An innovative evaluation method for performance of in-service asphalt pavement with semi-rigid base

Construction and Building Materials 235 (2020) 117376

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An innovative evaluation method for performance of in-service asphalt pavement with semi-rigid base Chao Jing a,b, Jinxi Zhang a,⇑, Bo Song a,c a

Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China School of Civil Engineering and Mechanics, Yanshan University, Qinhuangdao 066004, China c Beijing Road Engineering Quality Supervision Station, Beijing 100037, China b

h i g h l i g h t s
This paper presents a method for evaluating the performance of in-service pavements.
Seven indicators, including functional and structural performances, are investigated.
The comprehensive performance is evaluated using Principal Component Analysis.
The method is found to be effective in maintaining and utilizing existing roads.

a r t i c l e

i n f o

Article history: Received 29 November 2018 Received in revised form 13 September 2019 Accepted 24 October 2019

Keywords: Road engineering In-service asphalt pavement Semi-rigid base Comprehensive performance evaluation Principal Component Analysis (PCA)

a b s t r a c t To solve the issues involved in the inaccurate evaluation of the comprehensive performance of in-service asphalt pavements with semi-rigid base, in this study, extensive investigation and field testing are conducted on certain asphalt pavements with semi-rigid base in Beijing. A total of over 1000 sets of FWD data, more than 120 sets of radar spectra, and 138 cores are collected, and utilized for building a comprehensive performance evaluation system for asphalt pavements; seven indicators, including the functional and structural performances, are selected. The comprehensive performance of the pavement is analyzed and evaluated by principal component analysis (PCA). The results show that the comprehensive performance evaluation method for in-service asphalt pavements with semi-rigid base, proposed in this paper can get the accurate evaluation including the total score, score of each principal component and ranking of each pavement section. Furthermore, the comprehensive performance of in-service asphalt pavements with semi-rigid base is not necessarily related to the highway classification and road age. In addition, evaluation method for in-service asphalt pavements with semi-rigid base proposed in this paper can also be conducive for maintaining and utilizing existing road structures; thereby, major and medium rehabilitation can be considerably reduced, supporting green and scientific road maintenance. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction With decades of rapid development and construction, the Chinese road transport infrastructure has witnessed remarkable achievements. By the end of 2018, China’s road network includes a total length of 4,846,500 km of national highway [1]. Asphalt pavement is the main type of pavement for the highways in China, in which the base layer is mostly semi-rigid in Beijing as well as in the entire country, accounting for more than 90% [2]. Pavement assets have been progressively ⇑ Corresponding author. E-mail addresses: [email protected] (C. Jing), [email protected] (J. Zhang). https://doi.org/10.1016/j.conbuildmat.2019.117376 0950-0618/Ó 2019 Elsevier Ltd. All rights reserved.

aging in China. Each year a tremendous amount of public funds are invested in numerous highway maintenance and rehabilitation projects to retain pavement serviceability [3]. Moreover, major rehabilitation needs to be carried out on the existing roads, which not only affects traffic operation, but also causes several problems. In particular, periodic reconstruction and major road rehabilitation without planning will not only result in unwanted expenditure, wastage, traffic congestion, and safety accidents, but will also result in environmental pollution, land waste, and resource consumption. However, due to the lack of accurate and precise evaluation of the comprehensive road performance, the existing road maintenance management at times reconstructs old roads with poor pavement conditions, incurring high costs, considerable disturbance, severe pollution, and high energy consumption. Therefore, this paper

