or low, and the database records the subjective segmentation quality evaluation results of each pair of algorithm segmentation, that is, which segmentation quality of each pair of algorithm segmentation is higher and which segmentation quality is lower.


### 3.2. Data Collection

According to the steps of constructing the training set in this paper, the segmentation block and the feature block are divided into categories to obtain differentK-value clustering centers. According to the evaluation process proposed in this paper, the segmentation block is compared to be evaluated with the training set of the corresponding block size to obtain each pair of segmentation maps in the ACS500 database at K = 3, K = 4, K = 5, and l at 7px, 11px, 15px, 19px, 23px, 27px, 31px, 35px evaluation scores under 8 pixel sizes [32, 33].


## 3.1. Experimental Design

This article uses the split evaluation database ACS500 database, statistical methods, unsupervised evaluation methods, and supervised evaluation methods to be consistent with the subjective evaluation results, to verify the effectiveness of this method, and compared with unsupervised evaluation methods and supervised methods, the competitive advantage of this method in segmentation quality evaluation performance [28, 29].The ACS500 database contains two algorithm segmentation results for each original image and several reference segmentations [30, 31]. The content contained in this database is very suitable for the study of asphalt pavement carried out in this paper. Among them, the segmentation quality between each pair of algorithm segmentation results is high or low, and the database records the subjective segmentation quality evaluation results of each pair of algorithm segmentation, that is, which segmentation quality of each pair of algorithm segmentation is higher and which segmentation quality is lower.


## 3.2. Data Collection

According to the steps of constructing the training set in this paper, the segmentation block and the feature block are divided into categories to obtain differentK-value clustering centers. According to the evaluation process proposed in this paper, the segmentation block is compared to be evaluated with the training set of the corresponding block size to obtain each pair of segmentation maps in the ACS500 database at K = 3, K = 4, K = 5, and l at 7px, 11px, 15px, 19px, 23px, 27px, 31px, 35px evaluation scores under 8 pixel sizes [32, 33].


## 4. Discussion

### 4.1. Evaluation and Analysis of Pavement Performance Indicators

#### 4.1.1. Analysis of Pavement Damage

As shown in Table1 and Figure 1, regardless of the effects of rutting, the PCI (pavement condition index) index is above 90%, and the pavement damage level is excellent; and after considering the effects of rutting, the PCI index is about 60%, and the pavement damage level Fair; there is a difference of two levels between the two. And the maintenance measures made according to PCI are also quite different: the former only needs daily maintenance-based and local minor repair methods, while the latter needs to take the middle repair cover maintenance measures.Table 1
PCI indicators (taking rutting into account).
LaneSectionSUP segmentSMA segmentAK segmentTraffic lane62.6462.6762.6762.6762.6662.6462.6462.6362.5362.6762.6762.66Overtaking lane62.6762.6762.6562.6762.6762.6Figure 1
PCI indicators.


#### 4.1.2. Analysis of Bearing Capacity of Pavement Structure

As shown in Figure2, in the short-term period after the road is opened to traffic, under the condition of no cracks, pits, and other damage on the road surface, the PCI index when only a small rut is considered is 62.7, and according to the corresponding maintenance regulations, this is necessary to repair the pavement in the middle of the road, which is seriously inconsistent with the actual situation [33].Figure 2
Converted PCI index.


#### 4.1.3. Analysis of Pavement Anti-skid Capability

Driving safety is related to many factors, and the antiskid ability of the road is one of them [34]. The factors that affect the pavement’s antiskid ability are pavement surface characteristics (fine structure and coarse structure), pavement humidity, and driving speed. In addition to the antiskid ability, factors such as road safety, weather, and driver status are all related to driving safety. Pavement skidding resistance index SRI (Pavement Skidding Resistance Index) is used as the evaluation index for pavement antiskid capability. A score above 90 is considered excellent, and a score above 80 is considered good. As shown in Table2 and Figure 3, the two-way road driving quality is better, the total average SRI is 91.2, the evaluation is “excellent,” and the left is slightly better than the right.Table 2
Pavement antiskid performance index.
Serial numberRightLeftSFCSRIEvaluation levelSFCSRIEvaluation level151.2592.4Excellent49.8691.4Excellent247.0388.9Good54.8394.6Excellent350.1291.6Excellent48.7190.5Excellent449.2690.9Excellent48.7990.5Excellent551.6692.7Excellent51.9192.9ExcellentFigure 3
Antiskid performance index of the designed road surface.