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precisely evaluates the comprehensive performance of in-service asphalt pavements and establishes the potential of in-service asphalt pavements with semi-rigid bases; thereby, it effectively reduces the period and frequency of major rehabilitation, and provides support for a scientific maintenance strategy. The performance or state evaluation of in-service pavements is crucial, and has received significant attention. In 2007, China promulgated the current Highway Performance Assessment Standards, JTG H20-2007, which defines five technical indicators for asphaltpavement performance evaluation, including the road surface damage, evenness, rutting, roughness performance, and structural strength. The performance qualification index (PQI) is a comprehensive index consisting of the pavement condition index (PCI), riding quality index (RQI), rutting depth index (RDI), skidding resistance index (SRI), and pavement structural strength index (PSSI). The pavement condition index (PCI) maily refers the characteristics of surface distresses such as cracking, Mansour Fakhri et al. [4] proposed a practical method for representing and determining the intensity of cracking through identifying the spatial distribution of pavement cracks at the project level using the locations of pavement surface cracks and GIS. Yuling Yao et al. [5] established a comprehensive evaluation index system for pavement performance based on the PCI, RQI, SRI, and RDI. Hui Jiang [6] applied an interval association function with the RQI, PCI, PSSI, and SFC to describe same, different, and inverse relationships between the measured sample indicators and the standard, to comprehensively evaluate the pavement performance with weights. Xiaofeng Wang et al. [7] proposed a genetic, neural network algorithm with the maximum crack rate, maximum rutting depth, standard deviation on the evenness, and antiskidding ability to analyze and evaluate the performance of highway asphalt pavements. Liang Song [8], Yongqing Zhang, Shuangying Jia [9], and Qunfang Hu et al. [10] introduced the grey theory and evaluated the performance of each section. Jian Gong et al. [11] proposed an asphalt pavement performance evaluation model with five indicators, including the evenness, rutting, antiskidding performance, damage condition, and deflection value. Danny Xiao et al. [12] used data (cracking, rutting, evenness) to evaluate the performance of permeable asphalt macadam bases. Kelvin C. P. Wang and Qiang Li [13] applied the grey clustering method to three indicators, including the cracking condition, rutting, and the international roughness index to evaluate the performance of asphalt pavements. AG Golroo [14] introduced a fuzzy set method to evaluate the pavement for the disease type, severity, density, and weight factors. Ajit Pratap Singh et al. [15] presented a pavement performance evaluation based on the international roughness index (IRI), surface modulus (E0), rutting depth, and skidding resistance index using a fuzzy analytic hierarchy process (FAHP) and fuzzy weighted average (FWA). Li Ming [16] introduced the artificial neural network method for evaluating pavements based on the international roughness index, damage rate, structural strength index, sideway force coefficient, and structural depth. Wang Guanhu et al. [17] proposed a grey theory method to evaluate the performance of airport pavements for the PCI, IRI, and SFC. Shi Qiu et al. [18] proposed a data-driven approach to conduct comprehensive pavement evaluation and ranking using a total of 8 parameters involving IRI, rutting, alligator cracking, longitudinal cracking, transverse cracking, patching and so on. Mansour Fakhri et al. [19] proposed a practical solution for pavement structural evaluation which developed a relationship between deflection bowl param-eters derived from Falling Weight Defl ectometer (FWD) and two pavement performance indices, International Roughness Index (IRI) and Pavement Surface Evaluation and Rating index (PASER), by the use of Artificial Neural Network (ANN) and regression models.

In a word, at present, researchers pay more attention to the performance evaluation of asphalt layer of asphalt pavement. However, for the asphalt pavement with semi-rigid base, the comprehensive performance evaluation of the in-service pavement should include more effective indicators not only refer to the functional performance but also structural performance, not only asphalt layer but also semi-rigid base. In addition, the semi-rigid base is hidden beneath the asphalt layer, so that its cracking condition and cavities is invisible and cannot be evaluated by the routine methods such as visual surveys or image identification [20]. Usually, the resilient deflection on the pavement surface were used for the evaluation of structural bearing capacity, but not for the evaluation of distress condition of the semi-rigid base [21]. In this case, the nondestructive detection based on FWD and Ground Penetrating Radar (GPR) detection and the destructive detection based on excavation for a core sample would be better choices to get a more reliable evaluation result. Therefore, in this paper, we conducted extensive research and field testing on semi-rigid base asphalt pavement in Beijing and perform the quantitative analysis and evaluation on the seven indicators including cracking (PCI), rutting (RDI), deflection, asphalt surface modulus, radar flaw detection, core sample integrity, and base core strength, aiming to complement previous research and provide support for scientific road maintenance strategy. 2. Methodology In this paper, a methodology is proposed, based on a case study, for assessing the comprehensive performance of in-service asphalt pavements with semi-rigid bases through field investigation and principal component analysis (PCA). The general framework of the assessment system for in-service asphalt pavements with semirigid bases is presented in Fig. 1. 2.1. Selection and identification of pavement sections Based on maintenance data and the pavement condition over the years, sections with road ages of more than 14 years with heavier traffic were selected; the basic information of the selected pavement sections are listed in Table 1. As such sections are about to face major rehabilitation, in recent years, the number of major rehabilitations to pavement in Beijing has been particularly large. This study establishes a multi-indicator evaluation system to fully mining the potential value of the pavement, for the precise comprehensive performance evaluation and ranking of these pavements, and for reducing the number of major rehabilitation sections. 2.2. Comprehensive performance evaluation system 2.2.1. Selection of evaluation indicators The current highway performance assessment standard, JTG H20-2007, defines five technical indicators for asphalt pavement performance evaluation, including the pavement surface damage, evenness, rutting, anti-skidding and structural strength. It is found in a wide investigation that damages of asphalt pavement with semi-rigid base in Beijing are mainly the cracking and rutting, and eveness and roughness indicators are mainly for the evaluation of comfort, which has little effect on the life of the pavement [22]. Therefore, it is undoubted that the cracking and rutting indicators should be included in the evaluation system. When the semi-rigid base layer is severely cracked or even loose, the measured deflection value is twice as that of the normal pavement. However, it is also found that there is no corresponding relationship between the pavement surface deflection and pavement surface damage;

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Fig. 1. Assessment of in-service asphalt pavement with semi-rigid base: methodological framework.

Table 1 Basic information and comprehensive performance evaluation indicators of each section. Road Section

Road Age (years)

Category

Surface course

Base course

Inner Ring of East Fifth Ring Road (IREF)

14

Highway

36 cm lime fly ash stabilized sand

Inner Ring of West Fifth Ring Road (IRWF)

14

Highway

Inner Ring of South Fifth Ring Road (IRSF)

14

Highway

Inner Ring of North Fifth Ring Road (IRNF)

14

Highway

Outer Ring of East Fifth Ring Road (OREF)

14

Highway

Outer Ring of West Fifth Ring Road (ORWF)

14

Highway

Outer Ring of South Fifth Ring Road (ORSF)

14

Highway

Outer Ring of North Fifth Ring Road (ORNF)

14

Highway

Shun Mi Road (SM)

25

Secondary road

Jing Jin Tang Road (JJT)

27

Highway

JianCai City West Road (JCCW)

24

Sub arterial urban

5 cm SMA + 6 cm dense graded asphalt concrete +7cm asphalt concrete 5 cm asphalt concrete + 6 cm dense graded asphalt concrete + 7 cm asphalt concrete 5 cm SMA +6cm dense graded asphalt concrete +7cm asphalt concrete 5 cm asphalt concrete + 6 cm dense graded asphalt concrete + 7 cm asphalt concrete 5 cm SMA +6cm dense graded asphalt concrete +7cm asphalt concrete 5 cm asphalt concrete +6cm dense graded asphalt concrete +7cm asphalt concrete 5 cm SMA +6cm dense graded asphalt concrete +7cm asphalt concrete 5 cm asphalt concrete + 6 cm dense graded asphalt concrete +7cm asphalt concrete 4cmAC-13 +4cmAC-16 5 cm SMA-16 +7cm AC-20 +12 cm asphalt macadam 5 cm AC +6cm asphalt macadam made in Mixing Station

only when the pavement surface presents obvious fatigue failure, the pavement surface deflection and tensile stress indicators show consistency. Therefore, the deflection indicator also needs to be included in the evaluation system.