#### 4.1.4. Evaluation and Analysis of Supplementary Indicators of Pavement Damage

As shown in Figure4, the transverse crack penetration of the reconstructed road section is relatively large, and the transverse crack penetration of mostroad sections is greater than 0.4, indicating that the transverse cracks penetrate the pavement more seriously. Inthe sections of K125 + 000∼K127 + 000 and K133 + 000∼K134 + 000, the transverse crack penetration is greater than 0.7, and the crack disease is more serious.Figure 4
Distribution of crack spacing.


## 4.1. Evaluation and Analysis of Pavement Performance Indicators

### 4.1.1. Analysis of Pavement Damage

As shown in Table1 and Figure 1, regardless of the effects of rutting, the PCI (pavement condition index) index is above 90%, and the pavement damage level is excellent; and after considering the effects of rutting, the PCI index is about 60%, and the pavement damage level Fair; there is a difference of two levels between the two. And the maintenance measures made according to PCI are also quite different: the former only needs daily maintenance-based and local minor repair methods, while the latter needs to take the middle repair cover maintenance measures.Table 1
PCI indicators (taking rutting into account).
LaneSectionSUP segmentSMA segmentAK segmentTraffic lane62.6462.6762.6762.6762.6662.6462.6462.6362.5362.6762.6762.66Overtaking lane62.6762.6762.6562.6762.6762.6Figure 1
PCI indicators.


### 4.1.2. Analysis of Bearing Capacity of Pavement Structure

As shown in Figure2, in the short-term period after the road is opened to traffic, under the condition of no cracks, pits, and other damage on the road surface, the PCI index when only a small rut is considered is 62.7, and according to the corresponding maintenance regulations, this is necessary to repair the pavement in the middle of the road, which is seriously inconsistent with the actual situation [33].Figure 2
Converted PCI index.


### 4.1.3. Analysis of Pavement Anti-skid Capability

Driving safety is related to many factors, and the antiskid ability of the road is one of them [34]. The factors that affect the pavement’s antiskid ability are pavement surface characteristics (fine structure and coarse structure), pavement humidity, and driving speed. In addition to the antiskid ability, factors such as road safety, weather, and driver status are all related to driving safety. Pavement skidding resistance index SRI (Pavement Skidding Resistance Index) is used as the evaluation index for pavement antiskid capability. A score above 90 is considered excellent, and a score above 80 is considered good. As shown in Table2 and Figure 3, the two-way road driving quality is better, the total average SRI is 91.2, the evaluation is “excellent,” and the left is slightly better than the right.Table 2
Pavement antiskid performance index.
Serial numberRightLeftSFCSRIEvaluation levelSFCSRIEvaluation level151.2592.4Excellent49.8691.4Excellent247.0388.9Good54.8394.6Excellent350.1291.6Excellent48.7190.5Excellent449.2690.9Excellent48.7990.5Excellent551.6692.7Excellent51.9192.9ExcellentFigure 3
Antiskid performance index of the designed road surface.


### 4.1.4. Evaluation and Analysis of Supplementary Indicators of Pavement Damage

As shown in Figure4, the transverse crack penetration of the reconstructed road section is relatively large, and the transverse crack penetration of mostroad sections is greater than 0.4, indicating that the transverse cracks penetrate the pavement more seriously. Inthe sections of K125 + 000∼K127 + 000 and K133 + 000∼K134 + 000, the transverse crack penetration is greater than 0.7, and the crack disease is more serious.Figure 4
Distribution of crack spacing.