36 cm lime fly ash stabilized sand 36 cm lime fly ash stabilized sand

36 cm lime fly ash stabilized sand 36 cm lime fly ash stabilized sand

36 cm lime fly ash stabilized sand

36 cm lime fly ash stabilized sand

36 cm lime fly ash stabilized sand

18 cm lime fly ash stabilized sand 20 cm cement stabilized sand

30 cm lime fly ash stabilized sand

In addition, the service life of asphalt pavements with semirigid base includes the fatigue life of the semi-rigid base layer and the residual life of the surface layer, under traffic load. The surface modulus also has considerable influence on the service life of

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Fig. 2. Relationship between maximum tensile stress at the bottom of semi-rigid base and fatigue life and modulus of asphalt surface [21].

asphalt pavements with semi-rigid bases, as shown in Fig. 2[23]; therefore, it needs to be included in the evaluation system. The Ground Penetrating Radar (GPR) detection method is an electromagnetic wave technique used for determining the distribution of the pavement medium. This method is highly accurate, efficient, flexible, and intuitive, and has been extensively used for pavement detection in China and other countries [24–26]. In particular, as the effect of interlayer bonding on the structural deformation is significant, this method can efficiently investigate the structure of each layer and the state of interlayer bonding [27]. However, the GPR method is more suitable for a wide range of general investigation. When detecting a local suspicious area, if the road status needs to be accurately evaluated, it is still necessary to excavate a core sample. As shown in Figs. 3 and 4, the GPR detection results of the two sections are partially uncompacted, but the core sample and road surface conditions are considerably different. The surface of the Jing Jin Tang expressway (K22 + 950) presents a net-shaped crack; the core samples obtained from here are all fragmented. The inner ring of the North Fifth Ring road (K96 + 111) shows good pavement conditions, and the core sample obtained from here is completely devoid of cracks. Therefore, a comprehensive performance evaluation system for semi-rigid-base asphalt pavements should also include radar flaw detection, base core sample integrity, and base core sample strength. Thus, the proposed comprehensive performance evaluation system for asphalt pavements with semi-rigid base includes seven indicators: cracking (PCI), rutting (RDI), deflection, asphalt surface modulus, radar flaw detection, core sample integrity, and base core strength.

2.2.2. Investigation of evaluation indicators In this paper, we conducted extensive research and field testing on a number of asphalt pavements with semi-rigid base in Beijing,

which mainly included non-destructive tests, and rapidly obtained road surface distress information using the automatic detection technology of the multifunctional inspection vehicles, as well as pavement deflection data, using the automatic deflection vehicles or falling weight deflectometer (FWD), where the FWD analysis technology is combined with the GPR detection technology. The damage in the pavement base layer is concealed and latent. Therefore, for damage detection, excavation and core sample drilling were performed, if necessary. A total of more than 1000 sets of FWD data, 120 radar maps, and 138 core samples were collected, as shown in Fig. 5, to build an asphalt pavement condition detection system, and explore the classification criteria and standards for the asphalt pavement condition. For the detection of the PCI and RQI, multifunctional inspection vehicles were utilized. For the detection of Ground Penetrating Radar (GPR), three measuring lines were arranged on the middle and both sides of the wheel path of each lane to continuously detect the internal damage to the pavement, as depicted in Fig. 6(a). By interpreting the radar output waveform image, damage such as voids and noncompactness within the pavement were identified [28], and the overall integrity of the pavement was preliminarily judged. For the investigation of deflection, it was performed by the Falling-Weight-Deflectometer (FWD). FWD works stationary. At a measuring point defined load impulses are applied to the pavements surface. By means of a geophone in the centre of loading and at defined intervals up to a distance of approximate two meters, the short-term deformation (deflection) of the surface will be detected [29], as shown in Fig. 6(b), the layout of measuring points in the middle and the outside of wheel path of each lane, a pair of measuring points are arranged every 20 m on the pavement surface. Full-sacle core samples, consisting of the asphalt layers and base course, were extracted from the spots determined by the analysis results of GPR and FWD deflection basin. In order to evaluate the mechanical property of asphalt layers and base course, IDT test and Resilient Modulus Test were carried out on asphalt mixture and lime fly ash stabilized sand, respectively. 2.3. Evaluation approach 2.3.1. Selection of evaluation method The comprehensive performance evaluation system for asphalt pavements with semi-rigid bases includes numerous indicators and diverse data; hence, there is inevitably some overlap between the indicators, i.e., a certain correlation, rendering the comprehensive performance evaluation of the pavement difficult. In Fig. 7, each axis represents a variable, that is, an evaluation index. In Fig. 7 shows the comprehensive performance index information of the four selected pavement sections, where the data are organized in seven dimensions, indicating that the observation value

Fig. 3. Jing Jin Tang Road (K22 + 950).

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Fig. 4. Inner Ring of North Fifth Ring Road(K96 + 111).

Fig. 5. Investigation on comprehensive performance of in-service asphalt pavement with semi-rigid base.

of the comprehensive performance of each road section is a point in the seven-dimensional space. Although only four sections are presented, the data is sufficient to demonstrate that the evaluation of the comprehensive performance on each road section cannot be easily and intuitively obtained. Therefore, PCA was conducted to reduce the dimensionality of the data, and eliminate the correlation and interaction of the indicators, before calculating the comprehensive performance. Note: The quantification of the indicators for the integrity of the core sample and radar detection was performed, based on expert opinion, as follows: For the integrity of the core sample, 6: integrity without crack, 5: integrity without wide crack, 4: integrity with subsurface layer loose, 3: partially cracked in crack location, 2: deeply cracked in transverse joint location, 1: core sample fragmentation. For radar detection, 6: good structural condition, 5: partially poor interlayer bonding, 4: poor interlay bonding with partial road base rarefied, 3: partially uncompacted around the rutting marks, 2: empty around the rutting marks, 1: bad interlayer bonding with serious empty base layer.

independent, comprehensive indicators, Zm ; instead of the original indicators, as equation (1).

8 Z 1 ¼ a11 X 1 þ a12 X 2 þ . . . þ a1p X p > > > < Z ¼ a X þ a X þ ... þ a X 2 21 1 22 2 2p p >       > > : Z m ¼ am1 X 1 þ am2 X 2 þ . . . þ amp X p The steps involved in the PCA are as follows:

1) Calculate the correlation coefficient matrix using Eqns. (2) and (3).

2

r11

r12

   r1P

6r 6 21 r22    r2P R¼6 .. .. .. .. 6 4 . . . . rP1 rP2    r PP

3 7 7 7 7 5

Pn   k1 ðxki  xi Þðxkj  xj Þ rij ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n P  2 P  2 ðxki  xi Þ ðxkj  xj Þ k1

2.3.2. Principle of principal component analysis Principal Component Analysis (PCA) is a method for data dimensionality reduction, and is currently being applied in road engineering and other fields [30–34], where the fundamental idea is to recombine the original redundant correlated indicators X1 ; X2 ; . . . ; XP (such as p indicators) into a smaller set of

ð1Þ

ð2Þ

ð3Þ

k1

where r ij ði; j ¼ 1; 2; . . . ; pÞ is the correlation coefficient between the original variables, xi and xj . Then, calculate the feature values and vectors: First, solve the characteristic equation, jkI  Rj ¼ 0, to find the eigenvalues,ki ði ¼ 1; 2; . . . ; pÞ, and sort them in the order of

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Fig. 6. Diagram of the investigation of GPR and FWD.

Fig. 7. The comprehensive performance index information of sections.

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magnitude, i.e., k1 P k2 P . . . P kp P 0; Then, calculate the feature vectors, ei ði ¼ 1; 2; . . . ; pÞ, corresponding to the eigenvalues, ki ði ¼ 1; 2; . . . ; pÞ. Next, calculate the principal component contribution rate and the cumulative contribution rate: The principal component contribution rate is given by Ppri ði ¼ i; 2; . . . ; pÞ, and the cumulative contribution rate is ck Pk1 m ck Pk1 . p c k1 k

In general, m principal components corresponding to feature values,ki ði ¼ 1; 2; . . . ; mÞ; whose cumulative contribution rate is 85% or more are finally selected as the dependent features. 2) Calculate the load of principal components, as follows:

pðzk ; xi Þ ¼

pffiffiffiffiffi ck eki ði; k ¼ 1; 2;    ; pÞ

ð4Þ

Fig. 8. Correlation of each evaluation indicators. Note: X1- PCI, X2- RDI, X3- 1/ deflection (mm), X4- surface modulus (MPa), X5- base core sample strength (MPa), X6- core sample integrity, and X7- Radar detection condition.

3) Calculate the score of the principal components

2

z11

z12

zn1

z22 .. . zn2

6z 6 21 Z¼6 6 .. 4 .

   z1m

strength and core sample integrity; there is a significant negative correlation between the RDI and the reciprocal of the deflection. The correlation analysis again confirms the necessity of applying PCA to evaluate the comprehensive performance of the pavement.