## 4.1.1. Analysis of Pavement Damage

As shown in Table1 and Figure 1, regardless of the effects of rutting, the PCI (pavement condition index) index is above 90%, and the pavement damage level is excellent; and after considering the effects of rutting, the PCI index is about 60%, and the pavement damage level Fair; there is a difference of two levels between the two. And the maintenance measures made according to PCI are also quite different: the former only needs daily maintenance-based and local minor repair methods, while the latter needs to take the middle repair cover maintenance measures.Table 1
PCI indicators (taking rutting into account).
LaneSectionSUP segmentSMA segmentAK segmentTraffic lane62.6462.6762.6762.6762.6662.6462.6462.6362.5362.6762.6762.66Overtaking lane62.6762.6762.6562.6762.6762.6Figure 1
PCI indicators.


## 4.1.2. Analysis of Bearing Capacity of Pavement Structure

As shown in Figure2, in the short-term period after the road is opened to traffic, under the condition of no cracks, pits, and other damage on the road surface, the PCI index when only a small rut is considered is 62.7, and according to the corresponding maintenance regulations, this is necessary to repair the pavement in the middle of the road, which is seriously inconsistent with the actual situation [33].Figure 2
Converted PCI index.


## 4.1.3. Analysis of Pavement Anti-skid Capability

Driving safety is related to many factors, and the antiskid ability of the road is one of them [34]. The factors that affect the pavement’s antiskid ability are pavement surface characteristics (fine structure and coarse structure), pavement humidity, and driving speed. In addition to the antiskid ability, factors such as road safety, weather, and driver status are all related to driving safety. Pavement skidding resistance index SRI (Pavement Skidding Resistance Index) is used as the evaluation index for pavement antiskid capability. A score above 90 is considered excellent, and a score above 80 is considered good. As shown in Table2 and Figure 3, the two-way road driving quality is better, the total average SRI is 91.2, the evaluation is “excellent,” and the left is slightly better than the right.Table 2
Pavement antiskid performance index.
Serial numberRightLeftSFCSRIEvaluation levelSFCSRIEvaluation level151.2592.4Excellent49.8691.4Excellent247.0388.9Good54.8394.6Excellent350.1291.6Excellent48.7190.5Excellent449.2690.9Excellent48.7990.5Excellent551.6692.7Excellent51.9192.9ExcellentFigure 3
Antiskid performance index of the designed road surface.


## 4.1.4. Evaluation and Analysis of Supplementary Indicators of Pavement Damage

As shown in Figure4, the transverse crack penetration of the reconstructed road section is relatively large, and the transverse crack penetration of mostroad sections is greater than 0.4, indicating that the transverse cracks penetrate the pavement more seriously. Inthe sections of K125 + 000∼K127 + 000 and K133 + 000∼K134 + 000, the transverse crack penetration is greater than 0.7, and the crack disease is more serious.Figure 4
Distribution of crack spacing.


## 5. Conclusions

The main factors affecting the decision of preventive maintenance measures include highway grade, highway technical status, traffic volume, capital, and cost effectiveness. This article summarizes the characteristics and advantages of various preventive maintenance measures through the introduction of current commonly used preventive maintenance measures, and provides a theoretical basis for the decision making of preventive maintenance measures for asphalt pavement.This paper points out the limitations of the existing road segment unit division method and introduces linear dimensionality reduction method andK-clustering method to divide the preventive maintenance road segment unit. The SPSS statistical product and service solution software is used to divide the pavement preventive maintenance unit. SPSS has the characteristics of convenient and fast in the process of dividing the maintenance unit.This paper uses interval mathematics and analytic hierarchy process to determine the weight coefficients of each index and reduces the influence of subjective factors. Considering the outstanding impact of different pavement performance on pavement comprehensive evaluation, nonlinear fuzzy comprehensive evaluation method is used. Comprehensive performance evaluation overcomes the shortcomings of the traditional fuzzy evaluation model and objectively reflects the state of the road network where the road section is located. However, due to space limitations, the method description part of this paper is still too rough, and the author expects better research methods to further study this topic in the future.



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*Source: 6414618-2022-07-30.xml*