3

   z2m 7 7 .. .. 7 7 . . 5    znm

ð5Þ

3.3. Determination of the number of principal components

To study the comprehensive performance of in-service asphalt pavements with semi-rigid base, we selected 11 sections with long service times, in Beijing, and performed indicator-based investigation on their comprehensive performance. The results are shown in Table 2.

We calculated the eigenvalues of the correlation coefficient matrix using SAS 9.4, and the difference between the upper and lower eigenvalues, the contribution rate of each principal component on the variance, and the cumulative contribution rate. As shown in Fig. 9, the contribution rate of the first, second, and the third principal components on the variance are 50.20%, 20.07%, and 15.96%, respectively. The cumulative contribution rate of the first three principal components is 86.23%, which can well summarize the data; hence, the fourth principal component and thereafter were not considered.

3.2. Correlation of evaluation indicators

3.4. Evaluation result of principal components

We performed correlation analysis on the comprehensive performance indicators. As shown in Fig. 8, the PCI is not related to the RDI. There is a significant positive correlation among the PCI and core sample strength, core sample integrity, and radar detection; deflection and surface modulus and core sample integrity; surface modulus and core sample strength; and core sample

The load amount of each evaluation indicator for each principal component factor is listed in Table 3, where the coefficientsX3, X5, and X6 in the first principal component are the largest, X2 in the second principal component has a significant positive coefficient, and X7 in the third principal component has the most significant negative coefficient, considerably exceeding the other indicators.

3. Case study 3.1. Data preparation

Table 2 Comprehensive performance evaluation indicators of each road section. Road Section

IREF IRWF IRSF IRNF OREF ORWF ORSF ORNF SM JJT JCCW

Basic Information

Comprehensive Performance Evaluation Indicators

Origin- destination

Age (year)

Category

PCI

RDI

Deflection (mm)

Modulus of asphalt layer (MPa)

Strength of core sample of base (MPa)

Integrity of core sample

Radar flaw detection

K12 + 900- K13 + 300 K75 + 700- K76 + 300 K37 + 820- K38 + 200 K96 + 100- K96 + 770 K13 + 000- K13 + 400 K76 + 197- K76 + 700 K30 + 400- K30 + 600 K96 + 972- K97 + 334 K13 + 000- K14 + 000 K28 + 500- K29 + 000 K2 + 411- K3 + 236

14 14 14 14 14 14 14 14 25 27 24

Highway Highway Highway Highway Highway Highway Highway Highway Secondary road Highway Sub arterial urban

77.28 89.9 92 90.5 84.46 79 90 87.2 89.34 85.97 67.2

77.8 70.8 85.2 84.8 84.4 85.4 66.8 86.8 90 85.2 80

15.14 5.57 7.56 9.95 16.4 8.12 9.62 18.83 22.76 29.04 36.51

3544 3550 3232 4590 2341 4833 3441 3929 1707 1020 1038

10.16 10.99 9.49 11.63 10.45 10.34 9.68 10.36 13.98 0 0

2 6 5 5 3 3 3 2 4 1 1

2 6 6 3 5 6 3 5 4 6 1

Note: The quantification of the core integrity and radar detection is shown in Fig. 7.

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Fig. 9. The eigenvalue and contribution rate of the principal component.

Therefore, the first principal component can be considered as a comprehensive indicator of the damage and structural strength, the second principal component as the indicator of the deformation, and the third principal component as the indicator of the interlayer bonding. The overall evaluation results of the comprehensive performance of each section are shown in Table 4. The best performance for the interlayer bonding index is demonstrated by the IRNF; the comprehensive performance of the IRWF is the best, while that of the JCCW as well as the IREF are the worst, considering the entire Fifth Ring road. In Fig. 10, the axes z1, z2 and z3 represents the damage and structural strength, the deformation and interlayer bonding, respectively. The comparison results of the damage and structural strength, the deformation and interlayer bonding are shown in Fig. 10; the best performance for the damage and structural strength is demonstrated by the IRWF, for the deformation by the SM, and for the interlayer bonding by the IRNF. Moreover, the better performance for both the indicators is exhibited by the SM, while that of the JCCW is the opposite. Fig. 11 illustrates that the trend of the scores of each principal component is consistent with the trend of the overall review. The highway classification, pavement structure, and age of each section on the Fifth Ring road are the same; however, the comprehensive performance evaluation results are considerably different, which may be caused by differences in the traffic load. Moreover, the comprehensive performance of asphalt pavements with semirigid base is not necessarily related to the highway classification and road age. Although the Shunmi road is a secondary road, with 25 service years, it demonstrates good comprehensive perfor-

Table 3 Factor load of each evaluation indicator for each principal component.

X1 X2 X3 X4 X5 X6 X7

Z1

Z2

Z3

0.40 0.10 0.45 0.38 0.41 0.48 0.28

0.30 0.73 0.29 0.20 0.06 0.01 0.50

0.18 0.39 0.27 0.42 0.55 0.03 0.51

Note: X1–X7 indicate the same as in Fig. 7. Z1 denotes comprehensive indicator of damage and structural strength; Z2 denotes indicator of deformation; and Z3 denotes indicator of the interlayer bonding.

mance; in addition, the Jing Jin Tang expressway has been in service for 27 years, but its comprehensive performance remains good. 4. Discussion Pavement assets have been progressively aging in China, and a lot of in-service asphalt pavements with semi-rigid base were built more than 10 years ago. Each year a tremendous amount of public funds are invested in numerous highway maintenance and rehabilitation projects to retain pavement serviceability [3]. In conventional pavement management including asphalt pavements with semi-rigid base, the maintenance strategies of pavement sections are based on JTG H20-2007, JTG H10-2009 and JTG D50-2017. On the premise of satisfying the strength, when PCI  80, the pavement of highway and first-class highway should be performed medium rehabilitation, and when the strength can not meet the requirement, major rehabilitation should be taken to improve the bearing capacity. As can be seen in Fig. 12, the pavement sections of JCCW, JJT and SM are need to be taken major rehabilitation for the insufficient strength, while the ORWF and IREF are need to be taken medium rehabilitation, so this is also the reason why the number of pavement sections which need to be taken major and medium rehabilitation is particularly large in recent years. It is difficult and sometimes too subjective for pavement engineers to determine priority of pavements maintenance if departs from official standards. More efforts are needed if a comprehensive evaluation system is to be established or modified based on the current practice. These concerns are addressed by the methodology developed in this study. In addition, Fig. 12 also demonstrates the ranking of each pavement section, such as the SM, though it is need a major rehabilitation, it has a higher evaluation and ranking than JCCW and JJT, similarly, the ORWF and IREF are need to be taken medium rehabilitation, they also have higher evaluations and rankings especially for ORWF. Therefore, there is no need to perform maintenance strategies on these pavement sections at the same time, but it is necessary to make the priority ranking of pavement sections. There is no doubt that the methodology developed in this study will be propitious to reduce the number and frequency of major and medium rehabilitation, save maintenance funds and reduce the problems caused by major and medium rehabilitation including traffic congestion, safety accidents, environmental pollution, land waste, and resource consumption.

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C. Jing et al. / Construction and Building Materials 235 (2020) 117376 Table 4 Comprehensive performance evaluation results of each road section. Road Section

IREF IRWF IRSF IRNF OREF ORWF ORSF ORNF SM JJT JCCW

Score Z1

Z2

Z3

0.89 2.77 1.69 1.46 0.26 0.87 0.59 0.08 0.00 2.24 3.93

1.29 1.08 0.82 0.11 0.74 0.17 1.81 0.93 1.49 1.42 1.26

1.05 1.37 0.63 1.11 0.07 0.41 0.42 0.70 0.87 1.70 0.09

Total score

Ranking

0.54 0.95 0.91 0.89 0.03 0.53 0.13 0.26 0.44 1.11 2.23

9 1 2 3 7 4 8 6 5 10 11

Fig. 10. Comparison of damage and structural strength, deformation and interlayer bonding.

Fig. 11. Comprehensive performance evaluation results of each section.

5. Conclusions In this paper, we conducted extensive research and field investigation on several asphalt pavements with semi-rigid base in

Beijing, using mainly nondestructive tests, and rapidly obtained the road-surface damage information using the automatic detection technology of multifunctional investigation vehicles, as well as the road deflection data using automatic deflection vehicles or

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Fig. 12. Comprehensive performance evaluation results of each section.

FWD, where FWD analysis technology was combined with GPR detection technology. In addition, as the damage in the pavement base layer is concealed and latent, damage detection methods, such as excavation and core sample drilling, were performed, if necessary. Based on above, we developed an evaluation method for the comprehensive performance of asphalt pavements. Seven indicators, including the functional and structural performances, were selected, and the comprehensive performance of asphalt pavements with semi-rigid base was analyzed and evaluated through PCA to obtain the following conclusions: a) The indicators included in the comprehensive performance evaluation system for in-service asphalt pavements with semi-rigid base, in this paper, were established to be scientific and comprehensive. Apart from the PCI and RDI, which are not related, the other indicators indicate correlation to a certain extent, demonstrating the necessity of applying PCA for evaluating the comprehensive performance of the pavement. b) Three main components were determined, among the seven indicators. The best performance for crack and structural strength was demonstrated by the inner ring of West Fifth Ring road (IRWF), while that for the deformation was demonstrated by the Shunmi road(SM). The best performance for both two indicators was demonstrated by the Shunmi road (SM), while that of the Jiancaicheng West road (JCCW) was the worst. Moreover, the best performance for the interlayer bonding indicator was demonstrated by the inner ring of the North Fifth Ring road (IRNF). c) Although the highway classification, pavement structure, and age of each section on the Fifth Ring road were the same, the comprehensive performance evaluation results were significantly different; this may be caused by differences in the traffic load. Moreover, the comprehensive performance of in-service asphalt pavements with semi-rigid base is not necessarily related to the highway classification and road age. d) The comprehensive performance evaluation s method for inservice asphalt pavements with semi-rigid base, proposed in this paper, can improve and accurately evaluate the comprehensive performance of in-service pavements, and is conducive for maintaining and utilizing existing road

structures; thereby, periodic reconstruction and major and medium rehabilitation can be considerably reduced, supporting green and scientific road maintenance. 6. Data availability The data used to support the findings of this study are available from the corresponding author upon request. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The research and publication of the article was funded by the National Key Research and Development Program of China (2018YFB1600302), National Natural Science Foundation of China (Grant No.51778027) and Natural Science Foundation of Hebei Province of China (Grant No. E2019203559). References [1] Ministry of transport of the People’s Republic of China. Statistical bulletin of transport industry in 2018. Beijing, China: 2019. [2] X. Cui, Q. Dong, F. Ni, X. Liang, Evaluation of Semi-rigid Base Performance Through Numerical Simulation and Data Mining of Pavement Deflection Basin, in: X. Shi, Z. Liu, J. Liu (eds) Proceedings of GeoShanghai 2018 International Conference: Transportation Geotechnics and Pavement Engineering. GSIC 2018. Springer, Singapore (2018) [3] Ministry of transport of the People’s Republic of China. Statistical bulletin of national toll roads in 2017. Beijing, China:2018 [4] Mansour Fakhri, Reza Shahni Dezfoulian, Amir Golroo, et al., Developing an approach for measuring the intensity of cracking based on geospatial analysis using GIS and automated data collection system. Int. J. Pavement Eng. (2019) in press [5] Yao Yu-ling, Li Xue-hong, Zhang Bi-chao.Integrative evaluation index system for preventive maintenance timing of asphalt pavement, J. Traffic Transp. Eng. 05 (2007) 48–53. [6] Hui Jiang, Mingwu Wang, Zhu Qikun, Yu Zhu, Pavement performance evaluation model based on interval connectional membership degree, J. Hefei Univ. Technol. (Nat. Sci.) 39 (08) (2016) 1089–1092. [7] Xiaofeng Wang, Panke Gao, Jinxi Wang, Study on Evaluation Method of Expressway Asphalt Pavement Performance Based on Hybrid Genetic Neural Network, Highway Eng. 42 (04) (2017) 219–222.

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